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The Kinematic Driver-Aerosol (KiD-A) intercomparison was established to test the hypothesis that detailed warm microphysical schemes provide a benchmark for lower-complexity bulk microphysics schemes. KiD-A is the first intercomparison to compare multiple Lagrangian cloud models (LCMs), size bin-resolved schemes, and double-moment bulk microphysics schemes in a consistent 1D dynamic framework and box cases. In the absence of sedimentation and collision–coalescence, the drop size distributions (DSDs) from the LCMs exhibit similar evolution with expected physical behaviors and good interscheme agreement, with the volume mean diameter (Dvol) from the LCMs within 1%–5% of each other. In contrast, the bin schemes exhibit nonphysical broadening with condensational growth. These results further strengthen the case that LCMs are an appropriate numerical benchmark for DSD evolution under condensational growth. When precipitation processes are included, however, the simulated liquid water path, precipitation rates, and response to modified cloud drop/aerosol number concentrations from the LCMs vary substantially, while the bin and bulk schemes are relatively more consistent with each other. The lack of consistency in the LCM results stems from both the collision–coalescence process and the sedimentation process, limiting their application as a numerical benchmark for precipitation processes. Reassuringly, however, precipitation from bulk schemes, which are the basis for cloud microphysics in weather and climate prediction, is within the spread of precipitation from the detailed schemes (LCMs and bin). Overall, this intercomparison identifies the need for focused effort on the comparison of collision–coalescence methods and sedimentation in detailed microphysics schemes, especially LCMs.

Cloud microphysics is critical for weather and climate prediction. In this work, we document updates and corrections to the cloud microphysical scheme used in the Community Earth System Model (CESM) and other models. These updates include a new nomenclature for the scheme, now called Parameterization of Unified Microphysics Across Scales (PUMAS), and the ability to run the scheme on graphics processing units (GPUs). The main science changes include refactoring an ice number limiter and associated changes to ice nucleation, adding vapor deposition onto snow, and introducing an implicit numerical treatment for sedimentation. We also detail the improvements in computational performance that can be achieved with GPU acceleration. We then show the impact of these scheme changes on the (a) mean state climate, (b) cloud feedback response to warming, and (c) aerosol forcing. We find that corrections are needed to the immersion freezing parameterization and that ice nucleation has important impacts on climate. We also find that the revised scheme produces less cloud liquid and ice but that this can be adjusted by changing the loss process for cloud liquid (autoconversion). Furthermore, there are few discernible effects of the PUMAS changes on cloud feedbacks but some reductions in the magnitude of aerosol–cloud interactions (ACIs). Small cloud feedback changes appear to be related to the implicit sedimentation scheme, with a number of factors affecting ACIs.

Comprehensive assessment of climate datasets is important for communicating model projections and associated uncertainties to stakeholders. Uncertainties can arise not only from assumptions and biases within the model but also from external factors such as computational constraint and data processing. To understand sources of uncertainties in global variable-resolution (VR) dynamical downscaling, we produced a regional climate dataset using the Model for Prediction Across Scales (MPAS; dynamical core version 4.0) coupled to the Community Atmosphere Model (CAM; version 5.4), which we refer to as CAM–MPAS hereafter. This document provides technical details of the model configuration, simulations, computational requirements, post-processing, and data archive of the experimental CAM–MPAS downscaling data. The CAM–MPAS model is configured with VR meshes featuring higher resolutions over North America as well as quasi-uniform-resolution meshes across the globe. The dataset includes multiple uniform- (240 and 120 km) and variable-resolution (50–200, 25–100, and 12–46 km) simulations for both the present-day (1990–2010) and future (2080–2100) periods, closely following the protocol of the North American Coordinated Regional Climate Downscaling Experiment. A deviation from the protocol is the pseudo-warming experiment for the future period, using the ocean boundary conditions produced by adding the sea surface temperature and sea-ice changes from the low-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-LR) in the Coupled Model Intercomparison Project Phase 5 to the present-day ocean state from a reanalysis product. Some unique aspects of global VR models are evaluated to provide background knowledge to data users and to explore good practices for modelers who use VR models for regional downscaling. In the coarse-resolution domain, strong resolution sensitivity of the hydrological cycles exists over the tropics but does not appear to affect the midlatitude circulations in the Northern Hemisphere, including the downscaling target of North America. The pseudo-warming experiment leads to similar responses of large-scale circulations to the imposed radiative and boundary forcings in the CAM–MPAS and MPI-ESM-LR models, but their climatological states in the historical period differ over various regions, including North America. Such differences are carried to the future period, suggesting the importance of the base state climatology. Within the refined domain, precipitation statistics improve with higher resolutions, and such statistical inference is verified to be negligibly influenced by horizontal remapping during post-processing. Limited (≈50 % slower) throughput of the current code is found on a recent many-core/wide-vector high-performance computing system, which limits the lengths of the 12–46 km simulations and indirectly affects sampling uncertainty. Our experience shows that global and technical aspects of the VR downscaling framework require further investigations to reduce uncertainties for regional climate projection.

Process-oriented diagnostics (PODs) aim to provide feedback for model developers through model analysis based on physical hypotheses. However, the step from a diagnostic based on relationships among variables, even when hypothesis-driven, to specific guidance for revising model formulation or parameterizations can be substantial. The POD may provide more information than a purely performance-based metric, but a gap between POD principles and providing actionable information for specific model revisions can remain. Furthermore, in coordinating diagnostics development, there is a trade-off between freedom for the developer, aiming to capture innovation, and near-term utility to the modeling center. Best practices that allow for the former, while conforming to specifications that aid the latter, are important for community diagnostics development that leads to tangible model improvements. Promising directions to close the gap between principles and practice include the interaction of PODs with perturbed physics experiments and with more quantitative process models as well as the inclusion of personnel from modeling centers in diagnostics development groups for immediate feedback during climate model revisions. Examples are provided, along with best-practice recommendations, based on practical experience from the NOAA Model Diagnostics Task Force (MDTF). Common standards for metrics and diagnostics that have arisen from a collaboration between the MDTF and the Department of Energy’s Coordinated Model Evaluation Capability are advocated as a means of uniting community diagnostics efforts.

EarthWorks is a high-resolution, coupled, global storm-resolving Earth System Model which is aimed at both weather and climate applications. All components share the same geodesic grid. The target grid spacing is 3.75 km for all components, and the target performance is one simulated year per wall clock day by 2025. While EarthWorks uses the CESM framework, its atmosphere, ocean and sea ice components are based on the MPAS (“Model for Prediction Across Scales”) dynamical cores. These components are coupled using the CMEPS (Community Mediator for Earth Prediction Systems) developed for CESM. Our presentation will focus on the results of both fully coupled and AMIP simulations with various horizontal grid spacings. We will also mention various problems encountered and overcome along the way.

For the Community Atmosphere Model version 6 (CAM6), an adjustment is needed to conserve dry air mass. This adjustment exposes an inconsistency in how CAM6’s energy budget incorporates water—in CAM6 water in the vapor phase has energy, but condensed phases of water do not. When water vapor condenses, only its latent energy is retained in the model, while its remaining internal, potential, and kinetic energy are lost. A global fixer is used in the default CAM6 model to maintain global energy conservation, but locally the energy tendency associated with water changing phase violates the divergence theorem. This error in energy tendency is intrinsically tied to the water vapor tendency, and reaches its highest values in regions of heavy rainfall, where the error can be as high as 40 W m−2 annually averaged. Several possible changes are outlined within this manuscript that would allow CAM6 to satisfy the divergence theorem locally. These fall into one of two categories: 1) modifying the surface flux to balance the local atmospheric energy tendency and 2) modifying the local atmospheric tendency to balance the surface plus top-of-atmosphere energy fluxes. To gauge which aspects of the simulated climate are most sensitive to this error, the simplest possible change—where condensed water still does not carry energy and a local energy fixer is used in place of the global one—is implemented within CAM6. Comparing this experiment with the default configuration of CAM6 reveals precipitation, particularly its variability, to be highly sensitive to the energy budget formulation. Significance Statement This study examines and explains spurious regional sources and sinks of energy in a widely used climate model. These energy errors result from not tracking energy associated with water after it transitions from the vapor phase to either liquid or ice. Instead, the model used a global fixer to offset the energy tendency related to the energy sources and sinks associated with condensed water species. We replace this global fixer with a local one to examine the model sensitivity to the regional energy error and find a large sensitivity in the simulated hydrologic cycle. This work suggests that the underlying thermodynamic assumptions in the model should be revisited to build confidence in the model-simulated regional-scale water and energy cycles.

Earth System Models (ESMs) must represent processes below the grid scale of a model using representations (parameterizations) of physical and chemical processes. As a tutorial exercise to understand diagnostics and parameterization, this work presents a representation of rainbows for an ESM: the Community Earth System Model version 2 (CESM2). Using the 'state' of the model, basic physical laws, and some assumptions, we generate a representation of this unique optical phenomena as a diagnostic output. Rainbow occurrence and it's possible changes are related to cloud occurrence and rain formation which are critical uncertainties for climate change prediction. The work highlights issues which are typical of many diagnostics parameterizations such as assumptions, uncertain parameters and the difficulty of evaluation against uncertain observations. Results agree qualitatively with limited available global 'observations' of Rainbows. Rainbows are seen in expected locations in the sub-tropics over the ocean where broken clouds and frequent precipitation occurs. The diurnal peak is in the morning over ocean and in the evening over land. The representation of rainbows is found to be quantitatively sensitive to the assumed amount of cloudiness and the amount of stratiform rain. Rainbows are projected to have decreased, mostly in the Northern Hemisphere, due to aerosol pollution effects increasing cloud coverage since 1850. In the future, continued climate change is projected to decrease cloud cover, associated with a positive cloud feedback. As a result the rainbow diagnostic projects that rainbows will increase in the future, with the largest changes at mid-latitudes. The diagnostic may be useful for assessing cloud parameterizations, and is an exercise in how to build and test parameterizations of atmospheric phenomena.

Realistic simulation of the Earth's mean-state climate remains a major challenge, and yet it is crucial for predicting the climate system in transition. Deficiencies in models' process representations, propagation of errors from one process to another, and associated compensating errors can often confound the interpretation and improvement of model simulations. These errors and biases can also lead to unrealistic climate projections and incorrect attribution of the physical mechanisms governing past and future climate change. Here we show that a significantly improved global atmospheric simulation can be achieved by focusing on the realism of process assumptions in cloud calibration and subgrid effects using the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The calibration of clouds and subgrid effects informed by our understanding of physical mechanisms leads to significant improvements in clouds and precipitation climatology, reducing common and long-standing biases across cloud regimes in the model. The improved cloud fidelity in turn reduces biases in other aspects of the system. Furthermore, even though the recalibration does not change the global mean aerosol and total anthropogenic effective radiative forcings (ERFs), the sensitivity of clouds, precipitation, and surface temperature to aerosol perturbations is significantly reduced. This suggests that it is possible to achieve improvements to the historical evolution of surface temperature over EAMv1 and that precise knowledge of global mean ERFs is not enough to constrain historical or future climate change. Cloud feedbacks are also significantly reduced in the recalibrated model, suggesting that there would be a lower climate sensitivity when it is run as part of the fully coupled E3SM. This study also compares results from incremental changes to cloud microphysics, turbulent mixing, deep convection, and subgrid effects to understand how assumptions in the representation of these processes affect different aspects of the simulated atmosphere as well as its response to forcings. We conclude that the spectral composition and geographical distribution of the ERFs and cloud feedback, as well as the fidelity of the simulated base climate state, are important for constraining the climate in the past and future.

Mixed-phase clouds greatly affect projections of future climate, with recent evaluations highlighting the influence of the ice nucleation process in these clouds. Here we explore how this process affects climate sensitivity using simulations of the Community Earth System Model version 2 (CESM2). Ice nucleation is found to influence simulated cloud feedbacks not just over extratropical low clouds but over most regions and levels of the troposphere. However, the otherwise major influence of ice nucleation on total cloud feedback is negated when holding global mean cloud phase to observed levels. In satellite-constrained model experiments, dissimilar ice nucleation realizations all result in a strongly positive total cloud feedback, as in the default model. Global-scale cloud phase is hence confirmed to be the dominant link between ice nucleation and climate sensitivity. Conversely, whether ice nucleation is treated as aerosol-sensitive is found to be of limited importance. A microphysics update from CESM1 to CESM2 had substantially weakened ice nucleation in mixed-phase clouds, in part due to a model issue. Our findings suggest that this contributed to increased climate sensitivity primarily by reducing a global-scale cloud phase bias. Despite the issue, CESM2’s ice nucleation appears to form more realistic mixed-phase clouds than either a corrected implementation or CESM1’s ice nucleation scheme.

Simulating whole atmosphere dynamics, chemistry, and physics is computationally expensive. It can require high vertical resolution throughout the middle and upper atmosphere, as well as a comprehensive chemistry and aerosol scheme coupled to radiation physics. An unintentional outcome of the development of one of the most sophisticated and hence computationally expensive model configurations is that it often excludes a broad community of users with limited computational resources. Here, we analyze two configurations of the Community Earth System Model Version 2, Whole Atmosphere Community Climate Model Version 6 (CESM2(WACCM6)) with simplified “middle atmosphere” chemistry at nominal 1 and 2 degree horizontal resolutions. Using observations, a reanalysis, and direct model comparisons, we find that these configurations generally reproduce the climate, variability, and climate sensitivity of the 1 degree nominal horizontal resolution configuration with comprehensive chemistry. While the background stratospheric aerosol optical depth is elevated in the middle atmosphere configurations as compared to the comprehensive chemistry configuration, it is comparable between all configurations during volcanic eruptions. For any purposes other than those needing an accurate representation of tropospheric organic chemistry and secondary organic aerosols, these simplified chemistry configurations deliver reliable simulations of the whole atmosphere that require 35% to 86% fewer computational resources at nominal 1 and 2 degree horizontal resolution, respectively.

Global climate models (GCMs) have advanced in many ways as computing power has allowed more complexity and finer resolutions. As GCMs reach storm-resolving scales, they need to be able to produce realistic precipitation intensity, duration, and frequency at fine scales with consideration of scale-aware parameterization. This study uses a state-of-the-art storm-resolving GCM with a nonhydrostatic dynamical core – the Model for Prediction Across Scales (MPAS), incorporated in the atmospheric component (Community Atmosphere Model, CAM) of the open-source Community Earth System Model (CESM), within the System for Integrated Modeling of the Atmosphere (SIMA) framework (referred to as SIMA-MPAS). At uniform coarse (here, at 120 km) grid resolution, the SIMA-MPAS configuration is comparable to the standard hydrostatic CESM (with a finite-volume (FV) dynamical core) with reasonable energy and mass conservation on climatological timescales. With the comparable energy and mass balance performance between CAM-FV (workhorse dynamical core) and SIMA-MPAS (newly developed dynamical core), it gives confidence in SIMA-MPAS's applications at a finer resolution. To evaluate this, we focus on how the SIMA-MPAS model performs when reaching a storm-resolving scale at 3 km. To do this efficiently, we compose a case study using a SIMA-MPAS variable-resolution configuration with a refined mesh of 3 km covering the western USA and 60 km over the rest of the globe. We evaluated the model performance using satellite and station-based gridded observations with comparison to a traditional regional climate model (WRF, the Weather Research and Forecasting model). Our results show realistic representations of precipitation over the refined complex terrains temporally and spatially. Along with much improved near-surface temperature, realistic topography, and land–air interactions, we also demonstrate significantly enhanced snowpack distributions. This work illustrates that the global SIMA-MPAS at storm-resolving resolution can produce much more realistic regional climate variability, fine-scale features, and extremes to advance both climate and weather studies. This next-generation storm-resolving model could ultimately bridge large-scale forcing constraints and better inform climate impacts and weather predictions across scales.

Estimates of the anthropogenic effective radiative forcing (ERF) trend have increased by 50% since 2000 (from +0.4 W m−2 decade−1 in 2000–09 to +0.6 W m−2 decade−1 in 2010–19), the majority of which is driven by changes in the aerosol ERF trend, as a result of aerosol emissions reductions. Here we study the extent to which observations of the climate system agree with these ERF assumptions. We use a large ERF ensemble from the IPCC’s Sixth Assessment Report (AR6) to attribute the anthropogenic contributions to global mean surface temperature (GMST), top-of-atmosphere radiative flux, and we use aerosol optical depth observations. The GMST trend has increased from +0.18°C decade−1 in 2000–09 to +0.35°C decade−1 in 2010–19, coinciding with the anthropogenic warming trend rising from +0.19°C decade−1 in 2000–09 to +0.24°C decade−1 in 2010–19. This, as well as observed trends in top-of-atmosphere radiative fluxes and aerosol optical depths, supports the claim of an aerosol-induced temporary acceleration in the rate of warming. However, all three observation datasets additionally suggest that smaller aerosol ERF trend changes are compatible with observations since 2000, since radiative flux and GMST trends are significantly influenced by internal variability over this period. A zero-trend-change aerosol ERF scenario results in a much smaller anthropogenic warming acceleration since 2000 but is poorly represented in AR6’s ERF ensemble. Short-term ERF trends are difficult to verify using observations, so caution is required in predictions or policy judgments that depend on them, such as estimates of current anthropogenic warming trend, and the time remaining to, or the outstanding carbon budget consistent with, 1.5°C warming. Further systematic research focused on quantifying trends and early identification of acceleration or deceleration is required.

The COVID-19 pandemic caused significant economic disruption in 2020 and severely impacted air traffic. We use a state-of-the-art Earth system model and ensembles of tightly constrained simulations to evaluate the effect of the reductions in aviation traffic on contrail radiative forcing and climate in 2020. In the absence of any COVID-19-pandemic-caused reductions, the model simulates a contrail effective radiative forcing (ERF) of 62 ± 59 mW m−2 (2 standard deviations). The contrail ERF has complex spatial and seasonal patterns that combine the offsetting effect of shortwave (solar) cooling and longwave (infrared) heating from contrails and contrail cirrus. Cooling is larger in June–August due to the preponderance of aviation in the Northern Hemisphere, while warming occurs throughout the year. The spatial and seasonal forcing variations also map onto surface temperature variations. The net land surface temperature change due to contrails in a normal year is estimated at 0.13 ± 0.04 K (2 standard deviations), with some regions warming as much as 0.7 K. The effect of COVID-19 reductions in flight traffic decreased contrails. The unique timing of such reductions, which were maximum in Northern Hemisphere spring and summer when the largest contrail cooling occurs, means that cooling due to fewer contrails in boreal spring and fall was offset by warming due to fewer contrails in boreal summer to give no significant annual averaged ERF from contrail changes in 2020. Despite no net significant global ERF, because of the spatial and seasonal timing of contrail ERF, some land regions would have cooled slightly (minimum −0.2 K) but significantly from contrail changes in 2020. The implications for future climate impacts of contrails are discussed.

Predictions of the Earth system, such as weather forecasts and climate projections, require models informed by observations at many levels. Some methods for integrating models and observations are very systematic and comprehensive (e.g., data assimilation), and some are single purpose and customized (e.g., for model validation). We review current methods and best practices for integrating models and observations. We highlight how future developments can enable advanced heterogeneous observation networks and models to improve predictions of the Earth system (including atmosphere, land surface, oceans, cryosphere, and chemistry) across scales from weather to climate. As the community pushes to develop the next generation of models and data systems, there is a need to take a more holistic, integrated, and coordinated approach to models, observations, and their uncertainties to maximize the benefit for Earth system prediction and impacts on society.

Successive IPCC reports have assessed the level of human-induced warming above preindustrial, but much less emphasis has been placed on quantifying the rate of anthropogenic warming, despite the rate of warming being a key variable for ambitious policymaking. The decadal global temperature anomaly trend can be considered a combination of the forced responses from the full range of radiatively-active pollutants, plus the additional trend introduced by natural variability over the previous decade.The global temperature anomaly trend likely increased in the 2010s, following a temporary pause through the 2000s. Estimates of the globally averaged radiative forcing (RF) timeseries, which are used to attribute the anthropogenic contribution to this recent behaviour, suggest a 50% increase in the anthropogenic RF trend, which largely arises from aerosol RF trend changes since 2000. When these RF timeseries are used to complete a global temperature anomaly attribution (following the technique outlined in the IPCC’s Special Report on the Global Warming of 1.5°C), they suggest that the attributed anthropogenic warming rate has increased by between 50 and 100% since 2000, pushing the estimated rate of net anthropogenic warming up to around 0.3°C/decade since 2010.We study the global observational evidence supporting the aerosol trends presented in these RF datasets, and thus aim to determine the likely anthropogenic contribution to the perceived warming acceleration behaviour since 2000. We argue that while observations do support the claim that RF trends are partly responsible for the warming trend (and importantly do support the best-estimate RF trend estimates in this ensemble), observational evidence is circumstantial, with a counterhypothesis that aerosol RFs make only a small contribution to the warming trend since 2000 consistently failing to be disproven across the full ensemble of RFs.This occurs because observed trends in radiative fluxes and global temperatures are significantly influenced by internal variability, principally ENSO and PDO, precluding a clearer assessment of the externally forced behaviour over the short global observational records we have. In light of this uncertainty, considerable caution is required in predictions or policy judgments that depend on the precise current anthropogenic warming trend, such as the time remaining to, or the outstanding carbon budget consistent with, a warming of 1.5°C, since these may be influenced considerably by recent changes in aerosol forcing behaviour.

Using information from the A-Train satellites, the properties and radiative effects of eastern Pacific Ocean boundary layer clouds are evaluated in the Community Atmosphere Model, version 5 (CAM5), from the summer of 2007 and 2008. The cloud microphysical properties are inferred using measurements from CloudSat and CALIPSO (CC) that are then used to calculate the broadband radiative flux profiles. Accounting appropriately for sampling differences between the measurements and the simulation, evidence of the “too few, too bright” low cloud bias is found in CAM5. Single-layer low clouds have a frequency of occurrence of 42% from CC, as compared with just 29% in CAM5, and the averaged cloud radiative kernel (CRK) for the model shows stronger cooling. For stratocumulus in particular, the cooling in the model CRK is larger by a factor of 2 relative to the observations, implying an overly sensitive tropical low cloud feedback. Differences in the day/night occurrence of stratocumulus help to explain some of the difference in the CRK. The cloud-type microphysics for liquid clouds is represented reasonably well by the model, with a tendency for smaller water paths and smaller effective radii. Overall, the occurrence and CRK have partially compensating errors such that the net cooling at the top of the atmosphere for eastern Pacific low clouds is −43 W m−2 in CAM5, as compared with −32 W m−2 from CC. The cooling effect in the model is accomplished by fewer low clouds with a narrower range of properties, as compared with more clouds with a broader range of properties in the observation-based dataset.

This study is the first to reach a global view of the precipitation process partitioning, using a combination of satellite and global climate modeling data. The pathways investigated are 1) precipitating ice (ice/snow/graupel) that forms above the freezing level and melts to produce rain (S) followed by additional condensation and collection as the melted precipitating ice falls to the surface (R); 2) growth completely through condensation and collection (coalescence), warm rain (W); and 3) precipitating ice (primarily snow) that falls to the surface (SS). To quantify the amounts, data from satellite-based radar measurements—CloudSat, GPM, and TRMM—are used, as well as climate model simulations from the Community Atmosphere Model (CAM) and the Met Office Unified Model (UM). Total precipitation amounts and the fraction of the total precipitation amount for each of the pathways is examined latitudinally, regionally, and globally. Carefully examining the contributions from the satellite-based products leads to the conclusion that about 57% of Earth’s precipitation follows pathway S, 15% R, 23% W, and 5% SS, each with an uncertainty of ±5%. The percentages differ significantly from the global climate model results, with the UM indicating smaller fractional S, more R, and more SS; and CAM showing appreciably greater S, negative R (indicating net evaporation below the melting layer), a much larger percentage of W and much less SS. Possible reasons for the wide differences between the satellite- and model-based results are discussed.

To assess deep convective parameterizations in a variety of GCMs and examine the fast-time-scale convective transition, a set of statistics characterizing the pickup of precipitation as a function of column water vapor (CWV), PDFs and joint PDFs of CWV and precipitation, and the dependence of the moisture–precipitation relation on tropospheric temperature is evaluated using the hourly output of two versions of the GFDL Atmospheric Model, version 4 (AM4), NCAR CAM5 and superparameterized CAM (SPCAM). The 6-hourly output from the MJO Task Force (MJOTF)/GEWEX Atmospheric System Study (GASS) project is also analyzed. Contrasting statistics produced from individual models that primarily differ in representations of moist convection suggest that convective transition statistics can substantially distinguish differences in convective representation and its interaction with the large-scale flow, while models that differ only in spatial–temporal resolution, microphysics, or ocean–atmosphere coupling result in similar statistics. Most of the models simulate some version of the observed sharp increase in precipitation as CWV exceeds a critical value, as well as that convective onset occurs at higher CWV but at lower column RH as temperature increases. While some models quantitatively capture these observed features and associated probability distributions, considerable intermodel spread and departures from observations in various aspects of the precipitation–CWV relationship are noted. For instance, in many of the models, the transition from the low-CWV, nonprecipitating regime to the moist regime for CWV around and above critical is less abrupt than in observations. Additionally, some models overproduce drizzle at low CWV, and some require CWV higher than observed for strong precipitation. For many of the models, it is particularly challenging to simulate the probability distributions of CWV at high temperature.

The Whole Atmosphere Community Climate Model version 6 (WACCM6) is a major update of the whole atmosphere modeling capability in the Community Earth System Model (CESM), featuring enhanced physical, chemical and aerosol parameterizations. This work describes WACCM6 and some of the important features of the model. WACCM6 can reproduce many modes of variability and trends in the middle atmosphere, including the Quasi‐Biennial Oscillation, Stratospheric Sudden Warmings and the evolution of Southern Hemisphere springtime ozone depletion over the 20th century. WACCM6 can also reproduce the climate and temperature trends of the 20th century throughout the atmospheric column. The representation of the climate has improved in WACCM6, relative to WACCM4. In addition, there are improvements in high latitude climate variability at the surface and sea ice extent in WACCM6 over the lower top version of the model (CAM6) that come from the extended vertical domain and expanded aerosol chemistry in WACCM6, highlighting the importance of the stratosphere and tropospheric chemistry for high latitude climate variability.