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Reanalyses and a High-Resolution Model Fail to Capture the “High Tail” of CAPE Distributions

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  • 1 aDepartment of the Geophysical Sciences, University of Chicago, Chicago, Illinois
  • | 2 bCenter for Robust Decision-making on Climate and Energy Policy, University of Chicago, Chicago, Illinois
  • | 3 cCollege of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
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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−1) are associated with high-impact weather events, and CAPE is widely used in convective parameterizations to help determine the strength and timing of convection. However, to date few studies have systematically evaluated CAPE biases in models in a climatological context, and none have addressed bias in the high tail of CAPE distributions. This work compares CAPE distributions in ~200 000 summertime proximity soundings from four sources: the observational radiosonde network [Integrated Global Radiosonde Archive (IGRA)], 0.125° reanalyses (ERA-Interim and ERA5), and a 4-km convection-permitting regional WRF simulation driven by ERA-Interim. Both reanalyses and the WRF Model consistently show too-narrow distributions of CAPE, with the high tail (>90th percentile) systematically biased low by up to 10% in surface-based CAPE and even more in alternate CAPE definitions. This “missing tail” corresponds to the most impacts-relevant conditions. CAPE bias in all datasets is driven by surface temperature and humidity: reanalyses and the WRF Model underpredict 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.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Elisabeth J. Moyer, moyer@uchicago.edu

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−1) are associated with high-impact weather events, and CAPE is widely used in convective parameterizations to help determine the strength and timing of convection. However, to date few studies have systematically evaluated CAPE biases in models in a climatological context, and none have addressed bias in the high tail of CAPE distributions. This work compares CAPE distributions in ~200 000 summertime proximity soundings from four sources: the observational radiosonde network [Integrated Global Radiosonde Archive (IGRA)], 0.125° reanalyses (ERA-Interim and ERA5), and a 4-km convection-permitting regional WRF simulation driven by ERA-Interim. Both reanalyses and the WRF Model consistently show too-narrow distributions of CAPE, with the high tail (>90th percentile) systematically biased low by up to 10% in surface-based CAPE and even more in alternate CAPE definitions. This “missing tail” corresponds to the most impacts-relevant conditions. CAPE bias in all datasets is driven by surface temperature and humidity: reanalyses and the WRF Model underpredict 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.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Elisabeth J. Moyer, moyer@uchicago.edu

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