Diagnosing the Impacts of Northern Hemisphere Surface Albedo Biases on Simulated Climate

Chad W. Thackeray Department of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario, Canada, and Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

Search for other papers by Chad W. Thackeray in
Current site
Google Scholar
PubMed
Close
,
Christopher G. Fletcher Department of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario, Canada

Search for other papers by Christopher G. Fletcher in
Current site
Google Scholar
PubMed
Close
, and
Chris Derksen Climate Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada

Search for other papers by Chris Derksen in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Many Earth system models contain substantial biases in the magnitude and seasonal cycle of the albedo of snow-covered surfaces. Various structural and parametric deficiencies have been identified as potential causes of these albedo biases, related to vegetation distribution and abundance, snow albedo, and the representation of snow interception by forest canopies. There is, however, little understanding of how the albedo biases directly influence simulated climate because of difficulties in isolating them from other complex processes and feedbacks. In this study, we conduct a number of novel simulations using the National Center for Atmospheric Research Community Earth System Model (CESM), replacing the model’s internal surface albedo calculation with values prescribed from observations or from other model simulations. Results show that while biases in surface albedo are largest in winter, those during spring have the greatest impact on surface climate because incoming solar radiation is much stronger. Correcting biases in the seasonal cycle of albedo in CESM reduces climatological temperature biases across the boreal region in spring and partially corrects Arctic sea level pressure biases, but due to compensating errors, overall climate biases are not always reduced. Additionally, we impose albedo patterns extracted from other climate models with large positive and negative albedo biases to illustrate the climate responses that can result. Prescribed surface albedo produces significant impacts on surface radiation, near-surface land temperatures, and, more rarely, atmospheric circulation. This is important because small changes to mean climate during spring can have major implications for the snow and surface radiation regimes.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-18-0083.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chad W. Thackeray, cwthackeray@ucla.edu

Abstract

Many Earth system models contain substantial biases in the magnitude and seasonal cycle of the albedo of snow-covered surfaces. Various structural and parametric deficiencies have been identified as potential causes of these albedo biases, related to vegetation distribution and abundance, snow albedo, and the representation of snow interception by forest canopies. There is, however, little understanding of how the albedo biases directly influence simulated climate because of difficulties in isolating them from other complex processes and feedbacks. In this study, we conduct a number of novel simulations using the National Center for Atmospheric Research Community Earth System Model (CESM), replacing the model’s internal surface albedo calculation with values prescribed from observations or from other model simulations. Results show that while biases in surface albedo are largest in winter, those during spring have the greatest impact on surface climate because incoming solar radiation is much stronger. Correcting biases in the seasonal cycle of albedo in CESM reduces climatological temperature biases across the boreal region in spring and partially corrects Arctic sea level pressure biases, but due to compensating errors, overall climate biases are not always reduced. Additionally, we impose albedo patterns extracted from other climate models with large positive and negative albedo biases to illustrate the climate responses that can result. Prescribed surface albedo produces significant impacts on surface radiation, near-surface land temperatures, and, more rarely, atmospheric circulation. This is important because small changes to mean climate during spring can have major implications for the snow and surface radiation regimes.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-18-0083.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chad W. Thackeray, cwthackeray@ucla.edu

Supplementary Materials

    • Supplemental Materials (PDF 1.23 MB)
Save
  • Allen, R. J., and C. S. Zender, 2010: Effects of continental-scale snow albedo anomalies on the wintertime Arctic oscillation. J. Geophys. Res., 115, D23105, https://doi.org/10.1029/2010JD014490.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allen, R. J., and C. S. Zender, 2011: Forcing of the Arctic Oscillation by Eurasian snow cover. J. Climate, 24, 65286539, https://doi.org/10.1175/2011JCLI4157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barlage, M., X. Zeng, H. Wei, and K. E. Mitchell, 2005: A global 0.05° maximum albedo dataset of snow-covered land based on MODIS observations. Geophys. Res. Lett., 32, L17405, https://doi.org/10.1029/2005GL022881.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bartlett, P. A., and D. L. Verseghy, 2015: Modified treatment of intercepted snow improves the simulated forest albedo in the Canadian Land Surface Scheme. Hydrol. Processes, 29, 32083226, https://doi.org/10.1002/hyp.10431.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, R. D., 2000: Northern Hemisphere snow cover variability and change, 1915–97. J. Climate, 13, 23392355, https://doi.org/10.1175/1520-0442(2000)013<2339:NHSCVA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, J. L., J. C. Furtado, M. A. Barlow, V. A. Alexeev, and J. E. Cherry, 2012: Arctic warming, increasing snow cover and widespread boreal winter cooling. Environ. Res. Lett., 7, 014007, https://doi.org/10.1088/1748-9326/7/1/014007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Boer, G., W. Chapman, J. E. Kay, B. Medeiros, M. D. Shupe, S. Vavrus, and J. Walsh, 2012: A characterization of the present-day Arctic atmosphere in CCSM4. J. Climate, 25, 26762695, https://doi.org/10.1175/JCLI-D-11-00228.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Douville, H., 2010: Relative contribution of soil moisture and snow mass to seasonal climate predictability: A pilot study. Climate Dyn., 34, 797818, https://doi.org/10.1007/s00382-008-0508-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dutra, E., C. Schär, P. Viterbo, and P. M. A. Miranda, 2011: Land-atmosphere coupling associated with snow cover. Geophys. Res. Lett., 38, L15707, https://doi.org/10.1029/2011GL048435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eltahir, E. A. B., 1998: A soil moisture–rainfall feedback mechanism: 1. Theory and observations. Water Resour. Res., 34, 765776, https://doi.org/10.1029/97WR03499.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Essery, R., 2013: Large-scale simulations of snow albedo masking by forests. Geophys. Res. Lett., 40, 55215525, https://doi.org/10.1002/grl.51008.

  • Flanner, M. G., K. M. Shell, M. Barlage, D. K. Perovich, and M. A. Tschudi, 2011: Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008. Nat. Geosci., 4, 151155, https://doi.org/10.1038/ngeo1062.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fletcher, C. G., P. J. Kushner, and J. Cohen, 2007: Stratospheric control of the extratropical circulation response to surface forcing. Geophys. Res. Lett., 34, L21802, https://doi.org/10.1029/2007GL031626.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fletcher, C. G., S. C. Hardiman, P. J. Kushner, and J. Cohen, 2009: The dynamical response to snow cover perturbations in a large ensemble of atmospheric GCM integrations. J. Climate, 22, 12081222, https://doi.org/10.1175/2008JCLI2505.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fletcher, C. G., C. W. Thackeray, and T. M. Burgers, 2015: Evaluating biases in simulated snow albedo feedback in two generations of climate models. J. Geophys. Res. Atmos., 120, 1226, https://doi.org/10.1002/2014JD022546.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gates, W. L., and Coauthors, 1999: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80, 2956, https://doi.org/10.1175/1520-0477(1999)080<0029:AOOTRO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gent, P. R., and Coauthors, 2011: The Community Climate System Model version 4. J. Climate, 24, 49734991, https://doi.org/10.1175/2011JCLI4083.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gong, G., D. Entekhabi, and J. Cohen, 2002: A large-ensemble model study of the wintertime AO–NAO and the role of interannual snow perturbations. J. Climate, 15, 34883499, https://doi.org/10.1175/1520-0442(2002)015<3488:ALEMSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gong, G., D. Entekhabi, J. Cohen, and D. Robinson, 2004: Sensitivity of atmospheric response to modeled snow anomaly characteristics. J. Geophys. Res., 109, D06107, https://doi.org/10.1029/2003JD004160.

    • Search Google Scholar
    • Export Citation
  • Grodsky, S. A., J. A. Carton, S. Nigam, and Y. M. Okumura, 2012: Tropical Atlantic biases in CCSM4. J. Climate, 25, 36843701, https://doi.org/10.1175/JCLI-D-11-00315.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Groisman, P., T. R. Karl, R. W. Knight, and G. L. Stenchikov, 1994: Changes of snow cover, temperature, and radiative heat balance over the Northern Hemisphere. J. Climate, 7, 16331656, https://doi.org/10.1175/1520-0442(1994)007<1633:COSCTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, T., S. Liang, and D.-X. Song, 2014: Analysis of global land surface albedo climatology and spatial-temporal variation during 1981–2010 from multiple satellite products. J. Geophys. Res. Atmos., 119, 10 28110 298, https://doi.org/10.1002/2014JD021667.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., Y. Kushnir, G. Ottersen, and M. Visbeck, 2003: An overview of the North Atlantic Oscillation. The North Atlantic Oscillation: Climatic Significance and Environmental Impact, Geophys. Monogr., Vol. 134, Amer. Geophys. Union, 1–35, https://doi.org/10.1029/134GM01.

    • Crossref
    • Export Citation
  • Kanamitsu, M., W. Ebisuzaki, J. Woollen, S. K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 16311644, https://doi.org/10.1175/BAMS-83-11-1631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar, 2000: A catchment-based approach to modeling land surface processes in a general circulation model 1. Model structure. J. Geophys. Res., 105, 24 80924 822, https://doi.org/10.1029/2000JD900327.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krinner, G., and Coauthors, 2018: ESM-SnowMIP: Assessing models and quantifying snow-related climate feedbacks. Geosci. Model Dev., 11, 50275049, https://doi.org/10.5194/gmd-2018-153.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawrence, D. M., and Coauthors, 2011: Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. J. Adv. Model. Earth Syst., 3, M03001, https://doi.org/10.1029/2011MS00045.

    • Search Google Scholar
    • Export Citation
  • Li, Y., T. Wang, Z. Zeng, S. Peng, X. Lian, and S. Piao, 2016: Evaluating biases in simulated land surface albedo from CMIP5 global climate models. J. Geophys. Res. Atmos., 121, 61786190, https://doi.org/10.1002/2016JD024774.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, S., Q. Wu, X. Ren, Y. Yao, S. R. Schroeder, and H. Hu, 2017: Modeled Northern Hemisphere autumn and winter climate responses to realistic Tibetan Plateau and Mongolia snow anomalies. J. Climate, 30, 94359454, https://doi.org/10.1175/JCLI-D-17-0117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., 2003: Spatial patterns of soil moisture connected to monthly-seasonal precipitation variability in a monsoon region. J. Geophys. Res., 108, 8856, https://doi.org/10.1029/2002JD003124.

    • Search Google Scholar
    • Export Citation
  • Liu, Z., and L. Wu, 2004: Atmospheric response to North Pacific SST: The role of ocean–atmosphere coupling. J. Climate, 17, 18591882, https://doi.org/10.1175/1520-0442(2004)017<1859:ARTNPS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loken, C., and Coauthors, 2010: SciNet: Lessons learned from building a power-efficient top-20 system and data centre. J. Phys. Conf. Ser., 256, 012026, https://doi.org/10.1088/1742-6596/256/1/012026.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loranty, M. M., L. T. Berner, S. J. Goetz, Y. Jin, and J. T. Randerson, 2014: Vegetation controls on northern high latitude snow-albedo feedback: Observations and CMIP5 model simulations. Global Change Biol., 20, 594606, https://doi.org/10.1111/gcb.12391.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ménard, C. B., J. Ikonen, K. Rautiainen, M. Aurela, A. N. Arslan, and J. Pulliainen, 2015: Effects of meteorological and ancillary data, temporal averaging, and evaluation methods on model performance and uncertainty in a land surface model. J. Hydrometeor., 16, 25592576, https://doi.org/10.1175/JHM-D-15-0013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mudryk, L. R., C. Derksen, P. J. Kushner, and R. Brown, 2015: Characterization of Northern Hemisphere snow water equivalent datasets, 1981–2010. J. Climate, 28, 80378051, https://doi.org/10.1175/JCLI-D-15-0229.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muller, J.-P., 2013: GlobAlbedo final product validation report. GlobAlbedo Rep. GlobAlbedo_FVR_V1.2, 104 pp., http://www.globalbedo.org/docs/GlobAlbedo_FVR_V1_2_web.pdf.

  • Neale, R. B., J. Richter, S. Park, P. H. Lauritzen, S. J. Vavrus, P. J. Rasch, and M. Zhang, 2013: The mean climate of the Community Atmosphere Model (CAM4) in forced SST and fully coupled experiments. J. Climate, 26, 51505168, https://doi.org/10.1175/JCLI-D-12-00236.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oleson, K. W., D. M. Lawrence, B. Gordon, M. G. Flanner, E. Kluzek, J. Peter, and X. Zeng, 2010: Technical description of version 4.0 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-478+STR, 257 pp.

  • Orsolini, Y. J., and N. K. Kvamstø, 2009: Role of Eurasian snow cover in wintertime circulation: Decadal simulations forced with satellite observations. J. Geophys. Res., 114, 112, https://doi.org/10.1029/2009JD012253.

    • Search Google Scholar
    • Export Citation
  • Qian, T., A. Dai, K. E. Trenberth, and K. W. Oleson, 2006: Simulation of global land surface conditions from 1948 to 2004. Part I: Forcing data and evaluations. J. Hydrometeor., 7, 953975, https://doi.org/10.1175/JHM540.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qu, X., and A. Hall, 2007: What controls the strength of snow-albedo feedback? J. Climate, 20, 39713981, https://doi.org/10.1175/JCLI4186.1.

  • Qu, X., and A. Hall, 2014: On the persistent spread in snow-albedo feedback. Climate Dyn., 42, 6981, https://doi.org/10.1007/s00382-013-1774-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robinson, D., and A. Frei, 2000: Seasonal variability of Northern Hemisphere snow extent using visible satellite data. Prof. Geogr., 52, 307315, https://doi.org/10.1111/0033-0124.00226.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roesch, A., 2006: Evaluation of surface albedo and snow cover in AR4 coupled climate models. J. Geophys. Res., 111, D15111, https://doi.org/10.1029/2005JD006473.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saito, K., and J. Cohen, 2003: The potential role of snow cover in forcing interannual variability of the major Northern Hemisphere mode. Geophys. Res. Lett., 30, 1302, https://doi.org/10.1029/2002GL016341.

    • Search Google Scholar
    • Export Citation
  • Schaaf, C., and Coauthors, 2002: First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ., 83, 135148, https://doi.org/10.1016/S0034-4257(02)00091-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., D. Lüthi, M. Litschi, and C. Schär, 2006: Land–atmosphere coupling and climate change in Europe. Nature, 443, 205209, https://doi.org/10.1038/nature05095.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobolowski, S., G. Gong, and M. Ting, 2010: Modeled climate state and dynamic responses to anomalous North American snow cover. J. Climate, 23, 785799, https://doi.org/10.1175/2009JCLI3219.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thackeray, C. W., and C. G. Fletcher, 2016: Snow albedo feedback: Current knowledge, importance, outstanding issues and future directions. Prog. Phys. Geogr., 40, 392408, https://doi.org/10.1177/0309133315620999.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thackeray, C. W., C. G. Fletcher, and C. Derksen, 2014: The influence of canopy snow parameterizations on snow albedo feedback in boreal forest regions. J. Geophys. Res. Atmos., 119, 98109821, https://doi.org/10.1002/2014JD021858.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thackeray, C. W., C. G. Fletcher, and C. Derksen, 2015: Quantifying the skill of CMIP5 models in simulating seasonal albedo and snow cover evolution. J. Geophys. Res. Atmos., 120, 58315849, https://doi.org/10.1002/2015JD023325.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thackeray, C. W., C. G. Fletcher, L. R. Mudryk, and C. Derksen, 2016: Quantifying the uncertainty in historical and future simulations of Northern Hemisphere spring snow cover. J. Climate, 29, 86478663, https://doi.org/10.1175/JCLI-D-16-0341.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thackeray, C. W., X. Qu, and A. Hall, 2018: Why do models produce spread in snow albedo feedback? Geophys. Res. Lett., 45, 62236231, https://doi.org/10.1029/2018GL078493.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., and J. M. Wallace, 2001: Regional climate impacts of the Northern Hemisphere annular mode. Science, 293, 8589, https://doi.org/10.1126/science.1058958.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verseghy, D., R. Brown, and L. Wang, 2017: Evaluation of CLASS snow simulation over eastern Canada. J. Hydrometeor., 18, 12051225, https://doi.org/10.1175/JHM-D-16-0153.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viovy, N., 2018: CRUNCEP version 7—Atmospheric forcing data for the Community Land Model. NCAR/UCAR research data archive, https://rda.ucar.edu/datasets/ds314.3/.

  • Visbeck, M. H., J. W. Hurrell, L. Polvani, and H. M. Cullen, 2001: The North Atlantic Oscillation: Past, present, and future. Proc. Natl. Acad. Sci. USA, 98, 12 87612 877, https://doi.org/10.1073/pnas.231391598.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L., J. N. S. Cole, P. Bartlett, D. Verseghy, C. Derksen, R. Brown, and K. von Salzen, 2016: Investigating the spread in surface albedo for snow-covered forests in CMIP5 models. J. Geophys. Res. Atmos., 121, 11041119, https://doi.org/10.1002/2015JD023824.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X., and J. R. Key, 2005: Arctic surface, cloud, and radiation properties based on the AVHRR polar pathfinder dataset. Part I: Spatial and temporal characteristics. J. Climate, 18, 25582574, https://doi.org/10.1175/JCLI3438.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Watanabe, M., and Coauthors, 2010: Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Climate, 23, 63126335, https://doi.org/10.1175/2010JCLI3679.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S., H. Y. Ma, J. S. Boyle, S. A. Klein, and Y. Zhang, 2012: On the correspondence between short- and long-time-scale systematic errors in CAM4/CAM5 for the year of tropical convection. J. Climate, 25, 79377955, https://doi.org/10.1175/JCLI-D-12-00134.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, T., 2005: Influence of the seasonal snow cover on the ground thermal regime: An overview. Rev. Geophys., 43, RG4002, https://doi.org/10.1029/2004RG000157.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 694 204 19
PDF Downloads 559 128 8