Understanding and Improving the Scale Dependence of Trigger Functions for Convective Parameterization Using Cloud-Resolving Model Data

Fengfei Song Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California

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Guang J. Zhang Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California

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Abstract

As the resolution of global climate model increases, whether trigger functions in current convective parameterization schemes still work remains unknown. In this study, the scale dependence of undilute and dilute dCAPE, Bechtold, and heated condensation framework (HCF) triggers is evaluated using the cloud-resolving model (CRM) data. It is found that all these trigger functions are scale dependent, especially for dCAPE-type triggers, with skill scores dropping from ~0.6 at the lower resolutions (128, 64, and 32 km) to only ~0.1 at 4 km. The average convection frequency decreases from 14.1% at 128 km to 2.3% at 4 km in the CRM data, but it increases rapidly in the dCAPE-type triggers and is almost unchanged in the Bechtold and HCF triggers across resolutions, all leading to large overpredictions at higher resolutions. In the dCAPE-type triggers, the increased frequency is due to the increased rate of dCAPE greater than the threshold (65 J kg−1 h−1) at higher resolutions. The box-and-whisker plots show that the main body of dCAPE in the correct prediction and overprediction can be separated from each other in most resolutions. Moreover, the underprediction is found to be corresponding to the decaying phase of convection. Hence, two modifications are proposed to improve the scale dependence of the undilute dCAPE trigger: 1) increasing the dCAPE threshold and 2) considering convection history, which checks whether there is convection prior to the current time. With these modifications, the skill at 16 km, 8 km, and 4 km can be increased from 0.50, 0.27, and 0.15 to 0.70, 0.61, and 0.53, respectively.

Current affiliation: Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington.

© 2018 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: Fengfei Song, songfengfei@gmail.com

Abstract

As the resolution of global climate model increases, whether trigger functions in current convective parameterization schemes still work remains unknown. In this study, the scale dependence of undilute and dilute dCAPE, Bechtold, and heated condensation framework (HCF) triggers is evaluated using the cloud-resolving model (CRM) data. It is found that all these trigger functions are scale dependent, especially for dCAPE-type triggers, with skill scores dropping from ~0.6 at the lower resolutions (128, 64, and 32 km) to only ~0.1 at 4 km. The average convection frequency decreases from 14.1% at 128 km to 2.3% at 4 km in the CRM data, but it increases rapidly in the dCAPE-type triggers and is almost unchanged in the Bechtold and HCF triggers across resolutions, all leading to large overpredictions at higher resolutions. In the dCAPE-type triggers, the increased frequency is due to the increased rate of dCAPE greater than the threshold (65 J kg−1 h−1) at higher resolutions. The box-and-whisker plots show that the main body of dCAPE in the correct prediction and overprediction can be separated from each other in most resolutions. Moreover, the underprediction is found to be corresponding to the decaying phase of convection. Hence, two modifications are proposed to improve the scale dependence of the undilute dCAPE trigger: 1) increasing the dCAPE threshold and 2) considering convection history, which checks whether there is convection prior to the current time. With these modifications, the skill at 16 km, 8 km, and 4 km can be increased from 0.50, 0.27, and 0.15 to 0.70, 0.61, and 0.53, respectively.

Current affiliation: Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington.

© 2018 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: Fengfei Song, songfengfei@gmail.com
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