Abstract: Climate models are becoming increasingly sophisticated as climate scientists continually work to improve the realism with which the processes influencing Earth's climate are represented. One example is the treatment of cloud microphysics: as complexity is added to cloud microphysical schemes, Earth's energy budget can respond to changes in climate forcings, such as carbon dioxide or aerosols, in new ways. This increase in degrees of freedom has illuminated larger spread in climate sensitivity across the latest generation of climate models participating Coupled Model Intercomparison Project, Phase 6, with more high climate sensitivity models (Zelinka et al., 2020, https://doi.org/10.1029/2019gl085782). Whilst the historical record gives us just over a century of data to apply toward climate sensitivity constraints (e.g., Nijsse et al., 2020, https://doi.org/10.5194/esd-11-737-2020), the ocean is still taking up much of the heat trapped by anthropogenic greenhouse gas emissions and the climate system is far from equilibrium which limits our understanding how climate sensitivity might change in response to long-term forced climate change. Here we discuss the valuable tests that paleoclimate reconstructions can provide the latest generation of climate models, as demonstrated by the recent study of Zhu et al., 2022, https://doi.org/10.1029/2021ms002776. Their study provides an example of the benefits for climate model development when climate models are confronted with simulating climates very different from today. Ideally the climate model development stage under future iterations of CMIP will involve such tests as an effort to constrain global climate sensitivity and the regional patterns of climate, such as polar amplification and subtropical aridification.

Abstract: Since many new generation Earth system models (ESMs) have been suggested to overestimate future global warming, the latest report of the Intergovernmental Panel on Climate Change used the constrained range of global warming instead of that in the raw ensemble. However, it is not clear how the constraints of climate change projections potentially reduce the uncertainty of impact assessments. Here, we show that the climate-related uncertainty of the economic impact of climate change in the world can be constrained. By applying an impact emulator, we estimate the economic impacts in nine sectors based on 67 ESMs’ future climate change projections and find that the impacts in eight sectors are closely related to the recent past trend of global mean temperature, which is the metric used for the constraint of global warming projections. Observational constraints lower the upper bound of the aggregate economic impact simulated by the single emulator from 2.9% to 2.5% of the world gross domestic product (the relative reduction of variance is 31%) under the medium greenhouse gas concentration scenarios. Our results demonstrate how advances in climate science can contribute to reducing climate-related uncertainties in impact assessments, while we do not examine uncertainties of emulators and impact models.

Abstract: This chapter examines the physical drivers of climate change. The global radiation budget is first examined followed by the greenhouse effect. The properties of the greenhouse gases (carbon dioxide, nitrous oxide, methane, halocarbons and water vapor) and their radiative forcing is addressed. The concept of global warming potential is introduced which addresses the combined effect of several greenhouse gases together over a specified time priod. Aerosols and clouds have complex effects on climate including both cooling and warming. The climate response can be quantified using the climate sensitivity parameter. Climate feedbacks, both long-term and short-term, are important and include albedo feedbacks. A simple model to quantify feedbacks is presented (Box 2.1). The partial pressure of carbon dioxide over the oceans is addressed and the thawing of permafrost.

Abstract: AbstractClimate change is a natural process that controls our planet’s climate on timescales varying from decades to millennia. The greenhouse effect drives this process, where some atmospheric gases retain radiation in the atmosphere. Changes in the Earth system, naturally or anthropogenically caused, perturb the Earth’s energy balance. The climate system response will depend on the complex feedback mechanisms involved in the process. Positive and negative feedback mechanisms will work together to produce a climate forcing that will be different from a simple linear response. Human activities release large amounts of greenhouse gases, perturbing the Earth’s energy balance. When climate feedback is considered, it is clear that human activities lead to an increase in temperatures due to an amplification of the greenhouse effect. This chapter provides an overview of climate change’s physical basis, the greenhouse effect, and climate feedback. We provide a view of climate change as part of natural climate variability and anthropogenic activities. We also overview the future impacts of climate change according to diverse anthropogenic greenhouse gas emission scenarios.KeywordsClimate changeGreenhouse effectClimate feedbackRepresentative concentration pathwaysSocioeconomic pathways

Abstract: Abstract. Planning for the impacts of climate change requires accurate projections by Earth System Models (ESMs). ESMs, as developed by many research centres, estimate changes to weather and climate as atmospheric Greenhouse Gases (GHGs) rise, and they inform the influential Intergovernmental Panel on Climate Change (IPCC) reports. ESMs are advancing the understanding of key climate system attributes. However, there remain substantial inter--ESM differences in their estimates of future meteorological change, even for a common GHG trajectory, and such differences make adaptation planning difficult. Until recently, the primary approach to reducing projection uncertainty has been to place emphasis on simulations that best describe the contemporary climate. Yet a model that performs well for present--day atmospheric GHG levels may not necessarily be accurate for higher GHG levels and vice-versa. A relatively new approach of Emergent Constraints (ECs) is gaining much attention as a technique to remove uncertainty between climate models. This method involves searching for an inter--ESM link between a quantity that we can measure now and another of major importance for in describing future climate. Combining the contemporary measurement with this relationship refines the future projection. Identified ECs exist for thermal, hydrological and geochemical cycles of the climate system. As ECs grow in influence on climate policy, the method is under intense scrutiny, creating a requirement to understand them better. We hypothesise that as many Earth System components vary in both space and time, their behaviours often satisfy large--scale Partial Differential Equations (PDEs). Such PDEs are valid at coarser scales than the equations coded in ESMs which capture finer high resolution gridbox--scale effects. We suggest that many ECs link to such an effective hidden PDE that is implicit in most or all ESMs. An EC may exist because its two quantities depend similarly on an ESM--specific internal bulk parameter in such a PDE, and with measurements constraining and revealing its (implicit) value. Alternatively, well--established process understanding coded at the ESM gridbox--scale, when aggregated, may generate a bulk parameter with a common ``emergent'' value across all ESMs. This single parameter may link uncertainties in a contemporary climate driver to those of a climate--related property of interest, the EC constraining the latter by measurements of the former. We offer illustrative examples of these concepts with generic differential equations and their solutions, placed in a conceptual EC framework.

Abstract: The climate is changing. The main reason is because of human-induced changes in atmospheric composition which produce warming from increased greenhouse gases. This is referred to as a forcing of the climate system. There are many other forcings, both natural and anthropogenic. The issue then is to determine the consequences in terms of the change in climate and its impacts. There is a direct response to just about any forcing, and in some cases that is the answer we seek. But in many or most cases, it is not so simple. Rather, the initial change provokes other responses, especially in the atmospheric and ocean circulation, that in turn cause other changes to occur. If the response amplifies the original change, then it is referred to as a positive feedback. Whereas if the response offsets and reduces the outcome, then it is a negative feedback. The size of some effects is quantified in Section 13.5.

Abstract: Climate change, primarily in the form of global warming, is one of the major challenges for human society in the 21st century. Ever-increasing population and energy demands are further accelerating global warming. There are various drivers in the Earth's climate system, which directly or indirectly influence climate change and vice versa. The knowledge about these drivers and their impacts on climate is needed to formulate the possible mitigation and adaptation strategies for climate change. Here we present a comprehensive overview of these climate drivers and their role in climate change. This chapter provides a basic understanding of essential climate variables such as greenhouse gases and black carbon, and their impacts on climate. The long-term datasets of carbon dioxide and black carbon along with the average global surface temperature are analyzed to understand the dynamical nature of climate change. About 28% increase of atmospheric carbon dioxide with the rate + 15 ppm/decade is observed during 1959–2019. The accelerated warming rate (+ 0.17°C/decade) in the last 50 years (1970–2019) as compared to + 0.07°C/decade in the last one and half century (1880–2019) substantiates the role of increased anthropogenic activities lately. The possible adaptation and mitigation strategies, including restoration of the degraded ecosystem, clean fuel energy, city planning, biochar amendment to soil, etc., are summarized to provide an overall perspective of climate change in the current scenario.

Abstract: This is our climate change status check, and the first three things to understand are that climate change is real, anthropogenic (i.e., caused by humans), and dangerous. To drive home those points, this chapter relies heavily on the science of the landmark IPCC climate assessments and particularly the most recent AR5 report. We drill down on a key figure from the AR5 to clarify the observed data since 1850 in respect of global average surface temperatures, sea level rise, atmospheric concentrations of several greenhouse gases, and annual CO2 emissions. With a second figure we examine the sources of climate forcing since 1950 and clarify the degree to which they are caused by humanity or by nature. We then divert to key facets that we don’t yet fully understand about climate change, including tipping points, “climate sensitivity” and the likely emissions pathway that humanity will choose over the remainder of the century. We conclude with the observation that irrespective of how these mysteries play out, substantial climate change is in our future. It’s coming.

Abstract: Abstract. Planning for the impacts of climate change requires accurate projections by Earth System Models (ESMs). ESMs, as developed by many research centres, estimate changes to weather and climate as atmospheric Greenhouse Gases (GHGs) rise, and they inform the influential Intergovernmental Panel on Climate Change (IPCC) reports. ESMs are advancing the understanding of key climate system attributes. However, there remain substantial inter--ESM differences in their estimates of future meteorological change, even for a common GHG trajectory, and such differences make adaptation planning difficult. Until recently, the primary approach to reducing projection uncertainty has been to place emphasis on simulations that best describe the contemporary climate. Yet a model that performs well for present--day atmospheric GHG levels may not necessarily be accurate for higher GHG levels and vice-versa. A relatively new approach of Emergent Constraints (ECs) is gaining much attention as a technique to remove uncertainty between climate models. This method involves searching for an inter--ESM link between a quantity that we can measure now and another of major importance for in describing future climate. Combining the contemporary measurement with this relationship refines the future projection. Identified ECs exist for thermal, hydrological and geochemical cycles of the climate system. As ECs grow in influence on climate policy, the method is under intense scrutiny, creating a requirement to understand them better. We hypothesise that as many Earth System components vary in both space and time, their behaviours often satisfy large--scale Partial Differential Equations (PDEs). Such PDEs are valid at coarser scales than the equations coded in ESMs which capture finer high resolution gridbox--scale effects. We suggest that many ECs link to such an effective hidden PDE that is implicit in most or all ESMs. An EC may exist because its two quantities depend similarly on an ESM--specific internal bulk parameter in such a PDE, and with measurements constraining and revealing its (implicit) value. Alternatively, well--established process understanding coded at the ESM gridbox--scale, when aggregated, may generate a bulk parameter with a common ``emergent'' value across all ESMs. This single parameter may link uncertainties in a contemporary climate driver to those of a climate--related property of interest, the EC constraining the latter by measurements of the former. We offer illustrative examples of these concepts with generic differential equations and their solutions, placed in a conceptual EC framework.

Abstract: This paper documents the experimental setup and general features of the coupled historical and future climate simulations with the first version of the U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv1.0). The future projected climate characteristics of E3SMv1.0 at the highest emission scenario (SSP5-8.5) designed in the Scenario Model Intercomparison Project (ScenarioMIP) and the SSP5-8.5 greenhouse gas (GHG) only forcing experiment are analyzed with a focus on regional responses of atmosphere, ocean, sea-ice, and land. Due to its high climate sensitivity, E3SMv1.0 is one of the CMIP6 models with the largest surface warming by the end of the 21st century under the high-emission SSP5-8.5 scenario. The global mean precipitation change is highly correlated to the global temperature change, while the spatial pattern of the change in runoff responds to the precipitation changes. The oceanic mixed layer generally shoals throughout the global ocean. The sea ice, especially in the Northern Hemisphere, rapidly decreases with large seasonal variability. The annual mean AMOC is overly weak with a slower change relative to other CMIP6 models. We detect a significant polar amplification in E3SMv1.0 from the atmosphere, ocean, and sea ice. Comparing the SSP5-8.5 all-forcing experiment with the GHG-only experiment, we find that the unmasking of the aerosol effects due to the decline of the aerosol loading in the future projection period causes accelerated warming in SSP5-8.5 all-forcing experiment. While the oceanic climate response is mainly controlled by the GHG forcing, the land runoff response is impacted primarily by forcings other than GHG over certain regions. However, the importance of the GHG forcing on the land runoff changes grows in the future climate projection period compared to the historical period.

Abstract: The climate feedback determines how Earth's climate responds to anthropogenic forcing. It has been more negative in recent decades than predicted by Earth system models due to a sea surface temperature `pattern effect', whereby warming is concentrated in the western tropical Pacific, where nonlocal radiative feedbacks are very negative. This phenomenon has however primarily been studied within climate models. We diagnose a pattern effect from historical records as an evolution of the climate feedback over the past five decades. The climate feedback has decreased by $0.8\pm0.5$ W/m$^2$K over the past 50 years, corresponding to a reduction in climate sensitivity. Earth system models' climate feedbacks instead increase over this period. Understanding and simulating this historical trend and its future evolution are critical for reliable climate projections.

Abstract: Abstract. Planning for the impacts of climate change requires accurate projections by Earth System Models (ESMs). ESMs, as developed by many research centres, estimate changes to weather and climate as atmospheric Greenhouse Gases (GHGs) rise, and they inform the influential Intergovernmental Panel on Climate Change (IPCC) reports. ESMs are advancing the understanding of key climate system attributes. However, there remain substantial inter--ESM differences in their estimates of future meteorological change, even for a common GHG trajectory, and such differences make adaptation planning difficult. Until recently, the primary approach to reducing projection uncertainty has been to place emphasis on simulations that best describe the contemporary climate. Yet a model that performs well for present--day atmospheric GHG levels may not necessarily be accurate for higher GHG levels and vice-versa. A relatively new approach of Emergent Constraints (ECs) is gaining much attention as a technique to remove uncertainty between climate models. This method involves searching for an inter--ESM link between a quantity that we can measure now and another of major importance for in describing future climate. Combining the contemporary measurement with this relationship refines the future projection. Identified ECs exist for thermal, hydrological and geochemical cycles of the climate system. As ECs grow in influence on climate policy, the method is under intense scrutiny, creating a requirement to understand them better. We hypothesise that as many Earth System components vary in both space and time, their behaviours often satisfy large--scale Partial Differential Equations (PDEs). Such PDEs are valid at coarser scales than the equations coded in ESMs which capture finer high resolution gridbox--scale effects. We suggest that many ECs link to such an effective hidden PDE that is implicit in most or all ESMs. An EC may exist because its two quantities depend similarly on an ESM--specific internal bulk parameter in such a PDE, and with measurements constraining and revealing its (implicit) value. Alternatively, well--established process understanding coded at the ESM gridbox--scale, when aggregated, may generate a bulk parameter with a common ``emergent'' value across all ESMs. This single parameter may link uncertainties in a contemporary climate driver to those of a climate--related property of interest, the EC constraining the latter by measurements of the former. We offer illustrative examples of these concepts with generic differential equations and their solutions, placed in a conceptual EC framework.

Abstract: This paper documents the experimental setup and general features of the coupled historical and future climate simulations with the first version of the U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv1.0). The future projected climate characteristics of E3SMv1.0 at the highest emission scenario (SSP5-8.5) designed in the Scenario Model Intercomparison Project (ScenarioMIP) and the SSP5-8.5 greenhouse gas (GHG) only forcing experiment are analyzed with a focus on regional responses of atmosphere, ocean, sea-ice, and land. Due to its high climate sensitivity, E3SMv1.0 is one of the CMIP6 models with the largest surface warming by the end of the 21st century under the high-emission SSP5-8.5 scenario. The global mean precipitation change is highly correlated to the global temperature change, while the spatial pattern of the change in runoff responds to the precipitation changes. The oceanic mixed layer generally shoals throughout the global ocean. The sea ice, especially in the Northern Hemisphere, rapidly decreases with large seasonal variability. The annual mean AMOC is overly weak with a slower change relative to other CMIP6 models. We detect a significant polar amplification in E3SMv1.0 from the atmosphere, ocean, and sea ice. Comparing the SSP5-8.5 all-forcing experiment with the GHG-only experiment, we find that the unmasking of the aerosol effects due to the decline of the aerosol loading in the future projection period causes accelerated warming in SSP5-8.5 all-forcing experiment. While the oceanic climate response is mainly controlled by the GHG forcing, the land runoff response is impacted primarily by forcings other than GHG over certain regions. However, the importance of the GHG forcing on the land runoff changes grows in the future climate projection period compared to the historical period.

Abstract: Using climate projections to evaluate future climate impacts and their associated risks requires a background knowledge of the nature of climate change, use of climate models to develop future projections, and knowledge of how to address climate scenario uncertainty. This chapter provides an overview of climate and climate change, some of the foundational climate science that underlies current climate change assessments, and a brief introduction to climate models and climate scenario uncertainty. Global projections of temperature and precipitation changes from the recent Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) and a brief comparison to the prior assessment (AR5) are provided. The main sources of uncertainty in these projections include climate variability, climate model differences and treatment of scientific knowledge gaps, and greenhouse gas (GHG) emissions. When projections are downscaled to local resolution, downscaling is an additional source of uncertainty. These uncertainties can be incorporated in assessments of climate impacts by choosing a range of scenarios that directly address the sources of uncertainty. Evaluating the likelihood of a given climate impact on animal health or management strategies requires consideration of the main sources of climate projection uncertainties. Adaptation requires consideration of global-to-regional contexts of climate changes and impacts, but also adaptive capacity.

Abstract: Abstract. Planning for the impacts of climate change requires accurate projections by Earth System Models (ESMs). ESMs, as developed by many research centres, estimate changes to weather and climate as atmospheric Greenhouse Gases (GHGs) rise, and they inform the influential Intergovernmental Panel on Climate Change (IPCC) reports. ESMs are advancing the understanding of key climate system attributes. However, there remain substantial inter--ESM differences in their estimates of future meteorological change, even for a common GHG trajectory, and such differences make adaptation planning difficult. Until recently, the primary approach to reducing projection uncertainty has been to place emphasis on simulations that best describe the contemporary climate. Yet a model that performs well for present--day atmospheric GHG levels may not necessarily be accurate for higher GHG levels and vice-versa. A relatively new approach of Emergent Constraints (ECs) is gaining much attention as a technique to remove uncertainty between climate models. This method involves searching for an inter--ESM link between a quantity that we can measure now and another of major importance for in describing future climate. Combining the contemporary measurement with this relationship refines the future projection. Identified ECs exist for thermal, hydrological and geochemical cycles of the climate system. As ECs grow in influence on climate policy, the method is under intense scrutiny, creating a requirement to understand them better. We hypothesise that as many Earth System components vary in both space and time, their behaviours often satisfy large--scale Partial Differential Equations (PDEs). Such PDEs are valid at coarser scales than the equations coded in ESMs which capture finer high resolution gridbox--scale effects. We suggest that many ECs link to such an effective hidden PDE that is implicit in most or all ESMs. An EC may exist because its two quantities depend similarly on an ESM--specific internal bulk parameter in such a PDE, and with measurements constraining and revealing its (implicit) value. Alternatively, well--established process understanding coded at the ESM gridbox--scale, when aggregated, may generate a bulk parameter with a common ``emergent'' value across all ESMs. This single parameter may link uncertainties in a contemporary climate driver to those of a climate--related property of interest, the EC constraining the latter by measurements of the former. We offer illustrative examples of these concepts with generic differential equations and their solutions, placed in a conceptual EC framework.

Abstract: Abstract. To estimate equilibrium climate sensitivity from a simulation where a step change in carbon dioxide concentrations is imposed, a common approach is to linearly extrapolate temperatures as a function of top-of-atmosphere energetic imbalance to estimate the equilibrium state (“effective climate sensitivity”). In this study, we find that this estimate may be biased in some models due to state-dependent energetic leaks. Using an ensemble of multi-millennial simulations of climate model response to a constant forcing, we estimate equilibrium climate sensitivity through Bayesian calibration of simple climate models which allow for responses from subdecadal to multi-millennial timescales. Results suggest potential biases in effective climate sensitivity in the case of particular models where radiative tendencies imply energetic imbalances which differ between pre-industrial and quadrupled CO2 states, whereas for other models even multi-thousand-year experiments are insufficient to predict the equilibrium state. These biases draw into question the utility of effective climate sensitivity as a metric of warming response to greenhouse gases and underline the requirement for operational climate sensitivity experiments on millennial timescales to better understand committed warming following a stabilization of greenhouse gases.