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contributor authorSong, Fengfei
contributor authorZhang, Guang J.
date accessioned2019-09-19T10:10:04Z
date available2019-09-19T10:10:04Z
date copyright6/27/2018 12:00:00 AM
date issued2018
identifier otherjcli-d-17-0660.1.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4262291
description abstractAbstractAs 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.
publisherAmerican Meteorological Society
titleUnderstanding and Improving the Scale Dependence of Trigger Functions for Convective Parameterization Using Cloud-Resolving Model Data
typeJournal Paper
journal volume31
journal issue18
journal titleJournal of Climate
identifier doi10.1175/JCLI-D-17-0660.1
journal fristpage7385
journal lastpage7399
treeJournal of Climate:;2018:;volume 031:;issue 018
contenttypeFulltext


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