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In this study, we evaluate mesoscale convective system (MCS) simulations in the second version of U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv2). E3SMv2 atmosphere model (EAMv2) is run at the uniform 0.25° horizontal resolution. We track MCSs consistently in the model and observations using PyFLEXTRKR algorithm, which defines MCSs based on both cloud top brightness temperature (Tb) and surface precipitation. Results from using only Tb to define MCSs are also discussed to understand the impact of different MCS tracking algorithms on MCS evaluation and provide additional insights into model errors in simulating MCSs. Our results show that EAMv2 simulated MCS precipitation is largely underestimated in tropical and extratropical regions. This is mainly attributed to the underestimated MCS genesis and underestimated precipitation intensity in EAMv2. Comparing the two MCS tracking methods, simulated MCS precipitation is increased if MCSs are defined with only cloud top Tb. The Tb‐based MCS tracking method, however, includes cloud systems with very weak precipitation. This illustrates the model issues in simulating heavy precipitation even though the convective cloud shield is overall well simulated from the moist convective processes. Furthermore, sensitivity experiments are performed to examine the impact of new cloud and convection parameterizations developed for EAMv3 on simulated MCSs. The new physics parameterizations help increase the relative contribution of convective precipitation to total precipitation in the tropics, but the simulated MCS properties are not significantly improved. This suggests that simulating MCSs still remain a challenge for the next version of E3SM. Plain Language Summary: Mesoscale convective systems (MCSs) are one of the largest forms of deep convective storms, which play an important role in the earth system. It is imperative for global climate models to reasonably simulate MCS properties. This study aims to evaluate simulated MCS properties in the second version of U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv2). We utilized two different approaches to define MCSs in the model and observations for consistent comparisons. Our results show that the E3SMv2 model underestimates MCS precipitation in the tropical and subtropical regions. The too few MCSs and overly weak precipitation intensity in individual MCSs are the primary reasons for this MCS precipitation bias. The simulated MCS precipitation becomes more comparable to the observations when surface precipitation is not included in the MCS definition. However, many cloud systems with very weak precipitation characteristics are included. This comparison illustrates the model issues in precipitation formation while convective cloud structures are overall well simulated. In addition, by examining the impact of new physics parameterizations that are developed for the next generation of E3SM model on the MCS simulation, we find simulating MCSs will remain a challenge for the next version of E3SM model. Key Points: Simulated mesoscale convective system (MCS) precipitation is substantially underestimated in E3SMv2 due to insufficient MCS genesis and rain rate in individual MCSsUtilizing different MCS tracking methods provides a more complete picture about the model capability in simulating MCSsMCS properties in E3SMv2 are not significantly improved with the new cloud and convection parameterizations developed for E3SMv3 [ABSTRACT FROM AUTHOR]

Among the physical processes controlling aerosol vertical profiles, in‐cloud wet removal is of utmost importance while its representation in global climate models (GCMs) is crude. In this study, we implement into the Community Atmosphere Model version 6 (CAM6) a physically‐based aerosol wet removal parameterization scheme that explicitly treats aerosol activation, removal and resuspension. Evaluation against in‐situ observations shows that the default scheme substantially overestimates the upper tropospheric black carbon (BC) and sea salt mass. Our physically‐based scheme reduces BC and sea salt mass by a factor of 10 and 1,000, respectively, in better agreement with observations. Also, the new scheme slightly increases number of aerosol particles between 12 nm and 4.8 μm in diameter, thereby mitigating the aerosol number underestimation in the default scheme. Our new scheme reduces the overestimation of coarse‐mode aerosol (0.5–4.8 μm) number. Overall, the aerosol property changes (mass decrease and number increase) reduce the cloud condensation nuclei (CCN) concentration at low supersaturation (i.e., 0.02% and 0.1%), and increase CCN at high supersaturations (i.e., 0.5% and 1%). Consequently, the global annual mean cloud liquid water path increases by 1.89 g m−2 and the ice water path increases by 0.51 g m−2. The global annual mean shortwave, longwave, and net cloud radiative forcing change by −1.06, 0.57, and −0.48 W m−2, respectively. Further improvement is needed to reflect the real physics that the removal efficiencies for aerosol mass and number are disproportionate and to advect cloud‐borne (activated) aerosols for a complete aerosol lifecycle. Plain Language Summary: In‐cloud aerosol wet removal through aerosol particle activation to become cloud droplets and the subsequent rainfall formation removes airborne aerosol particles from the atmosphere, largely impacting aerosol amount and distribution in the atmosphere. Global climate models (GCMs) usually overestimate aerosol mass concentrations throughout the atmosphere, particularly in the upper troposphere, hampering an accurate assessment of aerosol‐cloud‐climate interactions. The main cause of aerosol overestimation lies in the oversimplified treatment of in‐cloud aerosol wet removal processes. In this study, we incorporate a new aerosol wet removal scheme in a GCM, the Community Atmosphere Model version 6. The evaluation against observations of the aerosol profile simulations with our new scheme shows significant improvements over the default scheme that is characterized by overestimation of aerosol and insufficient aerosol wet removal, highlighting the importance of an accurate representation of aerosol wet removal for aerosol vertical profiles. With the newly improved aerosol wet removal scheme, the global annual mean net cloud radiative forcing is changed by −0.48 W m−2, indicating that our new aerosol wet removal results in clouds that are more reflective on a global scale. Key Points: Improved representations of aerosol activation, removal, and resuspension in the wet removal scheme are implemented into Community Atmosphere Model version 6Our improved wet removal scheme effectively reduces the systematic overestimation of aerosol mass and mitigates the underestimation of total aerosol (12 nm to 4.8 μm) numberThe global mean net cloud radiative forcing is changed by −0.48 W m−2 due to our aerosol wet removal improvement in terms of activation, removal, and resuspension [ABSTRACT FROM AUTHOR]

Accurate prediction of cloud radiative effect (CRE) is important to weather forecast and climate projection, and solar energy production—a major renewable energy source toward decarbonization. Here, we evaluate the capability of the Weather Research and Forecast (WRF) model to simulate solar irradiance on a short‐term timescale (days) against observations in a remote region in north China. Results illustrate that our WRF simulation systematically underestimates the CRE and three error sources are identified: (a) incorrectly predicted cloud occurrence (i.e., missed clouds and false clouds), (b) underestimated cloud condensate mass, and (c) simplified parameterization of solar irradiance extinction. The incorrect cloud occurrence is the leading bias source, because it occurred most frequently and results in a substantial magnitude of errors. The cloud occurrence bias is subject to simulations of large‐scale air ascends and planetary boundary layer turbulence. Even when cloud occurrence is correctly simulated, our WRF simulation still underestimates CRE. This is because (a) the shallow convection scheme and cloud microphysics scheme underestimate cloud condensate mass and (b) cloud water path that feeds in the radiation scheme neglects precipitating cloud condensates (i.e., raindrops and graupels). Furthermore, an evaluation of cases with small bias in cloud condensate mass and effective radius demonstrates the parameterization of solar irradiance extinction for clouds induces a mean root mean square deviation of 110 W/m2. A possible reason is the simplified calculation of cloud extinction efficiency by applying Monte Carlo integration. The gained knowledge is important for understanding CRE simulation and solar irradiance forecast. Key Points: The Weather Research and Forecast model simulation is found to underestimate cloud radiative effect (CRE) in a remote and semiarid regionMissed clouds and false clouds, that occur 50% of the time and cause up to 400 W/m2 bias in CRE, are the leading bias sourcesOther nonignorable bias sources include underestimated cloud condensate mass and the oversimplified radiative transfer scheme [ABSTRACT FROM AUTHOR]

We introduce a physics‐based aerosol wet removal scheme with unified treatments of aerosol transport and removal by convective clouds into the Community Atmosphere Model version 6. Since several important physical processes are still neglected or poorly represented in this new physics‐based scheme, we develop secondary improvements to the parameterizations of aerosol activation, resuspension, and cloud‐borne aerosol detrainment in this new scheme. Changes in the aerosol wet removal scheme cause tropospheric aerosol concentrations to decrease to different extents: compared to the control run, the physics‐based scheme significantly decreases aerosol burdens by up to 60% over the southern Pacific Ocean, whereas the secondary improvements mitigate the decreasing tendency. The burden changes also depend on aerosol chemical components: the sulfate mass decrease is compensated by secondary production, black carbon (BC) is effectively removed via increasing the hygroscopicity of particulate organic matter from 0 to 0.2, and dust shows the most spatially heterogeneous changes. Simulated aerosol profiles are evaluated against aircraft‐based observations over the Pacific and Atlantic Oceans. The secondary‐improved scheme reduces the overestimations of upper tropospheric BC and sea salt concentrations by a factor of 10 and 1,000, respectively, and reproduces the dependence of BC mass decrease rates on cloud types. Consideration of convective cloud‐borne aerosol detrainment plays the most important role in enhancing the aerosol wet removal and decreasing the positive biases of tropospheric BC and sea salt concentrations. We also summarize unresolved issues related to convective cloud genesis and microphysics, cloud‐borne aerosol evolution, and BC and dust emissions. Key Points: Aerosol wet removal by deep convective clouds significantly impacts global aerosol vertical profilesExplicit convective cloud microphysics in wet removal scheme reduces overestimation of black carbon and sea salt concentrations in upper troposphereParameterization of cloud‐borne (activated) aerosol detrainment from convective clouds to stratiform clouds is an important step in improving the model performance [ABSTRACT FROM AUTHOR]

Partitioning deep convective cloud condensates into components that sediment and detrain, known to be a challenge for global climate models, is important for cloud vertical distribution and anvil cloud formation. In this study, we address this issue by improving the convective microphysics scheme in the National Center for Atmospheric Research Community Atmosphere Model version 5.3 (CAM5.3). The improvements include: (1) considering sedimentation for cloud ice crystals that do not fall in the original scheme, (2) applying a new terminal velocity parameterization that depends on the environmental conditions for convective snow, (3) adding a new hydrometeor category, "rimed ice," to the original four‐class (cloud liquid, cloud ice, rain, and snow) scheme, and (4) allowing convective clouds to detrain snow particles into stratiform clouds. Results from the default and modified CAM5.3 models were evaluated against observations from the U.S. Department of Energy Tropical Warm Pool‐International Cloud Experiment (TWP‐ICE) field campaign. The default model overestimates ice amount, which is largely attributed to the underestimation of convective ice particle sedimentation. By considering cloud ice sedimentation and rimed ice particles and applying a new convective snow terminal velocity parameterization, the vertical distribution of ice amount is much improved in the midtroposphere and upper troposphere when compared to observations. The vertical distribution of ice condensate also agrees well with observational best estimates upon considering snow detrainment. Comparison with observed convective updrafts reveals that current bulk model fails to reproduce the observed updraft magnitude and occurrence frequency, suggesting spectral distributions be required to simulate the subgrid updraft heterogeneity. Key Points: Graupel is added to a convective microphysics scheme for global climate modelsNew convective ice particle terminal velocity schemes are implemented and convective snow is allowed to detrainVertical distribution of ice mass in the midtroposphere and upper troposphere is improved [ABSTRACT FROM AUTHOR]