Assessing Surface Solar Radiation Fluxes in the CMIP Ensembles

Alexander Loew Ludwig-Maximilians-Universität München, Munich, and Max Planck Institute for Meteorology, Hamburg, Germany

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Axel Andersson Deutscher Wetterdienst, Offenbach, Germany

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Jörg Trentmann Satellite-Based Climate Monitoring, Deutscher Wetterdienst, Offenbach, Germany

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Marc Schröder Satellite-Based Climate Monitoring, Deutscher Wetterdienst, Offenbach, Germany

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Abstract

Earth system models are indispensable tools in climate studies. The Coupled Model Intercomparison Project (CMIP) is a coordinated effort of the Earth system modeling community to intercompare existing models. An accurate simulation of surface solar radiation fluxes is of major importance for the accuracy of simulations of the near-surface climate in Earth system models. The present study provides a quantitative assessment of the accuracy and multidecadal changes of surface solar radiation fluxes for model results from two phases of CMIP. The entire archives of phase 5 of CMIP (CMIP5) and its predecessor phase 3 (CMIP3) are analyzed for present-day climate conditions. A relative model ranking is provided, and its uncertainty is quantified using different global observational records. It is shown that the choice of an observational dataset can have a major influence on relative model ranking between CMIP models. However the multidecadal variability of surface solar radiation fluxes, also known as global “dimming” or “brightening,” is largely underestimated by the CMIP models.

Corresponding author address: Alexander Loew, Ludwig-Maximilians-Universität München, Luisenstr. 37, 80333 Munich, Germany. E-mail: alexander.loew@lmu.de

Abstract

Earth system models are indispensable tools in climate studies. The Coupled Model Intercomparison Project (CMIP) is a coordinated effort of the Earth system modeling community to intercompare existing models. An accurate simulation of surface solar radiation fluxes is of major importance for the accuracy of simulations of the near-surface climate in Earth system models. The present study provides a quantitative assessment of the accuracy and multidecadal changes of surface solar radiation fluxes for model results from two phases of CMIP. The entire archives of phase 5 of CMIP (CMIP5) and its predecessor phase 3 (CMIP3) are analyzed for present-day climate conditions. A relative model ranking is provided, and its uncertainty is quantified using different global observational records. It is shown that the choice of an observational dataset can have a major influence on relative model ranking between CMIP models. However the multidecadal variability of surface solar radiation fluxes, also known as global “dimming” or “brightening,” is largely underestimated by the CMIP models.

Corresponding author address: Alexander Loew, Ludwig-Maximilians-Universität München, Luisenstr. 37, 80333 Munich, Germany. E-mail: alexander.loew@lmu.de
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  • Abramowitz, G., 2012: Towards a public, standardized, diagnostic benchmarking system for land surface models. Geosci. Model Dev., 5, 819827, doi:10.5194/gmd-5-819-2012.

    • Search Google Scholar
    • Export Citation
  • Abramowitz, G., R. Leuning, M. Clark, and A. Pitman, 2008: Evaluating the performance of land surface models. J. Climate, 21, 54685481, doi:10.1175/2008JCLI2378.1.

    • Search Google Scholar
    • Export Citation
  • Allen, R. J., J. R. Norris, and M. Wild, 2013: Evaluation of multidecadal variability in CMIP5 surface solar radiation and inferred underestimation of aerosol direct effects over Europe, China, Japan, and India. J. Geophys. Res. Atmos., 118, 63116336, doi:10.1002/jgrd.50426.

    • Search Google Scholar
    • Export Citation
  • Anav, A., G. Murray-Tortarolo, P. Friedlingstein, S. Sitch, S. Piao, and Z. Zhu, 2013: Evaluation of land surface models in reproducing satellite derived leaf area index over the high-latitude Northern Hemisphere. Part II: Earth system models. Remote Sens., 5, 36373661, doi:10.3390/rs5083637.

    • Search Google Scholar
    • Export Citation
  • Blyth, E., D. B. Clark, R. Ellis, C. Huntingford, S. Los, M. Pryor, M. Best, and S. Sitch, 2011: A comprehensive set of benchmark tests for a land surface model of simultaneous fluxes of water and carbon at both the global and seasonal scale. Geosci. Model Dev., 4, 255269, doi:10.5194/gmd-4-255-2011.

    • Search Google Scholar
    • Export Citation
  • Brovkin, V., L. Boysen, T. Raddatz, V. Gayler, A. Loew, and M. Claussen, 2013: Evaluation of vegetation cover and land-surface albedo in MPI-ESM CMIP5 simulations. J. Adv. Model. Earth Syst., 5, 4857, doi:10.1029/2012MS000169.

    • Search Google Scholar
    • Export Citation
  • Eyring, V., and Coauthors, 2016: ESMValTool (v1.0)—A community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP. Geosci. Model Dev., 9, 17471802, doi:10.5194/gmd-9-1747-2016.

    • Search Google Scholar
    • Export Citation
  • Flato, G., and Coauthors, 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 741–866. [Available online at https://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_Chapter09_FINAL.pdf.]

  • Gettelman, A., and Coauthors, 2012: A community diagnostic tool for chemistry climate model validation. Geosci. Model Dev., 5, 10611073, doi:10.5194/gmd-5-1061-2012.

    • Search Google Scholar
    • Export Citation
  • Gleckler, P. J., K. E. Taylor, and C. Doutriaux, 2008: Performance metrics for climate models. J. Geophys. Res., 113, D06104, doi:10.1029/2007JD008972.

  • Goddard, L., and Coauthors, 2013: A verification framework for interannual-to-decadal predictions experiments. Climate Dyn., 40, 245272, doi:10.1007/s00382-012-1481-2.

    • Search Google Scholar
    • Export Citation
  • Gregow, H., and Coauthors, 2015: User awareness concerning feedback data and input observations used in reanalysis systems. Adv. Sci. Res., 12, 6367, doi:10.5194/asr-12-63-2015.

    • Search Google Scholar
    • Export Citation
  • Hagemann, S., A. Loew, and A. Andersson, 2013: Combined evaluation of MPI-ESM land surface water and energy fluxes. J. Adv. Model. Earth Syst., 5, 259286, doi:10.1029/2012MS20008.

    • Search Google Scholar
    • Export Citation
  • Hinkelman, L. M., P. W. Stackhouse, B. A. Wielicki, T. Zhang, and S. R. Wilson, 2009: Surface insolation trends from satellite and ground measurements: Comparisons and challenges. J. Geophys. Res., 114, D00D20, doi:10.1029/2008JD011004.

    • Search Google Scholar
    • Export Citation
  • Hollmann, R., and Coauthors, 2013: The ESA Climate Change Initiative: Satellite data records for essential climate variables. Bull. Amer. Meteor. Soc., 94, 1541–1552, doi:10.1175/BAMS-D-11-00254.1.

  • IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, C. B. Field et al., Eds., Cambridge University Press, 582 pp.

  • Karlsson, K.-G., and Coauthors, 2013: CLARA-A1: A cloud, albedo, and radiation dataset from 28 yr of global AVHRR data. Atmos. Chem. Phys., 13, 53515367, doi:10.5194/acp-13-5351-2013.

    • Search Google Scholar
    • Export Citation
  • Kato, S., N. G. Loeb, F. G. Rose, D. R. Doelling, D. A. Rutan, T. E. Caldwell, L. Yu, and R. A. Weller, 2013: Surface irradiances consistent with CERES-derived top-of-atmosphere shortwave and longwave irradiances. J. Climate, 26, 27192740, doi:10.1175/JCLI-D-12-00436.1.

    • Search Google Scholar
    • Export Citation
  • Li, J.-L. F., D. E. Waliser, G. Stephens, S. Lee, T. L’Ecuyer, S. Kato, N. Loeb, and H.-Y. Ma, 2013: Characterizing and understanding radiation budget biases in CMIP3/CMIP5 GCMs, contemporary GCM, and reanalysis. J. Geophys. Res., 118, 81668184, doi:10.1002/jgrd.50378.

    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., B. A. Wielicki, D. R. Doelling, G. L. Smith, D. F. Keyes, S. Kato, N. Manalo-Smith, and T. Wong, 2009: Toward optimal closure of the earth’s top-of-atmosphere radiation budget. J. Climate, 22, 748766, doi:10.1175/2008JCLI2637.1.

    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., S. Kato, W. Su, T. Wong, F. G. Rose, D. R. Doelling, J. R. Norris, and X. Huang, 2012: Advances in understanding top-of-atmosphere radiation variability from satellite observations. Surv. Geophys., 33, 359385, doi:10.1007/s10712-012-9175-1.

    • Search Google Scholar
    • Export Citation
  • Loew, A., 2013: Terrestrial satellite climate data records: How long is long enough? A test case for the Sahel. Theor. Appl. Climatol., 115, 427–440, doi:10.1007/s00704-013-0880-6.

    • Search Google Scholar
    • Export Citation
  • Loew, A., 2015a: pycmbs, version 1.1.0. Zenodo, accessed 11 May 2015, doi:10.5281/zenodo.17486.

  • Loew, A., 2015b: Comprehensive CMIP5 model evaluation reports: Auxiliary material. Open Data LMU, doi:10.5282/ubm/data.70.

  • Loew, A., P. M. van Bodegom, J.-L. Widlowski, J. Otto, T. Quaife, B. Pinty, and T. Raddatz, 2014: Do we (need to) care about canopy radiation schemes in DGVMs? Caveats and potential impacts. Biogeosciences, 11, 18731897, doi:10.5194/bg-11-1873-2014.

    • Search Google Scholar
    • Export Citation
  • Luo, Y. Q., and Coauthors, 2012: A framework for benchmarking land models. Biogeosciences, 9, 38573874, doi:10.5194/bg-9-3857-2012.

  • Meehl, G. A., C. Covey, K. E. Taylor, T. Delworth, R. J. Stouffer, M. Latif, B. McAvaney, and J. F. B. Mitchell, 2007: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc., 88, 13831394, doi:10.1175/BAMS-88-9-1383.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., R. Moss, and K. Taylor, 2014: Climate model intercomparisons: Preparing for the next phase. Eos, Trans. Amer. Geophys. Union, 95, 7778, doi:10.1002/2014EO090001.

    • Search Google Scholar
    • Export Citation
  • Müller, R., U. Pfeifroth, C. Träger-Chatterjee, J. Trentmann, and R. Cremer, 2015: Digging the METEOSAT treasure—3 decades of solar surface radiation. Remote Sens., 7, 8067–8101, doi:10.3390/rs70608067.

    • Search Google Scholar
    • Export Citation
  • Raschke, E., S. Kinne, and P. W. Stackhouse, 2012: GEWEX Radiative Flux Assessment (RFA) volume 1: Assessment. WCRP Tech. Rep. 19/2012, 273 pp. [Available online at http://www.wcrp-climate.org/documents/GEWEX%20RFA-Volume%201-report.pdf.]

  • Reichler, T., and J. Kim, 2008: How well do coupled models simulate today’s climate? Bull. Amer. Meteor. Soc., 89, 303311, doi:10.1175/BAMS-89-3-303.

    • Search Google Scholar
    • Export Citation
  • Riihelä, A., T. Carlund, J. Trentmann, R. Müller, and A. Lindfors, 2015: Validation of CM SAF surface solar radiation datasets over Finland and Sweden. Remote Sens., 7, 66636682, doi:10.3390/rs70606663.

    • Search Google Scholar
    • Export Citation
  • Stackhouse, P. W., S. K. Gupta, S. J. Cox, T. Zhang, J. C. Mikovitz, and L. M. Hinkelman, 2011: 24.5-year data set released. GEWEX News, Vol. 21, No. 1, International GEWEX Project Office, Silver Spring, MD, 1012.

    • Search Google Scholar
    • Export Citation
  • Stevens, B., and Coauthors, 2013: Atmospheric component of the MPI-M Earth System Model: ECHAM6. J. Adv. Model. Earth Syst., 5, 146172, doi:10.1002/jame.20015.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, doi:10.1175/BAMS-D-11-00094.1.

    • Search Google Scholar
    • Export Citation
  • Wild, M., 2009: Global dimming and brightening: A review. J. Geophys. Res., 114, D00D16, doi:10.1029/2008JD011470.

  • Zhang, T., P. W. Stackhouse, S. K. Gupta, S. J. Cox, J. Colleen Mikovitz, and L. M. Hinkelman, 2013: The validation of the GEWEX SRB surface shortwave flux data products using BSRN measurements: A systematic quality control, production and application approach. J. Quant. Spectrosc. Radiat. Transfer, 122, 127140, doi:10.1016/j.jqsrt.2012.10.004.

    • 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.

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