• Armour, K., 2017: Energy budget constraints on climate sensitivity in light of inconstant climate feedbacks. Nat. Climate Change, 7, 331335, https://doi.org/10.1038/nclimate3278.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Block, K., and T. Mauritsen, 2013: Forcing and feedback in the MPI-ESM-LR coupled model under abruptly quadrupled CO2. J. Adv. Model. Earth Syst., 5, 676691, https://doi.org/10.1002/jame.20041.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bony, S., and Coauthors, 2006: How well do we understand climate change feedback processes? J. Climate, 19, 34453482, https://doi.org/10.1175/JCLI3819.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Budyko, M., 1969: The effect of solar radiation variations on the climate of the Earth. Tellus, 21, 611619, https://doi.org/10.3402/tellusa.v21i5.10109.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, Y., S. Liang, X. Chen, and T. He, 2015: Assessment of sea ice albedo radiative forcing and feedback over the Northern Hemisphere from 1982 to 2009 using satellite and reanalysis data. J. Climate, 28, 12481259, https://doi.org/10.1175/JCLI-D-14-00389.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cavalieri, D., C. Parkinson, P. Gloersen, and H. Zwally, 1996: Sea ice concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS passive microwave data. NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed 7 January 2019, https://doi.org/10.5067/8GQ8LZQVL0VL.

    • Crossref
    • Export Citation
  • Chou, M., and K. Lee, 1996: Parameterizations for the absorption of solar radiation by water vapor and ozone. J. Atmos. Sci., 53, 12031208, https://doi.org/10.1175/1520-0469(1996)053<1203:PFTAOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cowtan, K., and R. Way, 2014: Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Quart. J. Roy. Meteor. Soc., 140, 19351944, https://doi.org/10.1002/qj.2297.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Curry, J., J. Scramm, and E. Ebert, 1995: Sea ice-albedo climate feedback mechanism. J. Climate, 8, 240247, https://doi.org/10.1175/1520-0442(1995)008<0240:SIACFM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ding, Q., and Coauthors, 2017: Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice. Nat. Climate Change, 7, 289295, https://doi.org/10.1038/nclimate3241.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ding, Q., A. Schweiger, M. L. Heureux, E. Steig, D. Battisti, and N. Johnson, 2019: Fingerprints of internal drivers of Arctic sea ice loss in observations and model simulations. Nat. Geosci., 12, 2833, https://doi.org/10.1038/s41561-018-0256-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donohoe, A., and D. Battisti, 2011: Atmospheric and surface contributions to planetary albedo. J. Climate, 24, 44024417, https://doi.org/10.1175/2011JCLI3946.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donohoe, A., and D. Battisti, 2013: The seasonal cycle of atmospheric heating and temperature. J. Climate, 26, 49624980, https://doi.org/10.1175/JCLI-D-12-00713.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferreira, D., J. Marshall, and B. Rose, 2011: Climate determinism revisited: Multiple equilibria in a complex climate model. J. Climate, 24, 9921012, https://doi.org/10.1175/2010JCLI3580.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Flanner, M., K. Shell, M. Barlage, D. Perovich, and M. 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
  • 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, https://doi.org/10.1017/CBO9781107415324.018.

    • Crossref
    • Export Citation
  • Gorodetskaya, I. V., L. Tremblay, B. Liepert, M. A. Cane, and R. Cullather, 2008: The influence of cloud and surface properties on the Arctic Ocean shortwave radiation budget in coupled models. J. Climate, 21, 866882, https://doi.org/10.1175/2007JCLI1614.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hall, A., 2004: The role of surface albedo feedback in climate. J. Climate, 17, 15501568, https://doi.org/10.1175/1520-0442(2004)017<1550:TROSAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hall, A., and X. Qu, 2006: Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophys. Res. Lett., 33, 15501568, https://doi.org/10.1029/2005GL025127.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hansen, J., G. Russell, D. Rind, P. Stone, A. Lacis, S. Lebedeff, R. Ruedy, and L. Travis, 1983: Efficient three-dimensional global models for climate studies: Models I and II. Mon. Wea. Rev., 111, 609662, https://doi.org/10.1175/1520-0493(1983)111<0609:ETDGMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hansen, J., R. Ruedy, J. Glascoe, and M. Sato, 1999: GISS analysis of surface temperature change. J. Geophys. Res., 104, 30 99731 022, https://doi.org/10.1029/1999JD900835.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holland, M. M., and C. Bitz, 2003: Polar amplification of climate change in coupled models. Climate Dyn., 21, 221232, https://doi.org/10.1007/s00382-003-0332-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hummel, J., and R. Reck, 1979: A global surface albedo model. J. Adv. Model. Earth Syst., 18, 239253, https://doi.org/10.1175/1520-0450(1979)18[239:AGSAM]2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hwang, Y., D. Frierson, and J. Kay, 2011: Coupling between Arctic feedbacks and changes in poleward energy transport. Geophys. Res. Lett., 38, L17704, https://doi.org/10.1029/2011GL048546.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, J., and Coauthors, 2016: Assessing recent trends in high-latitude Southern Hemisphere surface climate. Nat. Climate Change, 6, 917926, https://doi.org/10.1038/nclimate3103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kato, S., and Coauthors, 2018: Surface irradiances of edition 4.0 Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) data product. J. Climate, 31, 45014527, https://doi.org/10.1175/JCLI-D-17-0523.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J., M. Holland, and A. Jahn, 2011: Inter-annual to multi-decadal Arctic sea ice extent trends in a warming world. Geophys. Res. Lett., 38, L15708, https://doi.org/10.1029/2011GL048008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J., M. Holland, C. Bitz, E. Blanchard-Wrigglesworth, A. Gettelman, A. Conley, and D. Bailey, 2012: The influence of local feedbacks and northward heat transport on the equilibrium Arctic climate response to increased greenhouse gas forcing. J. Climate, 25, 54335450, https://doi.org/10.1175/JCLI-D-11-00622.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindsay, R., M. Wensnaham, A. Schweiger, and J. Zhang, 2014: Evaluation of seven different atmospheric reanalysis products in the Arctic. J. Climate, 27, 25882606, https://doi.org/10.1175/JCLI-D-13-00014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., and Coauthors, 2018: Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) top-of-atmosphere (TOA) edition-4.0 data product. J. Climate, 31, 895918, https://doi.org/10.1175/JCLI-D-17-0208.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahlstein, I., and R. Knutti, 2012: September Arctic sea ice predicted to disappear near 2°C global warming above present. J. Geophys. Res., 117, D06104, https://doi.org/10.1029/2011JD016709.

    • 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 multi-model dataset: A new era in climate change research. Bull. Amer. Meteor. Soc., 88, 13831394, https://doi.org/10.1175/BAMS-88-9-1383.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morice, C. P., J. Kennedy, N. Rayner, and P. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset. J. Geophys. Res., 117, D08101, https://doi.org/10.1029/2011JD017187.

    • Search Google Scholar
    • Export Citation
  • North, G. R., 1984: The small ice cap instability in diffusive climate models. J. Atmos. Sci., 41, 33903395, https://doi.org/10.1175/1520-0469(1984)041<3390:TSICII>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., A. Conley, and F. M. Vitt, 2018: Surface and top-of-atmosphere radiative feedback kernels for CESM-CAM5. Earth Syst. Sci. Data, 10, 317324, https://doi.org/10.5194/essd-10-317-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perovich, D., T. Grenfell, B. Light, and P. Hobbs, 2002: Seasonal evolution of the albedo of multiyear Arctic sea ice. J. Geophys. Res., 107, 8044, https://doi.org/10.1029/2000JC000438.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pistone, K., I. Eisenman, and V. Ramanathan, 2014: Observational determination of albedo decrease caused by vanishing Arctic sea ice. Proc. Natl. Acad. Sci., 111, 33223326, https://doi.org/10.1073/pnas.1318201111.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pistone, K., I. Eisenman, and V. Ramanathan, 2019: Radiative heating of an ice-free Arctic Ocean. Geophys. Res. Lett., 46, 74747480, https://doi.org/10.1029/2019GL082914.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Polvani, L., and K. Smith, 2013: Can natural variability explain the observed Antarctic sea ice trends? New modeling evidence from CMIP5. Geophys. Res. Lett., 40, 31953199, https://doi.org/10.1002/grl.50578.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Previdi, M., 2010: Radiative feedbacks on global precipitation. Environ. Res. Lett., 5, 025211, https://doi.org/10.1088/1748-9326/5/2/025211.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qu, X., and A. Hall, 2005: Surface contribution to planetary albedo variability in the cryosphere regions. J. Climate, 18, 52395252, https://doi.org/10.1175/JCLI3555.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roe, G., 2009: Feedbacks, timescales, and seeing red. Annu. Rev. Earth Planet. Sci., 37, 930115, https://doi.org/10.1146/ANNUREV.EARTH.061008.134734.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rosenblum, E., and I. Eisenman, 2016: Faster Arctic sea ice retreat in CIMP5 than in CMIP3 due to volcanoes. J. Climate, 29, 91799188, https://doi.org/10.1175/JCLI-D-16-0391.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schneider, A., M. Flanner, and J. Perket, 2018: Multidecadal variability in surface albedo feedback across CMIP5 models. Geophys. Res. Lett., 45, 19721980, https://doi.org/10.1002/2017GL076293.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shell, K., J. Kiehl, and C. Shields, 2008: Using the radiative kernel technique to calculate climate feedbacks in NCAR’s Community Atmospheric Model. J. Climate, 21, 22692282, https://doi.org/10.1175/2007JCLI2044.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shu, Q., Z. Song, and F. Qiao, 2015: Assessment of sea ice simulations in the CMIP5 models. Cryosphere, 9, 399409, https://doi.org/10.5194/tc-9-399-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sledd, A., and T. L’Ecuyer, 2019: How much do clouds mask the impact of Arctic sea ice and snow cover variations? Different perspectives from observations and reanalyses. Atmosphere, 10, 12, https://doi.org/10.3390/atmos10010012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, C., and Coauthors, 2018: Understanding rapid adjustments to diverse forcing agents. Geophys. Res. Lett., 45, 12 02312 031, https://doi.org/10.1029/2018GL079826.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soden, B., and I. Held, 2006: An assessment of climate feedbacks in coupled ocean–atmosphere models. J. Climate, 19, 33543360, https://doi.org/10.1175/JCLI3799.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stocker, T., and Coauthors, 2013: Technical summary. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 33–115, https://doi.org/10.1017/CBO9781107415324.005.

    • Crossref
    • Export Citation
  • Stroeve, J., and D. Notz, 2015: Insights on past and future sea-ice evolution from combining observations and models. Global Planet. Change, 135, 119132, https://doi.org/10.1016/j.gloplacha.2015.10.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K., M. Crucifix, P. Braconnot, C. Hewitt, C. Doutriaux, A. Broccoli, J. Mitchell, and M. Webb, 2007: Estimating shortwave radiative forcing and response in climate models. J. Climate, 20, 25302543, https://doi.org/10.1175/JCLI4143.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K., R. Stouffer, and G. Meehl, 2012: An overview of cmip5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and J. T. Fasullo, 2010: Simulation of present-day and twenty-first-century energy budgets of the southern oceans. J. Climate, 23, 440454, https://doi.org/10.1175/2009JCLI3152.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vavrus, S., D. Waliser, A. Schweiger, and J. Francis, 2009: Simulations of 20th and 21st century Arctic cloud amount in the global climate models assessed in the IPCC AR4. Climate Dyn., 33, 10991115, https://doi.org/10.1007/s00382-008-0475-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X., and J. 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
  • Winton, M., 2006: Surface albedo feedback estimates from the AR4 climate models. J. Climate, 19, 359365, https://doi.org/10.1175/JCLI3624.1.

All Time Past Year Past 30 Days
Abstract Views 69 69 39
Full Text Views 14 14 6
PDF Downloads 19 19 8

The Effect of Atmospheric Transmissivity on Model and Observational Estimates of the Sea Ice Albedo Feedback

View More View Less
  • 1 Applied Physics Laboratory, University of Washington, Seattle, Washington
  • 2 Department of Atmospheric Sciences, University of Washington, Seattle, Washington
  • 3 Polar Science Center, Applied Physics Laboratory, University of Washington, Seattle, Washington
  • 4 Pacific Northwest National Laboratory, Richland, Washington
© Get Permissions
Restricted access

Abstract

The sea ice-albedo feedback (SIAF) is the product of the ice sensitivity (IS), that is, how much the surface albedo in sea ice regions changes as the planet warms, and the radiative sensitivity (RS), that is, how much the top-of-atmosphere radiation changes as the surface albedo changes. We demonstrate that the RS calculated from radiative kernels in climate models is reproduced from calculations using the “approximate partial radiative perturbation” method that uses the climatological radiative fluxes at the top of the atmosphere and the assumption that the atmosphere is isotropic to shortwave radiation. This method facilitates the comparison of RS from satellite-based estimates of climatological radiative fluxes with RS estimates across a full suite of coupled climate models and, thus, allows model evaluation of a quantity important in characterizing the climate impact of sea ice concentration changes. The satellite-based RS is within the model range of RS that differs by a factor of 2 across climate models in both the Arctic and Southern Ocean. Observed trends in Arctic sea ice are used to estimate IS, which, in conjunction with the satellite-based RS, yields an SIAF of 0.16 ± 0.04 W m−2 K−1. This Arctic SIAF estimate suggests a modest amplification of future global surface temperature change by approximately 14% relative to a climate system with no SIAF. We calculate the global albedo feedback in climate models using model-specific RS and IS and find a model mean feedback parameter of 0.37 W m−2 K−1, which is 40% larger than the IPCC AR5 estimate based on using RS calculated from radiative kernel calculations in a single climate model.

© 2020 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: Aaron Donohoe, adonohoe@u.washington.edu

Abstract

The sea ice-albedo feedback (SIAF) is the product of the ice sensitivity (IS), that is, how much the surface albedo in sea ice regions changes as the planet warms, and the radiative sensitivity (RS), that is, how much the top-of-atmosphere radiation changes as the surface albedo changes. We demonstrate that the RS calculated from radiative kernels in climate models is reproduced from calculations using the “approximate partial radiative perturbation” method that uses the climatological radiative fluxes at the top of the atmosphere and the assumption that the atmosphere is isotropic to shortwave radiation. This method facilitates the comparison of RS from satellite-based estimates of climatological radiative fluxes with RS estimates across a full suite of coupled climate models and, thus, allows model evaluation of a quantity important in characterizing the climate impact of sea ice concentration changes. The satellite-based RS is within the model range of RS that differs by a factor of 2 across climate models in both the Arctic and Southern Ocean. Observed trends in Arctic sea ice are used to estimate IS, which, in conjunction with the satellite-based RS, yields an SIAF of 0.16 ± 0.04 W m−2 K−1. This Arctic SIAF estimate suggests a modest amplification of future global surface temperature change by approximately 14% relative to a climate system with no SIAF. We calculate the global albedo feedback in climate models using model-specific RS and IS and find a model mean feedback parameter of 0.37 W m−2 K−1, which is 40% larger than the IPCC AR5 estimate based on using RS calculated from radiative kernel calculations in a single climate model.

© 2020 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: Aaron Donohoe, adonohoe@u.washington.edu
Save