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I believe that sound science and good sense will prevail in the face of irrational and scientifically baseless climate fears.

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https://19january2017snapshot.epa.gov/climate-change-science/causes-climate-change_.html

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Examines the science and arguments of global warming skepticism. Common objections like ‘global warming is caused by the sun’, ‘temperature has changed naturally in the past’ or ‘other planets are warming too’ are examined to see what the science really says.

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One way that the world's ocean affects weather and climate is by playing an important role in keeping our planet warm. The majority of radiation from the sun is absorbed by the ocean, particularly in tropical waters around the equator, where the ocean acts like a massive, heat-retaining solar panel. Land areas also absorb some sunlight, and the atmosphere helps to retain heat that would otherwise quickly radiate into space after sunset.

The ocean doesn't just store solar radiation; it also helps to distribute heat around the globe. When water molecules are heated, they exchange freely with the air in a process called evaporation. Ocean water is constantly evaporating, increasing the temperature and humidity of the surrounding air to form rain and storms that are then carried by trade winds. In fact, almost all rain that falls on land starts off in the ocean. The tropics are particularly rainy because heat absorption, and thus ocean evaporation, is highest in this area.

Outside of Earth's equatorial areas, weather patterns are driven largely by ocean currents. Currents are movements of ocean water in a continuous flow, created largely by surface winds but also partly by temperature and salinity gradients, Earth's rotation, and tides. Major current systems typically flow clockwise in the northern hemisphere and counterclockwise in the southern hemisphere, in circular patterns that often trace the coastlines.

Ocean currents act much like a conveyor belt, transporting warm water and precipitation from the equator toward the poles and cold water from the poles back to the tropics. Thus, ocean currents regulate global climate, helping to counteract the uneven distribution of solar radiation reaching Earth's surface. Without currents in the ocean, regional temperatures would be more extreme-super hot at the equator and frigid toward the poles-and much less of Earth's land would be habitable.

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How does the ocean affect climate and weather on land?

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One way that the world's ocean affects weather and climate is by playing an important role in keeping our planet warm. The majority of radiation from the sun is absorbed by the ocean, particularly in tropical waters around the equator, where the ocean acts like a massive, heat-retaining solar panel.

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Volcanic effects on climate: recent advances and future avenues - Bulletin of Volcanology

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Volcanic eruptions have long been studied for their wide range of climatic effects. Although global-scale climatic impacts following the formation of stratospheric sulfate aerosol are well understood, many aspects of the evolution of the early volcanic aerosol cloud and regional impacts are uncertain. In the last twenty years, several advances have been made, mainly due to improved satellite measurements and observations enabling the effects of small-magnitude eruptions to be quantified, new proxy reconstructions used to investigate the impact of past eruptions, and state-of-the-art aerosol-climate modelling that has led to new insights on how volcanic eruptions affect the climate. Looking to the future, knowledge gaps include the role of co-emissions in volcanic plumes, the impact of eruptions on tropical hydroclimate and Northern Hemisphere winter climate, and the role of eruptions in long-term climate change. Future model development, dedicated model intercomparison projects, interdisciplinary collaborations, and the application of advanced statistical techniques will facilitate more complex and detailed studies. Ensuring that the next large-magnitude explosive eruption is well observed will be critical in providing invaluable observations that will bridge remaining gaps in our understanding.

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visit this site https://www.science.org/doi/10.1126/science.aay3701

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i found this interesting. please check

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What Is the Sun’s Role in Climate Change? - NASA Science

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The Sun powers life on Earth; it helps keep the planet warm enough for us to survive. It also influences Earth’s climate: We know subtle changes in Earth’s orbit around the Sun are responsible for the comings and goings of the past ice ages. But the warming we’ve seen over the last few decades is […]

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this in nice for paleoclimate research

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National Centers for Environmental Information (NCEI)

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Climate research has revealed dramatic increases in temperatures since the late 19th century, but our analysis is limited by how long temperatures have been consistently sampled around the world, providing data that lasts for only a negligible fraction of the Earth's lifespan. How can researchers compare modern temperatures to past centuries and millennia?

The answer is paleoclimate research. By using various proxies, scientists can infer climate information from thousands or even millions of years ago. and contain physical or chemical evidence that can be reliably traced to certain time periods. For instance, scientists can observe the width of annual tree rings in temperature-sensitive (as opposed to moisture-sensitive) trees or the relative concentrations of oxygen isotopes in ice cores (at high latitudes, precipitation in colder conditions has a lower O-18/O-16 ratio as compared to warmer conditions).

Paleoclimate data allows us to better understand current trends in the context of centuries of climate variability.

Temperature and Carbon Dioxide Change

When broadly viewing the paleoclimate record, one clear trend emerges: when the concentration of carbon dioxide in the atmosphere increases, temperature increases along with it-and vice versa. These values may rise and fall over 100,000 year periods, but they do so at similar rates. This trend is consistent with the role of greenhouse gases in climate change.

Temperature change (light blue) and carbon dioxide change (dark blue) measured from the EPICA Dome C ice core in Antarctica. (Jouzel et al. 2007; Luthi et al. 2008).

Carbon dioxide is coupled to temperature in other ways. For instance, warmer temperatures release more carbon from soils and permafrost, warmer oceans absorb less carbon from the atmosphere, and warmer periods prevent the fertilization of marine phytoplankton that can take up excess carbon dioxide.

Paleoclimate data also reveals that climate change is about more than temperature. During times with lowest temperatures, snow lines were lower, continents were drier, and tropical monsoons were weaker. While some of these changes may be independent, some could be tied to changing levels of carbon dioxide. Understanding what changes may occur in the future and to what extent remains a topic of vigorous research.

Paleoclimatic Data for the Last 2,000 Years

Since the 1970s, paleoclimatologists have developed methods for reconstructing temperatures over the last 2,000 years based on a broad range of proxy data types. While some differences between these records may be due to their using diverse statistical methods, they show similar patterns of temperature variability over the last 2,000 years.

These paleoclimate reconstructions reveal three important conclusions:

Temperatures have increased over the last 50 years at an unprecedented rate.

Warmer than average temperatures are more widespread across the Northern Hemisphere since the mid-20th century than in any previous time.

Prior to 1850 CE, there was a millennial-scale cooling trend including a multi-century period with relatively cooler temperatures beginning in the 15th century.

Climate Model Simulations of the Last 1,000 Years

Paleoclimate records indicate that natural climate variations are a result of "forcings," factors that drive the climate system to change. These forcings include solar and volcanic activity. Some theorize that these forcings are behind global warming, rather than human-caused greenhouse gas emissions. We can test this using another tool: the climate model.

Climate models are computer programs that use physical laws to determine how climate has changed in the past and may change in the future. Models range from simple ones tracking only the most essential processes to complex ones which incorporate additional important interactions between the atmosphere, ocean, sea ice, and land surface. Through modeling studies, researchers have examined the contribution of greenhouse gas concentrations to global warming.

These climate model simulations have revealed that:

Solar and volcanic activities have been responsible for some variations in Northern Hemisphere temperature over the past 1,000 years.

Neither solar nor volcanic forcing can explain the dramatic warming of the 20th century. Natural changes in these forcings would actually have resulted in a small cooling since 1960 under a normal emissions pathway.

Only by adding the human-caused increase in greenhouse gas concentrations are the models able to explain the unprecedented warmth we are experiencing.

Paleoclimate Data Before 2,000 Years Ago

Studying data from well beyond 2,000 years ago, paleoclimatologists have discovered several periods of unusually high warmth. The Mid-Holocene Warm Period, observed about 6,000 years ago, was generally warmer than today during summer in the Northern Hemisphere.

This natural warming was caused by slow changes in the Earth's orbit, which caused shifts in the amount of solar radiation reaching each latitudinal band of the planet. Changes in the Earth's orbit also caused warming in the Penultimate Interglacial Period about 125,000 years ago. Based on proxy evidence, the global mean annual surface temperature appeared to be higher than pre-industrial by about 1° to 2°C, but this warming was not uniform across the globe. However, this slowly varying climate forcing mechanism cannot be responsible for warming over the last 100 years.

The Early Eocene Period, about 54 to 48 million years ago, stands out as distinctly warmer than today, particularly at high latitudes. The global surface temperature was 9° to 14°C higher than today, and carbon dioxide levels were very high, between 1,000 to 2,000 parts per million (levels in 2021 were 414.27 parts per million). Scientists think that increased volcanic activity was an important cause of these high levels of carbon dioxide. Model simulations support the hypothesis that carbon dioxide concentrations maintained high temperatures in the early Eocene.

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Climate Change in the Context of Paleoclimate

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Studying the world before thermometers provides a better understanding of future climate concerns.

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15.5: Anthropogenic Causes of Climate Change

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As shown in the previous section, prehistoric changes in climate have been very slow. Climate changes typically occur slowly over many millions of years. The climate changes observed today are rapid …

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Students explore how solar radiation, Earth’s surface and oceans, and greenhouse gases interact to cause global warming. They can change variables to determine how much greenhouse gas emissions might need to fall to mitigate the temperature increase.

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How We Use Models

Models help us to work through complicated problems and understand complex systems. They also allow us to test theories and solutions. From models as simple as toy cars and kitchens to complex representations such as flight simulators and virtual globes, we use models throughout our lives to explore and understand how things work.

Climate Models, and How They Work

Climate models are based on well-documented physical processes to simulate the transfer of energy and materials through the climate system. Climate models, also known as general circulation models or GCMs, use mathematical equations to characterize how energy and matter interact in different parts of the ocean, atmosphere, land. Building and running a climate model is complex process of identifying and quantifying Earth system processes, representing them with mathematical equations, setting variables to represent initial conditions and subsequent changes in climate forcing, and repeatedly solving the equations using powerful supercomputers.
Check out The Very, Very Simple Climate Model "
Climate Model Resolution

Climate models separate Earth's surface into a three-dimensional grid of cells. The results of processes modeled in each cell are passed to neighboring cells to model the exchange of matter and energy over time. Grid cell size defines the resolution of the model: the smaller the size of the grid cells, the higher the level of detail in the model. More detailed models have more grid cells, so they need more computing power.
See an animation showing different grid sizes " Explore practical information for those interested in participating in global climate model experiments "
Climate models also include the element of time, called a time step. Time steps can be in minutes, hours, days, or years. Like grid cell size, the smaller the time step, the more detailed the results will be. However, this higher temporal resolution requires additional computing power.

How are Climate Models Tested?

Once a climate model is set up, it can be tested via a process known as "hind-casting." This process runs the model from the present time backwards into the past. The model results are then compared with observed climate and weather conditions to see how well they match. This testing allows scientists to check the accuracy of the models and, if needed, revise its equations. Science teams around the world test and compare their model outputs to observations and results from other models.

Using Scenarios to Predict Future Climate

Once a climate model can perform well in hind-casting tests, its results for simulating future climate are also assumed to be valid. To project climate into the future, the climate forcing is set to change according to a possible future scenario. Scenarios are possible stories about how quickly human population will grow, how land will be used, how economies will evolve, and the atmospheric conditions (and therefore, climate forcing) that would result for each storyline.

In 2000, the Intergovernmental Panel on Climate Change (IPCC) issued its Special Report on Emissions Scenarios (SRES), describing four scenario families to describe a range of possible future conditions. Referred to by letter-number combinations such as A1, A2, B1, and B2, each scenario was based on a complex relationship between the socioeconomic forces driving greenhouse gas and aerosol emissions and the levels to which those emissions would climb during the 21st century. The SRES scenarios have been in use for more than a decade, so many climate model results describe their inputs using the letter-number combinations.

In 2013, climate scientists agreed upon a new set of scenarios that focused on the level of greenhouse gases in the atmosphere in 2100. Collectively, these scenarios are known as Representative Concentration Pathways or RCPs. Each RCP indicates the amount of climate forcing, expressed in Watts per square meter, that would result from greenhouse gases in the atmosphere in 2100. The rate and trajectory of the forcing is the pathway. Like their predecessors, these values are used in setting up climate models.
Learn more about RCPs "
Results of Current Climate Models

Around the world, different teams of scientists have built and run models to project future climate conditions under various scenarios for the next century. So the groups can make a fair comparison of their results, they run the same experiment. Because each climate model is slightly different, the results show a a range of projections. Though yearly values projected for temperature and precipitation differ among the models, the trend and magnitude of change is fairly consistent.

Global climate model results from groups around the world project that global temperature will continue to increase. They also show that human decisions and behavior we choose today will determine how dramatically climate will change in the future.
Check the World Climate Research Programme's experimental design for the most recent phase of the Coupled Modeled Intercomparison Project "
How are Climate Models Different from Weather Prediction Models?

Unlike weather forecasts, which describe a detailed picture of the expected daily sequence of conditions starting from the present, climate models are probabilistic, indicating areas with higher chances to be warmer or cooler and wetter or drier than usual. Climate models are based on global patterns in the ocean and atmosphere, and records of the types of weather that occurred under similar patterns in the past.
View maps showing short-term climate forecasts "

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Models help us to work through complicated problems and understand complex systems. They also allow us to test theories and solutions. From models as simple as toy cars and kitchens to complex representations such as flight simulators and virtual globes, we use models throughout our lives to explore and understand how things work.

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https://www.un.org/en/climatechange/science/causes-effects-climate-change

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https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions

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Climate change is any change occurring to the planet's climate either permanently or lasting for long periods of time.

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Natural vs anthropogenic climate change - Energy Education

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Climate change is any change occurring to the planet’s climate either permanently or lasting for long periods of time. It is the cumulative total of two related sources: anthropogenic climate change and natural climate change. Anthropogenic climate change is defined by the human impact on Earth’s climate while natural climate change are the natural climate cycles that have been and continue to occur throughout Earth’s history.[1]

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In my opinion, the current climate change is primarly anthropogenic (human-caused).

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Natural disasters like large volcanic eruptions can cool or warm the planet without human help.

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Cutting down so many trees means less oxygen and more carbon dioxide, which makes the Earth hotter.

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I am aware that climate change is partly natural, but we know human actions are contributing. By reducing pollution and using resources more efficiently, we can improve air quality, protect ecosystems and leave a healthier planet for future generations.

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https://dergipark.org.tr/tr/download/article-file/211152

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Nature at the end finds it s way and that is a miracle. Animals and plants have always adapted to changing climates without human influence.

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Landingpage - European Green Transformation Master

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Undoubtedly, climate change is a natural process, however, it seems to be sped up by human activity as well.

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Australia's climate is changing. Observed changes over the 20th century include increases in global average air and ocean temperature, rising global sea levels, long-term sustained widespread reduction of snow and ice cover, and changes in atmospheric and ocean circulation and regional weather patterns, which influence seasonal rainfall conditions. ​

These changes are caused by extra heat in the climate system due to the addition of greenhouse gases to the atmosphere. The additional greenhouse gases are primarily due to human activities such as the burning of fossil fuels (coal, oil, and natural gas), agriculture, and land clearing. These activities increase the amount of heat-trapping greenhouse gases in the atmosphere. The pattern of observed changes in the climate system is consistent with an increased greenhouse effect. Other climatic influences like volcanoes, the sun and natural variability cannot explain the timing and extent of the observed changes.

The evidence of climate change is supported by extensive scientific research performed and reported across the world. Past and present climate information is collected from observations and measurements of our environment, including trapped air in ice from thousands of years ago. Climate models are used to understand the causes of climate change and to project changes into the future.

Many of the impacts of climate change pose risks to human and natural systems, through more frequent and severe heat waves, coastal inundation due to sea level rise, disruptions to rainfall patterns and other effects. Analyses of a range of climate scenarios indicate the most severe risks of climate change can largely be mitigated if greenhouse gas emissions are reduced to the point they are no longer accumulating in the atmosphere.

Greenhouse gas effect

The greenhouse effect is a natural process that warms the Earth's surface. When the Sun's energy reaches the Earth's atmosphere, some of it is reflected back to space and some is absorbed and re-radiated by greenhouse gases.

Greenhouse gases include carbon dioxide, methane, nitrous oxide, ozone and some artificial chemicals such as chlorofluorocarbons (CFCs).

The absorbed energy warms the atmosphere and the surface of the Earth. This process maintains the Earth's temperature at around 33 degrees Celsius warmer than it would otherwise be, allowing life on Earth to exist.

Increased greenhouse effect

The problem we now face is that human activities - particularly burning fossil fuels (coal, oil and natural gas), agriculture and land clearing - are increasing the concentrations of greenhouse gases. This is increasing the greenhouse effect, which is contributing to warming of the Earth.

Greenhouse effect

Step 1: Solar radiation reaches the Earth's atmosphere - some of this is reflected back into space.

Step 2: The rest of the sun's energy is absorbed by the land and the oceans, heating the Earth.

Step 3: Heat radiates from Earth towards space.

Step 4: Some of this radiative heat is trapped by greenhouse gases in the atmosphere, keeping the Earth warm enough to sustain life.

Step 5: Human activities such as burning fossil fuels, agriculture and land clearing are increasing the amount of greenhouse gases released into the atmosphere.

Step 6: This is trapping extra heat, and causing the Earth's temperature to rise, along with other effects like ocean acidification noted in the figure below.

Observed changes

Alternate text for "There are multiple lines of evidence that show the climate system is changing"

There are multiple lines of evidence that show the climate system is changing. These include:

record high surface air temperatures

increased average number of hot days per year

decreased average number of cold days per year

increasing intensity and frequency of extreme events (e.g. fires, floods)

changing rainfall patterns

increasing sea surface temperatures

rising sea levels

increasing ocean heat content

increasing ocean acidification

changing Southern Ocean currents

melting ice caps and glaciers

decreasing Arctic sea ice

Australia is already experiencing the impacts of a changing climate, particularly changes associated with increases in temperature, frequency and intensity of heatwaves, hazardous fire weather and drought conditions. Climate observations and future projections show that these changes from the historical climate are ongoing and long-term, and that they cannot be explained by natural variability (though they do interact with underlying natural variability).

Australia's changing climate

Air temperatures have increased globally by around 1.2 degrees Celsius since the late 1800s. This is when modern meteorological record keeping became widespread and reliable enough to produce global datasets. Most of the warming from 1880 to present has occurred since the 1970s. The observed increase in temperatures has occurred across the globe, with rising temperatures recorded on all continents and in the ocean. 2016 and 2020 are tied for the world's warmest year on record, and years 2015 and 2017 were the equal second warmest years on record globally.

Australia's weather and climate are changing in response to a warming global climate. Australia has warmed on average by 1.44 ± 0.24 °C since 1910, with most warming occurring since 1950 and every decade since then being warmer than the ones before.

Australia's warmest year on record was 2019, with an average temperature 1.52°C above the 1961-1990 average. The seven years from 2013 to 2019 all rank in the warmest years on record. This long-term warming trend means that most years are now warmer than almost any observed during the 20th century. When relatively cooler years do occur, it is because natural drivers that typically cool Australia's climate, such as La Niña, act to partially offset the background warming trend (Figure 1).

Ocean warming and sea level rise

One of the strongest indicators of climate change is the amount of heat stored in the world's oceans. The heat content of oceans has increased during recent decades and accounts for more than 90 per cent of the total heat trapped by added greenhouse gases and accumulated by the land, air and ocean since the 1970s. Ocean warming is continuing, especially in the top several hundred metres of the ocean. In the Australian region, the average sea surface temperature for each decade since 1900 has been warmer than the previous decade (Figure 2).

Average sea surface temperature in the Australian region has warmed by more than 1°C since 1900, with eight of the ten warmest years on record occurring since 2010. The warmest year on record was 2016, associated with one of the strongest negative Indian Ocean Dipole events on record.

The greatest ocean warming in the Australian region since 1970 has occurred around southeastern Australia and Tasmania. The East Australian Current now extends farther south than in earlier decades, creating an area in the Tasman Sea where the warming rate is now twice the global average. There has also been warming across large areas of the Indian Ocean region to the southwest of Australia. Warming of the ocean has contributed to longer and more frequent marine heatwaves.

The increasing frequency of marine heatwaves around Australia in recent years has caused significant impacts on marine ecosystem health, marine habitats and species. These impacts include depleting kelp forests and sea grasses, a poleward shift in some marine species, and increased occurrence of disease. Recent marine heatwaves are the primary cause of mass coral bleaching and widespread damage to coral reefs around Australia, including the Great Barrier and Ningaloo reefs. Other pressures such as tropical cyclones, nutrient runoff and disease also affect the health of some areas of the Great Barrier Reef.

There is clear evidence that sea levels have risen as a result of climate change, based on observations from tide gauges, measurements of our past climate and satellite measurements.

Global mean sea level rise is accelerating. Tide gauge and satellite altimetry observations show that the rate of global mean sea level rise increased from 1.5 ± 0.2 cm per decade (1901-2000) to 3.5 ± 0.4 cm per decade (1993-2019). The dominant cause of global mean sea level rise since 1970 is anthropogenic climate change. Australia, like other nations, is already experiencing sea level rise. Sea level varies from year to year and from place to place, partly due to the natural variability of the climate system from the effect of climate drivers such as El Niño and La Niña. Based on satellite altimetry observations since 1993, the rates of sea level rise to the north and southeast of Australia have been significantly higher than the global average, whereas rates of sea level rise along the other coasts of the continent have been closer to the global average. (Figure 3).

Ocean currents are also changing, particularly in the Southern Ocean. Studies have shown that the deepest ocean water in the Antarctic, which is normally the coldest water, has warmed and become less saline since the 1980s. The world's deep ocean currents play a critical role in transporting heat around the planet, thus regulating the climate. Warming of these currents alters their ability to perform this function.

Another serious impact of the increasing concentration of atmospheric carbon dioxide is ocean acidification. Around a quarter of the carbon dioxide produced by humans is absorbed by the oceans. As the carbon dioxide dissolves in sea water it forms carbonic acid, making the ocean more acidic. There are early indications that some marine organisms are already being affected by ocean acidification.

Many marine species that have low mobility or rely on specific habitats or narrow temperature ranges for survival, such as marine plants, corals, and other invertebrates, may not be able to move to new or more suitable habitats. If they cannot move, then as the oceans become warmer and more acidic it becomes harder for these organisms to evolve quickly enough for populations to remain viable. For those marine organisms that form shells or bodies of calcium carbonate minerals, (for example, molluscs and corals), it also becomes more difficult for them to capture and absorb the minerals they need from the sea water to maintain and grow their bodies.

Initially these changes are likely to have the greatest impacts on marine environments such as coral reefs as well as the Arctic and Antarctic ecosystems. Eventually almost all aquatic ecosystems will be affected by increasing levels of ocean warming and acidification if greenhouse gas emissions are not curbed and reversed.

Extreme weather events

Extreme weather and climate events have serious impacts on our economy, society and environment. Extreme weather events include heatwaves, bushfires, tropical cyclones, cold snaps, extreme rainfall including flash flooding, and droughts.

There is increasing evidence that the frequency and intensity of many types of extreme weather events are changing. Extreme hot days in Australia are getting hotter, with the frequency of very hot (greater than 40°C) daytime temperatures increasing since the 1990s (Figure 4).

In addition, there has been an increase in extreme fire weather and fire season length across large parts of Australia since the 1950s, with a rapid increase in the late 1990s to early 2000s at many locations in south-eastern Australia.

Individual extreme events occur as a result of a number of contributing climatic factors. A growing body of research provides estimates of the relative contribution of natural variability and anthropogenic (human-caused) climate change to individual extreme events. For example, a study by the ARC Centre of Excellence for Climate System Science analysed the record heat and record low rainfall in winter 2017. It found that conditions were 60 times more likely to have occurred because of climate change than they would have otherwise.

Understanding the influences on such events helps us to better understand how and why extreme events are changing and allows us to plan for the future impacts of these events in Australia.

Rainfall patterns

Rainfall patterns are changing around the world. Research shows the global water cycle is intensifying with a warming climate, which means wet areas are likely to get wetter and dry regions are likely to be drier in response to climate change.

Australian rainfall is highly variable. Average annual rainfall has slightly increased since 1900, with a large increase in northwest Australia since 1970. Trends in rainfall over recent decades include:

increased spring and summer monsoonal rainfall across the north

higher than normal rainfall across the centre

decreased late autumn and winter rainfall across the south.

A climate change signal has been identified in the observed long-term reduction of cool season rainfall in southern Australia, with significant declines since the 1970s in southwest Western Australia (17 per cent), and since the mid-1990s in south-eastern Australia. South-eastern Australia experienced the most persistent rainfall deficit between 1997 and 2009 since records began at the start of the 20th Century.

More information on general climate impacts across Australia is provided in the State of the Climate 2020 report or on the website Climate Change in Australia.

Climate resources

Climate change science provides the information needed to understand and plan for climate change impacts, thereby increasing community resilience and reducing the cost of climate change impacts to society. Australian scientists are at the forefront of global efforts to understand the science behind climate change. Australia has a unique interest in and responsibility for leading climate change research in the southern hemisphere.

Climate change science is provided to the Australian Government by agencies such as the CSIRO and the Bureau of Meteorology, state and territory government agencies, as well as university research groups and cooperative research centres. Climate change science from these sources is reliable and quality assured because it goes through a stringent process of peer-review during which other scientists check the results of the research.

Peer-reviewed research

The peer-review process involves scientists evaluating the quality of other scientists' work. It provides a mechanism to quality control scientific discourse and peer-reviewed papers, by ensuring that the work is rigorous, coherent, uses past research and adds to what we already know. Climate change science that has been peer-reviewed therefore provides a reliable and quality assured source of information.

To publish in most scientific journals, conference proceedings, and to apply for grants, scientists have to go through a peer review process. The peer review process is usually a 'blind' review. This means that the authors do not know the identity of the reviewers. This process is designed to ensure evaluation is independent.

The Intergovernmental Panel on Climate Change (IPCC) assesses the peer-reviewed literature on climate change every five to six years, and publishes its findings in Assessment Reports. The IPCC reports are themselves subject to an intense peer-review process involving hundreds of scientific experts and government reviewers. This unprecedented level of peer and government review makes this compendium of climate change science one of the most scrutinised documents in the history of science.

Read more

See more

Temperature

Oceans

Extremes

Rainfall

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Australia's climate is changing. Observed changes over the 20th century include increases in global average air and ocean temperature, rising global sea levels, long-term sustained widespread reduction of snow and ice cover, and changes in atmospheric and ocean circulation and regional weather patterns, which influence seasonal rainfall conditions.​

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The Upper Atmosphere Is Cooling, Prompting New Climate Concerns

Climate as Seen from Space with NASA's Gavin Schmidt

The race to preserve Earth's historical climate record-its ice

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What Is the Evidence for Human-Caused Climate Change?

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Climate-warming trends over the past century are extremely likely due to human activities. Multiple studies show that 97 percent or more of climate scientists agree.

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The Earth's average surface temperature has risen about 1.2°C (2.2°F) since the late 19th century, with most warming occurring in the past 40 years. Global sea levels have risen by about 21–24 cm (8–9 in) since 1880, with about a third of this increase occurring in the last 25 years alone due to melting glaciers.

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Climate change: Natural and Anthropogenic causes - Yo Nature

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Climate change is the long term change in the Earth’s weather patterns which has natural causes like changes in the Earth’s orbit or anthropogenic causes.

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There is unequivocal evidence that Earth is warming at an unprecedented rate. Human activity is the principal cause.

While Earth's climate has changed throughout its history, the current warming is happening at a rate not seen in the past 10,000 years.

According to the Intergovernmental Panel on Climate Change (IPCC), "Since systematic scientific assessments began in the 1970s, the influence of human activity on the warming of the climate system has evolved from theory to established fact."¹

Scientific information taken from natural sources (such as ice cores, rocks, and tree rings) and from modern equipment (like satellites and instruments) all show the signs of a changing climate.

From global temperature rise to melting ice sheets, the evidence of a warming planet abounds.

The rate of change since the mid-20th century is unprecedented over millennia.

Earth's climate has changed throughout history. Just in the last 800,000 years, there have been eight cycles of ice ages and warmer periods, with the end of the last ice age about 11,700 years ago marking the beginning of the modern climate era - and of human civilization. Most of these climate changes are attributed to very small variations in Earth's orbit that change the amount of solar energy our planet receives.

The current warming trend is different because it is clearly the result of human activities since the mid-1800s, and is proceeding at a rate not seen over many recent millennia.1 It is undeniable that human activities have produced the atmospheric gases that have trapped more of the Sun's energy in the Earth system. This extra energy has warmed the atmosphere, ocean, and land, and widespread and rapid changes in the atmosphere, ocean, cryosphere, and biosphere have occurred.

Earth-orbiting satellites and new technologies have helped scientists see the big picture, collecting many different types of information about our planet and its climate all over the world. These data, collected over many years, reveal the signs and patterns of a changing climate.

Scientists demonstrated the heat-trapping nature of carbon dioxide and other gases in the mid-19th century.² Many of the science instruments NASA uses to study our climate focus on how these gases affect the movement of infrared radiation through the atmosphere. From the measured impacts of increases in these gases, there is no question that increased greenhouse gas levels warm Earth in response.

Scientific evidence for warming of the climate system is unequivocal.

Intergovernmental Panel on Climate Change

Ice cores drawn from Greenland, Antarctica, and tropical mountain glaciers show that Earth's climate responds to changes in greenhouse gas levels. Ancient evidence can also be found in tree rings, ocean sediments, coral reefs, and layers of sedimentary rocks. This ancient, or paleoclimate, evidence reveals that current warming is occurring roughly 10 times faster than the average rate of warming after an ice age. Carbon dioxide from human activities is increasing about 250 times faster than it did from natural sources after the last Ice Age.³

The Evidence for Rapid Climate Change Is Compelling:

Global Temperature Is Rising

The planet's average surface temperature has risen about 2 degrees Fahrenheit (1 degrees Celsius) since the late 19th century, a change driven largely by increased carbon dioxide emissions into the atmosphere and other human activities.4 Most of the warming occurred in the past 40 years, with the seven most recent years being the warmest. The years 2016 and 2020 are tied for the warmest year on record.5

Image credit: Ashwin Kumar, Creative Commons Attribution-Share Alike 2.0 Generic.

Ocean Acidification Is Increasing

Since the beginning of the Industrial Revolution, the acidity of surface ocean waters has increased by about 30%.13, 14 This increase is due to humans emitting more carbon dioxide into the atmosphere and hence more being absorbed into the ocean. The ocean has absorbed between 20% and 30% of total anthropogenic carbon dioxide emissions in recent decades (7.2 to 10.8 billion metric tons per year). 15, 16

Image credit: NOAA

Header image shows clouds imitating mountains as the sun sets after midnight as seen from Denali's backcountry Unit 13 on June 14, 2019. Credit: NPS/Emily Mesner
Image credit in list of evidence: Ashwin Kumar, Creative Commons Attribution-Share Alike 2.0 Generic.

Discover More Topics From NASA

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Takeaways The rate of change since the mid-20th century is unprecedented over millennia. Earth's climate has changed throughout history. Just in the last 800,000 years, there have been eight cycles of ice ages and warmer periods, with the end of the last ice age about 11,700 years ago marking the beginning of the modern climate [...]

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This thematic page includes key facts and resources about the intersection of climate change and Early Childhood Development.

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Volcanic eruptions are responsible for releasing molten rock, or lava, from deep within the Earth, forming new rock on the Earth's surface. But eruptions also impact the atmosphere.

The gases and dust particles thrown into the atmosphere during volcanic eruptions have influences on climate. Most of the particles spewed from volcanoes cool the planet by shading incoming solar radiation. The cooling effect can last for months to years depending on the characteristics of the eruption. Volcanoes have also caused global warming over millions of years during times in Earth's history when extreme amounts of volcanism occurred, releasing greenhouse gases into the atmosphere.

Even though volcanoes are in specific places on Earth, their effects can be more widely distributed as gases, dust, and ash get into the atmosphere. Because of atmospheric circulation patterns, eruptions in the tropics can have an effect on the climate in both hemispheres while eruptions at mid or high latitudes only have impact the hemisphere they are within.

Below is an overview of materials that make their way from volcanic eruptions into the atmosphere: particles of dust and ash, sulfur dioxide, and greenhouse gases like water vapor and carbon dioxide.

Particles of Dust and Ash

Volcanic ash or dust released into the atmosphere during an eruption shade sunlight and cause temporary cooling. Larger particles of ash have little effect because they fall out of the air quickly. Small ash particles form a dark cloud in the troposphere that shades and cools the area directly below. Most of these particles fall out of the atmosphere within rain a few hours or days after an eruption. But the smallest particles of dust get into the stratosphere and are able to travel vast distances, often worldwide. These tiny particles are so light that they can stay in the stratosphere for months, blocking sunlight and causing cooling over large areas of the Earth.

Sulfur from Volcanoes

Often, erupting volcanoes emit sulfur dioxide into the atmosphere. Sulfur dioxide is much more effective than ash particles at cooling the climate. The sulfur dioxide moves into the stratosphere and combines with water to form sulfuric acid aerosols. The sulfuric acid makes a haze of tiny droplets in the stratosphere that reflects incoming solar radiation, causing cooling of the Earth's surface. The aerosols can stay in the stratosphere for up to three years, moved around by winds and causing significant cooling worldwide. Eventually, the droplets grow large enough to fall to Earth.

Greenhouse Gases Emitted by Volcanoes

Volcanoes also release large amounts of greenhouse gases such as water vapor and carbon dioxide. The amounts put into the atmosphere from a large eruption doesn't change the global amounts of these gases very much. However, there have been times during Earth history when intense volcanism has significantly increased the amount of carbon dioxide in the atmosphere and caused global warming.

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I think our climate change is anthropogenic.

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Greenhouse gases affect Earth's energy balance and climate

The Sun serves as the primary energy source for Earth's climate. Some of the incoming sunlight is reflected directly back into space, especially by bright surfaces such as ice and clouds, and the rest is absorbed by the surface and the atmosphere. Much of this absorbed solar energy is re-emitted as heat (longwave or infrared radiation). The atmosphere in turn absorbs and re-radiates heat, some of which escapes to space. Any disturbance to this balance of incoming and outgoing energy will affect the climate. For example, small changes in the output of energy from the Sun will affect this balance directly.

If all heat energy emitted from the surface passed through the atmosphere directly into space, Earth's average surface temperature would be tens of degrees colder than today. Greenhouse gases in the atmosphere, including water vapour, carbon dioxide, methane, and nitrous oxide, act to make the surface much warmer than this because they absorb and emit heat energy in all directions (including downwards), keeping Earth's surface and lower atmosphere warm [Figure B1]. Without this greenhouse effect, life as we know it could not have evolved on our planet. Adding more greenhouse gases to the atmosphere makes it even more effective at preventing heat from escaping into space. When the energy leaving is less than the energy entering, Earth warms until a new balance is established.

Greenhouse gases emitted by human activities alter Earth's energy balance and thus its climate. Humans also affect climate by changing the nature of the land surfaces (for example by clearing forests for farming) and through the emission of pollutants that affect the amount and type of particles in the atmosphere.

Scientists have determined that, when all human and natural factors are considered, Earth's climate balance has been altered towards warming, with the biggest contributor being increases in CO ₂.

Human activities have added greenhouse gases to the atmosphere

The atmospheric concentrations of carbon dioxide, methane, and nitrous oxide have increased significantly since the Industrial Revolution began. In the case of carbon dioxide, the average concentration measured at the Mauna Loa Observatory in Hawaii has risen from 316 parts per million (ppm) in 1959 (the first full year of data available) to more than 411 ppm in 2019 [Figure B2]. The same rates of increase have since been recorded at numerous other stations worldwide. Since preindustrial times, the atmospheric concentration of CO ₂ has increased by over 40%, methane has increased by more than 150%, and nitrous oxide has increased by roughly 20%. More than half of the increase in CO ₂ has occurred since 1970. Increases in all three gases contribute to warming of Earth, with the increase in CO ₂ playing the largest role. See page B3 to learn about the sources of human emitted greenhouse gases. Learn about the sources of human emitted greenhouse gases.

Scientists have examined greenhouse gases in the context of the past. Analysis of air trapped inside ice that has been accumulating over time in Antarctica shows that the CO ₂ concentration began to increase significantly in the 19th century [Figure B3], after staying in the range of 260 to 280 ppm for the previous 10,000 years. Ice core records extending back 800,000 years show that during that time, CO ₂ concentrations remained within the range of 170 to 300 ppm throughout many "ice age" cycles - learn about the ice ages - and no concentration above 300 ppm is seen in ice core records until the past 200 years.

Measurements of the forms (isotopes) of carbon in the modern atmosphere show a clear fingerprint of the addition of "old" carbon (depleted in natural radioactive ¹⁴C) coming from the combustion of fossil fuels (as opposed to "newer" carbon coming from living systems). In addition, it is known that human activities (excluding land use changes) currently emit an estimated 10 billion tonnes of carbon each year, mostly by burning fossil fuels, which is more than enough to explain the observed increase in concentration. These and other lines of evidence point conclusively to the fact that the elevated CO ₂ concentration in our atmosphere is the result of human activities.

Fig b2. Measurements of atmospheric CO₂ since 1958 from the Mauna Loa Observatory in Hawaii (black) and from the South Pole (red) show a steady annual increase in atmospheric CO₂ concentration. The measurements are made at remote places like these because they are not greatly influenced by local processes, so therefore they are representative of the background atmosphere. The small up-and-down saw-tooth pattern reflects seasonal changes in the release and uptake of CO₂ by plants. Source: Scripps CO2 Program

Figure b3. CO₂ variations during the past 1,000 years, obtained from analysis of air trapped in an ice core extracted from Antarctica (red squares), show a sharp rise in atmospheric CO₂ starting in the late 19th century. Modern atmospheric measurements from Mauna Loa are superimposed in gray. Source: figure by Eric Wolff, data from Etheridge et al., 1996; MacFarling Meure et al., 2006; Scripps CO₂ Program.

Climate records show a warming trend

Estimating global average surface air temperature increase requires careful analysis of millions of measurements from around the world, including from land stations, ships, and satellites. Despite the many complications of synthesising such data, multiple independent teams have concluded separately and unanimously that global average surface air temperature has risen by about 1 °C (1.8 °F) since 1900 [Figure B4]. Although the record shows several pauses and accelerations in the increasing trend, each of the last four decades has been warmer than any other decade in the instrumental record since 1850.

Going further back in time before accurate thermometers were widely available, temperatures can be reconstructed using climate-sensitive indicators "proxies" in materials such as tree rings, ice cores, and marine sediments. Comparisons of the thermometer record with these proxy measurements suggest that the time since the early 1980s has been the warmest 40-year period in at least eight centuries, and that global temperature is rising towards peak temperatures last seen 5,000 to 10,000 years ago in the warmest part of our current interglacial period.

Many other impacts associated with the warming trend have become evident in recent years. Arctic summer sea ice cover has shrunk dramatically. The heat content of the ocean has increased. Global average sea level has risen by approximately 16 cm (6 inches) since 1901, due both to the expansion of warmer ocean water and to the addition of melt waters from glaciers and ice sheets on land. Warming and precipitation changes are altering the geographical ranges of many plant and animal species and the timing of their life cycles. In addition to the effects on climate, some of the excess CO ₂ in the atmosphere is being taken up by the ocean, changing its chemical composition (causing ocean acidification).

Figure b4. Earth's global average surface temperature has risen, as shown in this plot of combined land and ocean measurements from 1850 to 2019 derived from three independent analyses of the available data sets. The top panel shows annual average values from the three analyses, and the bottom panel shows decadal average values, including the uncertainty range (grey bars) for the maroon (HadCRUT4) dataset. The temperature changes are relative to the global average surface temperature, averaged from 1961−1990. Source: Based on IPCC AR5, data from the HadCRUT4 dataset (black), NOAA Climate.gov; data from UK Met Office Hadley Centre (maroon), US National Aeronautics and Space Administration Goddard Institute for Space Studies (red), and US National Oceanic and Atmospheric Administration National Centers for Environmental Information (orange).

Many complex processes shape our climate

Based just on the physics of the amount of energy that CO ₂ absorbs and emits, a doubling of atmospheric CO ₂ concentration from pre-industrial levels (up to about 560 ppm) would by itself cause a global average temperature increase of about 1 °C (1.8 °F). In the overall climate system, however, things are more complex; warming leads to further effects (feedbacks) that either amplify or diminish the initial warming.

The most important feedbacks involve various forms of water. A warmer atmosphere generally contains more water vapour. Water vapour is a potent greenhouse gas, thus causing more warming; its short lifetime in the atmosphere keeps its increase largely in step with warming. Thus, water vapour is treated as an amplifier, and not a driver, of climate change. Higher temperatures in the polar regions melt sea ice and reduce seasonal snow cover, exposing a darker ocean and land surface that can absorb more heat, causing further warming. Another important but uncertain feedback concerns changes in clouds. Warming and increases in water vapour together may cause cloud cover to increase or decrease which can either amplify or dampen temperature change depending on the changes in the horizontal extent, altitude, and properties of clouds. The latest assessment of the science indicates that the overall net global effect of cloud changes is likely to be to amplify warming.

The ocean moderates climate change. The ocean is a huge heat reservoir, but it is difficult to heat its full depth because warm water tends to stay near the surface. The rate at which heat is transferred to the deep ocean is therefore slow; it varies from year to year and from decade to decade, and it helps to determine the pace of warming at the surface. Observations of the sub-surface ocean are limited prior to about 1970, but since then, warming of the upper 700 m (2,300 feet) is readily apparent, and deeper warming is also clearly observed since about 1990.

Surface temperatures and rainfall in most regions vary greatly from the global average because of geographical location, in particular latitude and continental position. Both the average values of temperature, rainfall, and their extremes (which generally have the largest impacts on natural systems and human infrastructure), are also strongly affected by local patterns of winds.

Estimating the effects of feedback processes, the pace of the warming, and regional climate change requires the use of mathematical models of the atmosphere, ocean, land, and ice (the cryosphere) built upon established laws of physics and the latest understanding of the physical, chemical and biological processes affecting climate, and run on powerful computers. Models vary in their projections of how much additional warming to expect (depending on the type of model and on assumptions used in simulating certain climate processes, particularly cloud formation and ocean mixing), but all such models agree that the overall net effect of feedbacks is to amplify warming.

Human activities are changing the climate

Rigorous analysis of all data and lines of evidence shows that most of the observed global warming over the past 50 years or so cannot be explained by natural causes and instead requires a significant role for the influence of human activities.

In order to discern the human influence on climate, scientists must consider many natural variations that affect temperature, precipitation, and other aspects of climate from local to global scale, on timescales from days to decades and longer. One natural variation is the El Niño Southern Oscillation (ENSO), an irregular alternation between warming and cooling (lasting about two to seven years) in the equatorial Pacific Ocean that causes significant year-to-year regional and global shifts in temperature and rainfall patterns. Volcanic eruptions also alter climate, in part increasing the amount of small (aerosol) particles in the stratosphere that reflect or absorb sunlight, leading to a short-term surface cooling lasting typically about two to three years. Over hundreds of thousands of years, slow, recurring variations in Earth's orbit around the Sun, which alter the distribution of solar energy received by Earth, have been enough to trigger the ice age cycles of the past 800,000 years.

Fingerprinting is a powerful way of studying the causes of climate change. Different influences on climate lead to different patterns seen in climate records. This becomes obvious when scientists probe beyond changes in the average temperature of the planet and look more closely at geographical and temporal patterns of climate change. For example, an increase in the Sun's energy output will lead to a very different pattern of temperature change (across Earth's surface and vertically in the atmosphere) compared to that induced by an increase in CO ₂ concentration. Observed atmospheric temperature changes show a fingerprint much closer to that of a long-term CO ₂ increase than to that of a fluctuating Sun alone. Scientists routinely test whether purely natural changes in the Sun, volcanic activity, or internal climate variability could plausibly explain the patterns of change they have observed in many different aspects of the climate system. These analyses have shown that the observed climate changes of the past several decades cannot be explained just by natural factors.

How will climate change in the future?

Scientists have made major advances in the observations, theory, and modelling of Earth's climate system, and these advances have enabled them to project future climate change with increasing confidence. Nevertheless, several major issues make it impossible to give precise estimates of how global or regional temperature trends will evolve decade by decade into the future. Firstly, we cannot predict how much CO ₂ human activities will emit, as this depends on factors such as how the global economy develops and how society's production and consumption of energy changes in the coming decades. Secondly, with current understanding of the complexities of how climate feedbacks operate, there is a range of possible outcomes, even for a particular scenario of CO ₂ emissions. Finally, over timescales of a decade or so, natural variability can modulate the effects of an underlying trend in temperature. Taken together, all model projections indicate that Earth will continue to warm considerably more over the next few decades to centuries. If there were no technological or policy changes to reduce emission trends from their current trajectory, then further globally-averaged warming of 2.6 to 4.8 °C (4.7 to 8.6 °F) in addition to that which has already occurred would be expected during the 21st century [Figure B5]. Projecting what those ranges will mean for the climate experienced at any particular location is a challenging scientific problem, but estimates are continuing to improve as regional and local-scale models advance.

Figure b5. The amount and rate of warming expected for the 21st century depends on the total amount of greenhouse gases that humankind emits. Models project the temperature increase for a business-as-usual emissions scenario (in red) and aggressive emission reductions, falling close to zero 50 years from now (in blue). Black is the modelled estimate of past warming. Each solid line represents the average of different model runs using the same emissions scenario, and the shaded areas provide a measure of the spread (one standard deviation) between the temperature changes projected by the different models. All data are relative to a reference period (set to zero) of 1986-2005. Source: Based on IPCC AR5

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Climate change: evidence and causes | Royal Society

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Supplementary information for the project 'Climate Change: Evidence and causes'.

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Climate change is primarily caused by human activities, although natural factors also play a role.

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Greenhouse gases affect Earth's energy balance and climate

The Sun serves as the primary energy source for Earth's climate. Some of the incoming sunlight is reflected directly back into space, especially by bright surfaces such as ice and clouds, and the rest is absorbed by the surface and the atmosphere. Much of this absorbed solar energy is re-emitted as heat (longwave or infrared radiation). The atmosphere in turn absorbs and re-radiates heat, some of which escapes to space. Any disturbance to this balance of incoming and outgoing energy will affect the climate. For example, small changes in the output of energy from the Sun will affect this balance directly.

If all heat energy emitted from the surface passed through the atmosphere directly into space, Earth's average surface temperature would be tens of degrees colder than today. Greenhouse gases in the atmosphere, including water vapour, carbon dioxide, methane, and nitrous oxide, act to make the surface much warmer than this because they absorb and emit heat energy in all directions (including downwards), keeping Earth's surface and lower atmosphere warm [Figure B1]. Without this greenhouse effect, life as we know it could not have evolved on our planet. Adding more greenhouse gases to the atmosphere makes it even more effective at preventing heat from escaping into space. When the energy leaving is less than the energy entering, Earth warms until a new balance is established.

Greenhouse gases emitted by human activities alter Earth's energy balance and thus its climate. Humans also affect climate by changing the nature of the land surfaces (for example by clearing forests for farming) and through the emission of pollutants that affect the amount and type of particles in the atmosphere.

Scientists have determined that, when all human and natural factors are considered, Earth's climate balance has been altered towards warming, with the biggest contributor being increases in CO ₂.

Human activities have added greenhouse gases to the atmosphere

The atmospheric concentrations of carbon dioxide, methane, and nitrous oxide have increased significantly since the Industrial Revolution began. In the case of carbon dioxide, the average concentration measured at the Mauna Loa Observatory in Hawaii has risen from 316 parts per million (ppm) in 1959 (the first full year of data available) to more than 411 ppm in 2019 [Figure B2]. The same rates of increase have since been recorded at numerous other stations worldwide. Since preindustrial times, the atmospheric concentration of CO ₂ has increased by over 40%, methane has increased by more than 150%, and nitrous oxide has increased by roughly 20%. More than half of the increase in CO ₂ has occurred since 1970. Increases in all three gases contribute to warming of Earth, with the increase in CO ₂ playing the largest role. See page B3 to learn about the sources of human emitted greenhouse gases. Learn about the sources of human emitted greenhouse gases.

Scientists have examined greenhouse gases in the context of the past. Analysis of air trapped inside ice that has been accumulating over time in Antarctica shows that the CO ₂ concentration began to increase significantly in the 19th century [Figure B3], after staying in the range of 260 to 280 ppm for the previous 10,000 years. Ice core records extending back 800,000 years show that during that time, CO ₂ concentrations remained within the range of 170 to 300 ppm throughout many "ice age" cycles - learn about the ice ages - and no concentration above 300 ppm is seen in ice core records until the past 200 years.

Measurements of the forms (isotopes) of carbon in the modern atmosphere show a clear fingerprint of the addition of "old" carbon (depleted in natural radioactive ¹⁴C) coming from the combustion of fossil fuels (as opposed to "newer" carbon coming from living systems). In addition, it is known that human activities (excluding land use changes) currently emit an estimated 10 billion tonnes of carbon each year, mostly by burning fossil fuels, which is more than enough to explain the observed increase in concentration. These and other lines of evidence point conclusively to the fact that the elevated CO ₂ concentration in our atmosphere is the result of human activities.

Fig b2. Measurements of atmospheric CO₂ since 1958 from the Mauna Loa Observatory in Hawaii (black) and from the South Pole (red) show a steady annual increase in atmospheric CO₂ concentration. The measurements are made at remote places like these because they are not greatly influenced by local processes, so therefore they are representative of the background atmosphere. The small up-and-down saw-tooth pattern reflects seasonal changes in the release and uptake of CO₂ by plants. Source: Scripps CO2 Program

Figure b3. CO₂ variations during the past 1,000 years, obtained from analysis of air trapped in an ice core extracted from Antarctica (red squares), show a sharp rise in atmospheric CO₂ starting in the late 19th century. Modern atmospheric measurements from Mauna Loa are superimposed in gray. Source: figure by Eric Wolff, data from Etheridge et al., 1996; MacFarling Meure et al., 2006; Scripps CO₂ Program.

Climate records show a warming trend

Estimating global average surface air temperature increase requires careful analysis of millions of measurements from around the world, including from land stations, ships, and satellites. Despite the many complications of synthesising such data, multiple independent teams have concluded separately and unanimously that global average surface air temperature has risen by about 1 °C (1.8 °F) since 1900 [Figure B4]. Although the record shows several pauses and accelerations in the increasing trend, each of the last four decades has been warmer than any other decade in the instrumental record since 1850.

Going further back in time before accurate thermometers were widely available, temperatures can be reconstructed using climate-sensitive indicators "proxies" in materials such as tree rings, ice cores, and marine sediments. Comparisons of the thermometer record with these proxy measurements suggest that the time since the early 1980s has been the warmest 40-year period in at least eight centuries, and that global temperature is rising towards peak temperatures last seen 5,000 to 10,000 years ago in the warmest part of our current interglacial period.

Many other impacts associated with the warming trend have become evident in recent years. Arctic summer sea ice cover has shrunk dramatically. The heat content of the ocean has increased. Global average sea level has risen by approximately 16 cm (6 inches) since 1901, due both to the expansion of warmer ocean water and to the addition of melt waters from glaciers and ice sheets on land. Warming and precipitation changes are altering the geographical ranges of many plant and animal species and the timing of their life cycles. In addition to the effects on climate, some of the excess CO ₂ in the atmosphere is being taken up by the ocean, changing its chemical composition (causing ocean acidification).

Figure b4. Earth's global average surface temperature has risen, as shown in this plot of combined land and ocean measurements from 1850 to 2019 derived from three independent analyses of the available data sets. The top panel shows annual average values from the three analyses, and the bottom panel shows decadal average values, including the uncertainty range (grey bars) for the maroon (HadCRUT4) dataset. The temperature changes are relative to the global average surface temperature, averaged from 1961−1990. Source: Based on IPCC AR5, data from the HadCRUT4 dataset (black), NOAA Climate.gov; data from UK Met Office Hadley Centre (maroon), US National Aeronautics and Space Administration Goddard Institute for Space Studies (red), and US National Oceanic and Atmospheric Administration National Centers for Environmental Information (orange).

Many complex processes shape our climate

Based just on the physics of the amount of energy that CO ₂ absorbs and emits, a doubling of atmospheric CO ₂ concentration from pre-industrial levels (up to about 560 ppm) would by itself cause a global average temperature increase of about 1 °C (1.8 °F). In the overall climate system, however, things are more complex; warming leads to further effects (feedbacks) that either amplify or diminish the initial warming.

The most important feedbacks involve various forms of water. A warmer atmosphere generally contains more water vapour. Water vapour is a potent greenhouse gas, thus causing more warming; its short lifetime in the atmosphere keeps its increase largely in step with warming. Thus, water vapour is treated as an amplifier, and not a driver, of climate change. Higher temperatures in the polar regions melt sea ice and reduce seasonal snow cover, exposing a darker ocean and land surface that can absorb more heat, causing further warming. Another important but uncertain feedback concerns changes in clouds. Warming and increases in water vapour together may cause cloud cover to increase or decrease which can either amplify or dampen temperature change depending on the changes in the horizontal extent, altitude, and properties of clouds. The latest assessment of the science indicates that the overall net global effect of cloud changes is likely to be to amplify warming.

The ocean moderates climate change. The ocean is a huge heat reservoir, but it is difficult to heat its full depth because warm water tends to stay near the surface. The rate at which heat is transferred to the deep ocean is therefore slow; it varies from year to year and from decade to decade, and it helps to determine the pace of warming at the surface. Observations of the sub-surface ocean are limited prior to about 1970, but since then, warming of the upper 700 m (2,300 feet) is readily apparent, and deeper warming is also clearly observed since about 1990.

Surface temperatures and rainfall in most regions vary greatly from the global average because of geographical location, in particular latitude and continental position. Both the average values of temperature, rainfall, and their extremes (which generally have the largest impacts on natural systems and human infrastructure), are also strongly affected by local patterns of winds.

Estimating the effects of feedback processes, the pace of the warming, and regional climate change requires the use of mathematical models of the atmosphere, ocean, land, and ice (the cryosphere) built upon established laws of physics and the latest understanding of the physical, chemical and biological processes affecting climate, and run on powerful computers. Models vary in their projections of how much additional warming to expect (depending on the type of model and on assumptions used in simulating certain climate processes, particularly cloud formation and ocean mixing), but all such models agree that the overall net effect of feedbacks is to amplify warming.

Human activities are changing the climate

Rigorous analysis of all data and lines of evidence shows that most of the observed global warming over the past 50 years or so cannot be explained by natural causes and instead requires a significant role for the influence of human activities.

In order to discern the human influence on climate, scientists must consider many natural variations that affect temperature, precipitation, and other aspects of climate from local to global scale, on timescales from days to decades and longer. One natural variation is the El Niño Southern Oscillation (ENSO), an irregular alternation between warming and cooling (lasting about two to seven years) in the equatorial Pacific Ocean that causes significant year-to-year regional and global shifts in temperature and rainfall patterns. Volcanic eruptions also alter climate, in part increasing the amount of small (aerosol) particles in the stratosphere that reflect or absorb sunlight, leading to a short-term surface cooling lasting typically about two to three years. Over hundreds of thousands of years, slow, recurring variations in Earth's orbit around the Sun, which alter the distribution of solar energy received by Earth, have been enough to trigger the ice age cycles of the past 800,000 years.

Fingerprinting is a powerful way of studying the causes of climate change. Different influences on climate lead to different patterns seen in climate records. This becomes obvious when scientists probe beyond changes in the average temperature of the planet and look more closely at geographical and temporal patterns of climate change. For example, an increase in the Sun's energy output will lead to a very different pattern of temperature change (across Earth's surface and vertically in the atmosphere) compared to that induced by an increase in CO ₂ concentration. Observed atmospheric temperature changes show a fingerprint much closer to that of a long-term CO ₂ increase than to that of a fluctuating Sun alone. Scientists routinely test whether purely natural changes in the Sun, volcanic activity, or internal climate variability could plausibly explain the patterns of change they have observed in many different aspects of the climate system. These analyses have shown that the observed climate changes of the past several decades cannot be explained just by natural factors.

How will climate change in the future?

Scientists have made major advances in the observations, theory, and modelling of Earth's climate system, and these advances have enabled them to project future climate change with increasing confidence. Nevertheless, several major issues make it impossible to give precise estimates of how global or regional temperature trends will evolve decade by decade into the future. Firstly, we cannot predict how much CO ₂ human activities will emit, as this depends on factors such as how the global economy develops and how society's production and consumption of energy changes in the coming decades. Secondly, with current understanding of the complexities of how climate feedbacks operate, there is a range of possible outcomes, even for a particular scenario of CO ₂ emissions. Finally, over timescales of a decade or so, natural variability can modulate the effects of an underlying trend in temperature. Taken together, all model projections indicate that Earth will continue to warm considerably more over the next few decades to centuries. If there were no technological or policy changes to reduce emission trends from their current trajectory, then further globally-averaged warming of 2.6 to 4.8 °C (4.7 to 8.6 °F) in addition to that which has already occurred would be expected during the 21st century [Figure B5]. Projecting what those ranges will mean for the climate experienced at any particular location is a challenging scientific problem, but estimates are continuing to improve as regional and local-scale models advance.

Figure b5. The amount and rate of warming expected for the 21st century depends on the total amount of greenhouse gases that humankind emits. Models project the temperature increase for a business-as-usual emissions scenario (in red) and aggressive emission reductions, falling close to zero 50 years from now (in blue). Black is the modelled estimate of past warming. Each solid line represents the average of different model runs using the same emissions scenario, and the shaded areas provide a measure of the spread (one standard deviation) between the temperature changes projected by the different models. All data are relative to a reference period (set to zero) of 1986-2005. Source: Based on IPCC AR5

# data[].attributes.attachment_link.title
Climate change: evidence and causes | Royal Society

# data[].attributes.attachment_link.description
Supplementary information for the project 'Climate Change: Evidence and causes'.

# data[].attributes.attachment_link.readable_text
Greenhouse gases affect Earth's energy balance and climate

The Sun serves as the primary energy source for Earth's climate. Some of the incoming sunlight is reflected directly back into space, especially by bright surfaces such as ice and clouds, and the rest is absorbed by the surface and the atmosphere. Much of this absorbed solar energy is re-emitted as heat (longwave or infrared radiation). The atmosphere in turn absorbs and re-radiates heat, some of which escapes to space. Any disturbance to this balance of incoming and outgoing energy will affect the climate. For example, small changes in the output of energy from the Sun will affect this balance directly.

If all heat energy emitted from the surface passed through the atmosphere directly into space, Earth's average surface temperature would be tens of degrees colder than today. Greenhouse gases in the atmosphere, including water vapour, carbon dioxide, methane, and nitrous oxide, act to make the surface much warmer than this because they absorb and emit heat energy in all directions (including downwards), keeping Earth's surface and lower atmosphere warm [Figure B1]. Without this greenhouse effect, life as we know it could not have evolved on our planet. Adding more greenhouse gases to the atmosphere makes it even more effective at preventing heat from escaping into space. When the energy leaving is less than the energy entering, Earth warms until a new balance is established.

Greenhouse gases emitted by human activities alter Earth's energy balance and thus its climate. Humans also affect climate by changing the nature of the land surfaces (for example by clearing forests for farming) and through the emission of pollutants that affect the amount and type of particles in the atmosphere.

Scientists have determined that, when all human and natural factors are considered, Earth's climate balance has been altered towards warming, with the biggest contributor being increases in CO ₂.

Human activities have added greenhouse gases to the atmosphere

The atmospheric concentrations of carbon dioxide, methane, and nitrous oxide have increased significantly since the Industrial Revolution began. In the case of carbon dioxide, the average concentration measured at the Mauna Loa Observatory in Hawaii has risen from 316 parts per million (ppm) in 1959 (the first full year of data available) to more than 411 ppm in 2019 [Figure B2]. The same rates of increase have since been recorded at numerous other stations worldwide. Since preindustrial times, the atmospheric concentration of CO ₂ has increased by over 40%, methane has increased by more than 150%, and nitrous oxide has increased by roughly 20%. More than half of the increase in CO ₂ has occurred since 1970. Increases in all three gases contribute to warming of Earth, with the increase in CO ₂ playing the largest role. See page B3 to learn about the sources of human emitted greenhouse gases. Learn about the sources of human emitted greenhouse gases.

Scientists have examined greenhouse gases in the context of the past. Analysis of air trapped inside ice that has been accumulating over time in Antarctica shows that the CO ₂ concentration began to increase significantly in the 19th century [Figure B3], after staying in the range of 260 to 280 ppm for the previous 10,000 years. Ice core records extending back 800,000 years show that during that time, CO ₂ concentrations remained within the range of 170 to 300 ppm throughout many "ice age" cycles - learn about the ice ages - and no concentration above 300 ppm is seen in ice core records until the past 200 years.

Measurements of the forms (isotopes) of carbon in the modern atmosphere show a clear fingerprint of the addition of "old" carbon (depleted in natural radioactive ¹⁴C) coming from the combustion of fossil fuels (as opposed to "newer" carbon coming from living systems). In addition, it is known that human activities (excluding land use changes) currently emit an estimated 10 billion tonnes of carbon each year, mostly by burning fossil fuels, which is more than enough to explain the observed increase in concentration. These and other lines of evidence point conclusively to the fact that the elevated CO ₂ concentration in our atmosphere is the result of human activities.

Fig b2. Measurements of atmospheric CO₂ since 1958 from the Mauna Loa Observatory in Hawaii (black) and from the South Pole (red) show a steady annual increase in atmospheric CO₂ concentration. The measurements are made at remote places like these because they are not greatly influenced by local processes, so therefore they are representative of the background atmosphere. The small up-and-down saw-tooth pattern reflects seasonal changes in the release and uptake of CO₂ by plants. Source: Scripps CO2 Program

Figure b3. CO₂ variations during the past 1,000 years, obtained from analysis of air trapped in an ice core extracted from Antarctica (red squares), show a sharp rise in atmospheric CO₂ starting in the late 19th century. Modern atmospheric measurements from Mauna Loa are superimposed in gray. Source: figure by Eric Wolff, data from Etheridge et al., 1996; MacFarling Meure et al., 2006; Scripps CO₂ Program.

Climate records show a warming trend

Estimating global average surface air temperature increase requires careful analysis of millions of measurements from around the world, including from land stations, ships, and satellites. Despite the many complications of synthesising such data, multiple independent teams have concluded separately and unanimously that global average surface air temperature has risen by about 1 °C (1.8 °F) since 1900 [Figure B4]. Although the record shows several pauses and accelerations in the increasing trend, each of the last four decades has been warmer than any other decade in the instrumental record since 1850.

Going further back in time before accurate thermometers were widely available, temperatures can be reconstructed using climate-sensitive indicators "proxies" in materials such as tree rings, ice cores, and marine sediments. Comparisons of the thermometer record with these proxy measurements suggest that the time since the early 1980s has been the warmest 40-year period in at least eight centuries, and that global temperature is rising towards peak temperatures last seen 5,000 to 10,000 years ago in the warmest part of our current interglacial period.

Many other impacts associated with the warming trend have become evident in recent years. Arctic summer sea ice cover has shrunk dramatically. The heat content of the ocean has increased. Global average sea level has risen by approximately 16 cm (6 inches) since 1901, due both to the expansion of warmer ocean water and to the addition of melt waters from glaciers and ice sheets on land. Warming and precipitation changes are altering the geographical ranges of many plant and animal species and the timing of their life cycles. In addition to the effects on climate, some of the excess CO ₂ in the atmosphere is being taken up by the ocean, changing its chemical composition (causing ocean acidification).

Figure b4. Earth's global average surface temperature has risen, as shown in this plot of combined land and ocean measurements from 1850 to 2019 derived from three independent analyses of the available data sets. The top panel shows annual average values from the three analyses, and the bottom panel shows decadal average values, including the uncertainty range (grey bars) for the maroon (HadCRUT4) dataset. The temperature changes are relative to the global average surface temperature, averaged from 1961−1990. Source: Based on IPCC AR5, data from the HadCRUT4 dataset (black), NOAA Climate.gov; data from UK Met Office Hadley Centre (maroon), US National Aeronautics and Space Administration Goddard Institute for Space Studies (red), and US National Oceanic and Atmospheric Administration National Centers for Environmental Information (orange).

Many complex processes shape our climate

Based just on the physics of the amount of energy that CO ₂ absorbs and emits, a doubling of atmospheric CO ₂ concentration from pre-industrial levels (up to about 560 ppm) would by itself cause a global average temperature increase of about 1 °C (1.8 °F). In the overall climate system, however, things are more complex; warming leads to further effects (feedbacks) that either amplify or diminish the initial warming.

The most important feedbacks involve various forms of water. A warmer atmosphere generally contains more water vapour. Water vapour is a potent greenhouse gas, thus causing more warming; its short lifetime in the atmosphere keeps its increase largely in step with warming. Thus, water vapour is treated as an amplifier, and not a driver, of climate change. Higher temperatures in the polar regions melt sea ice and reduce seasonal snow cover, exposing a darker ocean and land surface that can absorb more heat, causing further warming. Another important but uncertain feedback concerns changes in clouds. Warming and increases in water vapour together may cause cloud cover to increase or decrease which can either amplify or dampen temperature change depending on the changes in the horizontal extent, altitude, and properties of clouds. The latest assessment of the science indicates that the overall net global effect of cloud changes is likely to be to amplify warming.

The ocean moderates climate change. The ocean is a huge heat reservoir, but it is difficult to heat its full depth because warm water tends to stay near the surface. The rate at which heat is transferred to the deep ocean is therefore slow; it varies from year to year and from decade to decade, and it helps to determine the pace of warming at the surface. Observations of the sub-surface ocean are limited prior to about 1970, but since then, warming of the upper 700 m (2,300 feet) is readily apparent, and deeper warming is also clearly observed since about 1990.

Surface temperatures and rainfall in most regions vary greatly from the global average because of geographical location, in particular latitude and continental position. Both the average values of temperature, rainfall, and their extremes (which generally have the largest impacts on natural systems and human infrastructure), are also strongly affected by local patterns of winds.

Estimating the effects of feedback processes, the pace of the warming, and regional climate change requires the use of mathematical models of the atmosphere, ocean, land, and ice (the cryosphere) built upon established laws of physics and the latest understanding of the physical, chemical and biological processes affecting climate, and run on powerful computers. Models vary in their projections of how much additional warming to expect (depending on the type of model and on assumptions used in simulating certain climate processes, particularly cloud formation and ocean mixing), but all such models agree that the overall net effect of feedbacks is to amplify warming.

Human activities are changing the climate

Rigorous analysis of all data and lines of evidence shows that most of the observed global warming over the past 50 years or so cannot be explained by natural causes and instead requires a significant role for the influence of human activities.

In order to discern the human influence on climate, scientists must consider many natural variations that affect temperature, precipitation, and other aspects of climate from local to global scale, on timescales from days to decades and longer. One natural variation is the El Niño Southern Oscillation (ENSO), an irregular alternation between warming and cooling (lasting about two to seven years) in the equatorial Pacific Ocean that causes significant year-to-year regional and global shifts in temperature and rainfall patterns. Volcanic eruptions also alter climate, in part increasing the amount of small (aerosol) particles in the stratosphere that reflect or absorb sunlight, leading to a short-term surface cooling lasting typically about two to three years. Over hundreds of thousands of years, slow, recurring variations in Earth's orbit around the Sun, which alter the distribution of solar energy received by Earth, have been enough to trigger the ice age cycles of the past 800,000 years.

Fingerprinting is a powerful way of studying the causes of climate change. Different influences on climate lead to different patterns seen in climate records. This becomes obvious when scientists probe beyond changes in the average temperature of the planet and look more closely at geographical and temporal patterns of climate change. For example, an increase in the Sun's energy output will lead to a very different pattern of temperature change (across Earth's surface and vertically in the atmosphere) compared to that induced by an increase in CO ₂ concentration. Observed atmospheric temperature changes show a fingerprint much closer to that of a long-term CO ₂ increase than to that of a fluctuating Sun alone. Scientists routinely test whether purely natural changes in the Sun, volcanic activity, or internal climate variability could plausibly explain the patterns of change they have observed in many different aspects of the climate system. These analyses have shown that the observed climate changes of the past several decades cannot be explained just by natural factors.

How will climate change in the future?

Scientists have made major advances in the observations, theory, and modelling of Earth's climate system, and these advances have enabled them to project future climate change with increasing confidence. Nevertheless, several major issues make it impossible to give precise estimates of how global or regional temperature trends will evolve decade by decade into the future. Firstly, we cannot predict how much CO ₂ human activities will emit, as this depends on factors such as how the global economy develops and how society's production and consumption of energy changes in the coming decades. Secondly, with current understanding of the complexities of how climate feedbacks operate, there is a range of possible outcomes, even for a particular scenario of CO ₂ emissions. Finally, over timescales of a decade or so, natural variability can modulate the effects of an underlying trend in temperature. Taken together, all model projections indicate that Earth will continue to warm considerably more over the next few decades to centuries. If there were no technological or policy changes to reduce emission trends from their current trajectory, then further globally-averaged warming of 2.6 to 4.8 °C (4.7 to 8.6 °F) in addition to that which has already occurred would be expected during the 21st century [Figure B5]. Projecting what those ranges will mean for the climate experienced at any particular location is a challenging scientific problem, but estimates are continuing to improve as regional and local-scale models advance.

Figure b5. The amount and rate of warming expected for the 21st century depends on the total amount of greenhouse gases that humankind emits. Models project the temperature increase for a business-as-usual emissions scenario (in red) and aggressive emission reductions, falling close to zero 50 years from now (in blue). Black is the modelled estimate of past warming. Each solid line represents the average of different model runs using the same emissions scenario, and the shaded areas provide a measure of the spread (one standard deviation) between the temperature changes projected by the different models. All data are relative to a reference period (set to zero) of 1986-2005. Source: Based on IPCC AR5

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Climate change: evidence and causes | Royal Society

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Supplementary information for the project 'Climate Change: Evidence and causes'.

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Increased Greenhouse Gas Concentrations: Direct atmospheric measurements from monitoring stations like Mauna Loa, Hawaii, show a substantial increase in key greenhouse gases:

Carbon Dioxide (CO2): Increased from pre-industrial levels of approximately 280 parts per million (ppm) to over 420 ppm (as of recent measurements). This increase is directly linked to the burning of fossil fuels, deforestation, and industrial processes.

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Trends in CO2 - NOAA Global Monitoring Laboratory

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GML conducts research on greenhouse gas and carbon cycle feedbacks, changes in aerosols, and surface radiation, and recovery of stratospheric ozone.

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Formal attribution studies using climate models and statistical methods consistently demonstrate that natural climate forcings (solar variations, volcanic eruptions) alone cannot explain the observed warming trend. These studies quantify the contribution of human activities to the observed changes.

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Authors Figures How to cite Expand all sections

Coordinating Lead Authors:

Veronika Eyring (Germany) and Nathan P. Gillett (Canada)

Lead Authors:

Krishna M. Achuta Rao (India), Rondrotiana Barimalala (South Africa/Madagascar), Marcelo Barreiro Parrillo (Uruguay), Nicolas Bellouin (United Kingdom/France), Christophe Cassou (France), Paul J. Durack (United States of America/Australia), Yu Kosaka (Japan), Shayne McGregor (Australia), Seung-Ki Min (Republic of Korea), Olaf Morgenstern (New Zealand/Germany), Ying Sun (China)

Contributing Authors:

Lisa Bock (Germany), Elizaveta Malinina (Canada/Russian Federation), Guðfinna Aðalgeirsdóttir (Iceland), Jonathan L. Bamber (United Kingdom), Chris Brierley (United Kingdom), Lee de Mora (United Kingdom), John P. Dunne (United States of America), John C. Fyfe (Canada), Peter J. Gleckler (United States of America), Peter Greve (Austria/Germany), Lukas Gudmundsson (Switzerland/Germany, Iceland), Karsten Haustein (United Kingdom, Germany/Germany), Ed Hawkins (United Kingdom), Benjamin J. Henley (Australia), Marika M. Holland (United States of America), Chris Huntingford (United Kingdom), Colin Jones (United Kingdom), Masa Kageyama (France), Rémi Kazeroni (Germany/France), Yeon-Hee Kim (Republic of Korea), Charles Koven (United States of America), Gerhard Krinner (France/Germany, France), Eunice Lo (United Kingdom/China), Daniel J. Lunt (United Kingdom), Sophie Nowicki (United States of America/France, United States of America), Adam S. Phillips (United States of America), Valeriu Predoi (United Kingdom), Krishnan Raghavan (India), Malcolm J. Roberts (United Kingdom), Jon Robson (United Kingdom), Lucas Ruiz (Argentina), Jean-Baptiste Sallée (France), Benjamin D. Santer (United States of America), Andrew P. Schurer (United Kingdom), Jessica Tierney (United States of America), Blair Trewin (Australia), Katja Weigel (Germany), Xuebin Zhang (Canada), Anni Zhao (United Kingdom/China), Tianjun Zhou (China)

Review Editors:

Tomas Halenka (Czech Republic), Jose A. Marengo Orsini (Brazil/Peru), Daniel Mitchell (United Kingdom)

Chapter Scientists:

Lisa Bock (Germany), Elizaveta Malinina (Canada/Russian Federation)

This chapter should be cited as:

Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423-552, doi: 10.1017/9781009157896.005 .

The evidence for human influence on recent climate change strengthened from the IPCC Second Assessment Report to the IPCC Fifth Assessment Report, and is now even stronger in this assessment. The IPCC Second Assessment Report (SAR, 1995) concluded 'the balance of evidence suggests that there is a discernible human influence on global climate'. In subsequent assessments (TAR, 2001; AR4, 2007; and AR5, 2013), the evidence for human influence on the climate system was found to have progressively strengthened. The AR5 concluded that human influence on the climate system is clear, evident from increasing greenhouse gas concentrations in the atmosphere, positive radiative forcing, observed warming, and physical understanding of the climate system. This chapter updates the assessment of human influence on the climate system for large-scale indicators of climate change, synthesizing information from paleo records, observations and climate models. It also provides the primary evaluation of large-scale indicators of climate change in this Report, complemented by fitness-for-purpose evaluation in subsequent chapters.

It is unequivocal that human influence has warmed the atmosphere, ocean and land since pre-industrial times. Combining the evidence from across the climate system increases the level of confidence in the attribution of observed climate change to human influence and reduces the uncertainties associated with assessments based on single variables. Large-scale indicators of climate change in the atmosphere, ocean, cryosphere and at the land surface show clear responses to human influence consistent with those expected based on model simulations and physical understanding. {3.8.1}

For most large-scale indicators of climate change, the simulated recent mean climate from the latest generation Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models underpinning this assessment has improved compared to the Coupled Model Intercomparison Project Phase 5 (CMIP5) models assessed in AR5 (high confidence ). Some differences from observations remain, for example in regional precipitation patterns. High-resolution models exhibit reduced biases in some but not all aspects of surface and ocean climate ( medium confidence), and most Earth system models, which include biogeochemical feedbacks, perform as well as their lower-complexity counterparts ( medium confidence). The multi-model mean captures most aspects of observed climate change well ( high confidence). The multi-model mean captures the proxy-reconstructed global-mean surface air temperature (GSAT) change during past high- and low-CO ₂ climates ( high confidence) and the correct sign of temperature and precipitation change in most assessed regions in the mid-Holocene ( medium confidence). The simulation of paleoclimates on continental scales has improved compared to AR5 ( medium confidence), but models often underestimate large temperature and precipitation differences relative to the present day ( high confidence). {3.8.2}

The likely range of human-induced warming in glo bal-me an surface air temperature (GSAT) in 201 0 -2019 relative to 185 0 -19 00 is 0.8°C-1.3°C, encompassing the observed warming of 0.9°C-1.2°C, while the change attributable to natural forcings is only −0.1°C to +0.1°C. The best estimate of human-induced warming is 1.07°C. Warming can now be attributed since 1850-1900, instead of since 1951 as done in AR5, thanks to a better understanding of uncertainties and because observed warming is larger. The likely ranges for human-induced GSAT and global mean surface temperature (GMST) warming are equal ( medium confidence). Attributing observed warming to specific anthropogenic forcings remains more uncertain. Over the same period, forcing from greenhouse gases likely increased GSAT by 1.0°C-2.0°C, while other anthropogenic forcings including aerosols likely decreased GSAT by 0.0°C-0.8°C. It is very likely that human-induced greenhouse gas increases were the main driver of tropospheric warming since comprehensive satellite observations started in 1979, and extremely likely that human-induced stratospheric ozone depletion was the main driver of cooling in the lower stratosphere between 1979 and the mid-1990s. {3.3.1}

The CMIP6 model ensemble reproduces the observed historical global surface temperature trend and variability with biases small enough to support detection and attribution of hu man-in duced warming (very high confidence ). The CMIP6 historical simulations assessed in this report have an ensemble mean global surface temperature change within 0.2°C of the observations over most of the historical period, and observed warming is within the 5-95% range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed 5-95% range of observed warming. CMIP6 models broadly reproduce surface temperature variations over the past millennium, including the cooling that follows periods of intense volcanism ( medium confidence). For upper air temperature, there is medium confidence that most CMIP5 and CMIP6 models overestimate observed warming in the upper tropical troposphere by at least 0.1°C per decade over the period 1979 to 2014. The latest updates to satellite-derived estimates of stratospheric temperature have resulted in decreased differences between simulated and observed changes of global mean temperature through the depth of the stratosphere ( medium confidence). {3.3.1}

The slower rate of GMST increase observed over 1998-2012 compared to 1951-2012 was a temporary event followed by a strong GMST increase (very high confidence ). Improved observational datasets since AR5 show a larger GMST trend over 1998-2012 than earlier estimates. All the observed estimates of the 1998-2012 GMST trend lie within the 10th-90th percentile range of CMIP6 simulated trends ( high confidence). Internal variability, particularly Pacific Decadal Variability, and variations in solar and volcanic forcings partly offset the anthropogenic surface warming trend over the 1998-2012 period ( high confidence). Global ocean heat content continued to increase throughout this period, indicating continuous warming of the entire climate system ( very high confidence). Since 2012, GMST has warmed strongly, with the past five years (2016-2020) being the warmest five-year period in the instrumental record since at least 1850 ( high confidence). {Cross-Chapter Box 3.1, 3.3.1; 3.5.1}

It is likely that human influence has contributed to moistening in the upper troposphere since 1979. Also, there is medium confidence that human influence contributed to a global increase in annual surface specific humidity, and medium confidence that it contributed to a decrease in surface relative humidity over mid-latitude Northern Hemisphere continents during summertime. {3.3.2}

It is likely that human influence has contributed to observed large-scale precipitation changes since the mid-20th century. New attribution studies strengthen previous findings of a detectable increase in Northern Hemisphere mid- to high-latitude land precipitation ( high confidence). Human influence has contributed to strengthening the zonal mean precipitation contrast between the wet tropics and dry subtropics ( medium confidence). Yet, anthropogenic aerosols contributed to decreasing global land summer monsoon precipitation from the 1950s to 1980s ( medium confidence). There is also medium confidence that human influence has contributed to high-latitude increases and mid-latitude decreases in Southern Hemisphere summertime precipitation since 1979 associated with the trend of the Southern Annular Mode toward its positive phase. Despite improvements, models still have deficiencies in simulating precipitation patterns, particularly over the tropical ocean ( high confidence). {3.3.2, 3.3.3, 3.5.2}

Human-induced greenhouse gas forcing is the main driver of the observed changes in hot and cold extremes on the global scale (virtually certain ) and on most continents (very likely ). It is likely that human influence, in particular due to greenhouse gas forcing, is the main driver of the observed intensification of heavy precipitation in global land regions during recent decades. There is high confidence in the ability of models to capture the large-scale spatial distribution of precipitation extremes over land. The magnitude and frequency of extreme precipitation simulated by CMIP6 models are similar to those simulated by CMIP5 models ( high confidence). {Cross-Chapter Box 3.2}

It is likely that human influence has contributed to the poleward expansion of the zonal mean Hadley cell in the Southern Hemisphere since the 1980s. There is medium confidence that the observed poleward expansion of the zonal mean Hadley cell in the Northern Hemisphere is within the range of internal variability. The causes of the observed strengthening of the Pacific Walker circulation since the 1980s are not well understood, and the observed strengthening trend is outside the range of trends simulated in the coupled models ( medium confidence). While CMIP6 models capture the general characteristics of the tropospheric large-scale circulation ( high confidence), systematic biases exist in the mean frequency of atmospheric blocking events, especially in the Euro-Atlantic sector, some of which reduce with increasing model resolution ( medium confidence). {3.3.3}

It is very likely that anthropogenic forcing, mainly due to greenhouse gas increases, was the main driver of Arctic sea ice loss since the late 1970s. There is new evidence that increases in anthropogenic aerosols have offset part of the greenhouse gas-induced Arctic sea ice loss since the 1950s ( medium confidence). In the Arctic, despite large differences in the mean sea ice state, loss of sea ice extent and thickness during recent decades is reproduced in all CMIP5 and CMIP6 models ( high confidence). By contrast, global climate models do not generally capture the small observed increase in Antarctic sea ice extent during the satellite era, and there is low confidence in attributing the causes of this change. {3.4.1}

It is very likely that human influence contributed to the observed reductions in Northern Hemisphere spring snow cover since 1950. The seasonal cycle in Northern Hemisphere snow cover is better reproduced by CMIP6 than by CMIP5 models ( high confidence). Human influence was very likely the main driver of the recent global, near-universal retreat of glaciers. It is very likely that human influence has contributed to the observed surface melting of the Greenland Ice Sheet over the past two decades, and there is medium confidence in an anthropogenic contribution to recent overall mass loss from the Greenland Ice Sheet. However, there is only limited evidence, with medium agreement, of human influence on Antarctic Ice Sheet mass balance through changes in ice discharge. {3.4.2, 3.4.3}

It is extremely likely that human influence was the main driver of the ocean heat content increase observed since the 1970s, which extends into the deeper ocean (very high confidence ). Since AR5, there is improved consistency between recent observed estimates and model simulations of changes in upper (<700 m) ocean heat content, when accounting for both natural and anthropogenic forcings. Updated observations and model simulations show that warming extends throughout the entire water column ( high confidence), with CMIP6 models simulating 58% of industrial-era heat uptake (1850-2014) in the upper layer (0-700 m), 21% in the intermediate layer (700-2000 m) and 22% in the deep layer (>2000 m). The structure and magnitude of multi-model mean ocean temperature biases have not changed substantially between CMIP5 and CMIP6 ( medium confidence). {3.5.1}

It is extremely likely that human influence has contributed to observed near-surface and subsurface ocean salinity changes since the mid-20th century. The associated pattern of change corresponds to fresh regions becoming fresher and salty regions becoming saltier ( high confidence). Changes to the coincident atmospheric water cycle and ocean-atmosphere fluxes (evaporation and precipitation) are the primary drivers of the observed basin-scale salinity changes ( high confidence). The observed depth-integrated basin-scale salinity changes have been attributed to human influence, with CMIP5 and CMIP6 models able to reproduce these patterns only in simulations that include greenhouse gas increases ( medium confidence). The basin-scale changes are consistent across models and intensify through the historical period ( high confidence). The structure of the biases in the multi-model mean has not changed substantially between CMIP5 and CMIP6 ( medium confidence). {3.5.2}

Combining the attributable contributions from glaciers, i ce-she et surface mass balance and thermal expansion, it is very likely that human influence was the main driver of the observed global mean sea level rise since at least 1971. Since AR5, studies have shown that simulations that exclude anthropogenic greenhouse gases are unable to capture the sea level rise due to thermal expansion (thermosteric) during the historical period and that model simulations that include all forcings (anthropogenic and natural) most closely match observed estimates. It is very likely that human influence was the main driver of the observed global mean thermosteric sea level increase since 1970. {3.5.3, 3.5.1, 3.4.3}

While observations show that the Atlantic Meridional Overturning Circulation (AMOC) has weakened from the mid-2000s to the mid-2010s (high confidence) and the Southern Ocean upper overturning cell has strengthened since the 1990s (low confidence ), observational records are too short to determine the relative contributions of internal variability, natural forcing, and anthropogenic forcing to these changes (high confidence ). No changes in Antarctic Circumpolar Current transport or meridional position have been observed. The mean zonal and overturning circulations of the Southern Ocean and the mean overturning circulation of the North Atlantic (the Atlantic Meridional Overturning Circulation, AMOC) are broadly reproduced by CMIP5 and CMIP6 models. However, biases are apparent in the modelled circulation strengths ( high confidence) and their variability ( medium confidence). {3.5.4}

The main driver of the observed increase in the amplitude of the seasonal cycle of atmospheric CO ₂ is enhanced fertilization of plant growth by the increasing concentration of atmospheric CO ₂ ( medium confidence) . However, there is only low confidence that this CO ₂ fertilization has also been the main driver of observed greening because land management is the dominating factor in some regions. Earth system models simulate globally averaged land carbon sinks within the range of observation-based estimates ( high confidence), but global-scale agreement masks large regional disagreements. {3.6.1}

It is virtually certain that the uptake of anthropogenic CO ₂ was the main driver of the observed acidification of the global surface open ocean. The observed increase in CO ₂ concentration in the subtropical and equatorial North Atlantic since 2000 is likely associated in part with an increase in ocean temperature, a response that is consistent with the expected weakening of the ocean carbon sink with warming. Consistent with AR5 there is medium confidence that deoxygenation in the upper ocean is due in part to human influence. There is high confidence that Earth system models simulate a realistic time evolution of the global mean ocean carbon sink. {3.6.2}

It is very likely that human influence has contributed to the observed trend towards the positive phase of the Southern Annular Mode (SAM) since the 1970s and to the associated strengthening and southward shift of the Southern Hemispheric extratropical jet in austral summer. The influence of ozone forcing on the SAM trend has been small since the early 2000s compared to earlier decades, contributing to a weaker SAM trend observed over 2000-2019 ( medium confidence). Climate models reproduce the summertime SAM trend well, with CMIP6 models outperforming CMIP5 models ( medium confidence). By contrast, the cause of the Northern Annular Mode (NAM) trend towards its positive phase since the 1960s and associated northward shifts of the Northern Hemispheric extratropical jet and storm track in boreal winter is not well understood. Models reproduce the observed spatial features and variance of the SAM and NAM very well ( high confidence). {3.3.3, 3.7.1, 3.7.2}

Human influence has not affected the principal tropical modes of interannual climate variability or their associated regional teleconnections beyond the range of internal variability (high confidence ). Further assessment since AR5 confirms that climate and Earth system models are able to reproduce most aspects of the spatial structure and variance of the El Niño-Southern Oscillation and Indian Ocean Basin and Dipole modes ( medium confidence). However, despite a slight improvement in CMIP6, some underlying processes are still poorly represented. In the Tropical Atlantic basin, which contains the Atlantic Zonal and Meridional modes, major biases in modelled mean state and variability remain. {3.7.3 to 3.7.5}

There is medium confidence that anthropogenic and volcanic aerosols contributed to observed changes in the Atlantic Multi-decadal Variability (AMV) index and associated regional teleconnections since the 1960s, but there is low confidence in the magnitude of this influence. There is high confidence that internal variability is the main driver of Pacific Decadal Variability (PDV) observed since pre-industrial times, despite some modelling evidence for potential human influence. Uncertainties remain in quantification of the human influence on AMV and PDV due to brevity of the observational records, limited model performance in reproducing related sea surface temperature (SST) anomalies despite improvements from CMIP5 to CMIP6 ( medium confidence), and limited process understanding of their key drivers. {3.7.6, 3.7.7}

This chapter assesses the extent to which the climate system has been affected by human influence and to what extent climate models are able to simulate observed mean climate, changes and variability. This assessment is the basis for understanding what impacts of anthropogenic climate change may already be occurring and informs our confidence in climate projections. Moreover, an understanding of the amount of human-induced global warming to date is key to assessing our status with respect to the Paris Agreement goals of holding the increase in global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C ( UNFCCC, 2016).

The evidence of human influence on the climate system has strengthened progressively over the course of the previous five IPCC assessments, from the Second Assessment Report that concluded 'the balance of evidence suggests a discernible human influence on climate' through to the Fifth Assessment Report (AR5) which concluded that 'it is extremely likely that human influence caused more than half of the observed increase in global mean surface temperature (GMST) from 1951 to 2010' (see also Sections 1.3.4 and 3.3.1.1). The AR5 concluded that climate models had been developed and improved since the Fourth Assessment Report (AR4) and were able to reproduce many features of observed climate. Nonetheless, several systematic biases were identified ( Flato et al., 2013). This chapter additionally builds on the assessment of attribution of global temperatures contained in the IPCC Special Report on Global Warming of 1.5°C (SR1.5; IPCC, 2018), assessments of attribution of changes in the ocean and cryosphere in the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; IPCC, 2019b), and assessments of attribution of changes in the terrestrial carbon cycle in the IPCC Special Report on Climate Change and Land (SRCCL, IPCC, 2019a).

This chapter assesses the evidence for human influence on observed large-scale indicators of climate change that are described in Cross-Chapter Box 2.2 and assessed in Chapter 2. It takes advantage of the longer period of record now available in many observational datasets. The assessment of the human-induced contribution to observed climate change requires an estimate of the expected response to human influence, as well as an estimate of the expected climate evolution due to natural forcings and an estimate of variability internal to the climate system (internal climate variability). For this we need high quality models, primarily climate and Earth system models. Since AR5, a new set of coordinated model results from the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6; Eyring et al., 2016a) has become available. Together with updated observations of large-scale indicators of climate change (Chapter 2), CMIP simulations are a key resource for assessing human influence on the climate system. Pre-industrial control and historical simulations are of most relevance for model evaluation and assessment of internal variability, and these simulations are evaluated to assess fitness-for-purpose for attribution, which is the focus of this chapter (see also ( Section 1.5.4). This chapter provides the primary evaluation of large-scale indicators of climate change in this Report, and is complemented by other fitness-for-purpose evaluations in subsequent chapters. CMIP6 also includes an extensive set of idealized and single forcing experiments for attribution ( Eyring et al., 2016a; Gillett et al., 2016). In addition to the assessment of model performance and human influence on the climate system during the instrumental era up to the present-day, this chapter also includes evidence from paleo-observations and simulations over past millennia ( Kageyama et al., 2018).

Whereas in previous IPCC assessment reports the comparison of simulated and observed climate change was done separately in a model evaluation chapter and a chapter on detection and attribution, in AR6 these comparisons are integrated together. This has the advantage of allowing a single discussion of the full set of explanations for any inconsistency in simulated and observed climate change, including missing forcings, errors in the simulated response to forcings, and observational errors, as well as an assessment of the application of detection and attribution techniques to model evaluation. Where simulated and observed changes are consistent, this can be interpreted both as supporting attribution statements, and as giving confidence in simulated future change in the variable concerned (see also Box 4.1). However, if a model's simulation of historical climate change has been tuned to agree with observations, or if the models used in an attribution study have been selected or weighted on the basis of the realism of their simulated climate response, this information would need to be considered in the assessment and any attribution results correspondingly tempered. An integrated discussion of evaluation and attribution supports such a robust and transparent assessment.

This chapter starts with a brief description of methods for detection and attribution of observed changes in Section 3.2, which builds on the more general introduction to attribution approaches in the Cross-Working Group Box on Attribution in Chapter 1. In this chapter we assess the detection of anthropogenic influence on climate on large spatial scales and long temporal scales, a concept related to, but distinct from, that of the emergence of anthropogenically-induced climate change from the range of internal variability on local scales and shorter time scales ( Section 1.4.2.2). The following sections address the climate system component by component, in each case assessing human influence and evaluating climate models' simulations of the relevant aspects of climate and climate change. This chapter assesses the evaluation and attribution of global, hemispheric, continental and ocean basin-scale indicators of climate change in the atmosphere and at the Earth's surface ( Section 3.3, cryosphere ( Section 3.4, ocean ( Section 3.5, and biosphere ( Section 3.6, and the evaluation and attribution of modes of variability (Section 3.7), the period of slower warming in the early 21st century (Cross-Chapter Box 3.1) and large-scale changes in extremes (Cross-Chapter Box 3.2). Model evaluation and attribution on sub-continental scales are not covered here, since these are assessed in the Atlas and in Chapter 10, and extreme event attribution is not covered since it is assessed in Chapter 11. Section 3.8 assesses multivariate attribution and integrative measures of model performance based on multiple variables, as well as process representation in different classes of models. The chapter structure is summarized in Figure 3.1.

Figure 3.1 | Visual guide to Chapter 3.

New methods for model evaluation that are used in this chapter are described in Section 1.5.4. These include new techniques for process-based evaluation of climate and Earth system models against observations that have rapidly advanced since the publication of AR5 ( Eyring et al., 2019) as well as newly developed CMIP evaluation tools that allow a more rapid and comprehensive evaluation of the models with observations ( Eyring et al., 2016a, b).

In this chapter, we use the Earth System Model Evaluation Tool (ESMValTool, Eyring et al., 2020; Lauer et al., 2020; Righi et al., 2020) and the NCAR Climate Variability Diagnostic Package (CVDP, Phillips et al., 2014) that is included in the ESMValTool to produce most of the figures. This ensures traceability of the results and provides an additional level of quality control. The ESMValTool code to produce the figures in this chapter was released as open source software at the time of the publication of this Report (see details in the Chapter Data Table, Table SM.3.1). Figures in this chapter are produced either using one ensemble member from each model, or using all available ensemble members and weighting each simulation by 1/( NM i), where N is the number of models and M i is the ensemble size of the i th model, prior to calculating means and percentiles. Both approaches ensure that each model used is given equal weight in the figures, and details on which approach is used are provided in the figure captions.

An introduction to recent developments in detection and attribution methods in the context of this Report is provided in the Cross-Working Group Box on Attribution in Chapter 1. Here we discuss new methods and improvements applicable to the attribution of changes in large-scale indicators of climate change which are used in this chapter.

3.2.1 Methods Based on Regression

Regression-based methods, also known as fingerprinting methods, have been widely used for detection of climate change and attribution of the change to different external drivers. Initially, these methods were applied to detect changes in global surface temperature, and were then extended to other climate variables at different time and spatial scales (e.g., Hegerl et al., 1996; Hasselmann, 1997; Allen and Tett, 1999; Gillett et al., 2003b; Zhang et al., 2007; Min et al., 2008a, 2011). These approaches are based on multivariate linear regression and assume that the observed change consists of a linear combination of externally forced signals plus internal variability, which generally holds for large-scale variables ( Hegerl and Zwiers, 2011). The regressors are the expected space-time response patterns to different climate forcings (fingerprints), and the residuals represent internal variability. Fingerprints are usually estimated from climate model simulations following spatial and temporal averaging. A regression coefficient which is significantly greater than zero implies that a detectable change is identified in the observations. When the confidence interval of the regression coefficient includes unity and is inconsistent with zero, the magnitude of the model simulated fingerprints is assessed to be consistent with the observations, implying that the observed changes can be attributed in part to a particular forcing. Variants of linear regression have been used to address uncertainty in the fingerprints due to internal variability ( Allen and Stott, 2003) as well as structural model uncertainty ( Huntingford et al., 2006).

In order to improve the signal-to-noise ratio, observations and model-simulated responses are usually normalized by an estimate of internal variability derived from climate model simulations. This procedure requires an estimate of the inverse covariance matrix of the internal variability, and some approaches have been proposed for more reliable estimation of this ( Ribes et al., 2009). A signal can be spuriously detected due to too-small noise, and hence simulated internal variability needs to be evaluated with care. Model-simulated variability is typically checked through comparing modelled variance from unforced simulations with the observed residual variance using a standard residual consistency test ( Allen and Tett, 1999), or an improved one ( Ribes and Terray, 2013). Imbers et al. (2014) tested the sensitivity of detection and attribution results to different representations of internal variability associated with short-memory and long-memory processes. Their results supported the robustness of previous detection and attribution statements for the global mean temperature change but they also recommended the use of a wider variety of robustness tests.

Some recent studies focused on the improved estimation of the scaling factor (regression coefficient) and its confidence interval. Hannart et al. (2014) described an inference procedure for scaling factors which avoids making the assumption that model error and internal variability have the same covariance structure. An integrated approach to optimal fingerprinting was further suggested in which all uncertainty sources (i.e., observational error, model error, and internal variability) are treated in one statistical model without a preliminary dimension reduction step ( Hannart, 2016). Katzfuss et al. (2017) introduced a similar integrated approach based on a Bayesian model averaging. On the other hand, DelSole et al. (2019) suggested a bootstrap method to better estimate the confidence intervals of scaling factors even in a weak-signal regime. It is notable that some studies do not optimize fingerprints, as uncertainty in the covariance introduces a further layer of complexity, but results in only a limited improvement in detection ( Polson and Hegerl, 2017).

Another fingerprinting approach uses pattern similarity between observations and fingerprints, in which the leading empirical orthogonal function obtained from the time-evolving multi-model forced simulation is usually defined as a fingerprint (e.g., Santer et al., 2013; Marvel et al., 2019; Bonfils et al., 2020). Observations and model simulations are then projected onto the fingerprint to measure the degree of spatial pattern similarity with the expected physical response to a given forcing. This projection provides the signal time series, which is in turn tested against internal variability, as estimated from long control simulations. As a way to extend this pattern-based approach to a high-dimensional detection variable at daily time scales, Sippel et al. (2019, 2020) proposed using the relationship pattern with a global climate change metric as a fingerprint. To solve the high-dimensional regression problem which makes regression coefficients not well constrained, they incorporated a statistical learning technique based on a regularized linear regression, which optimizes a global warming signal by giving lower weight to regions with large internal variability.

3.2.2 Other Probabilistic Approaches

Considering the difficulty in accounting for climate modelling uncertainties in the regression-based approaches, Ribes et al. (2017) introduced a new statistical inference framework based on an additivity assumption and likelihood maximization, which estimates climate model uncertainty based on an ensemble of opportunity and tests whether observations are inconsistent with internal variability and consistent with the expected response from climate models. The method was further developed by Ribes et al. (2021), who applied it to narrow the uncertainty range in the estimated human-induced warming. Hannart and Naveau (2018), on the other hand, extended the application of standard causal theory ( Pearl, 2009) to the context of detection and attribution by converting a time series into an event, and calculating the probability of causation, an approach which maximizes the causal evidence associated with the forcing. On the other hand, Schurer et al. (2018) employed a Bayesian framework to explicitly consider climate modelling uncertainty in the optimal regression method. Application of these approaches to attribution of large-scale temperature changes supports a dominant anthropogenic contribution to the observed global warming.

Climate change signals can vary with time and discriminant analysis has been used to obtain more accurate estimates of time-varying signals, and has been applied to different variables such as seasonal temperatures ( Jia and DelSole, 2012) and the South Asian monsoon ( Srivastava and DelSole, 2014). The same approach was applied to separate aerosol forcing responses from other forcings (X. Yan et al., 2016) and results using climate model output indicated that detectability of the aerosol response is maximized by using a combination of temperature and precipitation data. Paeth et al. (2017) introduced a detection and attribution method applicable for multiple variables based on a discriminant analysis and a Bayesian classification method. Finally, a systematic approach has been proposed to translating quantitative analysis into a description of confidence in the detection and attribution of a climate response to anthropogenic drivers ( Stone and Hansen, 2016).

Overall, these new fingerprinting and other probabilistic methods for detection and attribution as well as efforts to better incorporate the associated uncertainties have addressed a number of shortcomings in previously applied detection and attribution techniques. They further strengthen the confidence in attribution of observed large-scale changes to a combination of external forcings as assessed in the following sections.

Cross-Chapter Box 3.1 | Global Surface Warming Over the Early 21st Century

Contributors: Christophe Cassou (France), Yu Kosaka (Japan), John C. Fyfe (Canada), Nathan P. Gillett (Canada), Ed Hawkins (United Kingdom), Blair Trewin (Australia)

The AR5 found that the rate of global mean surface temperature (GMST) increase inferred from observations over the 1998-2012 period was lower than the rate of increase over the 1951-2012 period, and lower than the ensemble mean increase in historical simulations from CMIP5 climate models extended by Representative Concentration Pathway (RCP) scenario simulations beyond 2005

( Flato et al., 2013). This apparent slowdown of surface global warming compared to the 62-year rate was assessed with medium confidence to have been caused in roughly equal measure by a cooling contribution from internal variability and a reduced trend in external forcing (particularly associated with solar and volcanic forcing) in AR5 based on expert judgement ( Flato et al., 2013). In AR5 it was assessed that almost all CMIP5 simulations did not reproduce the observed slower warming, and that there was medium confidence that the trend difference from the CMIP5 ensemble mean was to a substantial degree caused by internal variability with possible contributions from forcing error and model response uncertainty. This Cross-Chapter Box assesses new findings from observational products and statistical and physical models on trends over the 1998-2012 period considered in AR5.

Updated observational and reanalyses datasets and comparison with model simulations

Since AR5, there have been version updates and new releases of most observational GMST datasets (Cross-Chapter Box 2.3). All the updated products now available consistently find stronger positive trends for 1998-2012 than those assessed in AR5 ( Cowtan and Way, 2014; Karl et al., 2015; Hausfather et al., 2017; Medhaug et al., 2017; Simmons et al., 2017; Risbey et al., 2018). Simmons et al. (2017) reported that the 1998-2012 GMST trends in the updated observational and reanalysis datasets available at that time ranged from 0.06°C to 0.14°C per decade, compared with the 0.05°C per decade on average reported in AR5, while the latest data products reported in Chapter 2 Table 2.4 show GMST or global mean near-surface air temperature (GSAT) trends over that period ranging from 0.12°C to 0.14°C per decade. The lowest trend in Simmons et al. (2017) is from HadCRUT4, now superseded by HadCRUT5, which shows a trend of 0.12°C per decade. The upward revision is mainly due to improved sea surface temperature (SST) datasets and infilling of surface temperature in locations with missing records in observational products, mainly in the Arctic (see Cross-Chapter Box 2.3 for details).

With these updates, all the observed trends assessed here lie within the 10th-90th percentile range of the simulated trends in the CMIP5 and CMIP6 simulations (Cross-Chapter Box 3.1, Figure 1a). This result is insensitive to whether model GSAT (based on surface air temperature) or GMST (based on a blend of surface air temperature over land and sea ice and SST over open ocean) is used, and to whether or not masking with the observational data coverage is applied. Therefore, the observed 1998-2012 trend is consistent with both the CMIP5 or CMIP6 multi-model ensemble of trends over the same period ( high confidence).

Cross-Chapter Box 3.1, Figure 1 | 15-year trends of global surface temperature for 1998-2012 and 2012-2026. (a, b) GSAT and GMST trends for 1998-2012 (a) and 2012-2026 (b). Histograms are based on GSAT in historical simulations of CMIP6 (red shading, extended by SSP2-4.5) and CMIP5 (grey shading; extended by RCP4.5). Filled and open diamonds at the top represent multi-model ensemble means of GSAT and GMST trends, respectively. Diagonal lines show histograms of HadCRUT5.0.1.0. Triangles at the top of (a) represent GMST trends from Berkeley Earth, GISTEMP, Kadow et al. (2020) and NOAAGlobalTemp-Interim, and the GSAT trend from ERA5. Selected CMIP6 members whose 1998-2012 trends are lower than the HadCRUT5.0.1.0 mean trend are indicated by purple shading (a) and (b). In (a), model GMST and GSAT, and ERA5 GSAT are masked to match HadCRUT data coverage. (c-d) Trend maps of annual near-surface temperature for 1998-2012 based on HadCRUT5.0.1.0 mean (c), and composited surface air temperature trends of subsampled CMIP6 simulations (d) with GSAT trends in the purple shaded area in (a). In (c), cross marks indicate trends that are not significant at the 10% level based on t-tests with serial correlation taken into account. The ensemble size used for each of the histograms and the trend composite is indicated at the top right of each of the panels (a, b, d). Model ensemble members are weighted with the inverse of the ensemble size of the same model, so that each model is equally weighted. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Internal variability

All the observation-based GMST and GSAT trends are lower than the multi-model mean GMST and GSAT trends of both CMIP5 and CMIP6 for 1998-2012 (Cross-Chapter Box 3.1, Figure 1a). This suggests a possible cooling contribution from internal variability during this period. This is supported by initialized decadal hindcasts, which account for the phase of the multi-decadal modes of variability (Sections 3.7.6and 3.7.7), and which reproduce observed global mean SST and GSAT trends better than uninitialized historical simulations ( Guemas et al., 2013; Meehl et al., 2014).

Studies since AR5 identify Pacific Decadal Variability (PDV) as the leading mode of variability associated with unforced decadal GSAT fluctuations, with additional influence from Atlantic Multi-decadal Variability (Annex IV.2.6, IV.2.7; Brown et al., 2015; Dai et al., 2015; Steinman et al., 2015; Pasini et al., 2017). PDV transitioned from positive (El Niño-like) to negative (La Niña-like) phases during the slow warming period (Figure 3.39f and Cross-Chapter Box 3.1, Figure 1c). Model ensemble members that capture the observed slower decadal warming under transient forcing, and time segments of model simulations that show decadal GSAT decreases under fixed radiative forcing, also feature negative PDV trends (Cross-Chapter Box 3.1, Figure 1d; Meehl et al., 2011, 2013, 2014; Maher et al., 2014; Middlemas and Clement, 2016), suggesting the influence of PDV. This is confirmed by statistical models with the PDV-GSAT relationship estimated from observations and model simulations ( Schmidt et al., 2014; Meehl et al., 2016b; Hu and Fedorov, 2017), selected ensemble members and time segments from model simulations where PDV by chance evolves in phase with observations over the slow warming period ( Huber and Knutti, 2014; Risbey et al., 2014), and coupled model experiments in which PDV evolution is constrained to follow the observations ( Kosaka and Xie, 2013, 2016; England et al., 2014; Watanabe et al., 2014; Delworth et al., 2015). Part of the PDV trend may have been driven by anthropogenic aerosols ( Smith et al., 2016); however, this result is model-dependent, and internally-driven PDV dominates the forced PDV signal in the CMIP6 multi-model ensemble ( Section 3.7.6). It is also notable that there is large uncertainty in the magnitude of the PDV influence on GSAT across models ( Deser et al., 2017a; C.-Y. Wang et al., 2017) and among the studies cited above. In addition to PDV, contributions to the reduced warming trend from wintertime Northern Hemisphere atmospheric internal variability, particularly associated with a trend towards the negative phase of the Northern Annular Mode/North Atlantic Oscillation (Annex IV.2.1; Guan et al., 2015; Saffioti et al., 2015; Iles and Hegerl, 2017) or the Cold Ocean-Warm Land (COWL) pattern ( Molteni et al., 2017; Yang et al., 2020) have been suggested, leading to regional continental cooling over a large part of Eurasia and North America (Cross-Chapter Box 3.1, Figure 1c; C. Li et al., 2015; Deser et al., 2017a; Gan et al., 2019).

Such internally-driven variation of decadal GSAT trends is not unique to the 1998-2012 period ( Section 1.4.2.1; Lovejoy, 2014; Roberts et al., 2015; Dai and Bloecker, 2019). Due to the nature of internal variability, surface temperature changes over the 1998-2012 period are regionally- and seasonally-varying (Cross-Chapter Box 3.1, Figure 1c; Trenberth et al., 2014; Zang et al., 2019). Further, there was no slowdown in the increasing occurrence of hot extremes over land ( Kamae et al., 2014; Seneviratne et al., 2014; Imada et al., 2017). Thus, the internally-driven slowdown of GSAT increase does not correspond to slowdown of warming everywhere on the Earth's surface.

Updated forcing

CMIP5 historical simulations driven by observed forcing variations ended in 2005 and were extended with RCP scenario simulations for model-observation comparisons beyond that date. Post AR5 studies based on updated external forcing show that while no net effect of updated anthropogenic aerosols is found on GSAT trends ( Murphy, 2013; Gettelman et al., 2015; Oudar et al., 2018), natural forcing by moderate volcanic eruptions in the 21st century ( Haywood et al., 2014; Ridley et al., 2014; Santer et al., 2014) and a prolonged solar irradiance minimum around 2009 compared to the normal 11-year cycle ( Lean, 2018) yield a negative contribution to radiative forcing, which was missing in CMIP5 (Figure 2.2). This explains part of the difference between observed and CMIP5 trends, as shown based on EMIC simulations ( Huber and Knutti, 2014; Ridley et al., 2014), statistical and mathematical models ( Schmidt et al., 2014; Lean, 2018), and process-based climate models ( Santer et al., 2014). However, in a single climate model study by Thorne et al. (2015), updating most forcings (greenhouse gas concentrations, solar irradiance, and volcanic and anthropogenic aerosols) available when the study was done made no significant difference to the 1998-2012 GMST trend from that obtained with original CMIP5 forcing. Potential underestimation of volcanic (negative) forcing may have played a role ( Outten et al., 2015). In the multi-model ensemble mean, the 1998-2012 GMST trends are almost equal in CMIP5 and CMIP6 (Cross-Chapter Box 3.1, Figure 1a), suggesting compensation by a higher transient climate response and equilibrium climate sensitivity in CMIP6 than CMIP5 (Section 7.5.6). To summarize, while there is medium confidence that natural forcing that was missing in CMIP5 contributed to the difference of observed and simulated GMST trends, confidence remains low in the quantitative contribution of net forcing updates.

Energy budget and heat redistribution

The early 21st century slower warming was observed in atmospheric temperatures, but the heat capacity of the atmosphere is very small compared to that of the ocean. Although there is noticeable uncertainty among observational products (H. Su et al., 2017) and observation quality changes through time, global ocean heat content continued to increase during the slower surface warming period ( very high confidence), at a rate consistent with CMIP5 and CMIP6 historical simulations (Sections 2.3.3.1, 3.5.1.3and 7.2.2.2). There is high confidence that the Earth's energy imbalance was larger in the 2000s than in the 1985-1999 period (Section 7.2.2.1), consistent with accelerating ocean heat uptake in the past two decades (Section 3.5.1.3). Internal decadal variability is mainly associated with redistribution of heat within the climate system (X.H. Yan et al., 2016; Drijfhout, 2018) while associated top of the atmosphere radiation anomalies are weak ( Palmer and McNeall, 2014). Heat redistribution in the top 350 m of the Indian and Pacific Oceans has been found to be the main contributor to reduced surface warming during the slower surface warming period ( Lee et al., 2015; Nieves et al., 2015; F. Liu et al., 2016), consistent with the simulated signature of PDV ( England et al., 2014; Maher et al., 2018a; Gastineau et al., 2019). Below 700 m, enhanced heat uptake over the slower surface warming period was observed mainly in the North Atlantic and Southern Ocean ( Chen and Tung, 2014), though whether this was a response to forcing or a unique signature of the slow GMST warming has been questioned (W. Liu et al., 2016).

Summary and implications

With updated observation-based GMST datasets and forcing, improved analysis methods, new modelling evidence and deeper understanding of mechanisms, there is very high confidence that the slower GMST and GSAT increase inferred from observations in the 1998-2012 period was a temporary event induced by internal and naturally-forced variability that partly offset the anthropogenic warming trend over this period. Nonetheless, the heating of the climate system continued during this period, as reflected in the continued warming of the global ocean ( very high confidence) and in the continued rise of hot extremes over land ( medium confidence). Considering all the sources of uncertainties, it is impossible to robustly identify a single cause of the early 2000s slowdown ( Hedemann et al., 2017; Power et al., 2017); rather, it should be interpreted as due to a combination of several factors ( Huber and Knutti, 2014; Schmidt et al., 2014; Medhaug et al., 2017).

A major El Niño event in 2014-2016 led to three consecutive years of record annual GMST with unusually strong heat release from the North-western Pacific Ocean ( Yin et al., 2018), which marked the end of the slower warming period ( Hu and Fedorov, 2017; J. Su et al., 2017; Cha et al., 2018). The past five-year period (2016-2020) is the hottest five-year period in the instrumental record up to 2020 ( high confidence). This rapid warming was accompanied by a PDV shift toward its positive phase (J. Su et al., 2017; Cha et al., 2018). A higher rate of warming following the 1998-2012 period is consistent with the predictions in AR5 Box 9.2 ( Flato et al., 2013) and with a statistical prediction system (Sévellec and Drijfhout, 2018). Initialized decadal predictions show higher GMST trends in the early 2020s compared to uninitialized simulations ( Thoma et al., 2015; Meehl et al., 2016a).

While some recent studies find that internal decadal GSAT variability may become weaker under GSAT warming, associated in part with reduced amplitude PDV ( Section 4.5.3.5; Brown et al., 2017), the weakening is small under a realistic range of warming. A large volcanic eruption would temporarily cool GSAT (Cross-Chapter Box 4.1). Thus, there is very high confidence that reduced and increased GMST and GSAT trends at decadal time scales will continue to occur in the 21st century ( Meehl et al., 2013; Roberts et al., 2015; Medhaug and Drange, 2016). However, such internal or volcanically forced decadal variations in GSAT trend have little effect on centennial warming ( England et al., 2015; Cross-Chapter Box 4.1).

Cross-Chapter Box 3.2 | Human Influence on Large-scale Changes in Temperature and Precipitation Extremes

Contributors: Nathan P. Gillett (Canada), Seung-Ki Min (Republic of Korea), Krishnan Raghavan (India), Ying Sun (China), Xuebin Zhang (Canada)

Understanding how temperature and precipitation extremes have changed at large scales and the causes of these changes is an important part of our overall assessment of human influence on the climate system. Chapter 11 assesses changes in extremes and their causes, while this Cross-Chapter Box summarizes relevant assessments and supporting evidence in Chapters 8 and 11 and relates changes in extremes to mean changes on global and continental scales.

Attribution of temperature extremes

One important aspect of various indicators of temperature extremes is their connection to mean temperature at local, regional and global scales. For example, the highest daily temperature in a summer is often highly correlated with the summer mean temperature. Model projections show that changes in temperature extremes are often closely related to shifts in mean temperature ( Seneviratne et al., 2016; Kharin et al., 2018). It is thus no surprise that changes in temperature extremes are consistent with warming mean temperature, with warming leading to more hot extremes and fewer cold extremes. Given the attribution of mean warming to human influence ( Section 3.3.1), and the connection between changes in mean and extreme temperatures, it is to be expected that anthropogenic forcing has also influenced temperature extremes.

( Chapter 11 assesses that there is high confidence that climate models can reproduce the mean state and overall warming of temperature extremes observed globally and in most regions, although the magnitude of the trends may differ, and the ability of models to capture observed trends in temperature-related extremes depends on the metric evaluated, the way indices are calculated, and the time periods and spatial scales considered (Section 11.3.3). There has been widespread evidence of human influence on various aspects of temperature extremes, at global, continental, and regional scales. This includes attribution to human influence of observed changes in intensity, frequency, and duration and other relevant characteristics at global and continental scales (Section 11.3.4). The left-hand panel of Cross-Chapter Box 3.2, Figure 1 clearly shows that long-term changes in the global mean annual maximum daily maximum temperature can be reproduced by both CMIP5 and CMIP6 models forced with the combined effect of natural and anthropogenic forcings, but cannot be reproduced by simulations under natural forcing alone. Consistent with the assessment for global mean temperature ( Section 3.3.1), aerosol changes are found to have offset part of the greenhouse gas induced increase in hot extremes globally and over most continents over the 1951-2015 period ( Hu et al., 2020; Seong et al., 2021), though greenhouse gas and aerosol influences are less clearly separable in observed changes in cold extremes.

( Chapter 11 assesses that it is virtually certain that human-induced greenhouse gas forcing is the main contributor to the observed increase in the likelihood and severity of hot extremes and the observed decrease in the likelihood and severity of cold extremes on global scales, and very likely that this applies on most continents.

Attribution of precipitation extremes

An important piece of evidence supporting the SREX and AR5 assessment that there is medium confidence that anthropogenic forcing has contributed to a global scale intensification of heavy precipitation during the second half of the 20th century is the evidence for anthropogenic influence on other aspects of the global hydrological cycle. The most significant aspect of that is the increase in atmospheric moisture content associated with warming which should, in general, lead to enhanced extreme precipitation, particularly associated with enhanced convergence in tropical and extratropical cyclones (Sections 8.2.3.2 and 11.4.1). Such a connection is supported by the fact that annual maximum one-day precipitation increases with global mean temperature at a rate similar to the increase in the moisture holding capacity in response to warming, both in observations and in model simulations. Additionally, models project an increase in extreme precipitation across global land regions even in areas in which total annual or seasonal precipitation is projected to decrease.

The overall performance of CMIP6 models in simulating extreme precipitation intensity and frequency is similar to that of CMIP5 models ( high confidence), and there is high confidence in the ability of models to capture the large-scale spatial distribution of precipitation extremes over land (Section 11.4.3). Evidence of human influence on extreme precipitation has become stronger since AR5. Considering changes in precipitation intensity averaged over all wet days, there is high confidence that daily mean precipitation intensities have increased since the mid-20th century in a majority of land regions, including Europe, North America and Asia, and it is likely that such an increase is mainly due to anthropogenic emissions of greenhouse gases (Sections 8.3.1.3 and 11.4.4). Section 11.4.4 also finds a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation than expected by chance, which can only be explained when anthropogenic greenhouse gas forcing is considered. The right-hand

Cross-Chapter Box 3.2

panel of Cross-Chapter Box 3.2, Figure 1 demonstrates the consistency between changes in global average annual maximum daily precipitation in the observations and model simulations under combined anthropogenic and natural forcing, and inconsistency with simulations under natural forcing alone. While there is more evidence in the literature to quantify the net anthropogenic influence on extreme precipitation than the influence of individual forcing components, a dominant contribution of greenhouse gas forcing to the long-term intensification of extreme precipitation on global and continental scales has recently been quantified separately from the influence of anthropogenic aerosol and natural forcings ( Dong et al., 2020; Paik et al., 2020b).

Chapter 11 assesses that it is likely that human influence, in particular due to greenhouse gas forcing, is the main driver of the observed intensification of heavy precipitation in global land regions during recent decades (Section 11.4.4).

Cross-Chapter Box 3.2, Figure 1 | Comparison of observed and simulated changes in global mean temperature and precipitation extremes. Time series of globally averaged five-year mean anomalies of the annual maximum daily maximum temperature (TXx in °C) and annual maximum 1-day precipitation (Rx1day as standardized probability index in %) between 1953 and 2017 from the HadEX3 observations and the CMIP5 and CMIP6 multi-model ensembles with natural and human forcing (top) and natural forcing only (bottom). For CMIP5, historical simulations for 1953-2005 are combined with corresponding RCP4.5 scenario runs for 2006-2017. For CMIP6, historical simulations for 1953-2014 are combined with SSP2-4.5 scenario simulations for 2015-2017. Numbers in brackets represent the number of models used. The time-fixed observational mask has been applied to model data throughout the whole period. Grid cells with more than 70% of data available between 1953 and 2017 plus data for at least three years between 2013 and 2017 are used. Coloured lines indicate multi-model means, while shading represents 5th-95th percentile ranges, based on all available ensemble members with equal weight given to each model ( Section 3.2). Anomalies are relative to 1961-1990 means. Figure is updated from Seong et al. (2021), their Figure 3 and Paik et al. (2020b), their Figure 3. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Seasonal snow cover is a defining climate feature of the northern continents. It is therefore of considerable interest that climate models correctly simulate this feature. It is discussed in more detail in Section 9.5.3, and observational aspects of snow cover are assessed in Section 2.3.2.2.

The AR5 noted the strong linear correlation between Northern Hemisphere snow cover extent (SCE) and annual-mean surface air temperature in CMIP5 models. It was assessed as likely that there had been an anthropogenic contribution to observed reductions in Northern Hemisphere snow cover since 1970 ( Bindoff et al., 2013). The AR5 assessed that CMIP5 models reproduced key features of observed snow cover well, including the seasonal cycle of snow cover over northern regions of Eurasia and North America, but had more difficulties in more southern regions with intermittent snow cover. The AR5 also found that CMIP5 models underestimated the observed reduction in spring snow cover over this period (Figure 3.22; see also Brutel-Vuilmet et al., 2013; Thackeray et al., 2016; Santolaria-Otín and Zolina, 2020). This behaviour has been linked to how the snow-albedo feedback is represented in models ( Thackeray et al., 2018a). The CMIP5 multi-model ensemble has been shown to represent the snow-albedo feedback more realistically than CMIP3, although models from some individual modelling centres have not improved or have even got worse ( Thackeray et al., 2018a). There is still a systematic overestimation of the albedo of boreal forest covered by snow ( Thackeray et al., 2015; Y. Li et al., 2016). Consequently, the snow albedo feedback might have been overestimated by CMIP5 models (Section 9.5.3; Xiao et al., 2017).

Figure 3.22 | Time series of Northern Hemisphere March-April mean snow cover extent (SCE) from observations, CMIP5 and CMIP6 simulations. The observations (grey lines) are updated Brown-NOAA (Brown and Robinson, 2011), Mudryk et al. (2020), and GLDAS2. CMIP5 (top) and CMIP6 (bottom) simulations of the response to natural plus anthropogenic forcing are shown in brown, natural forcing only in green, and the pre-industrial control simulation range is presented in blue. Five-year mean anomalies are shown for the 1923-2017 period with the x-axis representing the centre years of each five-year mean. CMIP5 all forcing simulations are extended by using RCP4.5 scenario simulations after 2005 while CMIP6 all forcing simulations are extended by using SSP2-4.5 scenario simulations after 2014. Shading indicates 5th-95th percentile ranges for CMIP5 and CMIP6 all and natural forcings simulations, and solid lines are ensemble means, based on all available ensemble members with equal weight given to each model (Section 3.2). The blue vertical bar indicates the mean 5th-95th percentile range of pre-industrial control simulation anomalies, based on non-overlapping segments. The numbers in brackets indicate the number of models used. Anomalies are relative to the average over 1971-2000. For models, SCE is restricted to grid cells with land fraction ≥50%. Greenland is excluded from the total area summation. Figure is modified from Paik and Min (2020), their Figure 1. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

CMIP6 models improve on CMIP5 models in producing slightly increased SCE versus CMIP5, correcting the low bias in CMIP5 ( Mudryk et al., 2020). The linear relationship noted above between GSAT and SCE also exists in CMIP6 ( Mudryk et al., 2020). Like CMIP5, the CMIP6 models capture the negative trend in spring snow cover that has occurred in recent decades (Figure 3.22). However, the median CMIP6 model now produces slightly stronger post-1981 declines in the March to April mean SCE than the CMIP5 median ( Mudryk et al., 2020). Until about 1980, the models produce a generally stable March to April SCE, but after that a substantial decline, reaching a loss of about 2 × 10km in 2012-2017 relative to the 1971-2000 average. Compared to earlier studies which found that models underestimate observed trends for the 1979-2005 period ( Brutel-Vuilmet et al., 2013), both CMIP5 and CMIP6 models show improved agreement with the observations over the period to 2017 (Figure 3.22). One remaining concern is a failure of most CMIP6 models to correctly represent the relationship between snow cover extent and snow mass, reflecting too slow seasonal increases and decreases of SCE in the models ( Mudryk et al., 2020).

Several CMIP5 and CMIP6 based studies have consistently attributed the observed Northern Hemisphere spring SCE changes ( Section 2.3.2.2) to anthropogenic influences ( Rupp et al., 2013; Najafi et al., 2016; Paik and Min, 2020), with the observed changes being found to be inconsistent with natural variability alone. Similarly, spring snow thickness (Snow Water Equivalent) changes on the scale of the Northern Hemisphere have been attributed to greenhouse gas forcing ( Jeong et al., 2017). Using individual forcing simulations from multiple CMIP6 models, Paik and Min (2020) detected greenhouse gas influence in the observed decrease of early spring SCE between 1925 and 2019, which was found to be separable from the responses to other forcings.

In summary, it is very likely that anthropogenic influence contributed to the observed reductions in Northern Hemisphere springtime snow cover since 1950. CMIP6 models better represent the seasonality and geographical distribution of snow cover than CMIP5 simulations ( high confidence). Both CMIP5 and CMIP6 models simulate strong declines in spring SCE during recent years, in general agreement with observations, causing the multi-model mean decreasing trend in spring SCE to now better agree with observations than in earlier evaluations. Evidence has yet to emerge that interactions between vegetation and snow, found problematic in CMIP5, have improved in CMIP6 models (Section 9.5.3). Such deficiencies in the representation of snow in climate models mean there is medium confidence in the simulation of snow cover over the northern continents in CMIP6 model simulations. The models consistently link snow extent to surface air temperature (Figure 9.24). With warming of near-surface air linked to anthropogenic influence, and particularly to greenhouse gas increases ( Section 3.3.1.1), this provides additional evidence that reductions in snow cover are also caused by human activity.

While ( Chapter 9 (Sections 9.4 and 9.5) discusses process understanding for glaciers and ice sheets, as well as evaluation of global and regional-scale glacier and ice-sheet models, our focus here is on the attribution of large-scale changes in glaciers and ice sheets. Land ice in the form of glaciers has been included in CMIP climate and Earth system models as components of the land surface models for many years. However, their representation is simplified and is omitted altogether in the less complex modelling systems. In CMIP3 ( Meehl et al., 2007) and CMIP5 ( Taylor et al., 2012) land ice area fraction, a component of land surface models, was defined as a time-independent quantity, and in most model configurations was preset at the simulation initialization as a permanent land feature. In CMIP6 considerable progress has been made in improving and evaluating the representation of modelled land ice. For glaciers, an example is the expansion of the Joint UK Land Environment Simulator (JULES) land surface model to enable elevated tiles, and hence more accurately simulate the altitudinal atmospheric effects on glaciers ( Shannon et al., 2019). Moreover, standalone glacier models have now been systematically compared in GlacierMIP ( Hock et al., 2019a; Marzeion et al., 2020). The Antarctic and Greenland Ice Sheets were absent in global climate models that pre-date CMIP6 ( Eyring et al., 2016a), however some preliminary analyses that used results from CMIP5 to drive standalone ice-sheet models were included in AR5 ( Church et al., 2013a). For the first time in CMIP, the latest CMIP6 phase includes a coordinated effort to simulate temporally evolving ice sheets within the Ice Sheet Model Intercomparison Project (ISMIP6; Box 9.3; Nowicki et al., 2016). Our understanding of aspects of the global water storage contained in glaciers and ice sheets, and their contribution to sea-level rise, has improved since AR5 and SROCC ( Hock et al., 2019b; Meredith et al., 2019) both in models and observations (see assessment of observations and model evaluation for the Greenland Ice Sheet in Sections 2.3.2.4.1 and 9.4.1; Antarctica in Sections 2.3.2.4.2 and 9.4.2; and glaciers in Sections 2.3.2.3 and 9.5.1).

Glaciers are defined as perennial surface land ice masses independent of the Antarctic and Greenland Ice Sheets (Sections 9.5 and 2.3.2.3). The AR5 assessed that anthropogenic influence had likely contributed to the retreat of glaciers observed since the 1960s ( Bindoff et al., 2013), based on a high level of scientific understanding and robust estimates of observed mass loss, internal variability, and glacier response to climatic drivers. The SROCC ( Hock et al., 2019b) concluded that atmospheric warming was very likely the primary driver of glacier recession.

In summary, considering together the SROCC assessment that atmospheric warming was very likely the primary driver of glacier recession, the results of Roe et al. (2017, 2021) and our assessment of the dominant role of anthropogenic influence in driving atmospheric warming ( Section 3.3.1), we conclude that human influence is very likely the main driver of the near-universal retreat of glaciers globally since the 1990s.

The global ocean plays an important role in the climate system, as it is responsible for transporting and storing large amounts of heat (Sections 3.5.1 and 9.2.2.1), freshwater (Sections 3.5.2 and 9.2.2.2) and carbon (Sections 3.6.2 and 5.2.1.3) that are exchanged with the atmosphere. Therefore, accurate ocean simulation in climate models is essential for the realistic representation of the climatic response to anthropogenic warming, including rates of warming, sea level rise and carbon uptake, and the representation of coupled modes of climate variability.

Ocean model development has advanced considerably since AR5 ( Section 1.5.3.1). Ongoing model developments since AR5 have focused on improving the realism of the simulated ocean in coupled models, with horizontal resolutions increasing to 10-100 km (from about 200 km in CMIP5), and increased vertical resolutions in many modelling systems of 0-1 m for near-surface levels (from the highest resolution of 10 m in CMIP5). These developments are aimed at improving the representation of the diurnal cycle and coupling to the atmosphere (e.g., Bernie et al., 2005, 2007, 2008). General improvements to simulated ocean fidelity in response to increasing resolution are expected ( Hewitt et al., 2017), and the effects of model resolution on the fidelity of ocean models are discussed in more detail in Sections 9.2.2 and 9.2.4.

In this section we assess the global and basin-scale properties of the simulated ocean, with a focus on evaluation of the realism of simulated ocean properties, and the detection and attribution of human-induced changes in the ocean over the period of observational coverage. Observed changes to ocean temperature ( Section 2.3.3.1), salinity ( Section 2.3.3.2), sea level ( Section 2.3.3.3) and ocean circulation ( Section 2.3.3.4) are reported in Chapter 2. A more process-based assessment of ocean changes, alongside the assessment of variability and changes in ocean properties with spatial scales smaller than ocean basins, is presented in Chapter 9.

Ocean temperature and ocean heat content are key physical variables considered for climate model evaluation and are primary indicators of a changing ocean climate. This section assesses the performance of climate models in representing the mean state ocean temperature and heat content ( Section 3.5.1.1), with a particular focus on the tropical oceans given the importance of air-sea coupling in these areas ( Section 3.5.1.2). This is followed by an assessment of detection and attribution studies of changes in ocean temperature and heat content ( Section 3.5.1.3). Changes in global surface temperature are assessed in Section 3.3.1.1.

In CMIP3 and CMIP5 models, large SST biases were found in the mid- and high latitudes ( Flato et al., 2013). In CMIP6, the Northern Hemisphere mid-latitude surface temperature biases appear to be marginally improved in the multi-model mean when contrasted to CMIP5 despite large biases remaining in a few models (Figures 3.23a and 3.24). There is a decreased spread of the zonal mean SST error between 50°N and 30°S, relative to CMIP5 (Figure 3.24a). On the other hand, the Southern Ocean's warm surface temperature bias remains (Figure 3.23a; Beadling et al., 2020), and is on average larger in CMIP6 than in CMIP5 models (Figures 3.23a and 3.24). This warm bias is often associated with persistent overlying atmospheric cloud biases ( Hyder et al., 2018). Several other large biases also appear to remain largely unresolved in CMIP6, particularly warm biases in excess of 1°C along the equatorial eastern continental boundaries of the tropical Atlantic and Pacific Oceans (Figure 3.23a).

Figure 3.23 | Multi-model mean bias of (a) sea surface temperature and (b) near-surface salinity, defined as the difference between the CMIP6 m ulti-mo del mean and the climatology from the World Ocean Atlas 2018. The CMIP6 multi-model mean is constructed with one realization of 46 CMIP6 historical experiments for the period 1995-2014 and the climatology from the World Ocean Atlas 2018 is an average over all available years (1955-2017). Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where ≥66% of models show change greater than the variability threshold and ≥80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than the variability threshold and <80% of all models agree on sign of change. For more information on the advanced approach, please refer to Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Overall, the simulated and observed trends in SST patterns are generally consistent for the historical period ( Olonscheck et al., 2020). The CMIP6 models generally represent the observed pattern of trends better than the CMIP5 models, and observed trends fall within the range of simulated trends over a larger area for CMIP6 models than for CMIP5 models ( Olonscheck et al., 2020).

Figure 3.25 | CMIP6 potential temperature and salinity biases for the global ocean, Atlantic Ocean, Pacific Ocean and Indian Ocean. Shown in colour are the time-mean differences between the CMIP6 historical multi-model climatological mean and observations, zonally averaged for each basin (excluding marginal and regional seas). The observed climatological values are obtained from the World Ocean (Atlas 2018 (WOA18, 1981-2010; Prepared by the Ocean Climate Laboratory, National Oceanographic Data Center, Silver Spring, MD, USA), and are shown as labelled black contours for each of the basins. The simulated annual mean climatologies for 1981 to 2010 are calculated from available CMIP6 historical simulations, and the WOA18 climatology utilized synthesized observed data from 1981 to 2010. Output from a total of 30 available CMIP6 models is used for the temperature panels (left column) and 28 models for the salinity panels (right column). Potential temperature units are °C and salinity units are the Practical Salinity Scale 1978 [PSS-78]. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Focusing on the deep ocean (>2000 m), the CMIP6 ensemble mean shows a prominent and consistent warm bias (Figure 3.25), in all basins except the equatorial and northern Pacific, which contrasts to a cold bias seen in CMIP5 ( Flato et al., 2013). We note that while an updated observational temperature dataset is used in this assessment (WOA09 was used in AR5, while WOA18, 1981-2010 is used in AR6), the deep-ocean warm bias remains and is approaching double the magnitude (about 0.5°C) of the equivalent CMIP5 multi-model mean bias, a feature which is particularly prominent in the Atlantic and southern Indian Oceans. Increased horizontal resolution as well as the choice of the vertical coordinate are reported to partly improve these biases in some models ( Adcroft et al., 2019; Rackow et al., 2019; Hewitt et al., 2020).

In summary, there is little improvement in the multi-model mean sea surface and zonal mean ocean temperatures from CMIP5 to CMIP6 ( medium confidence). Nevertheless, the CMIP6 models show a somewhat more realistic pattern of SST trends ( low confidence).

The ocean plays an important role as the Earth's primary energy store. The AR5 and SROCC assessed that the ocean accounted for more than 90% of the Earth's energy change since the 1970s ( Rhein et al., 2013; Bindoff et al., 2019). These assessments are consistent with recent studies assessed in Section 7.2 and Cross-Chapter Box 9.1, which find that 91% of the observed change in Earth's total energy from 1971 to 2018 was stored in the ocean ( von Schuckmann et al., 2020). The AR5 concluded that anthropogenic forcing has very likely made a substantial contribution to ocean warming above 700 m, whereas below 700 m, limited measurements restricted the assessment of ocean heat content changes in AR5 and prevented a robust comparison between observations and models ( Bindoff et al., 2013).

With the recent increase in ocean sampling by Argo to 2000 m ( Roemmich et al., 2015; Riser et al., 2016; von Schuckmann et al., 2016) and the resulting improvements in estimates of ocean heat content ( Abraham et al., 2013; Balmaseda et al., 2013; Durack et al., 2014b; Cheng et al., 2017; von Schuckmann et al., 2020), a more quantitative assessment of the global ocean heat content changes that extends into the intermediate ocean (700-2000 m) over the more recent period (from 2005 to the present; Durack et al., 2018) can be performed. Observed ocean heat content changes are discussed in Section 2.3.3.1, where it is reported that it is virtually certain that the global upper ocean (0-700 m) and very likely that the global intermediate ocean (700-2000 m) warmed substantially from 1971 to the present. Further, ocean layer warming contributions are reported as 61% (0-700 m), 31% (700-2000 m) and 8% (>2000 m) for the 1971 to 2018 period (Table 2.7). CMIP5 model simulations replicate this partitioning fairly well for the industrial-era (1865 to 2017) throughout the upper (0-700 m, 65%), intermediate (700-2000 m, 20%) and deep (>2000 m, 15%) layers ( Gleckler et al., 2016; Durack et al., 2018). The corresponding warming percentages for the multi-model mean of a subset of CMIP6 simulations over the 1850-2014 period are 58% for the upper, 21% for the intermediate, and 22% for the deep-ocean layers (Figure 3.26). These results are consistent with SROCC which assessed that it is virtually certain that both the upper and intermediate ocean warmed from 2004 to 2016, with an increased rate of warming since 1993 ( Bindoff et al., 2019). The spatial distribution of these changes for different ocean depths is assessed in Section 9.2.2.1.

Figure 3.26 | Global ocean heat content in CMIP6 simulations and observations. Time series of observed (black) and simulated (red) global ocean heat content anomalies with respect to 1995-2014 for the full ocean depth (left-hand panel); upper layer: 0-700 m (top right-hand panel); intermediate layer: 700-2000 m (middle right-hand panel); and the abyssal ocean: >2000 m (bottom right-hand panel). The best estimate observations (black solid line) for the period of 1971-2018, along with very likely ranges (black shading) are from Section 2.3.3.1. For the models (1860-2014), ensemble members from 15 CMIP6 models are used to calculate the multi-model mean values (red solid line) after averaging across simulations for each independent model. The very likely ranges in the simulations are shown in red shading. Simulation drift has been removed from all CMIP6 historical runs using a contemporaneous portion of the linear fit to each corresponding pre-industrial control run (Gleckler et al., 2012). Units are zettajoules (ZJ; 10 joule). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

The multi-model means of both CMIP5 and CMIP6 historical simulations forced with time varying natural and anthropogenic forcing shows robust increases in ocean heat content in the upper (0-700 m) and intermediate (700-2000 m) ocean ( high confidence) (Figure 3.26; Cheng et al., 2016, 2019; Gleckler et al., 2016; Bilbao et al., 2019; Tokarska et al., 2019). Temporary (<10 years) surface and subsurface cooling during and after large volcanic eruptions is also captured in the upper-ocean, and global mean ocean heat content ( Balmaseda et al., 2013). The ocean heat content increase is also reflected in the corresponding ocean thermal expansion which is a leading contributor to global mean sea level rise (Sections 3.5.3.2 and 9.2.4, and Box 9.1).

For the period 1971-2014, the rate of ocean heat uptake for the global ocean in the CMIP6 models is about 6.43 [2.08-8.66] ZJ yr, with the upper, intermediate and deeper layers respectively accounting for 68%, 16% and 16% of the full depth global heat uptake (Figure 3.26). Overall, the simulated ocean heat content changes are consistent with the updated and improved observational analyses, within the very likely uncertainty range defined for each (see also ( Section 2.3.3.1, Table 2.7; Domingues et al., 2008; Purkey and Johnson, 2010; Levitus et al., 2012; Good et al., 2013; Cheng et al., 2017; Ishii et al., 2017; Zanna et al., 2019) as well as with the ocean components of total Earth heating assessed in Section 7.2.2.2, Table 7.1. Nevertheless, large uncertainties remain, particularly in the deeper layers due to the poor temporal and spatial sampling coverage, particularly in the Atlantic, Southern and Indian Oceans ( Garry et al., 2019). The very likely ranges of the simulated trends for the full ocean depth and below 2000 m fall within the very likely range of observed uptake during the last two decades. In the intermediate layer, the multi-model ensemble mean mostly stays above the observed 5th-95th percentile range before the year 2000, and below that range after 2000. For the upper ocean, some individual model realizations show a reduced ocean heat content increase during the 1970s and 1980s, which is then compensated by a greater warming than the observations from the early 1990s. These discrepancies have been linked with a temporary increase in the Southern Ocean deep water formation rate, as well as with the models' strong aerosol cooling effects and high equilibrium climate sensitivity (see also Section 7.5.6 and Box 7.2; Andrews et al., 2019, 2020; Golaz et al., 2019; Dunne et al., 2020; Winton et al., 2020).

In summary, there is strong evidence for an improved understanding of the observed global ocean heat content increase. It is extremely likely that human influence was the main driver of the ocean heat content increase observed since the 1970s, which extends into the deeper ocean ( very high confidence). Updated observations, like model simulations, show that warming extends throughout the entire water column ( high confidence).

While ocean assessments have primarily focused on temperature changes, improved observational salinity products since the early 2000s have supported more assessment of long-term ocean salinity change and variability from AR4 ( Bindoff et al., 2007) to AR5 across both models and observations ( Flato et al., 2013; Rhein et al., 2013). The AR5 assessed that it was very likely that anthropogenic forcings have made a discernible contribution to surface and subsurface ocean salinity changes since the 1960s. The SROCC augmented these insights, noting that observed high latitude freshening and warming have very likely made the surface ocean less dense with a stratification increase of between 2.18% and 2.42% from 1970 to 2017 ( Bindoff et al., 2019). A recent observational analysis has expanded on these assessments, suggesting a very marked summertime density contrast enhancement across the mixed layer base of 6.2-11.6% per decade, driven by changes in temperature and salinity, which is more than six times larger than previous estimates ( Sallée et al., 2021). An idealized ocean modelling study suggests that the enhanced stratification can account for a third of the salinity enhancement signal since 1990 ( Zika et al., 2018). Thus, there has been an expansion of observed global- and basin-scale salinity change assessment literature since AR5, with many new studies reproducing the key patterns of long-term salinity change reported in AR5 ( Rhein et al., 2013), and linking these through modelling studies to coincident changes in evaporation-precipitation patterns at the ocean surface (Sections 2.3.1.3, 3.3.2, 8.2.2.1 and 9.2.2).

Unlike SSTs, simulated sea surface salinity (SSS) does not provide a direct feedback to the atmosphere. However, some recent work has identified indirect radiative feedbacks through sea-salt aerosol interactions ( Ayash et al., 2008; Amiri-Farahani et al., 2019; Z. Wang et al., 2019) that can act to strengthen tropical cyclones, and increase precipitation ( Balaguru et al., 2012, 2016; Grodsky et al., 2012; Reul et al., 2014; Jiang et al., 2019). The absence of a direct feedback is one of the primary reasons why salinity simulation is difficult to constrain in ocean modelling systems, and why deviations from the observed near-surface salinity mean state between models and observations are often apparent ( Durack et al., 2012; Shi et al., 2017).

When compared to the routine assessment of simulated SST, simulated SSS has not received the same research attention at global- to basin-scales. For CMIP3, there was reasonable agreement between the basin-scale patterns of salinity, with a comparatively fresher Pacific when contrasted to the salty Atlantic, and basin salinity maxima features aligning well with the corresponding atmospheric evaporation minus precipitation field ( Durack et al., 2012). Similar features are also reproduced in CMIP5 along with realistic variability in the upper layers, but less variability than observations at 300 m and deeper, especially in the poorly sampled Antarctic region ( Pierce et al., 2012). In a regional study, only considering the Indian Ocean, CMIP5 SSS was assessed and it was shown that model biases were primarily linked to biases in the precipitation field, with ocean circulation biases playing a secondary role ( Fathrio et al., 2017a). The sea surface salinity bias in CMIP6 models is shown in Figure 3.23b.

For the first time in AR5, alongside global zonal mean temperature, global zonal mean salinity bias with depth was assessed for the CMIP5 models. This showed a strong upper ocean (<300 m) negative salinity (fresh) bias of order 0.3 PSS-78, with a tendency toward a positive salinity (salty) bias (<0.25 PSS-78) in the Northern Hemisphere intermediate layers (200-3000 m) ( Flato et al., 2013). These biases are also present in CMIP6, albeit with slightly smaller magnitudes (Figure 3.25). Here we expand the global zonal mean bias assessment to consider the three independent ocean basins individually, which allows for an assessment as to which basin biases are dominating the global zonal mean. The basin with the most pronounced biases is the Atlantic, with a strong upper ocean (<300 m) fresh bias, of order 0.3 PSS-78 just like the global zonal mean, and a marked subsurface salinity bias that exceeds 0.5 PSS-78 in equatorial waters between 400-1000 m.

The Pacific Ocean shares the strongest similarity to the global bias, with a similar upper ocean (<300 m) fresh bias. Lower magnitude positive salinity biases (about 0.3 PSS-78) are also present in both hemispheres between 200 and 3000 m, and deeper in the Southern Hemisphere (Figure 3.25). The Indian Ocean shows similar features to the Southern Hemisphere Pacific, with a marked upper ocean (<500 m) fresh bias of order 0.3 PSS-78, and a strong near-surface positive bias of order 0.4 PSS-78 associated with the Arabian Sea (Figure 3.25).

For the Southern Ocean in CMIP5, considerable fresh biases exist through the water column, and are most pronounced in the ventilated layers representing the subtropical mode and intermediate water masses ( Sallée et al., 2013). A fresh bias in upper and intermediate layers of comparable magnitude is also seen in CMIP6 (Figure 3.25). The structure of the biases in the CMIP6 multi-model mean (which averages across many simulations with differing subsurface geographies and differing Southern Ocean salinity biases ( Beadling et al., 2020)) is similar to that evident in the CMIP5 multi-model mean, but with slightly smaller magnitudes. The Arctic Ocean also on average exhibits a surface-enhanced fresh bias in the upper ocean (Figure 3.25), which is much larger than its Southern Hemisphere counterpart.

In summary, the structure of the salinity biases in the multi-model mean has not changed substantially between CMIP5 and CMIP6 ( medium confidence), though there is limited evidence that the magnitude of subsurface biases has been reduced. Biases are sufficiently small to provide confidence in the utility of CMIP-class models for detection and attribution of ocean salinity.

AR5 concluded that it was very likely that anthropogenic forcings had made a discernible contribution to surface and subsurface ocean salinity changes since the 1960s ( Bindoff et al., 2013; Rhein et al., 2013). It highlighted that the spatial patterns of salinity trends, and the mean fields of salinity and evaporation minus precipitation are all similar, with an enhancement to Atlantic Ocean salinity and freshening in the Pacific and Southern Oceans. Since AR5 all subsequent work on assessing observed and modelled salinity changes has confirmed these results.

Figure 3.27 | Maps of multi-decadal salinity trends for the near-surface ocean. Units are Practical Salinity Scale 1978 [PSS-78] per decade. (Top) The best estimate (Section 2.3.3.2) observed trend (1950-2019, Durack and Wijffels, 2010). (Bottom) Simulated trend from the CMIP6 historical experiment multi-model mean (1950-2014). Black contours show the climatological mean salinity in increments of 0.5 PSS-78 (thick lines 1 PSS-78). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Figure 3.28 | Long-term trends in halosteric and thermosteric sea level in CMIP6 models and observations. Units are mm yr. In The right-hand column, three observed maps of 0 to 2000 m halosteric sea level trends are shown: top (D&W) from Durack and Wijffels (2010), 1950-2019, updated; upper-middle (EN4) from Good et al. (2013), 1950-2019, updated; and lower-middle (Ishii) from Ishii et al. (2017), 1955-2019, updated. Bottom-right: the CMIP6 historical multi-model mean (1950-2014). Red and orange colours show a halosteric contraction (enhanced salinity) and blue and green a halosteric expansion (reduced salinity). In The left-hand column , basin-integrated halosteric (top) and thermosteric (bottom) trends for the Atlantic and Pacific, the two largest ocean basins, are shown, where Pacific anomalies are presented on the x-axis and Atlantic on the y-axis. Observational estimates are presented in black, CMIP6 historical (all forcings) simulations are shown in orange squares, with the multi-model mean shown as a dark orange diamond with a black bounding box. CMIP6 hist-nat (historical natural forcings only) simulations are shown in green squares with the multi-model mean as a dark green diamond with a black bounding box. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

While observational sparsity considerably limits quantification of regional changes, a recent study by Friedman et al. (2017) assessed salinity changes in the Atlantic Ocean from 1896 to 2013 and confirmed the pattern of mid-to-low latitude enhanced salinity and high latitude North Atlantic freshening over this period exists even after accounting for the effects of the NAO and AMO.

Considering the bulk of evidence, it is extremely likely that human influence has contributed to observed near-surface and subsurface salinity changes across the globe since the mid-20th century. All available multi-decadal assessments have confirmed that the associated pattern of change corresponds to fresh regions becoming fresher and salty regions becoming saltier ( high confidence). CMIP5 and CMIP6 models are only able to reproduce these patterns in simulations that include greenhouse gas increases ( medium confidence). Changes to the coincident atmospheric water cycle and ocean-atmosphere fluxes (evaporation and precipitation) are the primary drivers of the basin-scale observed salinity changes ( high confidence). This result is supported by all available observational assessments, along with a growing number of climate modelling studies targeted at assessing ocean and water cycle changes. The basin-scale changes are consistent across models and intensify on centennial scales from the historical period through to the projections of future climate ( high confidence).

In keeping with the scope of this chapter, this section addresses global and basin-scale sea level changes, whereas regional and local sea level changes are assessed in Section 9.6. In AR5, the observed sea level budget was closed by considering all contributing factors including ocean warming, mass contributions from terrestrial storage, glaciers, and the Antarctic and Greenland ice sheets ( Church et al., 2013b). The SROCC found that the observed global mean sea level (GMSL) rise is consistent within uncertainties with the sum of the estimated observed contributions for 1993-2015 and 2006-2015.

The current generation of climate models do not fully resolve many of the components required to close the observed sea level budget, such as glaciers, ice sheets and land water storage (see Section 9.6 and Box 9.1). Consequently, most CMIP-based analyses of sea level change have focused on thermosteric sea level changes (i.e., thermal expansion due to warming) and ocean dynamic sea level change, both of which are simulated in the CMIP5-generation of models. The improved agreement between modelled thermal expansion and observed estimates during the historical period led the SROCC to assess a high confidence level in the simulated thermal expansion using climate models and high confidence in their ability to project future thermal expansion.

Since CMIP5 models do not include all necessary components of sea level change, this gap has been bridged by using offline models (for glacier melt and ice-sheet surface mass balance) driven by reanalyses and model output. Some studies have used offline mass inputs to account for dynamic ice-sheet and terrestrial contributions. Slangen et al. (2017) and Meyssignac et al. (2017) suggested including corrections to several contributions to sea level changes including to the Greenland surface mass balance and glacier contributions, based on differences between CMIP5-driven model results and reanalysis-driven results. This helps close the gap between models and observations for the 20th century globally, as well as providing better agreement with tide gauge observations in terms of interannual and multi-decadal variability at the regional scale.

In CMIP6, ice sheets (see Sections 3.4.3.2 and 9.4) are included for the first time in ISMIP6 ( Nowicki et al., 2016). There is also scope for new insights into terrestrial water contributions from land surface (and sub-surface) modelling in the Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP; van den Hurk et al., 2016). In parallel, the GlacierMIP project ( Hock et al., 2019a; Marzeion et al., 2020; see Sections 3.4.3.1 and 9.5) is also underway, and has provided more quantitative guidance and a comprehensive assessment of the uncertainties and best estimates of the current and future contributions of glaciers to the sea level budget.

The SROCC concluded with high confidence that the dominant cause of GMSL rise since 1970 is anthropogenic forcing. Prior to that, AR5 had concluded that it is very likely that there has been a substantial contribution from anthropogenic forcings to GMSL rise since the 1970s. Since AR5, several studies have identified a human contribution to observed sea level change resulting from a warming climate as manifest in thermosteric sea level change and the contribution from melting glaciers and ice sheets.

For the global mean thermosteric sea level change, Slangen et al. (2014) showed the importance of anthropogenic forcings (combined greenhouse gas and aerosol forcings) for explaining the magnitude of the observed changes between 1957 and 2005, considering the full depth of the ocean and natural forcings in order to capture the variability (see also Figure 3.29). Over the 1950-2005 period, Marcos and Amores (2014) found that human influence explains 87% of the 0-700 m global thermosteric sea level rise. Both thermosteric and regional dynamic patterns of sea level change in individual forcing experiments from CMIP5 were considered by Slangen et al. (2015) who showed that responses to anthropogenic forcings are significantly different from both internal variability and inter-model differences and that although greenhouse gas and anthropogenic aerosol forcings produce opposite GMSL responses, there are differences in the response on regional scales. Based on these studies, we conclude that it is very likely that anthropogenic forcing was the main driver of the observed global mean thermosteric sea level change since 1970.

Figure 3.29 | Simulated and observed global mean sea level change due to thermal expansion for CMIP6 models and observations relative to the baseline period 1

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Chapter 3: Human Influence on the Climate System

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Oceans play a crucial role in regulating global climate by absorbing and redistributing vast amounts of heat.

1. Ocean currents, driven by wind, temperature, and salinity differences, transport warm water from the equator towards the poles and cold water from the poles towards the equator, influencing regional temperatures and precipitation patterns.

2. Changes in ocean circulation patterns, such as the El Niño-Southern Oscillation (ENSO) in the Pacific Ocean and the Atlantic Meridional Overturning Circulation (AMOC), can cause significant regional and short-term global climate variability.

While natural climate oscillations like El Niño and La Niña can cause short-term temperature fluctuations and changes in weather patterns, they are not responsible for long-term global warming. However, they can exacerbate or dampen the effects of anthropogenic climate change in the short term.

Milankovitch Cycles: These cycles describe long-term variations in Earth's orbit around the Sun and its axial tilt. These variations influence the amount and distribution of solar energy reaching the Earth over tens of thousands to hundreds of thousands of years.

Eccentricity: Changes in the shape of Earth's orbit (from more circular to more elliptical) affect the total amount of solar radiation received annually. This cycle operates over approximately 100,000 years.

Obliquity: Variations in the tilt of Earth's axis (currently about 23.5°) change the intensity of seasons. A larger tilt leads to more extreme seasons. This cycle occurs over roughly 41,000 years.

Precession: The wobble of Earth's axis affects the timing of the seasons relative to Earth's orbit, influencing the contrast between seasons in each hemisphere. This cycle has a period of about 23,000 years.

Collectively, these cycles redistribute solar energy across the Earth and over time, playing a key role in long-term climate shifts, including the onset and retreat of ice ages. They don't significantly change the total amount of solar energy Earth receives, but rather where and when it is received

Milankovitch cycles are responsible for past ice ages and warm periods, but they occur over timescales of thousands of years. They cannot explain the rapid warming observed in the past century, which is far too fast for such orbital changes to account for.

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The vast majority of climate scientists agree that while natural factors have always played a role in Earth's climate, the rapid warming observed since the mid-20th century is primarily due to the increase in anthropogenic greenhouse gas emissions. The arguments for natural causes often fail to account for the full body of evidence and the magnitude of human influence on the atmospheric composition.

I found only papers that discuss the contributions of natural factors or the uncertainties in climate science, rather than outright rejecting the dominant role of human activities.

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What are the primary human activities contributing to the increase in greenhouse gas emissions?

The primary human activities driving the increase in greenhouse gas emissions include:

Burning Fossil Fuels: The combustion of coal, oil, and natural gas for energy production (electricity, heating, transportation, and industry) releases significant amounts of carbon dioxide (CO2​) and nitrous oxide (N2​O) into the atmosphere. This is the largest single contributor to increased greenhouse gas concentrations.

Deforestation and Land Use Change: Clearing forests for agriculture, urbanization, and other purposes reduces the number of trees that absorb CO2​ from the atmosphere. Burning forests also releases stored carbon. Changes in land management practices can also release greenhouse gases from the soil.

Agriculture: Livestock farming, particularly cattle and sheep, produces large amounts of methane (CH4​) during digestion. The use of nitrogen-based fertilizers in crop production releases nitrous oxide (N2​O). Rice cultivation in flooded paddies also generates methane.

Industrial Processes: Certain industrial processes release greenhouse gases such as fluorinated gases (e.g., HFCs, PFCs, SF6), which have very high global warming potentials. The production of cement also releases CO2​.

Waste Management: Landfills produce methane (CH4​) as organic waste decomposes.

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How do scientific models and data demonstrate the correlation between human activities and the observed rise in global temperatures?

Scientific models and data provide strong evidence for the correlation between human activities and the observed rise in global temperatures through several key lines of evidence:

Greenhouse Gas Concentration Data: Direct measurements from monitoring stations worldwide show a significant and continuous increase in the atmospheric concentrations of key greenhouse gases (CO2​, CH4​, N2​O) since the Industrial Revolution. These increases directly correspond with the timeline of increased fossil fuel burning and other human activities.

Isotopic Fingerprinting: The chemical "fingerprint" of the excess carbon dioxide in the atmosphere reveals that it originates from the burning of fossil fuels and deforestation, which have distinct isotopic ratios compared to naturally occurring carbon.

Climate Model Simulations: Sophisticated climate models that incorporate natural factors (solar variations, volcanic eruptions) cannot accurately reproduce the observed warming trend of the past century unless anthropogenic greenhouse gas emissions are included in the simulations. When human-caused emissions are factored in, the models closely match the observed temperature increases.

Attribution Studies: Formal attribution studies use statistical methods to determine the most likely causes of observed climate changes. These studies consistently conclude that human activities are the dominant driver of the warming observed since the mid-20th century, and that natural factors alone cannot explain the magnitude and pattern of the warming.

Observed Climate Changes: The observed warming is consistent with the predicted effects of increased greenhouse gas concentrations. For example, the upper atmosphere is cooling while the lower atmosphere warms, which is a signature of greenhouse gas trapping heat near the Earth's surface. Other changes like ocean warming, ice melt, and sea-level rise also align with the effects of increased greenhouse gases.

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What are the main arguments for anthropogenic causes of current climate changes?

The main arguments for anthropogenic causes of current climate changes are:

Overwhelming Scientific Consensus: The vast majority of climate scientists and leading scientific organizations worldwide agree that human activities are the primary driver of current global warming. This consensus is based on decades of research and multiple lines of evidence.

Direct Link Between Emissions and Atmospheric Concentration: Human activities are releasing unprecedented amounts of greenhouse gases into the atmosphere, leading to a clear and measurable increase in their concentrations.

Physical Understanding of the Greenhouse Effect: The greenhouse effect, where certain gases in the atmosphere trap heat, is a well-established scientific principle. Increasing the concentration of these gases leads to more heat being trapped.

Consistency Between Models and Observations: Climate models that include anthropogenic forcing accurately simulate the observed warming trends, while models that only consider natural factors cannot.

Attribution Studies Quantify Human Influence: Statistical analyses consistently show that natural factors alone are insufficient to explain the observed warming, and that human activities are the dominant contributor.

Observed Changes Align with Greenhouse Gas Effects: The patterns of warming (e.g., greater warming at night and in higher latitudes), the cooling stratosphere, and ocean acidification are all consistent with the effects of increased greenhouse gases.

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What geological and paleoclimatic evidence supports the theory that current climate changes are within the range of natural variability experienced by Earth in the past?

Geological and paleoclimatic evidence clearly shows that Earth's climate has experienced a wide range of natural variability throughout its history, including periods much warmer and much colder than today. This evidence comes from various sources:

Ice Cores: Ice cores from Greenland and Antarctica provide records of past atmospheric composition (including greenhouse gases) and temperatures stretching back hundreds of thousands of years, revealing natural cycles of warming and cooling associated with Milankovitch cycles.

Ocean Sediments: Analysis of marine sediments reveals long-term climate changes, including shifts in ocean temperatures, ice volume, and sea level over millions of years.

Tree Rings (Dendroclimatology): Tree ring patterns can provide high-resolution records of temperature and precipitation changes over the past centuries to millennia, showing natural variations in climate.

Fossil Pollen: The distribution of fossil pollen in lake sediments and peat bogs indicates changes in vegetation types over time, reflecting past climate shifts.

Coral Reefs: Coral growth patterns and isotopic composition can provide information about past sea surface temperatures and sea levels.

Geological Formations: The presence of glacial deposits in regions now ice-free and fossil evidence of tropical plants in high-latitude areas indicate significant past climate changes. Sedimentary rocks like coal (formed from ancient swamps) and evaporites (formed in arid conditions) also provide clues about past climates.

Isotope Geochemistry: Analysis of stable isotopes (e.g., oxygen-18, carbon-13) in various natural archives (ice cores, sediments, fossils) helps reconstruct past temperatures and environmental conditions.

This evidence confirms that Earth's climate system is dynamic and has undergone substantial natural fluctuations. For example, the glacial-interglacial cycles of the past few million years demonstrate large natural temperature swings.

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What are the main arguments for natural causes of current climate changes?

While the overwhelming scientific consensus attributes current climate change primarily to human activities, some arguments suggest that natural factors are the dominant cause:

Solar Variability: Proponents of this argument suggest that changes in the Sun's output are the main driver of recent warming. They might point to historical correlations between solar activity and climate fluctuations. However, as mentioned earlier, direct measurements show no significant increase in solar irradiance over the period of rapid warming.

Natural Climate Cycles: This argument suggests that the current warming is part of a natural long-term cycle, such as a recovery from the Little Ice Age or other internal climate variability. While natural cycles exist (like ENSO and Milankovitch cycles), the rate and magnitude of the current warming are far outside the range of these natural fluctuations, especially over the last century. Milankovitch cycles, for instance, operate over thousands of years and, in fact, suggest a long-term cooling trend should be occurring currently if only natural factors were at play.

Volcanic Activity: Some argue that volcanic eruptions release enough greenhouse gases to significantly contribute to warming. However, the annual CO2​ emissions from human activities are tens to hundreds of times greater than those from all the world's volcanoes combined. While volcanic aerosols cause temporary cooling, their long-term warming contribution is minor compared to anthropogenic emissions.

Ocean Currents: It's sometimes argued that changes in ocean currents are the primary driver of warming. While ocean currents redistribute heat and influence regional climates, they are themselves influenced by global temperature changes and cannot explain the overall global warming trend observed.

Data Limitations and Model Uncertainties: Some argue that the historical climate record is incomplete or that climate models are unreliable, leading to overestimation of human influence. However, multiple independent datasets using different methodologies show consistent warming trends, and climate models, while having uncertainties, accurately reproduce past warming when anthropogenic factors are included.

It's important to note that the vast majority of climate scientists agree that while natural factors have always played a role in Earth's climate, the rapid warming observed since the mid-20th century is primarily due to the increase in anthropogenic greenhouse gas emissions.

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I believe climate change is caused by humans because we burn a lot of oil, gas, and coal, which releases gases that heat up the planet.

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Scientists all over the world agree that humans are the main reason the climate is changing so fast.

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The Earth has always changed, but now it’s happening much quicker because of things people are doing.

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I believe that most of the ice at the poles is melting because of human activities, not just natural changes.

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Earth has always gone through warm and cold periods so there is true to the opinion about being a natural phenomenon also

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Climate change happens over thousands of years, and it’s part of Earth's natural cycle.

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I believe that climate change is mostly a natural process. The Earth has gone through many periods of warming and cooling long before humans had any major impact. Natural factors like volcanic eruptions, solar activity and ocean currents have always played a big role in changing the climate.