Convective Transition Statistics over Tropical Oceans for Climate Model Diagnostics: GCM Evaluation

Journal Article · 31 December 2019 · Journal of the Atmospheric Sciences

DOI: https://doi.org/10.1175/jas-d-19-0132.1 · OSTI ID:1802193

Kuo, Yi-Hung ^[1]; Neelin, J. David ^[2]; Chen, Chih-Chieh ^[3]; Chen, Wei-Ting ^[4]; Donner, Leo J. ^[5]; Gettelman, Andrew ^[3]; Jiang, Xianan ^[6]; Kuo, Kuan-Ting ^[4]; Maloney, Eric ^[7]; Mechoso, Carlos R. ^[2]; Ming, Yi ^[5]; Schiro, Kathleen A. ^[2]; Seman, Charles J. ^[5]; Wu, Chien-Ming ^[4]; Zhao, Ming ^[5]

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1. Univ. of California, Los Angeles, CA (United States); University of California, Los Angeles
2. Univ. of California, Los Angeles, CA (United States)
3. National Center for Atmospheric Research, Boulder, CO (United States)
4. National Taiwan Univ., Taipei (Taiwan)
5. Princeton Univ., NJ (United States). Geophysical Fluid Dynamics Laboratory
6. Univ. of California, Los Angeles, CA (United States). Joint Institute for Regional Earth System Science and Engineering
7. Colorado State Univ., Fort Collins, CO (United States)

To assess deep convective parameterizations in a variety of GCMs and examine the fast-time-scale convective transition, a set of statistics characterizing the pickup of precipitation as a function of column water vapor (CWV), PDFs and joint PDFs of CWV and precipitation, and the dependence of the moisture–precipitation relation on tropospheric temperature is evaluated using the hourly output of two versions of the GFDL Atmospheric Model, version 4 (AM4), NCAR CAM5 and superparameterized CAM (SPCAM). The 6-hourly output from the MJO Task Force (MJOTF)/GEWEX Atmospheric System Study (GASS) project is also analyzed. Contrasting statistics produced from individual models that primarily differ in representations of moist convection suggest that convective transition statistics can substantially distinguish differences in convective representation and its interaction with the large-scale flow, while models that differ only in spatial–temporal resolution, microphysics, or ocean–atmosphere coupling result in similar statistics. Most of the models simulate some version of the observed sharp increase in precipitation as CWV exceeds a critical value, as well as that convective onset occurs at higher CWV but at lower column RH as temperature increases. While some models quantitatively capture these observed features and associated probability distributions, considerable intermodel spread and departures from observations in various aspects of the precipitation–CWV relationship are noted. For instance, in many of the models, the transition from the low-CWV, nonprecipitating regime to the moist regime for CWV around and above critical is less abrupt than in observations. Additionally, some models overproduce drizzle at low CWV, and some require CWV higher than observed for strong precipitation. For many of the models, it is particularly challenging to simulate the probability distributions of CWV at high temperature.

• Research Organization: Univ. of California, Los Angeles, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: SC0011074
• OSTI ID: 1802193
• Journal Information: Journal of the Atmospheric Sciences, Journal Name: Journal of the Atmospheric Sciences Journal Issue: 1 Vol. 77; ISSN 0022-4928
• Publisher: American Meteorological SocietyCopyright Statement
• Country of Publication: United States
• Language: English