• Acosta Navarro, J. C., and Coauthors, 2020: Link between autumnal Arctic sea ice and Northern Hemisphere winter forecast skill. Geophys. Res. Lett., 47, e2019GL086753, https://doi.org/10.1029/2019GL086753.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barber, D. G., J. V. Lukovich, J. Keogak, S. Baryluk, L. Fortier, and G. H. R. Henry, 2009: The changing climate of the Arctic. Arctic, 61, 726, https://doi.org/10.14430/arctic98.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Best, M. J., and Coauthors, 2011: The Joint UK Land Environment Simulator (JULES), model description–Part 1: Energy and water fluxes. Geosci. Model Dev., 4, 677699, https://doi.org/10.5194/gmd-4-677-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bintanja, R., and E. C. van der Linden, 2013: The changing seasonal climate in the Arctic. Sci. Rep., 3, 1556, https://doi.org/10.1038/srep01556.

  • Bitz, C. M., M. M. Holland, E. C. Hunke, and R. E. Moritz, 2005: Maintenance of the sea-ice edge. J. Climate, 18, 29032921, https://doi.org/10.1175/JCLI3428.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blanchard-Wrigglesworth, E., K. C. Armour, C. M. Bitz, and E. DeWeaver, 2011: Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations. J. Climate, 24, 231250, https://doi.org/10.1175/2010JCLI3775.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Budikova, D., 2009: Role of Arctic sea ice in global atmospheric circulation: A review. Global Planet. Change, 68, 149163, https://doi.org/10.1016/j.gloplacha.2009.04.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chapman, W. L., and J. E. Walsh, 2007: Simulations of Arctic temperature and pressure by global coupled models. J. Climate, 20, 609632, https://doi.org/10.1175/JCLI4026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chevallier, M., D. Salas y Mélia, A. Voldoire, M. Déqué, and G. Garric, 2013: Seasonal forecasts of the pan-Arctic sea ice extent using a GCM-based seasonal prediction system. J. Climate, 26, 60926104, https://doi.org/10.1175/JCLI-D-12-00612.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chylek, P., J. Li, M. K. Dubey, M. Wang, and G. Lesins, 2011: Observed and model simulated 20th century Arctic temperature variability: Canadian Earth System Model CanESM2. Atmos. Chem. Phys. Discuss., 11, 22 89322 907, https://doi.org/10.5194/acpd-11-22893-2011.

    • Search Google Scholar
    • Export Citation
  • Comiso, J. C., and D. K. Hall, 2014: Climate trends in the Arctic as observed from space. Wiley Interdiscip. Rev.: Climate Change, 5, 389409, https://doi.org/10.1002/wcc.277.

    • Search Google Scholar
    • Export Citation
  • Dai, P., Y. Gao, F. Counillon, Y. Wang, M. Kimmritz, and H. R. Langehaug, 2020: Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model. Climate Dyn., 54, 38633878, https://doi.org/10.1007/s00382-020-05196-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Day, J. J., S. Tietsche, and E. Hawkins, 2014: Pan-Arctic and regional sea ice predictability: Initialization month dependence. J. Climate, 27, 43714390, https://doi.org/10.1175/JCLI-D-13-00614.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., R. Tomas, M. Alexander, and D. Lawrence, 2010: The seasonal atmospheric response to projected Arctic sea ice loss in the late twenty-first century. J. Climate, 23, 333351, https://doi.org/10.1175/2009JCLI3053.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ding, Q., J. M. Wallace, D. S. Battisti, E. J. Steig, A. J. Gallant, H. J. Kim, and L. Geng, 2014: Tropical forcing of the recent rapid Arctic warming in northeastern Canada and Greenland. Nature, 509, 209212, https://doi.org/10.1038/nature13260.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Döscher, R., T. Vihma, and E. Maksimovich, 2014: Recent advances in understanding the Arctic climate system state and change from a Sea ice perspective: A review. Atmos. Chem. Phys., 14, 13 57113 600, https://doi.org/10.5194/acp-14-13571-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drobot, S. D., and J. A. Maslanik, 2002: A practical method for long-range forecasting of ice severity in the Beaufort Sea. Geophys. Res. Lett., 29, 1213, https://doi.org/10.1029/2001GL014173.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunstone, N., D. Smith, A. Scaife, L. Hermanson, R. Eade, and N. Robinson, 2016: Skilful predictions of the winter North Atlantic Oscillation one year ahead. Nat. Geosci., 9, 809814, https://doi.org/10.1038/ngeo2824.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eicken, H., 2013: Arctic sea ice needs better forecasts. Nature, 497, 431433, https://doi.org/10.1038/497431a.

  • Francis, J. A., and S. J. Vavrus, 2012: Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophys. Res. Lett., 39, L06801, https://doi.org/10.1029/2012GL051000.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Francis, J. A., W. Chan, D. J. Leathers, J. R. Miller, and D. E. Veron, 2009: Winter Northern Hemisphere weather patterns remember summer Arctic sea-ice extent. Geophys. Res. Lett., 36, L07503, https://doi.org/10.1029/2009GL037274.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goosse, H., and Coauthors, 2018: Quantifying climate feedbacks in polar regions. Nat. Commun., 9, 1919, https://doi.org/10.1038/s41467-018-04173-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guemas, V., and Coauthors, 2016: A review on Arctic sea-ice predictability and prediction on seasonal to decadal time-scales. Quart. J. Roy. Meteor. Soc., 142, 546561, https://doi.org/10.1002/qj.2401.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haarsma, R. J., and Coauthors, 2016: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6. Geosci. Model Dev., 9, 41854208, https://doi.org/10.5194/gmd-9-4185-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagedorn, R., F. J. Doblas-Reyes, and T. N. Palmer, 2016: The rationale behind the success of multi-model ensembles in seasonal forecasting—I. Basic concept. Tellus, 57A, 219233, https://doi.org/10.1111/j.1600-0870.2005.00103.x.

    • Search Google Scholar
    • Export Citation
  • Harnos, K. J., M. L’Heureux, Q. Ding, and Q. Zhang, 2019: Skill of seasonal Arctic sea ice extent predictions using the North American Multimodel Ensemble. J. Climate, 32, 623638, https://doi.org/10.1175/JCLI-D-17-0766.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holland, M. M., D. A. Bailey, and S. Vavrus, 2010: Inherent sea ice predictability in the rapidly changing Arctic environment of the Community Climate System Model, version 3. Climate Dyn., 36, 12391253, https://doi.org/10.1007/s00382-010-0792-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Honda, M., J. Inoue, and S. Yamane, 2009: Influence of low Arctic sea-ice minima on anomalously cold Eurasian winters. Geophys. Res. Lett., 36, L08707, https://doi.org/10.1029/2008GL037079.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, C., S. Yang, Q. Wu, Z. Li, J. Chen, K. Deng, T. Zhang, and C. Zhang, 2016: Shifting El Niño inhibits summer Arctic warming and Arctic sea-ice melting over the Canada basin. Nat. Commun., 7, 11721, https://doi.org/10.1038/ncomms11721.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hunke, E. C., and W. H. Lipscomb, 2010: CICE: The Los Alamos Sea Ice Model documentation and software user’s manual. Los Alamos National Laboratory Tech. Rep. LA-CC-06-012, 76 pp., https://csdms.colorado.edu/w/images/CICE_documentation_and_software_user's_manual.pdf.

  • IPCC, 2007: Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.

  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp., https://doi.org/10.1017/CBO9781107415324.

    • Crossref
    • Export Citation
  • Jaiser, R., K. Dethloff, D. Handorf, A. Rinke, and J. Cohen, 2012: Planetary- and baroclinic-scale interactions between atmospheric and sea ice cover changes in the Arctic. Tellus, 64A, 11595, https://doi.org/10.3402/tellusa.v64i0.11595.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johannessen, O. M., and Coauthors, 2004: Arctic climate change: Observed and modelled temperature and sea-ice variability. Tellus, 56A, 328341, https://doi.org/10.3402/tellusa.v56i4.14418.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johannessen, O. M., S. I. Kuzmina, L. P. Bobylev, and M. W. Miles, 2016: Surface air temperature variability and trends in the Arctic: New amplification assessment and regionalisation. Tellus, 68, 28234, https://doi.org/10.3402/tellusa.v68.28234.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, C. M., P. Lemke, and T. P. Barnett, 1985: Linear prediction of sea ice anomalies. J. Geophys. Res., 90, 56655675, https://doi.org/10.1029/JD090iD03p05665.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kobayashi, S., and Coauthors, 2015: The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 548, https://doi.org/10.2151/jmsj.2015-001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krahmann, G., and M. Visbeck, 2003: Arctic Ocean sea ice response to northern annular mode-like wind forcing. Geophys. Res. Lett., 30, 1793, https://doi.org/10.1029/2003GL017354.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kwok, R., 2000: Recent changes in Arctic Ocean sea ice motion associated with the North Atlantic Oscillation. Geophys. Res. Lett., 27, 775778, https://doi.org/10.1029/1999GL002382.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, S., 2012: Testing of the tropically excited Arctic warming mechanism (TEAM) with traditional El Niño and La Niña. J. Climate, 25, 40154022, https://doi.org/10.1175/JCLI-D-12-00055.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, S., T. Gong, N. Johnson, S. B. Feldstein, and D. Pollard, 2011: On the possible link between tropical convection and the Northern Hemisphere Arctic surface air temperature change between 1958 and 2001. J. Climate, 24, 43504367, https://doi.org/10.1175/2011JCLI4003.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z., W. Zhang, M. F. Stuecker, H. Xu, F.-F. Jin, and C. Liu, 2019: Different effects of two ENSO types in Arctic surface temperature in boreal winter. J. Climate, 32, 49434961, https://doi.org/10.1175/JCLI-D-18-0761.1.

    • Crossref
    • 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, https://doi.org/10.1029/2007JC004259.

    • Search Google Scholar
    • Export Citation
  • MacLachlan, C., and Coauthors, 2015: Global Seasonal forecast system version 5 (GloSea5): A high-resolution seasonal forecast system. Quart. J. Roy. Meteor. Soc., 141, 10721084, https://doi.org/10.1002/qj.2396.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madec, G., B. Rachid, B. Clément, C. Andrew, D. Srdan, F. Rachel, and O. Paolo, 2013: NEMO ocean engine, version 3.4. IPSL Note du Pole de Modélisation 27, 333 pp.

  • Megann, A., and Coauthors, 2014: GO5.0: The joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications. Geosci. Model Dev., 7, 10691092, https://doi.org/10.5194/gmd-7-1069-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merryfield, W. J., W.-S. Lee, W. Wang, M. Chen, and A. Kumar, 2013: Multi-system seasonal predictions of Arctic sea ice. Geophys. Res. Lett., 40, 15511556, https://doi.org/10.1002/grl.50317.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mysak, L. A., and S. A. Venegas, 1998: Decadal climate oscillations in the Arctic: A new feedback loop for atmosphere–ice–ocean interactions. Geophys. Res. Lett., 25, 36073610, https://doi.org/10.1029/98GL02782.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Notz, D., and J. Marotzke, 2012: Observations reveal external driver for Arctic sea-ice retreat. Geophys. Res. Lett., 39, L08502, https://doi.org/10.1029/2012GL051094.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Overland, J. E., and M. Wang, 2010: Large-scale atmospheric circulation changes are associated with the recent loss of Arctic sea ice. Tellus, 62A, 19, https://doi.org/10.1111/j.1600-0870.2009.00421.x.

    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., and Coauthors, 2004: Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER). Bull. Amer. Meteor. Soc., 85, 853872, https://doi.org/10.1175/BAMS-85-6-853.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petoukhov, V., and V. A. Semenov, 2010: A link between reduced Barents-Kara sea ice and cold winter extremes over northern continents. J. Geophys. Res., 115, D21111, https://doi.org/10.1029/2009JD013568.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pithan, F., and T. Mauritsen, 2014: Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nat. Geosci., 7, 181184, https://doi.org/10.1038/ngeo2071.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rae, J. G. L., H. T. Hewitt, A. J. Keen, J. K. Ridley, A. E. West, C. M. Harris, E. C. Hunke, and D. N. Walters, 2015: Development of the Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled model. Geosci. Model Dev., 8, 22212230, https://doi.org/10.5194/gmd-8-2221-2015.

    • 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
  • Rigor, I. G., J. M. Wallace, and R. L. Colony, 2002: Response of sea ice to the Arctic oscillation. J. Climate, 15, 26482663, https://doi.org/10.1175/1520-0442(2002)015<2648:ROSITT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sassi, F., D. Kinnison, B. A. Boville, R. R. Garcia, and R. Roble, 2004: Effect of El Niño–Southern Oscillation on the dynamical, thermal, and chemical structure of the middle atmosphere. J. Geophys. Res., 109, D17108, https://doi.org/10.1029/2003JD004434.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Screen, J. A., and I. Simmonds, 2010: Increasing fall-winter energy loss from the Arctic Ocean and its role in Arctic temperature amplification. Geophys. Res. Lett., 37, L16707, https://doi.org/10.1029/2010GL044136.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Screen, J. A., I. Simmonds, C. Deser, and R. Tomas, 2013: The atmospheric response to three decades of observed Arctic sea ice loss. J. Climate, 26, 12301248, https://doi.org/10.1175/JCLI-D-12-00063.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and J. A. Francis, 2006: The Arctic amplification debate. Climatic Change, 76, 241264, https://doi.org/10.1007/s10584-005-9017-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and R. G. Barry, 2011: Processes and impacts of Arctic amplification: A research synthesis. Global Planet. Change, 77, 8596, https://doi.org/10.1016/j.gloplacha.2011.03.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and J. Stroeve, 2015: Arctic sea ice trends, variability and implications for seasonal ice forecasting. Philos. Trans. Roy. Soc. London, 373A, 20140159, https://doi.org/10.1098/rsta.2014.0159.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., A. P. Barrett, J. C. Stroeve, D. N. Kindig, and M. M. Holland, 2009: The emergence of surface-based Arctic amplification. Cryosphere, 3, 1119, https://doi.org/10.5194/tc-3-11-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sigmond, M., J. C. Fyfe, G. M. Flato, V. V. Kharin, and W. J. Merryfield, 2013: Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system. Geophys. Res. Lett., 40, 529534, https://doi.org/10.1002/grl.50129.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, D. M., and J. M. Murphy, 2007: An objective ocean temperature and salinity analysis using covariances from a global climate model. J. Geophys. Res., 112, C02022, https://doi.org/10.1029/2005JC003172.

    • Search Google Scholar
    • Export Citation
  • Valcke, S., 2013: The OASIS3 coupler: A European climate modelling community software. Geosci. Model Dev., 6, 373388, https://doi.org/10.5194/gmd-6-373-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walsh, J. E., 1980: Empirical orthogonal function and the statistical predictability of sea ice extent. Sea Ice Processes and Models, R. S. Pritchard, Ed., University of Washington Press, 373–384.

  • Walsh, J. E., 1983: The role of sea ice in climatic variability: Theories and evidence. Atmos.–Ocean, 21, 229242, https://doi.org/10.1080/07055900.1983.9649166.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walters, D. N., and Coauthors, 2017: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations. Geosci. Model Dev., 10, 14871520, https://doi.org/10.5194/gmd-10-1487-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, W., M. Chen, and A. Kumar, 2013: Seasonal prediction of Arctic sea ice extent from a coupled dynamical forecast system. Mon. Wea. Rev., 141, 13751394, https://doi.org/10.1175/MWR-D-12-00057.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, K. D., and Coauthors, 2015: The Met Office Global Coupled model 2.0 (GC2) configuration. Geosci. Model Dev., 8, 15091524, https://doi.org/10.5194/gmd-8-1509-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, N., and Coauthors, 2014: An inherently mass-conserving semi-implicit semi-Lagrangian discretization of the deep-atmosphere global nonhydrostatic equations. Quart. J. Roy. Meteor. Soc., 140, 15051520, https://doi.org/10.1002/qj.2235.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yoo, C., S. Feldstein, and S. Lee, 2011: The impact of the Madden–Julian oscillation trend on the Arctic amplification of surface air temperature during the 1979–2008 boreal winter. Geophys. Res. Lett., 38, L24804, https://doi.org/10.1029/2011GL049881.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., M. Steele, D. A. Rothrock, and R. W. Lindsay, 2004: Increasing exchanges of Greenland–Scotland ridge and their links with the North Atlantic Oscillation and Arctic sea ice. Geophys. Res. Lett., 31, L09307, https://doi.org/10.1029/2003GL019304.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Prediction of Arctic Temperature and Sea Ice Using a High-Resolution Coupled Model

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  • 1 Institute for Climate and Application Research/Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
  • | 2 Met Office, Exeter, United Kingdom
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Abstract

Under global warming, surface air temperature has risen rapidly and sea ice has decreased markedly in the Arctic. These drastic climate changes have brought about various severe impacts on the vulnerable environment and ecosystem there. Thus, accurate prediction of Arctic climate becomes more important than before. Here we examine the seasonal to interannual predictive skills of 2-m air temperature (2-m T) and sea ice cover (SIC) over the Arctic region (70°–90°N) during 1980–2014 with a high-resolution global coupled model called the Met Office Decadal Prediction System, version 3 (DePreSys3). The model captures well both the climatology and interannual variability of the Arctic 2-m T and SIC. Moreover, the anomaly correlation coefficient of Arctic-averaged 2-m T and SIC shows statistically significant skills at lead times up to 16 months. This is mainly due to the contribution of strong decadal trends. In addition, it is found that the peak warming trend of Arctic 2-m T lags the maximum decrease trend of SIC by 1 month, in association with the heat flux forcing from the ocean surface to lower atmosphere. While the predictive skill is generally much lower for the detrended variations, we find a close relationship between the tropical Pacific El Niño–Southern Oscillation and the Arctic detrended 2-m T anomalies. This indicates potential seasonal to interannual predictability of the Arctic natural variations.

© 2021 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: Jing-Jia Luo, jjluo@nuist.edu.cn

Abstract

Under global warming, surface air temperature has risen rapidly and sea ice has decreased markedly in the Arctic. These drastic climate changes have brought about various severe impacts on the vulnerable environment and ecosystem there. Thus, accurate prediction of Arctic climate becomes more important than before. Here we examine the seasonal to interannual predictive skills of 2-m air temperature (2-m T) and sea ice cover (SIC) over the Arctic region (70°–90°N) during 1980–2014 with a high-resolution global coupled model called the Met Office Decadal Prediction System, version 3 (DePreSys3). The model captures well both the climatology and interannual variability of the Arctic 2-m T and SIC. Moreover, the anomaly correlation coefficient of Arctic-averaged 2-m T and SIC shows statistically significant skills at lead times up to 16 months. This is mainly due to the contribution of strong decadal trends. In addition, it is found that the peak warming trend of Arctic 2-m T lags the maximum decrease trend of SIC by 1 month, in association with the heat flux forcing from the ocean surface to lower atmosphere. While the predictive skill is generally much lower for the detrended variations, we find a close relationship between the tropical Pacific El Niño–Southern Oscillation and the Arctic detrended 2-m T anomalies. This indicates potential seasonal to interannual predictability of the Arctic natural variations.

© 2021 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: Jing-Jia Luo, jjluo@nuist.edu.cn
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