Goals

On completing this module, students are expected to be able to:

• explain how sediments, ice cores, and tree rings record geological time;
• explain how proxy information on past climates is extracted from geological materials;
• infer the nature of climate change from proxy records;
• consider how ancient events can inform us of changes to come in the future.

Learning Outcomes

After completing this module, students should be able to answer the following questions (hint: each of these topics is the subject of one quiz question!):

• In what materials is evidence of ancient geologic environment recorded?
• What is the "Hockey Stick"?
• What gases do ice bubbles contain?
• What is a piston corer?
• What are the processes that fractionate oxygen and carbon isotopes?
• What are the other proxies for temperature?
• How do tree rings record climate?
• What are foraminifera?
• What are the characteristics of leaves from tropical environments?
• What is moraine?
• What are the changes that occurred during glacial-interglacial cycles?
• How was the PETM initiated and what happened during the event?
• What was the impact of global warming during the PETM on life?

Below is an overview of your assignments for this module. The list is intended to prepare you for the module and help you to plan your time.

Assignments

1. Lab 1: PETM
2. Submit Module 1 Lab 1 (Graded).
3. Take Module 1 Quiz.
4. Yellowdig Entry and Reply

Google the words “hockey stick,” and in addition to advertisements for sporting equipment, you will find links to one of the most chronicled and scrutinized datasets in modern science, the climate record of the last millennium. The curve was originally documented in a paper authored by Penn State Meteorology and Geoscience Professor Michael Mann and his colleagues. As shown in the figure below, the curve shows fairly constant temperatures or even a modest cooling from 1000 to about 1900 AD. About 1900, a sharp warming trend began, the base of the hockey stick. This warming has continued almost unabated to the present day. It so happens that the base of the stick corresponds to later stages of the industrial revolution, a time when the release of greenhouse gases from fossil fuels to the atmosphere spiked. The correlation of warming and the increase in levels of greenhouse gases, which are known to trap heat in the Earth’s atmosphere, is hard to argue with.

The famous "Hockey Stick" curve showing the increase in global temperatures from 1000 to 2000, with a steep increase around 1900.

Credit: Intergovernmental Panel on Climate Change 2001 (IPCC)

Perhaps the most scrutinized aspect of the hockey stick graph, and where the scientific controversy has focused, is that a part of the data set derives from temperature measurements that have been made since about 1900. The remainder of the curve derives from historical records and "proxy" measurements that are made indirectly. "Proxies" are substitute measurements that are made to determine the conditions at times before humans could measure climate directly. Such proxies serve as the basis for our understanding of past climate. In this module, we will learn how proxy measurements are made.

Fortunately, the history of the earth, including the evolution of its physical environment and its life forms, is beautifully preserved by a variety of Earth processes. For example, as we discussed briefly on the last page, sediments deposited in the ocean basins accumulate in a layer-cake fashion and entrap the fossil remains of once-living organisms. In addition, when these organisms, including species of clams and corals, were living, they recorded Earth’s climate history in the chemistry of layer upon layer of their shell. Images of a layered clamshell and coral skeleton are shown below.

Samples of Climate Records

Video: Sedimentation (1:02)

As we will detail below, chemical proxies allow the climate to be reconstructed from shell materials many hundreds of millions of years old. In fact, some of the best records of Earth’s climate are obtained from mile upon mile...., sorry kilometer upon kilometer, of cores removed from the ocean floor. The current ocean basins are less than 180 million years old, meaning that only the last 180 million years of Earth’s history are preserved in the current ocean. Fortunately, large-scale Earth tectonic processes have elevated vast piles of sedimentary rocks onto the land, where they are preserved in mountains and elsewhere. These materials can be sampled in road cuts and cores and climate information gleaned from them, taking our understanding of climate history much further back.

Foreground shows sedimentary rocks uplifted on land on the South Island, New Zealand.

Credit: ©Tim Bralower. Used with permission

The major ice sheets have accumulated layer by layer over time. These layers record temperature, precipitation, and wind patterns at the time the ice formed. Tiny gas bubbles trapped within the ice preserve the composition of the atmosphere including levels of CO₂ and methane (CH₄). Kilometer upon kilometer of cored ice and the bubbles within it preserve an incredible record of Earth's climate variations for hundreds of thousands of years. See the images shown below of the GISP2 Ice Core and a sample of atmospheric gas bubbles in ice.

Photographs of changing properties of the GISP2 Ice Core from Greenland. From near the surface (upper photograph): unconsolidated ice, to 1800 meters depth (middle photograph): layered consolidated ice, to near the base at 3050 meters depth (lower photograph): showing ice with eroded sediment.

Credit: National Air and Space Administration (NASA)

Video: Ice Accumulation (1:34)

The accumulating ice also preserves dust, smoke, pollen, and ash from volcanic eruptions. The texture of summer snow and winter snow is very different, and the resulting layers provide very accurate means to determine the age of ice cores, as does C-14 dating of the CO₂ trapped in ice bubbles. In this way, the composition of the atmosphere can be determined hundreds of thousands of years back in the past. Imagine this, the air that your great-great-great-great grandparents breathed has been collected in some lab around the world!

Sample from the Taylor Dome core in Antarctica. The depths are indicated, and scale is shown.

Credit: Conditions for bubble elongation in cold ice-sheet ice. Journal of Glaciology 45(149), 147-154 (1999). By Alley, R.B., and J.J. Fitzpatrick.

Sections of ocean sediments have been sampled extensively by a suite of coring operations dating back to the 1940s. These operations include a variety of different drill ships as well as rigs like the ones that are used in oil exploration. In all cases, the retrieval of continuous cores from the seafloor requires a drill string, a continuous line of pipe that is assembled to extend from the ship or platform to the seafloor.

Once the drill string reaches the seafloor, a metal core barrel is lowered through the pipe to recover sediment. Typically, each core that is extracted is between 2 and 10 m long. At the head of the core barrel, is the bottom hole assembly, a device that is positioned to cut the core. A variety of different techniques are used to cut sediment. Where the sediment is young and soft, as occurs in younger layers close to the seafloor, cores can be cut using a piston-coring device (see figure below). The piston is situated inside the core, and it is triggered when the bottom hole assembly is close to the ocean bottom. When it hits the bottom, the piston rapidly moves up, so the mud fills the empty pipe as it sinks. At the end of the bottom hole assembly is a drill bit that actually helps cut through the core. In the case of the piston core, the bit is a sharp, knife-like device that helps the rapidly advancing core cut readily through the soupy sediment. If the formation is harder, as occurs deeper in the sediment column where the overlying burial has led to compaction and cementation, cores can only be retrieved using a hard bit that cuts the rock by rotating at high speeds assisted by water, and, in some cases, drilling mud (this is known as rotary coring). Compared to the piston core which advances in seconds or less, this rotary coring can take a very long time, often up to an hour for each meter of core; moreover, some of the sediment can be lost during coring, as pressure from water or mud must be applied to cut through the rock. Once the coring mechanism is fully extended, a core catcher slips into place underneath the coring device and a wire line is used to retrieve the core to the ship or drilling platform.

Different devices used to extract cores from shallow sediment

Credit: Fritz Heide, Woods Hole Oceanographic Institution

Probably the best-known and longest-running drilling operation, the Integrated Ocean Drilling Program (IODP) has been in operation for more than 40 years. The program began as the Deep Sea Drilling Project in 1969 and is a collaborative scientific operation between a number of different countries. The ship chartered by the program is the JOIDES Resolution, a highly sophisticated drilling platform that has the ability to take cores in locations where the ocean is 6000 meters deep. The ship is about 150 meters long, with a drilling derrick that is about 50 meters high. The deepest hole drilled by IODP exceeds 2000 meters. The ship can operate in stormy seas with large waves and strong currents as it is held in position by powerful turbines, and the drill pipe is stabilized by a device called a heave compensator that keeps the drill bit on target even when the ship is listing. The Resolution has berths for about 120 engineers, scientists, drilling “rough necks,” and caterers, and typically stays out to sea for six- to eight-week expeditions.

The JOIDES-Resolution drillship, the platform used by the Integrated Ocean Drilling Program to extract cores from the ocean depths. The ship is 170 meters long and houses over 150 scientists and engineers.

Credit: JOIDES-Resolution, National Science Foundation (Public Domain)

As we will see below, valuable information about Earth’s climate has been obtained by drilling in the ocean basins. However, rocks that were deposited in continental environments and marine sediments that have been uplifted onto the continents via plate tectonics also provide important information about past climates. These rocks are much more readily accessible and can be sampled in road cuts, stream beds, and many other places. In addition, sedimentary rocks on land can be sampled via coring with much less expense than their oceanic counterparts.

Equipment used to extract cores from ice sheets

Credit: National Air and Space Administration (NASA) (Public Domain)

Samples of land materials are often taken by digging a trench to obtain fresher material beneath the weathered surface zone. If the material is indurated, samples of core are removed using a saw and samples of outcrop are removed using a hammer. Soft material usually in cores can be sampled by inserting plastic tubes into the sediment. Once the samples are taken, the material is ready for analysis.

Scientists sampling cores taken by the Integrated Ocean Drilling Program

Credit: The Consortium for Ocean Leadership

Over the long term, cores of sediment are usually kept in refrigerators to keep them from drying out. Cores of ice are housed in warehouse-sized refrigerators that are cooled to -30^oC. The longest ice core sampled is the three-kilometer long Dome C core from Antarctica that extends back some 750,000 years.

The following video provides an excellent overview of how cores are collected by the Integrated Ocean Drilling Program. Click the play button in the center of the video to watch it.

Video: Core on Deck!: The Journey of how the Samples travel from the Rig Floor to the Core Lab (7:26)

Oxygen Isotopes

Since we cannot travel back in time to measure temperatures and other environmental conditions, we must rely on proxies for these conditions locked up in ancient geological materials.

The most widely applied proxy in studying past climate change are the isotopes of the element oxygen. Isotopes refer to different elemental atomic configurations that have a variable number of neutrons (neutrally charged particles) but the same number of protons (positive charges) and electrons (negative charges). As you might remember from your chemistry classes, protons and neutrons have equivalent masses, whereas electrons are weightless. So, because different isotopes of the same element have different weights, they behave differently in nature.

Oxygen has three different isotopes: oxygen 16, oxygen 17 and oxygen 18. These isotopes are all stable (meaning they do not decay radioactively). O-16 is by far the most common isotope in nature, accounting for more than 99.8% of all oxygen atoms, and O-17 is exceedingly rare, but O-18 is abundant enough in nature to be measured. The masses of O-16 and O-18 are different enough that these isotopes are effectively separated by natural processes. This separation process is known as fractionation. Without going into too much detail, O-16 and O-18 are fractionated by the process of evaporation as well as when minerals, including shells of animals and plants, are precipitated from water. The main driver of the evaporation effect in most geological intervals is the amount of water that has been removed from the ocean and is sequestered in ice (see video clips below). Evaporation selectively removes the lighter isotope, O-16 from water leaving higher concentrations of the heavier isotope, O-18. Thus, shells and other materials formed in the ocean tend to have more O-18 during colder, glacial intervals than during warmer intervals. However, as a portion of the evaporate ends up falling as snow which is then converted to ice, the reverse holds for ice sheets, such as those in Greenland and Antarctica. Ice formed in glacial intervals has more O-16 than ice grown in warmer times. Superimposed on the evaporation effect is a temperature effect. Shells that grow in warmer water hold more O-16 than shells that grow in colder water, as explained in the following clips:

Video: Stable Isotopes 1 (1:34)

Video: Stable Isotopes 2 (1:42)

A range of materials from the geological record demonstrates significant variations in their levels of O-16 and O-18 as a result of changes in climate. These changes are a combination of temperature and evaporation effects. The materials include shells of clams, corals, and plankton called foraminifera that inhabit the surface of the ocean, as well as cores taken from glacial ice that has formed at high latitudes (a term that describes the cooler regions closer to the poles) and high elevations, and even stalactites formed in caves. Later in this module, we will study the isotope records of several intervals of rapid climate change in the geological record.

Other temperature proxies: Mg/Ca and TEX-86

As we have seen, there are a number of assumptions that need to be made to convert oxygen isotope values to temperature. At times when there were major ice sheets, the proxy can be difficult to apply and in fact, most applications of oxygen isotopes during cold intervals focus on changes in ice volume, not warming or cooling. Thus, it is fortunate that there are other proxies that can be applied to determine temperature. Here, we consider two of these, the ratio of magnesium to calcium in fossil CaCO3 shells and the TEX-86 ratio in organic carbon.

Mg/Ca

Organisms that construct shells of the two CaCO3 polymorphs, aragonite and calcite, include magnesium in very small amounts in their shells. As it turns out that amount is heavily dependent on temperature as a result of somewhat complex kinetic processes. The higher the temperature, the more Mg is included in the shell. Unfortunately, there are a few complications that need to be considered. Like oxygen isotopes, the Mg/Ca proxy requires that all of the calcite or aragonite is original and that it has not been altered during burial of the shell. Second, the ratios appear to differ from one species to another, thus there have to be careful calibrations for individual species. These calibrations focus on developing a relationship between the Mg/Ca of the shell and the temperature of the water in which it grew. This can only be done in living obviously. For extinct species, assumptions need to be made and they result in some uncertainty. Workers are careful to restrict their Mg/Ca analysis to individual species. Third, the Mg/Ca ratio of seawater has changed slowly over time and this ratio can impact the absolute temperatures when the focus of study extends over many millions of years. The proxy is most useful when the study interval is short, the fossil shells are unaltered and the species are still living. Since Mg/Ca is not impacted by the volume of ice as are oxygen isotopes, the proxy is often used along with the latter system to determine the volume of glaciers in cold geologic period.

TEX-86

TEX-86 is a complex organic (meaning material that is composed largely of carbon, nitrogen, and phosphorus) proxy. This proxy is applied to compounds made by archaea or single-celled prokaryotes (organisms that do not have a differentiated nucleus). The archaea in question live in the oceans and are called crenarchaeota, and the TEX-86 proxy is based on changes in the composition of the lipid membranes of these organisms. The detailed structure of the membrane changes with temperature. The proxy is called TEX-86 because it is based on the tetraether index in carbon-86 atoms (you do not need to remember this!). TEX-86 has been calibrated in the modern oceans, meaning the indices of samples have been compared to the temperatures of the water in which they grow. The main uncertainty of the system revolves around the fact that the organisms of interest do not live right on the ocean surface so the temperatures theoretically are lower than surface levels. In addition, it appears that the index is impacted by the growth rate of the organism. Application of the TEX-86 is restricted to marine sediments and is most useful where there are no shells to measure for oxygen isotopes or Mg/Ca. Regardless of the uncertainties, the TEX-86 proxy is extremely useful to check results from these other two systems. Where two different proxies agree for the temperature of an interval the results are much more certain.

Marine Thaumarchaeota basis of the paleothermometer Fluorescence in situ hybridization.

Credit: FISH; St. Barbara Channel, 80 m depth, photo by E.F. DeLong. Used with permission.

TetraEther indeX of lipids with 86 carbon atoms. You will later get to see how TEX-86, Mg/Ca and Oxygen isotopes compare in the lab for this module.

Credit: Appy Sluijs. Used with permission.

Carbon Isotopes

Oxygen is not the only abundant element with isotopes that can be used as environmental proxies. The isotopes of the element carbon also are used as proxies in environmental reconstruction. Carbon has a number of isotopes including C-14, which is radioactive, and two stable (i.e. non-radioactive) isotopes, C12 and C13. C-14 are widely applied in dating recently formed natural materials that contain significant amounts of carbon such as shells, charcoal, and even materials that contain trace amounts of carbon such as pots and cloth, since its abundance can be accurately determined using an accelerator, and its rate of decay is rapid.

Video: Stable Isotopes 3 (1:40)

However, because of its rapid decay, C-14 disappears in a short time (about 100,000 years). The isotopes C-12 and C-13 are not radioactive, are common in natural materials, and are fractionated by environmental processes in a way that they can be applied as proxies. C-12 has 6 protons and 6 neutrons and is the most abundant of all of the isotopes of carbon. C-13 has 6 protons and 7 neutrons and is much less abundant than C-12, but still can be measured by mass spectrometry (the technique used to measure the abundance of stable isotopes). C-12 and C13 are strongly fractionated during photosynthesis, when plants convert CO₂ and sunlight into food; it requires less energy for a plant to incorporate an atom of the lighter isotope C-12 than it does an atom of C-13. In fact, when plant material is formed via photosynthesis, thousands of times more C-12 is incorporated than is C-13. As we will see, carbon isotopes are key indicators of the sources of greenhouse gas in the atmosphere, both today and in the past. C isotopes can be used as proxies for nutrient cycling and deep water circulation, as well as the source of plant materials.

Tree Rings

Tree rings have been widely applied in climate reconstruction. Rings are produced as a result of seasonal variations in the growth rate of tree bark. Wood produced during rapid growth, for example in spring in temperate regions, tends to be less dense than wood produced during slower growth phases during the late summer and early autumn. Where seasonal growth is highly variable, as in temperate regions, rings are strongly etched in the bark.

Tree Rings

Credit: Tree Rings by Jonathon Colman is licensed under CC BY-NC-ND 2.0

As shown in the image above, tree rings are especially powerful paleo climate indicators because the number of rings can be counted to determine the age of the tree or the age of the ring that is providing paleoclimate information. The width of tree rings can be interpreted in terms of temperature and precipitation variations; however, there are a number of factors that complicate this interpretation.

Video: Tree Rings Explained (1:54)

Fossils

Fossils represent the remains of life that once thrived in the oceans or on land. They range in size from dinosaurs to shells of clams and oysters, all the way down to microscopic remains of algae, fractions of a millimeter in size. Although many groups of fossils can yield information on climate and environment, in this module we focus on the fossil remains of plants as well as those of the microplankton and microbenthos. These microfossils include a number of key groups that live in the ocean and in freshwater bodies such as lakes.

A fossil trilobite from the Paleozoic Era.

Credit: Trilobite by Joanna Bourne is licensed under CC BY-NC-ND 2.0

Diatoms

The diatoms are a group of plankton that makes their shell out of opal (microcrystalline SiO₂). These organisms are autotrophic, meaning they fix CO₂ dissolved in seawater or lake water via photosynthesis. The delicate diatom shell, or frustule, consists of two valves that fit together like a pillbox. The diatom cell lives inside the frustule and the plastids or chloroplasts use light as a source of energy in photosynthesis. Diatoms thrive where nutrient and silica levels are high: in lakes, the coastal ocean, and in the Southern Ocean, the ocean that circles the globe just north of Antarctica. Diatoms reproduce asexually (vegetatively), and, where conditions are right and nutrient levels elevated, diatoms can rapidly produce cell counts of many millions of cells per liter of seawater. In certain cases, as we will see in Module 7, these count levels are called red tides, and in some of these cases, diatoms produce toxins.

Dinoflagellates

The dinoflagellates are a group of plankton that is mostly autotrophic but can also be heterotrophic. The dinoflagellates thrive in the coastal ocean and have a complex life cycle in which a floating, motile stage alternates with a dormant resting stage. The dinoflagellates make their tests out of cellulose, which is rarely preserved; however, the cyst stage of dinoflagellates is composed of sporopollenin, a polymer that preserves readily during burial. The dinoflagellates are the group primarily responsible for red tides; we will discuss them in detail in Module 7.

Electron Microscope Image of Dinoflagellate cysts

Credit: © Hargraves/Darling. Used with permission.

Coccolithophores

The coccolithophores, like the diatoms and dinoflagellates, are a group of marine autotrophic plankton. The coccolithophores, however, make their delicate shell out of the mineral calcite, or CaCO3, and have a more ocean-wide distribution than the diatoms. Today, the coccolithophores are adapted to live where the diatoms and dinoflagellates cannot, in parts of the ocean where nutrient levels are lower. Like the other groups, the coccolithophores are able to produce extremely large numbers of cells per liter in so-called blooms. Unlike the other two groups, the coccolithophores are threatened by ocean acidification, as we will see in Module 7.

A coccolithophore of the species Coccolithus pelagicus

Credit: Wikipedia, licensed under CC BY 2.5

Pictures of Foraminifera and Coccolithophores

Foraminifera

The foraminifera are zooplankton, meaning they consume tiny phytoplankton to get their energy. The foraminifera, or forams as they are known, make their shell out of calcite or CaCO₃. The delicate foram shells have a variety of shapes and are adapted to the life mode of the individual species. The forams have two very different modes of life. The planktonic forams float passively in the water column, whereas the benthic forams live on the seabed, either resting on the bottom or burrowing into the soft sediment. The foraminifera have been critical in the reconstruction of ancient ocean temperatures via stable isotopes and trace element proxies, as we will see below.

The following video provides an overview of the foraminifera.

Video: Fossils and Rocks (2:10)

Radiolaria

The radiolaria are zooplankton like the foraminifera. The radiolaria are one of the longest-lived plankton groups, extending back to the early part of the Cambrian period about 500 million years before present. The group makes a spaceship-like test out of opal (SiO₂) and, like the diatoms, is restricted to places in the ocean where nutrient levels are elevated. The radiolaria thrive in the tropical regions where upwelling currents bring nutrients to the surface ocean.

Microscopic shells made of opal produced by radiolaria. Diameters of the shells are about a third of a millimeter

Credit: Radiolaria by dedree drees is licensed under CC-BY 2.0

Plant Fossils

Plant fossils have been widely used in paleoclimate studies. These studies are based firmly on the distribution of modern plant species and on physical characteristics of those plants that are related to climate. The distribution of tropical plant species such as palms, cycads or groups of ferns can be used as evidence for warmer climates in the geologic record, for example, during the Cretaceous when these groups are found in places such as Alaska that have much colder climates today. Plants also provide more quantitative proxies. For example, the morphology of the margin and size of leaves is closely related to temperature and precipitation, respectively. Warmer climates tend to produce leaves that are smoother, whereas colder climates tend to produce leaves that are more jagged in shape. Wetter climates tend to produce leaves that are larger than drier climates with the same temperatures. Using the shape and size of leaves from modern locations with a range of temperatures and rainfall, equations relating margin and size to temperature and rainfall have been developed and these allow these climatic parameters to be determined from studies of ancient leaves.

Fossil leaves showing the variety of shapes that can be used to reconstruct ancient climate

Credit: National Park Service (Public Domain)

The density of the stomata, small pores typically found on the underside of leaves that provide a pathway for CO₂ to enter the leaf, are related to the partial pressure of CO₂. Thus stomatal density has been used as a proxy of ancient CO₂

Graph showing the relationship between leaf margin morphology and temperature. Data come from modern leaf species

Credit: Dana Royer, Wesleyan University

Coals/Evaporites

Certain types of sedimentary rocks are, by their very nature, indicative of certain climatic conditions. For example, in the arid subtropical regions, evaporation rates are so high that waters near the margins of the oceans readily evaporate. Once salinities reach certain levels, a sequence of minerals begins to precipitate directly from seawater. These minerals include gypsum (CaSO₄H₂O), anhydrite (CaSO₄), dolomite (CaMg(CO₃)₂), halite (NaCl), and sylvite (KCl). The group of minerals is known as evaporites, and when found in the geologic record, are indicative of arid climates. Today the suite of evaporite minerals is being formed in the Persian Gulf. In the past, very thick sequences of evaporites were deposited in the Mediterranean during the late Miocene (6 million years ago), and in Texas and New Mexico during the Permian (250-300 million years ago).

The evaporite mineral halite (NaCl) formed in Death Valley, California as a result of evaporation of an ancient lake

Credit: Devil's Golf Course by Mark A. Wilson, licensed under CC BY-SA 3.0

Just as evaporates indicate hot and dry climates, occurrences of coal in the geologic record suggest hot and wet climates. Today in tropical and subtropical regions with ample rainfall, trees, and other plants grow quickly. For example, tropical rainforest cover large areas in the Amazon basin, and mangrove forests grow along subtropical coasts in places like the Mississippi Delta and the coast of Florida. When this vegetation dies, it is rapidly buried in sediment in rivers, deltas, and beaches. Once encased beneath hundreds of meters of overlying sediment, and heated and pressurized over millions of years, the vegetation is transformed into coal. So just as evaporites are indicative of hot and dry conditions, coal is a sign of hot and wet conditions in the geological record. Please take a few moments to check out the photographic evidence below.

Coals and Evaporites Examples

Ancient Climate Events

In the next stage of the module, we will consider a number of climate events that took place in the geological record. The events we chose to present have one thing in common; they begin very abruptly, thus they provide some lessons about modern climate change. The events include both warming and cooling intervals and span a large part of the geologic record from over 2 billion years ago to 8.2 thousand years ago.

Some of the most abrupt and dramatic climate changes occurred very recently in Earth’s past, a geologic heartbeat ago if we consider the complete 4.6 billion years of the planet’s history. Materials including sediments deposited in the deep sea, ice formed in massive glaciers, stalactites formed in caves, wooly mammoths, and other large mammals, and spores and pollen of plants, provide evidence for very large and frequent oscillations in Earth’s climate that began about 2.5 million years ago. These oscillations involve the repeated advance and retreat of glaciers in the Northern Hemisphere. At their peak, ice-covered the northern parts of North America, Europe, and Asia, and the climate fluctuations also caused major changes in vegetation and animal habitats, as well as significant changes in ocean circulation.

The Franz Josef Glacier, South Island New Zealand. Rocky material is eroded by the glacier and is called moraine.

Credit: © Tim Bralower. Used with permission.

Glaciers deposit very diagnostic landforms and sediments that are often full of large boulders eroded from wide swaths of land over which the ice has traveled. More than a century ago, geologists determined using such evidence that at the coldest time of the Pleistocene, glaciers covered Edinburgh, Scotland; Moscow, Russia; and Detroit and Chicago in the US. In fact, from the glacial deposits alone, glaciologists had inferred several major advances and retreats of the two major ice sheets, the Laurentide in North America and the Fennoscandian in Europe and Asia.

A drumlin in southern Germany. This landform was deposited under an ice sheet during the last ice age

Credit: Drumlin 1777 from Wikimedia, licensed under CC BY-SA 3.0

At the height of the last major glaciation, known as the Last Glacial Maximum (LGM),18,000 years before the present, ice sheets covered Chicago, Boston, Detroit, and Cleveland (see maps below).

Comparison between summer ice coverage from 18,000 years BP and modern-day observations

Credit: Data/image provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their Web site.

Our understanding of the climate of the Pleistocene surged in the 1950s when coring of sediment began in the deep sea and when the potential of oxygen isotopes in reconstructing ancient climate began to be realized. Cores showed dramatic alternations or cycles in the type of sediment with sharp color changes from red or pink to white or gray. The cycles were found to correspond to changes in the amount of the mineral CaCO₃ that is derived from the shells of deep-sea organisms. The alternations were interpreted as major switches in the circulation of the deep ocean with corresponding changes in the ventilation and corrosiveness of the waters in which the sediments were deposited. Planktonic foraminifera, in different phases of the cycles, were found to have different oxygen isotope ratios that were interpreted as fluctuating sea surface temperature and glacial ice volume.

Prominent CaCO₃ cycles in a Pleistocene core from the southern part of the South Atlantic Ocean

Credit: Ocean Drilling Program, NSF, and JOIDES (Public Domain)

From studying cores of ice and deep-sea sediment, we now know that there have been more than 25 different advances and retreats over the last 2.5 million years. In fact, as the sediment and ice cores were gleaned, it was found that a number of the proxies fluctuated in a regular and periodic fashion. It had long been known from theoretical astronomy that the Earth’s orbit around the sun varies as a function of regular fluctuations in the shape of the orbit (called Eccentricity), the tilt of the axis of rotation (called Tilt or Obliquity), and the wobble of that axis (called Precession) (please see video below). Since these fluctuations control the amount of solar insolation received at the Earth’s surface, there was known to be a strong climate effect. These changes are cyclic with regular frequencies (the time from beginning to end of one cycle). From astronomical theory, the Eccentricity cycle is known to have a frequency of 100,000 and 400,000 years (two different cycles), Tilt/Obliquity a frequency of 40,000 years and Precession a frequency of 20,000 years. The Pleistocene proxy records were found to contain some of the same frequencies as these orbital fluctuations, and this is proof that changes in the amount of solar insolation were the ultimate control on the Pleistocene ice ages. The figure below shows an oxygen isotope record with prominent 41,000 and 1000,000-year cycles.

Foraminiferal Oxygen isotope cycles from a deep-sea core showing glacial and interglacial cycles that intensify starting about 2.6 million years ago

Credit: Five Myr Climate Change from Wikimedia, licensed under CC BY-NC-ND 2.0

The video below provides an overview of how the Earth's orbit varies and how it affects climate.

Video: Earth's Orbit and Climate (1:49)

The figure below shows temperature (derived from O-isotopes), atmospheric CO₂ measured from gas bubbles, and dust concentrations in ice samples from the famous Vostok ice core from Antarctica. The climate fluctuations shown by these data are some of the most abrupt and regular in the geologic record. The data show a close relationship between temperature and atmospheric CO₂ content that is not fully understood but probably is related to intensified ocean circulation during glacial intervals that led to vigorous upwelling in the Southern Ocean. The Southern Ocean is one of the most productive areas in the oceans and intensified upwelling and photosynthesis could have led to the increased removal of CO₂ from the atmosphere. Intensified atmospheric circulation during the glacial periods is thought to have transported more dust over Antarctica causing the increase in dust concentrations.

Estimated temperature and measured CO₂ and dust concentrations for the last 400 thousand years in the Vostok ice core

Click for a text description of the Temperature variations, Carbon Dioxide, and Dust Concentration graphs.

The image displays three line graphs, each representing different environmental data from the Vostok ice core in Antarctica, plotted over time. The graphs are arranged vertically on a black background with white grid lines, and each is drawn in a distinct color.

1. Top Graph (Blue) - Temperature (Derived from O-Isotopes): This graph shows temperature variations over time, inferred from oxygen isotope ratios in the ice core. The blue line fluctuates significantly, with several peaks and troughs indicating abrupt climate changes. It starts at a moderate level, dips slightly, rises to a peak, experiences smaller fluctuations, and then trends upward toward the end, suggesting a general warming trend with periodic oscillations. These fluctuations are some of the most abrupt and regular in the geologic record, reflecting significant climate shifts.
2. Middle Graph (Green) - Atmospheric CO2 (Measured from Gas Bubbles): This graph depicts atmospheric CO2 concentrations, measured from gas bubbles trapped in the ice. The green line shows a fluctuating pattern with a noticeable upward trend over time. It begins at a lower level, dips slightly, rises sharply to a peak, dips again, and then steadily increases with minor fluctuations, ending at a higher CO2 level than at the start. The data suggest a close relationship between temperature and CO2 content, potentially linked to intensified ocean circulation during glacial periods, particularly in the Southern Ocean, where vigorous upwelling and increased photosynthesis may have removed CO2 from the atmosphere.
3. Bottom Graph (Red) - Dust Concentrations: This graph illustrates dust concentrations in the ice samples, plotted in red. The line is highly erratic, with frequent, sharp spikes and drops, resembling a noisy signal. The spikes vary in height, with some taller peaks early on, while the overall amplitude of fluctuations decreases slightly toward the end, though the line remains volatile. The increased dust concentrations are thought to result from intensified atmospheric circulation during glacial periods, which transported more dust over Antarctica.

The graphs collectively highlight the relationship between temperature, CO2 levels, and dust concentrations over time, with the Vostok ice core data revealing abrupt and regular climate fluctuations. The lack of specific axis labels makes it difficult to determine the exact time scale or numerical values, but the grid lines help visualize the relative changes in each dataset. The data suggest that glacial intervals, marked by lower temperatures, likely led to intensified ocean and atmospheric circulation, influencing CO2 removal and dust transport in the Southern Ocean and Antarctica.

Credit: Vostok Ice Core from Wikimedia, licensed under CC BY-NC-ND 2.0

As the study of the Pleistocene period has intensified, we now know that glacial-interglacial cycles also corresponded to:

• more pronounced temperature changes in the high latitudes than the low latitudes (regions near the tropics). Temperature changes in high latitude regions are thought to be about 10^oC between glacials and interglacials. The variation in oxygen isotope ratios of tropical planktonic foraminifera are thought to be largely a result of ice volume changes, not temperature changes;
• abrupt swings in atmospheric circulation with wind belts such as the Intertropical Convergence Zone (ITCZ) shifting latitudinally by several degrees;
• rises and falls of sea level by up to 120 meters and advances and retreat of the shoreline across the continental shelves;
• massive floods of freshwater down rivers such as the St. Lawrence and Mississippi rivers at times when the ice melted;
• northward and southward movements of vegetation belts across the continent.

You will notice one key thing from the temperature plot above. The temperatures were higher than today about 125,000 years ago -- this interval is known as isotope Stage 5e or more informally the last interglacial. Since this time was well before humans were producing large volumes of CO₂ the warming was a result of natural processes related to Earth’s orbital configuration and insolation. So this is a key interval and one very interesting facet of it is that sea levels were higher than they are today likely by between 2 and 5 meters. Since temperatures were about 2 degrees C higher during the last interglacial, this gives us some important constraints on how fast sea level may rise in coming decades depending on CO₂ emissions.

The last glacial peak occurred 18,000 years ago, and since that time the planet has been steadily warming (with a number of reversals as we will see shortly). Since these fluctuations in the Earth’s orbit continue, at some stage in the future, Earth will begin to cool. At the last glacial, maximum temperatures were considerably cooler in the high latitudes. Also, the sea level was over 120 meters lower.

Video: Feedback (:57)

These fluctuations have significant potential in informing us about future climate changes. For example, the shape of the glacial climate cycles illustrates that the warming arm is rapid but the cooling arm is much slower. This distinction tells us about the mechanics of positive and negative feedbacks in Earth's climate.

Check Your Understanding

At the other end of the temperature spectrum from the PETM are the Snowball Earth events that occurred in the Proterozoic Era (543 million to 2.5 billion years ago), a time when only very primitive organisms inhabited the planet and oxygen levels were considerably lower than today. The aptly named Snowball Earth represents some of the most bizarre climate conditions ever experienced by the Earth, with strong evidence for ice sheets in equatorial locations. The evidence comes from sedimentary layers with a mixture of fine particles and large boulders, that look just like the rocks deposited by modern glaciers. These layers are found at locations known to be near the poles, which is not surprising, but also at those that are thought to have been near the equator, which is really unusual. This apparent global distribution of layers is why the events are termed "Snowball Earth". The main snowball events occurred about 2220 million years ago (2.22 billion), 710 million years ago and 640 million years ago.

Reconstruction of the globe covered in ice during a Snowball Earth event.

Credit: NASA (Public Domain)

Underneath and at the front of modern glaciers lie thick sediments that are characterized by a wide mixture of different grain sizes, from clay all the way up to large, angular boulders. Very similar, jumbled rocks are found in the Snowball events. It is not a surprise that such deposits exist at high latitude locations where large ice sheets have existed at a number of times in the past. What is so unusual is that glaciers are thought to have covered the tropics. Continents have been moving throughout geologic time, so it's natural to ask how we know the locations of Snowball deposits back in the Proterozoic. The best evidence for this is the orientation of magnetic grains in the sediments.

Video: Evidence for Tropical Ice Sheets (1:36)

So how cold was the Snowball Earth? If, as it appears, the planet was covered by ice from pole to pole, all of the sun’s radiation would be reflected back to space and temperatures must have been frigid. Models suggest that the global average temperature was about -50^oC and the temperature at the equator would be similar to that at the poles today, about -20^oC. With these conditions, most parts of the planet would have been under ~1 km of ice. With no ability to retain heat, it is hard to imagine how Earth recovered from a Snowball event, but surprisingly the end of the glacial events appear to have been extraordinarily abrupt.

An integral part of the Snowball story is that the glacial layers are overlain by layers called cap carbonates, shown in the image to the right. These layers contain no glacial material at all, being composed entirely of the minerals calcite (CaCO₃ and dolomite (CaMg(CO₃). These carbonates look like they were deposited in relatively shallow water on platforms in a similar setting to the modern-day Bahamas. The carbonates often have large ripples formed by strong underwater currents, as well as evidence for bacterial activity.

Maroon glacial deposits at the base of the photograph from a 716 million-year-old Snowball Earth event in Yukon province Canada. The gray unit at the top of the photograph represents the terminating cap carbonate event

Credit: Francis Macdonald, Science Daily

The juxtaposition of glacial deposits and cap carbonates have fascinated geologists and spurred a lot of debate. Among the major questions that have been puzzled over are how glaciers could have melted so fast and how such concentrated carbonate material could have been deposited. The abrupt melting of ice must have been driven by the build-up of CO₂in the atmosphere. Atmospheric CO₂ is provided by processes such as volcanism, which presumably continued during the Snowball. CO₂ is removed by processes such as photosynthesis and weathering of rocks, however, neither of these processes would have been active during a Snowball. Thus, it is thought that CO₂would have risen unchecked to a point when a super greenhouse radiative effect was triggered, global temperatures rose rapidly, and the ice melted.

Boulders are indicative of glacial deposition in Snowball Earth sediments from Namibia, Southern Africa. The tan rocks at the top of the photograph show the sharp transition to the cap carbonate layer

Credit: Michael Hambrey from Glaciers Online

The origin of highly concentrated carbonate is likely connected to the ability of weather reactions to increase the saturation of minerals such as calcite and dolomite in the ocean. Perhaps more perplexing is how life, primitive as it was at the time, survived on a glacial planet. Available fossils suggest that organisms at the time included both cyanobacteria that used chemicals as a source of energy and also red algae that used light. Microbes are known to exist close to the surface of modern glaciers. Perhaps the ice was considerably thinner near the equator, or possibly there were small ice-free refuges. The Snowball remains a fascinating climatic puzzle.

Cap carbonates overlying the Snowball Earth glacial deposits. These layers are from 600 million-year-old rocks from China.

Credit: Yangtze Platform from Flickr is licensed under CC BY-NC-ND 2.0

The Paleocene-Eocene Thermal Maximum (PETM) at 56 million years before present is arguably the best ancient analog of modern climate change. The PETM involved more than 5^oC of warming in 15-20 thousand years (actually a little slower than rates of warming over the last 50 years), fueled by the input of more than 2000 gigatons (a gigaton is a billion tons!) of carbon into the atmosphere. The PETM was associated with the largest deep-sea mass extinction event in the last 93 million years and remarkable diversification of life in the surface ocean and on land. Because of its potential significance, geologists have swarmed to study the event, and it's been the topic of great interest, and more than a little controversy, for the last 25 years. For this reason, also, we devote considerable attention to the event here.

Evidence for global warming at the Paleocene Eocene thermal maximum from a core located off the coast of Antarctica.

Click for a text description of Evidence for Global Warming image.

The benthic foraminifera extinction is illustrated by the red arrow. Decreasing oxygen isotope values indicate the warming of the surface ocean (planktonic foraminiferal isotope values) and deep ocean (benthic foraminiferal isotope values). Decreasing carbon isotope values indicate the input of greenhouse gas from either organic (i.e. biological) or volcanic reservoirs. During 170k years C levels spiked, dropped but not to the previous level, O spiked and dropped back.

• Left Section (Core Description):
  • Vertical sediment core section labeled "Site 690"
  • Depth scale: 166 to 176 meters below seafloor (mbsf)
  • Geological periods marked:
    • Upper Paleocene at 172 mbsf
    • Paleocene-Eocene boundary at ~170 mbsf (indicated by a red arrow)
• Main Diagram (Isotope Graphs):
  • Two side-by-side graphs plotting isotopic data vs. depth (166 to 176 mbsf on y-axis)
  • Left Graph (δ¹³C):
    • Carbon isotope ratio (δ¹³C) in per mil (‰), ranging from -2 to 4‰ on x-axis
  • Right Graph (δ¹³O):
    • Oxygen isotope ratio (δ¹³O) in per mil (‰), ranging from -3 to 1‰ on x-axis
  • Data points connected by lines, representing:
    • Benthic (bottom-dwelling) species
    • Thermocline (mid-depth) species
    • Mixed layer planktonic (surface-dwelling) species
  • Labels for each category are provided on the diagram
• Key Observations:
  • Significant isotopic excursions around the Paleocene-Eocene boundary (~170 mbsf)
  • Notable shifts in both δ¹³C and δ¹³O values
  • δ¹³C record shows a sharp drop around the boundary
  • Data points exhibit variability, reflecting major environmental changes during the transition

Credit: © Kennett & Stott (1991). Used with permission.

The evidence for warming comes from a variety of sources, the most compelling of which is the oxygen isotopes of planktonic and benthic forams in deep-sea cores. These data suggest significant warming of 6-8^o C of ocean surface waters at a range of latitudes, as well as of deep waters. We don’t need to go into the detail here, but this converts to 4-5 ^oC of the average Earth temperature, which is pretty significant. For comparison, global warming since the industrial revolution is about 1.2 ^oC. Today, warming is a result of human activities, but only very primitive primates were around at the time of the PETM, and they did not drive cars (!), so what caused the warming back then?

Corresponding to the oxygen isotope shift is a large and negative 4 to 5 per mil change in carbon isotopes that is used to define the geological extent of the event. The isotope excursion has been identified in sediments deposited in the ocean and those laid down in terrestrial environments such as lakes and rivers. It is called a golden spike because it can be correlated around the world, and it marks a precise time horizon, in fact, the excursion is now the formal definition of the boundary between the Paleocene and Eocene eras. Moreover, the excursion in terrestrial sediments allows us to correlate the changes that occurred on land during the PETM with those that took place in the ocean. Below is what the PETM looks like in the Big Horn Basin in Wyoming.

Photograph of the PETM in the Big Horn Basin, Wyoming. These rocks were deposited by rivers and on floodplains. They contain an amazing fossil record, including early primates and leaves.

Credit: © Peter Wilf. Used with permission.

The content of CO₂ in the atmosphere increased 3-4 times during the PETM. Regardless of whether it comes from cars, factories or from non-human sources, CO₂ is a greenhouse gas, and it causes the atmosphere to warm. As a consequence, surface ocean temperatures at the peak of the event were extremely warm, especially in the high latitudes. Off the coast of Antarctica, a location today that is close to freezing, the oceans were about 20^oC (68 ^oF) at the peak of the PETM! Imagine jumping into the ocean from Antarctica today! Tropical ocean temperatures were really hot. A recent paper indicates that temperatures off the coast of West Africa were 36 ^o C which is 97 ^oF! Now I’ve been swimming in Miami in August, and it feels like a bathtub at 88 ^oF, but 97 ^oF is virtually uninhabitable! The PETM was also associated with major changes in the properties of the deep ocean. Unlike today, when deep ocean waters are characterized by temperatures close to freezing, PETM deep waters were 10-15^o C. This may not seem that warm all considering, but there is no doubt that is caused a fundamental change in the way circulation in the ocean worked. Sea level was quite a bit higher during the PETM, and the continents were in different positions, as shown on the map below.

Map showing the distribution of continents around the Atlantic Ocean during the PETM. The map shows that sea levels were higher.

Credit: © Chris Scotese. Used with permission.

Likely, the cause of these warm deep waters was that they came from different surface ocean locations than they do today (see Module 6 for more detail on the sources of modern deep waters), combined with the warming that took place in the surface ocean. As warmer waters hold less oxygen than cold waters, PETM deep waters in many locations likely were possibly close to a condition that is known as hypoxia (we will learn more about this in Module 6). The word hypoxia does not sound too distressing but imagine for a minute that you are a fish, and you needed oxygen for respiration. Hypoxia would have been truly awful for such a creature! Finally, the input of so much CO₂ into the ocean caused ocean waters to become more acidic and led to a condition known as ocean acidification (sorry to keep jumping ahead, but we will learn a lot more about this in Module 7!). Acidification of the deep ocean during the PETM is well accepted and is observed by complete dissolution of all CaCO₃ shells that rained down on the seafloor. For creatures that require a shell for protection against predators and to protect the soft cellular parts from the harsh ocean, acidification would have been disastrous. By comparison, the shallow ocean experienced a much more minor decrease in its acidity and shelly creatures continued to thrive there.

Photographs showing the Paleocene Eocene thermal maximum recovered in cores taken in the Pacific Ocean. The benthic foraminiferal extinction level is indicated by the horizontal arrow. The change in the color of the sediment is a result of deep ocean acidification.

Credit: Tim Bralower © Penn State University is licensed under CC BY-NC-SA 4.0

Before you read this section, please make sure that you have read the section on different fossil groups under Proxy techniques: Fossils and Rocks

Paleontologists have studied the response of many different groups of organisms in the PETM fossil record, from tiny plankton such as foraminifera in the oceans all the way up to small primates on land. The event also has a spectacular plant record, especially if you love tropical species. One of the prime areas on land is in the spectacular Big Horn Basin in Wyoming where geologists have scoured the rocks for primates and plant material and an exceptional record of the PETM exists, correlated precisely to the deep sea record using the carbon isotope shift. The land record shows the introduction of small primates and tiny ancestors to horses right near the beginning of the PETM along with a burst of tropical vegetation (refer to photos). It's almost impossible for the primates and horses to evolve so quickly, what is much more likely is that warming during the event led to the opening of land bridges in places such as Alaska and Siberia and that appearance in North America is merely an immigration from another continent such as Eurasia. The beautiful Big Horn plant record shows a change from moist subtropical vegetation before the event to dry subtropical vegetation during it. Imagine the landscape in northern Florida changing in a geologic heartbeat to that of dusty southern Texas.

The task of studying fossils is the ocean in part of the PETM is somewhat difficult. PETM cores from the deep sea are devoid of CaCO₃ in the early part of the PETM as a result of acidification of the deep ocean. This acidification was so strong that shells that had already been buried on the bottom of the ocean also dissolved! The chief biological impact of the PETM was the mass extinction of deep-sea benthic forams. Approximately 35-50% of all species of this group went extinct during the event. Almost certainly, acidification was the main culprit, but it couldn’t have helped that the sea floor was often hypoxic and, in some parts of the ocean, food supply was limited. Life on the bottom of the ocean during the PETM was not fun, that is for sure!

Benthic foraminifera from PETM cores from New Jersey showing changes across the event

Credit: © Peter Stassen. Used with permisssion.

Interestingly, the benthic forams were almost unaffected by the environmental impacts at the Cretaceous-Tertiary (K-T) boundary, a time when the dinosaurs and many other groups went extinct. This paradox reflects the fact that the PETM had a far more severe impact on deep-water environments, whereas the K-T boundary has more impact on the surface ocean and land.

Evidence for ocean acidification at the sea surface at the very outset of the PETM is more elusive, partially because the fossil materials of coccolithophores and planktonic foraminifera that would hold the evidence were dissolved as they rained down through the acidic deep ocean or lay on the seafloor. However, later in the event, the fossil record is spectacular. Coccolithophores display signs of deformation, which has been observed in modern species grown in the lab under elevated CO₂. So, it could be that there was a minor amount of acidification in the ocean surface. This is confirmed by a proxy for pH based on boron isotopes. But acidification was clearly not the driver of change among animals and plants that lived at the sea surface.

Although extinction was focused in the deep sea, the PETM actually did result in abrupt changes to life in the surface ocean, one of the most dramatic of which was the occurrence of blooms of dinoflagellates in the coastal oceans.

These dinoflagellate blooms, which can be thought of as ancient red tides, are a sign of major environmental stress in the coastal zone possibly as a result of the increased runoff of water from the land. This runoff delivered nutrients from the land which resulted in a process called eutrophication which led to rapid blooms of algal growth and hypoxia on the sea floor. Elsewhere in the oceans, the environmental changes during the PETM led to shifts in the distribution of plankton groups, with tropical species invading the high latitudes and high-latitude species dwindling in abundance. As we said earlier, the tropics could have been like a warm bathtub (close to 100^o F) so you can imagine all of these tiny coccolithophores and foraminifera fleeing these conditions in a mass exodus! Clearly, temperature was the big factor for surface plankton away from the coastal zone.

Another very cool aspect of the change in plankton is shown by the tropical and subtropical planktonic foraminifera, which contain a unique set of morphologies during the PETM. It’s unclear whether these morphologies represent new species or just variations of existing species, but it's pretty amazing that such variation arose in a geologic heartbeat and then disappeared at the end of the event. Quite possibly, these new forms represent the adaptation to a deeper environment, the species were forced to exodus the very surface of the ocean because of the uninhabitable conditions.

One final thing about the plankton during the PETM. At the end of the event, the distributions and abundance of different taxa reverted to close to where they were before. The structure of communities changed a little, but for the surface species, it was almost as if the event never happened. Life there was very resilient, but on the sea floor, it was a different story, life was altered forever.

Deformed nannoplankton (related to coccolithophores) from PETM boreholes in New Jersey, possible signs of ocean acidification

Credit: Tim Bralower © Penn State University is licensed under CC BY-NC-SA 4.0

The chief biological impact of the PETM was the mass extinction of deep-sea benthic forams. Approximately 35-50% of all species of this group went extinct during the event. Although extinction was focused in the deep sea, the PETM actually did result in abrupt changes to life in the surface ocean, one of the most dramatic of which was the occurrence of blooms of dinoflagellates in the coastal oceans.

Fossils of the dinoflagellate cyst Apectodinium from the Paleocene Eocene thermal maximum

Credit: © Appy Sluijs. Used with permission.

These dinoflagellate blooms, which can be thought of as ancient red tides, are a sign of major environmental stress in the coastal zone, possibly as a result of the increased runoff of water from the land. Elsewhere in the oceans, the environmental changes during the PETM led to shifts in the distribution of plankton groups, with tropical species invading the high latitudes and high-latitude species dwindling in abundance. However, at the end of the event, the distributions and abundance of different taxa reverted to close to where they were before the PETM.

The burst of CO₂ proposed for the PETM is consistent with the carbon isotope excursion as well as the rapid warming. Because the PETM involved very rapid warming and was caused by a burst of greenhouse gas, the event has been used to model the potential effects of modern climate change on life and the environment.

Faunas from the PETM

The source of the greenhouse gas is still the subject of active debate. One thing we know for sure, the PETM CO₂ could not have come from humans or those early primates! There are five potential candidate causes: (1) gas hydrates in marine sedimentary rocks, (2) coals in terrestrial sedimentary rocks, (3) extensive wildfires, (4) melting of permafrost, and (5) volcanism in the North Atlantic Ocean coincident with the opening of the sea between Norway and Greenland. Each of these sources could have liberated sufficient CO₂ or methane (CH₄) to cause the warming and the carbon isotope excursion. This is explained in the video below.

• Gas hydrates are ice-like structures that hold methane (CH₄) that, like CO₂, is a powerful greenhouse gas. These compounds are only stable at a combination of pressures and temperatures not found at the surface. Today, these conditions exist several hundred meters below the seafloor along continental margins like off the coast of Florida, but in the past, they may have been stable at shallower burial depths.
• Coal is found interlayered in rocks under the surface in Norway and Greenland, and it has been hypothesized that volcanic lava injections in the subsurface could have driven methane from the coal and released it to the atmosphere.
• There is some evidence for wildfire during the PETM. Sediments deposited during the event in New Jersey and Maryland contain charcoal. However, so do sediments above and below the event, so this is not unique.
• Permafrost is like frozen soil that is often rich in plant material, contains a lot of methane and is a possible source of warming today. The concern is that warming due to human CO₂ emission will melt permafrost, allowing it to release its methane into the atmosphere. Since the Earth was already warm before the PETM, there was probably not a lot of permafrost, so this mechanism is somewhat unlikely.
• Finally, volcanism occurred in the North Atlantic as the ocean opened up between Norway and Greenland. This volcanism included the injection of horizontal and vertical layers of magma (magma is lava below the Earth’s surface) as well as the release of volcanic CO₂ into the oceans and ultimately the atmosphere.

Geologists have several ways to decide between these different mechanisms. There are independent means of determining the sources of carbon. The best way is the carbon isotope ratio of the different sources, which is very different. For example, methane hydrate has a ratio of – 60; coal and permafrost are about -20, while volcanism has a ratio of about -6. This means to cause an isotope excursion of -4 to -5, there needs to be almost 10 times more volcanic carbon than methane hydrate carbon.

Another way is the volume of carbonate dissolved by acidification in the deep sea. This can be estimated by looking at changes in the amount of CaCO3 in different sites. The third is from the magnitude of the change in pH, determined from the boron isotope proxy. Since the results from one proxy are not unique, geologists must use two proxies to constrain the source of CO₂. These estimates give quite different results, unfortunately. Estimates from carbon isotopes and carbonate point to a source such as permafrost or coal, or a mix of volcanism and methane while those from boron and carbon isotopes and also point to a mixture from volcanism and one of the other sources. Thus, it is likely that there was a mixture of sources of greenhouse gas that fueled the PETM.

One of the key aspects of this debate is the evidence for some of these mechanisms actually exist. We know that there was volcanic activity in the North Atlantic, and dates from these lavas are almost precisely the same as the PETM. We know that this volcanic magma was injected through coal. There are also signs of disturbance on the sea floor off the coast of Florida that could have been caused by the release of methane hydrates. The evidence is thus stronger for volcanism, coal, and methane hydrate than it is for permafrost and wildfire, but the problem is far from solved.

One other key piece of evidence is the rate of CO₂ addition. Methane hydrate, coal, and permafrost tend to be released in a more catastrophic fashion, whereas volcanism tends to be a little slower over time. If CO₂ is released quickly, then a large part of it is absorbed in the surface ocean, leading to surface ocean acidification. On the other hand, CO₂ added slowly generally bypasses the surface ocean and acidifies the deep. The lack of evidence for significant surface ocean acidification is more consistent with slow emission and volcanism.

One of the most important parts of the PETM is that it allows us to learn how the Earth operates in a warm mode with higher CO₂ levels than today. As we learned, the current CO₂ concentration in the atmosphere is 400 parts per million (ppm) and the PETM levels were likely double or triple this concentration (800-1200 ppm). Warm conditions during the height of the PETM led to increased break down of minerals, a process called weathering, and this removed CO₂ from the atmosphere. It could be that increased weathering in the warm PETM atmosphere was the beginning of the end of the event. In addition, the process whereby the ocean absorbed CO₂ leading to dissolution of CaCO₃ would also have led to the termination of the event.

Video: Paleocene Eocene Thermal Max (2:00)

Lab 1: Global warming during the Paleocene-Eocene Thermal Maximum (PETM)

Download this lab worksheet as a Word document:Earth 103 Lab 1 Practice Word Document (Please download required files below.)

In this lab, we explore the magnitude of the warming that took place during the PETM based on proxy data. We compare PETM temperatures with current temperatures. We will look at Mg/Ca, oxygen isotope, and TEX-86 proxy temperatures from a number of cores of marine PETM sediments drilled in the oceans and on land, as well as margin analysis of leaves from land sections, to determine the temperature change. If you have not read the relevant material on proxies and the PETM in this module, please go back and do this before you begin the lab.

Note: The data for this lab are provided in Google Earth files. The goals of this lab are:

1. To compare the magnitude and rate of warming at the PETM with that of today;
2. To get comfortable with Google Earth software, especially turning maps on and off.

Files to Download

1. PETM Data
2. PETM Paleogeography
3. Modern SST

Get used to manipulating these files in Google Earth. This short video shows you the basics of how you will need to do this for the lab (and it will be critical for future labs too):

Video: Lab 1 Basics (3:15)

Instructions

Load the PETM Data.kmz file into Google Earth. The map shows continental positions and the extent of the ocean during the PETM. The red markers show the site positions that you will use for this lab. If you click on them, you will get several pieces of information: (1) the proxy used to determine temperature; (2) the temperature just before the PETM (pre-event) and (3) the PETM temperature. I recommend you make a table similar to the example below, that shows all of the data, as well as your calculated temperature change.

Next, practice turning on the modern sea surface temperature (SST) map in the Modern SST.kmz file to compate the PETM temperatures with those of today. 

Note that since the continents have moved a long way in 55 million years since the PETM, there are places in the PETM ocean which now lie in the position of the modern continents and thus don’t have equivalent modern temperatures (you will see some PETM sites in the dark blue area of the modern continents). But if you turn on the PETM Paleogeography kmz file, you will see more accurate reconstructions of the margins of the continents and the locations of the sites relative to them.

After you have made the table from the data that you collected and feel comfortable comparing the data with temperatures read from the temperature kmz maps, please answer the practice questions below. Once you feel good about your answers, go to Lab 1 in Module 1. There will be two labs available to you, Lab 1 Submission (Practice) and Lab 1 Submission (Graded). You will have a chance to answer practice questions and get the correct answers before taking the Lab 1 Graded questions for credit. Please make sure you fully understand the practice questions before you start the graded questions for credit. You may want to review some of the content in Module 1 to help prepare. Keep in mind that you will only get one chance to complete the graded labs.

Practice Questions

1. What is the temperature increase at Bass River during the PETM (give your answer in degrees centigrade)?
2. What is the temperature increase at Siberian Sea Site during the PETM (give your answer in degrees centigrade)? 
3. Which oceanic site (i.e., only site with numbers located in the ocean basins) has the smallest temperature change?
   
   Now turn on the Modern temperature map and answer the following questions:
4. Compared to modern temperatures, is the location of Site 277 warmer or colder during the PETM?

5. Which oceanic site (i.e., only sites with numbers located in the ocean basins) has the smallest temperature difference between PETM and modern?

End of Module Recap

In this module, you should have mastered the following concepts:

• Geological materials offer valuable archives of ancient climate.
• This record can be deciphered using proxies for climate including stable isotopes, fossils, and leaf margin analysis.
• Rapid change between cold and warm climate occurred during a number of intervals in the Earth's past including the Quaternary (0-2 million years ago), the Paleocene-Eocene thermal maximum (55 million years ago), and the Snowball Earth (0.64-2.2 billion years ago).
• These events have the potential to inform us about changes to come including the impact of climate change on environments including the ocean and on life, and the processes that terminate warming events.

Assignments

You should have read the contents of this module carefully, completed and submitted any labs, taken the Module Quiz and added your entry and reply in the Yellowdig discussion. If you have not done so already, please do so before moving on to the next module. Incomplete assignments will negatively impact your final grade.

Lab

• Lab 1: PETM

Goals

On completing this module, students are expected to be able to:

• describe climate trends from recent changes in temperature, atmospheric water content and glacier length and mass;
• explain the difference between weather, climate, and the importance of time scales when looking at climate data;
• infer temperature trends from instrumental and borehole data.

Learning Outcomes

After completing this module, students should be able to answer the following questions:

• How was climate related to the rise and fall of the Mayans?
• What were the Medieval Warm Period and the Little Ice Age?
• What is the difference between weather and climate?
• How are global temperatures calculated?
• Where is the “blade” of the Hockey Stick, and what is the change in global temperature since then?
• What is El Niño-Southern Oscillation, and how does it impact global climate?
• How do volcanic eruptions impact global temperatures?
• In what latitudes has temperature increased most since 1880?
• How do boreholes reflect temperature change?
• How and why do ocean temperatures change differently from air temperatures?
• How has the water content of the atmosphere changed in the last 40 years and why?
• How has global precipitation changed over the last few decades?
• How has the frequency and intensity of large storms changed recently?
• Where in the US has drought increased since 1950?
• How are changes in ice sheet area determined?
• How has the volume of ice on glaciers changed recently?
• How has Arctic sea ice changed recently?

Below is an overview of your assignments for this module. The list is intended to prepare you for the module and help you to plan your time.

Assignments

1. Lab 2: Hurricanes
2. Submit Module 2 Lab 2 (Graded).
3. Take Module 2 Quiz.
4. Yellowdig Entry and Reply

On June 29, 2021, the mercury at Portland, Oregon reached 116^oF. December 10 and 11, 2021 saw EF4 tornadoes roar through Kentucky and other states, killing 88 and wounding more than 630. In December 2021, 202 inches of snow fell in the Sierra Nevada of California. Hurricane Sandy made landfall in New Jersey on October 29th, 2012, a very late point in the year for a storm to reach the northeastern US coast. January 8th, 2013 was the hottest day ever for Australia with an average temperature for the entire continent of 40.3^oC (nearly 105^oF). The AVERAGE (day and night) temperature in Phoenix in June 2017 was almost 95 degrees. And nearly 61 inches of rain fell around Houston during Hurricane Harvey in August 2017. Each of these events provoked arguments in favor or against global warming. Yet, on their own, none of them were definitive proof one way or another.

To make a case for climate change, we need to find the average global climate signal, which is often difficult to discern from the noise of short-term variations and regional differences associated with what we call weather. It is very important to remember that climate is the time-averaged weather, so when we are talking about climate change, we are not talking about the weather that you experience on a daily or seasonal basis — a single heat wave is not evidence of global climate warming, just as one cold snap does not constitute global climate cooling. But repeated, unusual heat waves will shift the average temperature of a region, and this can be taken as a manifestation of a warming climate. The climate is inherently variable over time and space, so detecting a meaningful trend is a challenge that requires great care, a lot of data and often some complex statistics.

Now, a word of warning, you might find the remainder of this page a little dull! However, it happens to be one of the most essential topics of the whole issue of climate change. We absolutely have to understand the significance of trends if we are going to interpret them!

Let’s consider a hypothetical case to help you better understand the nature of the problem. The mean annual temperature is the average temperature over the course of a year, and it varies in a way that we can simulate with a randomly generated string of numbers (geoscientists often refer to such variation as “noise”) added to a constant, long-term mean temperature. We would see something like this:

Hypothetical Temperature History

Click for a text description of Hypothetical Temperature History

This is a line graph titled "Hypothetical Temperature History." The graph represents a dataset described as "random noise added to a constant long-term mean temperature." Here’s a detailed breakdown of the graph’s components:

Axes:

• The horizontal axis (x-axis) represents "Time in Years." It spans from 0 to 200 years, with major markers at intervals of 20 years (0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200).
• The vertical axis (y-axis) represents "Mean Annual Temperature" in degrees Celsius (°C). It ranges from 19.6°C at the bottom to 21.4°C at the top, with major markers at intervals of 0.2°C (19.6, 19.8, 20.0, 20.2, 20.4, 20.6, 20.8, 21.0, 21.2, 21.4).

Main Data:

• The graph shows a single blue line that represents the mean annual temperature over the 200-year period. The line fluctuates up and down in a jagged, irregular pattern, indicating random variations in temperature.
• These fluctuations are described as "random noise" added to a constant long-term average temperature.

Mean Temperature:

• A red dashed line runs horizontally across the graph at 20.4°C. This line is labeled "mean temperature," representing the constant long-term average temperature around which the data fluctuates.
• The blue line (temperature data) oscillates above and below this red dashed line throughout the graph.

Standard Deviation Band:

• A shaded green band surrounds the red dashed line (mean temperature). This band extends from 20.2°C to 20.6°C, meaning it covers a range of 0.2°C above and below the mean of 20.4°C.
• The band is labeled "± one standard deviation," indicating that most of the temperature data points (the blue line) fall within this range, as expected in a dataset with random noise following a normal distribution.
• The green band spans the entire width of the graph, from 0 to 200 years.

Highlighted Sections:

• There are two specific time periods highlighted on the graph:
  1. Blue Shaded Area (20 to 40 years):
     • Between the 20-year and 40-year marks on the x-axis, a vertical blue shaded rectangle highlights this time period.
     • During this period, the blue temperature line dips slightly below the mean temperature of 20.4°C, with some points approaching 20.2°C or slightly lower.
     • This suggests a short-term cooling trend within the random fluctuations.
  2. Pink Shaded Area (100 to 120 years):
     • Between the 100-year and 120-year marks on the x-axis, a vertical pink shaded rectangle highlights this time period.
     • During this period, the blue temperature line shows a noticeable peak, rising above the mean temperature.
     • A specific data point around the 110-year mark is marked with a red arrow pointing to the blue line, where the temperature reaches approximately 20.8°C.
     • This indicates a short-term warming trend within the random fluctuations.

General Trend:

• Despite the short-term fluctuations highlighted in the blue and pink sections, the overall long-term trend of the temperature remains constant at the mean of 20.4°C.
• The random noise causes the temperature to vary, but there is no consistent upward or downward trend over the 200 years.

Purpose of the Graph:

• The graph illustrates how random noise can create short-term variations in temperature data, even when the long-term average temperature remains constant.
• The highlighted sections (cooling between 20-40 years and warming between 100-120 years) show how random fluctuations can sometimes appear as temporary trends, but these do not reflect a change in the long-term mean.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

In this case, you might say that if the temperature strayed out of the green zone, you have unusually hot or cold weather, and you would expect this kind of temperature excursion to be a standard feature of the natural variability of the region's climate. But, the green zone has more or less fixed limits, so the standard for calling something a heat wave does not change over time.

Now, let’s look at another hypothetical case where the climate really is warming, but there is still the natural variability or noise on a shorter timescale.

In this case, the same random noise is added to a gradual increase, indicated by the straight red line, which represents a long-term warming trend.

Click for a text description of the Hypothetical Temperature History as long-term warming trend.

This is a line graph titled "Hypothetical Temperature History." The graph represents a dataset described as "random noise added to a long-term warming trend." Here’s a detailed breakdown of the graph’s components:

Axes:

• The horizontal axis (x-axis) represents "Time in Years." It spans from 0 to 200 years, with major markers at intervals of 20 years (0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200).
• The vertical axis (y-axis) represents "Mean Annual Temperature" in degrees Celsius (°C). It ranges from 19.0°C at the bottom to 24.0°C at the top, with major markers at intervals of 0.5°C (19.0, 19.5, 20.0, 20.5, 21.0, 21.5, 22.0, 22.5, 23.0, 23.5, 24.0).

Main Data:

• The graph shows a single blue line that represents the mean annual temperature over the 200-year period. The line fluctuates up and down in a jagged, irregular pattern, indicating random variations in temperature.
• These fluctuations are described as "random noise" added to a long-term warming trend.

Mean Temperature Trend:

• A red dashed line runs diagonally across the graph, sloping upward from the bottom left to the top right. This line is labeled "steady increase in mean temperature," representing the long-term warming trend.
• The red dashed line starts at approximately 19.5°C at year 0 and rises steadily to about 23.5°C by year 200, indicating a consistent increase in the mean temperature over time.
• The blue line (temperature data) oscillates above and below this red dashed line throughout the graph, but the overall trend of the blue line follows the upward slope of the red dashed line.

Standard Deviation Band:

• A shaded light blue band surrounds the red dashed line (mean temperature trend). This band extends approximately 0.2°C above and below the red dashed line at any given point.
• The band is labeled "± one standard deviation," indicating that most of the temperature data points (the blue line) fall within this range, as expected in a dataset with random noise following a normal distribution.
• The light blue band spans the entire width of the graph, from 0 to 200 years, and slopes upward along with the red dashed line.

Highlighted Sections:

• There are two specific time periods highlighted on the graph:
  1. Blue Shaded Area (20 to 40 years):
     • Between the 20-year and 40-year marks on the x-axis, a vertical blue shaded rectangle highlights this time period.
     • During this period, the blue temperature line dips slightly below the red dashed line (mean temperature trend), with some points approaching 19.5°C or slightly lower.
     • This suggests a short-term cooling anomaly within the overall warming trend.
  2. Pink Shaded Area (100 to 120 years):
     • Between the 100-year and 120-year marks on the x-axis, a vertical pink shaded rectangle highlights this time period.
     • During this period, the blue temperature line shows a noticeable peak, rising above the red dashed line.
     • A specific data point around the 110-year mark is marked with a red arrow pointing to the blue line, where the temperature reaches approximately 21.5°C, while the red dashed line (mean trend) is around 21.0°C at that point.
     • This indicates a short-term warming spike within the overall warming trend.

General Trend:

• The overall long-term trend of the temperature shows a steady increase, as indicated by the upward-sloping red dashed line. The mean temperature rises from about 19.5°C at year 0 to about 23.5°C by year 200, an increase of approximately 4°C over 200 years.
• The random noise causes the temperature to fluctuate around this trend, creating short-term variations like the cooling between 20-40 years and the warming spike between 100-120 years.
• Despite these fluctuations, the overall direction of the temperature data (blue line) follows the upward trend of the red dashed line.

Purpose of the Graph:

• The graph illustrates how random noise can create short-term variations in temperature data, even when there is a clear long-term warming trend.
• The highlighted sections (cooling between 20-40 years and warming between 100-120 years) show how random fluctuations can sometimes appear as temporary anomalies, but the long-term warming trend remains consistent.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

If you think of the upper edge of the green zone on the previous figure as indicating the line for defining a heat wave, look what happens in a case where there is a steady warming of the climate, with the same kind of weather causing the rapid ups and downs. If at time 0, we said that a temperature of 21°C was a heat wave, that becomes the mean climate temperature by time 60 in the above figure — so, what previously was a rare warm spell is now just the standard. This means that, by older standards, "heat waves" become more common as time goes on.

Note that, once again, we can find areas in this above figure where a shorter time period would seem to indicate cooling or warming. This reinforces the idea that we can’t really talk about climate change by looking at just a few years, and leads to the question of how much time we do need to look at to get a good understanding of climate change. In general, the longer, the better. But just to illustrate this, consider the following, where we take the same kind of hypothetical temperature record as above and systematically find the linear trends over time, with windows of varying length that slide along through the 200-year record.

Three graphs of Temperature History-linear trends

Click for a text description of History-linear trends.

The image consists of three line graphs stacked vertically, each showing the mean annual temperature over a span of approximately 200 years, from around year 0 to year 200. The y-axis of each graph represents the mean annual temperature, ranging from 20.0 to 22.5 degrees. The x-axis represents the years.

Each graph illustrates the temperature data with two types of lines:

• A blue line representing the raw mean annual temperature data, which fluctuates significantly year to year.
• A green line showing the linear trend of the temperature data over a specific time window.

The three graphs differ in the time window used to calculate the linear trend:

1. The top graph uses a time window of 10 years. The green linear trend line shows more variability, closely following the short-term fluctuations in the blue temperature data, but still indicates a general upward trend over the 200-year period.
2. The middle graph uses a time window of 20 years. The green linear trend line is smoother than in the 10-year window, showing less short-term variability but still reflecting an overall upward trend in temperature.
3. The bottom graph uses a time window of 50 years. The green linear trend line is the smoothest of the three, with minimal short-term fluctuations, clearly depicting a steady upward trend in mean annual temperature over the 200 years.

Overall, all three graphs demonstrate a consistent long-term increase in mean annual temperature, with the longer time windows (20 and 50 years) providing a clearer view of the overall trend by smoothing out short-term variations.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Why do we care about this? There are all sorts of reasons why this is important and interesting, but here are three primary reasons.

1. The first reason for studying recent climate is that if we understand how climate has been changing in the recent past, we can establish a trend that we can use to project into the future.
2. The second reason for studying recent climate is that knowing the history of climate change gives us a chance to understand how the climate system responds to various controls; for instance, we know the histories of solar insolation, fossil fuel burning, land clearing, addition of other pollutants to the atmosphere (other greenhouse gases and particles that block sunlight), and volcanic eruptions. Knowing these histories and knowing how the climate has changed, we are in a position to develop a good understanding of the role that each of these controls plays in changing the climate, which helps us predict future climate change with greater confidence.
3. Finally, if climate change is real, then we cannot ignore it when planning for our future. The consequences of future climate change may require some difficult choices, so we had better be sure that there is a firm basis for the reality of climate change.

Temperature is probably the most important observation regarding the global climate, but how to measure the temperature of something as large as the Earth is complex. Climate scientists have taken a variety of approaches to answering this question, depending on the timescale of interest; we’ll have a look at the results of these different approaches in this section.

The first approach is the most obvious — you use thermometer data; this is usually called the instrumental record of climate. Let’s take a look at what some of these data look like for a place familiar to the authors — State College, PA. These data come from the US Historical Climatology Network, where you can find data from stations around the US. These data are the monthly mean temperatures from 1849 to 1994, so there has already been some averaging of the data to remove the day-to-day variability.

The thin blue line is the monthly mean temperature, which shows a great deal of variation related to the annual cycle of temperature change here — a bit more than 20°C.

Click for a text description of the State College, PA Temperature Record graph.

Outline Description of Audio Waveform Visualization

1. Overview
   • Three line graphs stacked vertically.
   • Each graph displays monthly mean temperature over approximately 200 years.
   • X-axis: Years (from 0 to 200).
   • Y-axis: Monthly mean temperature (ranging from 20.0 to 22.5 degrees).
2. Graph Components
   • Thin Blue Line: Represents the monthly mean temperature data.
     • Shows significant variation due to the annual cycle of temperature change.
     • Variation is a bit more than 20°C, reflecting seasonal fluctuations.
   • Green Line: Represents the linear trend of the temperature data.
     • Calculated over different time windows for each graph.
3. Graph Details by Time Window
   • Top Graph: Time Window = 10 Years
     • Title: "Linear Trends; Time Window = 10 years".
     • Thin blue line fluctuates widely due to monthly and seasonal changes.
     • Green trend line shows more variability, following short-term trends.
     • Indicates a general upward trend over the 200-year period.
   • Middle Graph: Time Window = 20 Years
     • Title: "Linear Trends; Time Window = 20 years".
     • Thin blue line continues to show significant monthly variation.
     • Green trend line is smoother than the 10-year window, reducing short-term variability.
     • Still reflects an overall upward trend in temperature.
   • Bottom Graph: Time Window = 50 Years
     • Title: "Linear Trends; Time Window = 50 years".
     • Thin blue line displays the same monthly variation of over 20°C.
     • Green trend line is the smoothest, minimizing short-term fluctuations.
     • Clearly shows a steady upward trend in temperature over 200 years.
4. Overall Trend
   • All three graphs indicate a consistent long-term increase in monthly mean temperature.
   • The monthly data highlights a significant annual cycle with temperature variations exceeding 20°C.
   • Longer time windows (20 and 50 years) provide a clearer view of the upward trend by smoothing out monthly and seasonal fluctuations.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

What we are seeing in the above figure is weather, which is "noisy"; what we want is the climate record from this station, which is not obvious, but we will find it in the first lab exercise for this module. The data for this one station can give us a climate record for the immediate surroundings, but going from this record at one point to the global temperature requires a bit more work.

One approach is shown in the figure below. Say you have an array of weather stations on a map:

Schematic representation of a method to figure out the area represented by each weather station's temperature record. This is one of several different strategies.

Click for a text description of Area Represented by One Weather Station image.

• Overview
  • A schematic diagram illustrating a method to calculate the area represented by a weather station's temperature record.
  • Depicts one of several strategies for this purpose.
  • Set against a solid black background.
• Central Element
  • Hexagonal Shape:
    • A gray hexagon with black outlines, positioned centrally.
    • Represents a weather station's area of influence.
  • Central Star:
    • A blue star located at the center of the hexagon.
    • Likely represents the weather station itself.
• Surrounding Elements
  • Blue Stars:
    • Six blue stars of varying sizes scattered around the hexagon.
    • Positioned at different distances and directions from the central hexagon.
    • Likely represent other weather stations or reference points.
  • Connecting Arrows:
    • Red Arrows:
      • Three red arrows with dashed lines.
      • Extend from the central blue star to three of the surrounding blue stars (top-right, right, and bottom-right).
      • Indicate connections or distances between the central weather station and others.
    • Green Arrow:
      • One green arrow with a dashed line.
      • Extends from the central blue star to the top-left corner of the hexagon.
      • May represent a specific direction or boundary of influence.
• Interpretation
  • The hexagon likely defines the area of influence for the central weather station.
  • The surrounding stars and arrows suggest a method of triangulation or spatial analysis.
  • Red arrows may indicate distances or relationships to nearby stations for calculating the area.
  • The green arrow might highlight a specific boundary or direction in the method.
  • The diagram simplifies a strategy for determining how much area a weather station's temperature record represents.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

The general approach is to draw lines between a station and all of its nearest neighbors, then find the midpoints of these lines (circles in the figure) then make a polygon that connects the circles, giving an area (in gray). This area (in km²) represents a tiny fraction, f_i, of the Earth’s surface that is associated with the temperature, T_i, of this station. If you do this for all stations, and sum all the T_i x f_i values from each station, you would have a global temperature (all of the f_i values would add up to 1.00).

You can see from the above example that if you have good weather stations spread uniformly across the planet (land and sea) and they have been recording continuously for a long time, then one can take the mean annual temperature of each station and calculate a simple global average for each year, and thus the history of temperature change for our planet. But, as you might imagine, the stations are not uniformly distributed — they are clustered in populated countries — and the number of stations declines as you go backward in time, so the actual process of assembling an instrumental record takes some care. A variety of groups have done this using slightly different data sets and approaches. The trick here is in how you combine the individual temperature records to come up with a global average. This is complicated by the fact that some weather stations may have problems related to things like the "urban heat island effect." Man-made materials retain heat better than open land and the lack of trees also amplifies warming in cities, which are currently warming at double the rate of the global average! Thus, if urban development encroaches on a weather station, the urban heat island effect will make the local temperature rise for reasons that are unrelated to any regional climate change. Researchers have found ways of ensuring that this effect does not skew the results, and many different groups come up with results that are nearly identical, giving us confidence that the data analysis is sound. Just in case you are wondering, the mean surface temperature of the Earth as a whole is 15^o C (59^o F)!

There are a number of good estimates of the recent history of global temperature change, and they are shown, plotted at the same scale, in the figure below.

Temperature reconstructions for the past 170 years from a variety of groups

Click for a text description of the Global Anomalies Relative to 1961-1990 graph.

The image is a line graph displaying global temperature anomalies from 1850 to around 2025. The x-axis represents the calendar year, ranging from 1850 to 2030, with major ticks at intervals of 20 years (1850, 1870, 1900, 1930, 1950, 1970, 1990, 2010, 2030). The y-axis represents the global temperature anomalies in degrees Celsius (°C) relative to the mean for the period 1961–1990, ranging from -0.5°C to 1.5°C, with major ticks at intervals of 0.5°C.

The graph includes four distinct lines, each representing temperature anomaly data from a different research group, as indicated by a legend in the top left corner:

• Berkeley (Berkeley Earth Surface Temperature project from the University of California) is shown in purple.
• GISTEMP (from NASA) is shown in green.
• NOAA (from the National Oceanic and Atmospheric Administration) is shown in red.
• HadCRUT5 (from the Climate Research Unit of the University of East Anglia, England) is shown in light blue.

Each line shows the global temperature anomalies over time, with all four datasets exhibiting a similar overall trend: a general increase in temperature anomalies from 1850 to the present. The lines fluctuate year to year, reflecting natural variability, but the upward trend becomes more pronounced after around 1980. From 1850 to 1900, the anomalies are mostly negative (below 0°C), ranging between -0.5°C and 0°C, with some variability. From 1900 to 1980, the anomalies hover around 0°C, with fluctuations between -0.3°C and 0.3°C. After 1980, all four lines show a steady increase, reaching approximately 1.2°C to 1.5°C by 2025.

The four lines are closely aligned, indicating that the different groups—Berkeley, GISTEMP, NOAA, and HadCRUT5—produce similar results despite using slightly different approaches to data selection and conversion of station data into global temperatures. However, there are minor differences in the year-to-year fluctuations, reflecting the variations in methodology among the groups. The graph effectively illustrates the consensus on global warming trends across these

Credit: Timothy Bralower © Penn State University is licensed under CC BY-NC-SA 4.0

The figure above shows anomalies relative to the mean for 1960-1980. GISTEMP is from NASA, CRUTEM4 is from the Climate Research Unit of the University of East Anglia in England, Berkeley is from the Berkeley Earth Surface Temperature project from the University of California, and NOAA is from NOAA (no surprise here). These different groups use essentially the same data, but they have slightly different approaches to selecting which data to use and how to convert the station data into global temperatures.

It is interesting to see how similar the curves are given that they use different strategies for averaging the data, and some of the records are based on slightly different sets of weather stations. In particular, note that none of these estimates show a general cooling trend over this length of time — they all show warming. Back in the 1800s, there were fewer weather stations, and so it is more difficult to estimate global temperature back then (see figure below), but it gets steadily better as time goes on, and for the last few decades, we have excellent data due to the satellites that now circle the globe taking temperature measurements of every spot on Earth (more on this in a bit). What we see in the figure above is a detail of the blade of the "Hockey Stick" — beginning about 1900, the temperature starts to rise, then it flattens out a bit in the 1950s and early 1960s, and then it increases again at a faster pace since that time. The total warming since 1900 is about 1.1°C as a global average. And many recent years have broken records, 2024 was the warmest year on average and about 1.5°C above 1900 (but its too early to say that this number if permanent warming).   

Looking in more detail at the Berkeley temperature estimate, which is based on about 1.6 billion measurements, we can see that the uncertainty, indicated in the figure below by the green band surrounding the red line, gets progressively larger as we go back in time, but the uncertainty is practically zero for more recent decades.

This figure shows the Berkeley temperature reconstruction with the uncertainty indicated by the green zone, with the global mean temperature (as an anomaly) shown in the thick red line.

Click for a text description of the 1800-2011 Instrumental Temperature Record graph.

• Overview
  • A line graph titled "1800–2011 Instrumental Temperature Record."
  • Displays global annual temperature anomalies from 1800 to 2011.
  • Data sourced from the Berkeley Earth Surface Temperature project (noted as "data from Berkeley Earth Surface Temperature, 2011").
  • Includes a note: "1.6 Billion measurements!" indicating the dataset's scale.
• Axes
  • X-Axis: Time
    • Spans from 1800 to 2010.
    • Major ticks at 50-year intervals (1800, 1850, 1900, 1950, 2000).
  • Y-Axis: Global Annual Temperature Anomaly (°C)
    • Ranges from -2.5°C to 1.5°C.
    • Major ticks at 0.5°C intervals.
• Graph Elements
  • Thick Red Line: Global Mean Temperature Anomaly
    • Represents the global mean temperature as an anomaly over the period.
    • Starts around -1.5°C in 1800.
    • Fluctuates between -1.5°C and -0.5°C until around 1900.
    • Shows a gradual increase from 1900 to 1980.
    • Rises more sharply after 1980, reaching approximately 1.0°C by 2011.
  • Green Shaded Area: Uncertainty Zone
    • Surrounds the red line, indicating the uncertainty in the Berkeley temperature reconstruction.
    • Wider in earlier years (1800–1850), around ±0.5°C, reflecting greater uncertainty.
    • Narrows over time, especially after 1900, to about ±0.1°C by 2011.
    • Reflects improved data reliability as more measurements become available.
• Trend and Interpretation
  • Shows a clear long-term warming trend over the 211-year period.
  • Significant temperature increase observed, especially in the 20th and early 21st centuries.
  • Uncertainty decreases over time, correlating with the increase in measurement data.
  • Highlights the reliability of the temperature reconstruction, with more precise data in later years.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

This is a good point to explore a question about these records. Why does the annually averaged temperature rise and fall in such a complicated fashion? The sun does not vary in its brightness in such a dramatic fashion (the solar cycle related to sunspots can account for a global temperature variation of about a tenth of a degree), and the greenhouse gases that keep our planet warm do not vary in their concentration this much. Instead, it appears that a good deal of the variability seen in these records is related to things like volcanic eruptions and climate system oscillations like the El Niño – La Niña Southern Oscillation (ENSO), which is discussed in detail in Module 6. In short, ENSO is essentially a huge, sluggish, sloshing back and forth of warm water along the equator in the Pacific Ocean — it is like a wave that reflects back and forth between the two edges of the Pacific, and it has a global reach in terms of climate. During the El Niño phase of this oscillation, the warm water is pooled up on the eastern side of the equatorial Pacific and this has the effect of making the whole Earth warmer (the reasons for this are complex, but the effect is quite clear). Conversely, during the La Niña phase, the warm water is pooled up at the western edge of the equatorial Pacific and the whole globe tends to be cooler. The El Niño stage causes flooding rains in California, wet conditions in Florida (recommend you visit Disney during the La Niña!), but crippling drought in Australia and southern Africa.

The last 25 years of globally averaged instrumental surface temperature measurements from the HadCRUT3 dataset.

Click for a text description of the Surface Temperature Record graph.

The image is a line graph displaying global surface temperature anomalies from 1980 to around 2008. The x-axis represents the years, ranging from 1980 to 2010, with major ticks at 5-year intervals (1980, 1985, 1990, 1995, 2000, 2005). The y-axis represents the temperature anomaly in degrees Celsius (°C), ranging from -0.3°C to 0.7°C, with major ticks at intervals of 0.1°C.

The graph includes three types of data representations, as indicated by a legend in the top left corner:

• A blue line represents the monthly average temperature anomaly, showing significant short-term fluctuations.
• Black dots represent the annual average temperature anomaly, plotted at yearly intervals, providing a clearer view of year-to-year changes.
• A red line represents the five-year average temperature anomaly, smoothing out short-term variations to highlight longer-term trends.

The temperature anomaly data shows a general upward trend over the period. In 1980, the monthly average starts around 0°C, with the five-year average slightly below 0°C. There are noticeable dips and peaks in the monthly data, such as a dip around 1992 and peaks around 1998 and 2005. The annual averages (black dots) follow a similar pattern but with less variability, while the five-year average (red line) shows a steady increase, rising from near 0°C in 1980 to about 0.5°C by 2008.

Additional annotations on the graph include:

• A black vertical line labeled "Pinatubo Volcano," marking the 1991 eruption of Mount Pinatubo, which corresponds to a noticeable dip in temperature anomalies around 1992–1993 due to the cooling effect of volcanic aerosols.
• Horizontal dashed lines in blue and red, labeled "El Niño/La Niña," indicating periods of El Niño (red) and La Niña (blue) events. These events correspond to peaks (El Niño, e.g., 1998) and dips (La Niña, e.g., 1985, 1999) in the temperature anomalies, reflecting their influence on global temperatures.

Overall, the graph illustrates a clear warming trend over the 28-year period, with short-term variations influenced by natural events like volcanic eruptions and El Niño/La Niña cycles, while the five-year average highlights the long-term increase in global surface temperatures.

Credit: Robert Rohde from Wikipedia, licensed under CC BY-SA 3.0

The above figure shows the last 25 years of globally averaged instrumental surface temperature measurements. Also shown is the recent history of fluctuations in ENSO and the period of atmospheric disturbance due to the eruption of Mount Pinatubo in the Philippines in 1991, one of the largest of the 20th century; the volcano injected ash and sulfur gases into the upper atmosphere, where they blocked enough sunlight to cool the global climate for a period of about 3 years.

Satellites offer another way of studying temperature changes and they are not subject to the same problems associated with weather station data — they provide a complete coverage of surface temperature on land and at sea. But, as can be seen below, there is a very good agreement between satellite measurements and the weather station data (NOAA surface in the figure below). The only problem is that the satellite data only go back to about 1980.

Global Temperature Anomaly chart by year relative to 1979-2000

Click for a text description of the global Temperature Anomaly graph.

• Overview
  • A line graph showing global temperature anomalies from 1955 to around 2010.
  • Compares data from satellite and surface measurements.
  • Includes annotations for significant climate events affecting temperature.
• Axes
  • X-Axis: Year
    • Spans from 1955 to 2010.
    • Major ticks at 10-year intervals (1955, 1965, 1975, 1985, 1995, 2005).
  • Y-Axis: Global Temperature Anomaly (°C)
    • Ranges from -0.4°C to 0.6°C.
    • Relative to the 1979–2000 average.
    • Major ticks at 0.2°C intervals.
• Graph Elements
  • Data Sources (Lines):
    • RSS MSU Satellite: Orange line.
      • Represents temperature anomalies from the Remote Sensing Systems (RSS) Microwave Sounding Unit (MSU) satellite data.
    • UAH MSU Satellite: Green line.
      • Represents temperature anomalies from the University of Alabama in Huntsville (UAH) MSU satellite data.
    • NOAA Surface: Blue line.
      • Represents temperature anomalies from NOAA surface measurements.
  • Trends:
    • All three lines show a general upward trend in temperature anomalies over the period.
    • From 1955 to 1980, anomalies fluctuate around -0.2°C to 0°C.
    • After 1980, a steady increase is observed, with anomalies reaching around 0.4°C to 0.5°C by 2010.
    • The three datasets are closely aligned, with minor differences in year-to-year fluctuations.
• Climate Event Annotations
  • Shaded Areas and Labels:
    • La Niña/El Niño:
      • Blue shaded areas indicate La Niña events (e.g., 1975, 1989, 1999), corresponding to dips in temperature anomalies.
      • Red shaded areas indicate El Niño events (e.g., 1983, 1998), corresponding to peaks in temperature anomalies.
    • Volcanic Eruptions:
      • Brown shaded areas mark major volcanic eruptions with cooling effects:
        • "Agung" (1963): A dip in temperature around 1963–1964.
        • "El Chichón" (1982): A dip around 1982–1983.
        • "Pinatubo" (1991): A significant dip around 1991–1993.
  • Impact:
    • Volcanic eruptions cause temporary cooling, visible as dips in the temperature anomaly lines.
    • El Niño events cause temporary warming, visible as peaks, while La Niña events cause cooling, visible as dips.
• Interpretation
  • The graph shows a clear long-term warming trend from 1955 to 2010, consistent across satellite (RSS, UAH) and surface (NOAA) data.
  • Short-term fluctuations are influenced by natural climate events like El Niño, La Niña, and volcanic eruptions.
  • The agreement between satellite and surface data reinforces the reliability of the observed warming trend.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

The figure above shows the global instrumental temperature record in blue (NASA GISTEMP) is compared to two versions of the microwave sounder satellite (MSS) data of lower atmospheric temperatures (UAH from Univ. of Alabama, Huntsville; RSS from Remote Sensing Systems, Inc.). The timing of the ups and downs in the satellite record are a near-perfect match with the instrumental record, but the magnitude of change is greater according to the satellite measurements. For comparison, we show the history of the El-Niño La-Niña oscillation and periods of volcanic eruptions that load the atmosphere with tiny particles of sulfuric gas that block sunlight and cool the planet. The eruption of Pinatubo in the Philippines had a big effect, and strong El-Niño periods lead to warming — together, these two variables (volcanoes, and El-Niño) along with small fluctuations in sunlight, account for the majority of the "noise" in these records. Another important point of this figure is that it confirms that the instrumental temperature record does a good job of representing what actually happened.

Next, we look at the spatial variations in the temperature over different spans of time.

Temperature Anomalies comparing the 2023 Mean Temperatures versus 1951-1980

Click for a text description of the Temperature Anomalies image.

• Overview
  • A world map displaying global temperature anomalies for the year 2023.
  • Sourced from Berkeley Earth, as indicated by the website "www.BerkeleyEarth.org" in the bottom right corner.
  • Temperature anomalies are relative to the 1951–1980 average.
• Map Projection
  • Uses an elliptical map projection (likely a Mollweide projection).
  • Shows the entire globe, with continents outlined in black.
  • Centered on the Atlantic Ocean, with Africa in the middle, North and South America to the left, and Europe, Asia, and Australia to the right.
• Color Scale and Legend
  • Color Gradient:
    • Represents temperature anomalies in degrees Celsius (°C).
    • Ranges from -6°C (dark blue) to +6°C (dark red).
    • Gradient: Dark blue (-6°C), light blue (-1°C), white (0°C), yellow (0.5°C), orange (2°C), red (4°C), dark red (6°C).
  • Label:
    • Located at the bottom, reads "Relative to 1951–1980 Average" and "Temperature Anomaly (°C)."
• Temperature Anomaly Distribution
  • Warming (Positive Anomalies):
    • Most of the globe shows positive temperature anomalies (yellow to dark red).
    • North America, Europe, and Asia exhibit significant warming, with large areas in orange to red (2°C to 4°C).
    • Parts of the Arctic, northern Canada, and Siberia show the highest anomalies, reaching dark red (up to 6°C).
    • Africa, South America, and Australia also show widespread warming, mostly in yellow to orange (0.5°C to 2°C).
  • Cooling (Negative Anomalies):
    • Very few areas show negative anomalies (blue).
    • A small region in the Arctic near Greenland shows light blue (-1°C to 0°C).
  • Neutral Areas:
    • Some regions, particularly in the southern oceans and parts of the Pacific, are near neutral (white, around 0°C).
• Interpretation
  • The map indicates widespread global warming in 2023 compared to the 1951–1980 baseline.
  • The most significant warming occurs in the Northern Hemisphere, particularly in the Arctic, consistent with polar amplification.
  • Minimal cooling is observed, limited to a small area in the Arctic.
  • The predominance of yellow, orange, and red colors underscores a global trend of rising temperatures.

Credit: Berkeley Earth: www.berkeleyearth.org, licensed under CC BY-4.0

The figure above shows the difference in instrumentally determined surface temperatures between the period January 1999 through December 2008 and "normal" temperatures at the same locations, defined to be the average over the interval January 1940 to December 1980. The average increase on this graph is 0.48 °C, and the widespread temperature increase is considered to be an aspect of global warming. The most striking feature of this map is that the temperature changes have not been uniform across the globe; the high latitudes (above about 50 degrees) in the Northern Hemisphere have warmed more than any other part of the Earth, while the tropics warmed far less. But Antarctica has been warming significantly too, and, most recently in 2022 there have been record temperatures 20 °C warmer than normal!

We now turn our attention to the spatial pattern of temperature change over a much longer range of time — back to 1884. Below is an animation of the temperature change based on the instrumental record. It is worth remembering that the quality and quantity of the data get better and better as time goes on, so the early parts of this animation have more uncertainty connected to them.

The movie below is from NASA’s reconstruction of surface temperature since 1884, and it shows how Earth has warmed over the last century plus in a very, very graphic and indisputable way. Just in case you can't see this, 2016 was the warmest year on record, and 16 of the 17 warmest years have occurred since 2000!

Video: Global Warming: 1880-2021 (00:31) This video is not narrated.

Click here if the video above does not play

Play this movie and watch as the globe becomes dominated by the yellow, orange, and red colors signifying warmer temperatures. Note that the warming is not uniform across the globe, nor is it steady through time, but the warming trend is nevertheless clear to see.

Another way of looking at this history of warming is by taking the average temperature at each latitude for each year and then stringing those along the horizontal axis, as below:

Latitude-averaged temperature anomalies from the GISTEMP data shown in the movie referred to in the last figure. Here, time runs along the x-axis and each vertical swath of color represents the temperature anomalies for that time, averaged along lines of latitude.

Click for a text description of the 12-month zonal mean anomalies graph.

1. Overview
   • A heatmap titled "12-month zonal mean anomalies vs 1951–1980."
   • Displays global temperature anomalies across different latitudes over time.
   • Covers the period from 1850 to around 2025.
2. Axes
   • X-Axis: Time (Years)
     • Spans from 1850 to 2030.
     • Major ticks at 10-year intervals (1850, 1860, 1870, ..., 2020, 2030).
   • Y-Axis: Latitude
     • Ranges from -90° (South Pole) to +90° (North Pole).
     • Major ticks at 30° intervals (-90°, -60°, -30°, 0°, 30°, 60°, 90°).
3. Color Scale and Legend
   • Color Gradient:
     • Represents temperature anomalies in degrees Celsius (°C) relative to the 1951–1980 average.
     • Ranges from -4.73°C (dark purple) to +4.78°C (dark red).
     • Intermediate colors: purple (-3.87°C), blue (-3.01°C), light blue (-2.15°C), cyan (-1.29°C), light green (-0.43°C), white (0°C), yellow (0.43°C), orange (1.29°C), red (2.15°C), dark orange (3.01°C), dark red (3.87°C to 4.78°C).
   • Label:
     • Located at the bottom, indicating the color scale and temperature anomaly values.
4. Temperature Anomaly Distribution
   • 1850–1950 (Cooler Period):
     • Predominantly cooler anomalies (blues and purples) across most latitudes.
     • Southern Hemisphere (-90° to 0°) and Northern Hemisphere (0° to 90°) show temperatures below the 1951–1980 average.
     • Polar regions (near -90° and 90°) exhibit darker purples (up to -4.73°C).
     • Equatorial regions (around 0° latitude) are closer to neutral (white) or slightly negative (light blue).
   • 1950–1980 (Transition Period):
     • Gradual shift toward warmer anomalies.
     • Some areas, especially in the Northern Hemisphere, begin showing yellows (0.43°C).
     • Southern Hemisphere remains mostly neutral or slightly cool (white to light blue).
   • 1980–2025 (Warming Period):
     • Warmer anomalies (yellows, oranges, reds) become dominant across most latitudes.
     • Northern Hemisphere (30° to 90°) shows significant warming, with large areas in orange and red (1.29°C to 3.01°C).
     • Arctic (60° to 90°) exhibits extreme warming, with dark red patches (up to 4.78°C) by 2020–2025, indicating polar amplification.
     • Southern Hemisphere (-90° to 0°) warms more slowly, with yellows and oranges (0.43°C to 2.15°C).
     • Antarctic (-90° to -60°) shows some neutral or slight cooling areas (white to light blue).
5. Interpretation
   • Illustrates a clear global warming trend over the 175-year period.
   • Most pronounced temperature increases occur in the Arctic (60° to 90°) after 1980.
   • Earlier periods (1850–1950) and southern latitudes show cooler anomalies relative to the 1951–1980 baseline.
   • Polar amplification is evident, with the Arctic experiencing the most extreme warming.

Credit: Tim Bralower © Penn State University with NASA GISTEMP, 2021 and licensed under CC BY-NC-SA 4.0

In the figure above, as in the movie above, the temperatures are given as anomalies, or differences relative to a mean established from some arbitrary period of time (1951-1980 in this case). One thing that is clear is that the polar region of the Northern Hemisphere is the area that has warmed the most — more than 6°C during this time period, when the mean global temperature has risen by a bit less than 1°C. Also clear is the fact that, starting around 1990, nearly all of the globe is warming. It's pretty hard to argue with this plot, isn't it?

But if you are still unconvinced, we have another dataset up our sleeves that is completely independent of all of the atmospheric data we have shown so far: the ground has also warmed up!

We next turn our attention to a very different means of reconstructing the temperature — studies of the temperatures measured in boreholes (i.e., holes drilled into the ground) at various locations around the Earth. The temperature profiles (how temperature changes with depth) for three representative boreholes in eastern Canada are shown in the figure below:

Temperature profiles from three representative boreholes showing the unexpected curvature towards higher temperatures near the tops of the boreholes.

Click for a text description of the borehole temperature graphs.

The image contains three line graphs, each depicting the relationship between ocean depth and temperature in degrees Celsius (°C). The graphs are arranged side by side, sharing a common y-axis but with slightly different x-axis ranges for temperature.

The y-axis, labeled "Depth (m)," represents the ocean depth, ranging from 0 meters at the top to 500 meters at the bottom, with major ticks at 100-meter intervals (0, 100, 200, 300, 400, 500). The x-axis, labeled "Temperature (°C)," represents the water temperature, with each graph having a slightly different range:

• The left graph ranges from 4°C to 10°C, with major ticks at 4, 5, 6, 7, 8, 9, and 10.
• The middle graph ranges from 4°C to 10°C, with major ticks at 4, 5, 6, 7, 8, 9, and 10.
• The right graph ranges from 4°C to 11°C, with major ticks at 4, 5, 6, 7, 8, 9, 10, and 11.

Each graph features a single dashed line representing the temperature profile:

• In the left graph, the temperature starts at around 9°C at the surface (0 m), decreases gradually to about 7°C at 100 m, then drops more sharply to around 5°C at 300 m, and continues to decrease slowly, reaching approximately 4°C at 500 m.
• In the middle graph, the temperature begins at around 8°C at the surface, decreases steadily to about 6°C at 100 m, then continues to drop to around 5°C at 300 m, and stabilizes near 4°C at 500 m.
• In the right graph, the temperature starts at around 10°C at the surface, decreases to about 8°C at 100 m, then drops to around 6°C at 300 m, and reaches approximately 5°C at 500 m.

The graphs illustrate the typical ocean temperature profile, where temperature decreases with increasing depth, reflecting the thermocline—a layer in the ocean where temperature decreases rapidly with depth. The slight variations in the temperature ranges and profiles across the three graphs may indicate different locations, seasons, or conditions affecting the ocean's thermal structure.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Note that in all three cases, the temperature curves around to higher temperatures near the surface — this reflects a response of soil and bedrock to warming from the atmosphere. In the absence of warming at the surface due to climate change, these temperature profiles would tend to follow the trends represented in the lower few hundreds of meters, and this would intersect the surface at around 3-4°C.

The basic idea here is a surprising one — that the way temperature changes down a borehole at the present time tells us something about how the surface temperature has changed in the past. This is indeed a remarkable and useful reality of some fairly basic physics of heat flow. It also provides us with an excellent way to filter out the “noise” in the climate record and focus on the main trends.

Heat is just a measure of the kinetic or vibrational energy of the atoms in some substance. If something is hot, its atoms are vibrating very fast, and because vibrating atoms affect neighboring atoms, heat can be transmitted; we often talk about this heat transmission as heat flow. Heat flows from hot regions to cold regions, and the rate of heat flow is proportional to what we call the thermal gradient — the rate of temperature change with distance. In our case, for distance, we are talking about depth in the Earth, and the center of the Earth is very hot — about 5000°C. The surface, instead, is quite cool at 15°C, so heat from the Earth tends to flow out to the surface, and this process is cooling the Earth very slowly. This situation leads to a geothermal gradient (rate of change of temperature with depth) that tends to be more or less steady at around 20 or 30°C per kilometer. The heat released to the surface is tiny compared to the energy coming from the Sun, so this geothermal heat, on a global basis, does not affect the climate.

When the surface temperature rises and becomes hotter than the temperature just below the surface, heat moves down into the ground, but it does this quite slowly. When the surface temperature becomes colder, heat flows up from the ground, cooling the ground, and this cooling is transmitted downward slowly. This general idea is illustrated schematically in the figure below:

Schematic illustration of how the temperature below the surface responds to an abrupt increase in the surface temperature.

Click for a text description of the Surface Temperature image.

• Overview
  • A schematic diagram titled "Surface Temperature History."
  • Illustrates the process of heat penetration into the ground over four sequential time stages.
  • Consists of four panels, each showing a temperature-depth profile at a different time.
• Top Section: Surface Temperature History
  • A small graph at the top showing surface temperature over time.
  • X-axis: Time, labeled with four points (1, 2, 3, 4).
  • Y-axis: Temperature (not labeled with specific values).
  • The graph shows:
    • A flat line from Time 1 to Time 2 (labeled "Starting Condition – Steady State").
    • A sudden increase at Time 2 (labeled "Sudden Warming at Surface").
    • A flat line from Time 2 to Time 4, indicating sustained higher surface temperature.
• Main Panels: Temperature-Depth Profiles
  • Four panels labeled Time 1, Time 2, Time 3, and Time 4.
  • Each panel shows a vertical cross-section of the ground with the atmosphere above.
  • Y-Axis: Depth
    • Labeled "Depth" on the left side of each panel.
    • Extends from the surface (top) downward into the ground (bottom).
    • No specific depth values provided.
  • X-Axis: Temperature
    • Labeled "Temperature" at the top of each panel.
    • No specific temperature values provided.
    • Arrows indicate increasing temperature to the right.
• Panel Details
  • Time 1: Starting Condition – Steady State
    • A straight red line slopes downward from the surface to deeper ground.
    • Indicates a linear temperature gradient, typical of a steady-state condition where temperature decreases with depth.
  • Time 2: Sudden Warming at Surface
    • The red line shows a sharp increase in temperature at the surface (top of the panel).
    • The line then slopes downward, similar to Time 1, but starts at a higher temperature.
    • Indicates the immediate effect of surface warming, with heat beginning to penetrate downward.
  • Time 3: Ground Begins to Warm as Heat Flows Down
    • The red line shows a curved profile.
    • Near the surface, the temperature is high (same as Time 2).
    • The temperature decreases with depth but more gradually than in Time 1.
    • A label "Depth of Heat Penetration" with an arrow points to the depth where the curve begins to steepen, indicating how far the heat has penetrated.
  • Time 4: Warming Moves Down Slowly
    • The red line continues to show a curved profile.
    • The temperature near the surface remains high.
    • The curve is less steep than in Time 3, indicating that heat has penetrated deeper.
    • The "Depth of Heat Penetration" label and arrow show a greater depth compared to Time 3, reflecting the slow downward movement of heat over time.
• Interpretation
  • The diagram illustrates how surface warming affects subsurface temperatures over time.
  • Initially, the ground is in a steady state with a linear temperature gradient (Time 1).
  • A sudden increase in surface temperature (Time 2) initiates heat flow downward.
  • Over time, the heat penetrates deeper into the ground (Time 3 and Time 4), with the depth of penetration increasing gradually.
  • The process demonstrates the slow conduction of heat through the ground in response to surface temperature changes.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Each of the four rectangles shows the variation of temperature with depth above and below the surface at different times. At the beginning (Time 1), the temperature below the surface increases steadily, while it is constant above the surface. Then at Time 2, the surface temperature suddenly rises and is hotter than the ground right at the surface. By Time 3, the ground temperature right near the surface warms, but that warming does not penetrate very deeply. At Time 4, the surface temperature has continued to remain high, and the heat flowing down into the ground has reached a greater depth.

Rocks have a very low thermal conductivity (conductivity is the term used to describe the way heat is transported at the molecular level) compared to many other materials, which means that it can take a long time for rocks underground to respond to changes in surface temperatures. Because of the way that the heat flows through rocks, short-term changes are smoothed out as the heat diffuses through the rocks. This means that the borehole temperature profiles provide information only about changes in the long-term average temperature.

Unlike most other methods for studying paleoclimate, borehole thermometry does not need to be calibrated against the instrumental record. Hence, borehole thermometry provides an independent record of paleoclimate against which other paleoclimate techniques can be validated. Below, we see the results of the analysis of a global data set of borehole temperatures, which give us an estimate of the global temperature change.

The globally averaged temperature reconstruction for borehole temperature profiles around the world, compared to an instrumental global temperature reconstruction paired with a multi-proxy temperature reconstruction. The temperatures are anomalies relative to the 1961-1990 mean.

Click for a text description of the Borehold temperature reconstruction graph.

• Overview
  • A schematic diagram titled "Surface Temperature History."
  • Illustrates the process of heat penetration into the ground over four sequential time stages.
  • Consists of four panels, each showing a temperature-depth profile at a different time.
• Top Section: Surface Temperature History
  • A small graph at the top showing surface temperature over time.
  • X-axis: Time, labeled with four points (1, 2, 3, 4).
  • Y-axis: Temperature (not labeled with specific values).
  • The graph shows:
    • A flat line from Time 1 to Time 2 (labeled "Starting Condition – Steady State").
    • A sudden increase at Time 2 (labeled "Sudden Warming at Surface").
    • A flat line from Time 2 to Time 4, indicating sustained higher surface temperature.
• Main Panels: Temperature-Depth Profiles
  • Four panels labeled Time 1, Time 2, Time 3, and Time 4.
  • Each panel shows a vertical cross-section of the ground with the atmosphere above.
  • Y-Axis: Depth
    • Labeled "Depth" on the left side of each panel.
    • Extends from the surface (top) downward into the ground (bottom).
    • No specific depth values provided.
  • X-Axis: Temperature
    • Labeled "Temperature" at the top of each panel.
    • No specific temperature values provided.
    • Arrows indicate increasing temperature to the right.
• Panel Details
  • Time 1: Starting Condition – Steady State
    • A straight red line slopes downward from the surface to deeper ground.
    • Indicates a linear temperature gradient, typical of a steady-state condition where temperature decreases with depth.
  • Time 2: Sudden Warming at Surface
    • The red line shows a sharp increase in temperature at the surface (top of the panel).
    • The line then slopes downward, similar to Time 1, but starts at a higher temperature.
    • Indicates the immediate effect of surface warming, with heat beginning to penetrate downward.
  • Time 3: Ground Begins to Warm as Heat Flows Down
    • The red line shows a curved profile.
    • Near the surface, the temperature is high (same as Time 2).
    • The temperature decreases with depth but more gradually than in Time 1.
    • A label "Depth of Heat Penetration" with an arrow points to the depth where the curve begins to steepen, indicating how far the heat has penetrated.
  • Time 4: Warming Moves Down Slowly
    • The red line continues to show a curved profile.
    • The temperature near the surface remains high.
    • The curve is less steep than in Time 3, indicating that heat has penetrated deeper.
    • The "Depth of Heat Penetration" label and arrow show a greater depth compared to Time 3, reflecting the slow downward movement of heat over time.
• Interpretation
  • The diagram illustrates how surface warming affects subsurface temperatures over time.
  • Initially, the ground is in a steady state with a linear temperature gradient (Time 1).
  • A sudden increase in surface temperature (Time 2) initiates heat flow downward.
  • Over time, the heat penetrates deeper into the ground (Time 3 and Time 4), with the depth of penetration increasing gradually.
  • The process demonstrates the slow conduction of heat through the ground in response to surface temperature changes.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Clearly, the shapes of the curves, or the rates of temperature change over this time period, are in close agreement, which is important since they come from very different, independent data sources. The borehole temperature reconstruction does not match the last bit of this time period, in part because the measurements begin further down the hole, and many of the measurements were made in the 1980s and 1990s before the instrumental record ends.

The oceans have absorbed over 90% of the excess heat resulting from greenhouse gas emissions since the 1970s. So if it weren’t for the ocean, the land would be a lot hotter than it is today. Since water absorbs a lot of heat, the ocean’s temperature has not increased as fast as the land’s though, but there have been some alarming trends in the last year or so (2023-2024).

Daily Global Sea Surface Temperature records from 1981-2024 showing the significant increases in the last two years.

Click for a text description of the Daily Sea Surface Temperature graph.

This image is a line graph titled "Daily Sea Surface Temperature, World (60°S–60°N, 0–360°E)." The data comes from NOAA OISST version 2.1, and the image is credited to ClimateReanalyzer.org, Climate Change Institute, University of Maine.

• Y-Axis: Sea surface temperature in degrees Celsius
  • Range: 19.5°C to 21.5°C
• X-Axis: Months of the year (January to December)
• Data Representation:
  • Years 1981–2022: Thin, dashed black lines (dense cluster showing historical variability)
  • 1982–2011 Mean: Solid black line
  • 1982–2011 Mean ± 2σ: Dotted black lines
  • 2023: Solid orange line
    • Peaks mid-year, just above 21°C
  • 2024: Solid black line
    • Peaks early at 21.5°C, declines but stays above 2023 for most of the year
• Legend (at bottom):
  • Lists years 1981–2024
  • 2023 (orange), 2024 (black), historical mean, and ±2σ labeled

The graph highlights a notable increase in global sea surface temperatures in 2023 and 2024 compared to the historical data from 1981 to 2022.

Credit: Climate Reanalyzer, Climate Change Institute at the University of Maine, based on data from NOAA Optimum Interpolation Sea Surface Temperature (OISST). Climate Reanalyzer content is licensed under a Creative Commons Attribution 4.0 International License

Annual Global Sea Surface Temperature anomaly relative to the 1951-2000 time interval.

Click for a text description of the Annual Sea Surface Temperature Anomaly graph.

This image is a line graph titled "Annual Sea Surface Temperature Anomaly (°C) [1951-2000] World (90°S–90°N, 0°E–360°E)." The data is sourced from ECMWF ERA5 (0.5°x0.5° deg), and the image is credited to ClimateReanalyzer.org, Climate Change Institute, University of Maine.

• Y-Axis: Sea surface temperature anomaly in degrees Celsius
  • Range: -0.3°C to 0.8°C
• X-Axis: Years (1940 to 2020)
• Data Representation:
  • 1940–1980: Blue line with data points
    • Fluctuates mostly below 0°C, with dips to -0.2°C
    • Shaded blue area around the line indicates variability
  • 1980–2020: Red line with data points
    • Starts near 0°C, shows a general upward trend
    • Peaks around 0.7°C in 2020
    • Shaded red area around the line indicates variability
• Trend:
  • Early years (1940–1980): Mostly negative anomalies (cooler than the 1951–2000 average)
  • Later years (1980–2020): Increasingly positive anomalies (warmer than the 1951–2000 average)

The graph illustrates a clear warming trend in global sea surface temperatures from 1940 to 2020, with anomalies shifting from negative to significantly positive over the decades.

Source: Climate Reanalyzer, Climate Change Institute at the University of Maine, based on data from NOAA Optimum Interpolation Sea Surface Temperature (OISST). Climate Reanalyzer content is licensed under a Creative Commons Attribution 4.0 International License

The instrumental record of temperature change in the oceans goes back to about 1850 and consists of thermometer measurements made on water samples taken by merchant and navy ships as they sailed the world’s oceans. The data are understandably best for parts of the oceans along major trade routes, and they are less abundant further back in time. These measurements, just like the land-based weather station data, have to be gridded to come up with a global average sea surface temperature. As might be expected, the sea surface temperature record is similar to the global temperature records, in part because the oceans make up almost 75% of Earth’s surface. But even if we separate out the land surface temperature from the global record and compare it to the ocean surface temperature, they are quite similar, as seen in the figure below.

Global Anomalies relative by decade relative to 1961-1990 Sea Surface Temperature (blue curve) compared to Land Surface Temperature (green curve)

Click for a text description of the Global Anomalies graph.

This image is a line graph showing global sea surface temperature anomalies from 1850 to 2030. The data is presented relative to the 1961–1990 average, with two datasets compared: HadSST4 and CRUTEM5.

• Y-Axis: Global temperature anomalies in degrees Celsius
  • Range: -1.5°C to 1°C
• X-Axis: Calendar years (1850 to 2030)
• Data Representation:
  • HadSST4: Blue line
    • Represents sea surface temperature anomalies
    • Starts around -0.5°C in 1850, fluctuates, and rises to about 0.8°C by 2030
  • CRUTEM5: Green line
    • Represents land surface temperature anomalies
    • Starts around -0.5°C in 1850, fluctuates, and rises to about 0.9°C by 2030
• Trend:
  • Both datasets show similar patterns with fluctuations
  • General upward trend from 1850 to 2030
  • CRUTEM5 (land) shows slightly higher anomalies than HadSST4 (sea) in recent years
• Legend (top left):
  • HadSST4 (blue line)
  • CRUTEM5 (green line)

The graph illustrates a clear long-term increase in both sea and land surface temperature anomalies over the 180-year period, with land temperatures warming slightly more than sea temperatures in recent decades.

Credit: Timothy Bralower © Penn State University is licensed under CC BY-NC-SA 4.0

Although the two records are quite similar, there are some differences — the SST changes over a smaller range than the land surface temperature, and the land temperature is subject to more dramatic swings. This difference is largely due to the greater heat capacity of the oceans relative to the air — it takes a long time to heat and cool the oceans, but air temperature can change quite rapidly.

Measurements from a system of hundreds of buoys stationed throughout the oceans allow us to take the temperature of the oceans over a depth range of 2000 m. These measurements go back in time to 1955 and show that not just the surface of the oceans, but the whole upper half of the oceans are slowly warming — only about 0.1 to 0.2 °C averaged over the globe during the past 50 years — but this is a vast amount of water that has been warmed.

So, while the whole ocean has absorbed a huge amount of heat, its overall temperature has changed little. Nevertheless, the very surface of the ocean has warmed almost as much as the rest of Earth’s surface and from the middle of 2023 through to 2024 the surface warming has been quite alarming with temperatures almost a degree warmer than in 2016.

We should pause and make a point or two about these temperature reconstructions because they are very important to our understanding of how Earth's climate has been changing.

1. The temperature reconstructions from multiple proxies (see the Borehole Temperatures page) compare well with the instrumental record — this gives us a basis for thinking that the reconstructions are reliable. They are also in good agreement with the temperature reconstructed from borehole data — this gives us even more confidence in the multi-proxy reconstruction.
2. What we see is a very dramatic warming that begins around 1900 — the blade of the "Hockey Stick" — that is far larger in magnitude and far more abrupt than any climate change we see in the reconstructed climate history.
3. The recent warming does not appear to be part of a cycle — if it were part of a cycle, then we should expect to see a similar abrupt large cooling that preceded the warming, but nothing of the sort appears in the record.
4. The oceans have been warming slower than the land and are absorbing the majority of the excess heat as a result of greenhouse gas emission.

We now turn our attention to water in the atmosphere. Water is a tremendously important part of the climate system, and it has a huge influence on the weather we experience every day. Clouds are made of water droplets or tiny ice crystals, and obviously, precipitation is water; but you also can sense the hidden water vapor in the form of humidity. If you don't understand the concept of humidity, plan a trip to the Magic Kingdom in Orlando, or, worse still, New Orleans in August! As we will learn in Module 3, water is one of the most important ways of transporting energy in the climate system. When water evaporates, it takes heat energy from the surface and carries that heat with it until it condenses back into liquid water, at which point it releases that heat into the atmosphere — this is what powers energetic storms. If you watch a large fluffy cloud building up on a summer day, expanding and growing up to greater and greater heights, just remember that all of that swirling movement is driven by the energy releases from water vapor.

The evaporation of water speeds up when it gets warmer. You could confirm this by doing an experiment with two pots of water on the stove, with the burner beneath each set to a different temperature — the hotter one will always evaporate faster. The same is also true with Earth's climate system — a warmer planet means more evaporation, which means more energy added to the atmosphere. And warmer air can hold more moisture than can colder air. If we study the laws of thermodynamics, we find that for a 1°C increase in the air temperature, the atmospheric water content should increase by about 7%. Until recently, it was difficult to measure the global water content in the atmosphere, but with the advent of satellites, we can now do this.

Global map of humidity

Click for a text description of the global humidity map.

This image is a world map showing the difference from average humidity levels globally, measured in grams per kilogram (g/kg). The map uses a color gradient to indicate variations in humidity compared to the average.

• Map Type: World map
• Measurement: Difference from average humidity (g/kg)
• Color Scale (bottom of the map):
  • Range: -2 g/kg to 2 g/kg
  • Colors: Orange (drier, -2) to green (wetter, 2), with white at 0 (average)
• Regions with Notable Differences:
  • Drier Areas (orange):
    • Western North America
    • Central Africa
    • Parts of the Middle East
    • Northern South America
  • Wetter Areas (green):
    • Central Asia
    • Parts of Southeast Asia
    • Southern Africa
    • Eastern South America
  • Near Average (white):
    • Most of Europe, Australia, and the polar regions

The map highlights global variations in humidity, with significant drying in parts of North America and Africa, and increased moisture in regions like Central Asia and Southeast Asia.

Credit: Image provided by the NOAA, Boulder CO, USA (Public Domain)

This map above shows the relative changes in humidity (atmospheric water content) at the end of 2010 compared to the average over the period of satellite observations (1981 - 2010) — so this is a type of humidity anomaly map for the year 2011. The green areas are more humid than normal and the brown/orange areas are drier than normal. On the whole, you can see that the globe is moister than normal. You can also see that the eastern side of the equatorial Pacific Ocean is drier than normal — this is because 2010 was a La Niña year, and warm water was pooled up on the western side, cooler water on the eastern side; the cooler water evaporates less, hence the drier atmosphere above that region.

If we look at the longer record of the globe as a whole, we can see how the water content of the atmosphere has been changing over the last 40 years.

Change in global humidity over the last 40 years.

Click for a text description of the Global Atmospheric Humidity Change map.

This image is a line graph titled "Global Atmospheric Humidity Change," showing changes in specific humidity from 1970 to 2010. The data is presented as an anomaly in grams of water per kilogram of air, relative to the 1979–2003 mean, with an additional comparison to the Niño 3.4 SST Index.

• Y-Axis (Left): Specific humidity anomaly (g water/kg air)
  • Range: -0.2 to 0.4 g/kg
• Y-Axis (Right): Niño 3.4 SST Index anomaly (°C)
  • Range: -3°C to 4°C
• X-Axis: Years (1970 to 2010)
• Data Representation:
  • Specific Humidity Anomaly: Green line
    • Fluctuates around 0 g/kg
    • Peaks around 1998 at 0.3 g/kg, dips around 1992 to -0.1 g/kg
    • Shows a slight upward trend over time
  • Niño 3.4 SST Index Anomaly: Blue dashed line
    • Fluctuates more dramatically
    • Peaks around 1998 at 3°C, dips around 1975 and 1989 to -2°C
    • Also shows a slight upward trend
  • Background: Light purple shading
    • Represents variability in the Niño 3.4 SST Index

The graph illustrates a correlation between global atmospheric humidity and the Niño 3.4 SST Index, with both showing a slight increase over the 40-year period, alongside significant fluctuations tied to El Niño and La Niña events.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

The thick green line in the figure above is the global humidity anomaly (data from NOAA), with its best fit linear trend as the blue dashed line. Over this time period, the water content has risen by about 5%. The thin blue line is the history of the El Niño - La Niña oscillation — the seesaw sloshing of warm water back and forth along the equatorial Pacific Ocean. Positive values (scale on the left) indicate an El Niño year when more of the warm water sloshes over to the eastern Pacific (the South American side); negative values mean the warm water is pooled up on the western side near Indonesia. As can be seen, some of the fluctuations in global humidity correspond to the El Niño history, with more moisture generally associated with an El Niño year. But the general trend is rising humidity, and the El Niño history does not show a similar rise — this tells us that while El Niño is important, the underlying trend is more likely related to a warmer planet.

The central point here is that a warming Earth should have a more humid atmosphere and indeed that is the case, and more water vapor means more energy in the atmosphere.

Guadeloupe River Flooding Summer 2025

Summer 2025 will long be remembered for the deadly flooding along the Guadeloupe River in central Texas. The devastating flooding was caused by massive amounts of rain from the remnants of a tropical system. Up to 20 inches of rain fell over a few hours early on July 4. The area is prone to flash flooding because the steep limestone cliffs and thin soils do not allow much water to infiltrate. So much of the water ends up in the Guadeloupe. On July 4, the river in rose by 26 feet in 45 minutes in some places and crested up to 38 feet above normal in other places. Numerous summer camps for kids and other campgrounds were situated in the flood plain and these were ravaged by the rapidly rising water. At least 136 people were killed by the floodwaters, tragically including many young campers. There have been tough discussions about the lack of warning to the camps along the river. The NWS put out a warning for life threatening flash flooding early on July 4, but for a variety of reasons word did not reach the campers. That will clearly change in the future.

There is no doubt that the intensity of the rainfall that caused this devastating flooding is related to climate change. A key fact to understand is that for every one degree of temperature increase, an air mass can hold 3 % more moisture, so as the atmosphere warms it becomes wetter. The type of downpours that occurred in central Texas are now happening in many parts of the country and flash flooding is now part of life in many areas.

In the summer of 2021, Western Europe experienced severe and deadly flooding. The rainfall was extraordinary, possibly a 1000 year event as a storm system stalled for several days dumping 11 inches in 48 hours in eastern Belgium including 9 inches in the populated city of Liege, and up to 8 inches of rain in 9 hour period in Germany. Flooding occurred over a wide area including Belgium, Germany, Luxembourg, the Netherlands, Switzerland, and Austria. In Germany 243 people died, including 196 in Germany. In Belgium, floodwaters caused buildings to collapse and washed away cars. The floods were called the greatest natural disaster the country has ever experienced. In Germany, the Ahr river valley was particularly bad because gorges caused extensive flooding. The country’s flood warning system was cited for a monumental failure, although it appears that local authorities take some of the blame.

Flood damage in Pepinster, Belgium on 17 July 2021

Christophe Licoppe, European Commission, Attribution, via Wikimedia Commons

Fast forward to California in the winter of 2022-2023 and a series of atmospheric rivers bringing moisture from the tropics dumped huge amounts of rain on the coast and inland areas and snow on the Sierra Nevada. 78 trillion gallons of moisture fell during this time with over 30 inches and major flooding in lowland areas and up to 58 feet of snow in the mountains.

Video: Atmospheric rivers hitting California in January 2023 (0:12) (No Narration)

One near unanimous source of blame among scientists was climate change, with the flooding even exceeding current forecasts. Warmer atmosphere can hold more moisture. In addition, melting Arctic ice is causing the jet stream to weaken and this is leading the storms systems that move slower or stall. Combined these factors are leading to more extreme events including days of flooding rains.

As stated above, warm air holds more moisture than cooler air. What does a moister atmosphere mean for precipitation? It means two things — more precipitation over the globe and a higher frequency of extreme precipitation events. The spatial pattern of precipitation is complex — far more so than for temperature — and measuring the frequency of extreme events is a challenging statistical problem. But some progress has been made on these questions, at least for certain regions of the globe.

First, let's take a quick look at the precipitation pattern of the globe, which is now being measured in great detail by NASA's Aquarius satellite.

Rainfall averages in terms of mm/day for the month of March 2019.

Click for a text description of the rainfall averages map.

This image is a world map showing average rainfall over the last 30 days as of April 1, 2019. The map uses a color gradient to indicate rainfall amounts in millimeters per day across the globe.

• Map Type: World map
• Measurement: Average rainfall for the last 30 days (mm/day)
• Date: April 1, 2019
• Color Scale (bottom of the map):
  • Range: 0 mm/day to 20 mm/day
  • Colors: Blue (0 mm/day) to red (20 mm/day), with green and yellow in between
• Regions with Notable Rainfall:
  • High Rainfall (red, 15–20 mm/day):
    • Equatorial Pacific (stretching across the ocean)
    • Parts of Southeast Asia
    • Northern South America (Amazon region)
    • Southern Africa
  • Moderate Rainfall (yellow to green, 5–15 mm/day):
    • Central Africa
    • Parts of South America
    • Southeast Asia
  • Low Rainfall (blue, 0–5 mm/day):
    • Most of North America, Europe, and Australia
    • Northern Africa and the Middle East

The map highlights significant rainfall in equatorial regions, particularly the Pacific and Amazon, while showing drier conditions in North America, Europe, and Australia during this period.

Credit: NASA (Public Domain)

Rainfall anomaly for the month of March 2019

Click for a text description of the rainfall anomaly map.

This image is a world map showing rainfall anomalies over the last 30 days as of April 1, 2019. The map uses a color gradient to indicate deviations from average rainfall in millimeters per day across the globe.

• Map Type: World map
• Measurement: Rainfall anomalies for the last 30 days (mm/day)
• Date: April 1, 2019
• Color Scale (bottom of the map):
  • Range: -15 mm/day to 15 mm/day
  • Colors: Red (drier, -15) to blue (wetter, 15), with yellow at 0 (average)
• Regions with Notable Anomalies:
  • Drier Areas (red, -15 to -5 mm/day):
    • Northern South America (Amazon region)
    • Parts of Southeast Asia
  • Wetter Areas (blue, 5 to 15 mm/day):
    • Equatorial Pacific (stretching across the ocean)
    • Southern Africa
    • Parts of South America (southern Brazil)
  • Near Average (yellow, -5 to 5 mm/day):
    • Most of North America, Europe, and Australia
    • Central Africa

The map highlights significant rainfall deficits in the Amazon and Southeast Asia, while showing above-average rainfall in the equatorial Pacific and parts of southern Africa during this period.

Credit: NASA (Public Domain)

The two images above show the rainfall averages in terms of mm/day for the month of March 2019, above, and the rainfall anomaly for the same month, below.

Earlier satellites did not have the same resolution, but the record goes back to the late 1970s, allowing us to get a picture of the longer-term mean precipitation patterns, which can be seen in the video below. Watch this movie a few times through to see the annual patterns of precipitation, and focus especially on the region around India, where the summer monsoons show up beautifully.

Video:IMERG Monthly Precipitation Climatology (2001 - 2022) (00:16) This video is not narrated.

On a global scale, there is no clear sign that the amount of precipitation is increasing, as can be seen in this next figure, which plots the global average precipitation rate for each month.

Monthly average precipitation rates, averaged over the globe from 1979 to 2012.

Click for a text description of the Monthly average precipitation graph.

This image is a line graph titled "Globally Averaged Precipitation History From Satellites," showing the average precipitation rate from 1980 to 2010. The data is derived from satellite measurements and presented in millimeters per day.

• Y-Axis: Average precipitation rate (mm/day)
  • Range: 2.5 mm/day to 2.8 mm/day
• X-Axis: Years (1980 to 2010)
• Data Representation:
  • Raw Data: Pink line
    • Shows high-frequency fluctuations
    • Varies between 2.5 and 2.8 mm/day
  • Smoothed Data: Blue line
    • Represents a smoothed trend of the raw data
    • Fluctuates more gradually, staying around 2.6 to 2.7 mm/day
• Trend:
  • No clear long-term increase or decrease
  • Periodic fluctuations, with peaks around 1983, 1998, and 2007
  • Dips around 1992 and 2001

The graph indicates that global precipitation rates have remained relatively stable over the 30-year period, with short-term variations but no significant long-term trend.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

The figure above shows the history of the monthly average precipitation rates, averaged over the globe — kind of a difficult thing to swallow at first. Think of the values plotted along the y-axis as being the precipitation rate, averaged over the whole globe for a given month. As you can perhaps see, there is a strong seasonal cycle (with peaks in the fall each year) — I've removed the seasonal variation from the raw data to give the thicker blue line, showing variability that is not related to the seasonal cycle. One thing that is clear is that there is no general upward or downward trend over this period, although there are ups and downs, some of which correspond to the El Niño oscillations.

So, global precipitation, on the whole, is not going up, as far as the data reveal. What this means is that although the atmosphere is getting moister, the new addition of moisture is not coming out as precipitation — the atmosphere is retaining this extra water vapor.

But because the trend suggests there will be extra water vapor in the atmosphere in the future, when the conditions are right for a big precipitation event, the event might be bigger than today or in the past. In addition, predictions suggest there might be more storm events that exceed a certain threshold so that they are classified as extreme precipitation events.

More regionally, the El Niño cycle produces a dramatic change in precipitation patterns in parts of the globe, with Southeastern Australia becoming dry in summer and more prone to bushfires. In the US, El Niño corresponds to heavy winter rainfall in California as the so-called "Pineapple Express" picks up and transports heat and moisture from the tropical Pacific. Folks in California always hope that El Niño events put an end to drought. More on that in Module 8. El Niño is generally not a good time to visit Florida in the winter, as the same pattern extends across the southern US.

Impact of El Niño-La Niña on US Precipitation

Click for a text description of the El Niño-La Niña Precipitation map.

This image is a diagram titled "Typical January-March Weather Anomalies and Atmospheric Circulation During Moderate to Strong El Niño & La Niña." It consists of two maps of North America, one for El Niño and one for La Niña, showing weather patterns and anomalies during these climate events.

• El Niño Map (Top):
  • Title: El Niño
  • Atmospheric Features:
    • Low pressure (marked "L") over the eastern Pacific
    • Persistent extended Pacific jet stream (red arrow) stretching across the Pacific
    • Polar jet stream (purple arrow) over northern North America
  • Weather Anomalies:
    • Warm (red): Western Canada and Alaska
    • Wet (green): Southern U.S. and Mexico
    • Cool (blue): Central U.S.
    • Dry (brown): Northern U.S. and parts of Canada
• La Niña Map (Bottom):
  • Title: La Niña
  • Atmospheric Features:
    • High pressure (marked "H") over the eastern Pacific
    • Variable Pacific jet stream (orange arrow) with a wavy pattern
    • Polar jet stream (purple arrow) over northern North America
  • Weather Anomalies:
    • Cold (blue): Western Canada and Alaska
    • Wet (green): Pacific Northwest and Great Lakes region
    • Dry (brown): Southern U.S. and Mexico
    • Warm (red): Southeastern U.S.

The diagram illustrates how El Niño brings wetter and cooler conditions to the southern U.S. with a strong jet stream, while La Niña results in drier conditions in the south, wetter conditions in the northwest, and a more variable jet stream.

Credit: Image provided by the NOAA, Boulder CO, USA (Public Domain)

Drought is a very familiar foe in parts of the US, significant regions of Africa, and much of Australia. Drought is often called a creeping disaster, as it decimates a region slowly. We will talk a lot more about drought in Modules 8 and 9. Here, we briefly consider the recent record of drought. As we saw previously with research on drought and the Mayans, oxygen isotopes of stalactites can be interpreted in terms of precipitation. Another indicator of drought is the width of tree rings. One of the most comprehensive tree-ring data sets is shown in a compilation of precipitation in New Mexico from 137 BC to 1992 (The New York Times, The Longest Measure of Drought: 21 Centuries of Rainfall in New Mexico). The data set shows that with the exception of a wet phase in the last decade, much of the 20th century was dry in New Mexico, and, in general, the data suggest that western North America has become drier over the last 1000 years.

As it turns out, there is solid evidence that the severity of droughts has increased over the long term in many parts of the world. And, as it turns out, climate models predict that drought will become a part of everyday life in many regions in the coming century. In the US, these areas include large parts of the west and south central (Kansas, Oklahoma, Texas). Especially in California, climate models suggest that warmer years in the future are more likely to also be drier years, and this will increase the severity of future droughts.

So, how is the severity of drought quantified? The most widely used method to measure drought today and in the past is the Palmer Drought Severity Index (PDSI). This index is an accounting of the balance of supply of moisture via precipitation and demand for moisture via potential evapotranspiration (PE). The PE is the amount of water that could be evaporated given an unlimited supply of water. The National Oceanic and Atmospheric Association (NOAA) publishes a map of the PDSI over the US every month (see figure below).

Palmer Drought Severity Index, September 2021

Click for a text description of the Palmer Drought Severity Index map.

This image is a map of the United States titled "Palmer Drought Severity Index, September 2021," produced by the National Centers for Environmental Information (NOAA). The map uses a color gradient to show drought severity across the U.S. during this period.

• Map Type: U.S. map
• Measurement: Palmer Drought Severity Index (PDSI)
• Date: September 2021
• Color Scale (bottom of the map):
  • Range: -4.00 (extreme drought) to 4.00 (extremely moist)
  • Colors: Dark red (extreme drought, -4.00) to dark green (extremely moist, 4.00)
  • Categories: Extreme drought, severe drought, moderate drought, mid-range, moderately moist, very moist, extremely moist
• Regions with Notable Conditions:
  • Extreme Drought (dark red, -4.00):
    • Western U.S. (California, Nevada, Utah, Montana, Idaho)
    • Parts of the Upper Midwest (Minnesota, North Dakota)
  • Severe Drought (red, -3.00 to -3.99):
    • Much of the Western U.S. (Oregon, Washington, Wyoming, Colorado)
    • Parts of the Great Plains (Nebraska, South Dakota)
  • Moderate Drought (orange, -2.00 to -2.99):
    • Southwest (Arizona, New Mexico)
    • Parts of the Midwest (Iowa, Wisconsin)
  • Mid-Range (white, -1.99 to 1.99):
    • Central U.S. (Kansas, Oklahoma, Missouri)
    • Parts of the Northeast (New York, Pennsylvania)
  • Moderately Moist (light green, 2.00 to 2.99):
    • Southeast (Georgia, Alabama, Mississippi)
    • Parts of the Midwest (Illinois, Indiana)
  • Very Moist (green, 3.00 to 3.99):
    • Parts of the Southeast (Louisiana, Arkansas)
    • Parts of the Northeast (Maine, Vermont)
  • Extremely Moist (dark green, 4.00):
    • Small areas in the Southeast (parts of Louisiana)

The map shows severe to extreme drought conditions dominating the Western U.S. and Upper Midwest, while the Southeast and parts of the Northeast experienced moist to extremely moist conditions in September 2021.

Credit: Image provided by the NOAA, Boulder, CO, USA (Public Domain)

The PDSI has increased (become drier) for the driest parts of the globe since 1950. For most of the US, the PDSI has decreased for this time period. However, the last century has seen severe droughts in certain regions. For example, the major drought of the 1930s Dust Bowl in the Central US caused severe hardship for farmers and others in this region. The 1950s and 1980s also saw severe droughts in the Great Plains of the US. We will find out in Module 4 how models project that the severity of droughts will increase in many parts of the globe with future climate change. Moreover, we will learn more about the impact of drought on water supply in Module 8 and on food in Module 9.

Drought is currently at a critical level out west. Lake Powell, which is the second largest reservoir in the western US is currently at its lowest level in history and approaching the level where water and hydroelectric power supplies are threatened. This event would be especially devastating for communities that rely on the lake for water, such as Las Vegas, Nevada. We will come back to this issue in Module 8.

Lake Powell in 2007

Credit: Lake Powell - Arizona by PRA from Wikimedia (Creative Commons Attribution 2.5 Generic)

The latest (2022) report of the Intergovernmental Panel on Climate Change (IPCC), the large international team of scientists tasked with predicting our climate future and its impact, issues stern warnings about drought in the western US, Australia, the Mediterranean region and sub-Saharan Africa. Drought will have a major impact on human health, largely because of its effect on accessibility to clean drinking water, and require major adaptation of communities. Adaptation can involve conservation measures as well as finding new sources of water, for example desalinization. Such adaptation will be easier in developed countries like the US compared to the developing world, where countries like Yemen and Madagascar are already struggling with the devastating impact of drought and do not have the resources required to adapt.

Carcasses of sheep and goats in a severe drought in Somalia

Credit: SomalilandDrought007 by Oxfam East Africa from Flickr CC BY 2.0

Los Angeles Fires 2025

January 2025 will forever be remembered for the fires that decimated parts of Los Angeles. The fires lasted for 24 days caused a total of $250-275 billion in damage, an estimate that will likely keep rising. The fires destroyed 18,000 structures, burned 57,000 acres and caused 29 fatalities; 200,000 people were forced to evacuate. The fires started during a stretch of warm and very dry conditions, the driest 9-month period in California history with almost no rain at all, and were spread by strong Santa Ana winds which gusted up to 100 mph, producing literal firestorms that proved very difficult to contain.

It's still uncertain what triggered the fires, but it is likely sparks from electrical equipment. There were eight separate blazes, but two areas were most devastated, Pacific Palisades near the coast and Altadena more inland. The scale of the fires was unprecedented and firefighters had a hard time fighting so many fires simultaneously.

The Palisades fire in Pacific Palisades, Topanga and Malibu destroyed 6800 structures, burned 23,000 acres and killed 12 people. The fire spread extremely rapidly and covered 1200 acres in a matter of hours. Major evacuations were ordered because of the major threat and major traffic jams resulted. Residents reported leaving homes without most of their possessions and pets were also left behind. The area covered by the Palisades fire is the home of many Hollywood celebrities, so the fire got a lot of press coverage. The fire got so large so fast that hydrants ran out of water for a short period of time early in the blaze and this hindered firefighting efforts. The fire burned for 24 days till it was contained and became the third most destructive fire in California history.

The Eaton fire in Altadena destroyed 9000 structures including 4400 family homes and killed 17 people with 24 still missing. When the fire started the winds were so strong embers settled on structures a mile away and the fire spread very quickly, rapidly burning 1000 acres and growing to 2200 acres including surrounding mountainous areas. 100,000 people were placed under evacuation orders. The cause of the fire is unknown, but it was likely started by sparks from powerlines. Lawsuits have already been brought against Southern California Edison, the local power company. Like the Palisades fire, it took 24 days to extinguish the Eaton fire. By that time, it was the second most destructive fire in California history

The aftermath of the two fires included severe air pollution as the burnt structures that contained lead paint, asbestos, and vehicles, and solar powered batteries and panels, that contained various heavy metals. Wildfire smoke contains a number of pollutants including fine particulate matter (PM 2.5). These tiny particles can invade the lungs and penetrate the bloodstream causing a number of respiratory and cognitive illnesses along with cancer. Residents were told to stay inside even after the fires were extinguished. The threat continued as subsequent heavy rainfall led to mudflows laden with toxic materials.

Hawaii Fires 2023

Summer 2023 will be remembered for the truly devastating and catastrophic fires that destroyed the historic tourist city of Lahaina in Maui. As of the date of writing the fire caused 115 fatalities with 66 people still unaccounted for.  More than 2200 buildings were destroyed and damage expected to exceed $6 billion. The cause of the tragedy is still under investigation, but it was likely sparked by electric utility poles downed by hurricane force winds.  These same winds were responsible for the exceedingly rapid spread of the fires that made evacuation extremely difficult. The failure of warning systems to alert residents is also under investigation. Almost certainly, climate change is at least partially responsible for the fires. A lush tropical island, Maui like the rest of Hawaii, has become much drier since 1990 especially during the summer wet season. Hotter temperatures result in thinner clouds which hold less rainfall. Another critical factor is the area around Lahaina, once the site of sugar cane farms, is now covered by invasive grasses that acted like a tinderbox during the fires. Sadly the Lahaina tragedy is a sign of the future of many places around the globe.

Fire ravaged the waterfront in Lahaina, Maui on August 8th, 2023. Burned out cars and the remains of buildings are seen in Lahaina town in this image captured by U.S. Civil Air Patrol.

Credit: Lahaina Fire US Civil Air Patrol, U.S. Air Force (Public Domain)

The 2023 summer began with smoke-filled skies across the eastern US from fires raging in Canada. The quality of the air in New York City was the worst of any large city in the world. The last five years have brought devastating fires to California and other western states! Images from the news have shown San Francisco’s air full of smoke and glowing red. Every year brings new records and new tragedies. Deadly fires are not unique to California; in fact, Australia has a history of particularly devastating fires. And climate change is going to provide all of the ingredients for more fires in the future: more fuel (from extreme rainfall events), more effective natural ignition (often dry lightning) and often, sadly, arson, as well as the conditions to keep fires burning (drought and heat). The latest IPCC report issues some stern warnings about fire, predicting that the livelihood of millions of people will be impacted by wildfires in the future. The citizens of California, Australia and other parts of the world need to get used to apocalyptic fires. The last five years have been a wake up call.

The Camp Fire, November 2018

The deadly Camp fire, the most deadly and destructive fire in California history, began November 8^th, 2018 from a spark from an electrical wire. By the time it was finished two weeks later, the fire had burned more than 1500,000 acres around the town of Paradise. The fire occurred late in the season and was whipped up by strong winds and abundant fuel from the previous rainy season. A virtual firestorm quickly converged on Paradise, a ridge-top town that was used to fires but had grown fast and without good evacuation planning. On November 8^th, the evacuation order came too late, and many folks did not receive it because cell towers were down. Because the call was so late, evacuation could not occur in an orderly fashion, from one side of the town to the other. Hundreds of vehicles quickly jammed the three routes out of town and the fire quickly trapped many people in their cars. Folks who did not get the evacuation order were trapped in their homes. A total of 86 people in Paradise were killed and the town was almost completely burned down.

In the wake of the fire, the large utility company PG&E filed for bankruptcy to avoid major lawsuits. Turns out the company discussed turning off the power grid in the days before the fire began but decided not to. Now in the wake of the fire, companies will be much more likely to turn off the grid if there is a chance of fire and this will impact millions of customers in California.

As it turns out the Camp fire was only one of two major fires that began on November 8^th. The massive Woolsey fire west of LA also began that day, burned 97,000 acres, killed three people, and forced the evacuation of 295,000 people.

The Camp and Woolsey fires are the “new normal” for California and the state must deal with difficult issues such as vegetation management, sensible development, evacuation planning, and regulation of power companies.

Woosley Fire. The large smoke plume from the fire encroaching on Malibu on November 9, seen from the Pacific Coast Highway.

Credit: image by Cyclonebiskit from Wikipedia, licensed under CC BY-SA 4.0

The record breaking 2020 fire season

2020 was a record-breaking fire year in California. More acres burned than ever before (over 4.3 million), at least in recent times, with over 9200 separate fires including the first “gigafire”, the August Complex Fire that burned more than 1 million acres (over 1500 square miles)! Total damages exceeded $2.5 billion and 32 people perished. Although arson was to blame for some of the fires, more often they were sparked by lightning that occurred in thunderstorms without rain. The largest fires in Northern California, the SZU Lightning Complex fire, the LNU Lightning Complex Fire, and the August fire were all started by lightning strikes.

Video: California wildfires evening update: September 9, 2020 (9:32)

The fires were caused by the continued drought in the state and across the western US, and, indeed, large fires occurred over much of the region as far east as Colorado and as far north as Washington state. In all over 52,000 fires consumed almost 9 million acres.

The year was also notable for fires that encroached on Seattle and especially Portland. In fact, wildfires caused an especially scary situation in the latter metropolitan area where 40,000 people had to be evacuated and 500,000 lived in evacuation warning zones at the height of the blazes. These fires in the northern part of California, Oregon, and Washington were responsible for extremely hazardous air quality for weeks, and in all over 17 million people experienced unhealthy or hazardous air this year. In fact, the air in Marion County in Oregon was so polluted it registered off the measurement scale. The air quality in Northern California, including Sacramento and San Francisco, was unhealthy for weeks, and young, elderly, and people with asthma, COPD, and other preexisting conditions were advised to remain indoors. The smoke can cause burning eyes and lung ailments including bronchitis, and aggravate heart and lunch diseases. The elevated levels of fine particulates, including hazardous compounds, may also cause longer-term health issues. This public health situation was especially difficult as it superimposed on Covid-19.

2020 will be remembered for the scale of the western fires but also for their profound impact on public health. On top of the possibility of evacuation and loss of property, many residents of the western states were awakened to the prospect that their “new normal” fire season included toxic air.

Australian Bushfires

The 2020 fire season in California was the worst on record. However, these are not the most devastating fires in Earth’s recent past. Black Saturday Bushfires in February 2009 in Victoria, Australia were fueled by extraordinary heat and strong winds. At the peak of the inferno, there were some 400 blazes. Conditions leading up to the fires were extraordinary. Mercury hit 116 degrees in Melbourne in a heatwave that started the week before the fire, and in the peak of the summer drought, the dry brush was perfect fuel. On the fateful Saturday, the first fire was started by arson. Falling power lines and lightning ignited other fires. Fires consumed some 1.1 million acres, destroyed whole towns, caused some $4.4 billion in damage, and killed 173 people. Most of the damage was done in the first few days, but the blazes raged for weeks. One of the astounding aspects of the fires were observations by firefighters of sideways mini-tornadoes, technically called horizontal convective rolls. As the air at the surface warmed and rose, it was forced to move in a corkscrew pattern-oriented parallel to the ground. This created bands of alternating fast and slower surface winds. Fast winds surpassed 30 mph and ignited huge swaths of land in a catastrophic fire. There are terrifying stories of people getting swept up in the flames trying to escape the inferno on tiny mountain roads.

The late part of 2019 and the early stages of 2020 have brought more intense wildfire to Australia, especially near Sydney in the southeast. The season began very early as a result of the decade long drought and intense heatwaves (see Heatwave page this module). As of this reporting fires have burned millions of acres, destroyed thousands of buildings, and killed dozens of people and millions of animals including koala and kangaroos. The smoke has caused unhealthy air in eastern Australian cities and the plume has drifted as far as New Zealand. The fires have caused a political upheaval because Australia has a large emission of carbon per capita and produces a significant amount of energy from coal. Given how susceptible it is to climate change, many citizens feel that the country is not doing enough to curb emissions. 2019-2020 is a glimpse of the future for Australians.

Werombi Bushfire

Credit: image by Helitak430 from Wikipedia CC BY-SA 4.0

Relationship between fire and climate change

So, how is increased fire activity related to climate change? Fire is very much a part of the ecosystem in places like Australia, California, South Africa, and Southern Europe. Even before people were around, fires ignited by lightning occur regularly in environments with dry seasons, as nature’s way of germinating drought-resistant species and fertilizing the soil. But people have made fire events much more common. First, many fires are started by arson or in the cases of the Lahaina and Camp fires by downed electric utility poles. Clearly, heat and drought are good for fire, and as we have seen, both of these ingredients will increase in the future as a result of anthropogenic climate change. But the other key aspect of fire is fuel and which is supplied by precipitation and active growth of vegetation. Climate change is likely to cause more variability in temperature and precipitation that will create more contrast between drought and wet years. This will lead to greater fire risk. The heavy rains in California in the winter of 2016-2017 caused a significant growth of vegetation in uncultivated hills and canyons surrounding residential areas and this dried out in the hot and dry 2017 summer. Then, once the warm fall Santa Ana winds arrived, the recipe for disaster was all ready.

The main culprit for the increase in fires in the western US and Hawaii is the long-term drought. However, development in forested and brushy areas that are prone to burn. Clearing of brush in populated areas has led to the establishment of invasive plant species that grow very rapidly and provide fuel when they die back in the dry season. There has been constant friction between responsible forest management with regulation and development. The potential for gigafires will force a reckoning between planning and forestry departments in the future so that forest and brush can be managed through controlled burns. Many communities are also replanting native plant species and using animals to clear out areas overwhelmed by invasives. At the same time, growth needs to be managed so that safe evacuation can occur in the case of a large fire. Sadly, highly destructive wildfires are part of California’s future.

As are mudslides. The huge Thomas fire destroyed so much vegetation that held hill-slopes in place. They were followed in early January by major rains that led to torrents of mud from hills into valleys. The catastrophic mudslides killed at least 20 people and caused massive property damage. Fire and mud are intricately related.

Homes and streets of a neighborhood affected by the Santa Barbara County mudslides in Santa Barbara, California, Jan. 9, 2018.

Credit: U.S. Coast Guard (Public Domain)

One of the main messages from the 2022 IPCC report is the need to adapt to a future in which fire is more common and more deadly. In fact California is already making progress adapting its communities to live with the threat. In some areas, development is being banned in risky locations near forests for example, and best practices are being applied for clearing brush and maintaining fire breaks. Australia has been applying these best practices for a number of years also.

What is also clear from the IPCC report is that wildfires like drought will have a major impact on human health especially for children, the elderly and the poor. The impact will also be more severe in the developing world where fires set for the purpose of deforestation are having a major effect on human health as well as wiping out large numbers of species, a topic we will return to in Module 11.

Heat waves are days to week-long events with extremely high temperatures. These events are becoming more common with a changing climate, are forecasted to become frequent in many parts of the world in the future and occur earlier in the year (June 2022 has been a dress rehearsal for that!). Heatwaves are often part of extended droughts and associated with wildfires. They are major public health risks, especially for very young and older populations, as well as the poor who do not have access to air conditioning or basic hydration. In the developing world, heat waves can be very deadly, in India in 2015 more than 2200 people died due to excessive heat.

Some of the most drastic heatwaves occur in Australia, a continent which is also characterized by devastating drought and perilous wildfires. The last few years have seen major heatwaves across the continent, shattering local and continental temperature records. In 2013 Australia got so hot that they had to add new colors to the temperature map. There were days that year when the center of the continent topped 125 degrees F (52 degrees C).

This map of Australia shows maximum temperatures in December 2018 and January 2019.

Click for a text description of the Australia Maximum temperatures map.

This image is a map of Australia titled "Highest Maximum Temperature (°C) December 2018 to January 2019," produced by the Australian Bureau of Meteorology. The map uses a color gradient to show the highest maximum temperatures recorded across Australia during this period.

• Map Type: Australia map
• Measurement: Highest maximum temperature (°C)
• Period: December 2018 to January 2019
• Color Scale (right side of the map):
  • Range: 14°C to 48°C
  • Colors: Light blue (14°C) to dark purple (48°C), with green, yellow, orange, and red in between
• Regions with Notable Temperatures:
  • Extremely High (dark purple, 46°C to 48°C):
    • Central Australia (Northern Territory, parts of South Australia)
  • Very High (red to purple, 40°C to 46°C):
    • Most of Western Australia, Northern Territory, and South Australia
    • Parts of Queensland and New South Wales
  • High (orange to red, 34°C to 40°C):
    • Eastern Australia (Queensland, New South Wales)
    • Parts of Victoria
  • Moderate (yellow to green, 26°C to 34°C):
    • Coastal areas of Western Australia and Victoria
    • Tasmania
  • Lower (blue, 14°C to 26°C):
    • Small areas in Tasmania and southern Victoria

The map shows that central and northern Australia experienced extremely high temperatures, often exceeding 40°C, while coastal and southern regions, including Tasmania, had cooler maximum temperatures during this period.

Credit: Australian Government Bureau of Meteorology licensed under ;CC BY 3.0 AU

In 2019 Australia broke records again in a summer of wildfires, drought, and heat. On December 19, the average high for the whole continent was 107.4 degrees F (41.9 degrees C) shattering the record set the day before by over a degree C.

In the US, heatwaves in the desert southwest including Phoenix and Las Vegas are part of a normal summer. Phoenix regularly reaches 112 degrees F (44.4 degrees C) and it has been known to exceed 120 degrees F (48.9 degrees C). These are shade temperatures, corresponding temperatures can reach 168 degrees F (76 degrees C) in the sun right above the ground level. Because concrete traps heat, cities like Phoenix get particularly hot and are prone to heatwaves. In fact, this “heat island” effect makes cities as much as 7 degrees F warmer during the day. Nighttime often does not provide much relief, with temperatures above 80 deg F for many nights in a row.

June 2021 was a sign of things to come in the northwest US and western Canada, with temperatures topping out at 116 degrees F in Portland and 108 degrees F in Seattle. Lytton in British Columbia reached 121 degrees F, the hottest temperature ever recorded in Canada. These temperatures smashed records for these cities that are normally cool in the early summer.

Predicted high temperatures for western Washington on June 28, 2021

Click for a text description of the Western Washington temperatures map.

This image is a temperature forecast map for the Pacific Northwest of the United States, titled "High Temperatures June 28, 2021," produced by NOAA's National Weather Service in Seattle. The map shows predicted high temperatures in degrees Fahrenheit for various locations in Washington state on that date.

• Map Type: Regional map (Pacific Northwest, Washington state)
• Measurement: High temperatures (°F)
• Date: June 28, 2021
• Color Scale (bottom of the map):
  • Range: 65°F to 115°F
  • Colors: Green (65°F) to dark purple (115°F), with yellow, orange, and red in between
• Regions and Temperatures:
  • Extremely High (purple, 110°F to 115°F):
    • Seattle: 113°F
    • Olympia: 112°F
    • Bremerton: 109°F
    • Kent: 112°F
    • North Bend: 113°F
    • Snoqualmie Pass: 102°F
  • Very High (red, 100°F to 110°F):
    • Forks: 108°F
    • Quinault: 106°F
    • Aberdeen: 101°F
    • Everett: 107°F
    • Monroe: 113°F
    • Darrington: 109°F
  • High (orange to yellow, 90°F to 100°F):
    • Port Angeles: 94°F
    • Port Townsend: 90°F
    • Friday Harbor: 92°F
    • Mount Vernon: 105°F
    • Stevens Pass: 94°F
    • Mount Baker: 93°F
  • Moderate (green, 65°F to 90°F):
    • Neah Bay: 87°F
    • Westport: 79°F
    • Chehalis: 110°F (outlier in a cooler zone)
    • Paradise: 88°F
    • White Pass: 93°F
• Additional Info:
  • Created: 04:52 AM PDT, June 28, 2021
  • Source: weather.gov/Seattle

The map indicates an extreme heatwave across Washington state, with most areas experiencing temperatures above 100°F, and some coastal areas slightly cooler but still unusually warm for the region.

United States National Weather Service, Public domain, via Wikimedia Commons

The heat caused a minimum of 500 fatalities in the US and Canada among vulnerable populations, including the elderly. These cities are not adapted to extreme heat, with a low percentage (about 40%) of homes having air conditioning. The heat was so extreme that up to a billion of clams and other shellfish were cooked inside their shells in an ecologic catastrophe. The heat caused major wildfires to rage throughout the area, including the town of Lytton which was virtually destroyed. Several weeks later and further south, temperatures reached 130 deg G in Death Valley, matching the world record the hottest temperature ever recorded.

July 2022 saw record breaking temperatures in Europe and notably in London where it reached 40.2 deg C or 104.4 deg F, an all-time record. That city is not adapted to temperatures that high and will have to invest significantly in areas like air-conditioning public transportation in the future.

Heatwaves will become more common and more extreme in most places in the future as the planet warms. Europe and Australia are going to experience more and more of them, as are places in India. The southern US will seem more like the Middle East in the future, with cities like Austin and El Paso becoming as hot as Dubai is today and Phoenix approaching Baghdad, Iraq temperatures. Washington DC is going to seem more like Austin in the summer, Boston will seem more like Philly and Billings, Montana more like El Paso.

More dramatically, there may be places in the Middle East and Northern India where humans may not be able to live in the future because it is impossible for the human body to cope with the searing heat.  To be more precise, a wet bulb temperature that factors heat and humidity of 95 degrees F or 35 degrees C is where it is thought that a combination of kidney, heart, or even brain failure may commence, especially for vulnerable populations.

Temperatures in India in June 2019 (in degrees C).

Click for a text description of the Indian Temperatures map.

This image is a temperature map of India showing air temperatures in degrees Celsius across various cities. The map uses a color gradient to indicate temperature variations, highlighting a significant heatwave in the region.

• Map Type: India map
• Measurement: Air temperature (°C)
• Color Scale (bottom of the map):
  • Range: 38°C to 50°C
  • Colors: Yellow (38°C) to dark red (50°C), with orange and red in between
• Regions and Temperatures:
  • Extremely High (dark red, 48°C to 50°C):
    • Jacobabad (Pakistan, near the border): 50°C
    • Delhi: 48°C
    • Lucknow: 48°C
  • Very High (red, 45°C to 47°C):
    • Ludhiana: 48°C
    • Agra: 49°C
    • Kanpur: 48°C
    • Patna: 45°C
    • Bhopal: 46°C
    • Nagpur: 45°C
    • Jaipur: 47°C
  • High (orange, 40°C to 44°C):
    • Ahmedabad: 46°C
  • Moderate (yellow, 38°C to 40°C):
    • Hyderabad: 39°C
• Geographic Context:
  • Surrounding areas: Pakistan, Arabian Sea, Bay of Bengal
  • Scale: 200 km (bottom left)

The map shows a severe heatwave affecting northern and central India, with temperatures reaching up to 50°C in Jacobabad and exceeding 45°C in many cities, while southern regions like Hyderabad are relatively cooler at 39°C.

Credit: NASA Earth Observatory image by Joshua Stevens (using GEOS-5 data from the Global Modeling and Assimilation Office at NASA GSFC) (Public Domain)

Like drought and fire, the 2022 IPCC report stresses the need for adaptation to heat. This is already taking place in the developed world where cities are reducing the heat island effect by, for example, planting more trees, making roofs green by covering with plants, and using materials for pavement that reflects heat as opposed to absorbing it. Unfortunately, these strategies are more difficult to apply in developing nations where necessities like air conditioning are also less available. Thus, it is likely that heatwaves will become increasingly deadly in coming decades.

Green roof of Chicago City Hall

Credit: Chicago City Hall Green Roof by TonyTheTiger from Wikipedia is licensed under CC BY-SA 3.0

Antarctica and Greenland, the two large ice sheets, represent some of the most bleak and hostile places on Earth. Not many geoscientists have the mettle to explore these remote places, but they remain one of the essential frontiers for research. These large and thick ice sheets look relatively homogeneous compared to other parts of the planet, but in fact, their behavior is not completely understood. The fact remains that if the ice sheets on Antarctica and Greenland were to melt, a feat that cannot happen over decades or even centennial time scales, don't worry, global sea level would rise by about 70 meters which would drown most coastal cities such as New York, Shanghai, Mumbai, and Jakarta. Just this threat should cause global leaders to stay up at night, though!

Ice is the frozen segment of the atmospheric moisture cycle. As you will remember from the discussions of Snowball Earth and the Pleistocene ice ages in Module 1, it is a very important component of Earth’s climate system — ice is the most reflective material on the surface, and as such it can exert an important control on how much sunlight the Earth absorbs, which directly affects the Earth’s temperature.

Ice is also an important and highly sensitive indicator of climate change in the polar regions and in areas of high altitude where mountain glaciers occur. Glaciers will grow or shrink in response to changes in temperature and precipitation. The temperature response is pretty obvious — glaciers melt as the temperature rises. The precipitation response is perhaps less obvious, but glaciers can expand if winter precipitation increases, and they shrink if the winter precipitation decreases. Just as the ground temperatures respond somewhat sluggishly to surface temperature changes, glaciers respond sluggishly to climate changes, which is good in the sense that they give us a better sense of the important trends in climate change that might otherwise be obscured by short-term variations. In the following pages we discuss the key different types of ice and how it is changing: glaciers in mountainous regions, sea ice in the Arctic, and the large Antarctic and Greenland ice sheets.

We’ll start with some striking images taken by glaciologists around the world — these are photos of mountain glaciers taken from the same spot at different times, and they provide us with some fairly shocking observations on just how much glaciers can change and have done so recently.

Muir Glacier in Alaska, as seen in 1941 and 2004

Credit: Credit: Photographs by William Osgood Field (1941) and Bruce F. Molnia (2004). From the Glacier Photograph Collection. Boulder, Colorado USA: National Snow and Ice Data Center.

For another Alaskan example, we turn to satellite imagery of Columbia Glacier, which obviously does not reach back into time as far, but nevertheless, there are some dramatic changes evident here:

Aerial view of the Columbia Glacier, Alaska, 1986

Credit: NASA’s Earth Observatory (Public Domain)

The above scene is from 1986, and at this point in time, the end of the glacier (terminus) is located down near the bottom of the image. Below, we jump forward in time to 2011:

Aerial view of the Columbia Glacier, Alaska, 2011

Credit: Modified from NASA’s Earth Observatory (Public Domain) to show terminus locations

As you can see, the terminus here has retreated by about 15 km in just 25 years — a very impressive rate.

Near disappearance of the famous Grinnell Glacier in Glacier National Park from 1938 to 2005

Credit: US National Park Service

Grinnell Glacier, in Glacier National Park, Montana, as seen from the same vantage point over a 67 year period. The glaciers in this famous national park are all in such rapid retreat that the park may need a new name in a few decades.

Glaciers in the Alps are shrinking too. Check out SwissEduc Glaciers online to see one good example — at the bottom of the page is a comparison that flips back and forth from the past to the present as you move your mouse over the image.

The same story as seen in Alaska, Montana, and the Alps holds for glaciers in more tropical settings, as can be seen from the images of Qori Kalas glacier in the Andes Mountains of Peru, below.

The retreat of the Qori Kalas glacier in the Andes Mountains of Peru 1978 to 2004

Credit: Lonnie Thompson and the National Snow and Ice Data Center, University of Colorado, Boulder.

Studies of glaciers around the world show that an overwhelming majority are losing mass over time. In most cases, this loss of mass is reflected in the glaciers' retreating, so the length of the glacier becomes smaller. The figure below shows a selection of data from glaciers around the world, documenting this pattern of retreat.

This figure shows examples of glacier length records from different parts of the world. Data points are scarce before 1900; after 1900 a considerable number of records have an annual resolution. Data from Oerlemans, 2005, Science, v. 308, p. 675.

Click for a text description of the Glacier Length Records graph.

This image is a scatter plot showing the retreat of several glaciers over time, titled with the data points representing the length of glaciers relative to their 1950 positions. The glaciers tracked are from different regions, and the data spans from around 1650 to 2000.

• Graph Type: Scatter plot
• Y-Axis: Length relative to 1950 (km)
  • Range: -2 km to 3 km
• X-Axis: Years (1650 to 2000)
• Data Representation:
  • Nigardsbreen, Norway: Red line with dots
    • Starts at 3 km in 1650, steadily decreases to around 0 km by 2000
  • Hansbreen, Svalbard: Blue line with dots
    • Starts at 1 km in 1900, decreases to around -1 km by 2000
  • Franz-Josef Glacier, New Zealand: Green line with dots
    • Starts at 0 km in 1900, fluctuates, and ends around -1 km by 2000
  • Wedgemount, Western Canada: Black line with dots
    • Starts at 0 km in 1900, decreases to around -1 km by 2000
  • Sorbreen, Jan Mayen: Orange line with dots
    • Starts at 0 km in 1900, decreases to around -1 km by 2000
• Trend:
  • All glaciers show a general retreat (negative length change) over time
  • Nigardsbreen has the longest record and most significant retreat, losing about 3 km since 1650

The scatter plot illustrates a consistent retreat of glaciers across different regions over the past few centuries, with a notable acceleration in retreat since the 1900s.

Credit: Robert Rohde

The vast majority of glaciers on Earth are melting, and this melting began about the time that the temperature records indicate the beginning of warming, around the beginning of the 1900s.

If you combine the records of glacier length changes from around the world into one graph, we can get a pretty clear idea of what is happening.

Change in the average length of all glaciers around the world

Click for a text description of the World Glacier Length changes graph.

This image is a line graph showing the retreat of a glacier over time, with the data representing the glacier's length relative to its 1950 position. The graph is sourced from a study by Oerlemans (2005), published in Science (DOI: 10.1126/science.1107046).

• Graph Type: Line graph
• Y-Axis: Length relative to 1950 (meters)
  • Range: -500 m to 1500 m
• X-Axis: Years (1700 to 2000)
• Data Representation:
  • Glacier Length: Blue line
    • Starts around 1200 m in 1700
    • Remains relatively stable with minor fluctuations until around 1850
    • Begins a steady decline after 1850
    • Drops to around -500 m by 2000
• Trend:
  • Stable length from 1700 to 1850
  • Significant retreat (about 1500 m) from 1850 to 2000

The graph illustrates a clear and consistent retreat of the glacier over the past 150 years, with the most significant length reduction occurring after 1850.

Credit: Figure adapted from Oerlemans, 2005

On the graph above, the y-axis plots the length of the glaciers relative to their length in 1950 — so this is a kind of length anomaly. A positive number means that on average, glaciers were longer than they were in 1950; negative numbers mean they were shorter. Here, we can see that beginning around 1850, glaciers around the world begin to shrink, and this trend continues to the present. The average glacier has retreated almost 2 kilometers in this time.

It is possible to estimate the magnitude and history of temperature change needed to produce this history of glacial retreat, and Oerlemans (2005) did this using a simple model; the results are shown below.

Modeled temperature change required to cause glacial retreat compared to proxy and instrumental temperature data

Click for a text description of the Modeled temperature change graph.

This image is a line graph showing global temperature anomalies from 1600 to 2000, derived from three different data sources: glacier records, multi-proxy reconstructions, and instrumental measurements. The anomalies are measured in degrees Celsius relative to a baseline.

• Graph Type: Line graph
• Y-Axis: Temperature anomaly (°C)
  • Range: -0.4°C to 0.5°C
• X-Axis: Calendar years (1600 to 2000)
• Data Representation:
  • Glaciers: Blue line
    • Starts around -0.3°C in 1600
    • Remains relatively stable with minor fluctuations until 1850
    • Rises sharply after 1850, reaching about 0.4°C by 2000
  • Multi-Proxy: Green line
    • Starts around -0.2°C in 1600
    • Shows more variability, with fluctuations between -0.3°C and 0.1°C until 1900
    • Rises after 1900, reaching about 0.3°C by 2000
  • Instrumental: Red line
    • Starts around 1850 at 0°C
    • Shows high variability, with peaks and dips
    • Rises steadily after 1900, reaching about 0.4°C by 2000
• Trend:
  • All three datasets show a general increase in temperature anomalies after 1850
  • Glacier and instrumental data align closely in the 20th century, showing a sharp rise

The graph illustrates a significant warming trend in global temperatures since the mid-19th century, with all three data sources confirming a rise of about 0.4°C by the year 2000.

Credit: D. Bice; data compiled by Oerlemans, 2005

The thick blue line here is the temperature history needed to produce the timing and magnitude of the glacial retreat history shown in the previous figure. For comparison, we also see the instrumental temperature record in red (Jones and Moburg, 2003) and the temperature reconstruction based on multiple climate proxies (Mann et al., 1999). Note the excellent match with the instrumental record in the last century.

Just like their smaller counterparts, the huge ice sheets of Greenland and Antarctica are also shrinking. This is critical as melting of these ice sheets impacts climate through albedo feedback (Module 3) as well as global sea level (Module 10). Melting of all of the ice in these ice sheets would raise global sea level by about 70 meters (actually, Greenland would produce 6 meters and Antarctica about 60 meters). Don't worry, this isn’t going to happen any time soon, but the concern is that large glaciers on the edge of the continents are becoming increasingly unstable and will fail in coming years. The Thwaites glacier also known as the doomsday glacier is the one scientists are most concerned about. Collapse of this glacier could happen very rapidly and raise global sea level by 65 cm. We discuss this glacier in more detail in Module 10.

Given the remoteness and difficulty associated with studying these ice sheets, we only have good data on their size for the last decade, thanks to the advent of satellite systems that can monitor these glaciers. In particular, the GRACE satellite system has provided some very important data on the changes occurring in Greenland and Antarctica. This ingenious satellite system consists of a pair of satellites that are chasing each other in the same orbit around the Earth. The distance between the satellites changes according to subtle changes in gravity on the surface of Earth. As the lead satellite approaches a region of stronger gravity (due to more mass near the surface), it pulls away from the trailing satellite and then slows down as it passes the region of excess mass. In this way, the satellites can measure the subtle changes in gravity, and since the satellites pass over the same area every few days, they can detect changes in the gravity of a certain spot over time. If a big ice sheet loses mass due to melting, its gravitational effect on the satellites diminishes, and in this way, the satellites can detect the changes in the mass of these ice sheets — they are effectively “weighing” these glaciers, which is an extraordinary achievement. The results can be seen in the videos below. Note: videos do not have audio.

Video: Greenland Ice Loss as measured by GRACE and GRACE-FO (No Audio) (:52)

These simulations above show the time series of Greenland and Antarctic ice mass changes from GRACE satellite data. You can see that melting is concentrated near the edges of the ice sheets and occurs in fits and starts (i.e., it is not gradual). The edges of the ice sheets, the ice shelves that float on the ocean, are holding the ice sheets back in a process known as buttressing. So once the ice shelves melt fast, this speeds up the melting of the edges of the ice sheet.

Other satellites passing over these ice sheets can measure the areas where surface melting produces small ponds of melt during the melting season. Much of the meltwater freezes back into the ice, but in some places near the edges of the ice sheets, the water sinks down into crevasses and travels all the way to the bottom of the ice, where it can lubricate the base and help accelerate the flow of the ice.

Greenland melt anomaly

Credit: NASA's Earth Observatory (Public Domain)

The above image shows the Greenland melt anomaly, measured as the difference between the number of days on which melting occurred in 2007 compared to the average annual melting days from 1988-2006. The areas with the highest amounts of additional melt days appear in red, and areas with below-average melt days appear in blue. Although faint streaks of blue appear along the coastlines, namely in northwestern and southeastern Greenland, red and orange predominate, especially in the south.

Video: Antarctic ice loss 2002-2016. This video does not have audio. (:37)

The video below shows the dramatic loss of ice on Antarctica between 2002 and 2016. The loss is mostly around the edges of the continent and focused in a few areas.

One of the most striking examples of climate change is related to the Arctic sea ice; the video below shows the drastic changes in the extent of Arctic sea ice over the last 40 years. The ocean is now permanently open to shipping in the summer.

Video: Annual Arctic Sea Ice Minimum Area 1979-2023, With Graph (00:46) (No Narration)

This visualization shows the age of the Arctic sea ice between 1979 and 2023. Younger sea ice, or first-year ice, is shown in a dark shade of blue, while the ice that is four years old or older is shown as white. The graph displayed quantifies the area covered sea ice 4 or more years old in millions of square kilometers.

The Arctic sea ice undergoes large fluctuations over the course of a year, and, like all aspects of the climate system, there is a good deal of natural variability. So, while one or two extreme years do not necessarily make a trend, they may be part of a trend. Visit NASA's Earth Observatory to see a nice set of maps looking down on the North Pole showing the sea ice extent over the last 12 years; each map shows the long-term mean ice extent in a yellow line. One thing that becomes apparent is that there is much more variability in the end-of-summer minimum ice extent than the end-of-winter maximum; another thing that is apparent is that the reduction in Arctic sea ice is now a long term trend.

In the figure below, we see a summary of data on the ice extent, reported as an anomaly (departure from the mean), and it now becomes apparent that there has been a more or less steady decline since about 1970.

Sea ice extent departures from monthly means for the Northern Hemisphere. For January 1953 through December 1979, data have been obtained from the UK Hadley Centre and are based on operational ice charts and other sources. For January 1979 through December 2018, data are derived from passive microwave (SMMR / SSM/I).

Click for a text description of the Arctic Sea Ice Extent Standardized Anomalies graph.

This image is a line graph titled "Arctic Sea Ice Extent Standardized Anomalies, January 1953 – October 2018." The graph shows the deviation of Arctic sea ice extent from the 1981–2010 mean, measured in standard deviations, over the specified period.

• Graph Type: Line graph
• Y-Axis: Anomaly (standard deviations from 1981–2010 mean)
  • Range: -5 to 5 standard deviations
• X-Axis: Years (1953 to 2018)
• Data Representation:
  • Monthly Anomaly: Blue line
    • Shows high variability, fluctuating between -4 and 4 standard deviations
    • Peaks around 1955 and 1975, dips significantly after 2000
  • 12-Month Running Mean: Pink line
    • Smoothed trend of the monthly data
    • Starts around 0 in 1953, fluctuates slightly until 1980
    • Shows a steady decline after 1980, reaching around -3 by 2018
• Trend:
  • Early years (1953–1980): Fluctuations around the mean (0)
  • Post-1980: Consistent decline in sea ice extent
  • By 2018: Anomaly reaches approximately -3 standard deviations

The graph illustrates a significant and ongoing decline in Arctic sea ice extent since the 1980s, with the 12-month running mean showing a clear downward trend over the 65-year period.

Credit: Walt Meier and Julienne Stroeve, National Snow and Ice Data Center, University of Colorado, Boulder, State of the Cryosphere.

In addition to the reduced area of coverage, the Arctic ice is also becoming thinner. The thickness of the ice in the Arctic has been monitored for a long time by the US Navy, using submarines. Now, satellites can measure ice thickness. The comparison below shows a 40% to 50% decrease in the thickness of ice from the average over 1958-1976 compared to the present.

Data from Kwok and Rothrock (2009; doi:10.1029/2009GL039035).

Click here for a text alternative to the figure above

Ice Thickness (m) At Different Locations

Thickness at:	1958-1976	1993-1997	2003-2007
Chukchi Cap	1.9m	1.0m	0.7m
Beaufort Sea	1.9m	1.0m	1.0m
Canada Basin	3.4m	2.1m	1.7m
North Pole	3.7m	2.3m	1.8m
Nansen Basin	3.8m	2.0m	2.2m
Eastern Arctic	3.3m	1.3m	1.2m

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

This reduction in the coverage of Arctic sea ice is significant since it means that during the summer months when the sun is at its brightest in the polar region, and there are 24 hours of daylight, the reflective ice cover is being reduced, allowing for the absorption of a much greater quantity of solar energy that can then warm the whole polar region. This change in sea ice coverage is not just an Arctic phenomenon — in the Antarctic, the sea ice is also decreasing in area as the map below illustrates.

The extent of sea ice around Antarctic in February 2023 compared to the average extent from 1981-2010.

NASA

Finally, a word on sea level. The melting of ice sheets and mountain glaciers is partially responsible for a significant rise in sea level---20 cm in the last 100 years. However, thermal expansion of seawater as a result of warming is equally, if not more, important. Also, since sea ice is already at ocean level, its melting does not contribute to sea level rise.

Like many Americans, I used to dream of owning coastal property. I have experienced hurricanes - I went to help friends recover from Andrew in Miami in 1992, and I lived through Hurricane Fran in NC in 1996, so I’ve seen the destruction they can cause. But I still maintained my dream. Every time I go to the beach, I pick up real estate brochures; on a cold, snowy weekend, I would look at coastal properties on Zillow. But my dream is being replaced by practicality. Coastal property is a risky investment in the 21st century! The research shows that fueled by increased heat from global warming, hurricanes are becoming stronger, slower moving and wetter, all recipes for increased devastation of coastal communities.

Development in downtown Miami

Credit: Miami Palmed Skyline by Wyn Van Devanter, licensed under CC BY-SA 2.0

We are going to focus on storms from 2017, 2019, and 2020. We start by looking at the year 2017. Harvey, Irma, Maria, three massive hurricanes occurred in three weeks! The big question is whether 2017 was just an unusually active year, or if these monster storms are a new normal, the grand result of a warming planet.

Hurricane Harvey, August 2017

Each of these storms had some incredible elements. The eye of Harvey roared ashore in south Texas more than a hundred miles south of the booming metropolis of Houston, the fourth-largest city in the US with a population of close to 6 million people. The storm moved north towards the city and literally parked there for 4 or 5 days drawing moisture in from the warm Gulf of Mexico. By the time the storm moved on to the north, parts of Houston had received close to 52 inches of rain. Harvey dumped a grand total of 33 trillion gallons of water on Houston and points north, causing catastrophic flooding. Just for context, 33 trillion gallons would fill a cube with sides of 3 miles, it’s a massive amount of water! Several small creeks north of the city were over 19 feet over their banks. The storm was the single largest rain event in the lower 48 states of the US ever! The total damage, still rising as I write this, is estimated to be about $150 billion, again one of the largest ever catastrophes. Harvey caused 82 deaths in the US.

At the outset, Houston is a very flood-prone city. Houston lies on a flat plain near the ocean. The natural landscape is grassland that is drained by creeks called bayou. The city lies very close to the Gulf of Mexico, which gets very warm in the late summer, close to 87 degrees F! This warmth is transferred to the air keeping coastal areas warm and humid. The Gulf is an enormous heat and water factory. A key fact to understand is that for every one degree of temperature increase, an air mass can hold 3 % more moisture, so as the Gulf has warmed over recent years it contributes more energy and rain to hurricanes making them most intense and much wetter.

Houston Development

Credit: Tim Bralower © Penn State University is licensed under CC BY-NC-SA 4.0

Houston might have been able to absorb this change in its natural state, but the city has grown very rapidly over the last two decades as industry has surged and jobs have been plentiful. The city prides itself on being business-friendly and as a result, it has no zoning, meaning that there are few limits on construction. A shopping mall or factory can be located right next to a housing development or a city park. As a result, the city has a massive amount of concrete, roads, parking lots, and rooftops with very little consideration paid to drainage. This contributed in a major way to flooding during Harvey as the photographs below testify. Harvey was a 1000-year event, meaning the levels of rainfall are only expected that rarely. But as it turns out, the storm was the third 500-year event in the last three years. So, it is clear that it is part of a new normal. After Harvey the city has started to make minor changes to the way it is developing requiring new homes in the floodplain to be built higher, but it still is resisting zoning measures that would keep homes out of flood-prone areas.

Satellite view of Hurricane Harvey

Credit: ABI image captured by NOAA’s GOES-16 satellite (RAMMB/CIRA SLIDER) from Wikimedia Commons (Public Domain)

Flooding in Houston during Hurricane Harvey

Credit: SC National Guard from Wikimedia Commons (Public domain)

Video: When the roads turned to rivers: Texas in the aftermath of Hurricane Harvey (11:38)

Hurricane Irma, September 2017

Irma arose rapidly in the tropical Atlantic, and at one point it had sustained wind speeds of 185 mph. This storm intensified rapidly, which is a characteristic of a large hurricane, increasing by 45 mph in one day. hit the islands of Barbuda, Antigua, St Martin and St. Barthelemy and caused catastrophic damage in these locations. The island of Barbuda, in particular, was literally flattened. Irma next set her eyes on Cuba, where it came ashore as a magnitude 5 storm with sustained winds of 160 mph and a massive storm surge. The storm led to collapsed buildings and flooding of coastal areas including the historic Malecón in Havana. Fortunately, Cuban authorities had evacuated close to a million people from low-lying areas.

Damage from Irma on the island of St Martin

Credit: Hurricane Irma on Sint Maarten by Ministry of Defense, Netherlands from Wikimedia Commons licensed under CC0 1.0

A few kilometers make a major difference in the history of a hurricane and the landfall in Cuba, which was not initially predicted, weakened the storm significantly. Initial forecasts were for Irma to come ashore near Miami with wind speeds near 155 mph, but the storm tracked a little further to the west and made landfall in the Florida Keys with maximum winds of 130 mph, then again in Marco Island on the west coast of Florida with winds of 115 mph. As it turns out, there have been far larger storms in Florida, including Hurricane Andrew in 1992, which flattened the Miami suburbs. But Irma was yet another reminder of how vulnerable the state is to storms. Much of the southern half of Florida is a natural swamp or marshland that originally looked like the Everglades National Park. But as in Houston, commercial interests, and in the case of Florida, the desire of citizens to own a small piece of paradise, have led to massive construction in the last decade, runaway development with insufficient environmental regulation. So, instead of swampland that served to absorb moisture and drain it back towards the ocean, large expanses of concrete funnel it into walls of water in cities and suburbs. Mangroves forest that previously protected the coast has been flattened. In fact, the Florida environment was destroyed a long time before this, as the Army Corps of Engineers modified the natural drainage to provide water for the sugar industry.

Sawgrass prairie in the Everglades

Credit: Everglades Sawgrass Prairie by Moni3 from Wikimedia Commons, licensed under CC BY 3.0

Landsat image of the Everglades showing development in Miami and Fort Lauderdale in upper right

Credit: USGS Southern Everglades by USGS from Wikimedia Commons (Public Domain)

As it turns out, Irma was less of a wind event than a storm surge event. As a hurricane moves towards land, it pushes water ahead of it, literally a wall of water. The result is storm surge. Hurricane Katrina in 2005 was also a storm surge event, with a surge of 28 feet measured at Pass Christian, Mississippi, just outside of New Orleans, the largest surge ever measured in a US hurricane. New Orleans is at or even below sea level and its levee system, upgraded after Katrina, is designed to deal with surge, but still, it remains highly vulnerable. The surge from Irma was about 10 feet in the Keys and Marco Island, enough to cause significant damage. Nevertheless, the storm was generally viewed by experts as another wake-up call to what will likely happen in the future if a major storm with winds over 160 miles an hour and a 20-foot storm surge hits Miami or Tampa which is extremely flood-prone. In all, Irma caused over 100 fatalities, most of which were in the Caribbean.

Boats across US 1 from their usual spots in a Big Pine Key marina.

Credit: Boat on US 1 by Dan Chapman, U.S. Fish and Wildlife Service Southeast Region from Wikimedia Commons (Public Domain)

Hurricane Maria, September 2017

Barely a week later, monster storm Maria developed as Irma had…this storm also developed by rapid intensification with an increase in wind speed of over 60 mph in one day! By the time it hit Dominica, Maria had sustained winds of 160 mph and it caused utter devastation and killed 15 people, then it took aim at Puerto Rico. The storm hit the east coast of the island with sustained winds of 155 mph and dumped up to 3 feet of rain in mountainous areas. The impact on Puerto Rico is like the combined effect of Harvey in Houston and Irma in Barbuda. Maria caused massive destruction on the island. The power grid was destroyed leaving all 3.4 million residents without electricity. Many people had no running water for days, and sewers and cell phone networks were also out. Dams were in danger of breaching. 60,000 homes lost most or all of their roofs and only 392 out of 5000 miles of roads remained open. The storm defoliated a large number of trees on the island and led to the loss of 80 percent of the agriculture. The total damage is estimated at $90 billion, but that does not include the misery the storm caused humans. Diseases spread due to the lack of clean drinking water. The water-borne bacterial infection leptospirosis was widespread. Overall, the storm directly or indirectly led to as many as 3000 deaths but the true number may never be known, and, years later, the island is still recovering from the storm.

Hurricane Dorian, September 2019

None of these storms compared to Dorian that hammered the Bahamas in 2019. Dorian made landfall on September 1, 2019, on Grand Abaco Island with sustained winds of 185 mph and gusts over 220 mph making it one of the strongest storms on record in the Atlantic and Pacific.

Dorian was an unusual storm in several ways. The storm was enormous. Dorian was particularly deadly because the devastating winds were combined with an extremely slow forward motion of about 5 mph so that the storm ravaged the Bahamas for days. Devastating storms like Andrew and Katrina had much faster motion but the slow speed of Dorian made the damage much, much worse. After pummeling the Abacos, a group of islands in the northeast Bahamas, the storm went back over open water and made landfall without weakening on September 2 on Grand Bahama, the largest Bahama island, where it literally stalled for a day before weakening a little and moving back over open water. The damage to the Bahamas was truly catastrophic.

At landfall on the Abacos and again on Grand Bahama, Dorian’s intense winds were accompanied by a massive storm surge of about 6-9 meters (20-25 feet) and heavy rain. In total about a meter (3 feet) of rain fell over most of the northern Bahamas. There are harrowing tales of people clinging on to trees and other harrowing survival stories, but sadly many were not so fortunate. The official death toll from Dorian is 70 but is almost certainly much, much higher because there were many undocumented citizens living in shantytowns. Initially, there were over 1000 people missing, and now that number is around 300, so the death toll is likely to be 500-600. The true number may never be known.

Hurricane Ian, late September, 2022

Now to Hurricane Ian that hit southwest Florida in late September 2022. The storm was notable because of how rapidly it intensified, with windspeeds increasing from 75 mph to a 155 mph in just two days. The very large storm came ashore at Cayo Costa island just to the north of Fort Myers on September 28^th 2022. Sustained windspeeds at landfall were 150 mph likely with higher gusts. It was the fifth strongest storm ever to hit the 50 contiguous states.

But the damage in southwest Florida was not just inflicted by the wind. Since the path of the storm closely paralleled the coast as it approached land, and because the highest surge is in the right front quadrant of the storm due to its counter-clockwise circulation, the storm surge over large areas was devastating.

The storm surge was between up to 15 feet above normal sea level along the barrier islands of Captiva, Sanibel and Fort Myers Beach. This wall of water caused massive devastation in these areas as observed in the photographs below.

Ian moved slowly to the northeast direction across the Florida peninsula and this slow path caused heavy rainfall over a wide area, with precipitation totals up to 17 inches over a 12-24 hour period. This rainfall caused widespread flooding well inland in places such as Orlando.

One of the main stories of the storm was prediction. The different forecast models agreed closely as the storm approached southwest Florida, but because the path was so close to parallel to the coast a small change led to a major difference in the landfall location. Two days out the path was more northerly with the eye forecasted to make landfall near Tampa, but then a day out a minor jog in the forecast to the east shifted the eye well south. This change led to some delays in evacuation in the Fort Myers area.

The storm caused massive damage over a widespread area with catastrophic damage to housing along the coast, especially in Fort Myers, Sanibel Captiva and Port Charlotte. More than 2.7 million people lost power at the height of the storm and a large number without clean water. Overall the storm led to 136 fatalities in Florida and a total of $50 billion in damage.

Hurricane Helene, September 2024

Hurricane Helene arose as a depression in the Caribbean. The storm rapidly intensified over the unusually warm Gulf of Mexico as it moved towards the Big Bend region of Florida where it made landfall on the evening of September 26, 2024, as a category 4 hurricane with sustained winds of 140 mph. The hurricane was also very large and had a high storm surge along a long area of the western Florida coastline from about 10 feet in the Big Bend to about 7 feet in the Tampa Bay region, causing extensive flooding along the whole coast.

Since the storm was moving so rapidly it maintained its strength inland and it was still a hurricane when it hit southern Georgia. Wind damage was severe well inland in both states.

Helene was still a tropical storm when it moved across northern Georgia near Atlanta, South Carolina near Greenville, and North Carolina near Asheville. In these regions, some damage was caused by wind, but most of it was done by water. A stalled frontal boundary had dumped up to 9 inches of rain over the mountains of western North Carolina before the storm arrived. The ground was therefore already saturated when the storm arrived, and this led to extensive tree damage from tropical-storm-force winds. Rainfall totals of up to 32 inches in the mountainous terrain caused many landslides and mudslides. The water rushed down the hillslopes causing rivers to rise rapidly, and overflow their banks leading to extensive flash flooding. River levels were at record levels, for example, the French Broad and Swannanoa rivers in Asheville were up to 5 feet above historic levels, breaking a record set in 1916. The results were absolutely catastrophic in Asheville and small mountain towns all over western South Carolina, North Carolina, and Tennessee. Extensive flash flooding swept away single-family homes and mobile homes, and flooded businesses.

The most dire result of Helene was loss of life. The storm was the most deadly since Hurricane Katrina with over 280 dead. Over 100 of these deaths occurred in western North Carolina, where many more are still missing at the time of writing. The storm was also the costliest since Katrina with estimates of over $50 billion with billions of dollars of uninsured flood damages.

Hurricanes and climate change

So, finally, we come back to the question of whether climate change is responsible for the surge in powerful hurricanes in 2017-2022. At the outset, we must stress that this question cannot be answered unequivocally. However, there are several factors that make it safe to say that large storms will be more common in the future and that they will cause increasing amounts of damage. To develop, storms need warm temperatures (over 80 degrees), abundant moisture, and circulation, as you can see in the video below.

Video: Fuel for the Storm (2:19)

As we will see in the lab at the end of this module, the ocean has warmed by 1 to 2 degrees C (3-4 degrees F) over the last century, and this leads to a 12-16 percent increase in moisture (3% per degree). Thus, there is a lot more fuel for hurricane development. The formation of hurricanes is also helped by weather disturbances often off West Africa, but there is not yet a relationship between these events and climate change.

In the case of Harvey, the volume of water is clearly a result of an extra warm ocean; for Irma and Maria, the ferocity of the winds and the rapid intensification is also related to water temperature. For Dorian, size and slow movement is a result of a warmer atmosphere. So, climate change is adding fuel to the fire for large hurricane development, and 2017 is a harbinger of things to come. There is one other factor to consider, perhaps one that will prove the most devastating in decades to come. Sea level rise. The ocean is now about a foot higher than it was in 1900. Projections are for a possible 6-foot rise in sea level by the end of this century if we don’t cut greenhouse gas levels significantly. We will discuss the issue of sea-level rise in great detail in Module 10. Such a rise would mean that even a storm such as Irma with a moderate storm surge would be catastrophic.

As with other impacts of climate change, the latest IPCC report stresses the need for adaptation to the threat of stronger hurricanes. Coastal communities will need to adapt to this threat by building homes higher and stronger, building sea walls and surge barriers, and gradually pulling back from the coast, an initiative called managed retreat. This is already happening near New York City and elsewhere in the US but again will be much more difficult to achieve in the developing world.

The night of December 10 and early morning of December 11, 2021 saw devastating tornadoes tear across the central US from Arkansas to Kentucky. Up to 71 different tornadoes have been confirmed rated up to EF-4 with wind speeds up to 190 miles per hour. The tornadoes caused massive damage totaling $3.9 billion, destroying whole communities, causing 88 fatalities and over 600 injured. 71 tornadoes have been confirmed. The worst damage was in the town of Mayfield, Kentucky, which was leveled by a strong EF-4 tornado. Much of the center of the town was irreparably damaged with houses and stores receiving devastating damage, 22 people died in that town alone. The tornado forecasts were generally highly accurate that night, but sadly, warnings weren’t always acted upon. What was particularly significant about the outbreak is how late in the season they occurred. Tornadoes in the central US are frequent in spring, when warm and cold air-masses collide over the region. It is very unusual for these strong storms to occur in the winter.

Aerial view of EF4 damage in Mayfield Kentucky on December 12, 2021

Credit: 2021 December Tornado - Mayfield, KY by State Farm is licensed unde CC BY 2.0

By studying the network of weather stations across the US, researchers have found a couple of interesting results — the biggest storms recorded in a given year not including tornadoes are increasing in strength, and the frequency of extreme storms is also increasing. In addition the area known as "tornado alley" where touch-downs are common is shifting eastward from Texas, Oklahoma and Kansas to Arkansas, Louisiana, Mississippi, Alabama and Tennessee.

The above figure shows the results of the trends of the biggest storms of each year (not including tornadoes) as recorded by weather stations across the US, and you can see that although the pattern is somewhat complex, the general story here is that big storms are getting bigger.

Credit: Environment Minnesota Research & Policy Center

In addition, the frequency of extreme storms is also increasing. An extreme storm is one where the rate of precipitation exceeds by a certain amount the long-term mean rate of precipitation for a given site (so an extreme storm in a wet region like the northeast US has a much higher precipitation rate than an extreme storm for a drier region). So, you might be asking yourselves whether these results translate into more frequent, massive tornadoes. This is a somewhat controversial topic. The consensus answer is that with a warmer atmosphere, tornadoes will definitely become more powerful (just like hurricanes), but the word is still out whether they will become more frequent. What is obvious from the December 2021 tornadoes is that these powerful weather systems will occur throughout the year in the future.

The figure above shows the results of the trends in the frequency of extreme storms as recorded by weather stations across the US, and you can see that the overwhelming pattern is one of more frequent extreme events.

Credit: Environment Minnesota Research & Policy Center

Download this lab as a Word document: Lab 2: Hurricanes (Please download required files below.)

In this lab, we will observe the tracks of the largest storms of the last century, and learn about the impacts of those storms on land.

The goals of the lab are:

1. to determine the main causes of damage from storms including wind, rainfall and flooding, and storm surge;
2. to observe the relationship between storm intensity and warming.

Files to Download:

1. Hurricane Tracks
2. Temperature Anomalies

Instructions

There are two Google Earth maps to load, the first, Hurricane Tracks kmz file, shows tracks of storms from 1900 to 2017. The second, Temperature Anomalies kmz file, shows average August temperatures for each year calculated relative to the average temperature between 1900 and 1910. You can switch back and forth between maps. Both maps have sliders at the top left of the screen that allow you to look at storms as well as temperature over time. The storm tracks have points that show the wind speed and pressure at different stages in its development. We definitely recommend that you don’t try to look at the storms all at once or you will see a maze of lines. Please make sure that the slider at the top left has the relevant range of dates on it. Otherwise, you will not be able to view tracks for the desired storm. Also, there are a few storms including Betsy whose names do not show unless you zoom in close. Note also, we break up the 2000-2010 and 2010-2017 decades.

As in the lab for Module 1, we begin with some practice questions that you can take in the Lab 2 practice submission, where you will receive the answers to the questions. Once you feel good about these questions, move on to the graded assignment. If you have any questions about the practice questions, please let us know. Remember, you only get one attempt at the graded assignment.

The video below will help you with operations in Google Earth. We HIGHLY recommend you watch it to learn exactly how to manipulate the files and use the historical imagery.

Video: Controls for Module 2 Lab (06:35)

Practice Questions

Part A. In the first part of the lab, we look at the tracks of hurricanes. You will need to look for the storm names. Load the name of the storm once you have found it and click on a point to find the wind speed and pressure. For certain storms, we will include the storm surge as well as the precipitation in areas near the landfall. You will also need to observe the elevation of areas close to the coast and look at historical imagery to determine the impact of the storm on coastal communities.

1. What was the wind speed of Hurricane Gilbert just before US landfall (click on the point closest to land)?
2. What was the strongest storm in the 1960s at US landfall based on wind speed?
3. Observe the storm surge of Hurricane Rita at Gueydan, LA (10 feet) and from the elevation, which is the closest to the extent of inland flooding? (Make sure terrain is switched on to read elevations and run the cursor over the map to see how elevation changes around the location of interest).
   1. About half of the town would be flooded
   2. None of the town would be flooded
   3. All of the town would be flooded
4. What is the percentage of rainfall from Hurricane Georges near landfall compared to the average annual rainfall of southern Alabama (60 inches)? Please give your answer as a percentage.
5. Here we are going to look at historical imagery to answer questions about the impact of storms on urban areas and the landscape. We will look at the 2005 Hurricane Katrina and its impact on Gulfport Mississippi. Enter “Gulfport” in the search box and fly to an elevation between 2000 and 4000 feet above the city. Turn on the historical imagery (clock at top left) and go back and forth between July 2005 and August 29, 2005 (right after the storm) photos using the slider. You will need to click the + button on the slider to get that scale of time change. Answer the following questions:
   1. What is the yellow material in the streets and parking lots very close to the coast after the storm?
   2. What is missing from the marina after the storm after the storm? We are looking for single words so your answers can be one word.
   3. Look at the parking lot across the four-lane road from the harbor area, what are the white boxes? (Look at both photos.)
   4. Does it look like the neighborhoods a block away from the four-lane road are inhabited? Yes or No

Part B. In the second part of the lab, we will observe the change in temperatures of the Atlantic Ocean over the last century that is related to the generation of more powerful hurricanes. Load and turn on the temperature anomaly kmz. By pressing the year buttons on the left, you can observe the temperature anomalies in August every five years (from 1910 to 2000) and annually from (2000 to 2017) relative to the average temperature from 1900 to 1910 file.

Center the map over the Atlantic Ocean so you can see Africa as well as North America including the Gulf of Mexico. As we have learned, the warmer the temperature the more energy to fuel hurricanes as well as the ability to hold more moisture.

6. Which is the warmest year in the central Atlantic Ocean between 1910 and 1950?
7. Which of the following is a year that the Gulf of Mexico had the highest potential to fuel strong storms based on temperature?
   1. 1965
   2. 1975
   3. 1985
   4. 1995
   5. 2005
8. Which year would temperatures in the Atlantic have been favorable for hurricane development?
   1. 1960
   2. 2017
   3. 1990
   4. 2000
   5. 2007
9. What is the general trend for temperature change between 1900 and 2017?
   1. Warming
   2. Cooling
   3. Stayed consistent
10. Which decade would have been slow for hurricane generation in the Atlantic based on temperatures?
    1. 2000-2010
    2. 1990-2000
    3. 1970-1980

In this module, we have covered a broad range of observations that are pertinent to recent climate change. Here is a quick recap:

Surface Temperature

Instrumental Record

This goes back to 1880; multiple analyses yield similar results, indicating a warming of about 1-1.5°C averaged over the globe in the past 150 years. The majority of the warming has occurred in the last 50 years. The map-view pattern reveals that the polar region of the Northern Hemisphere has warmed much more than other regions of the globe.

Satellite Measurements

Available for a much shorter time period, these results essentially confirm the analysis of the instrumental temperature record.

Ocean Warming

Sea surface temperature (SST) records from ships go back to 1850 and make up an important part of the data that provide global temperature estimates; these records are similar, though somewhat subdued in comparison to just land surface temperatures. The SST, though, represents just the skin of the oceans; to see deeper, we rely on measurements from a system of buoys that shows the oceans are slowly warming — only about 0.1 to 0.2 °C averaged over the globe during the past 50 years — but this is the temperature change in the whole upper 700 m of the oceans, which is a vast amount of water. So while the ocean has absorbed a huge amount of heat, its overall temperature has changed little.

Proxy Reconstructions

Through the use of multiple proxies, the average global temperature has been reconstructed about 2000 years into the past. These results indicate a Medieval Warm Period (AD 950 – 1250) that was almost as warm as today, and a Little Ice Age (AD 1350 - 1850) that was more than a degree colder than today, followed by the modern warming trend.

Borehole Reconstructions

The temperature versus depth measurements from boreholes preserve a smoothed record of the history of surface temperature change; global studies of these records provide a smoothed temperature history that goes back to the year 1500. This temperature history is in good agreement with the instrumental record.

Ice

Glacier Lengths

Mountain glaciers from around the world are shrinking; some at astounding rates. The history and magnitude of melting indicate a warming history that closely matches the results from the instrumental surface temperature record.

Ice Sheet Mass

Satellite data reveal the mass changes of the two large ice sheets; both are losing mass fast (2.3e15 kg of ice in 6 yr), contributing about 8 mm to sea level rise in this short period.

Arctic Sea Ice

Submarine sonar readings and satellite measurements show that the sea ice in the Arctic Ocean is declining in thickness and in areal extent, so much so that the Northwest Passage is now open for a month or two each summer.

Sea Level

Based on tide gauges, this record shows that sea level has risen about 20 cm in the past 100 years (2 mm/yr). This rise is a result of the melting of ice combined with the thermal expansion of the warmer ocean. Much more on this is Module 11.

After reviewing all of the data, here is what the leading scientific academies of the US, UK, Russia, China, France, Germany, Italy, Brazil, Japan, Canada, and India (the G8 climate change roundtable first held in Davos, Switzerland in 2005) jointly concluded:

Climate change is real. There will always be uncertainty in understanding a system as complex as the world’s climate. However, there is now strong evidence that significant global warming is occurring. The evidence comes from direct measurements, of rising surface air temperatures and subsurface ocean temperatures and from phenomena such as increases in average global sea levels, retreating glaciers, and changes to many physical and biological systems. This warming has already led to changes in the Earth's climate.

This is the consensus from leading scientists around the world, not politicians, journalists, or business people who may stand to gain financially from taking a stand on climate change. The motivation of these scientists is to understand what the data mean and to help the broader public understand the implications of the data. At the end of the day, the data tell us that the climate is changing; the real challenge before us lies in finding ways to respond to this change through some combination of taking steps to minimize the change and finding ways to adapt to the change.

End of Module Recap:

In this module, you should have mastered the following concepts:

• the difference between weather and climate; the importance of averaging over a long enough time period to remove the "noise" of weather;
• how temperature records are analyzed to reveal meaningful information about climate change;
• many different groups have used different approaches to estimate the history of global temperature, and they all reach similar conclusions — the Earth is warming;
• looking further back in time, we see that recent warming is unusually abrupt and is not part of a natural cycle — but natural variation in climate is important, and it is superimposed on the warming trend;
• virtually everything we can think of measuring that records the temperature at the surface and the near sub-surface tell us the same general story about the warming — there has been about 1.1°C warming over the past 70 or so years, with most of it in the last 50, and most of it concentrated in the high latitude regions of the Northern Hemisphere, where warming reduces the area and time period of coverage by snow and ice, and where, consequently, the snow-free ground absorbs much more solar energy;
• glaciers are shrinking around the globe, and the shrinking coincides in time with the warming trend seen in the instrumental temperature record. Arctic sea ice is also shrinking, and at an alarming rate. The big ice sheets of Greenland and Antarctica are also melting, contributing to sea level rise.
• sea level is on the rise during the same time period that the other records indicate warming. Seawater expands as it gets warmer, so it takes up more room, and so sea level must rise. Melting glaciers and ice sheets (but not sea ice) contribute to sea level rise as well.
• along with a warming climate comes increased water vapor in the atmosphere, which means more energy in the atmosphere, thus increasing the chances for extreme weather events; The evidence indicates that heat waves and extreme rainfall events are increasing along a trajectory that is similar to the surface temperature warming; in contrast, the total amount of precipitation does not appear to be changing in a significant way for the Earth as a whole (although various regions have been getting drier or wetter).

Assignments

You should have read the contents of this module carefully, completed and submitted any labs, the Yellowdig Entry and Reply and taken the Module Quiz. If you have not done so already, please do so before moving on to the next module. Incomplete assignments will negatively impact your final grade.

Lab

• Lab 2: Hurricanes

Goals

On completing this module, students are expected to be able to:

• describe how energy is absorbed, stored, and moved around in Earth's climate system;
• distinguish how the amount of energy stored determines the temperature;
• interpret the importance of feedback mechanisms that make our climate system sensitive to forcings, but also provide a stabilizing influence;
• infer how temperature responds to changes in solar input, albedo, and greenhouse gas concentrations;
• evaluate how simple (i.e., STELLA) models can be used to make projections of climate variables.

Learning Outcomes

After completing this module, students should be able to answer the following questions:

• What are heat and thermal energy?
• What are the different types of electromagnetic radiation?
• What is blackbody radiation and what is the significance of the Stefan-Boltzmann law?
• What is emissivity and what is its significance?
• What is albedo and what are albedo values for different materials?
• What is the solar constant and how is it measured?
• What is insolation and what are its geographic and annual distributions?
• What does sunspot history look like and how is it related to solar intensity?
• What are the relative heat capacities of different materials?
• What is the greenhouse effect and what are the different greenhouse gasses?
• What are the basic energy flows in the atmosphere?
• What is positive and negative feedback and what are examples of each?
• What are the energy budgets of different latitudes?
• How is heat transferred in the atmosphere?
• How is heat transferred in the oceans?
• What is the Global Conveyor Belt and what is its significance?

Below is an overview of your assignments for this module. The list is intended to prepare you for the module and help you to plan your time.

Assignments

1. Lab 3: Climate Modeling
2. Submit Module 3 Lab 3 (Graded).
3. Take Module 3 Quiz.
4. Yellowdig Entry and Reply

We begin with a quick glimpse of the global climate — and then we’ll try to understand why it looks this way. But first, what does climate mean? In the simplest sense, it is the average weather of a region — the average temperature, rainfall, air pressure, humidity, cloud cover, wind direction, and wind speed. This means that climate is not the same as weather; weather implies a very short-term description of the atmospheric conditions, and it tends to change in a complex manner over short time scales, making it notoriously difficult to predict. In contrast, the climate is less variable — it smoothens out the variability of the short-term weather. This course is about climate, how it is changing, and what that means for our future; as we move through this class, you should remind yourself periodically that we are not talking about the weather — our time frame is much longer.

So, let’s have a look at the climate as expressed by temperature:

The average near-surface air temperature (sea surface temperature over the oceans) of the Earth for the period from 1961-1990.

Click for a text description of the World Surface Temperatures map.

This image is a world map showing the annual mean temperature across the globe, measured in both degrees Fahrenheit and Celsius. The map uses a color gradient to represent temperature variations, with colder regions in blue and warmer regions in red.

• Map Type: World map
• Measurement: Annual mean temperature
• Color Scale (bottom of the map):
  • Range: -40°F (-40°C) to 80°F (30°C)
  • Colors: Dark blue (-40°F/-40°C) to dark red (80°F/30°C), with purple, green, yellow, and orange in between
• Regions with Notable Temperatures:
  • Coldest (dark blue, -40°F/-40°C to 0°F/-18°C):
    • Polar regions (Arctic and Antarctic)
    • Northern Canada, Greenland, and Siberia
  • Cool (green to light blue, 0°F/-18°C to 40°F/4°C):
    • Northern Europe, parts of Russia, and the northern U.S.
  • Moderate (yellow to orange, 40°F/4°C to 60°F/15°C):
    • Southern Europe, the central U.S., and parts of China
  • Warm (red, 60°F/15°C to 80°F/30°C):
    • Most of Africa, South America, India, Southeast Asia, and Australia
    • Parts of the Middle East and Central America

The map illustrates the global distribution of annual mean temperatures, with the coldest temperatures in polar regions and the warmest in equatorial and tropical areas.

Credit: Annual Average Temperature Map by Robert Rohde from Wikimedia, licensed under CC BY-SA 3.0

As you can see, the equatorial regions are the warmest, and the poles are the coldest, with Antarctica being noticeably colder than the Arctic. The temperature varies more within the continents than the oceans, and there is a pronounced northward extension of warm water in the North Atlantic.

The global climate system is like a big machine receiving, moving, storing, transferring, and releasing heat or thermal energy. The machine consists of the oceans, the atmosphere, the land surface, and the biota on land and in the oceans; in short, it consists of everything at the Earth’s surface. The average state of this system — the global climate — is represented most simply by the pattern of temperatures and precipitation at the surface.

In order to really understand this complex machine, we will have to understand something about its parts, but we also need to begin with some fundamental ideas about energy, heat, and temperature, including the source of the energy for the climate system — the sun.

Useful Terms and Definitions Related to the Energy of the Climate System

Energy

In the broadest terms, energy is a quantity that has the ability to produce change in a physical system; it includes all kinds of kinetic energy (energy of motion) and potential energy (energy based on the body's position) and is measured in joules. One joule represents the amount of energy needed to exert a force of one Newton over a meter; so 1 Joule = 1Nm.

Power

Energy expended over a period of time is a measure of power, and in the context of climate, power is expressed in terms of Watts (1 Watt = 1 joule per second). This is also called a heat flux — the rate of energy flow.

Heat

This is simply the thermal energy of a body, measured in joules. Think of this as the average kinetic energy (vibrations) of the atoms of a material.

Heat Flux Density

This is a measure of how concentrated the energy flow is and is given in units of Watts per square meter.

Temperature

This is obviously closely related to heat, but it is the average kinetic energy within some body. Materials can be the same temperature, but they may have different amounts of thermal energy — for instance, a volume of water has much more thermal energy than a similar volume of air at the same temperature. Remember that there are 3 temperature scales: Fahrenheit, Celsius, and Kelvin. We’ll use Celsius and Kelvin, which have the same scale, just offset so that 0°C = 273°K.

We begin with a very simple analog model for our planet’s climate (figure below) in which solar energy enters the system, is absorbed (some will have been reflected), stored (some will have been transformed or put to work), and then released back into outer space. The amount of energy stored determines the temperature of the planet. The balance between the incoming energy and the outgoing energy determines whether the planet becomes cooler, warmer, or stays the same. Notice the little arrow connecting the box to the Energy Out flow — this means that the amount of energy released by the planet depends on how hot it is; when it is hotter, it releases, or emits, more energy and when it is cooler, it emits less energy. What this does is to drive this system to a state where the energy out matches the energy in — then, the temperature (energy stored) is constant. This energy balance, sometimes called radiative equilibrium, is at the heart of all climate models.

Systems diagram for a simple energy flow system. Energy is added to a body (a reservoir in systems language), is stored by the body, and then leaves the body. The amount of energy stored determines the temperature, which in turn controls how much energy is released. This relationship between the energy out and the energy stored makes a negative feedback mechanism that tends to drive the system to a steady state where the energy in and the energy out are equal, and thus the temperature is constant.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Now, let’s consider the connection between this idea of an energy flow system to the actual Earth. As shown in the figure below, this system includes the atmosphere, the oceans, volcanoes, plants, ice, mountains, and even people — it is intimately connected to the whole planet. We will get to some of these other components of the climate system later, but to begin with, we will focus on just the energy flows — the yellow and red arrows shown below.

Global Climate System

Click for a text description

The image is a labeled diagram titled "The Global Climate System," illustrating various processes and components that influence Earth's climate. It depicts interactions between the atmosphere, land, ocean, and Earth's interior, with numbered annotations and a key explaining the symbols used.

• Overall Structure:
  • The diagram is a cross-sectional view of Earth, showing the atmosphere, ocean, land (continental and oceanic crust), and the underlying lithospheric mantle and asthenosphere.
  • The asthenosphere is depicted in red at the bottom, indicating its semi-fluid nature.
• Atmospheric Components:
  • 1: Short-wavelength (SW) solar radiation (yellow wavy arrows) enters the atmosphere from the top.
  • 2: Some solar radiation is reflected back into space (yellow wavy arrows pointing upward).
  • 3: Long-wavelength (LW) radiation (red wavy arrows) is emitted from the Earth's surface upward.
  • 4: Clouds reflect solar radiation back into space (yellow arrows) and trap long-wavelength radiation (red arrows).
  • 5: Some solar radiation is absorbed by the atmosphere (yellow arrows curving within the atmosphere).
  • 7: Clouds release precipitation (blue arrows pointing downward), contributing to the water cycle.
  • 8: Evaporation from the ocean surface (blue arrows pointing upward) adds water vapor to the atmosphere.
  • 9: Evaporation from land surfaces (blue arrows pointing upward) also contributes to atmospheric moisture.
• Land and Ocean Components:
  • 6: Ice on land reflects solar radiation (yellow arrows bouncing off ice).
  • 10: Ocean currents (black arrows) show the movement of water within the ocean.
  • 11: Transfer of CO₂ between the ocean and atmosphere (green arrows) indicates carbon exchange.
  • 15: Ice melts, contributing to the water cycle (blue arrows from ice to ocean).
  • 16: Runoff from land to ocean (blue arrows) shows the movement of water.
  • 17: Human activities, depicted as factories and vehicles on land, release CO₂ into the atmosphere (green arrows).
• Geological Components:
  • 12: A volcano on the continental crust releases CO₂ (green arrows) and ash (gray cloud) into the atmosphere.
  • 13: Volcanic eruptions emit particles and gases (gray cloud) that can influence climate.
  • 14: Weathering of rocks on the continental crust (green arrows) removes CO₂ from the atmosphere.
• Earth's Interior:
  • The diagram shows the continental crust and oceanic crust as part of tectonic plates.
  • The lithospheric mantle (part of the plate) is labeled beneath both the continental and oceanic crust.
  • Black arrows indicate the movement of plates, with a divergent boundary at the oceanic crust where new crust is formed (red area).
  • The asthenosphere beneath the lithospheric mantle is shown in red, indicating its role in plate movement.
• Key (Bottom of Diagram):
  • Long-wavelength (LW) radiation: Red wavy arrows.
  • Short-wavelength (SW) solar radiation: Yellow wavy arrows.
  • Transfer of CO₂: Green arrows.
  • Movement of water: Blue arrows.
  • Movement of plates: Black arrows.

The diagram visually represents the complex interactions within the global climate system, highlighting the roles of solar radiation, the carbon cycle, the water cycle, geological processes, and human activities in shaping Earth's climate.

Credit: Penn State Department of Geosciences, Modeling Earth's Climate System with STELLA

Numbers in the figure refer to the following key:

1. Incoming short-wavelength solar radiation
2. Reflected short-wavelength solar radiation
3. Emission of long-wavelength radiation (heat) from surface
4. Absorption of heat by greenhouse gases and emission of heat from the atmosphere back to the surface (the greenhouse effect)
5. Emission of surface heat not absorbed by the atmosphere
6. Evaporation cools the surface, adds water to the atmosphere
7. Condensation of water vapor releases heat to the atmosphere, precipitation returns water to the surface
8. Evapotranspiration by plants cools the surface
9. Chemical weathering of rocks consumes atmospheric CO₂
10. Oceans store and transfer thermal energy
11. Sedimentation of organic material and limestone (CaCO₃) transfers carbon to sediment on the ocean floor
12. Melting and metamorphism of sediments sends carbon back to surface
13. Emission of CO₂ from volcanoes
14. Emission of CO₂ from burning fossil fuels
15. Cold oceans absorb atmospheric CO₂
16. Warm oceans release CO₂ to the atmosphere
17. Photosynthesis and respiration of plants and soil exchange CO₂ between the atmosphere and biosphere

The figure above includes some new words and concepts, including short-wavelength and long-wavelength radiation, that will make sense if we devote a bit of time to a review of some topics related to energy.

Brief Review of Electromagnetic Radiation

The energy we are concerned with here comes in the form of electromagnetic radiation, so it will help us to review some aspects of this form of energy. Electromagnetic (EM) radiation comes in a spectrum of waves, each consisting of an electrical and a magnetic oscillation of particles called photons; this spectrum is shown in the figure below:

The electromagnetic (EM) spectrum, showing wave types and corresponding wavelengths, with the detail of the more familiar visible light portion of the spectrum. The Sun emits energy in the ultraviolet to near infrared, while the Earth emits energy entirely in the infrared. Also shown are the temperatures of objects whose peak energy emission is associated with the corresponding wavelengths, according to Wien’s Law.

Click for a text description of elecromagnetic spectrum image.

The image is a diagram titled "The Electromagnetic Spectrum," illustrating the range of electromagnetic radiation types, their wavelengths, and their relationship to the temperature of objects emitting them. The diagram is structured vertically, with various segments representing different types of radiation, their wavelengths, and associated temperatures.

• Title: "The Electromagnetic Spectrum" is written at the top.
• Main Structure: The diagram is a vertical bar divided into segments, each representing a type of electromagnetic radiation, with wavelengths and temperatures labeled on the sides.
• Wavelength Scale (Right Side):
  • Wavelengths are shown in a logarithmic scale, ranging from 0.01 nm (nanometers) at the top to 1000 m (meters) at the bottom.
  • Specific wavelength ranges are marked:
    • 0.01 nm (10^-11 m) for gamma rays
    • 1 nm (10^-9 m) for X-rays
    • 10 nm for ultraviolet (UV)
    • 100 nm to 1000 nm (10^-6 m) for visible light (400 nm to 700 nm highlighted in a color gradient from purple to red)
    • 10 μm (micrometers, 10^-5 m) for infrared (IR)
    • 100 μm for thermal IR
    • 1000 μm (10^-3 m) for far IR
    • 1 cm (10^-2 m) for microwaves
    • 10 cm for radar
    • 1 m for FM radio and TV
    • 10 m to 1000 m (10³ m) for AM radio
• Types of Radiation (Center):
  • From top to bottom, the types of electromagnetic radiation are labeled:
    • Gamma rays
    • X-rays
    • Ultraviolet (UV)
    • Visible light (with a color gradient from purple at 400 nm to red at 700 nm)
    • Near IR (infrared)
    • Thermal IR
    • Far IR
    • Microwaves
    • Radar
    • FM radio, TV
    • AM radio
• Temperature Scale (Left Side):
  • The left side shows the temperature of objects whose energy peaks at specific wavelengths, based on blackbody radiation principles.
  • Temperatures are marked with corresponding radiation types:
    • 29,000°K (Kelvin) for objects emitting gamma rays
    • 290°K for objects emitting in the solar spectrum (visible light)
    • 29°K for objects emitting in the Earth's thermal IR spectrum
    • 2.9°K for objects emitting in the microwave spectrum
• Spectral Curves:
  • Two curves are overlaid on the diagram, showing the blackbody radiation spectra:
    • A yellow curve labeled "Solar" peaks around the visible light range (290°K), indicating the Sun's emission spectrum.
    • A blue curve labeled "Earth's" peaks in the thermal IR range (29°K), indicating Earth's emission spectrum.
• Visible Light Section:
  • The visible light portion (400 nm to 700 nm) is highlighted with a color gradient, transitioning from purple (400 nm) to blue, green, yellow, orange, and red (700 nm).

The diagram effectively illustrates the relationship between wavelength, type of electromagnetic radiation, and the temperature of objects emitting that radiation, emphasizing the Sun's peak in visible light and Earth's peak in thermal infrared.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

In the realm of physics, a blackbody is an idealized material that absorbs perfectly all EM radiation that it receives (nothing is reflected), and it also releases or emits EM radiation according to its temperature. Hotter objects emit more EM energy, and the energy is concentrated at shorter wavelengths. The relationship between temperature and the wavelength of the peak of the energy emitted is given by Wien’s Law, which states that the wavelength, lambda, is:

$\lambda =0.0029/T$ (λ is in m, T in kelvins)

But the energy emitted covers a fairly broad range, as described by Planck’s Law, as shown below:

The spectra of energy emitted from idealized blackbodies of different temperatures. Notice that the peaks of the spectra are shifted to longer wavelengths for cooler objects. For reference, the average surface temperature of Earth is 288 °K.

Click for a text description of Blackbody emissions graph.

The image is a graph titled "Blackbody Emission from Objects of Different Temperatures," illustrating the energy emitted by blackbodies at various temperatures as a function of wavelength, based on Planck's law of blackbody radiation. The graph also references Wien's Law to highlight the wavelength at which the energy emission peaks for each temperature.

• Title: "Blackbody Emission from Objects of Different Temperatures" is written at the top.
• Axes:
  • X-Axis (Wavelength): Labeled "Wavelength (μm)" and ranges from 0 to 30 micrometers (μm), with major ticks at intervals of 5 μm (0, 5, 10, 15, 20, 25, 30).
  • Y-Axis (Energy): Labeled "Energy Emitted" with arbitrary units, ranging from 0 to 6, with major ticks at intervals of 1 (0, 1, 2, 3, 4, 5, 6).
• Data Representation:
  • Three curves are plotted, each representing the energy emitted by a blackbody at a specific temperature:
    • 300°K (Kelvin): Plotted in blue.
    • 400°K: Plotted in green.
    • 500°K: Plotted in red.
  • Each curve shows the energy emitted across the wavelength range, with a distinct peak indicating the wavelength at which the maximum energy is emitted.
• Curve Characteristics:
  • 300°K (Blue Curve): Peaks around 10 μm, with a maximum energy of about 1 unit, then gradually decreases toward longer wavelengths.
  • 400°K (Green Curve): Peaks around 7.5 μm, with a maximum energy of about 2 units, showing a higher and sharper peak compared to the 300°K curve.
  • 500°K (Red Curve): Peaks around 5.5 μm, with a maximum energy of about 5 units, exhibiting the highest and sharpest peak among the three curves.
• Annotation:
  • A label near the 500°K curve states: "energy peaks at a wavelength of 0.0029/T (Wien's Law)," explaining that the peak wavelength is inversely proportional to the temperature (T) of the blackbody, as per Wien's Displacement Law. The constant 0.0029 is in meter-Kelvin units (m·K), so when divided by temperature in Kelvin, it gives the peak wavelength in meters (which is then converted to μm in the graph).
• Overall Trend:
  • As the temperature increases from 300°K to 500°K, the peak of the emission curve shifts to shorter wavelengths (from ~10 μm to ~5.5 μm), and the total energy emitted (the area under the curve) increases significantly, consistent with the Stefan-Boltzmann Law and Wien's Law.

The graph visually demonstrates how blackbody radiation varies with temperature, showing that hotter objects emit more energy and at shorter wavelengths, which is a fundamental concept in understanding thermal radiation and its role in climate science (e.g., Earth's and the Sun's emission spectra).

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

The total amount of energy radiated from an object is also a function of its temperature, in a relationship known as the Stefan-Boltzmann law, which looks like this:

where σ is the Stefan-Boltzmann constant, which is 5.67e-8 Wm^-2K^-4 (this is another way of writing 5.67 x 10^-8; so 100 is 1e2, 1000 is 1e3, one million is 1e6, etc.), T is temperature of the object in °K, and so F has units of W/m². If you multiply this by the surface area of an object, you get the total rate of energy given off by an object (remember that Watts are a measure of energy, Joules, per second). As you can see, the amount of energy emitted is very sensitive to the temperature, and that can be seen in the figure above if you think about the area beneath the curves of different color. This sensitivity to temperature is very important in establishing the radiative equilibrium or balance of something like our planet — if you add more energy, that warms the planet, and then it emits more energy, which tends to oppose the warming effect of more energy added. Conversely, if you decrease the energy added, the planet cools and emits far less energy, which tends to minimize the cooling. This is a very important example of a negative feedback mechanism, one that works in opposition to some imposed change. The thermostat in your house is another good example of a negative feedback — it works to stabilize the temperature in your house, bringing it into radiative equilibrium.

The version of the Stefan-Boltzmann law described above applies for an ideal blackbody object, but it can easily be adapted to describe all other objects by including something called the emissivity, as follows:

Here, epsilon is the emissivity, which is a unitless value that is a measure of how good an object is at emitting (giving off) energy via electromagnetic radiation. A blackbody has epsilon=1, but most objects have lower emissivities. A very shiny object has an emissivity close to 0, and human skin is between 0.6 to 0.8.

As mentioned earlier, an ideal blackbody will absorb all incident light, but in the real world, things absorb only part of the incident light. The fraction of light that is reflected by an object is called the albedo, which means whiteness in Latin. Black objects have an albedo close to 0, while white objects have an albedo of close to 1.0. The table below lists some representative albedos for Earth surface materials. Most of these albedos are sensitive to the angle at which the sunlight hits the surface; this is especially true for water. When the Sun is at angles of 40° and higher relative to the horizon, the albedo of the water is fairly constant, but as the angle decreases from 40°, the albedo increases dramatically so that it is about 0.5 at a Sun angle of 10° and 1.0 at a sun angle of 0°. You are aware of this in the form of glare coming off the water in the early morning or in the evening before sunset.

Most people have an intuitive sense for the effects of albedo on reflectance and solar energy absorption. This is why people wear white clothes in hot sunny climates and dark clothes in cold sunny climates. What should you wear if it is cloudy and cold?

In the above table, we see that the Earth’s average albedo is 0.31, but there is considerable variation in this value over the surface of the Earth and over time as well — this spatial and temporal variation in albedo of the Earth is shown in the figure below.

The albedo of the Earth during February and July as averaged over the period from 1974 to 1978.

Credit: Graphics created by David Bice © Penn State University is licensed under CC BY-NC-SA 4.0; data from Mitchell and Wallace, 1992, J. Climate, 5, 1140-1156.

We have already mentioned the idea of radiative equilibrium, where the incoming energy and the outgoing energy are in balance, resulting in a steady temperature, but now we are in a position to combine a few other ideas to express this notion in a simple equation that is at the heart of all climate models. Before we begin, we introduce the solar constant, which is the amount of incoming solar electromagnetic radiation per unit area. Just for your information, this amount is measured on a plane perpendicular to the Sun's rays and at the mean distance from the Sun to the Earth.

We begin with the energy (in units of W/m²):

Here, S is the solar constant — 1370 W/m², and a is the albedo, which is about .31 based on satellite measurements. Then we deal with the energy out, using the Stefan-Boltzmann law:

Combining energy in (E_in) and energy out (E_out), we get:

Now, we can solve this to find what the equilibrium temperature of our planet is:

adding numbers, $T=(343(1-.31)5.67e-8)1/4=254°K=-19°C=-2.2°F$

Yikes! This is too cold — we know the mean temperature of the Earth is more like 15°C (288°K or 59°F).  What have we left out? The simple answer is the emissivity, which makes sense since we know the Earth is not an ideal blackbody. (Remember that emissivity is a measure of how good an object is at emitting (giving off) energy via electromagnetic radiation; in the above, we have effectively assumed an emissivity of 1, which is for a perfect black body material). Using the equation above, let’s see what that emissivity number should be:

$$S(1-a)=\epsilon \sigma T4$$$$\epsilon =S(1-a)/(\sigma T4)=343(1-.31)(5.67e-8)(2884)=0.606$$

So, then, even if all of these equations have you seeing stars, what does this basically mean? There is something about the Earth that prevents it from emitting as much energy as it should. What is this something? It is the greenhouse effect — the key that makes our planet a nice place to live.

Insolation — Incoming Solar Radiation

It all starts with the Sun, where the fusion of hydrogen creates an immense amount of energy, heating the surface to around 6000°K; the Sun then radiates energy outwards in the form of ultraviolet and visible light, with a bit in the near-infrared part of the spectrum. By the time this energy gets out to the Earth, its intensity has dropped to a value of about 1370 W/m² —as we just saw this is often called the solar constant (even though it is not truly constant — it changes on several timescales):

Sun's energy shining onto Earth

Click for a text description of Sun's Energy SHining onto Earth.

The climate system begins with energy from the Sun. At Earth’s distance from the Sun, the light has an intensity of 1370 W/m² — this value is sometimes called the solar constant, although it does change over time. The Earth is so far away from the Sun that the incoming rays of energy are all essentially parallel (thin black lines within the yellow region). Note that the sunlight strikes the planet perpendicular to the surface near the equator, but it strikes at an oblique angle near the poles, such that L2>L1. This means that the insolation is more concentrated near the equator and weaker near the poles. In other words, the heat flux density (W/m²) is greater at the equator than at the poles.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Since the Earth spins, the insolation is spread out over an area 4 times greater than the disk shown in the figure above, so the solar constant translates into a value of 343 W/m². This is a bit less than six 60 Watt light bulbs shining on every square meter of the surface, which adds up to a lot of light bulbs since the total surface area of Earth is 5.1e14 m². How much energy do we get from the Sun in a year? Take 1370 W/m², multiply by the area of the disk (pi x r² where r=radius of Earth, 6.37e6 m), and this gives us an answer in Watts, which has units of joules per second, so if we then multiply by the number of seconds in a year, then we get the total energy in joules per year. The number is staggering — 5.56e24 Joules of energy — and is 10,000 times greater than all of the energy generated and consumed by humans each year.

Earth's Orbit, Axial Tilt, and Seasons

Click for a text description of Earth's Orbit, Axial Tilt and the Seasons

The Earth orbits around the Sun with its spin axis (the line connecting the North and South Poles) tilted at 23.4° from a line perpendicular to the orbital plane. This tilt, or obliquity, gives rise to the variation in seasons, and the larger the tilt angle, the greater the contrast in seasons (this tilt changes on a timescale of about 40,000 years). If the tilt were 0°, there would be no real difference between winter and summer; the difference in distance between perihelion (the closest point of the orbit) and aphelion (the farthest point) is very small at present, but this, too, changes. The degree of ellipticity is called the orbital eccentricity; it changes on timescales of 95,000, 125,000, and 405,000 years. A very nice animated version of Earth’s orbit can be found here.

• Central Elements:
  • The Sun is depicted in the center as an orange circle.
  • Earth's elliptical orbit around the Sun is shown as a black oval path, with Earth positioned at four points corresponding to the seasons.
• Earth’s Positions and Seasons:
  • Summer (Northern Hemisphere):
    • Earth is shown on the left side of the orbit, tilted with the Northern Hemisphere facing the Sun.
    • Labeled "Summer Northern Hemisphere."
    • An annotation reads: "the South Pole receives no sunlight during this part of the orbit."
    • The position is marked as "Aphelion – Jul. 4, 1.01671 AU," indicating Earth is farthest from the Sun (1.01671 Astronomical Units).
  • Spring (Northern Hemisphere):
    • Earth is shown at the top of the orbit, with its axis tilted at an angle.
    • Labeled "Spring Northern Hemisphere."
  • Winter (Northern Hemisphere):
    • Earth is shown on the right side of the orbit, tilted with the Northern Hemisphere facing away from the Sun.
    • Labeled "Winter Northern Hemisphere."
    • An annotation reads: "the North Pole receives no sunlight during this part of the orbit."
    • The position is marked as "Perihelion – Jan. 4, 0.98329 AU," indicating Earth is closest to the Sun (0.98329 Astronomical Units).
  • Fall (Northern Hemisphere):
    • Earth is shown at the bottom of the orbit, with its axis tilted at an angle.
    • Labeled "Fall Northern Hemisphere."
• Axial Tilt Annotation:
  • A label on the right side of the diagram reads: "Earth’s spin axis is tilted relative to the orbital plane," explaining the cause of seasonal variations.
  • The tilt angle is marked as 23.5° on the Earth diagrams.
• Visual Details:
  • Each Earth is depicted as a globe with visible continents, primarily showing North and South America.
  • The tilt of Earth’s axis is indicated by a dashed line running through the center of each Earth, with the North Pole (marked with a small symbol) tilted toward or away from the Sun depending on the season.
  • Arrows on the orbital path indicate the direction of Earth’s movement around the Sun (counterclockwise).

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

The insolation is not constant over the surface of the Earth — it is concentrated near the equator (first figure on the page) because of the curvature of the Earth. But, the situation is complicated by the fact that the Earth’s spin axis is tilted by 23.4° relative to a line perpendicular to the Earth’s orbital plane (see the second figure on the page), so that as Earth orbits around the Sun, the insolation is concentrated in the Northern Hemisphere (the Northern Hemisphere summer) and then the Southern Hemisphere (winter in the Northern Hemisphere). This tilt of the spin axis, also called the obliquity, is the main reason we have seasons.

Insolation with latitude graph

Click for a text description of Insolation vs. Latitude

The image is a graph titled "Insolation v. Latitude," showing the variation in average daily insolation (solar energy received per unit area) across different latitudes on Earth. The graph includes three curves representing insolation at different times of the year, with a note indicating Earth's tilt angle as 23.45°.

• Axes:
  • X-Axis (Latitude): Labeled "Latitude," ranging from -80° (Southern Hemisphere) to 80° (Northern Hemisphere), with major ticks at intervals of 20° (-80, -60, -40, -20, 0, 20, 40, 60, 80). The equator is at 0°.
  • Y-Axis (Insolation): Labeled "Average Daily Insolation W/m²," ranging from 0 to 600 watts per square meter (W/m²), with major ticks at intervals of 100 W/m² (0, 100, 200, 300, 400, 500, 600).
• Data Representation:
  • Three curves are plotted, each representing the average daily insolation at different times:
    • Winter Solstice (Dec. 21): Plotted in blue, labeled "winter Dec. 21 solstice."
    • Summer Solstice (June 21): Plotted in red, labeled "summer solstice."
    • Annual Average: Plotted in green, labeled "Annual avg."
• Curve Characteristics:
  • Winter Solstice (Dec. 21, Blue Curve):
    • Shows high insolation in the Southern Hemisphere, peaking at around 500 W/m² near -20° latitude.
    • Insolation decreases sharply toward the Northern Hemisphere, dropping to nearly 0 W/m² at 80° latitude (North Pole), reflecting minimal sunlight during the Northern Hemisphere's winter.
  • Summer Solstice (June 21, Red Curve):
    • Shows high insolation in the Northern Hemisphere, peaking at around 500 W/m² near 20° latitude.
    • Insolation decreases sharply toward the Southern Hemisphere, dropping to nearly 0 W/m² at -80° latitude (South Pole), reflecting minimal sunlight during the Southern Hemisphere's winter.
  • Annual Average (Green Curve):
    • Shows a more balanced distribution, peaking at around 400 W/m² near the equator (0° latitude).
    • Insolation gradually decreases toward both poles, reaching around 150 W/m² at ±80° latitude, reflecting the yearly average sunlight received.
• Overall Trend:
  • The graph illustrates how Earth's axial tilt causes significant seasonal variations in insolation, with the Northern Hemisphere receiving more sunlight during the summer solstice (June 21) and the Southern Hemisphere receiving more during the winter solstice (Dec. 21).
  • The annual average curve shows that the equator receives the most consistent and highest insolation year-round, while the poles experience the greatest seasonal extremes.

The graph effectively demonstrates the relationship between latitude, Earth's tilt, and the distribution of solar energy, which drives seasonal climate patterns

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

The tilt of the spin axis also means that day length changes, and these changes are most dramatic at the poles, which experience 24 hours of daylight during their summers and no daylight during their winters. The varying day length, along with the angle of incidence of the Sun’s rays, combine to control the average daily insolation variation (see figure above). On a yearly average, the equatorial region receives the most insolation, so we expect it to be the warmest, and indeed it is.

Earlier, we mentioned the Solar Constant — a measure of the amount of solar energy reaching Earth. In reality, this value is not a constant because the Sun is a dynamic star with lots of interesting changes occurring. One of the best known of these changes is the solar cycle, related to sunspots. Sunspots are dark regions on the surface of the Sun related to intense magnetic activity, and measurements have shown that the greater the number of sunspots, the greater the energy output of the Sun. Early observations of these sunspots revealed a pronounced cyclical pattern to them, varying on an 11-year cycle, as shown below.

Sunspot History and Solar Constant, comparing the number of sunspots per calendar year.

Click for a text description of the Sunspot History and Solar Constant graph.

The image is a graph showing two overlapping time series, likely representing climate-related data such as temperature anomalies or another paleoclimate proxy, over a period of time. The graph lacks specific labels for the axes and title, but the y-axis appears to represent a measurement scale, and the x-axis likely represents time, with specific years marked on the right side.

• Axes:
  • Y-Axis: The vertical axis ranges from -50 to 200, with major ticks at intervals of 50 (-50, 0, 50, 100, 150, 200). The units are not specified but could represent temperature anomalies (e.g., in °C or a proxy like δ¹⁸O in ‰).
  • X-Axis: The horizontal axis is not explicitly labeled with a time scale, but specific years are marked on the right side, suggesting a time series. The years are not shown on the x-axis itself but are inferred from the right-side labels.
• Data Representation:
  • Two curves are plotted:
    • A blue line representing one dataset.
    • A pink line representing another dataset.
  • Both lines show similar patterns, indicating they might be measuring related variables or the same variable from different sources.
• Curve Characteristics:
  • Both the blue and pink lines exhibit significant variability, with frequent peaks and troughs.
  • The values generally fluctuate between -50 and 150, with occasional peaks reaching close to 200.
  • The two lines closely follow each other, suggesting a strong correlation between the datasets, though there are slight differences in amplitude and timing of peaks.
• Year Markers (Right Side):
  • Specific years are marked on the right side of the graph, corresponding to certain points on the curves:
    • 1967 at the top (around 150 on the y-axis).
    • 1964.5 (around 100 on the y-axis).
    • 1964 (around 50 on the y-axis).
    • 1963.5 (around 0 on the y-axis).
    • 1963 (around -50 on the y-axis).
  • These years suggest the data spans at least from 1963 to 1967, though the full time range of the graph is not clear without x-axis labels.
• Overall Trend:
  • The graph shows cyclical fluctuations with no clear long-term trend over the visible period.
  • The data appears to oscillate around a mean value (possibly around 50 on the y-axis), with periodic increases and decreases.
  • The close alignment of the blue and pink lines indicates consistency between the two datasets, possibly representing different measurements of the same phenomenon or related climate variables.

The graph likely represents a paleoclimate or climate variability record, showing short-term fluctuations over a few years in the 1960s, but without specific labels, the exact nature of the data (e.g., temperature, isotopic ratios, or another proxy) remains unclear.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0;

Here, in blue, we see the annual number of sunspots and in red we see the reconstructed solar intensity or Solar Constant. The reconstruction is made by studying the relationship between sunspot number and solar intensity in the last few decades, where we have good direct measurements of the solar intensity — this provides a relationship that is fairly simple and directly proportional. Higher sunspot numbers correspond to higher solar intensity. Both records are characterized by a strong 11-year cycle, often called the sunspot cycle.

The magnitude of variation in the Solar Constant, however, is quite small, and we shall see in our lab activity for this module that this amounts to a very small change in the temperature of the Earth.

When our planet absorbs and emits energy, the temperature changes, and the relationship between energy change and temperature change of a material is wrapped up in the concept of heat capacity, sometimes called specific heat. Simply put, the heat capacity expresses how much energy you need to change the temperature of a given mass. Let’s say we have a chunk of rock that weighs one kilogram, and the rock has a heat capacity of 2000 Joules per kilogram per °C — this means that we would have to add 2000 Joules of energy to increase the temperature of the rock by 1 °C. If our rock had a mass of 10 kg, we’d need 20,000 Joules to get the same temperature increase. In contrast, water has a heat capacity of 4184 Joules per kg per °K, so you’d need twice as much energy to change its temperature by the same amount as the rock.

Cooling history of air and water

Click for a text description of Cooling history of air and water

This image is a line graph showing the cooling of air temperature over time in comparison to a constant water temperature. The graph plots temperature in Kelvin against time in hours, illustrating the cooling process of air.

• Graph Type: Line graph
• Y-Axis: Temperature (K)
  • Range: 17 K to 293 K
• X-Axis: Hours
  • Range: 0 to 200 hours
• Data Representation:
  • Water Temperature (T_water): Red line
    • Constant at 293 K (labeled as 2) throughout the 200 hours
  • Air Temperature (T_air): Blue line
    • Starts at 155 K (labeled as 1) at 0 hours
    • Decreases rapidly within the first 50 hours, approaching 17 K
    • Levels off near 17 K after 100 hours, remaining stable through 200 hours
• Trend:
  • Air temperature cools significantly from 155 K to near 17 K within the first 100 hours
  • Water temperature remains unchanged at 293 K

The graph demonstrates the rapid cooling of air over time while the water temperature remains constant, highlighting the difference in thermal behavior between air and water over a 200-hour period.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

The heat capacity of a material, along with its total mass and its temperature, tell us how much thermal energy is stored in a material. For instance, if we have a square tub full of water one meter deep and one meter on the sides, then we have one cubic meter of water. Since the density of water is 1000 kg/m³, this tub has a mass of 1000 kg. If the temperature of the water is 20 °C (293 °K), then we multiply the mass (1000) times the heat capacity (4184) times the temperature (293) in °K to find that our cubic meter of water has 1.22e9 (1.2 billion) Joules of energy. Consider for a moment two side-by-side cubic meters of material — one cube is water, the other air. Air has a heat capacity of about 1000 Joules per kg per °K and a density of just 1.2 kg/m³, so its initial energy would be 1000 x 1 x 1.2 x 293 = 351,600 Joules — a tiny fraction of the thermal energy stored in the water. If the two cubes are at the same temperature, they will radiate the same amount of energy from their surfaces, according to the Stefan-Boltzmann law described above. If the energy lost in an interval of time is the same, the temperature of the cube of air will decrease much more than the water, and so in the next interval of time, the water will radiate more energy than the air, yet the air will have cooled even more, so it will radiate less energy. The result is that the temperature of the water cube is much more stable than the air — the water changes much more slowly; it holds onto its temperature longer. The figure above shows the results of a computer model that tracks the temperature of these two cubes.

One way to summarize this is to say that the higher the heat capacity, the greater the thermal inertia, which means that it is harder to get the temperature to change. This concept is an important one since Earth is composed of materials with very different heat capacities — water, air, and rock; they respond to heating and cooling quite differently.

The heat capacities for some common materials are given in the table below.

Earlier, we noticed that if you do the energy balance calculation to figure out the temperature of our planet, it suggests that Earth should be -19 °C, which is 34 °C colder than the observed average global temperature of 15 °C. Why is Earth warmer than it should be? The answer lies in the greenhouse effect — gases in our atmosphere (including CO₂, CH₄ (methane) and H₂O water vapor) trap much of the emitted heat and then re-radiate it back to Earth’s surface. This means that the energy leaving our planet from the top of the atmosphere is less than one would expect given the known temperature of our planet. As mentioned earlier, this effect can be represented in the simple energy balance equation as a term called the emissivity.

The fact of this greenhouse effect comes out of the very simple calculation we did above, but it can also be observed in great detail from satellite measurements of the infrared energy leaving Earth’s atmosphere.

As we discussed on the topic of black body radiation, the temperature of a body (a planet, for instance) gives us a sense of what the spectrum of energy should look like — that is, a range of wavelengths and intensity of radiation at those wavelengths. For the Earth, this spectrum, as seen from satellites looking down on the surface, is very different from the expected. The figure below shows the difference between the expected and the observed.

CO₂ H₂0 CH4 O₃ absorb energy differences

Click for a text description of the diagram above

The image consists of two related diagrams illustrating the spectrum of energy emitted by Earth and the absorption of this energy by greenhouse gases across different wavelengths. The diagrams highlight the role of greenhouse gases in trapping Earth's emitted energy, contributing to the greenhouse effect. The image is credited to Robert Rhode and includes a source link.

• Top Diagram (Energy Emission Spectrum):
  • Axes:
    • X-Axis (Wavelength): Labeled "Wavelength," ranging from 1 μm (micrometer) to 70 μm, with major ticks at 1, 5, 10, and 70 μm.
    • Y-Axis (Energy Emitted): Labeled "Energy Emitted," with no specific units provided, but the scale is relative, showing the intensity of emitted energy.
  • Data Representation:
    • Yellow Curve: Represents the theoretical blackbody emission spectrum for a planet at Earth's temperature (~288 K) without greenhouse gases, peaking around 10 μm.
    • Red Curve: Represents the actual spectrum of energy emitted by Earth, showing significant reductions at certain wavelengths due to absorption by greenhouse gases.
    • The area between the yellow and red curves is shaded red, indicating the energy absorbed by the atmosphere.
  • Trend: The yellow curve follows a smooth blackbody radiation curve, while the red curve shows dips at specific wavelengths where greenhouse gases absorb energy, reducing the amount of energy escaping to space.
• Bottom Diagram (Absorption by Greenhouse Gases):
  • Title and Label: The bottom diagram shows the "% Absorption From All Greenhouse Gases" and breaks down the absorption contributions from individual gases.
  • Axes:
    • X-Axis (Wavelength): Matches the top diagram, ranging from 1 μm to 70 μm, with major ticks at 1, 5, 10, and 70 μm.
    • Y-Axis (Absorption): For the topmost graph, labeled "% Absorption From All Greenhouse Gases," ranging from 0 to 100%, with major ticks at 0, 50, and 100%.
  • Data Representation:
    • Topmost Graph (Yellow with Blue Shading): Shows the percentage of Earth’s emitted energy absorbed by all greenhouse gases combined (blue shading) at each wavelength, with the red curve from the top diagram overlaid to show the emitted energy that escapes.
    • Individual Gas Absorption Graphs: Below the combined absorption graph, separate graphs show the absorption spectra for specific greenhouse gases:
      • Water Vapor (Blue): Strong absorption around 5–7 μm and beyond 20 μm.
      • Carbon Dioxide (Green): Significant absorption around 4 μm and 15 μm.
      • Oxygen and Ozone (Gray): Absorption primarily around 9–10 μm.
      • Methane (Brown): Absorption around 3.5 μm and 7–8 μm.
      • Nitrous Oxide (Dark Brown): Minor absorption around 4.5 μm and 8 μm.
    • A label on the right side reads: "these absorption spectra add up to the total absorption spectrum for green-house gases," indicating that the combined absorption (topmost graph) is the sum of the individual contributions.
  • Trend: The absorption graphs show that different gases absorb energy at specific wavelengths, with water vapor and carbon dioxide being the most significant contributors to the overall absorption.

The diagrams together illustrate how greenhouse gases absorb Earth’s outgoing infrared radiation, reducing the energy that escapes to space and contributing to the greenhouse effect. The absorption spectra of individual gases highlight their specific roles in this process.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

In fact, the same thing happens to the energy the Earth receives from the Sun — various gases in the atmosphere absorb that energy, so the amount we receive on the surface is less than what arrives at the top of the atmosphere.

Spectrum of Solar Radiation

Click for a text description of Spectrum of Solar Radiation

The image is a graph titled "Spectrum of Solar Radiation," showing the energy intensity of solar radiation across different wavelengths, comparing the radiation above the atmosphere to that at sea level. It highlights the effects of atmospheric absorption by various gases.

• Title: The title at the top reads: "Spectrum of Solar Radiation."
• Axes:
  • X-Axis (Wavelength): Labeled "Wavelength (nm)," ranging from 250 to 2500 nanometers (nm), with major ticks at intervals of 250 nm (250, 500, 750, 1000, 1250, 1500, 1750, 2000, 2250, 2500).
  • Y-Axis (Energy Intensity): Labeled "Energy Intensity (W/m²/nm)," ranging from 0 to 2.5 watts per square meter per nanometer (W/m²/nm), with major ticks at intervals of 0.5 (0, 0.5, 1.0, 1.5, 2.0, 2.5).
• Data Representation:
  • Two curves are plotted:
    • Radiation Above the Atmosphere: Represented by a smooth yellow curve labeled "Radiation Above the Atmosphere," also referred to as "Black Body at 5250°C." This curve approximates the Sun's emission as a blackbody at 5250°C (approximately 5523 K, close to the Sun's surface temperature of ~5773 K).
    • Radiation at Sea Level: Represented by a red curve labeled "Radiation at sea level," showing the solar radiation after passing through the atmosphere.
  • The area between the yellow and red curves is shaded red, indicating the energy absorbed by the atmosphere.
• Spectral Regions:
  • The graph is divided into three regions along the x-axis:
    • UV (Ultraviolet): From 250 nm to ~400 nm.
    • Visible: From ~400 nm to ~700 nm.
    • Infrared: From ~700 nm to 2500 nm.
  • These regions are marked with vertical dashed lines separating UV, visible, and infrared.
• Absorption Bands:
  • Specific absorption bands are labeled along the red curve, indicating where atmospheric gases absorb solar radiation:
    • O₃ (Ozone): Absorbs strongly in the UV range (around 250–350 nm).
    • O₂ (Oxygen): Absorbs around 750 nm.
    • H₂O (Water Vapor): Absorbs at multiple wavelengths, notably around 900 nm, 1100 nm, 1400 nm, and 1900 nm.
    • CO₂ (Carbon Dioxide): Absorbs around 2000 nm.
  • These absorption bands cause dips in the red curve, showing reduced energy intensity at sea level compared to above the atmosphere.
• Curve Characteristics:
  • Yellow Curve (Above Atmosphere): Peaks around 500 nm in the visible range at an intensity of about 2.0 W/m²/nm, then gradually decreases toward longer wavelengths, reaching near 0 W/m²/nm by 2500 nm.
  • Red Curve (At Sea Level): Follows the yellow curve but with significant reductions at specific wavelengths due to absorption. It peaks slightly below 2.0 W/m²/nm around 500 nm and shows pronounced dips corresponding to the absorption bands of O₃, O₂, H₂O, and CO₂.
• Overall Trend:
  • The graph illustrates that while solar radiation above the atmosphere follows a smooth blackbody curve, the radiation reaching sea level is significantly altered by atmospheric absorption.
  • The visible range (400–700 nm) experiences relatively less absorption, allowing most of the sunlight in this range to reach the surface, while UV and infrared regions are more heavily absorbed by atmospheric gases.

The graph effectively demonstrates the impact of Earth's atmosphere on incoming solar radiation, highlighting the role of specific gases in absorbing energy at different wavelengths.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

How do gases absorb this energy? It is basically a matter of vibrations of gas molecules being in sync with some of the frequencies of energy associated with insolation or infrared energy given off by Earth. You can think of the bonds between atoms in an H₂O molecule like springs that stretch, twist, and bend at specific frequencies (nice animation of H₂O movement), and if energy hits those molecules at just the right frequency, the bonds of the molecule absorb that energy and oscillate and stretch and twist more strongly.

There are numerous ways to demonstrate this heat-trapping ability of some gases — here is a nice laboratory demonstration of heat-trapping — but you can also think of the difference between the cold nighttime temperatures when the air is dry (little water vapor) compared to the warmer nighttime temperatures when the air is humid. The fact of the greenhouse effect is one of the most important things to understand about our climate system. This greenhouse effect, which is probably better described as warming produced by heat-trapping gases, is incredibly powerful — it returns more energy to the surface than we absorb from the Sun, and its strength is closely tied to the global carbon cycle, and thus the oceans, and all the biota on Earth.

Let’s try to put a lot of this together now and have a glance at the energy budget for Earth’s climate. The figure below attempts to illustrate where all the energy goes in the climate system. We start with 100 units of energy, which represents the total amount of energy Earth receives from the Sun in a year.

Energy Flows in the Climate System

Click here for a text description of Energy Flows in the Climate System

The image is a diagram titled "Energy Flows in the Climate System," illustrating the flow of solar energy through Earth's climate system, including the atmosphere and surface reservoirs. It quantifies the energy in units (relative to 100 units of incoming solar radiation) and highlights processes like reflection, absorption, heat transfer, and the greenhouse effect. The diagram is attributed to Kiehl and Trenberth (1997).

• Overall Structure:
  • The diagram is divided into three main sections: incoming solar radiation (left), the atmosphere (center), and the surface reservoir (bottom). Arrows and numerical values indicate the flow and distribution of energy.
• Incoming Solar Radiation (Left Side):
  • 100 units: Represented as a yellow arrow labeled "Incoming Solar Radiation," entering from the top left.
  • 9 units: Reflected off the land surface, labeled "Insolation Reflected off Land Surface" (yellow arrow pointing back upward).
  • 23 units: Reflected by clouds and aerosols, labeled "Insolation Reflected by Clouds & Aerosols" (yellow arrow pointing upward).
  • Total Reflected: 9 + 23 = 32 units of the incoming 100 units are reflected back to space.
• Atmosphere (Center Section):
  • 19 units: Absorbed by the atmosphere, labeled "Absorbed by Atmosphere" (yellow arrow curving into the atmosphere).
  • 16.5 units: Labeled "Atmosphere Reservoir," indicating the energy stored in the atmosphere.
  • 11 units: Lost to space as heat, labeled "Heat Lost to Space" (purple arrow pointing upward).
  • 133 units: Transferred from the surface to the atmosphere, labeled "Heat Transfer to Atmosphere" (red arrow pointing upward).
  • 57 units: Radiated into space from the top of the atmosphere, labeled "Heat Radiated into Space from top Atmosphere" (purple arrow pointing upward).
  • 95 units: Returned to the surface via the greenhouse effect, labeled "Heat Returned to Surface (Greenhouse Effect)" (green arrow pointing downward).
  • A section labeled "Energy Recycles" indicates the cycling of energy between the surface and atmosphere.
• Surface Reservoir (Bottom Section):
  • 49 units: Absorbed by the surface, labeled "Insolation Absorbed by Surface" (yellow arrow pointing downward).
  • 271.2 units: Labeled "Surface Reservoir (30% land; 70% water)," indicating the total energy at the surface.
  • A note within the surface reservoir states: "(boxed values indicate thermal energy stored in a reservoir)," referring to the 271.2 units.
  • Another note states: "(circled values indicate energy flows)," referring to the other numerical values in the diagram.
• Energy Balance Note (Bottom Left):
  • A note at the bottom left reads: "Here, 100 energy units = ~5.56E24 Joules/yr; the total annual solar energy received (averages ~342 W/m² over the surface of the Earth)," providing the scale for the energy units used in the diagram.
• Visual Elements:
  • The diagram uses color-coded arrows to represent different energy flows:
    • Yellow for incoming and reflected solar radiation.
    • Red for heat transfer from the surface to the atmosphere.
    • Purple for heat lost to space.
    • Green for heat returned to the surface via the greenhouse effect.
  • Clouds are depicted in the atmosphere, and the surface is divided into land (30%) and water (70%).

The diagram effectively illustrates the energy balance of Earth's climate system, showing how incoming solar radiation is distributed, reflected, absorbed, and re-radiated, with a significant portion recycled through the greenhouse effect, as quantified by Kiehl and Trenberth in 1997.

Credit: © Kiehl and Trenberth, 1997 Used with permission

When the insolation strikes the atmosphere, 23 units are reflected back to space from clouds and aerosols, which are tiny particles suspended in the atmosphere. Another 19 units are absorbed by the atmosphere, as described in the figure above, thus adding thermal energy to the atmosphere. The remaining 58 units of energy reach the Earth’s surface, where 9 units are reflected back into space, and the remaining 49 units are absorbed by the surface, warming the planet. The Earth’s surface is mostly water, and by virtue of its temperature and heat capacity, it has a lot more thermal energy than the atmosphere (271.2 vs 16.5). Energy flows up from the surface to the atmosphere in a variety of ways — mainly by emission of infrared radiation, heat transfer by evaporation, and then condensation of water. When water evaporates, it “steals” energy from the surface; this energy is needed to make the phase change from liquid to vapor, and the same energy is then released when water vapor condenses to form liquid water droplets. As you can see from the diagram, the combined flow of energy from the surface is greater than the amount we get from the sun! Of this energy given off by the surface, a little bit (7 units) escapes the atmosphere because there are no gases that absorb infrared energy at wavelengths between 10 to 15 microns; the rest is absorbed by the atmosphere, which then emits infrared energy from its top to outer space and from its bottom back to the surface; this atmospheric absorption of infrared energy and its return to the surface is called the greenhouse effect. Since the bottom of the atmosphere is much warmer than the top, much more energy is returned to the Earth’s surface than is emitted to outer space.

The remarkable thing to observe and remember here is that the surface receives almost twice as much energy from the greenhouse effect than it does directly from the Sun! But, if you look at the diagram a bit, you can see that the energy sent to the surface from the atmosphere is essentially recycled energy, whose origin is the Sun.

Energy Flows in the Climate System

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The image is a diagram titled "Energy Flows in the Climate System," illustrating the flow of solar energy through Earth's climate system, including the atmosphere and surface reservoirs. It quantifies the energy in units (relative to 100 units of incoming solar radiation) and highlights processes like reflection, absorption, heat transfer, and the greenhouse effect. The diagram is attributed to Kiehl and Trenberth (1997).

• Overall Structure:
  • The diagram is divided into three main sections: incoming solar radiation (left), the atmosphere (center), and the surface reservoir (bottom). Arrows and numerical values indicate the flow and distribution of energy.
• Incoming Solar Radiation (Left Side):
  • 100 units: Represented as a yellow arrow labeled "Incoming Solar Radiation," entering from the top left.
  • 9 units: Reflected off the land surface, labeled "Insolation Reflected off Land Surface" (yellow arrow pointing back upward).
  • 23 units: Reflected by clouds and aerosols, labeled "Insolation Reflected by Clouds & Aerosols" (yellow arrow pointing upward).
  • Total Reflected: 9 + 23 = 32 units of the incoming 100 units are reflected back to space.
• Atmosphere (Center Section):
  • 19 units: Absorbed by the atmosphere, labeled "Absorbed by Atmosphere" (yellow arrow curving into the atmosphere).
  • 16.5 units: Labeled "Atmosphere Reservoir," indicating the energy stored in the atmosphere.
  • 11 units: Lost to space as heat, labeled "Heat Lost to Space" (purple arrow pointing upward).
  • 133 units: Transferred from the surface to the atmosphere, labeled "Heat Transfer to Atmosphere" (red arrow pointing upward).
  • 57 units: Radiated into space from the top of the atmosphere, labeled "Heat Radiated into Space from top Atmosphere" (purple arrow pointing upward).
  • 95 units: Returned to the surface via the greenhouse effect, labeled "Heat Returned to Surface (Greenhouse Effect)" (green arrow pointing downward).
  • A section labeled "Energy Recycles" indicates the cycling of energy between the surface and atmosphere.
• Surface Reservoir (Bottom Section):
  • 49 units: Absorbed by the surface, labeled "Insolation Absorbed by Surface" (yellow arrow pointing downward).
  • 271.2 units: Labeled "Surface Reservoir (30% land; 70% water)," indicating the total energy at the surface.
  • A note within the surface reservoir states: "(boxed values indicate thermal energy stored in a reservoir)," referring to the 271.2 units.
  • Another note states: "(circled values indicate energy flows)," referring to the other numerical values in the diagram.
• Energy Balance Note (Bottom Left):
  • A note at the bottom left reads: "Here, 100 energy units = ~5.56E24 Joules/yr; the total annual solar energy received (averages ~342 W/m² over the surface of the Earth)," providing the scale for the energy units used in the diagram.
• Visual Elements:
  • The diagram uses color-coded arrows to represent different energy flows:
    • Yellow for incoming and reflected solar radiation.
    • Red for heat transfer from the surface to the atmosphere.
    • Purple for heat lost to space.
    • Green for heat returned to the surface via the greenhouse effect.
  • Clouds are depicted in the atmosphere, and the surface is divided into land (30%) and water (70%).

The diagram effectively illustrates the energy balance of Earth's climate system, showing how incoming solar radiation is distributed, reflected, absorbed, and re-radiated, with a significant portion recycled through the greenhouse effect, as quantified by Kiehl and Trenberth in 1997.

Credit: © Kiehl and Trenberth, 1997 Used with permission

The view of the climate system depicted in the adjacent figure is one of stability — energy flows in and out, in perfect balance, so the temperature of the earth should stay the same. But if we can learn anything from studying Earth’s history, we learn that change is the rule and stability the exception. When change occurs, it almost always brings feedback mechanisms into play — they can accentuate and dampen change, and they are incredibly important to our climate system. There are many good examples of feedback mechanisms, but here are a few to illustrate the idea.

Ice — Albedo Feedback

Ice reflects sunlight better than almost any other material on Earth, and in reflecting sunlight, it lowers the amount of insolation absorbed by Earth, which makes it colder. If the Earth becomes colder, more ice may grow, covering more area and thus reflecting even more insolation, which in turn cools the Earth further. Thus cooling instigates ice expansion, which promotes additional cooling, and so on — this is clearly a cycle that feeds back on itself to encourage the initial change. Since this chain of events furthers the initial change that triggered the whole thing, it is called a positive feedback (but note that the change may not be good from our perspective). Positive feedback mechanisms tend to lead to runaway change — some small initial change is thus accentuated into a major change.

Weathering Feedback

Rocks exposed at the surface interact with water and the atmosphere and undergo a set of chemical and physical changes we call weathering. The chemical part of weathering often involves the consumption of carbonic acid (formed from water and carbon dioxide) in dissolving minerals in rocks. This process of weathering is thus a sink for atmospheric carbon dioxide, which is an important greenhouse gas. If you remove carbon dioxide from the atmosphere, you weaken the greenhouse effect and this leads to cooling of the Earth. Like many chemical reactions, this chemical weathering occurs more rapidly in hotter climates, which are associated with higher levels of carbon dioxide. So consider a scenario in which some warming occurs; this will encourage faster weathering, which will consume carbon dioxide, which will lead to cooling. In this case, the initial change triggered a set of processes that countered the initial change — this is called a negative feedback (even though it may have beneficial results) because it works in opposition to the change that triggered it.

Cloud Feedback

Another important negative feedback mechanism involves the formation of clouds. On the whole, clouds in today's climate have a slight net cooling effect — this is the balance of the increased albedo due to low clouds and the increased greenhouse effect caused by high cirrus clouds. As a general rule, as the atmosphere gets warmer, it can hold more water vapor, and with more water vapor, we expect more clouds, and the increased clouds will then tend to limit the warming that initiated the increased clouds — thus we have another negative feedback mechanism.

Positive and Negative Feedbacks — Yin and Yang

In Asian philosophy, yin and yang can be thought of as interacting, interconnected forces that are essential components of a dynamic system. In the Earth system, positive and negative feedbacks are a bit like yin and yang — they are essential components of the whole system that ultimately play an important role in maintaining a more or less stable state. Positive feedback mechanisms enhance or amplify some initial change, while negative feedback mechanisms stabilize a system and prevent it from getting into extreme states. In many respects, the history of Earth’s climate system can be seen as a bit of a battle between these two types of feedback, but in the end, the negative feedbacks win out and our climate is generally stable with a limited range of change (excepting, of course, a few extremes such as the Snowball Earth events back around 750 Myr ago).

Positive and Negative Feedback Mechanisms

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The image consists of two sets of diagrams illustrating feedback mechanisms in the climate system, specifically focusing on positive and negative feedback loops. The diagrams use arrows and labels to show the relationships between different climate variables.

• Top Section: Positive Feedback Mechanism
  • Left Diagram (Cooling Cycle):
    • Components and Flow:
      • "Cooling" (in blue) leads to "Ice Growth" (with a "+" sign indicating a positive relationship).
      • "Ice Growth" leads to "Increase Albedo" (more ice reflects more sunlight).
      • "Increase Albedo" leads to "Less Insolation Absorbed" (less solar energy absorbed due to higher reflectivity).
      • "Less Insolation Absorbed" loops back to "Cooling," completing the cycle.
    • Description: This cycle shows that cooling promotes ice growth, which increases albedo (reflectivity), reducing the absorption of solar energy and further enhancing cooling—a self-reinforcing loop.
  • Right Diagram (Warming Cycle):
    • Components and Flow:
      • "Warming" (in red) leads to "Ice Melting" (with a "+" sign indicating a positive relationship).
      • "Ice Melting" leads to "Decrease Albedo" (less ice means less reflectivity).
      • "Decrease Albedo" leads to "More Insolation Absorbed" (more solar energy absorbed due to lower reflectivity).
      • "More Insolation Absorbed" loops back to "Warming," completing the cycle.
    • Description: This cycle shows that warming causes ice to melt, decreasing albedo, which increases the absorption of solar energy and further enhances warming—another self-reinforcing loop.
• Bottom Section: Negative Feedback Mechanism
  • Title: "Negative Feedback Mechanism" is written below the positive feedback section.
  • Left Diagram (Warming to Cooling):
    • Components and Flow:
      • "Warming" (in red) leads to "Increased Weathering" (with a "−" sign indicating a negative relationship).
      • "Increased Weathering" leads to "Weaker Greenhouse" (weathering removes CO₂ from the atmosphere, reducing the greenhouse effect).
      • "Weaker Greenhouse" leads to "Cooling" (in blue).
      • "Cooling" loops back to "Warming," completing the cycle.
    • Description: This cycle shows that warming increases weathering, which weakens the greenhouse effect by removing CO₂, leading to cooling—a self-regulating loop that counteracts the initial warming.
  • Right Diagram (Cooling to Warming):
    • Components and Flow:
      • "Cooling" (in blue) leads to "Decreased Weathering" (with a "−" sign indicating a negative relationship).
      • "Decreased Weathering" leads to "Stronger Greenhouse" (less CO₂ removal allows the greenhouse effect to strengthen).
      • "Stronger Greenhouse" leads to "Warming" (in red).
      • "Warming" loops back to "Cooling," completing the cycle.
    • Description: This cycle shows that cooling reduces weathering, allowing CO₂ to accumulate and strengthen the greenhouse effect, leading to warming—a self-regulating loop that counteracts the initial cooling.
• Visual Elements:
  • Arrows indicate the direction of influence between variables.
  • "+" signs in the positive feedback diagrams indicate that the variables reinforce each other.
  • "−" signs in the negative feedback diagrams indicate that the variables counteract each other.
  • "Cooling" is written in blue, and "Warming" is written in red to differentiate the temperature changes.

The diagrams effectively illustrate how positive feedback mechanisms (like the ice-albedo feedback) amplify climate changes, while negative feedback mechanisms (like the weathering-greenhouse feedback) act to stabilize the climate system by counteracting changes.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Earth’s climate systems are characterized by thresholds, levels that once crossed herald a new climate state. For example, we often talk about the 1.5 or 2^oC thresholds across which the impacts of climate change become dangerous, for example including long heatwaves, devastating droughts and more common extreme weather events. Thresholds can become tipping points if, once crossed, there is no going back from the new climate state, at least temporarily. One of the best examples of a tipping point is the cessation in Atlantic meridional overturning circulation (AMOC) which we will learn about in Module 6 and which is a key driver of heat transport around the globe. This circulation drives the formation of ocean deep waters which in return feeds the Gulf Stream which warms northern latitudes including Western Europe, making them more habitable. It also fuels monsoon rains in places like India. In the past, AMOC has turned off, driving Northern Europe into an ice age. The system is highly complex and difficult to predict, but there have been warning signs that it has been edging closer to that potentially devastating tipping point in recent decades. The system becomes more variable as a tipping point is reached, and that variability is currently showing signs of increasing. Tipping points may involve positive feedbacks. For example, melting and disintegration of the West Antarctic Ice sheet will lower planetary albedo resulting in further melting, and at some stage the feedback will make the system irreversible, at least temporarily. Another example is the melting of permafrost in the Arctic region. Tipping points can lead to cascading changes if they impact one another, for example, significant melting of Antarctic ice can cause enough warming to exacerbate permafrost melting. More examples of tipping points are showing in the figure below.

Possible tipping elements in the climate system

Click for a text description of the Tipping Elements map.

The image is a world map highlighting various regions with potential climate tipping points, where small changes in climate conditions could lead to significant, often irreversible shifts in the Earth system. The map uses color-coded regions and labels to indicate specific tipping elements and their potential impacts.

• Map Overview:
  • The map is a global projection centered on the Atlantic Ocean, showing continents in gray with specific regions highlighted in colors (red, green, blue, orange) to indicate climate tipping points.
  • Labels are placed over the highlighted regions to describe the tipping elements and their potential changes.
• Highlighted Regions and Tipping Points:
  • Arctic and Northern Regions:
    • Melt of Greenland Ice-Sheet: Highlighted in orange over Greenland, indicating potential ice loss due to warming.
    • Climatic Change-Induced Ozone Hole?: Highlighted in orange over the Arctic, suggesting possible changes in atmospheric chemistry.
    • Boreal Forest Dieback: Highlighted in green over northern North America (Canada and Alaska) and northern Eurasia (Siberia), indicating potential forest loss due to climate stress.
    • Permafrost and Tundra Loss?: Highlighted in blue over northern Siberia, suggesting potential thawing of permafrost and loss of tundra ecosystems.
  • Atlantic Ocean:
    • Atlantic Deep Water Formation: Highlighted in orange in the North Atlantic, indicating potential disruption of ocean circulation patterns like the Atlantic Meridional Overturning Circulation (AMOC).
  • South America:
    • Dieback of Amazonas Rainforest: Highlighted in green over the Amazon Basin, indicating potential forest loss due to drying and deforestation.
  • Africa:
    • Sahel Greening: Highlighted in green over the Sahel region (south of the Sahara Desert), suggesting potential vegetation growth due to changing rainfall patterns.
    • West African Monsoon Shift: Highlighted in green over West Africa, indicating potential changes in monsoon patterns.
  • Indian Subcontinent:
    • Indian Monsoon Chaotic Multistability: Highlighted in red over India, suggesting potential unpredictable shifts in monsoon behavior.
  • Antarctica:
    • Instability of West Antarctic Ice Sheet: Highlighted in blue over West Antarctica, indicating potential ice sheet collapse and sea level rise.
    • Changes in Antarctic Bottom Water Formation?: Highlighted in blue around Antarctica, suggesting potential disruption of deep ocean water formation.
  • Global Ocean:
    • Change in ENSO Amplitude or Frequency: Highlighted in red over the equatorial Pacific Ocean, indicating potential changes in the El Niño-Southern Oscillation (ENSO) patterns.
• Color Coding:
  • Red: Used for regions with potential changes in ENSO and the Indian Monsoon, indicating high-impact, dynamic shifts.
  • Green: Used for regions like the Amazon, Sahel, West Africa, and boreal forests, indicating potential ecosystem changes (dieback or greening).
  • Blue: Used for polar regions (Siberia, Antarctica), indicating ice and permafrost-related tipping points.
  • Orange: Used for the Arctic, Greenland, and North Atlantic, indicating ice melt and ocean circulation changes.
• Visual Elements:
  • The map uses a gray background for continents not highlighted, with colored overlays for the tipping point regions.
  • Labels are written in black, with question marks in some cases (e.g., "Climatic Change-Induced Ozone Hole?" and "Permafrost and Tundra Loss?") indicating uncertainty or ongoing research.

The map effectively illustrates the global distribution of climate tipping points, highlighting regions where the climate system may undergo significant and potentially irreversible changes due to global warming and other climate stressors.

CodeOne (blank map), DeWikiMan (additional elements), CC BY-SA 4.0, via Wikimedia Commons

Tipping points represent one of the most dire threats of climate change due to their irreversible nature. Because of this, they are very much a key driver of the warming thresholds and emissions targets.

Energy Flows in the Climate System

Click here for a text description of Energy Flows in the Climate System

The image is a diagram titled "Energy Flows in the Climate System," illustrating the flow of solar energy through Earth's climate system, including the atmosphere and surface reservoirs. It quantifies the energy in units (relative to 100 units of incoming solar radiation) and highlights processes like reflection, absorption, heat transfer, and the greenhouse effect. The diagram is attributed to Kiehl and Trenberth (1997).

• Overall Structure:
  • The diagram is divided into three main sections: incoming solar radiation (left), the atmosphere (center), and the surface reservoir (bottom). Arrows and numerical values indicate the flow and distribution of energy.
• Incoming Solar Radiation (Left Side):
  • 100 units: Represented as a yellow arrow labeled "Incoming Solar Radiation," entering from the top left.
  • 9 units: Reflected off the land surface, labeled "Insolation Reflected off Land Surface" (yellow arrow pointing back upward).
  • 23 units: Reflected by clouds and aerosols, labeled "Insolation Reflected by Clouds & Aerosols" (yellow arrow pointing upward).
  • Total Reflected: 9 + 23 = 32 units of the incoming 100 units are reflected back to space.
• Atmosphere (Center Section):
  • 19 units: Absorbed by the atmosphere, labeled "Absorbed by Atmosphere" (yellow arrow curving into the atmosphere).
  • 16.5 units: Labeled "Atmosphere Reservoir," indicating the energy stored in the atmosphere.
  • 11 units: Lost to space as heat, labeled "Heat Lost to Space" (purple arrow pointing upward).
  • 133 units: Transferred from the surface to the atmosphere, labeled "Heat Transfer to Atmosphere" (red arrow pointing upward).
  • 57 units: Radiated into space from the top of the atmosphere, labeled "Heat Radiated into Space from top Atmosphere" (purple arrow pointing upward).
  • 95 units: Returned to the surface via the greenhouse effect, labeled "Heat Returned to Surface (Greenhouse Effect)" (green arrow pointing downward).
  • A section labeled "Energy Recycles" indicates the cycling of energy between the surface and atmosphere.
• Surface Reservoir (Bottom Section):
  • 49 units: Absorbed by the surface, labeled "Insolation Absorbed by Surface" (yellow arrow pointing downward).
  • 271.2 units: Labeled "Surface Reservoir (30% land; 70% water)," indicating the total energy at the surface.
  • A note within the surface reservoir states: "(boxed values indicate thermal energy stored in a reservoir)," referring to the 271.2 units.
  • Another note states: "(circled values indicate energy flows)," referring to the other numerical values in the diagram.
• Energy Balance Note (Bottom Left):
  • A note at the bottom left reads: "Here, 100 energy units = ~5.56E24 Joules/yr; the total annual solar energy received (averages ~342 W/m² over the surface of the Earth)," providing the scale for the energy units used in the diagram.
• Visual Elements:
  • The diagram uses color-coded arrows to represent different energy flows:
    • Yellow for incoming and reflected solar radiation.
    • Red for heat transfer from the surface to the atmosphere.
    • Purple for heat lost to space.
    • Green for heat returned to the surface via the greenhouse effect.
  • Clouds are depicted in the atmosphere, and the surface is divided into land (30%) and water (70%).

The diagram effectively illustrates the energy balance of Earth's climate system, showing how incoming solar radiation is distributed, reflected, absorbed, and re-radiated, with a significant portion recycled through the greenhouse effect, as quantified by Kiehl and Trenberth in 1997.

Here 100 energy units = 5.56e24J/year, the total annual solar energy received averages 342 W/m^@ over the surface of the Earth

Incoming solar radiation: 100

Insolation Reflected by Clouds and Aerosols: 23

Insolation Reflected off Land Surface: 9

Insolation Absorbed by Surface: 49

Atmosphere Reservoir: 16.5

Surface Reservoir (30% Land, 70% Water): 271.2

Heat Transfer to Atmosphere: 133

Heat Lost to Space: 11

Heat returned to Surface (Greenhouse Effect): 95

Heat Radiated into Space from top of Atmosphere: 57

Credit: © Kiehl and Trenberth, 1997 Used with permission

The diagram we have just been considering (repeated above), presents a good overview of how energy flows through the Earth’s climate system, but it does not give us a sense of how that energy is distributed across the surface of the globe and there are some important things to be learned from looking at this spatial pattern. For many years now, satellites have been monitoring these energy flows using spectrometers that measure the intensity of energy at different wavelengths flowing to the Earth and from the Earth. So, let’s see what can be learned from a quick study of these satellite views. First, we consider the insolation at the top of the atmosphere averaged for the month of March.

The insolation averaged over March 2003 from NASA’s CERES satellite. In March, the insolation is at its greatest right at the equator and drops off to nearly zero at the poles. Note that this is the insolation before it interacts with the atmosphere, so it is a fairly simple pattern.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Of course, not all of this insolation strikes the surface — remember that just 49% of it reaches the ground. If we then look at the insolation reaching the ground, we see the following:

The total radiation reaching the surface of the Earth for March 1985-1989, from NASA’s ERBE experiment. The red and orange areas receive higher levels of radiation, while the blue areas are receive lower levels of radiation.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Notice that the highest flux is about 190 W/m²; far less than the maximum of almost 440 W/m² that reaches the top of the atmosphere. The difference is due to reflection from clouds, reflection from the surface, and absorption by atmospheric gases.

Now, let’s look at what comes back from the Earth, in the form of long wavelength energy, for the same time period.

The emitted long wavelength energy, averaged over March 1985-1989, from NASA’s ERBE experiment. This is the emitted infrared energy after it interacts with the atmosphere (the satellite is way above the atmosphere, looking down on Earth). The complex pattern reflects variability in surface temperature and concentrations of greenhouse gases.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

At the simplest level, we see that the tropics emit much more energy than the poles. This makes sense since we know they are warmer, and the Stefan-Boltzmann law tells us that the amount of energy emitted varies as the fourth power of temperature, and the tropics are warmer because they receive much more insolation (see figures above). Looking closely at the nearest above image, we see some interesting variations near the tropics — look at South America, Central Africa, and Indonesia, where the emitted energy is far less than we see elsewhere at these same latitudes. Why is this? Is it colder there? No, it is not colder there, which leads to another question — is the atmosphere above these regions absorbing more of the infrared energy emitted by the surface? Recall that one of the main heat-absorbing gases is water, and where you have a lot of water, you have a lot of clouds. So, let’s have a look at the typical average cloud cover for this time of year.

The cloud fraction, averaged over March 2005, from NASA’s CERES satellite. White is 100% cloud cover, while dark blue is 0% cloud cover. Note that the white areas of high cloud cover correspond to the regions where there is less long wavelength energy emitted.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

So, it is indeed the case that the amount of energy leaving the Earth varies according not only to the temperature but also to the concentration of heat-absorbing gases such as water.

Recall that we are focused on the energy budget here and whenever you do a budget, at the end, you look at the balance between what is coming in and what is going out. So, let’s do that now with the energy as measured by the satellites.

The difference between energy coming in and energy leaving the Earth for March 1985-1989, from NASA’s ERBE experiment. The red areas are places where more energy is coming in than is going out, while the blue areas are places where more energy is leaving than is coming in. So the red areas have a surplus of insolation, while the blue areas are running a deficit relative to insolation.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

As can be seen in the figure above, the tropics receive more energy than they emit, while the poles emit more than they receive. This picture can also be seen in a somewhat simpler diagram in which we average the net energy flow at each latitude.

The insolation reaching the surface averaged over March 1960, from NASA’s ERBE experiment.

Click for a text description of the Net Energy: Insolation - LW Emission graph.

The image is a graph titled "Net Energy: Insolation - LW Emission, March, 1960," showing the net energy balance (incoming solar radiation minus outgoing longwave radiation) across different latitudes on Earth for the month of March 1960. The graph highlights regions of energy surplus and deficit.

• Axes:
  • X-Axis (Latitude): Labeled "Latitude," ranging from -90° (South Pole) to 90° (North Pole), with major ticks at intervals of 30° (-90, -60, -30, 0, 30, 60, 90). The equator is at 0°.
  • Y-Axis (Net Radiation): Labeled "Net Radiation (W/m²)," ranging from -160.0 to 160.0 watts per square meter (W/m²), with major ticks at intervals of 32.0 W/m² (-160.0, -96.0, -32.0, 32.0, 96.0, 160.0). The zero line represents a balance between incoming and outgoing radiation.
• Data Representation:
  • The graph is a single curve plotted in red, representing the net energy (insolation minus longwave emission) at each latitude.
  • The area above the zero line (positive values) is shaded gray and labeled "Energy Surplus," indicating where incoming solar radiation exceeds outgoing longwave radiation.
  • The areas below the zero line (negative values) are shaded with a crosshatch pattern and labeled "Energy Deficit," indicating where outgoing longwave radiation exceeds incoming solar radiation.
• Curve Characteristics:
  • Energy Surplus: The curve peaks around the equator (0° latitude), reaching a maximum of approximately 96 W/m², indicating a significant energy surplus in the tropics. The surplus extends from about -30° to 30° latitude.
  • Energy Deficit: The curve dips below the zero line at higher latitudes, showing an energy deficit. It reaches a minimum of around -160 W/m² at the poles (-90° and 90° latitude), indicating a significant energy deficit in polar regions.
  • The curve crosses the zero line (net energy balance) at approximately -30° and 30° latitude, marking the transition between energy surplus and deficit zones.
• Overall Trend:
  • The graph shows a clear latitudinal pattern: the tropics (near the equator) experience an energy surplus due to high insolation and relatively lower longwave emission, while the polar regions experience an energy deficit due to low insolation (especially in March, during the polar night in the Southern Hemisphere) and high longwave emission.
  • The energy surplus in the tropics drives global atmospheric and oceanic circulation, as excess energy is transported toward the poles to balance the energy deficit.

The graph effectively illustrates the latitudinal variation in Earth's energy balance for March 1960, highlighting the role of solar insolation and longwave emission in creating energy surpluses and deficits that influence global climate patterns.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

The atmosphere and oceans are constantly flowing, and this motion is critical to the climate system. What makes them flow? In general, the movement is due to pressure differences — things flow from regions of high pressure to low pressure and the resulting surface winds distribute heat at the Earth's surface.

Movement of air masses from high to low-pressure regions.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

The pressure changes are themselves due to density and height differences — higher density in the air or the oceans leads to higher pressures. The density differences are due to changes in composition and temperature; this works slightly differently for air and water. In air, the important compositional variable is water vapor content — more water means lower density air. When we say more water, we mean that for a given number of molecules in a volume of air, a greater percentage of them are water, and water, as shown below, is lighter than a nitrogen molecule, which is the most abundant molecule in our atmosphere.

Density of Air

Inverse relationship with Temperature:

Higher temp = lower density → rising air

Weight of H₂O= 18

Lower temp = higher density → sinking air

Weight of N₂ = 28

Inverse relationship with water content:

More water = lower density → rising air

Less water = higher density → sinking air

As indicated above, density differences can cause either rising or sinking of air masses. Because Earth’s gravity decreases as you move away from the surface, there is a kind of equilibrium profile of density with height above the surface, as shown by the green curve below:

Lowering density of an air mass causes it to elevate

Click for a text description of the density of air masses.

The image is a simple graph labeled "Equilibrium profile of density," depicting the relationship between density and an unspecified variable, likely altitude or depth, in a fluid medium such as the atmosphere or ocean. The graph consists of a single curve on a black background with minimal labeling.

• Curve:
  • The curve is plotted in cyan and shows a smooth, exponential-like decay.
  • It starts at a higher value on the left side and decreases as it moves to the right, approaching a lower value asymptotically.
  • The curve suggests that density decreases with increasing altitude (if the x-axis represents height in the atmosphere) or increases with depth (if the x-axis represents depth in the ocean).
• Axes:
  • The graph lacks explicit axes labels or numerical scales.
  • The x-axis likely represents the variable with which density changes (e.g., altitude, depth, or another parameter), increasing from left to right.
  • The y-axis likely represents density, decreasing from left to right as the curve slopes downward.
• Trend:
  • The curve indicates an equilibrium state where density decreases exponentially with increasing altitude (or increases with depth), which is typical in atmospheric or oceanic profiles due to gravitational effects and pressure gradients.

The graph visually represents a fundamental concept in atmospheric or oceanic science, showing how density varies in a stable, equilibrium state, likely influenced by factors such as gravity, temperature, and pressure. The lack of specific axes labels makes the exact context (e.g., atmosphere vs. ocean) ambiguous, but the shape of the curve is characteristic of such profiles.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

If we lower the density of air at the surface from A to B, then the air rises from B to C. Then, if we increase the density of air at point C, moving it to D, it will sink back down to point A near the surface.

We start with the movement of the atmosphere, which we will try to make as simple as possible by first concentrating on the flow as seen in a vertical slice from pole to pole. The story begins at the equator, where air is warmed and lots of evaporation adds water to the air, giving it a low density:

Air masses spread out laterally when they reach the tropopause.

Click for a text description of the air masses spreading out diagram.

The image is a simple graph depicting a temperature profile in the atmosphere, likely illustrating the concept of the lapse rate or temperature variation with altitude. The graph consists of a single curve on a black background with minimal labeling.

• Curve:
  • The curve is plotted in blue with a dashed line style.
  • It forms a smooth, parabolic shape, starting at a higher value on the left, reaching a peak at the center, and then decreasing symmetrically to a lower value on the right.
  • The curve likely represents temperature as a function of altitude, with the peak indicating a temperature maximum (possibly at the tropopause or another atmospheric layer) and the decrease on either side representing cooling with increasing altitude.
• Axes:
  • The graph lacks explicit axes labels or numerical scales.
  • The x-axis likely represents altitude, increasing from left to right.
  • The y-axis likely represents temperature, with the peak at the center indicating the highest temperature in the profile.
• Additional Element:
  • At the bottom center of the graph, there is a small rectangular box with the symbol "L" inside it, possibly indicating the lapse rate (the rate of temperature decrease with altitude) or another related parameter.
• Trend:
  • The curve suggests a temperature profile where temperature increases with altitude up to a certain point (the peak) and then decreases, which is characteristic of the troposphere-stratosphere transition in the atmosphere.

The graph visually represents a fundamental concept in atmospheric science, showing how temperature varies with altitude in a simplified manner, likely focusing on the lapse rate or a specific atmospheric layer.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

This air rises until it gets to the top of the tropopause, which is a bit like a lid on the lower atmosphere. It then diverges, with some of the air flowing north and some flowing south. As it rises and moves away from the equator, the air gets colder, water vapor condenses and rains out and the air grows drier — the cooling and drying both make the air grow denser and by the time it reaches about 30°N and 30°S latitude, it begins to sink down to the surface.

Sinking of air masses at sub-tropical latitudes.

Click for a text description of the sinking of air masses diagram.

• Diagram Overview:
  • A simple diagram illustrating a low-pressure system in the atmosphere.
  • Features a blue dashed parabolic curve on a black background.
• Curve Details:
  • The blue dashed curve dips to a minimum at the center.
  • Represents a pressure profile with altitude or horizontal distance.
  • Indicates the lowest pressure at the center of the curve.
• Labeling:
  • A light blue box labeled "Low pressure" is placed at the center below the curve.
  • Marks the point of lowest pressure in the system.
• Surrounding Elements:
  • Two red upward-curving shapes on either side of the "Low pressure" label.
  • Indicate higher pressure regions surrounding the low-pressure center.
• Interpretation:
  • Suggests the structure of a low-pressure system where air rises at the center.
  • Often associated with weather phenomena like storms due to the pressure gradient.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

The sinking air is dense and dry, creating zones of high pressure in each hemisphere that are associated with very few clouds and rainfall — these are the desert latitudes. The sinking air hits the ground and then diverges. Some flows south and some flows north; the parts of this divergent flow that return towards the equator complete a loop or a convection cell, a Hadley Cell, named after Hadley, a famous meteorologist. Now, let’s turn our attention to the air that flows away from the equator. Moving along the surface, it warms and picks up water vapor, and so, its density decreases, and it eventually rises up when it gets to somewhere between 45° and 60° latitude in each hemisphere.

Rising of air masses between 45 and 60 degrees.

Click for a text description of the Rising of air masses diagram.

• Diagram Overview:
  • A simple diagram illustrating a high-pressure system in the atmosphere.
  • Features a blue dashed parabolic curve on a black background.
• Curve Details:
  • The blue dashed curve peaks at the center, forming a convex shape.
  • Represents a pressure profile with altitude or horizontal distance.
  • Indicates the highest pressure at the center of the curve.
• Pressure Elements:
  • Two red upward-curving shapes on either side of the center, indicating low-pressure regions.
  • Three light blue boxes with a "J" symbol, placed at the far left, center, and far right below the curve, representing high-pressure points.
• Interpretation:
  • Suggests the structure of a high-pressure system where air sinks at the center.
  • Often associated with clear, stable weather due to the sinking air in the high-pressure region.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0.

Once again, the rising air runs into the tropopause (which is lower at these higher latitudes) and diverges, with some of it returning toward the equator, thus completing another convection cell called the Ferrel Cell. The air that flows pole-ward sinks down at the poles, creating yet another convection cell known as the Polar Hadley Cell. These convection cells create bands of low and high pressure that roughly follow lines of latitude that exert a big influence on the climate at different latitudes. The air flowing within these convection cells does not simply move north and south as depicted above — the Coriolis effect alters the flow directions, giving us a surface pattern that is dominated by winds flowing east and west.

Simplified global atmospheric circulation.

Click for a text description of the Simplified global atmospheric circulation image.

• Diagram Overview:
  • A diagram titled "Simplified View of Global Circulation in the Atmosphere."
  • Depicts a cross-section of Earth's atmosphere, showing circulation patterns from the equator to the poles.
• Atmospheric Layers and Latitudes:
  • The troposphere is shown at the top, with the tropopause as a wavy line.
  • Latitudes are marked: 0° (equator), 30°, 60°, and 90° (poles) on both hemispheres.
• Circulation Cells:
  • Hadley Cells: Near the equator (0° to 30°), with rising air at the equator (low pressure) and sinking air at 30° (high pressure).
  • Ferrel Cells: Between 30° and 60°, with rising air at 60° (low pressure) and sinking air at 30° (high pressure).
  • Polar Hadley Cells: Between 60° and 90°, with rising air at 60° (low pressure) and sinking air at the poles (low pressure).
• Pressure and Flow Patterns:
  • Red arrows indicate rising air (low pressure, labeled "LOW P") at 0° and 60°, and sinking air (high pressure) at 30° and the poles.
  • Blue arrows show the direction of air movement within the cells, looping between high and low pressure zones.
  • Polar fronts are marked at 60° latitude in both hemispheres, where air masses converge.
• Weather Patterns:
  • Green labels indicate dominant weather patterns:
    • "Precipitation Dominates" at 0° and 60° (rising air leads to rain).
    • "Evaporation Dominates" at 30° (sinking air leads to dry conditions).
• Interpretation:
  • Illustrates the three-cell model of atmospheric circulation (Hadley, Ferrel, Polar Hadley).
  • Shows how pressure differences drive global wind patterns and weather, with precipitation and evaporation varying by latitude.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Note that the boundary between the Polar Hadley Cell and the Ferrel Cell (often called the Polar Front, and associated with the mid-latitude jet stream) is highly variable, with big loops in it. These loops, or waves, change over time to a much greater extent than the boundaries between the other convection cells.

The Coriolis Effect arises because our planet is spinning, which means that objects near the equator are moving at much faster velocities than objects at higher latitudes. If you were standing on the equator, you would be traveling at about 1600 km/hr; if you were standing at the North Pole, you would be traveling at 0 km/hr. This means that a parcel of air moving across the surface moves into regions where the whole planet is traveling either slower or faster. The physics of this phenomenon are well-understood, and without getting into the mathematics behind it, we can summarize it with 4 simple statements:

1. objects moving in the Northern Hemisphere get deflected to the right as you look in the direction of motion;
2. objects moving in the Southern Hemisphere get deflected to the left as you look in the direction of motion;
3. the strength of this effect, this deflection, is greater as you approach the poles; and
4. the strength of the effect is more important at higher velocities (e.g., a glacier does not respond to Coriolis).

Let’s think for a minute about what this general circulation of the atmosphere does. Among other things, it mixes the atmosphere quite thoroughly, and this means that the concentrations of things like greenhouse gases get homogenized. It also means that heat gets transferred. Polar air finds its way toward lower latitudes, where it cools the surface and in so doing warms itself, and warmer air finds its way to higher latitudes, where it gives up its heat to the surroundings and thus cools.

But this general circulation does more — it drives the circulation of the surface waters in the ocean.

The oceans swirl and twirl under the influence of the winds, Coriolis, salinity differences, the edges of the continents, and the shape of the deep ocean floor. We will discuss ocean circulation in detail in Module 6, but since ocean currents are critical agents of heat transport, we must include them here as well. In general, the surface currents of the oceans are driven by winds, Coriolis, and the edges of continents, and the deep currents that mix the oceans are driven by density changes related to temperature and salinity as well as the shape of the deep ocean floor.

The pattern of circulation is shown in the figure below, which represents the average paths of flow; on a shorter term, the flow is dominated by eddies that spin around.

Surface ocean current patterns.

Click for a text description of the Ocean surface currents image.

• Map Overview:
  • A world map titled "Ocean Surface Currents," showing major ocean currents and gyres.
  • Continents are in beige, oceans in white, with currents depicted as arrows.
• Ocean Gyres:
  • North Pacific Gyre: Labeled in green, with currents like the Kuroshio and California currents (blue arrows).
  • North Atlantic Gyre: Labeled in green, includes the Gulf Stream and North Atlantic Drift (red arrows).
  • South Pacific Gyre: Labeled in green, with the Peru Current (blue arrows).
  • South Atlantic Gyre: Labeled in green, includes the Brazil Current (red arrows).
  • South Indian Gyre: Labeled in green, with the Agulhas Current (red arrows).
• Equatorial Currents:
  • North Equatorial Currents: Labeled in both Pacific and Atlantic (blue arrows).
  • South Equatorial Currents: Labeled in Pacific, Atlantic, and Indian Oceans (blue arrows).
  • Equatorial Counter Currents: Labeled between North and South Equatorial Currents (red arrows).
• Circum-Antarctic Currents:
  • Labeled in green around Antarctica, with blue arrows indicating the Antarctic Circumpolar Current.
  • Antarctic Subpolar: Labeled in green, with blue arrows showing subpolar flow.
• Other Currents:
  • Oyashio Current: In the North Pacific (blue arrows).
  • West Australian Current: In the Indian Ocean (blue arrows).
  • Benguela Current: In the South Atlantic (blue arrows).
• Flow Patterns:
  • Red arrows indicate warm currents (e.g., Gulf Stream, Brazil Current).
  • Blue arrows indicate cold currents (e.g., Peru Current, Oyashio Current).
  • Arrows show the direction of flow, forming large gyres in each ocean basin.
• Interpretation:
  • Illustrates the global pattern of ocean surface currents driven by wind and the Coriolis effect.
  • Highlights the role of gyres in redistributing heat and influencing climate.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

In this map, the different colors correspond to the warm currents (red), cold currents (blue), and currents that move mostly along lines of latitude and thus do not transport waters across a temperature gradient (black). These latter currents may involve warm or cold water, but they do not move that water to warmer or colder places. As mentioned earlier, these arrows depict average flow paths, but on a shorter timescale, the water is involved in eddies that move along the directions indicated by these arrows. These ubiquitous eddies are important since they mix up the surface of the oceans, just as swirling a spoon in a coffee cup mixes the coffee. There are several ways of forming eddies, including intermittent winds combining with the Coriolis effect, opposing currents interacting with each other, and currents interacting with coastlines. As this pattern of currents indicates, surface ocean circulation moves a lot of warm water to colder portions of the Earth; it also moves cold water back down to warmer regions — the net effect is to exchange heat and bring the tropics and the poles a little closer to each other in terms of temperature. Or, in other words, this (along with the winds) moves surplus energy from the tropics to the regions of energy deficit near the poles.

It is important to realize that these currents, by themselves, would eventually homogenize the temperature on the surface, were it not for the huge difference in solar energy between the tropics and the poles. In addition, the strength of these air and ocean currents is sensitive to the temperature difference between the poles and the equator — the greater the temperature difference, the stronger the currents.

The surface currents described above are generally confined to the upper hundred meters or so of the oceans, and considering that the average depth of the oceans is about 4000 meters, the surface currents represent a very small part of the ocean system. The rest of the oceans are also in motion, moving much more slowly under the influence of density differences caused by temperature and salinity changes. Cold, salty water is dense, while warm, fresh water is light, and the resulting density differences drive a system of flows sometimes referred to as the thermohaline circulation. In today’s world, there are two principal places where deep waters form — the North Atlantic and Antarctica, as shown below:

The Global Conveyor Belt system of surface and deep currents.

Click for a text description of the Global Conveyor Belt system image.

• Map Overview:
  • A world map titled "Thermohaline Circulation," showing global ocean circulation patterns.
  • Continents are in beige, oceans in white, with currents depicted as colored arrows.
• Circulation Patterns:
  • Surface Flow (Red Arrows): Warm surface currents flow from the equator toward the poles, e.g., in the Atlantic from the Gulf of Mexico to the North Atlantic.
  • Deep Flow (Blue Arrows): Cold deep currents flow from the poles toward the equator, e.g., from the North Atlantic southward.
  • Deepest Flow (Purple Arrows): The deepest currents, primarily in the Southern Ocean, connecting the Atlantic, Indian, and Pacific Oceans.
• Key Features:
  • Yellow dots mark areas of deep water formation, mainly in the North Atlantic (near Greenland) and the Southern Ocean (near Antarctica).
  • Arrows show a continuous loop, indicating the global conveyor belt of ocean circulation.
• Annotations:
  • A note in the bottom right reads: "about 1 kyr to make a loop," indicating the circulation takes about 1,000 years to complete.
  • The map is credited to "Rahmstorf, 2002" in the bottom left.
• Color Coding:
  • Red arrows for surface flow (warm water).
  • Blue arrows for deep flow (cold water).
  • Purple arrows for the deepest flow.
• Interpretation:
  • Illustrates the thermohaline circulation, driven by temperature and salinity differences.
  • Shows how surface and deep currents connect globally, redistributing heat and influencing climate.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0, modified from Rahmstorf, 2002

In the North Atlantic, warm, salty water from the Gulf Stream comes into contact with cold Arctic air, and as the water cools it becomes very dense and sinks to the bottom of the ocean — this is called the North Atlantic Deep Water (NADW). When NADW forms, a tremendous amount of heat is transferred from the water to the air; this heat is equivalent to about 30% of the thermal energy received by the whole polar region, so it can influence the Arctic climate in a major way. In the Antarctic, as sea ice forms at the edge of the ice sheet, pure water is removed from seawater, thus increasing the salinity of the remaining water; the resulting density increase makes this the densest water in oceans, and it sinks to the bottom — this water mass is called the Antarctic Bottom Water (ABW). Of these two deep water flows, the NADW is much greater, and it flows in a complex path, hugging the bottom of the ocean as it moves through the Atlantic and into the Indian and Pacific Oceans, by which point it has warmed and mixed with the surrounding water to rise back up into the surface, where it starts its return path back into the North Atlantic, completing the loop in something like a thousand years. This flow is sometimes called the Global Conveyor Belt (we will talk a lot more about this in Module 6), and it represents an important means of mixing the global oceans.

These deep currents are very important to the global climate system in a couple of ways. One of these ways, described above, is the way that NADW formation influences the Arctic climate; this, in turn, can influence the formation or melting of ice in the polar region, which can trigger the ice-albedo feedback mechanism (see below). Another way these deep currents influence the global climate is by transporting CO₂ to the deep waters of the oceans. The CO₂ is dissolved into the seawater at the surface, so when deep waters form, they bring that CO₂ with them, thus removing it from the atmosphere. What this does is to effectively increase the volume of ocean water that can hold CO₂, which increases the total mass of carbon the oceans can hold. Indeed, these deep currents are already transporting anthropogenic CO₂ and other gases such as CFCs into the deep ocean (we will talk a lot more about this in Modules 5 and 7).

In this activity, we’ll explore some relatively simple aspects of Earth’s climate system, through the use of several STELLA models. STELLA models are simple computer models that are perfect for learning about the dynamics of systems — how systems change over time. The question of how Earth’s climate system changes over time is of huge importance to all of us, and we’ll make progress towards understanding the dynamics of this system through experimentation with these models. In a sense, you could say that we are playing with these models, and watching how they react to changes; these observations will form the basis of a growing understanding of system dynamics that will then help us understand the dynamics of Earth’s real climate system.

End of Module Recap: Please go down this list carefully and make sure you understand all of the points below.

• Earth's climate system is essentially an energy balance system, where the energy in (from sunlight or insolation) is balanced by the energy emitted (energy out) (via heat or infrared radiation).
• The energy in varies with latitude and season (and also orbital cycles like precession, axial tilt, and eccentricity mentioned in Module 1 regarding the Ice Ages). The energy in also varies as a function of the albedo (fraction of sunlight reflected); ice and snow have high albedo, water has low albedo, and the land surface is in between, varying according to the type and density of vegetation.
• The energy out depends on temperature and the greenhouse effect, or emissivity, as described by the Stefan-Boltzmann Law.
• The rate at which different parts of the Earth warm and cool is a function of the heat capacity; a larger heat capacity means that things warm and cool more slowly, and they also store much more heat.
• The greenhouse effect is not a theory — it is directly measured by satellites and represents a kind of energy recycling mechanism wherein particular gases in the atmosphere absorb heat emitted from the surface and then re-radiate some of that heat back to the surface.
• Without the greenhouse effect, our planet would be about 33 °C colder!
• Water, carbon dioxide, and methane are the main greenhouse gases. Water contributes the greatest amount of warming, but the atmosphere is saturated with water, and it cycles through the atmosphere very fast, so it cannot drive climate change (even though it is a very important part of the climate system). Carbon dioxide, on the other hand, is not close to saturation, and it cycles through the atmosphere more slowly, so it can drive climate change. Methane is far less abundant in the atmosphere and is quickly converted to carbon dioxide, so it is less important as a greenhouse gas.
• Our climate system is filled with feedback mechanisms. Positive feedback mechanisms like the ice-albedo feedback are triggered by a small climate change and then enhance the strength of that climate change. Negative feedbacks like the weathering feedback are triggered by a small climate change and then act to counter that change — these tend to stabilize the climate.
• If we look at the Earth as a whole, we see that the tropics get more heat than they emit back to space, and the polar regions emit more heat back to space than they get — the balance on a global scale comes about from the transport of heat through the winds and ocean currents.
• Winds and ocean currents are initiated by density differences that create pressure differences (or gradients). Pressure gradients (change in pressure over a certain distance) drive these flows, but the flows are modified by the Coriolis effect caused by the spinning of the Earth.
• Finally, we looked at the effects of historical variations in the four major drivers of the Earth’s climate system — the amount of sunlight, volcanoes, greenhouse gases, and pollutants (aerosols) — on a simple climate model. We saw that combined, they make our climate model warm and cool in a pattern that is pretty close to the reconstructed temperature history, and that among these, the greenhouse gas effect is by far the most important factor in the climate change of the last 100 years or so.

Assignments

You should have read the contents of this module carefully, completed and submitted any labs, the Yellowdig Entry and Reply, and taken the Module Quiz. If you have not done so already, please do so before moving on to the next module. Incomplete assignments will negatively impact your final grade.

Lab

• Lab 3: Climate Modeling.

Goals

On completing this module, students are expected to be able to:

• explain how general circulation climate models work;
• describe the different emission scenarios that are used for future model predictions and distinguish their relative impact;
• evaluate regional climate model predictions for the worst-case emissions scenario;
• assess how scientists communicate model predictions to policymakers.

Learning Outcomes

After completing this module, students should be able to answer the following questions:

• What does GCM stand for?

• What does the typical model grid look like?
• What are the inputs that drive models?
• What is the significance of pressure in models?
• Key to this module: What are the economic and environmental bases for the four main emission scenarios, A2, A1B, B1, and B2?
• What is the main driver for each scenario, and how does it change in the next century?
• Predicted temperature increase for each scenario in 2100?
• Which part of the globe warms the most and which the least under the different scenarios?
• Under A2, how much does the US warm in summer and winter?
• What parts of the US become drier under A1B and A2?
• Globally, where does stream flow decrease most drastically under A1B?

Below is an overview of your assignments for this module. The list is intended to prepare you for the module and help you to plan your time.

Assignments

1. Take Module 4 Quiz.
2. Yellowdig Entry and Reply

Ever wonder how weather forecasts are so accurate? How predictions are made over days and weeks? How hurricanes and blizzards are forecasted? General circulation models (GCMs) are instrumental in weather forecasting. They are highly detailed grid-based simulations of weather that use atmospheric physics to predict events over hours, days, and even further into the future. These models are commonly used to predict climate change over years, decades, and centuries. GCMs have become more and more accurate as the physics of the atmosphere has become better understood. As computers have become more capable computationally, the models have become more accessible to the general public. Before, they required a mainframe computer. You can now run them on laptops! In this module, we explore how GCMs work.

In a highly simplified sense, the operation of GCMs can be thought of in a few basic steps.

1. Divide up the atmosphere and oceans into a complex 3-D grid; each grid may represent 2° of latitude and 2° of longitude (roughly 200 km on a side), and the models typically have 20 - 40 vertical layers, which would give you about half a million cells.
2. Assign the starting conditions for each grid — the type of material (air, soil, water, etc.), temperature, salinity of the oceans, humidity of the air, greenhouse gas concentrations, insolation, and a whole host of other variables and constants. Typically, these starting conditions are a simplified snapshot of the current climate on Earth.

3. Based on the temperature, salinity, and humidity, the program calculates the pressure in each grid cell, which combines with the rotation of the Earth to determine the velocities in each cell (see figure below). The velocities then determine how the cells will exchange heat, moisture, salinity, etc., with their neighboring cells. The program then makes all of these transfers, and then updates the conditions of each cell — these new conditions then determine how things will move in the next time step. These calculations are done in very short time increments (typically a few minutes), and the result is that they can simulate the circulation of the atmosphere and the oceans.

   

   Mean Atmospheric Pressure and Winds from the NCAR GCM. The air pressure at the surface is shown in the colors — blue represents low pressure; red is high pressure. Also shown are the approximate average wind directions and strengths that would result from this map of pressure difference. The winds move from high-pressure areas to low-pressure areas, but they are bent by the Coriolis effect.

   The map illustrates global atmospheric pressure patterns and surface wind circulation, highlighting high-pressure systems in the subtropical regions and low-pressure zones near the equator and polar regions, with corresponding wind patterns.

   Click for a text description of Air Pressure and Winds map.

   This image is a world map titled "Mean Annual Atmospheric Pressure at Sea Level and Surface Winds," showing a 10-year simulation from the CCM1 PMIP Pre-Industrial model with computed sea surface temperatures from the cm1 R15L12 model. The map displays sea level pressure in hectopascals (hPa) and surface wind patterns, sourced from the National Center for Atmospheric Research (NCAR).

   Map Type: World map Measurement:

   • Sea level pressure (hPa)
   • Surface winds (m/s)

   Color Scale (bottom of the map):

   • Pressure: 1000 hPa (blue) to 1020 hPa (red)
   • Wind speed: 0 m/s (no arrows) to 4.9 m/s (longer arrows)

   Regions with Notable Pressure:

   • High Pressure (red, 1015–1020 hPa):
     • North Pacific (Aleutian High)
     • South Pacific (near South America)
     • North Atlantic (Azores High)
   • Low Pressure (blue, 1000–1005 hPa):
     • Equatorial regions (near the Intertropical Convergence Zone)
     • Southern Ocean (around Antarctica)

   Surface Winds:

   • Represented by black arrows
   • Stronger winds (longer arrows) in the Southern Ocean and North Atlantic
   • Weaker winds (shorter arrows) in equatorial regions

   Additional Info:

   • Data source: NCAR
   • Data specifics: Min = 999.0 hPa, Max = 1032.1 hPa

   Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

   This figure comes from a run of the NCAR model, called CCM (Community Climate Model) and represents the atmospheric pressure at sea level averaged over 10 years. Also shown is the pattern of winds that results from the combination of the forces due to the pressure differences (air flows from high to low pressure, driven by a Pressure Gradient Force and the Coriolis Force, which is related to the rotation of the Earth. The length of the arrows is proportional to the strength of the winds. Note that the model produces belts of pressure that are very similar to the observed pressure belts — low pressure near the equator, high pressure at 30N and 30S, low pressure at 50-60N and 50-60S, and high pressure again near the poles, patterns we learned about in Module 3.

4. Based on temperature, pressure, flow patterns, and humidity, the models simulate the formation of clouds, which then impact the albedo (reflectance of sunlight) and the absorption of heat emitted from the surface.
5. The models also calculate the evaporation of water from the surface and the precipitation of water, and its runoff over the land back to the oceans. The evaporation and precipitation are associated with big transfers of energy, and the model keeps track of this, too.
6. All models represent land topography. Many models also include representations of photosynthesis on land, the exchange of CO² between the plants, soil, oceans, and atmosphere, and sedimentation in the ocean.

This is obviously a very important question — if we are to rely on these models to guide our decisions about the future, we need to have some confidence that the models are good. The most important approach is to see if the model can simulate the known climate history. We set the model up to represent the state of the climate at some point in the past — say 1900 — and then we see how well the model can reproduce what actually happened.

As you can see in the figure below, the models are collectively quite good:

Temperature anomaly by year

Click for a text description of the Temperature anomaly graph.

This image is a line graph showing global temperature anomalies from 1900 to 2000, measured in degrees Celsius. The graph includes multiple data series to represent uncertainty, with annotations marking significant volcanic eruptions that influenced global temperatures.

• Graph Type: Line graph
• Y-Axis: Temperature anomaly (°C)
  • Range: -1.0°C to 1.0°C
• X-Axis: Years (1900 to 2000)
• Data Representation:
  • Temperature Anomalies: Multiple black lines
    • Represent various data series, showing uncertainty
    • Fluctuate between -0.5°C and 0.5°C until 1980
    • Rise sharply after 1980, reaching around 0.8°C by 2000
  • Mean Trend: Red line
    • Smoothed average of the black lines
    • Starts around -0.3°C in 1900
    • Shows a gradual increase, with a steeper rise after 1980
• Annotations (Volcanic Eruptions):
  • Santa Maria: Around 1902
  • Agung: Around 1963
  • El Chichón: Around 1982
  • Pinatubo: Around 1991
  • Each eruption corresponds to a temporary dip in temperature
• Trend:
  • General upward trend in temperature anomalies over the century
  • Temporary cooling periods following major volcanic eruptions

The graph illustrates a long-term warming trend in global temperatures over the 20th century, with notable short-term cooling events caused by volcanic eruptions, followed by a significant temperature increase after 1980.

Credit: Intergovernmental Panel on Climate Change (IPCC)

In this figure, the black line is the instrumental global average temperature (as an anomaly, which is a departure from the mean value from 1901 to 1950), the yellow lines represent the output from 58 model runs by 14 different models, and the red is the average of those 58 runs. The vertical gray lines are times of major volcanic eruptions, which are always followed by a few years of cooler temperatures.

Now, let’s look at how closely the models can simulate the spatial pattern of temperatures over the Earth. To begin with, we’ll look at January temperatures for the time between 2003 and 2005 — here is what we can reconstruct from observations (which are better in some places than others — we know the Sea Surface Temperature (SST) much better than the land temperature, since SST is very precisely measured by satellites).

Observed mean January temperature, 2003 – 2005.

Click for a text description of the Global surface Observed mean temperatures image.

This image is a globe view showing daytime surface temperatures across North America, with data sourced from SIO, NOAA, U.S. Navy, NGA, and GEBCO, and presented via Google Earth. The map uses a color gradient to represent temperatures in degrees Celsius, captured at a specific location and altitude.

• Map Type: Globe view (North America focus)
• Measurement: Daytime surface temperature (°C)
• Location and Altitude:
  • Coordinates: 0°19'46.45" N, 52°32'03.18" W
  • Elevation: 0 m
  • Eye altitude: 131372.71 km
• Color Scale (bottom of the map):
  • Range: -60°C to 50°C
  • Colors: Dark blue (-60°C) to dark red (50°C), with green, yellow, and orange in between
• Regions with Notable Temperatures:
  • Coldest (dark blue, -60°C to -20°C):
    • Arctic regions (northern Canada, Greenland)
  • Cool (green to light blue, -20°C to 0°C):
    • Central and northern Canada
    • Parts of Alaska
  • Moderate (yellow to orange, 0°C to 20°C):
    • Most of the continental U.S.
    • Southern Canada
  • Warm (red, 20°C to 50°C):
    • Southern U.S. (Texas, Arizona, Florida)
    • Mexico and Central America
• Additional Info:
  • Data source: SIO, NOAA, U.S. Navy, NGA, GEBCO
  • Copyright: 2012 Cnes/Spot Image
  • Platform: Google Earth

The globe view highlights a stark temperature gradient, with extremely cold temperatures in the Arctic, moderate temperatures across most of the U.S., and warmer conditions in the southern U.S. and Central America.

Credit: Google Earth

Now, we look at the same time period from a model simulation.

Model mean January temperature 2003 – 2005

Click for a text description of the Global surface Model mean temperatures image.

This image is a globe view showing daytime surface temperatures across North America, with data sourced from SIO, NOAA, U.S. Navy, NGA, and GEBCO, and presented via Google Earth. The map uses a color gradient to represent temperatures in degrees Celsius, captured at a specific altitude.

• Map Type: Globe view (North America focus)
• Measurement: Daytime surface temperature (°C)
• Altitude:
  • Eye altitude: 131372.71 km
• Color Scale (bottom of the map):
  • Range: -60°C to 50°C
  • Colors: Dark blue (-60°C) to dark red (50°C), with green, yellow, and orange in between
• Regions with Notable Temperatures:
  • Coldest (dark blue, -60°C to -20°C):
    • Arctic regions (northern Canada, Greenland)
  • Cool (green to light blue, -20°C to 0°C):
    • Central and northern Canada
    • Parts of Alaska
  • Moderate (yellow to orange, 0°C to 20°C):
    • Most of the continental U.S.
    • Southern Canada
  • Warm (red, 20°C to 50°C):
    • Southern U.S. (Texas, Arizona, Florida)
    • Mexico and Central America
• Additional Info:
  • Data source: SIO, NOAA, U.S. Navy, NGA, GEBCO
  • Copyright: 2012 Cnes/Spot Image
  • Platform: Google Earth

The globe view highlights a significant temperature gradient, with extremely cold temperatures in the Arctic, moderate temperatures across most of the U.S., and warmer conditions in the southern U.S. and Central America.

Credit: Google Earth

You can see that in general, they are quite similar to each other, but we can gain a bit more insight into the relationship between the observations and the model by subtracting the model from the observations — the result is this:

Observed temperatures

Click for a text description of the Differences between Observed and Model Temperatures image.

This image is a globe view showing the difference between observed and modeled surface temperatures across North America, with data sourced from SIO, NOAA, U.S. Navy, NGA, and GEBCO, and presented via Google Earth. The map uses a color gradient to represent temperature differences in degrees Celsius, captured at a specific altitude.

• Map Type: Globe view (North America focus)
• Measurement: Observation minus model temperature (°C)
• Altitude:
  • Eye altitude: 131372.71 km
• Color Scale (bottom of the map):
  • Range: -30°C to 30°C
  • Colors: Dark blue (-30°C) to dark red (30°C), with green, yellow, and orange in between
• Regions with Notable Differences:
  • Colder than Modeled (blue, -30°C to -10°C):
    • Arctic regions (northern Canada, Greenland)
  • Slightly Colder than Modeled (green to light blue, -10°C to 0°C):
    • Central and northern Canada
    • Parts of Alaska
  • Near Model Prediction (yellow to orange, 0°C to 10°C):
    • Most of the continental U.S.
    • Southern Canada
  • Warmer than Modeled (red, 10°C to 30°C):
    • Southern U.S. (Texas, Arizona, Florida)
    • Mexico and Central America
• Additional Info:
  • Data source: SIO, NOAA, U.S. Navy, NGA, GEBCO
  • Copyright: 2012 Cnes/Spot Image
  • Platform: Google Earth

The globe view highlights discrepancies between observed and modeled temperatures, with the Arctic showing colder-than-modeled conditions, the U.S. generally aligning with model predictions, and the southern U.S. and Central America being warmer than modeled.

Credit: Google Earth

Here, you can see that the model and the observations are generally quite close, within a couple of degrees of zero, where zero would be a perfect match. Areas that are yellow to orange are regions where the actual temperature is greater than the modeled temperature; blue areas are regions where the actual temperature is lower than the model. It would be a challenging task to figure out the cause of these differences, but at a very fundamental level, it is related to the fact that things like clouds are very important to the climate system, and the processes that actually form clouds occur on such a small scale that the models cannot resolve them. Cloud formation is one example of what the modelers call a sub-grid process, and modelers have to devise clever ways of getting around this. This is an area where refinements continue to occur, but for the time being, we see that the models do an impressive job, but not a perfect job. As you can see in the above results, models tend to underestimate the temperatures on land, so we should consider model results for the future to also underestimate the true temperatures.

If we average over longer time periods and also average many model results together, we get what climate modelers refer to as an ensemble mean, and these ensemble means do a remarkably good job of matching the observations, as is shown below.

Ensemble Mean

Click for a text description of the Ensemble mean image.

This image is a world map labeled "a)" showing temperature anomalies in degrees Celsius across the globe. The map uses a color gradient to indicate deviations from the average temperature, with contour lines marking specific temperature values.

• Map Type: World map
• Measurement: Temperature anomaly (°C)
• Color Scale (bottom of the map):
  • Range: -5°C to 5°C
  • Colors: Dark blue (-5°C) to dark red (5°C), with light blue, white, and yellow in between
• Regions with Notable Anomalies:
  • Colder than Average (blue, -5°C to -1°C):
    • Northern North America (Canada, Alaska)
    • Parts of Siberia
    • Contour lines: -16°C in northern Canada, -8°C in Siberia
  • Warmer than Average (red, 1°C to 5°C):
    • Southern Ocean (around Antarctica)
    • Contour lines: 48°C near Antarctica
  • Near Average (white to yellow, -1°C to 1°C):
    • Most of Europe, Africa, and South America
    • Contour lines: 0°C in central Africa, 24°C in the equatorial Pacific
• Additional Features:
  • Latitude lines: 60°N, 30°N, EQ, 30°S, 60°S
  • Longitude lines: -180°, -120°, -60°, 0°, 60°, 120°, 180°

The map highlights significant temperature anomalies, with colder-than-average conditions in northern North America and Siberia, and much warmer-than-average conditions in the Southern Ocean near Antarctica.

Credit: Intergovernmental Panel on Climate Change ( IPCC)

In this figure, the observed annual mean temperatures for the time period 1961-1990 are represented by the black contour lines, labeled in °C (-56°C in Antarctica and about 24°C in equatorial Africa). The colors represent the model temperatures (from 14 models, for the same 1961-1990 time period) minus the observations; positive values mean the models estimate temperatures that are too high. On the whole, the models slightly underestimate the temperatures, and they have particular problems at very high latitudes and in areas that are topographically complex.

As mentioned above, one of the important aspects of GCMs is that they calculate the precipitation, and this provides another means of evaluating how good the models are. The figure below shows a comparison of the observed annual precipitation and the average of the 14 models used by the IPCC study.

Comparison of the observed annual precipitation and the average of the 14 models used by the IPCC study

Click for a text description of the annual precipitation comparison image.

This image consists of two world maps, labeled "a)" and "b)," showing sea surface height anomalies in centimeters across the globe. The maps use a color gradient to indicate variations in sea surface height, likely related to oceanographic phenomena such as El Niño or La Niña.

• Map Type: Two world maps (a and b)
• Measurement: Sea surface height anomaly (cm)
• Color Scale (bottom of the maps):
  • Range: 0 cm to 330 cm
  • Colors: Yellow (0 cm) to dark blue (330 cm), with orange, red, and purple in between
• Map a):
  • Regions with Notable Anomalies:
    • High Anomalies (blue, 240–330 cm):
      • Equatorial Pacific (stretching from South America to Indonesia)
    • Moderate Anomalies (red to purple, 120–240 cm):
      • Surrounding areas of the equatorial Pacific
    • Low Anomalies (yellow to orange, 0–120 cm):
      • Most of the Atlantic and Indian Oceans
      • Polar regions
  • Latitude Lines: 60°N, 30°N, EQ, 30°S, 60°S
  • Longitude Lines: 0°, 60°, 120°, 180°, -120°, -60°
• Map b):
  • Regions with Notable Anomalies:
    • High Anomalies (blue, 240–330 cm):
      • Central equatorial Pacific (more concentrated than in map a)
    • Moderate Anomalies (red to purple, 120–240 cm):
      • Surrounding areas of the equatorial Pacific
    • Low Anomalies (yellow to orange, 0–120 cm):
      • Most of the Atlantic and Indian Oceans
      • Polar regions
  • Latitude Lines: 60°N, 30°N, EQ, 30°S, 60°S
  • Longitude Lines: 0°, 60°, 120°, 180°, -120°, -60°

The maps illustrate variations in sea surface height, with map a) showing a broader high anomaly across the equatorial Pacific, and map b) showing a more concentrated high anomaly in the central equatorial Pacific, suggesting changes in ocean conditions between the two periods.

Credit: Intergovernmental Panel on Climate Change (IPCC)

As can be seen, the models, on average, do quite well at simulating the global pattern of precipitation.

AOGCMs calculate the circulation and temperature of the world’s oceans, and we compare their ability in that regard by comparing the model-generated temperatures averaged over 1957 to 1990 with the observations from that time period.

Latitude-averaged observed ocean temperature from 1957 to 1990.

Click for a text description of the Ocean temperature image.

This image is a cross-sectional diagram showing ocean temperature anomalies in degrees Celsius across a latitudinal range from 90°N to 90°S, with depth extending from the surface to 4000 meters. The diagram uses a color gradient and contour lines to indicate temperature variations at different depths and latitudes.

• Diagram Type: Cross-sectional view
• Y-Axis: Depth (meters)
  • Range: 0 m to 4000 m
• X-Axis: Latitude
  • Range: 90°N to 90°S
• Color Scale (bottom of the diagram):
  • Range: -2.5°C to 2.5°C
  • Colors: Dark blue (-2.5°C) to dark red (2.5°C), with light blue, white, and yellow in between
• Temperature Anomalies:
  • Colder than Average (blue, -2.5°C to -0.5°C):
    • Near the surface at 90°N (Arctic)
    • Contour lines: -2.5°C at the surface
  • Warmer than Average (red, 0.5°C to 2.5°C):
    • Surface waters from 60°N to 60°S
    • Contour lines: 15°C at the surface near the equator, 12.5°C at 200 m depth
  • Near Average (white to yellow, -0.5°C to 0.5°C):
    • Deeper waters (below 1000 m) across all latitudes
    • Contour lines: 0°C at 1000 m depth
• Additional Features:
  • Contour lines: 0°C, 2.5°C, 5°C, 7.5°C, 10°C, 12.5°C, 15°C, 20°C
  • Latitude markers: 90°N, 60°N, 30°N, EQ, 30°S, 60°S, 90°S

The diagram illustrates that surface waters, particularly in the tropics, are significantly warmer than average, while deeper waters remain closer to average temperatures, with the Arctic surface showing colder-than-average conditions.

Credit: Intergovernmental Panel on Climate Change (IPCC)

In this figure, we see the latitude-averaged observed ocean temperature from 1957 to 1990 in the contours, and the average model ocean temperatures minus the observed temperatures in colors. For most of the oceans, the models are ± 1°C from the observations.

In sum, it should be fairly clear that although these models are incredibly complicated, they do a fairly good job of reproducing the temporal and spatial characteristics of our climate, especially when we look at longer time averages. In other words, if we compare the model results for a given day with the actual observations of that day, the agreement is not very good; the agreement gets better on a monthly-averaged basis, and it gets even better on an annually-averaged basis. Today, models are in a constant state of improvement, with certain elements, such as the impact of clouds, needing a lot more understanding.

Before we look at the results of GCMs that run into the future, we have to understand a few things about the experimental setups that go into these models. In order to do these experiments, the modelers have to apply forcings just as we did with our simple climate model in Module 3, and the primary variable is CO₂ (carbon emissions) added to the atmosphere through human activities such as burning fossil fuels, farming, making cement, etc.

The IPCC (Intergovernmental Panel on Climate Change) has developed a whole set of scenarios (about 40) that represent the possible carbon emissions history for the next 100 years. The carbon emission is used as a key variable in driving climate modeling for each scenario. These scenarios are known as Representative Concentration Pathways (RCPs) and each one is based on an emissions trajectory. The IPCC has focused on several individual RCPs that provide a range of emissions and climate scenarios. At the outset for simplicity in this module we do not use the RCP emissions terminology because it is not very intuitive. Instead, we use groups of RCPs known as families (also developed by the IPCC) defined by the severity of the cuts (or lack thereof!) and the amount of cooperation between countries. Each family or scenario is based on a bunch of assumptions about population growth, economic growth, and choices we might make regarding steps to minimize carbon emissions. You can read more about the whole set of emissions scenarios at Wikipedia: Special Report on Emissions Scenarios. But for our purposes, we'll focus on 3 families representing very different emissions scenarios.

The basis of the different emission scenarios.

Click for a text description of the emission scenarios image.

This image is a 2x2 matrix diagram illustrating four climate change scenarios based on two axes: economic focus (global vs. regional) and environmental focus (economic vs. environmental). Each quadrant represents a different scenario, labeled A1, A2, B1, and B2, with distinct characteristics.

• Diagram Type: 2x2 matrix
• Axes:
  • X-Axis: Economic focus
    • Left: Global
    • Right: Regional
  • Y-Axis: Environmental focus
    • Top: Economic
    • Bottom: Environmental
• Quadrants:
  • A1 (Top Left, Orange):
    • Global economic focus
    • Economic priority
    • Labels: B-balanced, F1-fossil intensive, T-non fossil
  • A2 (Top Right, Red):
    • Regional economic focus
    • Economic priority
  • B1 (Bottom Left, Green):
    • Global economic focus
    • Environmental priority
  • B2 (Bottom Right, Blue):
    • Regional economic focus
    • Environmental priority

The matrix visually categorizes future climate scenarios based on whether societies prioritize global or regional economic development and whether they focus on economic growth or environmental sustainability, with A1 including sub-scenarios emphasizing different energy strategies.

Credit: Tim Bralower © Penn State University is licensed under CC BY-NC-SA 4.0

The first of these scenarios is called SRES A2, and it is commonly known as business-as-usual — in other words, it leads to a continuation of increased annual carbon emissions that follows the recent history. In effect, this scenario represents a somewhat divided world, one in which we just can't reach agreements on what to do about limiting emissions of CO₂, so each country does what seems to be in its own best interests. This world is characterized by independently operating, self-reliant nations. This world also includes a continuously increasing population — it does not level off during this time period. Above all, in this world, decisions are based primarily on perceived economic interests, and the assumption is made that these interests do not include the development of alternative energy sources.

The second scenario, called SRES A1B is a bit more optimistic. This scenario envisions an integrated world characterized by rapid economic growth, a population that reaches 9 billion by 2050 and then declines gradually, and the rapid development of alternative energy sources that facilitate increased economic growth while limiting and eventually reducing carbon emissions. This scenario also assumes that there will be rapid development and sharing of technologies that help us reduce our energy consumption. One of the keys to this scenario is that countries are integrated — they act together and find ways to improve the conditions for everyone on Earth. At this point in time, A1B is an optimistic but realistic scenario; B1 would take a revolution in the way the world economies work. And A2 or "business as usual," well, we will show you this is not the road we want to travel!

The third scenario, called SRES B1, represents an even more integrated, more ecologically friendly world, but one in which there is still steady and strong economic growth. As in scenario SRES A1B, the population in this scenario peaks at 9 billion in 2050 and then declines. One way to think of this scenario is that it represents a rapid, strong, and global commitment to the reduction of carbon emissions — it represents the best we could possibly do, and yet it does not rely on miracle technologies. The only real miracle it requires is that we all quickly figure out how to think and act globally and not focus solely on our own national interests. Most projections ignore scenario B2, as the combination of regional and environmental strategies is highly unlikely.

In graphical form, here are the three emissions scenarios.

Emissions of fossil fuels in gigatons of carbon per year under the three IPCC emissions scenarios discussed above

Click for a text description of the fossil fuels emissions graph.

This image is a line graph comparing three different trends over time, represented by three distinct lines in blue, pink, and brown. The graph lacks labeled axes, but it appears to show changes in some variable over a period, possibly related to climate or environmental data.

• Graph Type: Line graph
• Lines:
  • Blue Line:
    • Starts low, rises to a peak around the midpoint
    • Declines steadily afterward, ending lower than the starting point
  • Pink Line (with dots):
    • Starts low, rises gradually
    • Peaks slightly after the blue line, then declines slowly
    • Ends slightly above the starting point
  • Brown Line:
    • Starts low, rises steadily throughout
    • Shows a consistent upward trend with no decline
    • Ends significantly higher than the starting point
• Trend:
  • Blue line shows a rise and fall pattern
  • Pink line shows a moderate rise with a slight decline
  • Brown line shows a continuous increase

The graph illustrates three distinct trends over time, with the blue line peaking and declining, the pink line showing a more moderate rise and fall, and the brown line indicating a steady increase throughout the period.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Each scenario shows emissions of carbon to the atmosphere (mainly from fossil fuel burning — FFB) in units of Gigatons of carbon per year (Gt C/yr; a GT is a billion tons!), so this is an annual rate. In terms of a STELLA model (which we will return to in the next Module), this represents a flow into the atmosphere. Roughly half of the carbon emitted will remain in the atmosphere and lead to a stronger greenhouse effect, which will, in turn, increase global temperature and change the climate in a variety of ways.

Next, we'll have a look at the main driver for emissions reduction, the Paris Climate Agreement and then frame the model scenarios in terms of their implications for climate change and climate policy.

The Paris Accord is a really big deal. This global climate agreement brokered by the United Nations and signed in December 2015 by some 195 countries went into effect in October 2016. A key goal of the accord is for all member countries to reduce greenhouse gas emissions to keep global temperature rise below 2^o C of pre-industrial levels (measured in 1880). This is a threshold level above which most scientists agree that the impacts of climate change will be catastrophic, including flooding of large coastal cities by sea level rise, brutal heat waves, and droughts that could cause widespread starvation in developing countries. The agreement also lays the groundwork for countries to strive to reduce emissions to keep temperature increase below 1.5^o C or 2^o C. 1.5^o C is considered to be the best case scenario given that we are currently at 1.1^o C above 1880 levels, and it would need very drastic emissions cuts very quickly. Above 1.5^o C low-lying island nations in the Pacific and Indian Oceans would likely end up underwater. The agreement also acknowledges that 2^o C is a more likely warming target.  As we've seen before, this is a significant amount of warming but much more favorable than the 3 or 4^oC which would produce catastrophic climate effects.

Parties and signatories of the Paris Climate Agreement

Click for a text description of the Paris Climate Agreement map.

This image is a world map highlighting specific regions in different colors, likely indicating participation in a global agreement, organization, or event. The map uses color coding to differentiate between regions with distinct statuses.

• Map Type: World map
• Color Coding:
  • Dark Blue:
    • Most of Europe
    • Represents a specific group, possibly indicating full participation or membership
  • Yellow:
    • Middle Eastern countries: Iraq, Syria, Lebanon, Jordan, and parts of North Africa (Libya, Egypt)
    • Represents a different status, possibly indicating partial participation or a specific regional grouping
  • Light Blue:
    • Rest of the world (North and South America, most of Africa, Asia, Australia)
    • Represents another status, possibly indicating non-participation or a different level of involvement
• Additional Features:
  • Country borders are outlined in black
  • Oceans are in black, and polar regions (Arctic and Antarctic) are in white

The map visually distinguishes between regions with different levels of involvement in a global context, with Europe in dark blue, parts of the Middle East and North Africa in yellow, and the rest of the world in light blue.

Credit: Paris Agreement by L.tak from Wikipedia is licensed under CC BY-SA 4.0

There have been numerous previous climate agreements in the past, most notably the 1997 Kyoto Protocol that laid out stringent emissions targets for the countries that signed on. One of the reasons the Paris Accord was adopted by so many nations (only Syria and Nicaragua did not sign initially, but both now have) is because the impacts of climate change are becoming increasingly urgent. Although 127 countries signed on to Kyoto, the US did not.

The second reason the accord was so widely adopted is that the emission targets are voluntary, set by the individual countries based on what they believe is feasible. For example, the US’s goal was to reduce emissions by 26 percent by 2025. Once a country sets its target, it is required to abide by it and present supporting monitoring data. A country can change targets every five years. The downside of the flexibility is that many experts believe that with current targets, 1.5^o C is impossible, 2^o C is highly unlikely and 3^o C is more realistic. So, countries will have to reduce emissions radically and rapidly to stave off the highly adverse impacts of climate change.

Two other key provisions of the Paris Accord is that richer developed countries have made a financial commitment to help poorer developing nations meet their targets, although there were no firm amounts in the agreement. President Obama pledged $2.5 billion to this fund while he was in office, but only $500 million was paid. Another key provision is that the agreement recognizes and addresses deforestation as a key element of emission reduction and for countries to use forest management strategies as part of their emissions goals. In fact at the climate summit in 2021, 100 countries including Brazil where deforestation has been particularly devastating, agreed to stop deforestation entirely by 2030, which would be a major step forward.

For the US, one of the key components of the emissions reduction strategies was the Clean Power Plan, introduced in 2014 under President Obama. The CPP set out to reduce emissions from electrical power generation by 32% based on the reduction of emissions from coal-fired power plants and the conversion to renewable sources of energy including wind, solar, and geothermal.

So, the strength of the Paris accord is that it is voluntary, highly transparent and collaborative. The downside is that it is voluntary! The general fear is that the agreement may not go far enough, fast enough.

Back and Forth History of the Paris Agreement

However, that said, this is by far the most widely adopted agreement and the global scope is a massive accomplishment. So, it was a major disappointment on June 1^st, 2017 when President Donald Trump signaled that the US would withdraw from the Paris Accord in 2020, the earliest time the US could pull out under the agreement guidelines. The fear is that if the US, the second largest producer of carbon, does not abide by its emissions goals, other countries might not as well. Trump's reasons were largely economic, that the conversion to renewable energy would be too expensive and hinder the bottom line of businesses, especially the fossil fuel industries. However, as we will see in this class, conversion to renewables has begun and has the potential to be a very large and highly profitable business. Moreover, cities, states, and businesses themselves, especially those in the northwest and on the west coast are already committed to reducing emissions, so at least part of the US will continue to collaborate with other countries to address the critical issue of climate change.

Trump's overall strategy involves defunding and repealing the Clean Power Plan, which happened in 2019. It was replaced by the Affordable Clean Energy Rule, which was invalidated by the courts in 2021.

Former President Biden reentered the Paris Agreement on 19 February 2021 and set even more ambitious US emissions targets than those originally agreed upon. The Inflation Reduction Act passed in 2022 includes $369 billion to help individuals, communities, and industries switch to renewable energy. This would help the US meet its Paris targets, and possibly more importantly, the legislation shows important leadership on the international stage. However, fast forward to 2025 and President Trump signed an executive order to leave the Paris agreement on 2026. if this happens the US will be one of four countries outside of the agreement along with Iran, Libya and Yemen. After this announcement Former New York Mayor Michael Bloomberg committed to funding the US financial commitments to the agreement. So stay tuned on that. Also in 2025 most of the clean energy funding for the Inflation Reduction Act was repealed by Congress, a majot setback for the US meeting its Paris goals.

We will refer to the Paris Agreement in the remainder of the course. In this module, we discuss how models simulate the climate of the future. In closing, remember the 2^oC number, it's going to come up over and over again.

In this section, we explore GCM predictions for future changes in temperature, precipitation, and surface water using three of the emission scenarios, A2, A1B, and B1. The models are driven primarily by estimates of CO2 input over the coming decades in each scenario.

In this section, we explore the predictions from GCMs regarding the temperature under different IPCC emissions scenarios described in the previous section. As part of the 2022 IPCC Assessment Report, all of the major GCM modeling groups around the world ran their models with the same Representative Concentration Pathways (RCPs) and emissions scenario families (A2, A1B, and B1) in order to provide the best estimate of what the future climate might look like under these scenarios. Each model is different, and so their results are also different. The figure below gives a sense of how much variability and similarity there is in these models. Remember that climate scientists believe that it is key that we maintain the warming below 2^oC; above that level the consequences, including drought, heatwaves, melting of ice sheets, could be dire.

Graph to show SRES A2 global mean temperatures Global Mean Temperatures of GCMs under the A2 ("Business as Usual") Emissions Scenario

Click for a text description of the SRES A2 global mean temperatures graph.

This image is a line graph displaying the output of multiple Global Climate Models (GCMs) under the SRES A2 scenario, often referred to as the "business as usual" scenario. The graph shows the average global temperature anomaly in degrees Celsius from 1900 to 2099, with each thin colored line representing a different GCM's projection.

• Graph Type: Line graph
• Y-Axis: Global temperature anomaly (°C)
  • Range: Not explicitly labeled, but spans approximately -1°C to 5°C
• X-Axis: Years (1900 to 2099)
• Data Representation:
  • Individual GCMs: Multiple thin colored lines (blue, red, green, yellow, orange, purple, etc.)
    • Each line represents a different GCM's projection
    • Lines start around -0.5°C in 1900, showing historical fluctuations
    • All lines exhibit a general upward trend, with increasing spread by 2099
    • Spread in 2099: Roughly 2°C to 5°C, showing a similar range of warming
  • Mean of GCMs: Thick gray line
    • Represents the average of all GCM outputs
    • Starts around -0.5°C in 1900
    • Rises steadily, reaching approximately 3.2°C above the present temperature by 2099
• Trend:
  • 1900–2000: Lines show historical warming of about 1.1°C, with fluctuations
  • 2000–2099: All models project continued warming, with the mean increasing by about 3.2°C
  • The projected warming by 2099 is roughly three times the warming experienced in the last century (1.1°C)

The graph illustrates the projected global temperature increase under the SRES A2 scenario, with a significant spread among GCMs but a consistent upward trend, culminating in a mean warming of 3.2°C by the end of the century, highlighting the potential for substantial climate change under a business-as-usual emissions pathway.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Each of the thin colored lines represents the output from a different GCM — here we see the average global temperature through time, starting in 1900 and going until 2099, using the SRES A2 scenario, which is the one we sometimes call "business as usual." There is obviously a big spread in the results, but they all have more or less the same general form and a similar range (the difference between the minimum and maximum temperatures). The thicker gray line is the mean from all these models, and we can see that by the end of the century, the mean rises by about 3.2 °C above the present temperature — this is roughly three times the warming we have experienced in the last century (about 1.1^oC).

The differences in these curves reflect, among other things, different starting conditions, and if we force them to all have the same temperature today, the similarities are more apparent:

Global Mean Temperatures of GCMs under the A2 ("Business as Usual") Emissions Scenario (forced on today's temperature)

Click for a text description of the Global Mean Temperatures of GCMs under the A2 graph.

This image is a line graph showing the relative global temperature changes projected by multiple climate models over the period from 1900 to 2099. Each thin colored line represents the output from a different climate model, illustrating the variability in their projections.

• Graph Type: Line graph
• Y-Axis: Relative temperature change (°C)
  • Range: Not explicitly labeled, but spans approximately -1°C to 5°C
• X-Axis: Years (1900 to 2099)
• Data Representation:
  • Individual Models: Multiple thin colored lines (blue, red, green, yellow, orange, purple, etc.)
    • Each line represents a different climate model's projection
    • Lines start around -0.5°C in 1900, showing historical fluctuations
    • All lines exhibit a general upward trend, with increasing spread by 2099
    • Spread in 2099: Less than 2°C (roughly 2°C to 4°C)
    • Most models fall within 0.5°C of the mean temperature
  • Mean Temperature: Thick gray line
    • Represents the average of all model outputs
    • Starts around -0.5°C in 1900
    • Rises steadily, reaching approximately 3.2°C by 2099
• Trend:
  • 1900–2000: Lines show historical fluctuations with a slight upward trend
  • 2000–2099: All models project continued warming, with the mean increasing to 3.2°C
  • The mean warming of 3.2°C by the end of the century is noted as disastrous for the planet

The graph highlights the variability among climate models in projecting future temperature changes, with a spread of less than 2°C by 2099, but most models clustering within 0.5°C of the mean. The mean warming of 3.2°C underscores the potential for significant and harmful climate impacts.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

This view gives a sense of how much the models differ in their relative temperature changes over the next century. You can see that by the end of the century, the spread of temperatures is a bit less than 2°C, but most of the models fall within a half a degree from the mean temperature — the thick gray line. As we have discussed, this mean warming of 3.2 °C would be disastrous for the planet.

The lesson here is that the similarities in the models are far more important than their differences, and that they forecast a significant temperature rise by the end of the century as we essentially continue with our emissions of carbon into the atmosphere.

Next, let’s have a look at what the models say about the different emissions scenarios. Just to refresh your memories, here are those emissions scenarios again:

Emissions of fossil fuels in gigatons of carbon per year under the three IPCC emissions scenarios

Click for a text description of the IPCC Emisisons scenarios graph.

This image is a line graph comparing three different trends over time, represented by three distinct lines in blue, pink, and brown. The graph lacks labeled axes, but it appears to show changes in some variable over a period, possibly related to climate or environmental data.

• Graph Type: Line graph
• Lines:
  • Blue Line:
    • Starts low, rises to a peak around the midpoint
    • Declines steadily afterward, ending lower than the starting point
  • Pink Line (with dots):
    • Starts low, rises gradually
    • Peaks slightly after the blue line, then declines slowly
    • Ends slightly above the starting point
  • Brown Line:
    • Starts low, rises steadily throughout
    • Shows a consistent upward trend with no decline
    • Ends significantly higher than the starting point
• Trend:
  • Blue line shows a rise and fall pattern
  • Pink line shows a moderate rise with a slight decline
  • Brown line shows a continuous increase

The graph illustrates three distinct trends over time, with the blue line peaking and declining, the pink line showing a more moderate rise and fall, and the brown line indicating a steady increase throughout the period.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Recall that in these emissions scenarios, we talk about the annual rate of carbon emissions to the atmosphere and that carbon takes the form of CO₂ in the atmosphere.

Here are the corresponding global temperature histories from the collection of models for these different scenarios:

As expected, the A2 scenario results in the highest temperature rise, followed by the A1B and the B1 scenarios. Remember that the A1B scenario represents a pretty optimistic view of how we will react to the challenge of climate change, but even in that case, the temperature rises by about 2.3 °C in the next century, which is above the Paris 2.0^oC target. And even in the dramatic reduction in emissions envisioned in the B1 scenario, the temperature still rises by about 1.4°C — greater than what we’ve experienced in the last century. The lesson here is that we need to be prepared for continued climate change even if we take steps to limit carbon emissions into the atmosphere. And note that only B1 keeps us below the catastrophic 2.0^oC threshold discussed earlier.

Next, we turn to the really interesting aspect of the GCM results, which are the spatial patterns of climate change. Why is this so interesting and important? The reason is that what really matters to us is how the climate changes in key areas -- areas that will affect sea level through melting of glacial ice, areas where people are concentrated, and areas where we produce food to feed ourselves. Remember that the climate of the Earth is highly variable, and if we talk about a global temperature rise of 3.2 °C, we have to remember that the temperature will rise more than that in some places (such as the polar regions) and less than that in the tropics.

We begin with a look at the climate of the future as predicted by the NCAR (National Center for Atmospheric Research in Boulder, Colorado) model — this model seems to often fall close to the mean of all the other models.

Projected January and July Warming for Future Decades Compared to the 1960-1990 mean under Scenario A1B

Click for a text description of the NCAR prjected climate image.

This image consists of four world maps showing temperature anomalies in Kelvin (K) under the SRES A1B scenario for two different periods: July 2046–2065 and July 2080–2099, as well as January 2046–2065 and January 2080–2099. Each map uses a color gradient to indicate temperature changes relative to a baseline.

• Diagram Type: Four world maps
• Measurement: Temperature anomaly (K)
• Color Scale (bottom of the maps):
  • Range: -3.0 K to 3.0 K
  • Colors: Dark blue (-3.0 K) to dark red (3.0 K), with light blue, white, and orange in between
• Maps:
  • July 2046–2065 Temperature Anomaly (Top Left):
    • Warmer than Average (red, 1.5 K to 3.0 K):
      • Central Africa, Middle East, India
    • Slightly Warmer (orange, 0 K to 1.5 K):
      • Most of North America, Europe, Asia
    • Near Average or Cooler (white to blue, -3.0 K to 0 K):
      • Parts of the Arctic, southern South America
  • January 2046–2065 Temperature Anomaly (Top Right):
    • Warmer than Average (red, 1.5 K to 3.0 K):
      • Arctic regions, northern North America, Siberia
    • Slightly Warmer (orange, 0 K to 1.5 K):
      • Most of the Northern Hemisphere
    • Near Average or Cooler (white to blue, -3.0 K to 0 K):
      • Southern Hemisphere, especially southern South America
  • July 2080–2099 Temperature Anomaly (Bottom Left):
    • Warmer than Average (red, 1.5 K to 3.0 K):
      • Central Africa, Middle East, India, central North America
    • Slightly Warmer (orange, 0 K to 1.5 K):
      • Most of the globe
    • Near Average or Cooler (white to blue, -3.0 K to 0 K):
      • Small areas in the Arctic, southern South America
  • January 2080–2099 Temperature Anomaly (Bottom Right):
    • Warmer than Average (red, 1.5 K to 3.0 K):
      • Arctic regions, northern North America, Siberia
    • Slightly Warmer (orange, 0 K to 1.5 K):
      • Most of the Northern Hemisphere
    • Near Average or Cooler (white to blue, -3.0 K to 0 K):
      • Southern Hemisphere, especially southern South America
• Additional Features:
  • Latitude lines: 90°N to 90°S
  • Longitude lines: 180°W to 180°E

The maps illustrate projected temperature anomalies under the SRES A1B scenario, showing significant warming in July and January for both periods, with the most pronounced increases in the Arctic during January and in tropical regions during July, intensifying by the end of the century.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Here, we see 4 views of the surface temperature anomaly relative to the 1960-1990 mean. The upper 2 panels represent the mean temperature anomaly for the 20-year period from 2046 to 2065 in the month of July on the left and January on the right. In the lower 2 panels, we see similar views for the 20-year period from 2080 to 2099. The color scale below is for all of the panels and shows the anomaly in °K, but you can also think of this as °C.

Let’s start with the mid-century forecast. It calls for moderate warming in the range of 2 °C relative to the 1960-1990 mean. For both months, the warming is slightly higher on land than in the oceans, and there are a couple of spots of cooling: at the southern edge of the Sahara, one in the Southern Ocean around Antarctica, and one in the North Atlantic. The striking feature of this model result is the big change in the high latitudes of the Northern Hemisphere in January, where a large region will experience much warmer winter temperatures — up to 10° C warmer. The changes are even more dramatic for the end of the century, with the northern winters warming by up to 20° C above the 1960-1990 mean! This clearly spells the end of polar ice, which already is at its lowest extent ever. Notice also that much of Canada and Siberia warm by up to 10° C; this has important implications for the reduction in permafrost, which will increase the flow of carbon into the atmosphere via positive feedback (more on this in the next module).

The pattern of warm winters is important for a number of reasons. For one thing, it means that there will be less growth of ice in glaciers during the time of the year that they accumulate ice; thus they will shrink faster, and sea level will rise at a higher rate. Another unexpected result of warmer winters is increased problems from some insect pests whose populations normally are greatly reduced by cold winters, and when it does not get cold enough in the winter, they expand their range and cause greater damage. This is already happening in the western US with the pine bark beetle, whose population has exploded in recent years, leading to the decimation of large forested areas. These dead pine trees are then fuel for large forest fires whose scale exceeds fires in the historical record.

On the other hand, warmer winters lead to lower energy demands for heating, but this is offset by the greater energy demands for cooling during the summer months (the season where cooling would be required will also increase).

For comparison, we now compare the end-of-century forecast for the other 2 scenarios, A2, which is our business as usual case, and B1, which is our optimistic case with the A1B forecast.

First, we have the A2 scenario, which leads to the highest warming:

Projected January and July Warming for Future Decades Compared to the 1960-1990 mean for Scenario A2 and A1B

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

For July, this scenario leads to warming of the continents that is between 5 and 10° C — very little of the US would escape warming in excess of about 6° C; the same is true for much of Europe. Winter shows an even more dramatic warming, with vast regions warming by more than 20 °C. A2 would result in a major increase in the number of days over 90^o F in places such as Atlanta Austin, Dallas and Phoenix literally half of the days of the year would exceed that level of discomfort. And polar ice would melt much faster than in A1B or B1.

Now, for the other extreme, the B1 scenario at the end of the century:

Projected January and July Warming for Future Decades Compared to the 1960-1990 mean under Scenario B1

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

As expected, the warming is far less dramatic, but still shows an impressive warming at the high latitudes of the Northern Hemisphere in the winter months. But, for most of the land areas, where people are concentrated and where we grow a lot of our food, the climate changes generally do not exceed a warming of 2°C.

These comparisons demonstrate the importance of reducing emissions to scenario B1 levels.

Next, we turn to precipitation, and we will be looking at anomaly maps, showing the difference between the model’s precipitation rate (i.e., mm/month or mm/day) and the average precipitation rate for 1960-1990. It will help us to begin with a glimpse of actual typical precipitation rates for the two months of interest here — January and July. Below, we see this pair of maps:

Global Map of Precipitation, July 2001-December 2019

Click for a text description of the July 2010 global precipitation image.

This image is a world map titled "Global Precipitation July 2010," showing the distribution of precipitation in millimeters per day (mm/day) across the globe for that month. The map uses a color gradient to indicate precipitation levels, with data statistics provided at the bottom.

• Map Type: World map
• Measurement: Precipitation (mm/day)
• Color Scale (bottom of the map):
  • Range: 0.0 mm/day to 15.0 mm/day
  • Colors: Dark blue (0.0 mm/day) to dark red (15.0 mm/day), with light blue, yellow, and orange in between
• Regions with Notable Precipitation:
  • High Precipitation (red, 12.0–15.0 mm/day):
    • Equatorial regions, particularly the Intertropical Convergence Zone (ITCZ)
    • Central America, northern South America (Amazon Basin)
    • West Africa, India, and Southeast Asia
  • Moderate Precipitation (yellow to orange, 3.0–12.0 mm/day):
    • Parts of the eastern U.S., eastern China, and northern Australia
  • Low Precipitation (blue, 0.0–3.0 mm/day):
    • Most of the Northern Hemisphere (outside the ITCZ)
    • Southern South America, southern Africa, and central Australia
• Data Statistics (bottom of the map):
  • Minimum: 0.0 mm/day
  • Maximum: 35.8 mm/day
  • Mean: 2.7 mm/day

The map illustrates global precipitation patterns for July 2010, with the highest rainfall concentrated along the equator, particularly in Central America, the Amazon, West Africa, and Southeast Asia, while much of the Northern and Southern Hemispheres outside these regions experienced lower precipitation.

Credit: Global Precipitation Measurement by NASA (Public Domain)

Global Map of Precipitation, January 2001-December 2019

Click for a text description of the January 2010 global precipitation image.

This image is a world map titled "Global Precipitation January 2010," showing the distribution of precipitation in millimeters per day (mm/day) across the globe for that month. The map uses a color gradient to indicate precipitation levels, with data statistics provided at the bottom.

• Map Type: World map
• Measurement: Precipitation (mm/day)
• Color Scale (bottom of the map):
  • Range: 0.0 mm/day to 15.0 mm/day
  • Colors: Dark blue (0.0 mm/day) to dark red (15.0 mm/day), with light blue, yellow, and orange in between
• Regions with Notable Precipitation:
  • High Precipitation (red, 12.0–15.0 mm/day):
    • Equatorial regions, particularly the Intertropical Convergence Zone (ITCZ)
    • Northern South America (Amazon Basin), Central America
    • Southeast Asia, parts of Indonesia, and northern Australia
  • Moderate Precipitation (yellow to orange, 3.0–12.0 mm/day):
    • Southern Brazil, parts of West Africa
    • Eastern Australia, parts of the southern U.S.
  • Low Precipitation (blue, 0.0–3.0 mm/day):
    • Most of the Northern Hemisphere (North America, Europe, Asia)
    • Southern Africa, central Australia, and southern South America
• Data Statistics (bottom of the map):
  • Minimum: 0.0 mm/day
  • Maximum: 26.2 mm/day
  • Mean: 2.8 mm/day

The map illustrates global precipitation patterns for January 2010, with the highest rainfall concentrated along the equator, particularly in the Amazon Basin, Southeast Asia, and northern Australia, while much of the Northern Hemisphere and non-equatorial Southern Hemisphere regions experienced lower precipitation.

Credit: Global Precipitation Measurement by NASA (Public Domain)

Here, red is used to show high rainfall, and blue is used to show low rainfall. The precipitation rates here are shown in terms of millimeters of rain per day (the high value here of 15 mm/day is about 0.6 inches/day). In the eastern US, for instance, the July rainfall is about 1 mm/day.

Looking into the future, we will again utilize the results from the NCAR model, looking at 20-yr. means from the climate model results to get a smoothed out version of the results. We focus here on the SRES A1B scenario, looking at the months of July and January.

Projected change in precipitation for future decades compared to today under emission scenario A1B

Click for a text description of the Scenario A1B precipitation anomaly maps.

This image consists of four world maps showing precipitation anomalies under the SRES A1B scenario for two different periods: July 2040–2069 and July 2070–2099, as well as January 2040–2069 and January 2070–2099. The anomalies are measured in kg m^-2 s^-1, with a color gradient indicating changes in precipitation relative to a baseline.

• Diagram Type: Four world maps
• Measurement: Precipitation anomaly (kg m^-2 s^-1)
• Color Scale (bottom of the maps):
  • Range: -0.0001 to 0.0001 kg m^-2 s^-1
  • Colors: Dark blue (-0.0001) to dark red (0.0001), with light blue, white, and yellow in between
• Maps:
  • July 2040–2069 Precipitation Anomaly (Top Left):
    • Increased Precipitation (red, 0.00005 to 0.0001 kg m^-2 s^-1):
      • Central Africa, northern South America, Southeast Asia
    • Decreased Precipitation (blue, -0.0001 to -0.00005 kg m^-2 s^-1):
      • Mediterranean, parts of the Middle East, southern North America
    • Near Average (white to yellow, -0.00005 to 0.00005 kg m^-2 s^-1):
      • Most of the Northern Hemisphere, Australia
  • January 2040–2069 Precipitation Anomaly (Top Right):
    • Increased Precipitation (red, 0.00005 to 0.0001 kg m^-2 s^-1):
      • Northern North America, northern Europe, Siberia
    • Decreased Precipitation (blue, -0.0001 to -0.00005 kg m^-2 s^-1):
      • Parts of the Southern Hemisphere, including southern South America
    • Near Average (white to yellow, -0.00005 to 0.00005 kg m^-2 s^-1):
      • Equatorial regions, most of Africa
  • July 2070–2099 Precipitation Anomaly (Bottom Left):
    • Increased Precipitation (red, 0.00005 to 0.0001 kg m^-2 s^-1):
      • Central Africa, northern South America, Southeast Asia (more pronounced than 2040–2069)
    • Decreased Precipitation (blue, -0.0001 to -0.00005 kg m^-2 s^-1):
      • Mediterranean, Middle East, southern North America (more intense than earlier period)
    • Near Average (white to yellow, -0.00005 to 0.00005 kg m^-2 s^-1):
      • Parts of the Northern Hemisphere, Australia
  • January 2070–2099 Precipitation Anomaly (Bottom Right):
    • Increased Precipitation (red, 0.00005 to 0.0001 kg m^-2 s^-1):
      • Northern North America, northern Europe, Siberia (more pronounced than 2040–2069)
    • Decreased Precipitation (blue, -0.0001 to -0.00005 kg m^-2 s^-1):
      • Southern South America, parts of the Southern Hemisphere
    • Near Average (white to yellow, -0.00005 to 0.00005 kg m^-2 s^-1):
      • Equatorial regions, most of Africa
• Additional Features:
  • Latitude lines: 90°N to 90°S
  • Longitude lines: 180°W to 180°E

The maps illustrate projected precipitation anomalies under the SRES A1B scenario, showing increased precipitation in tropical regions during July and in northern high latitudes during January, with decreased precipitation in the Mediterranean and southern regions, intensifying by the end of the century.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

The units here are a bit odd — kilograms per meter squared per second — but it is still a rate, and if we do a conversion, we find that 0.0001 of these units equals 25 centimeters per month, or a bit less than one centimeter per day, which is 10 millimeters per day. Have a look at the map for July 2040 to 2069. In the eastern US, the precipitation anomaly is about 2e-5 kg/m2s. At first, it is difficult to get a sense of whether this is important. This anomaly translates to about 45 mm/month, or about 1.5 mm/day. From the July 2001-December 2019 map above, we see that the typical rate for July in this region is on the order of about 4-5 mm/day, so an increase of 1.5 mm/day is about a 30% increase — fairly significant.

Interestingly, there is not much of a change in the anomaly for this region (eastern US) as we move to the end-of-the-century map in the lower left panel above. And in both cases, the western US is drier by a bit. If we look at the January maps, we see a bigger change between the two time periods, getting drier by the 2080-2099 period. For January, it is important to note that in the Western US, the precipitation is decreased; this could mean problems for the water supply out west, where the winter snows, as they melt in the summer, represent a major part of the water budget.

What about the other scenarios? Below, we see the precipitation anomaly maps for the A2 scenario — the one that leads to a hotter climate — for the 2080-2099 time period.

Projected change in precipitation for 2080-2099 compared to today under emission scenario A2

Click for a text description of the Scenario A2 Precipitation anomaly maps.

This image consists of two world maps showing precipitation flux anomalies under the SRES A2 scenario for the period 2080–2099, one for July and one for January. The anomalies are measured in kg m^-2 s^-1, with a color gradient indicating changes in precipitation relative to a baseline, and additional scales for percentage changes and millimeters per month.

• Diagram Type: Two world maps
• Measurement: Precipitation flux anomaly (kg m^-2 s^-1)
• Color Scale (bottom of the maps):
  • Range: -1.8e-4 to 1.8e-4 kg m^-2 s^-1
  • Colors: Dark blue (-1.8e-4) to dark red (1.8e-4), with light blue, white, and yellow in between
  • Additional Scales:
    • Percentage change: -42% to 42%
    • mm/month: -125 to 125 mm/month
• Maps:
  • July 2080–2099 Precipitation Anomaly (Left):
    • Increased Precipitation (red, 0.9e-4 to 1.8e-4 kg m^-2 s^-1, 21% to 42%, 62.5 to 125 mm/month):
      • Central Africa, northern South America, Southeast Asia
    • Decreased Precipitation (blue, -1.8e-4 to -0.9e-4 kg m^-2 s^-1, -42% to -21%, -125 to -62.5 mm/month):
      • Mediterranean, Middle East, southern North America, parts of Australia
    • Near Average (white to yellow, -0.9e-4 to 0.9e-4 kg m^-2 s^-1, -21% to 21%, -62.5 to 62.5 mm/month):
      • Most of the Northern Hemisphere, southern South America
  • January 2080–2099 Precipitation Anomaly (Right):
    • Increased Precipitation (red, 0.9e-4 to 1.8e-4 kg m^-2 s^-1, 21% to 42%, 62.5 to 125 mm/month):
      • Northern North America, northern Europe, Siberia
    • Decreased Precipitation (blue, -1.8e-4 to -0.9e-4 kg m^-2 s^-1, -42% to -21%, -125 to -62.5 mm/month):
      • Southern South America, parts of the Southern Hemisphere
    • Near Average (white to yellow, -0.9e-4 to 0.9e-4 kg m^-2 s^-1, -21% to 21%, -62.5 to 62.5 mm/month):
      • Equatorial regions, most of Africa, Australia

The maps illustrate projected precipitation anomalies under the SRES A2 scenario for 2080–2099, showing increased precipitation in tropical regions during July and in northern high latitudes during January, with significant decreases in the Mediterranean, southern North America, and parts of the Southern Hemisphere.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Compared to the A1B scenario for the same time period, we see a generally drier picture for the US, though the differences are actually quite small. Note that in this scenario, as in others, the tropics get wetter.

For scenario SRES B1, the one where the temperature at the end of the century is only slightly higher than the present, the precipitation changes are generally quite small, as can be seen in the map below:

Projected change in precipitation for 2080-2099 compared to today under emission scenario B1

Click for a text description of the Scenario B1 Precipitation anomaly maps.

This image is a world map titled "Global Precip Anomaly SRES B1 July 2080-2099," showing precipitation flux anomalies in kg m^-2 s^-1 under the SRES B1 scenario for the period of July 2080–2099. The map uses a color gradient to indicate changes in precipitation relative to a baseline, with data statistics provided at the bottom.

• Map Type: World map
• Measurement: Precipitation flux anomaly (kg m^-2 s^-1)
• Color Scale (bottom of the map):
  • Range: -1.0e-04 to 1.0e-04 kg m^-2 s^-1
  • Colors: Dark red (-1.0e-04) to dark blue (1.0e-04), with yellow, white, and light blue in between
• Regions with Notable Anomalies:
  • Increased Precipitation (blue, 0.5e-05 to 1.0e-04 kg m^-2 s^-1):
    • Northern North America, northern Europe, Siberia
    • Parts of Southeast Asia and the equatorial Pacific
  • Decreased Precipitation (red, -1.0e-04 to -0.5e-05 kg m^-2 s^-1):
    • Central America, northern South America (Amazon Basin)
    • Parts of West Africa and the Mediterranean
  • Near Average (white to yellow, -0.5e-05 to 0.5e-05 kg m^-2 s^-1):
    • Most of the Southern Hemisphere, including southern South America, Africa, and Australia
    • Parts of the Northern Hemisphere, including central North America and central Asia
• Data Statistics (bottom of the map):
  • Minimum: -5.2e-05 kg m^-2 s^-1
  • Maximum: 7.1e-05 kg m^-2 s^-1

The map illustrates projected precipitation anomalies under the SRES B1 scenario for July 2080–2099, showing increased precipitation in northern high latitudes and parts of the equatorial Pacific, while regions like Central America, the Amazon Basin, and the Mediterranean experience decreased precipitation.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0

Here, the very light colors indicate slight increases and decreases in the precipitation rate for July — the same picture holds for January as well.

In summary, the precipitation results indicate that in general, there are very complicated patterns of precipitation change, and for much of the globe, they are quite minimal. The model predicts that there will be wetter areas and drier areas, and what really matters is how these changes correlate with the regions where people live and grow their food. Under the A2 scenario (business-as-usual), we should expect a generally drier western and south-central US and a generally wetter northeastern U.S. We will revisit this issue in Module 8.

As was mentioned in the previous section on precipitation predictions from GCMs, there are some important implications for water supplies. In this section, we will take a look at how the model results might impact surface water in the future.

Surface water is of great importance since it is the primary source of water for agriculture. It is estimated that 69% of worldwide water use is for irrigation, and of this, about 62% comes from surface water which includes streams, lakes, and reservoirs.

When rain falls on the surface, most of it is absorbed by the soil, and then from there, it slowly migrates to streams, and from there into lakes and reservoirs, but it ultimately leaves a region, through stream flow and evaporation, to return to the oceans or the atmosphere — this is just a part of the large global water cycle. The total amount of water flowing in the streams of a region provides a useful measure of how much water is available for agriculture. We'll discuss this in depth in Module 9.

It takes around 3,000 liters of water to produce enough food to satisfy one person's daily dietary needs. This is a considerable amount when compared to that required for drinking, which is just 2-5 liters. To produce food for over 7 billion people who inhabit the planet today requires the water that would fill a canal ten meters deep, 100 meters wide and 7.1 million kilometers long – that's enough to circle the globe 180 times!

As we have seen, a GCM will predict the amount of rainfall over the surface of the Earth, and if we combine that with a model of the topography of the land areas (which is included in the GCMs), we can figure out how much water will flow as surface water through different regions. This has been done by taking the average precipitation from 20 GCMs operating under the SRES A1B scenario and then calculating how that surface water flow compares to the long-term average from 1900 to 1970. The resulting data provide us with a very good idea of what to expect in the future if we follow the A1B scenario.

The results of the surface water predictions can be seen here, but we will focus in on 3 snapshots from this history in a series of maps. We begin with a view of the predictions for the year 2020.

Percent change in runoff in 2020 relative to 1900-1970 for emission scenario A1B

Click for a text description of the 2020 runoff change map

This image is a world map titled "% Runoff Change, Relative to 1900-1970 for the SRES A1B scenario." The map shows projected changes in runoff (the flow of water over the Earth's surface) as a percentage relative to the 1900–1970 baseline, based on the SRES A1B climate scenario, which assumes rapid economic growth and balanced energy sources.

• Map Type: World map
• Measurement: Percentage change in runoff (%)
• Color Scale (bottom of the map):
  • Range: -60.0% to 60.0%
  • Colors: Red (decrease, -60.0%) to blue (increase, 60.0%), with white at 0% (no change)
  • Data range: Min -28.7%, Max 65.8%
• Regions with Notable Changes:
  • Significant Decrease (red, -60% to -12%):
    • Southern Europe (Mediterranean region)
    • Parts of Central America
    • Small areas in the Middle East
  • Moderate Decrease (orange, -12% to 0%):
    • Western U.S.
    • Parts of South America (southern Brazil, Argentina)
    • Central Africa
  • No Change (white, around 0%):
    • Most of North America, Australia, and Asia
    • Large parts of South America and Africa
  • Moderate Increase (light blue, 0% to 12%):
    • Northern Europe
    • Parts of Russia and Central Asia
    • Eastern Africa
  • Significant Increase (blue, 12% to 60%):
    • Arctic regions (northern Canada, Siberia)
    • Parts of Southeast Asia
    • Small areas in South America (northern Andes)

The map illustrates a varied global impact on runoff, with significant reductions projected in southern Europe and parts of Central America, while increases are expected in Arctic regions and parts of Southeast Asia under the SRES A1B scenario.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0; Data from Milly, et al., 2005

This image is a 30-year average, centered on the year 2020. The blue areas will see an increase in streamflow, and the red areas will see a decrease. The map includes contour lines that separate the values according to the labeled tick marks on the map scale. As we might expect, the changes are relatively small at this point, but the Mediterranean region has noticeably less streamflow.

As we move forward in time, to 2050, the changes become more dramatic.

Percent change in runoff in 2050 relative to 1900-1970 for emission scenario A1B

Click for a text description of the 2050 runoff change map.

This image is a world map titled "% Runoff Change, Relative to 1900-1970 for the SRES A1B scenario." The map illustrates projected changes in runoff (surface water flow) as a percentage compared to the 1900–1970 baseline, according to the SRES A1B climate scenario, which assumes rapid economic growth and balanced energy sources.

• Map Type: World map
• Measurement: Percentage change in runoff (%)
• Color Scale (bottom of the map):
  • Range: -60.0% to 60.0%
  • Colors: Red (decrease, -60.0%) to blue (increase, 60.0%), with white at 0% (no change)
  • Data range: Min -45.4%, Max 100.3%
• Regions with Notable Changes:
  • Significant Decrease (red, -60% to -12%):
    • Southern Europe (Mediterranean region)
    • Parts of Central America
    • Small areas in the Middle East
  • Moderate Decrease (orange, -12% to 0%):
    • Western U.S.
    • Parts of South America (southern Brazil, Argentina)
    • Central Africa
    • Northern Australia
  • No Change (white, around 0%):
    • Most of North America, eastern Brazil, and sub-Saharan Africa
    • Large parts of Asia and Australia
  • Moderate Increase (light blue, 0% to 12%):
    • Northern Europe
    • Parts of Russia and Central Asia
    • Eastern Africa
  • Significant Increase (blue, 12% to 60%):
    • Arctic regions (northern Canada, Siberia)
    • Parts of Southeast Asia
    • Southern South America (southern Chile, Argentina)

The map highlights a varied global pattern of runoff changes, with notable reductions projected in southern Europe and Central America, and increases in Arctic regions and Southeast Asia under the SRES A1B scenario.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0; Data from Milly, et al., 2005

And as we continue into the future, the picture in 2080 looks like this:

Percent change in runoff in 2080 relative to 1900-1970 for emission scenario A1B

Click for a text description of the 2080 runoff change map.

This image is a world map titled "% Runoff Change, Relative to 1900-1970 for the SRES A1B scenario." It shows projected changes in runoff (surface water flow) as a percentage compared to the 1900–1970 baseline, based on the SRES A1B climate scenario, which assumes rapid economic growth and balanced energy sources.

• Map Type: World map
• Measurement: Percentage change in runoff (%)
• Color Scale (bottom of the map):
  • Range: -60.0% to 60.0%
  • Colors: Red (decrease, -60.0%) to blue (increase, 60.0%), with white at 0% (no change)
  • Data range: Min -58.7%, Max 100.9%
• Regions with Notable Changes:
  • Significant Decrease (red, -60% to -12%):
    • Southern Europe (Mediterranean region)
    • Parts of Central America
    • Small areas in the Middle East and Central Asia
  • Moderate Decrease (orange, -12% to 0%):
    • Western U.S.
    • Parts of South America (southern Brazil, Argentina)
    • Central Africa
    • Northern Australia
  • No Change (white, around 0%):
    • Most of North America, eastern Brazil, and sub-Saharan Africa
    • Large parts of Asia and Australia
  • Moderate Increase (light blue, 0% to 12%):
    • Northern Europe
    • Parts of Russia and Central Asia
    • Eastern Africa
  • Significant Increase (blue, 12% to 60%):
    • Arctic regions (northern Canada, Siberia)
    • Southern South America (southern Chile, Argentina)
    • Parts of Southeast Asia

The map depicts a varied global pattern of runoff changes under the SRES A1B scenario, with significant reductions in southern Europe and Central America, and notable increases in Arctic regions and southern South America.

Credit: David Bice © Penn State University is licensed under CC BY-NC-SA 4.0; Data from Milly, et al., 2005