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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Climate models have substantial biases in the climatological latitude of the Southern Hemisphere eddy‐driven jet and the time scale of annular mode variability and disagree on the jet response to climate change. Zonally symmetric dry dynamical cores are often used for idealized modeling of the jet response to forcing and its sensitivity to model setup changes. The limits to which these models represent the key mechanisms that control the jet in complex models or the real world have not been systematically investigated. Here we show that substantial intermodel differences in jet latitude and strength can arise from differences in dynamical cores and resolved topography. Including topography and a more realistic surface drag in a dry model substantially alters the jet response to changes in drag strength. Using real‐world maps, enhanced drag over land shifts the jet poleward, whereas enhanced drag over the ocean leads to an equatorward shift. No universal relationship between annular mode time scale and forced response emerges in the dry model with topography. These results suggest that zonally symmetric models with Rayleigh drag lack important mechanisms that control the behavior of the midlatitude jet in coupled climate models. A dry model with topography and quadratic surface drag can fill this gap in the model hierarchy. Plain Language Summary: Weather and climate models struggle to correctly represent the midlatitude westerlies and often place them too far equatorward in the Southern Hemisphere. Reduced models that omit the effects of moisture and clouds and have a very simple representation of surface drag are often used to study how the westerlies respond to different forcings. Here we show that the response of the midlatitude westerlies to forcings can change dramatically when a slightly more realistic representation of surface drag is used in a model. We also show that large differences between models can arise from the way a model is constructed, independently of the representation of physical processes such as surface friction. Key Points: Using quadratic instead of linear drag can reverse the midlatitude jet response to drag changesEnhanced drag over land shifts the SH jet poleward in a model with topography and quadratic dragIntermodel differences in the dry dynamics setup have a strong impact on the jet latitude [ABSTRACT FROM AUTHOR]

The Energy Exascale Earth System Model Atmosphere Model version 1, the atmospheric component of the Department of Energy's Energy Exascale Earth System Model is described. The model began as a fork of the well‐known Community Atmosphere Model, but it has evolved in new ways, and coding, performance, resolution, physical processes (primarily cloud and aerosols formulations), testing and development procedures now differ significantly. Vertical resolution was increased (from 30 to 72 layers), and the model top extended to 60 km (~0.1 hPa). A simple ozone photochemistry predicts stratospheric ozone, and the model now supports increased and more realistic variability in the upper troposphere and stratosphere. An optional improved treatment of light‐absorbing particle deposition to snowpack and ice is available, and stronger connections with Earth system biogeochemistry can be used for some science problems. Satellite and ground‐based cloud and aerosol simulators were implemented to facilitate evaluation of clouds, aerosols, and aerosol‐cloud interactions. Higher horizontal and vertical resolution, increased complexity, and more predicted and transported variables have increased the model computational cost and changed the simulations considerably. These changes required development of alternate strategies for tuning and evaluation as it was not feasible to "brute force" tune the high‐resolution configurations, so short‐term hindcasts, perturbed parameter ensemble simulations, and regionally refined simulations provided guidance on tuning and parameterization sensitivity to higher resolution. A brief overview of the model and model climate is provided. Model fidelity has generally improved compared to its predecessors and the CMIP5 generation of climate models. Plain Language Summary: This study provides an overview of a new computer model of the Earth's atmosphere that is used as one component of the Department of Energy's latest Earth system model. The model can be used to help understand past, present, and future changes in Earth's behavior as the system responds to changes in atmospheric composition (like pollution and greenhouse gases), land, and water use and to explore how the atmosphere interacts with other components of the Earth system (ocean, land, biology, etc.). Physical, chemical, and biogeochemical processes treated within the atmospheric model are described, and pointers to previous and recent work are listed to provide additional information. The model is compared to present‐day observations and evaluated for some important tests that provide information about what could happen to clouds and the environment as changes occur. Strengths and weaknesses of the model are listed, as well as opportunities for future work. Key Points: A brief description and evaluation is provided for the atmospheric component of the Department of Energy's Energy Exascale Earth System ModelModel fidelity has generally improved compared to predecessors and models participating in past international model evaluationsStrengths and weaknesses of the model, as well as opportunities for future work, are described [ABSTRACT FROM AUTHOR]

The Max Planck Institute Grand Ensemble (MPI‐GE) is the largest ensemble of a single comprehensive climate model currently available, with 100 members for the historical simulations (1850–2005) and four forcing scenarios. It is currently the only large ensemble available that includes scenario representative concentration pathway (RCP) 2.6 and a 1% CO2 scenario. These advantages make MPI‐GE a powerful tool. We present an overview of MPI‐GE, its components, and detail the experiments completed. We demonstrate how to separate the forced response from internal variability in a large ensemble. This separation allows the quantification of both the forced signal under climate change and the internal variability to unprecedented precision. We then demonstrate multiple ways to evaluate MPI‐GE and put observations in the context of a large ensemble, including a novel approach for comparing model internal variability with estimated observed variability. Finally, we present four novel analyses, which can only be completed using a large ensemble. First, we address whether temperature and precipitation have a pathway dependence using the forcing scenarios. Second, the forced signal of the highly noisy atmospheric circulation is computed, and different drivers are identified to be important for the North Pacific and North Atlantic regions. Third, we use the ensemble dimension to investigate the time dependency of Atlantic Meridional Overturning Circulation variability changes under global warming. Last, sea level pressure is used as an example to demonstrate how MPI‐GE can be utilized to estimate the ensemble size needed for a given scientific problem and provide insights for future ensemble projects. Key Points: The 100‐member MPI‐GE is currently the largest publicly available ensemble of a comprehensive climate modelMPI‐GE currently has the most forcing scenarios of all large ensemble projects: RCP2.6, RCP4.5, RCP8.5, and 1% CO2The power of MPI‐GE is to estimate the forced response and internal variability, including changing variability, to unprecedented precision [ABSTRACT FROM AUTHOR]

We analyze the atmospheric processes that explain the large changes in radiative feedbacks between the two latest climate configurations of the Hadley Centre Global Environmental model. We use a large set of atmosphere‐only climate change simulations (amip and amip‐p4K) to separate the contributions to the differences in feedback parameter from all the atmospheric model developments between the two latest model configurations. We show that the differences are mostly driven by changes in the shortwave cloud radiative feedback in the midlatitudes, mainly over the Southern Ocean. Two new schemes explain most of the differences: the introduction of a new aerosol scheme and the development of a new mixed‐phase cloud scheme. Both schemes reduce the strength of the preexisting shortwave negative cloud feedback in the midlatitudes. The new aerosol scheme dampens a strong aerosol‐cloud interaction, and it also suppresses a negative clear‐sky shortwave feedback. The mixed‐phase scheme increases the amount of cloud liquid water path (LWP) in the present day and reduces the increase in LWP with warming. Both changes contribute to reducing the negative radiative feedback of the increase of LWP in the warmer climate. The mixed‐phase scheme also enhances a strong, preexisting, positive cloud fraction feedback. We assess the realism of the changes by comparing present‐day simulations against observations and discuss avenues that could help constrain the relevant processes. Key Points: Large changes in shortwave cloud feedbacks exist between two recent configurations of Met Office modelChanges in midlatitude feedbacks explain most of the global‐mean differencesThe new aerosol and mixed‐phase schemes explain most of the feedback differences [ABSTRACT FROM AUTHOR]

Comprehensive climate models exhibit a large spread in the magnitude of projected poleward eddy‐driven jet shift in response to warming. The spread has been connected to the radiative response to warming. To understand how different radiative assumptions alone affect the jet shift in response to warming, we introduce a new clear‐sky longwave radiation hierarchy that spans idealized (gray versus four bands; without or with interactive water vapor) through comprehensive (correlated‐k) radiation in the same general circulation model. The new hierarchy is used in an aquaplanet configuration to explore the impact of radiation on the jet stream response to warming, independent of mean surface temperature and meridional surface temperature gradient responses. The gray radiation scheme produces a split jet and its eddy‐driven jet shifts equatorward as climate warms, whereas the storm track shifts equatorward then poleward. Including four longwave bands leads to a merged jet that shifts slightly poleward with warming, and the storm track shifts monotonically poleward. Including interactive water vapor makes the poleward jet shift comparable to the jet shift with comprehensive radiation and interactive water vapor. These jet and storm track differences are linked to the radiation response of the stratospheric temperature, the tropopause height, and the meridional gradient of the radiative forcing to warming. Dynamically, the equatorward jet shift with the gray scheme is consistent with reduced wave reflection on the poleward flank of the jet, whereas the poleward jet shift with the other schemes is consistent with increased eddy length scale that favors equatorward wave propagation. Key Points: A new radiation climate model hierarchy is used to examine the impact of radiation on jet stream response to warmingGray radiation leads to equatorward shift, but adding four bands and interactive water vapor leads to agreement with comprehensive radiationEquatorward and poleward jet shifts are linked to increased poleward wave propagation and increased eddy length scale, respectively [ABSTRACT FROM AUTHOR]

This study explores the extent to which convective aggregation interacts with sea surface temperature (SST) and affects climate sensitivity. For this purpose, radiative‐convective equilibrium simulations are run with a general circulation model coupled to an ocean mixed layer, and several types of perturbations are imposed to the ocean‐atmosphere system. Convective aggregation turns out to be much more sensitive to temperature in coupled experiments than in prescribed SST experiments. But changes in convective aggregation induced by a doubling of the CO2 concentration are always smaller than changes associated with the transition from a non‐aggregated to an aggregated state. If aggregation changes were acting alone, they would exert a strong negative feedback on global mean surface temperature. However, in a coupled framework, aggregation changes interact with the SST and generate SST gradients that strengthen the positive low‐cloud feedback associated with changes in SST pattern. This overcompensates the negative feedback due to aggregation changes and leads to a larger equilibrium climate sensitivity than in the absence of SST gradients. Although this effect might be model specific, interactions between convective aggregation and the spatial distribution of SST appear crucial to assess the impact of convective aggregation on climate sensitivity. Key Points: Convective aggregation interacts strongly with surface temperature gradientsAggregation can increase climate sensitivity when coupled with an interactive slab ocean, rather than decreasing itThe impact of convective aggregation on climate should also be investigated in coupled frameworks [ABSTRACT FROM AUTHOR]

Effects of free‐tropospheric thermodynamic properties and large‐scale subsidence on the nocturnal growth of marine stratocumulus clouds are investigated through large‐eddy simulation based on Flight 5 of the Physics of Stratocumulus Top research campaign. It was characterized by a weak inversion and moist troposphere. Sensitivity simulations are performed using variations in the subsidence and free‐tropospheric temperature and humidity. The results confirm that the cloud‐top entrainment instability parameter κ alone cannot unambiguously control the behaviors of marine stratocumulus due to the opposite response of liquid water path (LWP) to changes in the humidity and temperature jumps with the same variation in κ, thereby both jumps should be considered separately. However, sometimes it could be considered as a joint factor of the humidity and temperature jumps controlling LWP even though cloud‐top entrainment instability does not occur or κ < 0. An obvious role of subsidence is to push the inversion layer down nearly identically and at the same rate under different ambient conditions. Enhanced subsidence, as expected, diminishes the entrainment rate, cloud‐top height, cloud thickness, and LWP. However, it has a small influence on the inversion thickness and the dynamic instability across the inversion. An LWP budget analysis shows that the direct effect of subsidence is a small reduction in the LWP, but the indirect effect of subsidence is large due to contributions from other physical processes. Therefore, subsidence is an important physical process controlling the LWP budget, even though its direct contribution is significantly smaller than the largest contributions from both radiation and entrainment. Key Points: Effects of subsidence and inversion jumps on nonclassical MSc are investigated using implicit large‐eddy simulationsContributions of individual physical processes to LWP tendency are analyzedIn contrast to classical MSc, weakened inversion increases entrainment and LWP in nonclassical MSc [ABSTRACT FROM AUTHOR]

The article presents research designed to calculate the historical aerosol effective radiative forcing (ERF) in the HadGEM3‐GA7 climate model. The authors offer their evaluation regarding the suitability of this model for inclusion in the UK Earth system model, UKESM1.

The parameterization of moist convection contributes to uncertainty in climate modeling and numerical weather prediction. Machine learning (ML) can be used to learn new parameterizations directly from high‐resolution model output, but it remains poorly understood how such parameterizations behave when fully coupled in a general circulation model (GCM) and whether they are useful for simulations of climate change or extreme events. Here we focus on these issues using idealized tests in which an ML‐based parameterization is trained on output from a conventional parameterization and its performance is assessed in simulations with a GCM. We use an ensemble of decision trees (random forest) as the ML algorithm, and this has the advantage that it automatically ensures conservation of energy and nonnegativity of surface precipitation. The GCM with the ML convective parameterization runs stably and accurately captures important climate statistics including precipitation extremes without the need for special training on extremes. Climate change between a control climate and a warm climate is not captured if the ML parameterization is only trained on the control climate, but it is captured if the training includes samples from both climates. Remarkably, climate change is also captured when training only on the warm climate, and this is because the extratropics of the warm climate provides training samples for the tropics of the control climate. In addition to being potentially useful for the simulation of climate, we show that ML parameterizations can be interrogated to provide diagnostics of the interaction between convection and the large‐scale environment. Plain Language Summary: Small‐scale features such as clouds are typically represented in climate models by simplified physical models, and these simplified models introduce errors and uncertainties. A promising alternative approach is to use machine learning to train a statistical model to represent small‐scale processes based on output from expensive physics‐based models that better represent the small‐scale processes. Here we use idealized tests to explore the implications of incorporating a machine‐learning model of atmospheric convection in a climate model. We find that such an approach can give accurate simulations of mean climate and heavy rainfall events. The machine‐learning model does not work well for global warming if it is only trained on the current climate. However, it does work well for global warming if trained on both the current and warmer climates, and it works surprisingly well if only trained on the warmer climate. We also show that the machine‐learning model can be used to better understand the underlying physical processes. Key Points: Random‐forest parameterization of convection gives accurate GCM simulations of climate and precipitation extremes in idealized testsClimate change captured when trained on control and warm climate, or only on warm climate, but not when trained only on control climateMachine‐learning parameterizations can also be interrogated to generate diagnostics of interaction of convection with the environment [ABSTRACT FROM AUTHOR]

Abstract: Because weather and climate models must capture a wide variety of spatial and temporal scales, they rely heavily on parameterizations of subgrid‐scale processes. The goal of this study is to demonstrate that the assumptions used to couple these parameterizations have an important effect on the climate of version 0 of the Energy Exascale Earth System Model (E3SM) General Circulation Model (GCM), a close relative of version 1 of the Community Earth System Model (CESM1). Like most GCMs, parameterizations in E3SM are sequentially split in the sense that parameterizations are called one after another with each subsequent process feeling the effect of the preceding processes. This coupling strategy is noncommutative in the sense that the order in which processes are called impacts the solution. By examining a suite of 24 simulations with deep convection, shallow convection, macrophysics/microphysics, and radiation parameterizations reordered, process order is shown to have a big impact on predicted climate. In particular, reordering of processes induces differences in net climate feedback that are as big as the intermodel spread in phase 5 of the Coupled Model Intercomparison Project. One reason why process ordering has such a large impact is that the effect of each process is influenced by the processes preceding it. Where output is written is therefore an important control on apparent model behavior. Application of k‐means clustering demonstrates that the positioning of macro/microphysics and shallow convection plays a critical role on the model solution. [ABSTRACT FROM AUTHOR]

Abstract: Key developments have been made to the NCAR Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM‐X). Among them, the most important are the self‐consistent solution of global electrodynamics, and transport of O+ in the F‐region. Other ionosphere developments include time‐dependent solution of electron/ion temperatures, metastable O+ chemistry, and high‐cadence solar EUV capability. Additional developments of the thermospheric components are improvements to the momentum and energy equation solvers to account for variable mean molecular mass and specific heat, a new divergence damping scheme, and cooling by O(3P) fine structure. Simulations using this new version of WACCM‐X (2.0) have been carried out for solar maximum and minimum conditions. Thermospheric composition, density, and temperatures are in general agreement with measurements and empirical models, including the equatorial mass density anomaly and the midnight density maximum. The amplitudes and seasonal variations of atmospheric tides in the mesosphere and lower thermosphere are in good agreement with observations. Although global mean thermospheric densities are comparable with observations of the annual variation, they lack a clear semiannual variation. In the ionosphere, the low‐latitude E × B drifts agree well with observations in their magnitudes, local time dependence, seasonal, and solar activity variations. The prereversal enhancement in the equatorial region, which is associated with ionospheric irregularities, displays patterns of longitudinal and seasonal variation that are similar to observations. Ionospheric density from the model simulations reproduces the equatorial ionosphere anomaly structures and is in general agreement with observations. The model simulations also capture important ionospheric features during storms. [ABSTRACT FROM AUTHOR]

The Superparameterized Community Atmosphere Model (SPCAM) is used to identify the dynamical and organizational properties of tropical extreme rainfall events on two scales. We compare the mesoscales resolved by General Circulation Models (GCMs) and the convective scales resolved by Cloud-Resolving Models (CRMs) to reassess and extend on previous results from GCMs and CRMs in radiative-convective equilibrium. We first show that the improved representation of subgridscale dynamics in SPCAM allows for a close agreement with the 7%/K Clausius-Clapeyron rate of increase in mesoscale extremes rainfall rates. Three contributions to changes in extremes are quantified and appear consistent in sign and relative magnitude with previous results. On mesoscales, the thermodynamic contribution (5.8%/K) and the contribution from mass flux increases (2%/K) enhance precipitation rates, while the upward displacement of the mass flux profile (-1.1%/K) offsets this increase. Convective-scale extremes behave similarly except that changes in mass flux are negligible due to a balance between greater numbers of strong updrafts and downdrafts and lesser numbers of weak updrafts. Extremes defined on these two scales behave as two independent sets of rainfall events, with different dynamics, geometries, and responses to climate change. In particular, dynamic changes in mesoscale extremes appear primarily sensitive to changes in the large-scale mass flux, while the intensity of convective-scale extremes is not. In particular, the increases in mesoscale mass flux directly contribute to the intensification of mesoscale extreme rain, but do not seem to affect the increase in convective-scale rainfall intensities. These results motivate the need for better understanding the role of the large-scale forcing on the formation and intensification of heavy convective rainfall. [ABSTRACT FROM AUTHOR]

WTG balance is used to examine how changes in the moist thermodynamic structure of the tropics affect the MJO in two simulations of the Superparameterized Community Earth System Model (SP-CESM), one at preindustrial (PI) levels of [ABSTRACT FROM AUTHOR]

In this study, the stochastic deep convection parameterization of Plant and Craig (PC) is implemented in the Community Atmospheric Model version 5 (CAM5) to incorporate the stochastic processes of convection into the Zhang-McFarlane (ZM) deterministic deep convective scheme. Its impacts on deep convection, shallow convection, large-scale precipitation and associated dynamic and thermodynamic fields are investigated. Results show that with the introduction of the PC stochastic parameterization, deep convection is decreased while shallow convection is enhanced. The decrease in deep convection is mainly caused by the stochastic process and the spatial averaging of input quantities for the PC scheme. More detrained liquid water associated with more shallow convection leads to significant increase in liquid water and ice water paths, which increases large-scale precipitation in tropical regions. Specific humidity, relative humidity, zonal wind in the tropics, and precipitable water are all improved. The simulation of shortwave cloud forcing (SWCF) is also improved. The PC stochastic parameterization decreases the global mean SWCF from −52.25 W/m2 in the standard CAM5 to −48.86 W/m2, close to −47.16 W/m2 in observations. The improvement in SWCF over the tropics is due to decreased low cloud fraction simulated by the stochastic scheme. Sensitivity tests of tuning parameters are also performed to investigate the sensitivity of simulated climatology to uncertain parameters in the stochastic deep convection scheme. [ABSTRACT FROM AUTHOR]

Understanding the interactions between agricultural production and climate is necessary for sound decision-making in climate policy. Gridded and high-resolution crop simulation has emerged as a useful tool for building this understanding. Large uncertainty exists in this utilization, obstructing its capacity as a tool to devise adaptation strategies. Increasing focus has been given to sources of uncertainties for climate scenarios, input-data, and model, but uncertainties due to model parameter or calibration are still unknown. Here, we use publicly available geographical data sets as input to the Environmental Policy Integrated Climate model (EPIC) for simulating global-gridded maize yield. Impacts of climate change are assessed up to the year 2099 under a climate scenario generated by HadEM2-ES under RCP 8.5. We apply five strategies by shifting one specific parameter in each simulation to calibrate the model and understand the effects of calibration. Regionalizing crop phenology or harvest index appears effective to calibrate the model for the globe, but using various values of phenology generates pronounced difference in estimated climate impact. However, projected impacts of climate change on global maize production are consistently negative regardless of the parameter being adjusted. Different values of model parameter result in a modest uncertainty at global level, with difference of the global yield change less than 30% by the 2080s. The uncertainty subjects to decrease if applying model calibration or input data quality control. Calibration has a larger effect at local scales, implying the possible types and locations for adaptation. [ABSTRACT FROM AUTHOR]

Climate models with variable-resolution grids offer a computationally less expensive way to provide more detailed information and increased accuracy by resolving processes that cannot be adequately represented by a coarser grid. This study uses the Model for Prediction Across Scales-Atmosphere (MPAS-A), consisting of a nonhydrostatic dynamical core and a subset of Weather Research and Forecasting (WRF) model physics, to investigate the potential benefits of using tropical channel refinement. The simulations are performed with an idealized aquaplanet configuration using 30 and 240 km global grid spacing, and two variable-resolution grids spanning the same grid spacing range; one with a narrow (20°S-20°N) and one with a wide (30°S-30°N) tropical channel refinement. Increasing resolution in the tropics impacts both the tropical and extratropical circulation. Compared to the 30 km global grid, both refined channel simulations exhibit slightly stronger updrafts inside the Hadley cell resulting in more resolved precipitation. Using a wider tropical refinement leads to a closer correspondence with the global high-resolution grid. While different grid spacings produce similar cloud size distributions that are consistent with observations, the dependence of precipitation rate on cloud size varies among simulations. The refined channel simulations show improved tropical and extratropical precipitation relative to the global coarse simulation. All simulations show a single precipitation peak centered on the equator. Although the results show that tropical refinement is an effective method for avoiding artifacts due to grid resolution sensitivities seen in earlier studies that only refined a portion of the tropics, some biases remain well inside of the refinement region. [ABSTRACT FROM AUTHOR]

Observations and regional climate modeling (RCM) studies demonstrate that global climate models (GCMs) are unreliable for predicting changes in extreme precipitation. Yet RCM climate change simulations are subject to boundary conditions provided by GCMs and do not interact with large-scale dynamical feedbacks that may be critical to the overall regional response. Limitations of both global and regional modeling approaches contribute significant uncertainty to future rainfall projections. Progress requires a modeling framework capable of capturing the observed regional-scale variability of rainfall intensity without sacrificing planetary scales. Here the United States summer rainfall response to quadrupled CO2 climate change is investigated using conventional (CAM) and superparameterized (SPCAM) versions of the NCAR Community Atmosphere Model. The superparameterization approach, in which cloud-resolving model arrays are embedded in GCM grid columns, improves rainfall statistics and convective variability in global simulations. A set of 5 year time-slice simulations, with prescribed sea surface temperature and sea ice boundary conditions harvested from preindustrial and abrupt four times CO2 coupled Community Earth System Model (CESM/CAM) simulations, are compared for CAM and SPCAM. The two models produce very different changes in mean precipitation patterns, which develop from differences in large-scale circulation anomalies associated with the planetary-scale response to warming. CAM shows a small decrease in overall rainfall intensity, with an increased contribution from the weaker parameterized convection and a decrease from large-scale precipitation. SPCAM has the opposite response, a significant shift in rainfall occurrence toward higher precipitation rates including more intense propagating Central United States mesoscale convective systems in a four times CO2 climate. [ABSTRACT FROM AUTHOR]

A coupled climate model is used to examine the impact of an increase in the mixing due to mesoscale eddies on the global climate system. A sixfold increase in the Redi mixing coefficient ARedi, which is within the admissible range of variation, has the overall effect of warming the global-mean surface air and sea surface temperatures by more than 1°C. Locally, sea surface temperatures increase by up to 7°C in the North Pacific and by up to 4°C in the Southern Ocean, with corresponding impacts on the ice concentration and ice extent in polar regions. However, it is not clear that the changes in heat transport from tropics to poles associated with changing this coefficient are primarily responsible for these changes. We found that the changes in the transport of heat are often much smaller than changes in long-wave trapping and short-wave absorption. Additionally, changes in the advective and diffusive transport of heat toward the poles often oppose each other. However, we note that the poleward transport of salt increases near the surface as ARedi increases. We suggest a causal chain in which enhanced eddy stirring leads to increased high-latitude surface salinity reducing salt stratification and water column stability and enhancing convection, triggering two feedback loops. In one, deeper convection prevents sea ice formation, which decreases albedo, which increases SW absorption, further increasing SST and decreasing sea ice formation. In the other, increased SST and reduced sea ice allow for more water vapor in the atmosphere, trapping long-wave radiation. Destratifying the polar regions is thus a potential way in which changes in ocean circulation might warm the planet. [ABSTRACT FROM AUTHOR]