Reanalyses and a high-resolution model fail to capture the `high tail' of CAPE distributions

TL;DR

This study evaluates CAPE distribution biases in models and reanalyses, revealing a consistent underestimation of extreme CAPE values, which are crucial for severe weather prediction, due to biases in surface temperature and humidity.

Contribution

It provides the first large-scale systematic assessment of CAPE biases in models and reanalyses, highlighting the underrepresentation of the high tail of CAPE distributions.

Findings

Reanalyses and models underestimate the high tail of CAPE by up to 20%.

Biases in surface temperature and humidity drive CAPE underestimation.

Reducing land surface and boundary layer model errors is essential for accurate CAPE simulation.

Abstract

Convective available potential energy (CAPE) is of strong interest in climate modeling because of its role in both severe weather and in model construction. Extreme levels of CAPE ($>$ 2000 J/kg) are associated with high-impact weather events, and CAPE is widely used in convective parametrizations to help determine the strength and timing of convection. However, to date no study has systematically evaluated CAPE biases in models in a climatological context, in an assessment large enough to characterize the high tail of the CAPE distribution. This work compares CAPE distributions in over 200,000 summertime proximity soundings from four sources: the observational radiosonde network (IGRA), 0.125 degree reanalysis (ERA-Interim and ERA5), and a 4 km convection-permitting regional WRF simulation driven by ERA-Interim. Both reanalyses and model consistently show too-narrow distributions of CAPE, with the high tail ($>$ 95th percentile) systematically biased low by up to 10% in surface-based CAPE and 20% at the most unstable layer. This "missing tail" corresponds to the most impacts-relevant conditions. CAPE bias in all datasets is driven by bias in surface temperature and humidity: reanalyses and model undersample observed cases of extreme heat and moisture. These results suggest that reducing inaccuracies in land surface and boundary layer models is critical for accurately reproducing CAPE.