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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.

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.

Significant uncertainty lies in representing the rain droplet size distribution (DSD) in bulk cloud microphysics schemes and in the derivation of parameters of the function fit to the spectrum from the varying moments of a DSD. Here we evaluate the suitability of gamma distribution functions (GDFs) for fitting rain DSDs against observed disdrometer data. Results illustrate that double-parameter GDFs with prescribed or diagnosed positive spectral shape parameters μ fit rain DSDs better than the Marshall–Palmer distribution function (with μ = 0). The relative errors of fitting the spectrum moments (especially high-order moments) decrease by an order of magnitude [from O(102) to O(101)]. Moreover, introduction of a triple-parameter GDF with mathematically solved μ decreases the relative errors to O(100). Based on further investigation of potential combinations of the three prognostic moments for triple-moment cloud microphysical schemes, it is found that the GDF with parameters determined from predictions of the zeroth, third, and fourth moments (the 034 GDF) exhibits the best fit to rain DSDs compared to other moment combinations. Therefore, we suggest that the 034 prognostic moment group should replace the widely accepted 036 group to represent rain DSDs in triple-moment cloud microphysics schemes. An evaluation of the capability of GDFs to represent rain DSDs demonstrates that 034 GDF exhibits accurate fits to all observed DSDs except for rarely occurring extremely wide spectra from heavy precipitation and extremely narrow spectra from drizzle. The knowledge gained from this assessment can also be used to improve cloud microphysics retrieval schemes and data assimilation.

The solar irradiance is an important parameter for the entire earth climate system since it governs the physical processes at the land surface (i.e., glacier melting, potential evapotranspiration, and diurnal cycling development of the planetary boundary layer (PBL)), and impacts on water and energy balance in the earth atmosphere (Ramanathan et al., 2001;Rosenfeld, 2006aRosenfeld, , 2006b. One objective of the studies focusing on man-made climate change is to quantify the variation of surface solar irradiance directly and indirectly caused by air pollutants from anthropogenic emissions (Jing & Suzuki, 2018;Powers et al., 2017). Additionally, solar irradiance forecast plays a key role in accelerating the ongoing global transition from conventional energy to renewable energy (Jimenez et al., 2016). Therefore, representation of physical processes controlling solar irradiance in the atmospheric models is important to estimate the radiation budget of the earth system, assess climate change, and accelerate the common application of green energy.The accuracy of simulating solar irradiance damping by aerosols, clouds, and light-absorbing gases during the transport from the top of the atmosphere to the earth's surface is usually unsatisfactory (Feng & Wang, 2019). The uncertainty of the solar irradiance simulation in mesoscale and global models is believed to intimately link to cloud radiative effect (CRE) since clouds usually show much higher solar irradiance extinct efficiency and sharper evolution than the gases and aerosols (Paquin-Ricard et al., 2010;Ruiz-Arias et al., 2016). Simulation of cloud-solar irradiance effect consists of two parts: (a) simulating cloud properties and (b) quantifying the solar irradiance extinction in the cloudy atmosphere. Therefore, solar irradiance forecast is determined by three