• Bitz, C. M., , D. S. Battisti, , R. E. Moritz, , and J. A. Beesley, 1996: Low-frequency variability in the Arctic atmosphere, sea ice, and upper-ocean climate system. J. Climate, 9, 394408, doi:10.1175/1520-0442(1996)009<0394:LFVITA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Blanchard-Wrigglesworth, E., , K. Armour, , C. M. Bitz, , and E. deWeaver, 2011a: Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations. J. Climate, 24, 231–250, doi:10.1175/2010JCLI3775.1.

    • Search Google Scholar
    • Export Citation
  • Blanchard-Wrigglesworth, E., , C. M. Bitz, , and M. H. Holland, 2011b: Influence of initial conditions and climate forcing on predicting Arctic sea ice. Geophys. Res. Lett., 38, L18503, doi:10.1029/2011GL048807.

    • Search Google Scholar
    • Export Citation
  • Day, J., , S. Tietsche, , and E. Hawkins, 2014: Pan-Arctic and regional sea ice predictability: Initialization month dependence. J. Climate, 27, 4371–4390, doi:10.1175/JCLI-D-13-00614.1.

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

  • Gregory, J., , P. Stott, , D. Cresswell, , N. Rayner, , C. Gordon, , and D. Sexton, 2002: Recent and future changes in Arctic sea ice simulated by the HadCM3 AOGCM. Geophys. Res. Lett., 29, 2175, doi:10.1029/2001GL014575.

    • Search Google Scholar
    • Export Citation
  • Holland, M. M., , D. A. Bailey, , and S. Vavrus, 2011: Inherent sea ice predictability in the rapidly changing Arctic environment of the Community Climate System Model, version 3. Climate Dyn., 36, 1239–1253, doi:10.1007/s00382-010-0792-4.

    • Search Google Scholar
    • Export Citation
  • Jahn, A., and Coauthors, 2012: Late-twentieth-century simulation of Arctic sea ice and ocean properties in the CCSM4. J. Climate, 25, 14311452, doi:10.1175/JCLI-D-11-00201.1.

    • Search Google Scholar
    • Export Citation
  • Kwok, R., , and D. A. Rothrock, 2009: Decline in Arctic sea ice thickness from submarine and ICESat records: 1958–2008. Geophys. Res. Lett., 36, L15501, doi:10.1029/2009GL039035.

    • Search Google Scholar
    • Export Citation
  • Large, W. G., , and S. G. Yeager, 2004: Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies. NCAR Tech. Note NCAR/TN-460+STR, 105 pp, doi:10.5065/D6KK98Q6.

  • Lindsay, R. W., , and J. Zhang, 2006a: Assimilation of ice concentration in an ice-ocean model. J. Atmos. Oceanic Technol., 23, 742749, doi:10.1175/JTECH1871.1.

    • Search Google Scholar
    • Export Citation
  • Lindsay, R. W., , and J. Zhang, 2006b: Arctic Ocean ice thickness: Modes of variability and the best locations from which to monitor them. J. Phys. Oceanogr., 36, 496506, doi:10.1175/JPO2861.1.

    • Search Google Scholar
    • Export Citation
  • Lindsay, R. W., , J. Zhang, , A. J. Schweiger, , and M. A. Steele, 2008: Seasonal predictions of ice extent in the Arctic Ocean. J. Geophys. Res., 113, C02023, doi:10.1029/2007JC004259.

    • Search Google Scholar
    • Export Citation
  • Maykut, G., 1985: An introduction to ice in the polar oceans. Tech. Rep. APL-UW 8510, Applied Physics Laboratory, University of Washington, 116 pp.

  • Notz, D., 2009: The future of ice sheets and sea ice: Between reversible retreat and unstoppable loss. Proc. Natl. Acad. Sci. USA, 106 (49), 20 59020 595, doi:10.1073/pnas.0902356106.

    • Search Google Scholar
    • Export Citation
  • Schweiger, A., , R. Lindsay, , J. Zhang, , M. Steele, , H. Stern, , and R. Kwok, 2011: Uncertainty in modeled Arctic sea ice volume. J. Geophys. Res., 116, C00D06, doi:10.1029/2011JC007084.

    • Search Google Scholar
    • Export Citation
  • Stroeve, J. C., , M. C. Serreze, , M. M. Holland, , J. E. Kay, , J. Malanik, , and A. P. Barrett, 2012: The Arctic’s rapidly shrinking sea ice cover: A research synthesis. Climatic Change, 110, 10051027, doi:10.1007/s10584-011-0101-1.

    • Search Google Scholar
    • Export Citation
  • Stroeve, J. C., , L. Hamilton, , C. M. Bitz, , and E. Blanchard-Wrigglesworth, 2014: Predicting September sea ice: Ensemble skill of the SEARCH sea ice outlook 2008–2013. Geophys. Res. Lett., 41, 24112418, doi:10.1002/2014GL059388.

    • Search Google Scholar
    • Export Citation
  • Thorndike, A., 1992: A toy model linking atmospheric thermal radiation and sea ice growth. J. Geophys. Res.,97 (C6), 9401–9410, doi:10.1029/92JC00695.

  • Tietsche, S., and Coauthors, 2014: Seasonal to interannual Arctic sea ice predictability in current global climate models. Geophys. Res. Lett., 41, 1035–1043, doi:10.1002/2013GL058755.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., , and D. Rothrock, 2003: Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates. Mon. Wea. Rev., 131, 845861, doi:10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
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Characteristics of Arctic Sea-Ice Thickness Variability in GCMs

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  • 1 Department of Atmospheric Sciences, University of Washington, Seattle, Washington
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Abstract

Skillful Arctic sea ice forecasts may be possible for lead times of months or even years owing to the persistence of thickness anomalies. In this study sea ice thickness variability is characterized in fully coupled GCMs and sea ice–ocean-only models (IOMs) that are forced with an estimate of observations derived from atmospheric reanalysis and satellite measurements. Overall, variance in sea ice thickness is greatest along Arctic Ocean coastlines. Sea ice thickness anomalies have a typical time scale of about 6–20 months, a time scale that lengthens about a season when accounting for ice transport, and a typical length scale of about 500–1000 km. The range of these scales across GCMs implies that an estimate of the number of thickness monitoring locations needed to characterize the full Arctic basin sea ice thickness variability field is model dependent and would vary between 3 and 14. Models with a thinner mean ice state tend to have ice-thickness anomalies that are generally shorter lived and smaller in amplitude but have larger spatial scales. Additionally, sea ice thickness variability in IOMs is damped relative to GCMs in part due to strong negative coupling between the dynamic and thermodynamic processes that affect sea ice thickness. The significance for designing prediction systems is discussed.

Corresponding author address: Edward Blanchard-Wrigglesworth, Department of Atmospheric Sciences, University of Washington, Box 351640, Seattle, WA 98195-1640. E-mail: ed@atmos.washington.edu

Abstract

Skillful Arctic sea ice forecasts may be possible for lead times of months or even years owing to the persistence of thickness anomalies. In this study sea ice thickness variability is characterized in fully coupled GCMs and sea ice–ocean-only models (IOMs) that are forced with an estimate of observations derived from atmospheric reanalysis and satellite measurements. Overall, variance in sea ice thickness is greatest along Arctic Ocean coastlines. Sea ice thickness anomalies have a typical time scale of about 6–20 months, a time scale that lengthens about a season when accounting for ice transport, and a typical length scale of about 500–1000 km. The range of these scales across GCMs implies that an estimate of the number of thickness monitoring locations needed to characterize the full Arctic basin sea ice thickness variability field is model dependent and would vary between 3 and 14. Models with a thinner mean ice state tend to have ice-thickness anomalies that are generally shorter lived and smaller in amplitude but have larger spatial scales. Additionally, sea ice thickness variability in IOMs is damped relative to GCMs in part due to strong negative coupling between the dynamic and thermodynamic processes that affect sea ice thickness. The significance for designing prediction systems is discussed.

Corresponding author address: Edward Blanchard-Wrigglesworth, Department of Atmospheric Sciences, University of Washington, Box 351640, Seattle, WA 98195-1640. E-mail: ed@atmos.washington.edu
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