Improved Convective Ice Microphysics Parameterization in the NCAR CAM Model

Abstract

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.

Mesoscale Convective Systems Simulated by a High‐Resolution Global Nonhydrostatic Model Over the United States and China

“…Differences between the microphysics parameterizations used in NICAM and the model evaluated by Feng, Song, et al. (2021) may also contribute to differences in the simulated MCS CCS and precipitation characteristics (Krueger et al., 1995; Lin et al., 2021). For example, cloud microphysics has important control on ice processes in the CCS and hence the CCS size (Feng et al., 2018).…”

“…The opposite biases in models with and without parameterized convection are consistent with Maher et al (2018), who state that model bias may also originate from processes other than convection schemes. Differences between the microphysics parameterizations used in NICAM and the model evaluated by Feng, Song, et al (2021) may also contribute to differences in the simulated MCS CCS and precipitation characteristics (Krueger et al, 1995;Lin et al, 2021). For example, cloud microphysics has important control on ice processes in the CCS and hence the CCS size (Feng et al, 2018).…”

An Improved Representation of Aerosol Wet Removal by Deep Convection and Impacts on Simulated Aerosol Vertical Profiles

“…Wang et al, 2009;Xie & Zhang, 2000), which may contribute to the slight underestimation of sea salt concentrations in the tropical regions (e.g., Figure 9c). Additionally, we need to accurately partition the deep convective cloud condensates into the sedimented and detained portions by improving the parameterizations of precipitating particle fall speeds and convective updraft vertical velocity (Lin et al, 2021). Moreover, we need to replace the double-moment treatment of rain droplet size distribution with a triple-moment one to eliminate the overestimation of drizzle rainfall rate and the underestimation of heavy rainfall rate (Shan, 2018;Shan et al, 2020).…”

Revealing Bias of Cloud Radiative Effect in WRF Simulation: Bias Quantification and Source Attribution

“…One of the reasons could be the systematic underestimation of condensates mass of low-level clouds caused by the shallow convection scheme and PBL scheme (Lin et al, 2015), as discussed right above. Additionally, the current cloud microphysics schemes suffer from the incapability of accurately representing cloud condensate size distribution and microphysical process rates, such as the incapability of the gamma function to represent the wide and truncated raindrop size distributions (Shan et al, 2014(Shan et al, , 2020, and the underestimated solid-phase particle sedimentation and rimming rates (Lin et al, 2021). All the deficiencies in parameterization schemes mentioned above could induce biases in the simulation of cloud microphysics that underestimate total cloud water condensate mass.…”