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Title: The importance of considering sub-grid cloud variability when using satellite observations to evaluate the cloud and precipitation simulations in climate models

Abstract

Abstract. Satellite cloud observations have become an indispensable tool for evaluatinggeneral circulation models (GCMs). To facilitate the satellite and GCMcomparisons, the CFMIP (Cloud Feedback Model Inter-comparison Project)Observation Simulator Package (COSP) has been developed and is nowincreasingly used in GCM evaluations. Real-world clouds and precipitation canhave significant sub-grid variations, which, however, are often ignored oroversimplified in the COSP simulation. In this study, we use COSP cloudsimulations from the Super-Parameterized Community Atmosphere Model (SPCAM5)and satellite observations from the Moderate Resolution ImagingSpectroradiometer (MODIS) and CloudSat to demonstrate the importance ofconsidering the sub-grid variability of cloud and precipitation when usingthe COSP to evaluate GCM simulations. We carry out two sensitivity tests:SPCAM5 COSP and SPCAM5-Homogeneous COSP. In the SPCAM5 COSP run, the sub-gridcloud and precipitation properties from the embeddedcloud-resolving model (CRM) of SPCAM5 are used to drive the COSP simulation, while inthe SPCAM5-Homogeneous COSP run only grid-mean cloud and precipitationproperties (i.e., no sub-grid variations) are given to the COSP. We find thatthe warm rain signatures in the SPCAM5 COSP run agree with the MODIS andCloudSat observations quite well. In contrast, the SPCAM5-Homogeneous COSPrun which ignores the sub-grid cloud variations substantially overestimatesthe radar reflectivity and probability of precipitation compared to thesatellite observations, as well as themore » results from the SPCAM5 COSP run. Thesignificant differences between the two COSP runs demonstrate that it isimportant to take into account the sub-grid variations of cloud andprecipitation when using COSP to evaluate the GCM to avoid confusing andmisleading results.« less

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [2];  [2]; ORCiD logo [3]
  1. Univ. of Maryland Baltimore County (UMBC), Baltimore, MD (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Nanjing Univ., Nanjing (China)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of Maryland Baltimore County (UMBC), Baltimore, MD (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1544106
Grant/Contract Number:  
SC0014641; AC06-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
Geoscientific Model Development (Online)
Additional Journal Information:
Journal Name: Geoscientific Model Development (Online); Journal Volume: 11; Journal Issue: 8; Journal ID: ISSN 1991-9603
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Geology

Citation Formats

Song, Hua, Zhang, Zhibo, Ma, Po-Lun, Ghan, Steven, and Wang, Minghuai. The importance of considering sub-grid cloud variability when using satellite observations to evaluate the cloud and precipitation simulations in climate models. United States: N. p., 2018. Web. doi:10.5194/gmd-11-3147-2018.
Song, Hua, Zhang, Zhibo, Ma, Po-Lun, Ghan, Steven, & Wang, Minghuai. The importance of considering sub-grid cloud variability when using satellite observations to evaluate the cloud and precipitation simulations in climate models. United States. doi:10.5194/gmd-11-3147-2018.
Song, Hua, Zhang, Zhibo, Ma, Po-Lun, Ghan, Steven, and Wang, Minghuai. Fri . "The importance of considering sub-grid cloud variability when using satellite observations to evaluate the cloud and precipitation simulations in climate models". United States. doi:10.5194/gmd-11-3147-2018. https://www.osti.gov/servlets/purl/1544106.
@article{osti_1544106,
title = {The importance of considering sub-grid cloud variability when using satellite observations to evaluate the cloud and precipitation simulations in climate models},
author = {Song, Hua and Zhang, Zhibo and Ma, Po-Lun and Ghan, Steven and Wang, Minghuai},
abstractNote = {Abstract. Satellite cloud observations have become an indispensable tool for evaluatinggeneral circulation models (GCMs). To facilitate the satellite and GCMcomparisons, the CFMIP (Cloud Feedback Model Inter-comparison Project)Observation Simulator Package (COSP) has been developed and is nowincreasingly used in GCM evaluations. Real-world clouds and precipitation canhave significant sub-grid variations, which, however, are often ignored oroversimplified in the COSP simulation. In this study, we use COSP cloudsimulations from the Super-Parameterized Community Atmosphere Model (SPCAM5)and satellite observations from the Moderate Resolution ImagingSpectroradiometer (MODIS) and CloudSat to demonstrate the importance ofconsidering the sub-grid variability of cloud and precipitation when usingthe COSP to evaluate GCM simulations. We carry out two sensitivity tests:SPCAM5 COSP and SPCAM5-Homogeneous COSP. In the SPCAM5 COSP run, the sub-gridcloud and precipitation properties from the embeddedcloud-resolving model (CRM) of SPCAM5 are used to drive the COSP simulation, while inthe SPCAM5-Homogeneous COSP run only grid-mean cloud and precipitationproperties (i.e., no sub-grid variations) are given to the COSP. We find thatthe warm rain signatures in the SPCAM5 COSP run agree with the MODIS andCloudSat observations quite well. In contrast, the SPCAM5-Homogeneous COSPrun which ignores the sub-grid cloud variations substantially overestimatesthe radar reflectivity and probability of precipitation compared to thesatellite observations, as well as the results from the SPCAM5 COSP run. Thesignificant differences between the two COSP runs demonstrate that it isimportant to take into account the sub-grid variations of cloud andprecipitation when using COSP to evaluate the GCM to avoid confusing andmisleading results.},
doi = {10.5194/gmd-11-3147-2018},
journal = {Geoscientific Model Development (Online)},
number = 8,
volume = 11,
place = {United States},
year = {2018},
month = {8}
}

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