A recent study by Agard and Emanuel (2017) proposed a simple equation for a quantity that scales with convective available potential energy (CAPE) that can be directly calculated from a limited number of environmental sounding parameters without lifting a hypothetical air parcel. This scaling CAPE was applied in a specific idealized framework, but the extent to which it can predict true CAPE in the real world has not been tested. This work uses reanalysis data over the U.S to demonstrate that this scaling CAPE does indeed scale very closely with CAPE, following a linear relationship with a scaling factor of 0.44. We then explain why they scale together via a step-by-step derivation of the theoretical assumptions linking scaling CAPE and real CAPE and their manifestation in the historical data. Overall, this work demonstrates that CAPE can be predicted from large-scale environmental parameters alone, which may be useful for a wide range of applications in weather and climate.

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.