Journal
JOURNAL OF THE ATMOSPHERIC SCIENCES
Volume 72, Issue 10, Pages 3996-4014Publisher
AMER METEOROLOGICAL SOC
DOI: 10.1175/JAS-D-14-0265.1
Keywords
Cloud microphysics; Climate models; Cloud parameterizations; Model evaluation; performance; Single column models
Categories
Funding
- National Aeronautics and Space Administration (NASA) [NNN13D455T]
- JAXA/EarthCARE project
- Joint DECC/Defra Met Office Hadley Centre Climate Programme [GA01101]
- Department of Energy Office of Science, Decadal and Regional Climate Prediction
- DOE [DE-AC06-76RLO 1830]
- Ministry of Education, Culture, Sports, Science and Technology of Japan [23310014, 23340137]
- Ministry of the Environment of Japan [S-10]
- Grants-in-Aid for Scientific Research [23310014, 23340137] Funding Source: KAKEN
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This study examines the warm rain formation process over the global ocean in global climate models. Methodologies developed to analyze CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations are employed to investigate the cloud-to-precipitation process of warm clouds and are applied to the model results to examine how the models represent the process for warm stratiform clouds. Despite a limitation of the present study that compares the statistics for stratiform clouds in climate models with those from satellite observations, including both stratiform and (shallow) convective clouds, the statistics constructed with the methodologies are compared between the models and satellite observations to expose their similarities and differences. A problem common to some models is that they tend to produce rain at a faster rate than is observed. These model characteristics are further examined in the context of cloud microphysics parameterizations using a simplified one-dimensional model of warm rain formation that isolates key microphysical processes from full interactions with other processes in global climate models. The one-dimensional model equivalent statistics reproduce key characteristics of the global model statistics when corresponding autoconversion schemes are assumed in the one-dimensional model. The global model characteristics depicted by the statistics are then interpreted as reflecting behaviors of the autoconversion parameterizations adopted in the models. Comparisons of the one-dimensional model with satellite observations hint at improvements to the formulation of the parameterization scheme, thus offering a novel way of constraining key parameters in autoconversion schemes of global models.
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