Simulating Clouds with Global Climate Models: A Comparison of CMIP5 Results with CMIP3 and Satellite Data

Axel Lauer International Pacific Research Center, University of Hawaii at Manoa, Honolulu, Hawaii

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Kevin Hamilton International Pacific Research Center, and Department of Meteorology, University of Hawaii at Manoa, Honolulu, Hawaii

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Abstract

Clouds are a key component of the climate system affecting radiative balances and the hydrological cycle. Previous studies from the Coupled Model Intercomparison Project phase 3 (CMIP3) showed quite large biases in the simulated cloud climatology affecting all GCMs as well as a remarkable degree of variation among the models that represented the state of the art circa 2005. Here the progress that has been made in recent years is measured by comparing mean cloud properties, interannual variability, and the climatological seasonal cycle from the CMIP5 models with satellite observations and with results from comparable CMIP3 experiments. The focus is on three climate-relevant cloud parameters: cloud amount, liquid water path, and cloud radiative forcing. The comparison shows that intermodel differences are still large in the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations, and reveals some small improvements of particular cloud properties in some regions in the CMIP5 ensemble over CMIP3. In CMIP5 there is an improved agreement of the modeled interannual variability of liquid water path and of the modeled longwave cloud forcing over mid- and high-latitude oceans with observations. However, the differences in the simulated cloud climatology from CMIP3 and CMIP5 are generally small, and there is very little to no improvement apparent in the tropical and subtropical regions in CMIP5.

Comparisons of the results from the coupled CMIP5 models with their atmosphere-only versions run with observed SSTs show remarkably similar biases in the simulated cloud climatologies. This suggests the treatments of subgrid-scale cloud and boundary layer processes are directly implicated in the poor performance of current GCMs in simulating realistic cloud fields.

Corresponding author address: Axel Lauer, IPRC, SOEST, University of Hawaii at Manoa, 1680 East–West Rd., POST Bldg. 401, Honolulu, HI 96822. E-mail: lauera@hawaii.edu

Abstract

Clouds are a key component of the climate system affecting radiative balances and the hydrological cycle. Previous studies from the Coupled Model Intercomparison Project phase 3 (CMIP3) showed quite large biases in the simulated cloud climatology affecting all GCMs as well as a remarkable degree of variation among the models that represented the state of the art circa 2005. Here the progress that has been made in recent years is measured by comparing mean cloud properties, interannual variability, and the climatological seasonal cycle from the CMIP5 models with satellite observations and with results from comparable CMIP3 experiments. The focus is on three climate-relevant cloud parameters: cloud amount, liquid water path, and cloud radiative forcing. The comparison shows that intermodel differences are still large in the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations, and reveals some small improvements of particular cloud properties in some regions in the CMIP5 ensemble over CMIP3. In CMIP5 there is an improved agreement of the modeled interannual variability of liquid water path and of the modeled longwave cloud forcing over mid- and high-latitude oceans with observations. However, the differences in the simulated cloud climatology from CMIP3 and CMIP5 are generally small, and there is very little to no improvement apparent in the tropical and subtropical regions in CMIP5.

Comparisons of the results from the coupled CMIP5 models with their atmosphere-only versions run with observed SSTs show remarkably similar biases in the simulated cloud climatologies. This suggests the treatments of subgrid-scale cloud and boundary layer processes are directly implicated in the poor performance of current GCMs in simulating realistic cloud fields.

Corresponding author address: Axel Lauer, IPRC, SOEST, University of Hawaii at Manoa, 1680 East–West Rd., POST Bldg. 401, Honolulu, HI 96822. E-mail: lauera@hawaii.edu
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  • Alekseev, V. A., E. M. Volodin, V. Ya. Galin, V. P. Dymnikov, and V. N. Lykossov, 1998: Modelling of the present-day climate by the atmospheric model of INM RAS “DNM GCM.” INM Tech. Rep. N2086-B98, Institute of Numerical Mathematics, Russian Academy of Sciences, 208 pp.

  • Arora, V. K., and Coauthors, 2011: Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys. Res. Lett., 38, L05805, doi:10.1029/2010GL046270.

    • Search Google Scholar
    • Export Citation
  • Barkstrom, B. R., 1984: The Earth Radiation Budget Experiment (ERBE). Bull. Amer. Meteor. Soc., 65, 11701185.

  • Bleck, R., 2002: An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates. Ocean Modell., 4, 5588.

  • Bodas-Salcedo, A., and Coauthors, 2011: COSP: Satellite simulation software for model assessment. Bull. Amer. Meteor. Soc., 92, 10231043.

    • Search Google Scholar
    • Export Citation
  • Bony, S., 1997: Sea surface temperature and large-scale circulation influences on tropical greenhouse effect and cloud radiative forcing. J. Climate, 10, 20552077.

    • Search Google Scholar
    • Export Citation
  • Bony, S., and J.-L. Dufresne, 2005: Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys. Res. Lett., 32, L20806, doi:10.1029/2005GL023851.

    • Search Google Scholar
    • Export Citation
  • Bony, S., M. Webb, C. Bretherton, S. A. Klein, P. Siebesma, G. Tselioudis, and M. Zhang, 2011: CFMIP: Towards a better evaluation and understanding of clouds and cloud feedbacks in CMIP5 models. CLIVAR Exchanges, Vol. 2, No. 56, International CLIVAR Project Office, Southampton, United Kingdom, 20–24.

  • Cess, R. D., and Coauthors, 1990: Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J. Geophys. Res., 95 (D10), 16 60116 615.

    • Search Google Scholar
    • Export Citation
  • Chen, W.-T., C. P. Woods, J.-L. F. Li, D. E. Waliser, J.-D. Chern, W.-K. Tao, J. H. Jiang, and A. M. Tompkins, 2011: Partitioning CloudSat ice water content for comparison with upper tropospheric ice in global atmospheric models. J. Geophys. Res., 116, D19206, doi:10.1029/2010JD015179.

    • Search Google Scholar
    • Export Citation
  • Collins, M., S. F. B. Tett, and C. Cooper, 2001: The internal climate variability of HadCM3, a version of the Hadley Centre Coupled Model without flux adjustments. Climate Dyn., 17, 6181.

    • Search Google Scholar
    • Export Citation
  • Collins, W. D., and Coauthors, 2006: The Community Climate System Model: CCSM3. J. Climate, 19, 21222143.

  • Collins, W. J., and Coauthors, 2008: Evaluation of the HadGEM2 model. Hadley Center Tech. Note 74, 47 pp. [Available online at http://www.metoffice.gov.uk/media/pdf/8/7/HCTN_74.pdf.]

  • Collins, W. J., and Coauthors, 2011: Development and evaluation of an earth-system model—HadGEM2. Geosci. Model Dev., 4, 10511075, doi:10.5194/gmd-4-1051-2011.

    • Search Google Scholar
    • Export Citation
  • Donner, L. J., and Coauthors, 2011: The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J. Climate, 24, 34833519.

    • Search Google Scholar
    • Export Citation
  • Dufresne J.-L., and Coauthors, 2013: Climate change projections using the IPSL-CM5 earth system model: From CMIP3 to CMIP5. Climate Dyn., 40, 21232165 , doi:10.1007/s00382-012-1636-1.

    • Search Google Scholar
    • Export Citation
  • Flato, G. M., 2005: The third generation Coupled Global Climate Model. [Available online at http://www.ec.gc.ca/ccmac-cccma/default.asp?lang=En&n=1299529F-1.]

  • Galin, V. Ya., E. M. Volodin, and S. P. Smyshliaev, 2003: Atmosphere general circulation model of INM RAS with ozone dynamics. Russ. Meteor. Hydrol., 5, 1322.

    • Search Google Scholar
    • Export Citation
  • Gent, P. R., and Coauthors, 2011: The Community Climate System Model version 4. J. Climate, 24, 49734991.

  • GFDL Global Atmospheric Model Development Team, 2004: The new GFDL global atmosphere and land model AM2–LM2: Evaluation with prescribed SST simulations. J. Climate, 17, 46414673.

    • Search Google Scholar
    • Export Citation
  • Gordon, C., C. Cooper, C. A. Senior, H. T. Banks, J. M. Gregory, T. C. Johns, J. F. B. Mitchell, and R. A. Wood, 2000: The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Climate Dyn., 16, 147168.

    • Search Google Scholar
    • Export Citation
  • Gordon, H. B., and Coauthors, 2002: The CSIRO Mk3 Climate System Model. CSIRO Atmospheric Research Tech. Paper 60, 130 pp. [Available online at http://www.cmar.csiro.au/e-print/open/gordon_2002a.pdf.]

  • Hartmann, D. L., and D. Doelling, 1991: On the net radiative effectiveness of clouds. J. Geophys. Res., 96 (D1), 869891.

  • Hasumi, H., and S. Emori, Eds., 2004: K-l coupled GCM (MIROC) description. K-l Tech. Rep. 1, Center for Climate System Research, University of Tokyo, Tokyo, Japan, 34 pp. [Available online at http://www.ccsr.u-tokyo.ac.jp/kyosei/hasumi/MIROC/tech-repo.pdf.]

  • Hourdin, F., and Coauthors, 2006: The LMDZ4 general circulation model: Climate performance and sensitivity to parameterized physics with emphasis on tropical convection. Climate Dyn., 27, 787813.

    • Search Google Scholar
    • Export Citation
  • Hourdin, F., and Coauthors, 2013: Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model. Climate Dyn., 40, 21672192, doi:10.1007/s00382-012-1411-3, in press.

    • Search Google Scholar
    • Export Citation
  • Jiang, J. H., and Coauthors, 2012: Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A-Train” satellite observations. J. Geophys. Res., 117, D14105, doi:10.1029/2011JD017237.

    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and Coauthors, 2012: Exposing global cloud biases in the Community Atmosphere Model (CAM) using satellite observations and their corresponding instrument simulators. J. Climate, 25, 51905207.

    • Search Google Scholar
    • Export Citation
  • Kirkevåg, A., and Coauthors, 2013: Aerosol–climate interactions in the Norwegian earth system model—NorESM1-M. Geosci. Model Dev., 6, 207244, doi:10.5194/gmd-6-207-2013.

    • Search Google Scholar
    • Export Citation
  • Lauer, A., K. Hamilton, Y. Wang, V. T. J. Phillips, and R. Bennartz, 2010: The impact of global warming on marine boundary layer clouds over the eastern Pacific—A regional model study. J. Climate, 23, 58445863.

    • Search Google Scholar
    • Export Citation
  • Li, L., and Coauthors, 2010: Development and evaluation of GAMIL2.0 and FGOALS2.0-g. Preprints, Fifth C20C Workshop, Beijing, China, IAP, Chinese Academy of Sciences, 29 pp. [Available online at http://www.lasg.ac.cn/C20C/UserFiles/File/c20c-ljli.pdf.]

  • Lin, J.-L., 2007: The double-ITCZ problem in IPCC AR4 coupled GCMs: Ocean–atmosphere feedback analysis. J. Climate, 20, 44974525.

  • Martin, G. M., M. A. Ringer, V. D. Pope, A. Jones, C. Dearden, and T. J. Hinton, 2006: The physical properties of the atmosphere in the new Hadley Centre Global Environmental Model, HadGEMl. Part I: Model description and global climatology. J. Climate, 19, 12741301.

    • Search Google Scholar
    • Export Citation
  • McFarlane, N. A., G. J. Boer, J.-P. Blanchet, and M. Lazare, 1992: The Canadian Climate Centre second-generation general circulation model and its equilibrium climate. J. Climate, 5, 10131044.

    • Search Google Scholar
    • Export Citation
  • Medeiros, B., B. Stevens, I. M. Held, M. Zhao, D. L. Williamson, J. G. Olson, and C. S. Bretherton, 2008: Aquaplanets, climate sensitivity, and low clouds. J. Climate, 21, 49744991.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., C. Covery, T. Delworth, M. Latif, B. McAvaney, J. F. B. Mitchell, R. J. Stouffer, and K. E. Taylor, 2007: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc., 88, 13831394.

    • Search Google Scholar
    • Export Citation
  • Mizuta, R., and Coauthors, 2012: Climate simulations using MRI-AGCM3.2 with 20-km grid. J. Meteor. Soc. Japan, 90, 233258.

  • Nam, C. C. W., and J. Quaas, 2012: Evaluation of clouds and precipitation in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data. J. Climate, 25, 49754992.

    • Search Google Scholar
    • Export Citation
  • O’Dell, C. W., F. J. Wentz, and R. Bennartz, 2008: Cloud liquid water path from satellite-based passive microwave observations: A new climatology over the global oceans. J. Climate, 21, 17211739.

    • Search Google Scholar
    • Export Citation
  • Park, S., and C. B. Leovy, 2004: Marine low-cloud anomalies associated with ENSO. J. Climate, 17, 34483469.

  • Pope, V., M. L. Gallani, P. R. Rowntree, and R. A. Stratton, 2000: The impact of new physical parameterizations in the Hadley Centre climate model: HadAM3. Climate Dyn., 16, 123146.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. S. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 54735496.

    • Search Google Scholar
    • Export Citation
  • Roeckner, E., and Coauthors, 1996: The atmospheric general circulation model ECHAM4: Model description and simulation of present-day climate. Rep. 218, Max Planck Institute for Meteorology, Hamburg, Germany, 90 pp. [Available online at http://www.mpimet.mpg.de/fileadmin/publikationen/Reports/MPI-Report_218.pdf.]

  • Roeckner, E., and Coauthors, 2003: The atmospheric general circulation model ECHAM5. Part I: Model description. Rep. 349, Max Planck Institute, Hamburg, Germany, 127 pp. [Available online at http://www.mpimet.mpg.de/fileadmin/publikationen/Reports/max_scirep_349.pdf.]

  • Roeckner, E., and Coauthors, 2006: Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model. J. Climate, 19, 37713791.

    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., and L. C. Garder, 1993: Validation of ISCCP cloud detections. J. Climate, 6, 23702393.

  • Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80, 22612287.

  • Rossow, W. B., A. W. Walker, D. E. Beuschel, and M. D. Roiter, 1996: International Cloud Climatology Project (ISCCP) documentation of new cloud datasets. WMO/TD-737, 115 pp.

  • Rotstayn, L. D., M. A. Collier, M. R. Dix, Y. Feng, H. B. Gordon, S. P. O’Farrell, I. N. Smith, and J. Syktus, 2010: Improved simulation of Australian climate and ENSO-related climate variability in a GCM with an interactive aerosol treatment. Int. J. Climatol., 30, 10671088, doi:10.1002/joc.1952.

    • Search Google Scholar
    • Export Citation
  • Russell, G. L., J. R. Miller, and D. Rind, 1995: A coupled atmosphere–ocean model for transient climate change studies. Atmos.–Ocean, 33, 683730.

    • Search Google Scholar
    • Export Citation
  • Sakamoto, T. T., and Coauthors, 2012: MIROC4h—A new high-resolution atmosphere–ocean coupled general circulation model. J. Meteor. Soc. Japan, 90, 325359.

    • Search Google Scholar
    • Export Citation
  • Salas-Mélia, D., and Coauthors, 2005: Description and validation of the CNRM-CM3 global coupled model. CNRM Working Note 103, 36 pp. [Available online at http://www.cnrm.meteo.fr/scenario2004/paper_cm3.pdf.]

  • Schmidt, G. A., and Coauthors, 2006: Present-day atmospheric simulations using GISS ModelE: Comparison to in situ, satellite, and reanalysis data. J. Climate, 19, 153192.

    • Search Google Scholar
    • Export Citation
  • Solomon, S., D. Qin, M. Manning, M. Marquis, K. Averyt, M. M. B. Tignor, H. L. Miller Jr., and Z. Chen, Eds., 2007: Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.

  • Stowasser, M., K. Hamilton, and G. J. Boer, 2006: Local and global climate feedbacks in models with differing climate sensitivities. J. Climate, 19, 193209.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106 (D7), 71837192.

  • Taylor, K. E., R. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498.

    • Search Google Scholar
    • Export Citation
  • Voldoire, A., and Coauthors, 2013: The CNRM-CM5.1 global climate model: Description and basic evaluation. Climate Dyn., 40, 20912121, doi:10.1007/s00382-011-1259-y.

    • Search Google Scholar
    • Export Citation
  • Volodin, E. M., N. A. Dianskii, and A. V. Gusev, 2010: Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations. Atmos. Oceanic Phys., 46, 414431, doi:10.1134/S000143381004002X.

    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., K.-W. Seo, S. Schubert, and E. Njoku, 2007: Global water cycle agreement in the climate models assessed in the IPCC AR4. Geophys. Res. Lett., 34, L16705, doi:10.1029/2007GL030675.

    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., and Coauthors, 2009: Cloud ice: A climate model challenge with signs and expectations of progress. J. Geophys. Res., 114, D00A21, doi:10.1029/2008JD010015.

    • Search Google Scholar
    • Export Citation
  • Washington, W. M., and Coauthors, 2000: Parallel climate model (PCM) control and transient simulations. Climate Dyn., 16, 755774.

  • Watanabe, M., and Coauthors, 2010: Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Climate, 23, 63126335.

    • Search Google Scholar
    • Export Citation
  • Watanabe, S., and Coauthors, 2011: MIROC-ESM 2010: Model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev., 4, 845872.

    • Search Google Scholar
    • Export Citation
  • Weare, B. C., 2004: A comparison of AMIP II model cloud layer properties with ISCCP D2 estimates. Climate Dyn., 22, 281292.

  • Wetzel, P., E. Maier-Reimer, M. Botzet, J. Jungclaus, N. Keenlyside, and M. Latif, 2006: Effects of ocean biology on the penetrative radiation in a coupled climate model. J. Climate, 19, 39733987.

    • Search Google Scholar
    • Export Citation
  • Wielicki, B. A., B. R. Barkstrom, E. F. Harrison, R. B. Lee III, G. L. Smith, and J. E. Cooper, 1996: Clouds and the Earth’s Radiant Energy System (CERES): An Earth Observing System experiment. Bull. Amer. Meteor. Soc., 77, 853868.

    • Search Google Scholar
    • Export Citation
  • Wu, T., and Coauthors, 2010: The Beijing Climate Center for Atmospheric General Circulation Model (BCC-AGCM2.0.1): Description and its performance for the present-day climate. Climate Dyn., 34, 123147.

    • Search Google Scholar
    • Export Citation
  • Yu, Y., X. Zhang, and Y. Guo, 2004: Global coupled ocean–atmosphere general circulation models in LASG/IAP. Adv. Atmos. Sci., 21, 444455.

    • Search Google Scholar
    • Export Citation
  • Yukimoto, S., and Coauthors, 2006: Present-day climate and climate sensitivity in the Meteorological Research Institute Coupled GCM version 2.3 (MRI-CGCM2.3). J. Meteor. Soc. Japan, 84, 333363.

    • Search Google Scholar
    • Export Citation
  • Yukimoto, S., and Coauthors, 2011: Meteorological Research Institute–Earth System Model version 1 (MRI-ESM1): Model description. Tech. Rep. 64, Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan, 83 pp.

  • Zhang, M. H., and Coauthors, 2005: Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements. J. Geophys. Res., 110, D15S02, doi:10.1029/2004JD005021.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., W. B. Rossow, A. A. Lacis, V. Oinas, and M. I. Mishchenko, 2004: Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data. J. Geophys. Res., 109, D19105, doi:10.1029/2003JD004457.

    • Search Google Scholar
    • Export Citation
  • Zhao, M., I. M. Held, S.-J. Lin, and G. A. Vecchi, 2009: Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J. Climate, 22, 66536678.

    • Search Google Scholar
    • Export Citation
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