Too Frequent and Too Light Arctic Snowfall With Incorrect Precipitation Phase Partitioning in the MIROC6 GCM

Cloud-phase partitioning has been studied in the context of cloud feedback and climate sensitivity; however, precipitation-phase partitioning also has a significant role in controlling the energy budget and sea ice extent. Although some global models have introduced a more sophisticated precipitation parameterization to reproduce realistic cloud and precipitation processes, the effects on the process representation of mixed- and ice-phase precipitation are poorly understood. Here, we evaluate how different precipitation modeling (i.e., diagnostic [DIAG] vs. prognostic [PROG] schemes) affects the simulated precipitation phase and occurrence frequency. Two versions of MIROC6 were used with the satellite simulator COSP2. Although the PROG scheme significantly improves the simulated cloud amount and snowfall rates, the phase partitioning, frequency, and intensity of precipitation with the PROG scheme are still biased, and are even worse than with the DIAG scheme. We found a "too frequent and too light" Arctic snowfall bias in the PROG, which cannot be eliminated by model tuning. The cloud-phase partitioning is also affected by the different approaches used to consider precipitation. The ratio of supercooled liquid water is underrepresented by switching from the DIAG to PROG scheme, because some snowflakes are regarded to be cloud ice. Given that the PROG precipitation retains more snow in the atmosphere, the underestimation becomes apparent when other models incorporate the PROG scheme. This depends on how much precipitation is within the clouds in the model. Our findings emphasize the importance of correctly reproducing the phase partitioning of cloud and precipitation, which ultimately affects the simulated climate sensitivity. Plain Language Summary This study examined cloud and precipitation phase partitioning (i.e., the ratio between liquid and ice) in the Arctic using the MIROC6 global climate model (GCM). Despite recent advances in precipitation modeling by GCMs, the associations between the macrostructures (i.e., cloud coverage and precipitation rate) and phase partitioning have been little studied. Prognostic treatment of precipitation, which is a more sophisticated parameterization, yields seasonal and annual cloud cover and snowfall that are in better agreement with satellite observations. However, it tends to generate snowfall too frequently and too lightly, resulting in the misrepresentation of precipitation phase partitioning. In addition, there is a risk of overestimating the ratio of cloud ice to cloud liquid by including prognostic precipitation. The bias is difficult to remove by model tuning alone. If the models misrepresent the precipitation phase partitioning, then the bias will further influence feedback processes in a future warming scenario through the snow-to-rain phase change, similar to the cloud phase feedback. Our findings emphasize the importance of conducting process-oriented model evaluations on a regional scale.