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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:
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. https://www.osti.gov/servlets/purl/1424109.
@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 = {2017},
month = {11}
}

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Works referenced in this record:

A review of cloud top height and optical depth histograms from MISR, ISCCP, and MODIS
journal, January 2010

  • Marchand, Roger; Ackerman, Thomas; Smyth, Mike
  • Journal of Geophysical Research, Vol. 115, Issue D16
  • DOI: 10.1029/2009JD013422

Characterizing Observed Midtopped Cloud Regimes Associated with Southern Ocean Shortwave Radiation Biases
journal, August 2014


A Hybrid Cloud Regime Methodology Used to Evaluate Southern Ocean Cloud and Shortwave Radiation Errors in ACCESS
journal, August 2015

  • Mason, Shannon; Fletcher, Jennifer K.; Haynes, John M.
  • Journal of Climate, Vol. 28, Issue 15
  • DOI: 10.1175/JCLI-D-14-00846.1

Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models
journal, January 2005


Accelerating Progress in Global Atmospheric Model Development through Improved Parameterizations: Challenges, Opportunities, and Strategies
journal, July 2010

  • Jakob, Christian
  • Bulletin of the American Meteorological Society, Vol. 91, Issue 7
  • DOI: 10.1175/2009BAMS2898.1

Precipitation and Latent Heating Characteristics of the Major Tropical Western Pacific Cloud Regimes
journal, September 2008


The Atmospheric Radiation Measurement Program
journal, January 2003

  • Ackerman, Thomas P.; Stokes, Gerald M.
  • Physics Today, Vol. 56, Issue 1
  • DOI: 10.1063/1.1554135

Advances in Understanding Clouds from ISCCP
journal, November 1999


Identification and analysis of atmospheric states and associated cloud properties for Darwin, Australia: DYNAMIC STATES AND CLOUDS AT DARWIN
journal, March 2012

  • Evans, S. M.; Marchand, R. T.; Ackerman, T. P.
  • Journal of Geophysical Research: Atmospheres, Vol. 117, Issue D6
  • DOI: 10.1029/2011JD017010

Changes in precipitation with climate change
journal, March 2011


Comparison of Satellite-, Model-, and Radiosonde-Derived Convective Available Potential Energy in the Southern Great Plains Region
journal, May 2017

  • Gartzke, Jessica; Knuteson, Robert; Przybyl, Grace
  • Journal of Applied Meteorology and Climatology, Vol. 56, Issue 5
  • DOI: 10.1175/JAMC-D-16-0267.1

Boundary layer control on convective available potential energy: Implications for cumulus parameterization
journal, November 2003

  • Donner, Leo J.; Phillips, Vaughan T.
  • Journal of Geophysical Research: Atmospheres, Vol. 108, Issue D22
  • DOI: 10.1029/2003JD003773

Convective quasi-equilibrium in midlatitude continental environment and its effect on convective parameterization
journal, January 2002


The Radiative, Cloud, and Thermodynamic Properties of the Major Tropical Western Pacific Cloud Regimes
journal, April 2005

  • Jakob, Christian; Tselioudis, George; Hume, Timothy
  • Journal of Climate, Vol. 18, Issue 8
  • DOI: 10.1175/JCLI3326.1

GCM intercomparison of global cloud regimes: present-day evaluation and climate change response
journal, March 2007


Regime-based evaluation of cloudiness in CMIP5 models
journal, April 2016


The Dynamical Core, Physical Parameterizations, and Basic Simulation Characteristics of the Atmospheric Component AM3 of the GFDL Global Coupled Model CM3
journal, July 2011

  • Donner, Leo J.; Wyman, Bruce L.; Hemler, Richard S.
  • Journal of Climate, Vol. 24, Issue 13
  • DOI: 10.1175/2011JCLI3955.1

A Bootstrap Technique for Testing the Relationship between Local-Scale Radar Observations of Cloud Occurrence and Large-Scale Atmospheric Fields
journal, November 2006

  • Marchand, Roger; Beagley, Nathaniel; Thompson, Sandra E.
  • Journal of the Atmospheric Sciences, Vol. 63, Issue 11
  • DOI: 10.1175/JAS3772.1

Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels
journal, June 2012

  • Zelinka, Mark D.; Klein, Stephen A.; Hartmann, Dennis L.
  • Journal of Climate, Vol. 25, Issue 11
  • DOI: 10.1175/JCLI-D-11-00248.1

Cloud fraction at the ARM SGP site: reducing uncertainty with self-organizing maps
journal, February 2015

  • Kennedy, Aaron D.; Dong, Xiquan; Xi, Baike
  • Theoretical and Applied Climatology, Vol. 124, Issue 1-2
  • DOI: 10.1007/s00704-015-1384-3

Cloud Vertical Distribution across Warm and Cold Fronts in CloudSat –CALIPSO Data and a General Circulation Model
journal, June 2010

  • Naud, Catherine M.; Del Genio, Anthony D.; Bauer, Mike
  • Journal of Climate, Vol. 23, Issue 12
  • DOI: 10.1175/2010JCLI3282.1

Objective Determination of Cloud Heights and Radar Reflectivities Using a Combination of Active Remote Sensors at the ARM CART Sites
journal, May 2000


Cluster analysis of cloud regimes and characteristic dynamics of midlatitude synoptic systems in observations and a model
journal, January 2005


Evaluation of midlatitude cloud properties in a weather and a climate model: Dependence on dynamic regime and spatial resolution
journal, January 2002


Tropical Intraseasonal Variability in Version 3 of the GFDL Atmosphere Model
journal, January 2013


Changes of storm properties in the United States: Observations and multimodel ensemble projections
journal, July 2016


Regimes of the North Australian Wet Season
journal, December 2009

  • Pope, Mick; Jakob, Christian; Reeder, Michael J.
  • Journal of Climate, Vol. 22, Issue 24
  • DOI: 10.1175/2009JCLI3057.1

COSP: Satellite simulation software for model assessment
journal, August 2011

  • Bodas-Salcedo, A.; Webb, M. J.; Bony, S.
  • Bulletin of the American Meteorological Society, Vol. 92, Issue 8
  • DOI: 10.1175/2011BAMS2856.1

Objective Classification of Precipitating Convective Regimes Using a Weather Radar in Darwin, Australia
journal, May 2009

  • Caine, Simon; Jakob, Christian; Siems, Steven
  • Monthly Weather Review, Vol. 137, Issue 5
  • DOI: 10.1175/2008MWR2532.1

The ERA-Interim reanalysis: configuration and performance of the data assimilation system
journal, April 2011

  • Dee, D. P.; Uppala, S. M.; Simmons, A. J.
  • Quarterly Journal of the Royal Meteorological Society, Vol. 137, Issue 656
  • DOI: 10.1002/qj.828

Cluster Analysis of North Atlantic–European Circulation Types and Links with Tropical Pacific Sea Surface Temperatures
journal, August 2008

  • Fereday, D. R.; Knight, J. R.; Scaife, A. A.
  • Journal of Climate, Vol. 21, Issue 15
  • DOI: 10.1175/2007JCLI1875.1

A Systematic Relationship between Intraseasonal Variability and Mean State Bias in AGCM Simulations
journal, November 2011

  • Kim, Daehyun; Sobel, Adam H.; Maloney, Eric D.
  • Journal of Climate, Vol. 24, Issue 21
  • DOI: 10.1175/2011JCLI4177.1

Variability of the Australian Monsoon and Precipitation Trends at Darwin
journal, November 2014


Evaluation of Hydrometeor Occurrence Profiles in the Multiscale Modeling Framework Climate Model Using Atmospheric Classification
journal, September 2009

  • Marchand, Roger; Beagley, Nathaniel; Ackerman, Thomas P.
  • Journal of Climate, Vol. 22, Issue 17
  • DOI: 10.1175/2009JCLI2638.1