Biases in U.S. severe convective storm environmentsdriven by biases in mean state near-surface moist static energy across CMIP6 models

<p>This database provides data (in NetCDF format) and code 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 spring SCS environments vary widely in magnitude and spatial pattern, though most models do well in reproducing the climatological pattern and a few (MPI and CNRM) also reproduce the overall magnitude. SCS biases are driven by biases in extreme convective available potential energy. These biases are ultimately found to be driven by biases in mean-state near-surface moist static energy (MSE), indicating that the SCS environments depend strongly on the near-surface mean state. Results are similar for fall, but not summer or winter when free-tropospheric biases are also important. 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."</p>