  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.
