Bias in CMIP6 historical U.S. severe convective environments driven by bias in mean-state near-surface moist static energy

<p>This database provides data (in NetCDF format) for reproducing figures in the manuscript of Chavas&Li (2022), with the abstract of the manuscript described below.</p>

<p>Abstract: "This work evaluates how well Coupled Model Intercomparison Project 6 (CMIP6) models reproduce the climatology of North American SCS environments in ERA5 reanalysis and examines what drives biases across models. Biases in Springtime SCS environments vary widely in magnitude and spatial pattern, but most models do well in reproducing the climatological pattern and a few also reproduce the overall magnitude. SCS bias is driven by bias in extreme CAPE. This bias is ultimately found to be driven by bias in mean-state near-surface moist static energy (MSE), indicating that the SCS environments depend strongly on the near-surface mean state. Results are broadly similar to Spring across all seasons, particularly Summer. Biases differ strongly across parent models but weakly across child models of the same parent. These outcomes help identify models well-suited for studying climate effects on SCS environments and also provide a foundation for improving model performance in the future."</p>