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Cash, Benjamin A.; Schneider, Edwin K.; Bengtsson, Lennart (2007)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
Subjects:

Classified by OpenAIRE into

arxiv: Physics::Atmospheric and Oceanic Physics
Model differences in projections of global mean and regional climate change due to increasing greenhouse gases are investigated using two atmospheric general circulation models (AGCMs): ECHAM4 (Max Planck Institute, version 4) and CCM3 (National Center for Atmospheric Research Community Climate Model version 3). We replace the ECHAM4 short-wave processes (including routines for short-wave radiation, aerosols, cloud liquid water path and cloud droplet size distribution) with the corresponding parametrizations from CCM3. We also eliminate sea-ice in both models. We find that the resulting ‘hybrid’-ECHAM4 model has the same global mean temperature sensitivity (defined as the difference in temperature between the 2× CO2 and 1× CO2 integrations at equilibrium) and similar regional temperature change patterns as CCM3. The global mean precipitation sensitivity was only slightly affected; indicating different processes control this. Investigation of top of the atmosphere radiative feedbacks in the standard-ECHAM4 and hybrid-ECHAM4 models show that the differences in global mean temperature sensitivity and regional temperature change patterns can be attributed primarily to a stronger, negative, cloud short-wave feedback in the tropics of the hybrid-ECHAM4 model. However, comparison of the hybrid-ECHAM4 model to CCM3 reveals large differences in partitioning of the cloud feedbacks between long-wave and short-wave in the two models. This suggests that the global mean temperature sensitivity and regional temperature change patterns respond primarily to the magnitude and distribution of the top of the atmosphere feedbacks and are relatively insensitive to the partitioning between individual processes.
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    • Andronova, N. and Schlesinger, M. E. 2001. Objective estimation of the probability distribution for climate sensitivity. J. Geophys. Res. 106(D19), 22 605-22 612.
    • Alexeev, V. A., Langen, P. L. and Bates, J. R. 2005. Polar amplification of surface warming on an aquaplanet in “ghost forcing” experiments without sea ice feedbacks. Clim. Dyn. 24, 655-666.
    • Boer, G. J., Stouffer, R. J., Dix, M., Noda, A., Senior, C. A. and co-authors. 2001. Projections of future climate change.In: Climate Change 2001. The Scientific Basis. (eds J. T. Houghton, Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden and co-editors). Cambridge University Press, Cambridge, 525-582.
    • Boer, G. J. and Yu, B. 2003. Climate sensitivity and response. Clim. Dyn. 20, 415-429.
    • Bony, S. and Dufresne, J.-L. 2005. Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys. Res. Lett., doi:10.1029/2005GL023851.
    • Brieglieb, B. P. 1992. Delta-Eddington approximation for solar radiation in the NCAR Community Climate Model. J. Geophys. Res. 97, 7603- 7612.
    • Cash, Benjamin A., Schneider, Edwin K. and Bengtsson, Lennart 2005. Origin of regional climate differences: Role of boundary conditions and model formulation in two GCMs. Clim. Dyn. 25, 709-723, doi:10.1007/s00382-005-0069-5.
    • Cess, R. D., Potter, G. L., Blanchet, J. P., Boer, G. J., Ghan, S. J. and co-authors. 1990. Intercomparison and interpretation of climate feedback processes in nineteen atmospheric general circulation models. J. Geophys. Res. 95, 16 601-16 615.
    • Cess, R. D., Potter, G. L., Zhang, M. H., Blanchet, J. P., Chalita, S. and co-authors. 1991. Intercomparison of snow-climate feedback as produced by 17 general circulation models. Science 253, 888- 892.
    • Cess, R. D., Zhang, M. H., Ingram, W. J., Potter, G. L., Alekssev, V. and co-authors. 1996. Cloud feedback in atmospheric general circulation models: an update. J. Geophys. Res. 101, 12 791-12 794.
    • Colman, R. A. 2003. A comparison of climate feedbacks in General Circulation Models. Clim. Dyn. 20, 865-873.
    • Colman, R. A. and McAvaney, B. J. 1995. Sensitivity of the climate response of an atmospheric general circulation model to changes in the convective parameterization and horizontal resolution. J. Geophys. Res. 100, 3155-3172.
    • Colman, R. A., McAvaney, B. J., Fraser, J. R., Rikus, L. J. and Dahni, R. R. 1994. Snow and cloud feedbacks modeled by an atmospheric general circulation model. Clim. Dyn. 9, 253-265.
    • Covey, C., AchutaRao, K. M., Cubasch, U., Jones, P., Lambert, S. J. and co-authors. 2003. An overview of results from the Coupled Model Intercomparison Project. Global Planet. Change 37, 103-133.
    • Dickinson, R. E. 1986. How will climate change? The Greenhouse Effect, Climate Change and Ecosystems. (eds B. Bolin, B. R. Doos, J. Jager and R. A. Warrick), SCOPE 29, Wiley, Chichester, 206-270.
    • Forest, C. E., Stone, P. H., Sokolov, A. P., Allen, M. R. and Webster, M. D. 2002. Quantifying uncertainties in the climate system properties with the use of recent climate observations. Science 295, 113- 117.
    • Fouquart, Y. and Bonnel, B. 1980. Computations of solar heating of the Earth's atmosphere: A new parameterization. Beitr. Phys. Atmos. 53, 35-62.
    • Gates, W. L., Mitchell, J. F. B., Boer, G. J., Cubasch, U. and Meleshko, V. P. 1992. Climate modeling, climate prediction and model validation. In: Climate Change 1992. The Supplementary Report to the IPCC Scientific Assessment. (eds J. T. Houghton, B. A. Callander and S. K. Varney). Cambridge University Press, Cambridge, 97-134.
    • Gregory, J. M., Stouffer, R. J., Raper, S. C. B., Stott, P. A. and Rayner, N. A. 2002. An observationally based estimate of the climate sensitivity. J. Climate 15(22), 3117-3121.
    • Harvey, L. D. D. and Kaufman, R. K. 2002. Simultaneously constraining climate sensitivity and aerosol radiative forcing. J. Climate 15(20), 2837-2861.
    • Kattenberg, A., Giorgi, F., Grassl, H., Meehl, G. A., Mitchell, J. F. B. and co-authors. 1995. Climate models-projections of future climate. In: Climate Change 1995. The Science of Climate Change. (eds J. T. Houghton, L. G. Meira Filho, B. A. Callander, N. Harris, A. Kattenberg and K. Maskell). Cambridge University Press, Cambridge, 285-357.
    • Kiehl, J. T., Hack, J. J., Bonan, G. B., Boville, B. B., Briegleb, B. P. and co-authors. 1996. Description of the NCAR Community Climate Model (CCM3). NCAR Technical Note. NCAR/TN-420+STR, Boulder, Colorado.
    • Knutti, R., Stocker, T. F., Joos, F. and Plattner, G. K. 2002. Constraints on radiative forcing and future climate change from observations and climate model ensembles. Nature 416, 719-723.
    • Koepke, P., Hess, M., Schult, I. and Shettle, E. P. 1997. Global Aerosol Data Set, Report No. 243, Max-Planck-Institut fu¨r Meteorologie, Hamburg, ISSN 0937-1060.
    • Meehl, G. A., Boer, G. J., Covey, C., Latif, M. and Stouffer, R. J. 2000. The Coupled Model Intercomparison Project (CMIP). Bull. Amer. Met. Soc. 81, 313-318.
    • Mitchell, J. F. B., Manabe, S., Tokioka, T. and Meleshko, V. 1990. Equilibrium climate change. Climate Change: The IPCC Scientific Assessment. (eds J. T. Houghton, G. J. Jenkins and J. J. Ephraums). Cambridge University Press, Cambridge, 131-172.
    • Murphy, J., Sexton, D. M., Barnett, D. N., Jones, G. S., Webb, M. J. and co-authors 2004. Quantification of modeling uncertainties in a large ensemble of climate change simulations. Nature 430, 768-772.
    • Ra¨isa¨nen, J. 2001. CO2-induced climate change in CMIP2 experiments: quantification of agreement and role of internal variability. J. Climate 14, 2088-2104.
    • Ramaswamy, V., Boucher, O., Haigh, J., Hauglustaine, D., Haywood, J. and co-authors. 2001. Radiative forcing of climate change. In: Climate Change 2001. The Scientific Basis. (eds J. T. Houghton, Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden and co-editors). Cambridge University Press, Cambridge, 349-416.
    • Randall, D. A., Cess, R. D., Blanchet, J. P., Chalita, S., Colman, R. and co-authors. 1994. Analysis of snow feedbacks in fourteen general circulation models. J. Geophys. Res. 99, 20 757-20 771.
    • Rockel, B., Raschke, E. and Weyres, B. 1991. A parameterization of broad band radiative transfer properties of water, ice, and mixed clouds. Beitr. Physik Atmos. 64, 1-12.
    • Roeckner, E., Arpe, K., Bengtsson, L., Christoph, M., Claussen, M. and co-authors. 1996. The atmospheric general circulation model ECHAM-4: model description and simulation of present-day climate. Max Planck Institute for Meteorology Rep. 218, Hamburg, Germany, 90.
    • Rotstayn, L. D. 1999. Climate sensitivity of the CSIRO GCM: Effect of cloud modeling assumptions. J. Climate 12, 334-356.
    • Schneider, E. K., Kirtman, B. P. and Lindzen, R. S. 1999. Upper tropospheric water vapor and climate sensitivity. J. Atmos. Sci. 56, 1649- 1658.
    • Schneider, E. K. 2002. The causes of differences between equatorial Pacific SST simulations of two coupled ocean-atmosphere general circulation models. J. Climate 15, 449-469.
    • Senior, C. A. and Mitchell, J. F. B. 1993. Carbon dioxide and climate: the impact of cloud parameterization. J. Climate 6, 393- 418.
    • Slingo, A. 1989. A GCM parameterization for the shortwave radiative properties of water clouds. J. Atmos. Sci. 46, 1419- 1427.
    • Soden, B. J., Broccoli, A. J. and Hemler, R. S. 2004. On the use of cloud forcing to estimate feedback. J. Climate 17, 3661-3665.
    • Soden, B. J. and Held, I. M. 2006. An assessment of climate feedbacks in coupled ocean-atmosphere models. J. Climate 19, 3354- 3360.
    • U. S. National Academy of Sciences 1979. Carbon Dioxide and Climate: A Scientific Assessment. National Academy of Sciences, Washington, 22.
    • Tsushima, Y., Abe-Ouchi, A. and Manabe, S. 2005. Radiative damping of annual variation in global mean surface temperature: comparison between observed and simulated feedback. Clim. Dyn.. doi:10.1007/s00382-005-0002-y.
    • Webb, M. J. and co-authors. 2006. On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Clim. Dyn.. doi:10.1007/s00382-006-0111-2.
    • Yao, M.-S. and Del Genio, A. D. 1999. Effect of cloud parameterization on the simulation of climate change in the GISS GCM. J. Climate 12, 761-779.
    • Zhang, M. H., Hack, J. J., Kiehl, J. T. and Cess, R. D. 1994. Diagnostic study of climate feedback processes in atmospheric general circulation models. J. Geophys. Res. 99, 5525-5537.
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