Data for the publication "Significant Increase in Graupel and Lightning Occurrence in a Warmer Climate Simulated by Prognostic Graupel Parameterization"

This dataset includes a set of 11yr simulations using the MIROC6 global aerosol-climate model under the pre-industrial (PI, aerosol emission at the year 1850), present-day (PD, aerosol emission at the year 2000), and future warming (SST+4K, a uniform 4 K increase in sea surface temperature) conditions. The data are used in the manuscript entitled "Significant Increase in Graupel and Lightning Occurrence in a Warmer Climate Simulated by Prognostic Graupel Parameterization"

Significant increase in graupel and lightning occurrence in a warmer climate simulated by prognostic graupel parameterization

Abstract There is little consensus among global climate models (CGMs) regarding the response of lightning flash rates to past and future climate change, largely due to graupel not being included in models. Here a two-moment prognostic graupel scheme was incorporated into the MIROC6 GCM and applied in three experiments involving pre-industrial aerosol, present-day, and future warming simulations. The new microphysics scheme performed well in reproducing global distributions of graupel, convective available potential energy, and lightning flash rate against satellite retrievals and reanalysis datasets. The global mean lightning rate increased by 7.1% from the pre-industrial period to the present day, which was attributed to increased graupel occurrence. The impact of future warming on lightning activity was more evident, with the rate increasing by 18.4 $$\%\,\textrm{K}^{-1}$$ % K - 1 through synergistic contributions of destabilization and increased graupel. In the Arctic, the lightning rate depends strongly on the seasonality of graupel, emphasizing the need to incorporate graupel into GCMs for more accurate climate prediction

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

cbeall123/COSPv2.0: UpdatedWRDs

Updated warm rain diagnostics as featured in: Beall, Charlotte M.; Ma, Po-Lun; Christensen, Matthew W.; Mülmenstädt, Johannes; Varble, Adam; Suzuki, Kentaroh; Michibata, Takuro (2023). "Droplet collection efficiencies estimated from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions." Submitted to Atmospheric Chemistry and Physics