• Bony, S., and Coauthors, 2006: How well do we understand climate change feedback processes? J. Climate, 19, 34453482.

  • Budyko, M., 1969: The effect of solar radiation variations on the climate of the earth. Tellus, 21, 611619.

  • Curry, J. A., and E. E. Ebert, 1992: Annual cycle of radiation fluxes over the Arctic Ocean: Sensitivity to cloud optical properties. J. Climate, 5, 12671280.

    • Search Google Scholar
    • Export Citation
  • Donohoe, A., 2011: Radiative and dynamic controls of global scale energy fluxes. Ph.D. thesis, University of Washington, 137 pp.

  • Donohoe, A., and D. Battisti, 2011: Atmospheric and surface contributions to planetary albedo. J. Climate, 24, 44014417.

  • Enderton, D., and J. Marshall, 2009: Controls on the total dynamical heat transport of the atmosphere and oceans. J. Atmos. Sci., 66, 15931611.

    • Search Google Scholar
    • Export Citation
  • Fasullo, J. T., and K. E. Trenberth, 2008a: The annual cycle of the energy budget. Part I: Global mean and land–ocean exchanges. J. Climate, 21, 22972312.

    • Search Google Scholar
    • Export Citation
  • Fasullo, J. T., and K. E. Trenberth, 2008b: The annual cycle of the energy budget. Part II: Meridional structures and poleward transports. J. Climate, 21, 23132325.

    • Search Google Scholar
    • Export Citation
  • Graves, C., W. Lee, and G. North, 1993: New parameterizations and sensitivities for simple climate models. J. Geophys. Res., 98 (D3), 50255036.

    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., M. E. Ockert-Bell, and M. L. Michelsen, 1992: The effect of cloud type on earth’s energy balance: Global analysis. J. Climate, 5, 12811304.

    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., L. Moy, and Q. Fu, 2001: A global surface albedo model. J. Climate, 14, 44954511.

  • Held, I., and B. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699.

  • Hwang, Y., and D. Frierson, 2011: Increasing atmospheric poleward energy transport with global warming. Geophys. Res. Lett., 37, L24807, doi:10.1029/2010GL045440.

    • Search Google Scholar
    • Export Citation
  • Kiehl, J., 1994: On the observed near cancellation between longwave and shortwave cloud forcing in tropical regions. J. Climate, 7, 559565.

    • Search Google Scholar
    • Export Citation
  • Lucarini, V., and F. Ragone, 2011: Energetics of climate models: Energy balance and meridional enthalpy transports. Rev. Geophys., 49, RG1001, doi:10.1029/2009RG000323.

    • Search Google Scholar
    • Export Citation
  • Lucarini, V., K. Fraedrich, and F. Ragone, 2011: New results on the thermodynamical properties of the climate system. J. Atmos. Sci., 68, 24382458.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., C. Covey, 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
  • North, G. R., 1975: Theory of energy-balance climate models. J. Atmos. Sci., 32, 20332043.

  • Robock, A., 1980: The seasonal cycle of snow cover, sea ice and surface albedo. Mon. Wea. Rev., 108, 267285.

  • Rose, B., and J. Marshall, 2009: Ocean heat transport, sea ice, and multiple climate states: Insights from energy balance models. J. Atmos. Sci., 66, 28282843.

    • Search Google Scholar
    • Export Citation
  • Rutan, D., F. Rose, N. Smith, and T. Charlock, 2001: Validation data set for CERES surface and atmospheric radiation budget (SARB). GEWEX/WCRP Newsletter, Vol. 11, No. 1, International GEWEX Project Office, Silver Spring, MD, 11–12.

  • Sellers, W. D., 1969: A global climatic model based on the energy balance of the earth-atmosphere system. J. Appl. Meteor., 8, 392400.

    • 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.

  • Stone, P., 1978: Constraints on dynamical transports of energy on a spherical planet. Dyn. Atmos. Oceans, 2, 123139.

  • Thompson, D., and S. Solomon, 2002: Interpretation of recent Southern Hemisphere climate change. Science, 84, 895899.

  • Trenberth, K. E., and J. M. Caron, 2001: Estimates of meridional atmosphere and ocean heat transports. J. Climate, 14, 34333443.

  • Trenberth, K. E., and J. T. Fasullo, 2010: Simulation of present day and 21st century energy budgets of the southern oceans. J. Climate, 23, 440454.

    • Search Google Scholar
    • Export Citation
  • Vonder Haar, T., and A. Oort, 1973: New estimate of annual poleward energy transport by Northern Hemisphere oceans. J. Phys. Oceanogr., 3, 169172.

    • Search Google Scholar
    • Export Citation
  • Warren, S., and S. Schneider, 1979: Seasonal simulation as a test for uncertainties in the parameterizations of a Budyko–Sellers zonal climate model. J. Atmos. Sci., 36, 13771391.

    • Search Google Scholar
    • Export Citation
  • Wielicki, B., B. Barkstrom, E. Harrison, R. Lee, G. Smith, and J. 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
  • Wunsch, C., 2005: The total meridional heat flux and its oceanic and atmospheric partition. J. Climate, 18, 43744380.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 894 433 9
PDF Downloads 3567 483 5

What Determines Meridional Heat Transport in Climate Models?

View More View Less
  • 1 Department of Atmospheric Sciences, University of Washington, Seattle, Washington
Restricted access

Abstract

The annual mean maximum meridional heat transport (MHTMAX) differs by approximately 20% among coupled climate models. The value of MHTMAX can be expressed as the difference between the equator-to-pole contrast in absorbed solar radiation (ASR*) and outgoing longwave radiation (OLR*). As an example, in the Northern Hemisphere observations, the extratropics (defined as the region with a net radiative deficit) receive an 8.2-PW deficit of net solar radiation (ASR*) relative to the global average that is balanced by a 2.4-PW deficit of outgoing longwave radiation (OLR*) and 5.8 PW of energy import via the atmospheric and oceanic circulation (MHTMAX). The intermodel spread of MHTMAX in the Coupled Model Intercomparison Project Phase 3 (CMIP3) simulations of the preindustrial climate is primarily (R2 = 0.72) due to differences in ASR* while model differences in OLR* are uncorrelated with the MHTMAX spread. The net solar radiation (ASR*) is partitioned into contributions from (i) the equator-to-pole contrast in incident radiation acting on the global average albedo and (ii) the equator-to-pole contrast of planetary albedo, which is further subdivided into components due to atmospheric and surface reflection. In the observations, 62% of ASR* is due to the meridional distribution of incident radiation, 33% is due to atmospheric reflection, and 5% is due to surface reflection. The intermodel spread in ASR* is due to model differences in the equator-to-pole gradient in planetary albedo, which are primarily a consequence of atmospheric reflection differences (92% of the spread), and is uncorrelated with differences in surface reflection. As a consequence, the spread in MHTMAX in climate models is primarily due to the spread in cloud reflection properties.

Corresponding author address: Aaron Donohoe, University of Washington, Box 351640, 408 ATG Bldg., Seattle, WA 98195. E-mail: aaron@atmos.washington.edu

Abstract

The annual mean maximum meridional heat transport (MHTMAX) differs by approximately 20% among coupled climate models. The value of MHTMAX can be expressed as the difference between the equator-to-pole contrast in absorbed solar radiation (ASR*) and outgoing longwave radiation (OLR*). As an example, in the Northern Hemisphere observations, the extratropics (defined as the region with a net radiative deficit) receive an 8.2-PW deficit of net solar radiation (ASR*) relative to the global average that is balanced by a 2.4-PW deficit of outgoing longwave radiation (OLR*) and 5.8 PW of energy import via the atmospheric and oceanic circulation (MHTMAX). The intermodel spread of MHTMAX in the Coupled Model Intercomparison Project Phase 3 (CMIP3) simulations of the preindustrial climate is primarily (R2 = 0.72) due to differences in ASR* while model differences in OLR* are uncorrelated with the MHTMAX spread. The net solar radiation (ASR*) is partitioned into contributions from (i) the equator-to-pole contrast in incident radiation acting on the global average albedo and (ii) the equator-to-pole contrast of planetary albedo, which is further subdivided into components due to atmospheric and surface reflection. In the observations, 62% of ASR* is due to the meridional distribution of incident radiation, 33% is due to atmospheric reflection, and 5% is due to surface reflection. The intermodel spread in ASR* is due to model differences in the equator-to-pole gradient in planetary albedo, which are primarily a consequence of atmospheric reflection differences (92% of the spread), and is uncorrelated with differences in surface reflection. As a consequence, the spread in MHTMAX in climate models is primarily due to the spread in cloud reflection properties.

Corresponding author address: Aaron Donohoe, University of Washington, Box 351640, 408 ATG Bldg., Seattle, WA 98195. E-mail: aaron@atmos.washington.edu
Save