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A reference or "no‐feedback" radiative response to warming is fundamental to understanding how much global warming will occur for a given change in greenhouse gases or solar radiation incident on the Earth. The simplest estimate of this radiative response is given by the Stefan‐Boltzmann law as −4σTe‾3≈−3.8 ${-}4\sigma {\overline{{T}_{e}}}^{3}\approx -3.8$ W m−2 K−1 for Earth's present climate, where Te‾ $\overline{{T}_{e}}$ is a global effective emission temperature. The comparable radiative response in climate models, widely called the "Planck feedback," averages −3.3 W m−2 K−1. This difference of 0.5 W m−2 K−1 is large compared to the uncertainty in the net climate feedback, yet it has not been studied carefully. We use radiative transfer models to analyze these two radiative feedbacks to warming, and find that the difference arises primarily from the lack of stratospheric warming assumed in calculations of the Planck feedback (traditionally justified by differing constraints on and time scales of stratospheric adjustment relative to surface and tropospheric warming). The Planck feedback is thus masked for wavelengths with non‐negligible stratospheric opacity, and this effect implicitly acts to amplify warming in current feedback analysis of climate change. Other differences between Planck and Stefan‐Boltzmann feedbacks arise from temperature‐dependent gas opacities, and several artifacts of nonlinear averaging across wavelengths, heights, and different locations; these effects partly cancel but as a whole slightly destabilize the Planck feedback. Our results point to an important role played by stratospheric opacity in Earth's climate sensitivity, and clarify a long‐overlooked but notable gap in our understanding of Earth's reference radiative response to warming. Plain Language Summary: Earth's climate is stable because a warmer planet loses more energy to space, at infrared wavelengths invisible to the naked eye. The rate of change of this energy loss as the planet warms provides an estimate how Earth's energy balance responds to warming, which is simple enough to write on a small piece of paper. When scientists investigate the warming predicted by climate models, they often start from a similar but not identical calculation of how Earth's energy balance responds to warming. This calculation, based on model output, is about 15% less stabilizing than the simple pencil‐and‐paper estimate. In this paper, we explore the causes of this 15% difference between the pencil‐and‐paper estimate and the calculations using climate models. We show that the difference is primarily caused by the lack of assumed warming in climate models high in Earth's atmosphere, where temperatures are not closely linked to surface warming. This lack of warming acts as a hidden destabilizing feedback in current analysis of climate models. Key Points: Earth's reference radiative response, or "Planck feedback," is ∼0.5 W m−2 K−1 less stabilizing than a Stefan‐Boltzmann estimateWe find this deviation is mostly due to the assumed lack of stratospheric warming in calculations of the Planck feedbackThe lack of stratospheric warming serves as an implicit positive feedback in analysis of climate model warming [ABSTRACT FROM AUTHOR]

Numerical models have been developed to investigate and understand responses of biogeochemical cycle to global changes. Steady state, when a system is in dynamic equilibrium, is generally required to initialize these model simulations. However, the spin‐up process that is used to achieve steady state pose a great burden to computational resources, limiting the efficiency of global modeling analysis on biogeochemical cycles. This study introduces a new Semi‐Analytical Spin‐Up (SASU) to tackle this grand challenge. We applied SASU to Community Land Model version 5 and examined its computational efficiency and accuracy. At the Brazil site, SASU is computationally 7 times more efficient than (or saved up to 86% computational cost in comparison with) the traditional native dynamics (ND) spin‐up to reach the same steady state. Globally, SASU is computationally 8 times more efficient than the accelerated decomposition spin‐up and 50 times more efficient than ND. In summary, SASU achieves the highest computational efficiency for spin‐up on site and globally in comparison with other spin‐up methods. It is generalizable to wide biogeochemical models and thus makes computationally costly studies (e.g., parameter perturbation ensemble analysis and data assimilation) possible for a better understanding of biogeochemical cycle under climate change. Plain Language Summary: Land carbon cycle models, a critical component of earth system models, have been developed to investigate and understand ecological responses to global changes. However, the model spin‐up process to achieve steady state are usually time‐consuming and pose great computational burden. The spin‐up problem hinders our ability to study some key issues in land carbon cycle modeling, such as sensitivity analysis. This paper applies a new SASU framework to accelerate spin‐up of Community Land Model matrix version 5. Compared with traditional native dynamic approach, SASU speeds up the spin‐up by approximately 50 times, achieving the fastest spin‐up speed than all the previously recorded methods. Our SASU method is applicable to biogeochemical models and, therefore, liberates the models for computationally costly studies, such as parameter perturbation ensemble analysis and data assimilation. Key Points: A new semi‐analytical spin‐up (SASU) framework combines the default accelerated spin‐up method and matrix analytical algorithmSASU accelerates CLIM5 spin‐up by tens of times, becoming the fastest method to our knowledgeSASU is applicable to most biogeochemical models and enables computationally costly study, for example, sensitivity analysis [ABSTRACT FROM AUTHOR]

This paper presents the response to anthropogenic forcing in the GISS‐E2.1 climate models for the 21st century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The experiments were performed using an updated and improved version of the NASA Goddard Institute for Space Studies (GISS) coupled general circulation model that includes two different versions for atmospheric composition: A non‐interactive version (NINT) with prescribed composition and a tuned aerosol indirect effect and the One‐Moment Aerosol model (OMA) version with fully interactive aerosols which includes a parameterized first indirect aerosol effect on clouds. The effective climate sensitivities are 3.0°C and 2.9°C for the NINT and OMA models, respectively. Each atmospheric version is coupled to two different ocean general circulation models: The GISS ocean model (E2.1‐G) and HYCOM (E2.1‐H). We describe the global mean responses for all future scenarios and spatial patterns of change for surface air temperature and precipitation for four of the marker scenarios: SSP1‐2.6, SSP2‐4.5, SSP4‐6.0, and SSP5‐8.5. By 2100, global mean warming ranges from 1.5°C to 5.2°C relative to 1,850–1,880 mean temperature. Two high‐mitigation scenarios SSP1‐1.9 and SSP1‐2.6 limit the surface warming to below 2°C by the end of the 21st century, except for the NINT E2.1‐H model that simulates 2.2°C of surface warming. For the high emission scenario SSP5‐8.5, the range is 4.6–5.2°C at 2100. Due to about 15% larger effective climate sensitivity and stronger transient climate response in both NINT and OMA CMIP6 models compared to CMIP5 versions, there is a stronger warming by 2100 in the SSP emission scenarios than in the comparable Representative Concentration Pathway (RCP) scenarios in CMIP5. Changes in sea ice area are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with the largest sea ice area decreases occurring during September in the Northern Hemisphere in both E2.1‐G (−1.21 × 106 km2/°C) and E2.1‐H models (−0.94 × 106 km2/°C). Both coupled models project decreases in the Atlantic overturning stream function by 2100. The largest decrease of 56%–65% in the 21st century overturning stream function is produced in the warmest scenario SSP5‐8.5 in the E2.1‐G model, comparable to the reduction in the corresponding CMIP5 GISS‐E2 RCP8.5 simulation. Both low‐end scenarios SSP1‐1.9 and SSP1‐2.6 also simulate substantial reductions of the overturning (9%–37%) with slow recovery of about 10% by the end of the 21st century (relative to the maximum decrease at the middle of the 21st century). Plain Language Summary: The projections of future climate change are uncertain because they are dependent on different possible scenarios of human‐caused emissions and their interaction with natural forcings, internal climate variability, and inter‐model differences. This paper presents the results of the climate model of the NASA Goddard Institute for Space Studies, GISS‐E2.1, for the anthropogenically forced climate response for the twenty first century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The sensitivity of the model response to different magnitudes of anthropogenic forcings in the twenty‐first‐century scenarios were performed using two different versions for the atmospheric composition and two different ocean general circulation models. Compared to CMIP5 GISS‐E2 versions, the CMIP6 GISS‐E2.1 climate model shows a stronger warming by 2100 in comparable scenarios due to larger effective climate sensitivity and transient climate response. Both climate models with two different ocean components project decreases in the Atlantic overturning stream function by 2100. Key Points: GISS E2.1 model with different configurations is used to carry out 134 Shared Socioeconomic Pathway (SSP) experimentsGISS‐E2.1 climate model shows a stronger warming by 2,100 in comparable Representative Concentration Pathway scenarios in CMIP5 due to larger effective climate sensitivity and stronger transient climate responseBoth coupled models, E2.1‐G and E2.1‐H, project decreases in the Atlantic overturning stream function by 2100 with the largest decrease in the warmest scenario SSP5‐8.5 in the E2.1‐G model [ABSTRACT FROM AUTHOR]

We describe the approach taken to develop the United Kingdom's first community Earth system model, UKESM1. This is a joint effort involving the Met Office and the Natural Environment Research Council (NERC), representing the U.K. academic community. We document our model development procedure and the subsequent U.K. submission to CMIP6, based on a traceable hierarchy of coupled physical and Earth system models. UKESM1 builds on the well‐established, world‐leading HadGEM models of the physical climate system and incorporates cutting‐edge new representations of aerosols, atmospheric chemistry, terrestrial carbon, and nitrogen cycles and an advanced model of ocean biogeochemistry. A high‐level metric of overall performance shows that both models, HadGEM3‐GC3.1 and UKESM1, perform better than most other CMIP6 models so far submitted for a broad range of variables. We point to much more extensive evaluation performed in other papers in this special issue. The merits of not using any forced climate change simulations within our model development process are discussed. First results from HadGEM3‐GC3.1 and UKESM1 include the emergent climate sensitivity (5.5 and 5.4 K, respectively) which is high relative to the current range of CMIP5 models. The role of cloud microphysics and cloud‐aerosol interactions in driving the climate sensitivity, and the systematic approach taken to understand this role, is highlighted in other papers in this special issue. We place our findings within the broader modeling landscape indicating how our understanding of key processes driving higher sensitivity in the two U.K. models seems to align with results from a number of other CMIP6 models. Plain Language Summary: The United Kingdom has taken a community approach to model development and delivery of simulations to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The papers in this special issue characterize and evaluate the U.K. models and highlight emerging properties of the models, such as the climate sensitivity. Understanding of the reasons for an increase in sensitivity in these new models is highlighted here, and similarities to our findings from other modeling centers are discussed. Key Points: The United Kingdom has developed its first community Earth system model and delivered a traceable hierarchy of models to CMIP6We applied a process‐based evaluation strategy in model development but chose not to use historic trends or measures of climate responseThe U.K. models exhibit higher climate sensitivity than seen in CMIP5 arising in part from more positive cloud feedbacks [ABSTRACT FROM AUTHOR]

Abstract: The Global Coupled 3 (GC3) configuration of the Met Office Unified Model is presented. Among other applications, GC3 is the basis of the United Kingdom's submission to the Coupled Model Intercomparison Project 6 (CMIP6). This paper documents the model components that make up the configuration (although the scientific descriptions of these components are in companion papers) and details the coupling between them. The performance of GC3 is assessed in terms of mean biases and variability in long climate simulations using present‐day forcing. The suitability of the configuration for predictability on shorter time scales (weather and seasonal forecasting) is also briefly discussed. The performance of GC3 is compared against GC2, the previous Met Office coupled model configuration, and against an older configuration (HadGEM2‐AO) which was the submission to CMIP5. In many respects, the performance of GC3 is comparable with GC2, however, there is a notable improvement in the Southern Ocean warm sea surface temperature bias which has been reduced by 75%, and there are improvements in cloud amount and some aspects of tropical variability. Relative to HadGEM2‐AO, many aspects of the present‐day climate are improved in GC3 including tropospheric and stratospheric temperature structure, most aspects of tropical and extratropical variability and top‐of‐atmosphere and surface fluxes. A number of outstanding errors are identified including a residual asymmetric sea surface temperature bias (cool northern hemisphere, warm Southern Ocean), an overly strong global hydrological cycle and insufficient European blocking. [ABSTRACT FROM AUTHOR]

This paper introduces the Flexible Global Ocean‐Atmosphere‐Land System Model: Grid‐Point Version 3 (FGOALS‐g3) and evaluates its basic performance based on some of its participation in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) experiments. Our results show that many significant improvements have been achieved by FGOALS‐g3 in terms of climatological mean states, variabilities, and long‐term trends. For example, FGOALS‐g3 has a small (−0.015°C/100 yr) climate drift in 700‐yr preindustrial control (piControl) runs and smaller biases in climatological mean variables, such as the land/sea surface temperatures (SSTs) and seasonal soil moisture cycle, compared with its previous version FGOALS‐g2 during the historical period. The characteristics of climate variabilities, for example, Madden‐Julian oscillation (MJO) eastward/westward propagation ratios, spatial patterns of interannual variability of tropical SST anomalies, and relationship between the East Asian Summer Monsoon and El Niño–Southern Oscillation (ENSO), are well captured by FGOALS‐g3. In particular, the cooling trend of globally averaged surface temperature during 1940–1970, which is a challenge for most CMIP3 and CMIP5 models, is well reproduced by FGOALS‐g3 in historical runs. In addition to the external forcing factors recommended by CMIP6, anthropogenic groundwater forcing from 1965 to 2014 was incorporated into the FGOALS‐g3 historical runs. Plain Language Summary: The sixth phase of the Coupled Model Intercomparison Project (CMIP6) is a crucial support for the sixth Assessment Report of Intergovernmental Panel on Climate Change (IPCC AR6) and will also provide important foundation for research in climate change in the next few years. This paper gives the description of FGOALS‐g3 model, its experiment configures, and the experiments conducted according to the experimental design of CMIP6 and evaluates the preliminary performance of model simulation. This work offers references to CMIP6 data users and provides enormous output data sets for assessing and understanding climate change. Key Points: This paper describes FGOALS‐g3 and its experiment design for CMIP6Historical, preindustrial, and scenario simulations are evaluatedClimate drift is small in preindustrial simulation, and mean climate and climate variabilities at different temporal scales are realistic in historical runs [ABSTRACT FROM AUTHOR]

This paper describes the GISS‐E2.1 contribution to the Coupled Model Intercomparison Project, Phase 6 (CMIP6). This model version differs from the predecessor model (GISS‐E2) chiefly due to parameterization improvements to the atmospheric and ocean model components, while keeping atmospheric resolution the same. Model skill when compared to modern era climatologies is significantly higher than in previous versions. Additionally, updates in forcings have a material impact on the results. In particular, there have been specific improvements in representations of modes of variability (such as the Madden‐Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice. The effective climate sensitivity to 2 × CO2 is slightly higher than previously at 2.7–3.1°C (depending on version) and is a result of lower CO2 radiative forcing and stronger positive feedbacks. Plain Language Summary: This paper describes the latest iteration of the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS) climate model, which will be used for understanding historical climate change and to make projections for the future. We compare the model output to a wide range of observations over the recent era (1979–2014) and show that there has been a significant increase in how well the model performs compared to the previous version from 2014, particularly in the Southern Ocean, though some persistent biases remain. The model has a temperature response to the increase of carbon dioxide that is slightly higher than previous versions but is well within the range expected from observational and past climate constraints. Key Points: GISS‐E2.1 is an updated climate model version for use within the CMIP6 projectAtmospheric composition is calculated consistently in all model versionsResults demonstrate a significant improvement in skill in a climate model without changes to atmospheric resolution [ABSTRACT FROM AUTHOR]

Advances in high‐performance computing have enabled large‐eddy simulations (LES) of turbulence, convection, and clouds. However, their potential to improve parameterizations in global climate models (GCMs) is only beginning to be harnessed, with relatively few canonical LES available so far. The purpose of this paper is to begin creating a public LES library that expands the training data available for calibrating and evaluating GCM parameterizations. To do so, we use an experimental setup in which LES are driven by large‐scale forcings from GCMs, which in principle can be used at any location, any time of year, and in any climate state. We use this setup to create a library of LES of clouds across the tropics and subtropics, in the present and in a warmer climate, with a focus on the transition from stratocumulus to shallow cumulus over the East Pacific. The LES results are relatively insensitive to the choice of host GCM driving the LES. Driven with large‐scale forcing under global warming, the LES simulate a positive but weak shortwave cloud feedback, adding to the accumulating evidence that low clouds amplify global warming. Plain Language Summary: Clouds remain one of the largest uncertainties in our understanding and in predictions of climate change because it is challenging to represent their small‐scale dynamics in climate models. However, high‐resolution simulations can provide faithful simulations of clouds and turbulence in limited areas, and these can be used to calibrate climate models. So far, only a limited set of simulations has been used for calibration of climate models, with focus on a few specific locations. This study presents an experimental setup that allows the high‐resolution simulations to be run anywhere on the globe, in any climate state, driven by output from different climate models. The setup is used to create a library of high‐resolution simulations of clouds across the tropics and subtropics in both the current and a warmer climate. The library substantially expands the data set available for the calibration and evaluation of climate models. Key Points: A library of high‐resolution simulations of clouds is created with large‐eddy simulations (LES) driven by a few global climate models (GCMs)Clouds simulated by LES driven by different GCMs are more similar to one another than the corresponding GCM‐simulated cloudsUnder global warming, LES‐simulated low clouds exhibit a weak but positive feedback on warming [ABSTRACT FROM AUTHOR]

Characteristics of, and fundamental differences between, the radiative‐convective equilibrium (RCE) climate states following the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) protocols in the Community Atmosphere Model version 5 (CAM5) and version 6 (CAM6) are presented. This paper explores the characteristics of clouds, moisture, precipitation and circulation in the RCE state, as well as the tropical response to surface warming, in CAM5 and CAM6 with different parameterizations. Overall, CAM5 simulates higher precipitation rates that result in larger global average precipitation, despite lower outgoing longwave radiation compared to CAM6. Differences in the structure of clouds, particularly the amount and vertical location of cloud liquid, exist between the CAM versions and can, in part, be related to distinct representations of shallow convection and boundary layer processes. Both CAM5 and CAM6 simulate similar peaks in cloud fraction, relative humidity, and cloud ice, linked to the usage of a similar deep convection parameterization. These anvil clouds rise and decrease in extent in response to surface warming. More generally, extreme precipitation, aggregation of convection, and climate sensitivity increase with warming in both CAM5 and CAM6. This analysis provides a benchmark for future studies that explore clouds, convection, and climate in CAM with the RCEMIP protocols now available in the Community Earth System Model. These results are discussed within the context of realistic climate simulations using CAM5 and CAM6, highlighting the usefulness of a hierarchical modeling approach to understanding model and parameterization sensitivities to inform model development efforts. Plain Language Summary: Clouds, circulations and rainfall in the tropics play an important role in Earth's climate. However, global climate models differ in their representation of these features, contributing to uncertainty in future climate projections. One useful tool to better understand model differences and inform efforts to improve models is to analyze idealized configurations. We explore two different numerical representations of an idealized atmosphere relevant to tropical regions to determine the impact on the characteristics of clouds, rainfall and circulations as well as the tropical atmospheric response to warming. We show that our idealized models mirror differences in the low‐cloud structure in the deep tropics of more realistic models. This work also finds similarities between the two numerical representations, such as a decrease in the spatial extent of high altitude clouds with warming. As the surface is warmed, both versions also show increases in the clustering of clouds, the likelihood of extreme precipitation rates, and an estimate of how much warming would occur in response to a doubling of carbon dioxide. Key Points: A new radiative‐convective equilibrium (RCE) configuration is added to the official release of Community Earth System Model (CESM) to facilitate the use of Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) in the CESM hierarchyThe Community Atmosphere Model version 5 and Community Atmosphere Model version 6 (CAM6) RCEMIP climate states and variation across SST experiments are documented in the context of the CESM hierarchyDiffering parameterizations lead to a reduction of low‐level clouds in CAM6, consistent with differences in more realistic simulations [ABSTRACT FROM AUTHOR]

We present an unprecedented set of high‐resolution climate simulations, consisting of a 500‐year pre‐industrial control simulation and a 250‐year historical and future climate simulation from 1850 to 2100. A high‐resolution configuration of the Community Earth System Model version 1.3 (CESM1.3) is used for the simulations with a nominal horizontal resolution of 0.25° for the atmosphere and land models and 0.1° for the ocean and sea‐ice models. At these resolutions, the model permits tropical cyclones and ocean mesoscale eddies, allowing interactions between these synoptic and mesoscale phenomena with large‐scale circulations. An overview of the results from these simulations is provided with a focus on model drift, mean climate, internal modes of variability, representation of the historical and future climates, and extreme events. Comparisons are made to solutions from an identical set of simulations using the standard resolution (nominal 1°) CESM1.3 and to available observations for the historical period to address some key scientific questions concerning the impact and benefit of increasing model horizontal resolution in climate simulations. An emerging prominent feature of the high‐resolution pre‐industrial simulation is the intermittent occurrence of polynyas in the Weddell Sea and its interaction with an Interdecadal Pacific Oscillation. Overall, high‐resolution simulations show significant improvements in representing global mean temperature changes, seasonal cycle of sea‐surface temperature and mixed layer depth, extreme events and in relationships between extreme events and climate modes. Plain Language Summary: Although the current generation of climate models has demonstrated high fidelity in simulating and projecting global temperature change, these models show large uncertainties when it comes to questions concerning how rising global temperatures will impact local weather conditions. This is because the resolution (~100 km) at which the majority of climate models simulate the climate is not fine enough to resolve these small‐scale regional features. Conducting long‐term (multi‐centuries) high‐resolution (~10 km) climate simulations has been a great challenge for the research community due to the extremely high computational demands. Through international collaboration, we are able to address this challenge by delivering an unprecedented set of multi‐century high‐resolution climate simulations using the Community Earth System Model (CESM), capable of directly representing tropical cyclones and extreme rainfall events. In this paper, we give an overall assessment of the value and benefit of the high‐resolution CESM climate simulations by making a direct comparison to an identical set of low‐resolution CESM simulations. We showcase some of the major improvements of the high‐resolution CESM in simulating global mean temperature changes, seasonal cycle of sea‐surface temperature, and extreme events, such as tropical cyclones and relationships between tropical cyclones and El Niño‐Southern Oscillation. Key Points: An unprecedented set of multi‐century high‐resolution Community Earth System Model (CESM) simulations is describedHigh‐resolution CESM simulations reveal a potential role of Southern Ocean polynyas in multidecadal climate variabilityHigh‐resolution CESM exhibits significantly improved simulations of extreme events, such as tropical cyclones and atmospheric rivers [ABSTRACT FROM AUTHOR]

Understanding climate-cropland interactions and their impact on future projections in West Africa motivated the recent development of a modeling framework that asynchronously couples four models for regional climate, crop growth, socioeconomics, and cropland allocation. This modeling framework can be applied to a future time slice using an equilibrium approach or to a continuous projection using a transient approach. This paper compares the differences between these two approaches, examines the transient dynamics of the system, and evaluates its impact on future projections. During the course of projection up to mid-century, food demand is projected to increase monotonically, while the projected crop yield shows a high degree of temporal dynamics due to strong climate variability. Such temporal dynamics are not accounted for by the equilibrium approach. As a result, the transient approach projects a generally faster future expansion of cropland, with the largest differences over Benin, Burkina Faso, Ghana, Senegal, and Togo. Despite the relative large differences between the two approaches in projecting land cover changes associated with cropland expansion, the projected future climate changes are fairly similar. While the additional cropland expansion in the transient approach favors a wet signal, both the transient and equilibrium approaches project a future decrease of rainfall in the western part of West Africa and an increase in the eastern part. For quantifying climate changes, the equilibrium application of the modeling framework is likely to be sufficient; for assessing climate impact on agricultural sectors and devising mitigation and adaptation strategies, transient dynamics is important. [ABSTRACT FROM AUTHOR]

With the advent of climate change as a major challenge of our time, Earth system modeling has become highly policy‐relevant regulatory science. In this situation, the social mechanisms that play a role in any scientific endeavor become particularly exposed. By discussing historical, philosophical, and sociological (HPS) aspects of the field's current "cultures of prediction" together with the physical science community in a physical science journal, we aim to provide an entry point into HPS reasoning for climate scientists interested in reflecting on their field and science in general. This paper, first, introduces our perspective on "science as culture" and climate modeling as "regulatory science" and, second, highlights and connects relevant ideas from the three commentaries that follow it. In so doing, we hope to give a fuller picture of climate science, the interplay it engenders between HPS and the physical sciences, the distinctions that it gives rise to as compared to some of the more traditional, exact, sciences in which it is rooted and its place in society including its role in scientific policy advice. Key Points: With the advent of climate change as a major challenge of our time, Earth system modeling has become highly policy relevantIn this situation, the social mechanisms that play a role in any scientific endeavor become particularly exposedThis paper introduces perspectives on "science as culture" and highlights relevant ideas from the three subsequent commentaries [ABSTRACT FROM AUTHOR]

The Alfred Wegener Institute Climate Model (AWI‐CM) participates for the first time in the Coupled Model Intercomparison Project (CMIP), CMIP6. The sea ice‐ocean component, FESOM, runs on an unstructured mesh with horizontal resolutions ranging from 8 to 80 km. FESOM is coupled to the Max Planck Institute atmospheric model ECHAM 6.3 at a horizontal resolution of about 100 km. Using objective performance indices, it is shown that AWI‐CM performs better than the average of CMIP5 models. AWI‐CM shows an equilibrium climate sensitivity of 3.2°C, which is similar to the CMIP5 average, and a transient climate response of 2.1°C which is slightly higher than the CMIP5 average. The negative trend of Arctic sea‐ice extent in September over the past 30 years is 20–30% weaker in our simulations compared to observations. With the strongest emission scenario, the AMOC decreases by 25% until the end of the century which is less than the CMIP5 average of 40%. Patterns and even magnitude of simulated temperature and precipitation changes at the end of this century compared to present‐day climate under the strong emission scenario SSP585 are similar to the multi‐model CMIP5 mean. The simulations show a 11°C warming north of the Barents Sea and around 2°C to 3°C over most parts of the ocean as well as a wetting of the Arctic, subpolar, tropical, and Southern Ocean. Furthermore, in the northern middle latitudes in boreal summer and autumn as well as in the southern middle latitudes, a more zonal atmospheric flow is projected throughout the year. Plain Language Summary: The Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) participates for the first time with a global climate model in the Coupled Model Intercomparison Project 6 (CMIP6). The results of CMIP6 and previous model comparison projects feed into the next assessment report of the Intergovernmental Panel on Climate Change (IPCC). The IPCC assessment reports include information on past and expected climate change in the future and is written for policy‐ and decision‐makers as well as for the general public. The main characteristics of the AWI climate model are described and compared to models from previous intercomparison projects. The projected global warming in AWI‐CM is similar to the average warming predicted by climate models in the previous intercomparison project. However, the Arctic sea‐ice extent declines faster than typical previous estimates. Areas that are wet in present‐day climate become wetter, and areas that are dry in present‐day climate become drier in the future—consistent with previous climate model simulations. The ocean currents remain rather stable in the AWI climate projections, which leads to a continued warm Gulf stream and therefore an only slightly reduced warming of the North Atlantic and parts of Europe compared to other middle‐latitude regions. Key Points: The paper describes contributions of AWI‐CM, which employs a sea‐ice ocean component formulated on unstructured meshes, to CMIP6Equilibrium climate sensitivity is similar to average of CMIP5 projections; transient climate response is slightly above averageResponse patterns are similar to CMIP5 with more pronounced Arctic sea ice loss and a more stable AMOC compared to other systems [ABSTRACT FROM AUTHOR]

The performance of global ocean biogeochemical models can be quantified as the misfit between modeled tracer distributions and observations, which is sought to be minimized during parameter optimization. These models are computationally expensive due to the long spin‐up time required to reach equilibrium, and therefore optimization is often laborious. To reduce the required computational time, we investigate whether optimization of a biogeochemical model with shorter spin‐ups provides the same optimized parameters as one with a full‐length, equilibrated spin‐up over several millennia. We use the global ocean biogeochemical model MOPS with a range of lengths of model spin‐up and calibrate the model against synthetic observations derived from previous model runs using a derivative‐free optimization algorithm (DFO‐LS). When initiating the biogeochemical model with tracer distributions that differ from the synthetic observations used for calibration, a minimum spin‐up length of 2,000 years was required for successful optimization due to certain parameters which influence the transport of matter from the surface to the deeper ocean, where timescales are longer. However, preliminary results indicate that successful optimization may occur with an even shorter spin‐up by a judicious choice of initial condition, here the synthetic observations used for calibration, suggesting a fruitful avenue for future research. Plain Language Summary: Global ocean biogeochemical models allow us to simulate ocean biological and chemical variables throughout the global ocean, and are necessary for climate change projections. They are very computationally expensive due to the required spin‐up needed for the model to reach a steady state, and so any improvements to the model need to be sought in an efficient way. One way we investigate here is to first make the model less computationally expensive by using a shorter spin‐up, and then we apply an efficient optimization algorithm to tune the model to observations. By shortening the spin‐up from 3,000 to 2,000 years we show that we can still reach a successfully optimized model. Preliminary results indicate that successful optimization may occur with an even shorter spin‐up when the biogeochemical model is initialized from a more accurate state, which highlights a future avenue for research that may encourage more systematic tuning of computationally expensive ocean biogeochemical models. Key Points: Global ocean biogeochemical models are computationally expensive due to the long spin‐up time required to reach equilibriumA shortened spin up of 2,000 years during parameter optimization can be successfully optimizedHow short a spin‐up one can successfully optimize is influenced by the parameters being calibrated and the initial conditions of the model [ABSTRACT FROM AUTHOR]

Terrestrial, aquatic, and marine ecosystems regulate climate at local to global scales through exchanges of energy and matter with the atmosphere and assist with climate change mitigation through nature‐based climate solutions. Climate science is no longer a study of the physics of the atmosphere and oceans, but also the ecology of the biosphere. This is the promise of Earth system science: to transcend academic disciplines to enable study of the interacting physics, chemistry, and biology of the planet. However, long‐standing tension in protecting, restoring, and managing forest ecosystems to purposely improve climate evidences the difficulties of interdisciplinary science. For four centuries, forest management for climate betterment was argued, legislated, and ultimately dismissed, when nineteenth century atmospheric scientists narrowly defined climate science to the exclusion of ecology. Today's Earth system science, with its roots in global models of climate, unfolds in similar ways to the past. With Earth system models, geoscientists are again defining the ecology of the Earth system. Here we reframe Earth system science so that the biosphere and its ecology are equally integrated with the fluid Earth to enable Earth system prediction for planetary stewardship. Central to this is the need to overcome an intellectual heritage to the models that elevates geoscience and marginalizes ecology and local land knowledge. The call for kilometer‐scale atmospheric and ocean models, without concomitant scientific and computational investment in the land and biosphere, perpetuates the geophysical view of Earth and will not fully provide the comprehensive actionable information needed for a changing climate. Plain Language Summary: Terrestrial ecosystems provide a natural solution to planetary warming by storing carbon, dissipating surface heating through evapotranspiration, and other processes. That forests, in particular, influence climate is a centuries‐old premise, but its potential for planetary stewardship has not been realized. In an acrimonious controversy spanning several centuries, managing forests to purposely change climate was advocated, legislated, and resoundingly dismissed as unscientific. Similar intellectual bias is evident in today's Earth system science and the associated Earth system models, which are the state‐of‐the‐art models used to inform climate policy. The popular characterization of Earth system science lauds its interdisciplinary melding of physics, chemistry, and biology, but the models emphasize the physics and fluid dynamics of the atmosphere and oceans and present a limited perspective of terrestrial ecosystems in the Earth system. Ecologists studying the living world increasingly have a voice in Earth system science as we move beyond the physical basis for climate change to Earth system prediction for planetary stewardship. As we once again look to forests to solve a climate problem, we must surmount the disciplinary narrowness that failed to answer the forest‐climate question in the past and that continues to limit the interdisciplinary potential of Earth system science. Key Points: Nature‐based climate solutions have been advocated for centuries, but have been distorted by academic bias and colonialist prejudiceEarth system science, while recognizing the climate services of the biosphere, has a geophysical bias in interdisciplinary collaborationTo realize the potential for planetary stewardship, Earth system models must embrace the living world equally with the fluid world [ABSTRACT FROM AUTHOR]

This is the first paper that analyzes data from atmosphere model intercomparison project‐type climate simulations using a cloud‐system‐resolving global nonhydrostatic model without cumulus parameterization focussing particulaly on the relationship between clouds and circulation, and their changes due to global warming. The decrease in fractional coverage of low clouds is key to evaluating cloud radiative effects, because changes in shortwave cloud radiative effects overwhelm those of longwave cloud radiative effects. Thus, improved evaluation of low clouds is important, even in high‐resolution climate simulations. An analysis of heat redistribution by explicitly computed clouds revealed that column‐integrated heating rate due to phase changes correlates highly with vertical velocity at the altitude corresponding to 500 hPa and is closely linked to column water vapor, similar to the present climate result. Using data from year 1 to year 5, the effective climate sensitivity was evaluated to be 3.6−3.7°C. Possible convective aggregation is also examined using an index of modified subsidence fraction and characteristic changes in the number of cold pools. Despite previous idealized‐planet simulations showing more aggregated tropical convection under warmer conditions, here we show a decrease in the subsidence fraction and an increase in the number of smaller cold pools, suggesting that it is possible to realize less convective organization with warming under real atmospheric conditions. Key Points: We used global nonhydrostatic climate simulations to study how clouds and large‐scale circulations change in response to global warmingChanges in the shortwave cloud radiative effect are key to evaluating the net cloud radiative effect, even in high‐resolution simulationsWe found no evidence of an increased amount of tropical convective aggregation in response to global warming [ABSTRACT FROM AUTHOR]

Peatlands are poorly represented in global Earth system modeling frameworks. Here we add a peatland‐specific land surface hydrology module (PEAT‐CLSM) to the Catchment Land Surface Model (CLSM) of the NASA Goddard Earth Observing System (GEOS) framework. The amended TOPMODEL approach of the original CLSM that uses topography characteristics to model catchment processes is discarded, and a peatland‐specific model concept is realized in its place. To facilitate its utilization in operational GEOS efforts, PEAT‐CLSM uses the basic structure of CLSM and the same global input data. Parameters used in PEAT‐CLSM are based on literature data. A suite of CLSM and PEAT‐CLSM simulations for peatland areas between 40°N and 75°N is presented and evaluated against a newly compiled data set of groundwater table depth and eddy covariance observations of latent and sensible heat fluxes in natural and seminatural peatlands. CLSM's simulated groundwater tables are too deep and variable, whereas PEAT‐CLSM simulates a mean groundwater table depth of −0.20 m (snow‐free unfrozen period) with moderate temporal fluctuations (standard deviation of 0.10 m), in significantly better agreement with in situ observations. Relative to an operational CLSM version that simply includes peat as a soil class, the temporal correlation coefficient is increased on average by 0.16 and reaches 0.64 for bogs and 0.66 for fens when driven with global atmospheric forcing data. In PEAT‐CLSM, runoff is increased on average by 38% and evapotranspiration is reduced by 19%. The evapotranspiration reduction constitutes a significant improvement relative to eddy covariance measurements. Plain Language Summary: Peatlands are wetlands in which plant matter has accumulated over thousands of years under almost permanently water‐logged conditions. Alterations in these conditions as a result of global climate change can lead to the release of the huge peatland carbon pool as carbon dioxide over much shorter timescales than were required for accumulation. The additional emissions would amplify global warming. A better representation of the peatland hydrology in global Earth system models can help quantify how peatlands respond to a changing climate. In this paper, we add a peatland‐specific land surface hydrology module to the land surface model used in NASA's GEOS Earth system modeling framework. Comparisons of numerical simulations encompassing northern peatlands against field observations show that the new model version significantly improves our ability to capture the hydrological dynamics of peatlands. The new peatland representation in GEOS offers new opportunities, including the potential for merging model information and remote sensing observations in a way that improves our understanding of the overall role played by peatlands in the global water and carbon cycles. Key Points: A peatland‐specific land surface hydrology was added to an Earth system model and constrained by literature data, without parameter tuningSimulations were evaluated with a data set of groundwater table depth and evapotranspiration with unprecedented coverage in high latitudesThe peatland model version performs significantly better in terms of hydrological variables over peatlands than the operational model [ABSTRACT FROM AUTHOR]

Abstract: A decadal climate prediction system named as IAP‐DecPreS was constructed in the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, based on a fully coupled model FGOALS‐s2 and a newly developed initialization scheme, referred to as EnOI‐IAU. In this paper, we introduce the design of the EnOI‐IAU scheme, assess the accuracies of initialization integrations using the EnOI‐IAU and preliminarily evaluate hindcast skill of the IAP‐DecPreS. The EnOI‐IAU scheme integrates two conventional assimilation approaches, ensemble optimal interpolation (EnOI) and incremental analysis update (IAU). The EnOI and IAU were applied to calculate analysis increments and incorporate them into the model, respectively. Three continuous initialization (INIT) runs were conducted for the period of 1950–2015, in which observational sea surface temperature (SST) from the HadISST1.1 and subsurface ocean temperature profiles from the EN4.1.1 data set were assimilated. Then nine‐member 10 year long hindcast runs initiated from the INIT runs were conducted for each year in the period of 1960–2005. The accuracies of the INIT runs are evaluated from the following three aspects: upper 700 m ocean temperature, temporal evolution of SST anomalies, and dominant interdecadal variability modes, Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). Finally, preliminary evaluation of the ensemble mean of the hindcast runs suggests that the IAP‐DecPreS has skill in the prediction of the PDO‐related SST anomalies in the midlatitude North Pacific and AMO‐related SST anomalies in the tropical North Atlantic. [ABSTRACT FROM AUTHOR]

Vegetation gross primary productivity (GPP) is the single largest carbon flux of the terrestrial biosphere which, in turn, is responsible for sequestering 25%–30% of anthropogenic carbon dioxide emissions. The ability to model GPP is therefore critical for calculating carbon budgets as well as understanding climate feedbacks. Earth system models (ESMs) have the capability to simulate GPP but vary greatly in their individual estimates, resulting in large uncertainties. We describe a machine learning (ML) approach to investigate two key factors responsible for differences in simulated GPP quantities from ESMs: the relative importance of different atmospheric drivers and differences in the representation of land surface processes. We describe the different steps in the development of our interpretable ML framework including the choice of algorithms, parameter tuning, training and evaluation. Our results show that ESMs largely agree on the physical climate drivers responsible for GPP as seen in the literature, for instance drought variables in the Mediterranean region or radiation and temperature in the Arctic region. However differences do exist since models don't necessarily agree on which individual variable is most relevant for GPP. We also explore a distance measure to attribute GPP differences to climate influences versus process differences and provide examples for where our methods work (South Asia, Mediterranean) and where they are inconclusive (Eastern North America). Plain Language Summary: Gross primary productivity (GPP) is the rate at which plants remove carbon dioxide from the atmosphere during photosynthesis. Carbon dioxide is a greenhouse gas and excess in the atmosphere causes global warming and climate change. Changes in the amounts of atmospheric carbon dioxide will impact the entire Earth System. We therefore need the ability to accurately calculate GPP, especially for different possible carbon usage pathways in the future. Earth system models or ESMs allow us to simulate various processes happening in the earth's atmosphere and biosphere including photosynthesis and can help us estimate GPP changes for such different pathways. However, ESMs can vary significantly in their simulated GPP estimates making it difficult to have confidence in using these estimates. We describe a ML framework to better understand where ESMs differ in calculating GPP so that we can address knowledge gaps in models. This approach allows us to understand the processes involved without having to run computationally expensive simulations. With improved models, we can also improve our ability to predict climate change outcomes for the future. Key Points: A machine learning framework to advance our understanding of the terrestrial carbon cycle in earth system models or ESMs is proposedDifferences in the relative importance of atmospheric drivers of gross primary productivity highlights differences across modelsA method to attribute differences in productivity estimates from ESMs due to process representation versus atmospheric forcing is demonstrated [ABSTRACT FROM AUTHOR]

The internal variability pertains to fluctuations originating from processes inherent to the climate component and their mutual interactions. On the other hand, forced variability delineates the influence of external boundary conditions on the physical climate system. A methodology is formulated to distinguish between internal and forced variability within the surface air temperature. The noise‐to‐noise approach is employed for training a neural network, drawing an analogy between internal variability and image noise. A large training data set is compiled using surface air temperature data spanning from 1901 to 2020, obtained from an ensemble of Atmosphere‐Ocean General Circulation Model simulations. The neural network utilized for training is a U‐Net, a widely adopted convolutional network primarily designed for image segmentation. To assess performance, comparisons are made between outputs from two single‐model initial‐condition large ensembles, the ensemble mean, and the U‐Net's predictions. The U‐Net reduces internal variability by a factor of four, although notable discrepancies are observed at the regional scale. While demonstrating effective filtering of the El Niño Southern Oscillation, the U‐Net encounters challenges in capturing the changes in the North Atlantic Ocean. This methodology holds potential for extension to other physical variables, facilitating insights into the climate change triggered by external forcings over the long term. Plain Language Summary: To anticipate future climate change, it is crucial to detect and understand the impacts of human activities. However, distinguishing the effects of anthropogenic forcing from natural climate variations in observational data is challenging. Natural climate variability, known as internal variability, arises from the chaotic nature of atmospheric and oceanic circulation, and from the interactions among the ocean, atmosphere, and land. Here, a novel approach is introduced to distinguish the changes caused by human activities from internal variability. It is applied to the surface air temperature evolution from 1901 to 2020. This method uses an artificial neural network designed to separate the internal from the human‐induced variability. An unprecedented number of climate model simulations are used, enabling precise estimation of human‐forced variability in these climate models. The spatio‐temporal variations are distinguished by applying a well‐known methodology previously used to remove noise from images. The method's performance is evaluated, revealing errors regarding the internal variability that are typically one‐fourth of the actual variations. Regions with important internal variability or with low agreement among models exhibit the largest errors. Overall, the skills are comparable to other existing approaches, but improvements are anticipated. Key Points: We present a new method to separate the forced and internal variability of the surface air temperatureWe utilize a U‐Net trained with global climate models outputs and implement a noise to noise methodology to eliminate internal variabilityThe results are assessed through the utilization of very large ensemble simulations of two distinct climate models [ABSTRACT FROM AUTHOR]

We describe the baseline model configuration and simulation characteristics of the Geophysical Fluid Dynamics Laboratory (GFDL)'s Land Model version 4.1 (LM4.1), which builds on component and coupled model developments over 2013–2019 for the coupled carbon‐chemistry‐climate Earth System Model Version 4.1 (ESM4.1) simulation as part of the sixth phase of the Coupled Model Intercomparison Project. Analysis of ESM4.1/LM4.1 is focused on biophysical and biogeochemical processes and interactions with climate. Key features include advanced vegetation dynamics and multi‐layer canopy energy and moisture exchanges, daily fire, land use representation, and dynamic atmospheric dust coupling. We compare LM4.1 performance in the GFDL Earth System Model (ESM) configuration ESM4.1 to the previous generation component LM3.0 in the ESM2G configuration. ESM4.1/LM4.1 provides significant improvement in the treatment of ecological processes from GFDL's previous generation models. However, ESM4.1/LM4.1 likely overestimates the influence of land use and land cover change on vegetation characteristics, particularly on pasturelands, as it overestimates the competitiveness of grasses versus trees in the tropics, and as a result, underestimates present‐day biomass and carbon uptake in comparison to observations. Plain Language Summary: The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a new Land Model (LM4.1) as part of its 4th generation coupled model development. This model includes advances from the previous generation and introduces a new vegetation demography model, multi‐layer canopy, plant hydraulics, fire, and land use representation as well as dynamic atmospheric dust coupling. Coupled within an Earth System Model (ESM4.1), LM4.1 features an improved representation of many ecological processes from the previous generation of GFDL ESMs. Key Points: A new land model LM4.1 is developed at the Geophysical Fluid Dynamics Laboratory (GFDL) for the next‐generation Earth System Model (ESM) ESM4.1LM4.1 integrates age‐height structured vegetation dynamics, multi‐layer canopy‐soil‐snow energy exchanges, and prognostic fires and mineral dustESM4.1/LM4.1 improves patterns of land surface climate and carbon cycle compared to the previous generation GFDL model ESM2G/LM3.0 [ABSTRACT FROM AUTHOR]

In this paper, the recently developed variable-resolution option within the Community Earth System Model (VR-CESM) is assessed for long-term regional climate modeling of California at 0.25° (∼28 km) and 0.125° (∼14 km) horizontal resolutions. The mean climatology of near-surface temperature and precipitation is analyzed and contrasted with reanalysis, gridded observational data sets, and a traditional regional climate model (RCM)-the Weather Research and Forecasting (WRF) model. Statistical metrics for model evaluation and tests for differential significance have been extensively applied. With only prescribed sea surface temperatures, VR-CESM tended to produce a warmer summer (by about 1-3°C) and overestimated overall winter precipitation (about 25%-35%) compared to reference data sets. Increasing resolution from 0.25° to 0.125° did not produce a statistically significant improvement in the model results. By comparison, the analogous WRF climatology (constrained laterally and at the sea surface by ERA-Interim reanalysis) was ∼1-3°C colder than the reference data sets, underestimated precipitation by ∼20%-30% at 27 km resolution, and overestimated precipitation by ∼65-85% at 9 km. Overall, VR-CESM produced comparable statistical biases to WRF in key climatological quantities. This assessment highlights the value of variable-resolution global climate models (VRGCMs) in capturing fine-scale atmospheric processes, projecting future regional climate, and addressing the computational expense of uniform-resolution global climate models. [ABSTRACT FROM AUTHOR]

Climate change will have potentially significant effects on freshwater quality due to increases in river and lake temperatures, changes in the magnitude and seasonality of river runoff, and more frequent and severe extreme events. These physical impacts will in turn have economic consequences through effects on riparian development, river and reservoir recreation, water treatment, harmful aquatic blooms, and a range of other sectors. In this paper, we analyze the physical and economic effects of changes in freshwater quality across the contiguous U.S. in futures with and without global-scale greenhouse gas mitigation. Using a water allocation and quality model of 2119 river basins, we estimate the impacts of various projected emissions outcomes on several key water quality indicators, and monetize these impacts with a water quality index approach. Under mitigation, we find that water temperatures decrease considerably and that dissolved oxygen levels rise in response. We find that the annual economic impacts on water quality of a high emissions scenario rise from $1.4 billion in 2050 to $4 billion in 2100, leading to present value mitigation benefits, discounted at 3%, of approximately $17.5 billion over the 2015-2100 period. [ABSTRACT FROM AUTHOR]

The general circulation models used to simulate global climate typically feature resolution too coarse to reproduce many smaller-scale processes, which are crucial to determining the regional responses to climate change. A novel approach to downscale climate change scenarios is presented which includes the interactions between the North Atlantic Ocean and the European shelves as well as their impact on the North Atlantic and European climate. The goal of this paper is to introduce the global ocean-regional atmosphere coupling concept and to show the potential benefits of this model system to simulate present-day climate. A global ocean-sea ice-marine biogeochemistry model (MPIOM/HAMOCC) with regionally high horizontal resolution is coupled to an atmospheric regional model (REMO) and global terrestrial hydrology model (HD) via the OASIS coupler. Moreover, results obtained with ROM using NCEP/NCAR reanalysis and ECHAM5/MPIOM CMIP3 historical simulations as boundary conditions are presented and discussed for the North Atlantic and North European region. The validation of all the model components, i.e., ocean, atmosphere, terrestrial hydrology, and ocean biogeochemistry is performed and discussed. The careful and detailed validation of ROM provides evidence that the proposed model system improves the simulation of many aspects of the regional climate, remarkably the ocean, even though some biases persist in other model components, thus leaving potential for future improvement. We conclude that ROM is a powerful tool to estimate possible impacts of climate change on the regional scale. [ABSTRACT FROM AUTHOR]

This contribution describes the ocean biogeochemical component of the Geophysical Fluid Dynamics Laboratory's Earth System Model 4.1 (GFDL‐ESM4.1), assesses GFDL‐ESM4.1's capacity to capture observed ocean biogeochemical patterns, and documents its response to increasing atmospheric CO2. Notable differences relative to the previous generation of GFDL ESM's include enhanced resolution of plankton food web dynamics, refined particle remineralization, and a larger number of exchanges of nutrients across Earth system components. During model spin‐up, the carbon drift rapidly fell below the 10 Pg C per century equilibration criterion established by the Coupled Climate‐Carbon Cycle Model Intercomparison Project (C4MIP). Simulations robustly captured large‐scale observed nutrient distributions, plankton dynamics, and characteristics of the biological pump. The model overexpressed phosphate limitation and open ocean hypoxia in some areas but still yielded realistic surface and deep carbon system properties, including cumulative carbon uptake since preindustrial times and over the last decades that is consistent with observation‐based estimates. The model's response to the direct and radiative effects of a 200% atmospheric CO2 increase from preindustrial conditions (i.e., years 101–120 of a 1% CO2 yr−1 simulation) included (a) a weakened, shoaling organic carbon pump leading to a 38% reduction in the sinking flux at 2,000 m; (b) a two‐thirds reduction in the calcium carbonate pump that nonetheless generated only weak calcite compensation on century time‐scales; and, in contrast to previous GFDL ESMs, (c) a moderate reduction in global net primary production that was amplified at higher trophic levels. We conclude with a discussion of model limitations and priority developments. Plain Language Summary: This paper describes and evaluates the ocean biogeochemical component of the Geophysical Fluid Dynamics Laboratory's Earth System Model 4.1 (GFDL‐ESM4.1). GFDL‐ESM4.1 was developed to study the past, present, and future evolution of the Earth system under scenarios for natural and anthropogenic drivers of Earth system change, including greenhouse gases and aerosols. The response of the ocean's vast carbon and heat reservoirs to accumulating greenhouse gases greatly reduces their atmospheric and terrestrial impacts, but also puts ocean environments and the marine resources they support at risk. Relative to previous models, GFDL‐ESM4.1 improves the representation of (a) ocean food webs connecting plankton and fish; (b) biological processes influencing the sequestration of carbon in the deep ocean; and (c) land‐atmosphere‐ocean nutrient exchanges. While simulations have biases, they capture many critical aspects of the global ocean carbon cycle and ocean ecosystem, including the observed uptake of anthropogenic carbon over the last ~150 yr. Projections suggest that continued CO2 increases could significantly decrease ocean productivity and the ocean's capacity to sequester atmospheric carbon. Key Points: Enhanced plankton food webs, remineralization, and Earth system linkages yield skillful global carbon cycle and ecosystem simulationsOcean productivity estimates improved, but phosphate limitation and hypoxia overestimated in some areasHigh CO2 and associated warming substantially reduce the biological pump and ocean productivity across trophic levels [ABSTRACT FROM AUTHOR]

This contribution describes the ocean biogeochemical component of the Geophysical Fluid Dynamics Laboratory's Earth System Model 4.1 (GFDL‐ESM4.1), assesses GFDL‐ESM4.1's capacity to capture observed ocean biogeochemical patterns, and documents its response to increasing atmospheric CO2. Notable differences relative to the previous generation of GFDL ESM's include enhanced resolution of plankton food web dynamics, refined particle remineralization, and a larger number of exchanges of nutrients across Earth system components. During model spin‐up, the carbon drift rapidly fell below the 10 Pg C per century equilibration criterion established by the Coupled Climate‐Carbon Cycle Model Intercomparison Project (C4MIP). Simulations robustly captured large‐scale observed nutrient distributions, plankton dynamics, and characteristics of the biological pump. The model overexpressed phosphate limitation and open ocean hypoxia in some areas but still yielded realistic surface and deep carbon system properties, including cumulative carbon uptake since preindustrial times and over the last decades that is consistent with observation‐based estimates. The model's response to the direct and radiative effects of a 200% atmospheric CO2 increase from preindustrial conditions (i.e., years 101–120 of a 1% CO2 yr−1 simulation) included (a) a weakened, shoaling organic carbon pump leading to a 38% reduction in the sinking flux at 2,000 m; (b) a two‐thirds reduction in the calcium carbonate pump that nonetheless generated only weak calcite compensation on century time‐scales; and, in contrast to previous GFDL ESMs, (c) a moderate reduction in global net primary production that was amplified at higher trophic levels. We conclude with a discussion of model limitations and priority developments. Plain Language Summary: This paper describes and evaluates the ocean biogeochemical component of the Geophysical Fluid Dynamics Laboratory's Earth System Model 4.1 (GFDL‐ESM4.1). GFDL‐ESM4.1 was developed to study the past, present, and future evolution of the Earth system under scenarios for natural and anthropogenic drivers of Earth system change, including greenhouse gases and aerosols. The response of the ocean's vast carbon and heat reservoirs to accumulating greenhouse gases greatly reduces their atmospheric and terrestrial impacts, but also puts ocean environments and the marine resources they support at risk. Relative to previous models, GFDL‐ESM4.1 improves the representation of (a) ocean food webs connecting plankton and fish; (b) biological processes influencing the sequestration of carbon in the deep ocean; and (c) land‐atmosphere‐ocean nutrient exchanges. While simulations have biases, they capture many critical aspects of the global ocean carbon cycle and ocean ecosystem, including the observed uptake of anthropogenic carbon over the last ~150 yr. Projections suggest that continued CO2 increases could significantly decrease ocean productivity and the ocean's capacity to sequester atmospheric carbon. Key Points: Enhanced plankton food webs, remineralization, and Earth system linkages yield skillful global carbon cycle and ecosystem simulationsOcean productivity estimates improved, but phosphate limitation and hypoxia overestimated in some areasHigh CO2 and associated warming substantially reduce the biological pump and ocean productivity across trophic levels [ABSTRACT FROM AUTHOR]

Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each aerosol mode, resulting in high degree of internal mixing of BC with non‐BC species and large mass ratio of coating to BC (RBC, the mass ratio of non‐BC species to BC in BC‐containing particles). To improve BC mixing state representation, we coupled a machine learning (ML) model of BC mixing state index trained on particle‐resolved simulations to the CAM6 with MAM4 (MAM4‐ML). In MAM4‐ML, we use RBC to partition accumulation mode particles into two new modes, BC‐free particles and BC‐containing particles. We adjust RBC to make the modeled BC mixing state index (χmode) match the one predicted by the ML model (χML). On a global average, the mass fraction of BC‐containing particles in accumulation mode decreases from 100% (MAM4‐default) to 48% (MAM4‐ML). The globally averaged χmode decreases from 78% (MAM4‐default) to 63% (MAM4‐ML, 19% reduction) and agrees well with χML (66%). The RBC decreases by 52% for accumulation mode and better agrees with observations. The hygroscopicity drops by 9% for BC‐containing particles in accumulation mode, leading to a 20% reduction in the BC activation fraction. The surface BC concentration increases most (6.9%) in the Arctic, and the BC burden increases by 4%, globally. Our study highlights the application of the ML model for improving key aerosol processes in GCMs. Plain Language Summary: Black carbon (BC) mixing state is an important property for assessing impacts of BC on human health and climate. It describes how BC is distributed among the particle population and within individual particles. Due to computational costs, global climate models (GCMs) consider aerosols as either external mixtures (e.g., bulk models) or fully internal mixtures with uniform composition within a certain size range (e.g., an aerosol bin in sectional models or an aerosol mode in modal models). To improve BC mixing state representation, we coupled a machine learning (ML) model learned from particle‐resolved simulations to the online simulations of the Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4‐ML). We used the mass ratio of coating to BC (RBC) to determine the mass fraction of BC‐free particles and BC‐containing particles, and we adjusted RBC to make the modeled BC mixing state match ML model. The MAM4‐ML model reduces the degree of internal mixture of BC substantially. In addition, MAM4‐ML RBC is reduced by 52%, leading to a 20% reduction in BC activation fraction and a 4% increase in BC burden. This study proposes a method for the application of the ML to improve GCMs. Key Points: Machine learning model trained on particle‐resolved simulations has been coupled to CAM6 MAM4 to improve BC mixing state representationThe BC mixing state index is reduced by 19%, and the mass ratio of coating to BC is reduced by 52%, in better agreement with observationsDue to the drop in hygroscopicity of BC‐containing aerosol (9%), BC activation fraction decreased by 20%, and BC burden increased by 4% [ABSTRACT FROM AUTHOR]

Earth System Models (ESMs) project that the terrestrial carbon sink will continue to grow as atmospheric CO2 increases, but this projection is uncertain due to biases in the simulated climate and how ESMs represent ecosystem processes. In particular, the strength of the CO2‐fertilization effect, which is modulated by nutrient cycles, varies substantially across models. This study evaluates land carbon balance uncertainties for the Canadian Earth System Model (CanESM) by conducting simulations where the latest version of CanESM's land surface component is driven offline with raw and bias‐adjusted CanESM5 climate forcing data. To quantify the impact of nutrient limitation, we complete simulations where the nitrogen cycle is enabled or disabled. Results show that bias adjustment improves model performance across most ecosystem variables, primarily due to reduced biases in precipitation. Turning the nitrogen cycle on increases the global land carbon sink during the historical period (1995–2014) due to enhanced nitrogen deposition, placing it within the Global Carbon Budget uncertainty range. During the future period (2080–2099), the simulated land carbon sink increases in response to bias adjustment and decreases in response to the dynamic carbon‐nitrogen interaction, leading to a net decrease when both factors are acting together. The dominating impact of the nitrogen cycle demonstrates the importance of representing nutrient limitation in ESMs. Such efforts may produce more robust carbon balance projections in support of global climate change mitigation policies such as the 2015 Paris Agreement. Plain Language Summary: The implementation of global climate change policies relies on our ability to predict how the global carbon cycle will evolve in the future. Climate models project that the biosphere will continue to absorb more CO2 than it emits, keeping atmospheric CO2 levels lower than they would be otherwise. However, the strength of this net CO2 uptake varies considerably among models. This is because of differences in the simulated climate as well as the use of different methods for simulating plant growth. This study evaluates the importance of both factors by running one model with different climate data sets and model configurations. Our results show that the future net CO2 uptake by plants increases when removing biases in climatic conditions and decreases when accounting for the impact of soil nutrients on plant growth, leading to a net decrease when both factors are acting together. The dominating impact of the nutrients demonstrates the importance of representing nutrient limitation in climate models. Such efforts may produce more robust carbon balance projections in support of global climate change mitigation policies such as the 2015 Paris Agreement. Key Points: Bias adjustment of climate forcing improves model performance across most variables, primarily due to reduced biases in precipitationThe inclusion of the N cycle increases the simulated C sink during the historical period, placing it within the observed uncertainty rangeThe future C sink increases with bias adjustment and decreases with the N cycle, resulting in a net decrease when both factors are at play [ABSTRACT FROM AUTHOR]

Deep learning (DL) has emerged as a promising tool to downscale climate projections at regional‐to‐local scales from large‐scale atmospheric fields following the perfect‐prognosis approach. Given their complexity, it is crucial to properly evaluate these methods, especially when applied to changing climatic conditions where the ability to extrapolate/generalize is key. In this work, we intercompare several DL models extracted from the literature for the same challenging use‐case (downscaling temperature in the CORDEX North America domain) and expand standard evaluation methods building on eXplainable Artificial Intelligence (XAI) techniques. Specifically, we introduce two novel XAI‐based diagnostics—Aggregated Saliency Map and Saliency Dispersion Maps—and show how they can be used to unravel the internal behavior of these models, aiding in their design and evaluation. This work advocates for the introduction of XAI techniques into deep downscaling evaluation frameworks, especially when working with large regions and/or under climate change conditions. Plain Language Summary: Due to limitations in the computational resources available, General Circulation Models (GCMs) are often used to simulate the climate system over coarse resolution grids. This hampers the applicability of GCM products in the regional‐to‐local scale, highly demanded by different socio‐economic sectors. Statistical downscaling aims to solve this problem by generating high‐resolution climate fields. Recently, machine learning techniques—particularly deep learning (DL) models—have shown promising results in this task. These models are first trained in a historical period through observational data sets, and then applied to the GCM outputs of plausible future scenarios, thus generating high‐resolution climate change products. To assess the performance of these methods, a number of evaluation metrics have been proposed considering both the skill to reproduce present climate conditions and the ability to generalize changing conditions. Here, we illustrate the possibilities of eXplainable Artificial Intelligence (XAI) techniques to expand the evaluation framework for deep downscaling methods, introducing new XAI‐derived diagnostics to unravel their internal behavior. The results show the usefulness of incorporating XAI techniques into statistical downscaling evaluation frameworks, especially when working with large regions and/or under climate change conditions. Key Points: Explainable artificial intelligence (XAI) facilitates the evaluation of deep downscaling models by unraveling their internal behaviorXAI techniques can detect structural problems not revealed by standard evaluation, which may be relevant for understanding model differencesXAI techniques can assess the relevance and locality of the predictors of deep learning models, helping to assess their physical consistency [ABSTRACT FROM AUTHOR]

CGILS-the CFMIP-GASS Intercomparison of Large Eddy Models (LESs) and single column models (SCMs)-investigates the mechanisms of cloud feedback in SCMs and LESs under idealized climate change perturbation. This paper describes the CGILS results from 15 SCMs and 8 LES models. Three cloud regimes over the subtropical oceans are studied: shallow cumulus, cumulus under stratocumulus, and well-mixed coastal stratus/stratocumulus. In the stratocumulus and coastal stratus regimes, SCMs without activated shallow convection generally simulated negative cloud feedbacks, while models with active shallow convection generally simulated positive cloud feedbacks. In the shallow cumulus alone regime, this relationship is less clear, likely due to the changes in cloud depth, lateral mixing, and precipitation or a combination of them. The majority of LES models simulated negative cloud feedback in the well-mixed coastal stratus/stratocumulus regime, and positive feedback in the shallow cumulus and stratocumulus regime. A general framework is provided to interpret SCM results: in a warmer climate, the moistening rate of the cloudy layer associated with the surface-based turbulence parameterization is enhanced; together with weaker large-scale subsidence, it causes negative cloud feedback. In contrast, in the warmer climate, the drying rate associated with the shallow convection scheme is enhanced. This causes positive cloud feedback. These mechanisms are summarized as the 'NESTS' negative cloud feedback and the 'SCOPE' positive cloud feedback (Negative feedback from Surface Turbulence under weaker Subsidence-Shallow Convection PositivE feedback) with the net cloud feedback depending on how the two opposing effects counteract each other. The LES results are consistent with these interpretations. [ABSTRACT FROM AUTHOR]

The new Max-Planck-Institute Earth System Model (MPI-ESM) is used in the Coupled Model Intercomparison Project phase 5 (CMIP5) in a series of climate change experiments for either idealized CO2-only forcing or forcings based on observations and the Representative Concentration Pathway (RCP) scenarios. The paper gives an overview of the model configurations, experiments related forcings, and initialization procedures and presents results for the simulated changes in climate and carbon cycle. It is found that the climate feedback depends on the global warming and possibly the forcing history. The global warming from climatological 1850 conditions to 2080-2100 ranges from 1.5°C under the RCP2.6 scenario to 4.4°C under the RCP8.5 scenario. Over this range, the patterns of temperature and precipitation change are nearly independent of the global warming. The model shows a tendency to reduce the ocean heat uptake efficiency toward a warmer climate, and hence acceleration in warming in the later years. The precipitation sensitivity can be as high as 2.5% K−1 if the CO2 concentration is constant, or as small as 1.6% K−1, if the CO2 concentration is increasing. The oceanic uptake of anthropogenic carbon increases over time in all scenarios, being smallest in the experiment forced by RCP2.6 and largest in that for RCP8.5. The land also serves as a net carbon sink in all scenarios, predominantly in boreal regions. The strong tropical carbon sources found in the RCP2.6 and RCP8.5 experiments are almost absent in the RCP4.5 experiment, which can be explained by reforestation in the RCP4.5 scenario. [ABSTRACT FROM AUTHOR]

Uncertainties associated with the response of shallow clouds to global warming remain a great challenge for climate projection. Due to the small size of these clouds, parameterizations are required to represent them in both current‐ and next‐generation climate models. We present a quantitative evaluation of several important assumptions used in both mass‐flux and Assumed‐PDF Higher‐Order Closure parameterizations of shallow convection. We use large‐eddy simulations (LESs) of four different shallow convection regimes as benchmarks and apply a qt (total moisture)–w (vertical velocity) quadrant analysis technique to identify the "coherent structures" of moist and dry up/down‐drafts. The statistics of these coherent structures and the environment are then used to evaluate commonly used assumptions in mass‐flux parameterizations related to (a) the convective downdrafts and (b) the subplume variability of convective updrafts. For quantitative evaluation of the PDF closure used in AP‐HOCs, we perform offline calculations with the PDF closure used in the Cloud Layers Unified By Bi‐normals and the Simplified Higher‐Order Closure schemes by supplying the closure with statistical quantities directly calculated from the LESs. Then, the impact of parameterization assumptions embedded in the PDF closure in the same two categories is analyzed and understood through comparisons of the output from the closure with the statistics of the moist and dry up/down‐drafts from the LESs. Plain Language Summary: This study examines critically several key simplifying assumptions about convective downdrafts and small‐scale variability associated with shallow clouds that are made in two types of shallow convection parameterizations widely used in numerical weather and climate models and demonstrates quantitatively the impact of these assumptions on the parameterized cloud water content, depth, and turbulent transport. Key Points: A qt–w quadrant analysis is applied to identify convective coherent structures in large‐eddy simulations of various shallow cloud regimesStatistics from the quadrant analysis is used to evaluate assumptions in mass‐flux and Assumed‐PDF Higher‐Order Closure shallow convection parameterizationsSimplifying assumptions on convective downdrafts and subplume variability are linked to parameterization deficiencies [ABSTRACT FROM AUTHOR]

Frost is damaging to plants when air temperature drops below their tolerance threshold. The set of mechanisms used by cold‐tolerant plants to withstand freezing is called "hardening" and typically take place in autumn to protect against winter damage. The recent incorporation of a hardening scheme in the demographic vegetation model FATES opens up the possibility to investigate frost mortality to vegetation. Previously, the hardening scheme was used to improve hydraulic processes in cold‐tolerant plants. In this study, we expand upon the existing hardening scheme by implementing hardiness‐dependent frost mortality into CLM5.0‐FATES to study the impacts of frost on vegetation in temperate and boreal sites from 1950 to 2015. Our results show that the original freezing mortality approach of FATES, where each plant type had a fixed freezing tolerance threshold—an approach common to many other dynamic vegetation models, was restricted to predicting plant type distribution. The main results emerging from the new scheme are a high autumn and spring frost mortality, especially at colder sites, and increasing mid‐winter frost mortality due to global warming, especially at warmer sites. We demonstrate that the new frost scheme is a major step forward in dynamically representing vegetation in ESMs by for the first time including a level of frost tolerance that is responding to the environment and includes some level of cost (implicitly) and benefit. By linking hardening and frost mortality in a land surface model, we open new ways to explore the impact of frost events in the context of global warming. Plain Language Summary: Frost can lead to partial or full mortality of a plant, unless it has evolved a tolerance to cold weather to avoid the formation of ice crystals in its tissues. Frost mortality is poorly represented in most advanced dynamic vegetation models, which are our primary tools for predicting climate change and to understand its effects on the biosphere. In one of those models, the functionally assembled simulator (FATES), a minimum temperature threshold is assigned to each plant type to represent the limit at which it will start to incur frost injury. In this study, we focus on making the fixed temperature threshold of FATES variable in time and space—depending on the local conditions of temperature and light. Our results show that the new frost mortality scheme can simulate autumn and spring frost mortality, also for cold‐adapted species, and increases mid‐winter frost mortality due to variable winter weather—especially at warmer sites. This is in contrast to the original frost mortality scheme, which was only applicable to predict the distribution of plant types across climate zones. The implementation presented here opens up new ways to explore the spatial evolution of frost occurrences and intensities under changing climatic conditions. Key Points: We compare the original freezing mortality implementation in FATES (based on hard‐coded temperature thresholds) to a more dynamic approach (based on hardening)In contrast to the original approach, a new frost scheme is able to predict the damaging effects of extreme winter events and late spring frost eventsWe show that autumn and spring frost mortality is large, especially at colder sites, and that mid‐winter frost mortality increases with global warming [ABSTRACT FROM AUTHOR]

Atmospheric gravity waves (GWs) span a broad range of length scales. As a result, the un‐resolved and under‐resolved GWs have to be represented using a sub‐grid scale (SGS) parameterization in general circulation models (GCMs). In recent years, machine learning (ML) techniques have emerged as novel methods for SGS modeling of climate processes. In the widely used approach of supervised (offline) learning, the true representation of the SGS terms have to be properly extracted from high‐fidelity data (e.g., GW‐resolving simulations). However, this is a non‐trivial task, and the quality of the ML‐based parameterization significantly hinges on the quality of these SGS terms. Here, we compare three methods to extract 3D GW fluxes and the resulting drag (Gravity Wave Drag [GWD]) from high‐resolution simulations: Helmholtz decomposition, and spatial filtering to compute the Reynolds stress and the full SGS stress. In addition to previous studies that focused only on vertical fluxes by GWs, we also quantify the SGS GWD due to lateral momentum fluxes. We build and utilize a library of tropical high‐resolution (Δx = 3 km) simulations using weather research and forecasting model. Results show that the SGS lateral momentum fluxes could have a significant contribution to the total GWD. Moreover, when estimating GWD due to lateral effects, interactions between the SGS and the resolved large‐scale flow need to be considered. The sensitivity of the results to different filter type and length scale (dependent on GCM resolution) is also explored to inform the scale‐awareness in the development of data‐driven parameterizations. Plain Language Summary: Gravity waves (GWs) present a challenge to climate prediction: waves on scales of O(1)–O(100) km can neither be systematically measured with conventional observational systems, nor properly represented (resolved) in operational climate models, which have a typical grid spacing on the order of 100 km. Therefore, in these climate models, small‐scale GWs must be parameterized, or estimated, based on the resolved (large‐scale) flow. The primary effects of these small‐scale waves on the resolved flow is the so‐called sub‐grid scale drag (Gravity Wave Drag [GWD]), resulting from the propagation and breaking of these waves. Existing GW parameterizations in general circulation models are all highly simplified; for example, they only account for vertical propagation of GWs. With growing computing power, a promising alternative approach is to use machine learning to develop data‐driven parameterizations. However, this requires to first generate reliable high‐resolution computer simulations and then extract GWD from these simulations. This study follows these steps, compares different extraction methods, and describes some challenges and pathways to make advances. Furthermore, our results suggest that the horizontal propagation of GWs should be included in parameterizations too, however, extra care is needed in order to extract the resulting GWD from high‐resolution data. Key Points: In a library of weather research and forecasting model simulations, we compare methods for estimating 3D gravity wave drag force that are un‐ and under‐resolved by general circulation modelsFor drag associated with vertical fluxes, different methods agree on time‐ and zonal‐mean but not on instantaneous spatiotemporal patternsDrag associated with horizontal fluxes is significant but is very sensitive to the estimation methodology [ABSTRACT FROM AUTHOR]

Previous work has found that as the surface warms the large‐scale tropical circulations weaken, convective anvil cloud fraction decreases, and atmospheric static stability increases. Circulation changes inevitably lead to changes in the humidity and cloud fields which influence the surface energetics. The exchange of mass between the boundary layer (BL) and the midtroposphere has also been shown to weaken in global climate models. What has remained less clear is how robust these changes in the circulation are to different representations of convection, clouds, and microphysics in numerical models. We use simulations from the Radiative‐Convective Equilibrium Model Intercomparison Project to investigate the interaction between overturning circulations, surface temperature, and atmospheric moisture. We analyze the underlying mechanisms of these relationships using a 21‐member model ensemble that includes both General Circulation Models and Cloud‐system Resolving Models. We find a large spread in the change of intensity of the overturning circulation. Both the range of the circulation intensity, and its change with warming can be explained by the range of the mean upward vertical velocity. There is also a consistent decrease in the exchange of mass between the BL and the midtroposphere. However, the magnitude of the decrease varies substantially due to the range of responses in both mean precipitation and mean precipitable water. We hypothesize based on these results that despite well understood thermodynamic constraints, there is still a considerable ability for the cloud fields and the precipitation efficiency to drive a substantial range of tropical convective responses to warming. Plain Language Summary: Tropical large‐scale overturning circulations are expected to weaken with warming. This weakening is the result of precipitation increasing at a slower rate than the atmospheric water vapor. Because precipitation and water vapor are important measures of how energy flows through the atmosphere it is important to understand how they will respond to a warming climate. We use two methods to calculate the change of the overturning circulation in 21 different simulations of the tropical atmosphere. This group of 21 models includes high resolution models that resolve cloud systems, and global models with grid‐spacing of about 100 km. We show that a weakening circulation that results from increasing atmospheric stability and increased water vapor is a robust result across most models. But across the group of models there is a large range of magnitudes in the response of the circulation to warming. This variability is well explained by the magnitude of the mean upward vertical velocity. Higher resolution models do not have a narrower range of responses. Narrowing this range of responses will depend on developing a better understanding of what drives the variations in atmospheric stability, surface fluxes of latent energy, and relative humidity. Key Points: The overturning tropical circulation weakens as the surface warms in the majority of radiative convective equilibrium models examinedInter‐model spread of circulation intensity and change of intensity are highly correlated with ascending velocity spread at 500 hPaVariability of both the clear‐sky heating and static stability result in large variations of the subsidence velocity [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: In each successive generation of climate models the representation of climate processes becomes more realistic and more complex. The more sophisticated characterization of cloud processes has resulted in some models warming much more in response to increasing atmospheric carbon dioxide concentrations due to stronger cloud feedbacks; referred to as having a higher climate sensitivity. The Community Earth System Model version 2 (CESM2) is one such model. When used to simulate the Last Glacial Maximum (LGM), CESM2 has a global temperature around 5°C colder than surface temperature reconstructions and previous generations of this model. By investigating the updated cloud scheme, Zhu et al., 2022, https://doi.org/10.1029/2021ms002776 were able to identify and modify issues with the cloud microphysical scheme that were contributing to this problem. They subsequently produced a new version of CESM2 which can simulate the LGM more accurately without degrading the simulation of the 20th century climate. The Zhu et al., 2022, https://doi.org/10.1029/2021ms002776 study is an example of how a paleoclimate can be used to identify issues with cloud schemes not traditionally recognized in the model development and validation process. Incorporating paleoclimate simulations earlier in the model development and validation process may help constrain cloud feedbacks and subsequently climate sensitivity. Key Points: Paleoclimates provide valuable tests for the latest generation of climate modelsZhu et al., 2022, https://doi.org/10.1029/2021ms002776 provide an example of the paleoclimate benefits for climate model developmentFuture iterations of CMIP will ideally incorporate paleoclimate tests during model development [ABSTRACT FROM AUTHOR]

What role does convection play in cloud feedbacks? What role does convective aggregation play in climate? A flurry of recent studies explores 'self-aggregation' of moist convection in diverse simulations using explicit convection and interactive radiation. The implications involve upper level dry areas acting as infrared windows-the climate system's 'radiator fins.' A positive feedback maintains these: dry columns undergo radiative cooling which drives descent and further drying. If the resulting clumpiness of vapor and cloud fields depends systematically on global temperature, then convective organization could be a climate system feedback. How reconcilable and how relevant are these interesting but idealized studies? [ABSTRACT FROM AUTHOR]

A three‐dimensional variably saturated flow model was developed for assessing future global water resources and parameterized for groundwater pumping. We applied this model to an actual watershed to verify its validity as an Earth System Model. For global applicability, the parameterization method for multi‐layered groundwater pumping was developed and verified through comparison with observations and MODFLOW results. The parameterization proposed in this study is applicable even when multiple groundwater pumping wells are present within one horizontal computational grid and when the well spans multiple vertical grids. This method can be applied at the global scale without parameters such as the well radius, for which data may be difficult to obtain. The parameterization recreated seasonal and annual variations in the observed values. Furthermore, the results were comparable to those of MODFLOW. However, the calculation results were overestimated relative to the observed values. This overestimation was likely to be due to active groundwater pumping in the Central Valley before the start of the unsteady‐state calculation. Therefore, the groundwater level at the beginning of the unsteady‐state calculation was calculated using observed values, improving reproducibility. Furthermore, as observed groundwater levels are unlikely to be available at the global scale, steady‐state calculations were conducted over 15 and 60 years considering groundwater pumping. However, the results were not as reproducible as those obtained using observed groundwater levels. These results suggest that the groundwater level set at the beginning of the calculation is important for global‐scale groundwater flow calculation. Plain Language Summary: Water resources are expected to become strained in the future due to climate change and population growth. The main focus of water resource assessments conducted to date has been on river water. Although groundwater is an important component of water resources, it has generally not been considered. This is largely due to the lack of groundwater models that can be applied at a global scale. Therefore, we developed a new groundwater model that was parameterized for groundwater pumping and validated in plains. The results of this study indicate that parameterization of groundwater pumping can produce reasonable results relative to observed values, and that the groundwater level set at the beginning of the calculation is important for the evaluation and prediction of global water resources. In particular, collection of observed groundwater levels or estimation of groundwater pumping are key steps toward more realistic prediction. Key Points: A three‐dimensional variably saturated flow model for use as an Earth System Model was developed and parameterized for groundwater pumpingThe validation results reproduced observations to some extent and were comparable to those of MODFLOW in the Central ValleyThe results show that setting the groundwater level at the beginning of the unsteady‐state simulation is important for global‐scale [ABSTRACT FROM AUTHOR]

Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single‐ and multi‐variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer‐wise relevance propagation, a neural network explainability tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These "indicator patterns" vary in time and between climate models, providing a template for investigating inter‐model differences in the time evolution of the forced response. This work demonstrates how neural networks and their explainability tools can be harnessed to identify patterns of the forced signal within combined fields. Plain Language Summary: Using machine learning tools called neural networks, we identify patterns of the changing climate within climate model data. Changes in the climate can be identified earlier when detecting patterns within maps of multiple variables and seasons than for single maps alone. By visualizing the patterns learned by the neural networks, we can identify which regions, variables, and seasons are most important for detecting climate change. These patterns offer insight into how climate change is represented in different climate models, and how the patterns of climate change will evolve over time. Key Points: Neural networks and their explainability tools can be harnessed to identify patterns of forced change within combined fieldsCombined fields of input allow for earlier detection of the emergence of a forced climate responseExplainable AI techniques can be used to identify patterns that describe the emergence and evolution of forced climate change [ABSTRACT FROM AUTHOR]

Spin‐up is essential to provide initial conditions for land surface models (LSM) when they cannot be given reliably as in the application to regional permafrost change studies. In this study, the impacts of spin‐up strategy including total spin‐up length and cycling scheme on modeling of permafrost dynamics on the Qinghai‐Tibet Plateau (QTP) were evaluated through two groups of experiments using a modified Noah LSM. The first group aims to test different total spin‐up lengths and the second group for different cycling schemes. The results show that the presence of permafrost prolongs the convergence of the model. Vertically, the slowest convergence is observed at the permafrost table. The insufficiency of total spin‐up length is prone to underestimate permafrost area and overestimate the degradation rate. Different cycling schemes considerably affect the resulting initial thermal fields and result in degradation rates with a difference of 3.37 × 103 km2/a on the QTP, which exceeds the difference (2.92 × 103 km2/a) in the degradation rates reported in existing studies. The multi‐year cycling scheme is generally preferred, but overlong cycle length should be avoided to prevent the introduction of climate change trends in the spin‐up period. We recommend a spin‐up strategy of a 500‐year cycling with the first 5‐ to 10‐year of forcing for modeling permafrost on the QTP with the Noah LSM. Our findings highlight the importance of the spin‐up strategy, which is usually neglected in present LSM‐based permafrost modeling studies. Plain Language Summary: Recently, land surface models (LSM) have been applied to model regional permafrost changes in the context of climate change. In these applications, spin‐up is typically used to provide initial conditions for the model. However, the way we generate the spin‐up forcing strongly affects the resulting initial fields at the end of the spin‐up simulation and the accuracy of the subsequent simulation of permafrost changes. This study aims to investigate these impacts through two groups of experiments using the Qinghai‐Tibet Plateau (QTP) as the study area where the Noah LSM is used to simulate permafrost changes in response to decadal climate changes. It is found that convergence is slower in permafrost regions than non‐permafrost regions. Different spin‐up strategies affect the initial thermal fields to varying degrees. However, they can cause large differences in permafrost area change and degradation rate in follow‐up simulations driven by real climate forcing, exceeding the uncertainties associated with choices of different methods and data in existing studies. In the end, we recommend a strategy of 500‐year cycling with the first 5‐ to 10‐year of available meteorological data for the QTP LSM applications. Key Points: Convergence is slower in permafrost regions and different spin‐up strategies have varying degrees of influence on the initial fieldSpin‐up strategies can lead to a difference in permafrost degradation rate comparable to the differences of the rates in existing studiesA spin‐up strategy of a 500‐year cycling with the first 5‐ to 10‐year of forcing is recommended for permafrost simulation on the Qinghai‐Tibet Plateau [ABSTRACT FROM AUTHOR]

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ‐GUESS, ORCHIDEE, SiB‐3, and SiB‐CASA. All models were wrapped in a software framework driven with common forcing data, spin‐up, and run protocols specified by the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901–2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open‐source, cloud‐based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges. Plain Language Summary: Comparing models is fundamental to our understanding of how the land surface responds to changes in climate. However, these comparisons are challenging to conduct, requiring the organization of multiple, decentralized teams throughout the world. We explored centralizing these models on a single supercomputing system. The models were all run the same way. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) the centralized system takes a lot of burden off individual teams; (b) running models the same way helps to identify differences in how the world is represented in the models; and (c) the system allows us to run many model experiments relatively quickly. The challenges are: (a) lots of models require lots of data storage and transfer needs; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. Another system, called PEcAn, which has a lot of tools that can help overcome these challenges, can potentially be used in future work. Key Points: We ran nine terrestrial biosphere models centralized on a common computing frameworkThe Farm allows multiple MIP experiments to be run relatively quickly and uniformlyChallenges included technological demand, model versioning, and interpretation of results [ABSTRACT FROM AUTHOR]

This study evaluates the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the observed climate variability in the historical simulation of the Coupled Model Intercomparison Phase 6 (CMIP6). TaiESM1 is developed on the basis of the Community Earth System Model version 1.2.2, with the inclusion of several new physical schemes and improvements in the atmosphere model. The new additions include an improved triggering function in the cumulus convection scheme, a revised distribution‐based formula in the cloud fraction scheme, a new aerosol scheme, and a unique scheme for three‐dimensional surface absorption of shortwave radiation that accounts for the influence of complex terrains. In contrast to the majority of model evaluation processes, which focus mainly on the climatological mean, this evaluation focuses on climate variability parameters, including the diurnal rainfall cycle, precipitation extremes, synoptic eddy activity, intraseasonal fluctuation, monsoon evolution, and interannual and multidecadal atmospheric and oceanic teleconnection patterns. A series of intercomparisons between the simulations of TaiESM1 and CMIP6 models and observations indicate that TaiESM1, a participating model in CMIP6, can realistically simulate the observed climate variability at various time scales and are among the leading CMIP6 models in terms of many key climate features. Plain Language Summary: Earth System Models are important tools for understanding how earth system changes and providing information for mitigation and adaption facing future climate changes. Taiwan Earth System Model version 1 (TaiESM1) is developed in the Research Center for Environmental Changes, Academia Sinica, Taiwan, with the goal to join the effort of the international model intercomparison—the Coupled Model Intercomparison Phase 6 (CMIP6). Based on the Community Earth System Model version 1, key improvements of TaiESM1 are in the representation of how raining systems starts, how cloud covers shade, how aerosols form and dissipate, and how topography reflects sunlight. We compare TaiESM1s performance with observations and other CMIP6 models to understand TaiESM1's capability in simulating climate systems and future climate changes. We especially focus on evaluating the climate variability on a wide range of time scales, i.e., how climate systems change on those of within one day, day‐to‐day, seasonal, year‐to‐year, and decades‐to‐decades. Our analysis shows that TaiESM1 can realistically represent the observed climate variability and would help to expand our understandings of the earth system with the global scientific community and society as a whole. Key Points: Climate variability in the historical simulation of Taiwan Earth System Model version 1 (TaiESM1) is evaluatedWhile still subject to several common biases, TaiESM1's performance of mean climate states is among the leading CMIP6 modelsTaiESM1 is capable of realistically simulating key climate features, including the diurnal rainfall, monsoon evolution, and teleconnection [ABSTRACT FROM AUTHOR]

The assessment of current and future risks for natural and human systems associated with climate change largely relies on numerical simulations performed with state‐of‐the‐art climate models. Various steps are involved in the development of such models, from development of individual components of the climate system up to free parameter calibration of the fully coupled model. Here, we describe the final tuning phase for the IPSL‐CM6A‐LR climate model. This phase alone lasted more than 3 years and relied on several pillars: (i) the tuning against present‐day conditions given a small adjustment of the ocean surface albedo to compensate for the current oceanic heat uptake, (ii) the release of successive versions after adjustments of the individual components, implying a systematic and recurrent adjustment of the atmospheric energetics, and (iii) the use of a few metrics based on large scale variables such as near‐global mean temperature, summer Arctic sea‐ice extent, as targets for the tuning. Successes, lessons and prospects of this tuning strategy are discussed. Plain Language Summary: Evaluating current and future risks for natural and human systems associated with climate change is largely based on numerical simulations performed with models of the climate system, which includes the atmosphere, the land, the ocean, the cryosphere, and the oceanic and terrestrial biosphere. Various steps are involved in the development of such models. First, models for individual components are developed and tested. Second, many aspects are represented with parameterizations that summarize the effect of a missing process, such as those happening on scales that are smaller than the model grid sizes. The parameterizations in turn involve many parameters, sometimes poorly estimated from observations, that have to be calibrated. Here, we describe the final tuning phase of the IPSL‐CM6A‐LR climate model, which includes several novel aspects: first, the choice to calibrate the model against present‐day observations, which implies taking into account the transient nature of the observed climate; second, the systematic and recurrent adjustment of the atmospheric radiative budget; third, the use of a few large scale observable variables as targets. Successes, lessons and prospects of this tuning strategy are discussed. Key Points: The tuning process of IPSL‐CM6A‐LR under present‐day control conditions is describedThe associated continuous atmospheric energetics adjustment is presentedSuccesses, lessons and prospects of the IPSL‐CM6A‐LR tuning strategy are discussed [ABSTRACT FROM AUTHOR]

Abstract This is the first paper that analyzes data from atmosphere model intercomparison project‐type climate simulations using a cloud‐system‐resolving global nonhydrostatic model without cumulus parameterization focussing particulaly on the relationship between clouds and circulation, and their changes due to global warming. The decrease in fractional coverage of low clouds is key to evaluating cloud radiative effects, because changes in shortwave cloud radiative effects overwhelm those of longwave cloud radiative effects. Thus, improved evaluation of low clouds is important, even in high‐resolution climate simulations. An analysis of heat redistribution by explicitly computed clouds revealed that column‐integrated heating rate due to phase changes correlates highly with vertical velocity at the altitude corresponding to 500 hPa and is closely linked to column water vapor, similar to the present climate result. Using data from year 1 to year 5, the effective climate sensitivity was evaluated to be 3.6−3.7°C. Possible convective aggregation is also examined using an index of modified subsidence fraction and characteristic changes in the number of cold pools. Despite previous idealized‐planet simulations showing more aggregated tropical convection under warmer conditions, here we show a decrease in the subsidence fraction and an increase in the number of smaller cold pools, suggesting that it is possible to realize less convective organization with warming under real atmospheric conditions.

An advanced aerosol treatment, with a focus on semivolatile nitrate formation, is introduced into the Community Atmosphere Model version 5 with interactive chemistry (CAM5‐chem) by coupling the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) with the 7‐mode Modal Aerosol Module (MAM7). An important feature of MOSAIC is dynamic partitioning of all condensable gases to the different fine and coarse mode aerosols, as governed by mode‐resolved thermodynamics and heterogeneous chemical reactions. Applied in the free‐running mode from 1995 to 2005 with prescribed historical climatological conditions, the model simulates global distributions of sulfate, nitrate, and ammonium in good agreement with observations and previous studies. Inclusion of nitrate resulted in ∼10% higher global average accumulation mode number concentrations, indicating enhanced growth of Aitken mode aerosols from nitrate formation. While the simulated accumulation mode nitrate burdens are high over the anthropogenic source regions, the sea‐salt and dust modes respectively constitute about 74% and 17% of the annual global average nitrate burden. Regional clear‐sky shortwave radiative cooling of up to −5 W m−2 due to nitrate is seen, with a much smaller global average cooling of −0.05 W m−2. Significant enhancements in regional cloud condensation nuclei (at 0.1% supersaturation) and cloud droplet number concentrations are also attributed to nitrate, causing an additional global average shortwave cooling of −0.8 W m−2. Taking into consideration of changes in both longwave and shortwave radiation under all‐sky conditions, the net change in the top of the atmosphere radiative fluxes induced by including nitrate aerosol is −0.7 W m−2. Plain Language Summary: Atmospheric aerosols and aerosol‐cloud interactions continue to be a major source of uncertainty in global climate models that are used to assess the impacts of anthropogenic emissions on climate change. A notable fraction of aerosols is composed of ammonium nitrate, which forms in the atmosphere when ammonia combines with nitric acid produced from oxidation of nitrogen oxides. Both precursor gases are emitted in large amounts from anthropogenic activities as well as natural sources. However, a faithful numerical representation of nitrate aerosol in global models has been difficult owing to the semivolatile nature of ammonium nitrate. In this work, we introduce and evaluate an advanced and computationally efficient aerosol chemistry module in a state‐of‐the‐science global climate model to properly simulate the dynamics of nitrate aerosol formation and its interactions with the naturally occurring sea‐salt and dust aerosols. Inclusion of nitrate results in about 10% higher global average number concentrations of aerosols in the size range that efficiently interacts with solar radiation and acts as seeds upon which cloud droplets can form. Consequently, nitrate accounts for an additional radiative cooling, largely due to the changes in cloud formation. Key Points: A modal version of the advanced aerosol chemistry module MOSAIC is developed and introduced in a climate model to simulate nitrate aerosolMOSAIC provides an accurate and efficient treatment for dynamically partitioning semivolatile gases over to entire aerosol size distributionThe modeled global distribution of nitrate is in good agreement with observations and its impact on the radiative effects is quantified [ABSTRACT FROM AUTHOR]

Subkilometer processes are critical to the physics of aerosol‐cloud interaction (ACI) but have been dependent on parameterizations in global model simulations. We thus report the strength of ACI in the Ultra‐Parameterized Community Atmosphere Model (UPCAM), a multiscale climate model that uses coarse exterior resolution to embed explicit cloud‐resolving models with enough resolution (250 m horizontal, 20 m vertical) to quasi‐resolve subkilometer eddies. To investigate the impact on ACIs, UPCAM's simulations are compared to a coarser multiscale model with 4 km horizontal resolution. UPCAM produces cloud droplet number concentrations (Nd) and cloud liquid water path (LWP) values that are higher than the coarser model but equally plausible compared to observations. Our analysis focuses on the Northern Hemisphere (20–50°N) oceans, where historical aerosol increases have been largest. We find similarities in the overall radiative forcing from ACIs in the two models, but this belies fundamental underlying differences. The radiative forcing from increases in LWP is weaker in UPCAM, whereas the forcing from increases in Nd is larger. Surprisingly, the weaker LWP increase is not due to a weaker increase in LWP in raining clouds, but a combination of weaker increase in LWP in nonraining clouds and a smaller fraction of raining clouds in UPCAM. The implication is that as global modeling moves toward finer than storm‐resolving grids, nuanced model validation of ACI statistics conditioned on the existence of precipitation and good observational constraints on the baseline probability of precipitation will become key for tighter constraints and better conceptual understanding. Plain Language Summary: How aerosol particles impact the climate through their interactions with clouds is a significant source of uncertainty in quantifying the drivers of climate change over the past hundred years. Global climate models have so far been heavily reliant on approximations of the physical processes that occur at subkilometer scales, even though processes at those scales are important for representing the physics behind aerosol‐cloud interactions. To address this gap, we develop and run a multiscale global model that embeds a finer‐scale model (250 m in the horizontal and 20 m in the vertical) within the columns of a coarser‐resolution global model. A pair of simulations with preindustrial and present‐day aerosol emissions is used to quantify the impact of human aerosol emissions. They show that the climate impact of resolving subkilometer resolutions is relatively small. However, this masks some key differences. The increase in cloud water with increasing aerosols is substantially weaker when subkilometer motions are resolved. Most of this weakening is due to a weaker response in nonraining clouds and there being fewer clouds that rain in the high‐resolution model. The simulation results point to observations of specific processes that can help further constrain the impact of aerosols on clouds and climate. Key Points: Aerosol‐cloud interactions in a global model that resolves subkilometer processes are compared to those in a coarser 4 km modelOver the Northern Hemisphere ocean, resolving subkilometer scales leads to a weaker increase in liquid water path with aerosolsWeaker LWP increase is due to fewer precipitating clouds and weaker LWP increase in nonraining clouds [ABSTRACT FROM AUTHOR]

Abstract This paper describes the GISS‐E2.1 contribution to the Coupled Model Intercomparison Project, Phase 6 (CMIP6). This model version differs from the predecessor model (GISS‐E2) chiefly due to parameterization improvements to the atmospheric and ocean model components, while keeping atmospheric resolution the same. Model skill when compared to modern era climatologies is significantly higher than in previous versions. Additionally, updates in forcings have a material impact on the results. In particular, there have been specific improvements in representations of modes of variability (such as the Madden‐Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice. The effective climate sensitivity to 2 × CO2 is slightly higher than previously at 2.7–3.1°C (depending on version) and is a result of lower CO2 radiative forcing and stronger positive feedbacks.

Spinning up a highly complex, coupled Earth system model (ESM) is a time consuming and computationally demanding exercise. For models with interactive ice sheet components, this becomes a major challenge, as ice sheets are sensitive to bidirectional feedback processes and equilibrate over glacial timescales of up to many millennia. This work describes and demonstrates a computationally tractable, iterative procedure for spinning up a contemporary, highly complex ESM that includes an interactive ice sheet component. The procedure alternates between a computationally expensive coupled configuration and a computationally cheaper configuration where the atmospheric component is replaced by a data model. By periodically regenerating atmospheric forcing consistent with the coupled system, the data atmosphere remains adequately constrained to ensure that the broader model state evolves realistically. The applicability of the method is demonstrated by spinning up the preindustrial climate in the Community Earth System Model Version 2 (CESM2), coupled to the Community Ice Sheet Model Version 2 (CISM2) over Greenland. The equilibrium climate state is similar to the control climate from a coupled simulation with a prescribed Greenland ice sheet, indicating that the iterative procedure is consistent with a traditional spin‐up approach without interactive ice sheets. These results suggest that the iterative method presented here provides a faster and computationally cheaper method for spinning up a highly complex ESM, with or without interactive ice sheet components. The method described here has been used to develop the climate/ice sheet initial conditions for transient, ice sheet‐enabled simulations with CESM2‐CISM2 in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Plain Language Summary: Experiments with Earth system models typically use the preindustrial (1850 CE) climate as a reference point when examining the climate response to a given experiment scenario. A preindustrial simulated climate state is therefore important to develop and represent consistently, which often requires long and computationally expensive spin‐up or equilibration simulations. The latest generation Earth system models include time‐evolving ice sheet components, which complicate the task of generating a self‐consistent simulated preindustrial climate. Additional complexity arises because ice sheets interact with the rest of the climate system through complex processes and feedbacks and respond slowly to climate change over many millennia. This equilibration timescale is computationally intractable using traditional spin‐up/equilibration simulation techniques. To circumvent this challenge, we present a novel method for generating an internally consistent climate state that is suitable for models with interactive ice sheet components. This method uses fewer computational resources than traditional simulation methods, while generating a climate consistent with more expensive methods. We demonstrate the viability of the method by generating the preindustrial control climate in the Community Earth System Model Version 2 (CESM2), which includes an interactive Greenland ice sheet. Key Points: We describe a computationally tractable, iterative procedure for spinning up a coupled Earth system‐ice sheet modelEquilibrium state from the iterative procedure is similar to a more expensive traditional model spin‐up with prescribed ice sheetsThe procedure is used for developing initial conditions for transient, fully coupled simulations in the Coupled Model Intercomparison Project phase 6 [ABSTRACT FROM AUTHOR]

Climate simulations for the North Atlantic and Europe for recent and future conditions simulated with the regionally coupled ROM model are analyzed and compared to the results from the MPI‐ESM. The ROM simulations also include a biogeochemistry and ocean tides. For recent climate conditions, ROM generally improves the simulations compared to the driving model MPI‐ESM. Reduced oceanic biases in the Northern Atlantic are found, as well as a better simulation of the atmospheric circulation, notably storm tracks and blocking. Regarding future climate projections for the 21st century following the RCP 4.5 and 8.5 scenarios, MPI‐ESM and ROM largely agree qualitatively on the climate change signal over Europe. However, many important differences are identified. For example, ROM shows an SST cooling in the Subpolar Gyre, which is not present in MPI‐ESM. Under the RCP8.5 scenario, ROM Arctic sea ice cover is thinner and reaches the seasonally ice‐free state by 2055, well before MPI‐ESM. This shows the decisive importance of higher ocean resolution and regional coupling for determining the regional responses to global warming trends. Regarding biogeochemistry, both ROM and MPI‐ESM simulate a widespread decline in winter nutrient concentration in the North Atlantic of up to ~35%. On the other hand, the phytoplankton spring bloom in the Arctic and in the North‐Western Atlantic starts earlier, and the yearly primary production is enhanced in the Arctic in the late 21st century. These results clearly demonstrate the added value of ROM to determine more detailed and more reliable climate projections at the regional scale. Plain Language Summary: We downscale present climate and future climate change projections for the North Atlantic and Europe using a regionally coupled Earth System Model including atmosphere, ocean, river runoff, and ocean biogeochemistry components. This approach allows us to attain higher spatial resolution and to a more accurate representation of key physical processes, yielding a better simulation of present climate at regional and local scales when compared to the driving global climate model. Future climate change projections show more detail at regional and local scale, mostly related to the improvement in the representation of orography and bathymetry. These improvements along with a better representation of the interactive ocean‐atmosphere coupling lead to other remarkable differences with the driving global climate model: (1) colder Sea Surface Temperature in the Subpolar Gyre region, indicating a local convection collapse and a Atlantic Meridional Overturning Circulation slowdown; (2) a seasonal free‐ice Arctic is reached by 2055 under RCP8.5 scenario, well before projected by the driving global climate model; and (3) stronger reduction in nutrients in the North Atlantic by the end of the 21st century. These results clearly demonstrate the added value of the regionally coupled model system to determine more reliable climate projections at the regional scale. Key Points: The regionally coupled model shows reduced oceanic biases in the North Atlantic and a better simulation of the atmospheric circulationThe Arctic reaches a seasonally ice‐free state by 2055 with the regionally coupled model under RCP8.5 scenarioChanges in biogeochemistry are generally stronger and show enhanced details in the regionally coupled model [ABSTRACT FROM AUTHOR]