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Title: Diagnosing Cloud Biases in the GFDL AM3 Model With Atmospheric Classification

Abstract

In this paper, we define a set of 21 atmospheric states, or recurring weather patterns, for a region surrounding the Atmospheric Radiation Measurement Program's Southern Great Plains site using an iterative clustering technique. The states are defined using dynamic and thermodynamic variables from reanalysis, tested for statistical significance with cloud radar data from the Southern Great Plains site, and are determined every 6 h for 14 years, creating a time series of atmospheric state. The states represent the various stages of the progression of synoptic systems through the region (e.g., warm fronts, warm sectors, cold fronts, cold northerly advection, and high-pressure anticyclones) with a subset of states representing summertime conditions with varying degrees of convective activity. We use the states to classify output from the NOAA/Geophysical Fluid Dynamics Laboratory AM3 model to test the model's simulation of the frequency of occurrence of the states and of the cloud occurrence during each state. The model roughly simulates the frequency of occurrence of the states but exhibits systematic cloud occurrence biases. Comparison of observed and model-simulated International Satellite Cloud Climatology Project histograms of cloud top pressure and optical thickness shows that the model lacks high thin cloud under all conditions, but biasesmore » in thick cloud are state-dependent. Frontal conditions in the model do not produce enough thick cloud, while fair-weather conditions produce too much. Finally, we find that increasing the horizontal resolution of the model improves the representation of thick clouds under all conditions but has little effect on high thin clouds. However, increasing resolution also changes the distribution of states, causing an increase in total cloud occurrence bias.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4];  [5]
  1. Princeton Environmental Inst., NJ (United States); Geophysical Fluid Dynamics Lab., Princeton, NJ (United States)
  2. Univ. of Washington, Seattle, WA (United States). Dept. of Atmospheric Sciences; Joint Inst. for the Study of the Atmosphere and Ocean, Seattle, WA (United States)
  3. Geophysical Fluid Dynamics Lab., Princeton, NJ (United States); Program in Atmospheric and Oceanic Sciences, Princeton, NJ (United States)
  4. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  5. Geophysical Fluid Dynamics Lab., Princeton, NJ (United States)
Publication Date:
Research Org.:
Univ. of Washington, Seattle, WA (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Princeton Environmental Inst., NJ (United States); Geophysical Fluid Dynamics Lab., Princeton, NJ (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); Princeton Univ. (United States)
OSTI Identifier:
1424109
Report Number(s):
LLNL-JRNL-731041
Journal ID: ISSN 2169-897X
Grant/Contract Number:
AC52-07NA27344; SC0002472
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Geophysical Research: Atmospheres
Additional Journal Information:
Journal Volume: 122; Journal Issue: 23; Journal ID: ISSN 2169-897X
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; clouds; model evaluation; clustering; classification; GCMs; weather patterns

Citation Formats

Evans, Stuart, Marchand, Roger, Ackerman, Thomas, Donner, Leo, Golaz, Jean-Christophe, and Seman, Charles. Diagnosing Cloud Biases in the GFDL AM3 Model With Atmospheric Classification. United States: N. p., 2017. Web. doi:10.1002/2017JD027163.
Evans, Stuart, Marchand, Roger, Ackerman, Thomas, Donner, Leo, Golaz, Jean-Christophe, & Seman, Charles. Diagnosing Cloud Biases in the GFDL AM3 Model With Atmospheric Classification. United States. doi:10.1002/2017JD027163.
Evans, Stuart, Marchand, Roger, Ackerman, Thomas, Donner, Leo, Golaz, Jean-Christophe, and Seman, Charles. Thu . "Diagnosing Cloud Biases in the GFDL AM3 Model With Atmospheric Classification". United States. doi:10.1002/2017JD027163.
@article{osti_1424109,
title = {Diagnosing Cloud Biases in the GFDL AM3 Model With Atmospheric Classification},
author = {Evans, Stuart and Marchand, Roger and Ackerman, Thomas and Donner, Leo and Golaz, Jean-Christophe and Seman, Charles},
abstractNote = {In this paper, we define a set of 21 atmospheric states, or recurring weather patterns, for a region surrounding the Atmospheric Radiation Measurement Program's Southern Great Plains site using an iterative clustering technique. The states are defined using dynamic and thermodynamic variables from reanalysis, tested for statistical significance with cloud radar data from the Southern Great Plains site, and are determined every 6 h for 14 years, creating a time series of atmospheric state. The states represent the various stages of the progression of synoptic systems through the region (e.g., warm fronts, warm sectors, cold fronts, cold northerly advection, and high-pressure anticyclones) with a subset of states representing summertime conditions with varying degrees of convective activity. We use the states to classify output from the NOAA/Geophysical Fluid Dynamics Laboratory AM3 model to test the model's simulation of the frequency of occurrence of the states and of the cloud occurrence during each state. The model roughly simulates the frequency of occurrence of the states but exhibits systematic cloud occurrence biases. Comparison of observed and model-simulated International Satellite Cloud Climatology Project histograms of cloud top pressure and optical thickness shows that the model lacks high thin cloud under all conditions, but biases in thick cloud are state-dependent. Frontal conditions in the model do not produce enough thick cloud, while fair-weather conditions produce too much. Finally, we find that increasing the horizontal resolution of the model improves the representation of thick clouds under all conditions but has little effect on high thin clouds. However, increasing resolution also changes the distribution of states, causing an increase in total cloud occurrence bias.},
doi = {10.1002/2017JD027163},
journal = {Journal of Geophysical Research: Atmospheres},
number = 23,
volume = 122,
place = {United States},
year = {Thu Nov 16 00:00:00 EST 2017},
month = {Thu Nov 16 00:00:00 EST 2017}
}

Journal Article:
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