• Auclair, G., and L. B. Tremblay, 2018: The role of ocean heat transport in rapid sea ice declines in the Community Earth System Model large ensemble. J. Geophys. Res. Oceans, 123, 89418957, https://doi.org/10.1029/2018JC014525.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Babb, D. G., J. Landy, D. Barber, and R. Galley, 2019: Winter sea ice export from the Beaufort Sea as a preconditioning mechanism for enhanced summer melt: A case study of 2016. J. Geophys. Res. Oceans, 124, 65756600, https://doi.org/10.1029/2019JC015053.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barber, D., and R. Massom, 2007: The role of sea ice in Arctic and Antarctic polynyas. Polynyas: Windows to the World, W. Smith and D. Barber, Eds., Elsevier Oceanography Series, Vol. 74, Elsevier, 1–54.

    • Crossref
    • Export Citation
  • Bareiss, J., and K. Görgen, 2005: Spatial and temporal variability of sea ice in the Laptev Sea: Analyses and review of satellite passive-microwave data and model results, 1979 to 2002. Global Planet. Change, 48, 2854, https://doi.org/10.1016/j.gloplacha.2004.12.004.

    • 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
  • Blanken, H., L. B. Tremblay, S. Gaskin, and A. Slavin, 2017: Modelling the long-term evolution of worst-case Arctic oil spills. Mar. Pollut. Bull., 116, 315331, https://doi.org/10.1016/j.marpolbul.2016.12.070.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brunette, C., B. Tremblay, and R. Newton, 2019: Winter coastal divergence as a predictor for the minimum sea ice extent in the Laptev Sea. J. Climate, 32, 10631080, https://doi.org/10.1175/JCLI-D-18-0169.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bushuk, M., R. Msadek, M. Winton, G. A. Vecchi, R. Gudgel, A. Rosati, and X. Yang, 2017a: Summer enhancement of Arctic sea ice volume anomalies in the September-ice zone. J. Climate, 30, 23412362, https://doi.org/10.1175/JCLI-D-16-0470.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bushuk, M., R. Msadek, M. Winton, G. A. Vecchi, R. Gudgel, A. Rosati, and X. Yang, 2017b: Skillful regional prediction of Arctic sea ice on seasonal timescales. Geophys. Res. Lett., 44, 49534964, https://doi.org/10.1002/2017GL073155.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bushuk, M., M. Winton, D. B. Bonan, E. Blanchard-Wrigglesworth, and T. Delworth, 2020: A mechanism for the Arctic sea ice spring predictability barrier. Geophys. Res. Lett., 47, e2020GL088335, https://doi.org/10.1029/2020GL088335.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, Y., S. Liang, X. Chen, T. He, D. Wang, and X. Cheng, 2017: Enhanced wintertime greenhouse effect reinforcing Arctic amplification and initial sea-ice melting. Sci. Rep., 7, 8462, https://doi.org/10.1038/s41598-017-08545-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CERES Science Team, 2018: CERES EBAF Ed 4.0 data quality summary. 38 pp., https://ceres.larc.nasa.gov/documents/DQ_summaries/CERES_EBAF_Ed4.0_DQS.pdf.

  • CERES Science Team, 2019a: CERES Energy Balanced and Filled (EBAF) TOA monthly means data in netCDF edition 4.1. NASA Langley Atmospheric Science Data Center DAAC, accessed June 2019, https://doi.org/10.5067/TERRA-AQUA/CERES/EBAF-TOA_L3B004.1.

    • Crossref
    • Export Citation
  • CERES Science Team, 2019b: CERES Energy Balanced and Filled (EBAF) TOA and surface monthly means data in netCDF edition 4.1. NASA Langley Atmospheric Science Data Center DAAC, accessed June 2019, https://doi.org/10.5067/TERRA-AQUA/CERES/EBAF_L3B.004.1.

    • Crossref
    • Export Citation
  • CERES Science Team, 2020: CERES_EBAF-Surface Ed 4.1 data quality summary. 21 pp., https://ceres.larc.nasa.gov/documents/DQ_summaries/CERES_EBAF-Surface_Ed4.1_DQS.pdf.

  • Chevallier, M., and D. Salas-Mélia, 2012: The role of the sea ice thickness distribution in the Arctic sea ice potential predictability: A diagnostic approach with a coupled GCM. J. Climate, 25, 30253038, https://doi.org/10.1175/JCLI-D-11-00209.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choi, Y.-S., B.-M. Kim, S.-K. Hur, S.-J. Kim, J.-H. Kim, and C.-H. Ho, 2014: Connecting early summer cloud-controlled sunlight and late summer sea ice in the Arctic. J. Geophys. Res. Atmos., 119, 11 08711 099, https://doi.org/10.1002/2014JD022013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Comiso, J. C., 2012: Large decadal decline of the Arctic multiyear ice cover. J. Climate, 25, 11761193, https://doi.org/10.1175/JCLI-D-11-00113.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeRepentigny, P., B. Tremblay, R. Newton, and S. Pfirman, 2016: Patterns of sea ice retreat in the transition to a seasonally ice-free Arctic. J. Climate, 29, 69937008, https://doi.org/10.1175/JCLI-D-15-0733.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeRepentigny, P., A. Jahn, L. B. Tremblay, R. Newton, and S. Pfirman, 2020: Increased transnational sea ice transport between neighboring Arctic states in the 21st century. Earth’s Future, 8, e2019EF001284, https://doi.org/10.1029/2019EF001284.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emery, W. J., C. Fowler, and J. Maslanik, 1995: Satellite remote sensing of ice motion. Oceanographic Applications of Remote Sensing, M. Ikeda and F. W. Dobson, Eds., CRC Press, 367–379.

  • Francis, J. A., E. Hunter, J. Key, and X. Wang, 2005: Clues to variability in Arctic minimum sea ice extent. Geophys. Res. Lett., 32, L21501, https://doi.org/10.1029/2005GL024376.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gardner, A. S., and M. J. Sharp, 2010: A review of snow and ice albedo and the development of a new physically based broadband albedo parameterization. J. Geophys. Res., 115, F01009, https://doi.org/10.1029/2009JF001444.

    • Search Google Scholar
    • Export Citation
  • Guemas, V., M. Chevallier, M. Déqué, O. Bellprat, and F. Doblas-Reyes, 2016: Impact of sea ice initialization on sea ice and atmosphere prediction skill on seasonal timescales. Geophys. Res. Lett., 43, 38893896, https://doi.org/10.1002/2015GL066626.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamilton, L. C., and J. Stroeve, 2016: 400 predictions: The SEARCH Sea Ice Outlook 2008–2015. Polar Geogr., 39, 274287, https://doi.org/10.1080/1088937X.2016.1234518.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., 2016: Climate sensitivity and feedback mechanisms. Global Physical Climatology, 2nd ed., D. L. Hartmann, Ed., Elsevier, 293–323, https://doi.org/10.1016/B978-0-12-328531-7.00010-4.

    • Crossref
    • 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, 12391253, https://doi.org/10.1007/s00382-010-0792-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, Y., G. Chou, Y. Xie, and N. Soulard, 2019: Radiative control of the interannual variability of Arctic sea ice. Geophys. Res. Lett., 46, 98999908, https://doi.org/10.1029/2019GL084204.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hutchings, J. K., and I. G. Rigor, 2012: Role of ice dynamics in anomalous ice conditions in the Beaufort Sea during 2006 and 2007. J. Geophys. Res. Oceans, 117, C00E04, https://doi.org/10.1029/2011JC007182.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kapsch, M., R. Graversen, M. Tjernström, and R. Bintanja, 2016: The effect of downwelling longwave and shortwave radiation on Arctic summer sea ice. J. Climate, 29, 11431159, https://doi.org/10.1175/JCLI-D-15-0238.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kashiwase, H., K. I. Ohshima, S. Nihashi, and H. Eicken, 2017: Evidence for ice-ocean albedo feedback in the Arctic Ocean shifting to a seasonal ice zone. Sci. Rep., 7, 8170, https://doi.org/10.1038/s41598-017-08467-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kato, S., and et al. , 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
  • Kimura, N., A. Nishimura, Y. Tanaka, and H. Yamaguchi, 2013: Influence of winter sea-ice motion on summer ice cover in the Arctic. Polar Res., 32, 20 193, https://doi.org/10.3402/polar.v32i0.20193.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, M., D. Veron, and H. Huntley, 2020: Early predictors of seasonal Arctic sea-ice volume loss: The impact of spring and early-summer cloud radiative conditions. Ann. Glaciol., 61, 392400, https://doi.org/10.1017/aog.2020.60.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krumpen, T., M. Janout, K. I. Hodges, R. Gerdes, F. Girard-Ardhuin, J. A. Höleman, and S. Willmes, 2013: Variability and trends in Laptev Sea ice outflow between 1992–2011. Cryosphere, 7, 349363, https://doi.org/10.5194/tc-7-349-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kwok, R., 2006: Contrasts in sea ice deformation and production in the Arctic seasonal and perennial ice zones. J. Geophys. Res., 111, C11S22, https://doi.org/10.1029/2005JC003246.

    • Search Google Scholar
    • Export Citation
  • Kwok, R., 2018: Arctic sea ice thickness, volume, and multiyear ice coverage: losses and coupled variability (1958–2018). Environ. Res. Lett., 13, 105005, https://doi.org/10.1088/1748-9326/aae3ec.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kwok, R., G. Spreen, and S. Pang, 2013: Arctic sea ice circulation and drift speed: Decadal trends and ocean currents. J. Geophys. Res. Oceans, 118, 24082425, https://doi.org/10.1002/jgrc.20191.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lenetsky, J., B. Tremblay, C. Brunette, and G. Meneghello, 2021: Sub-seasonal predictability of Arctic Ocean sea ice conditions: Bering Strait and Ekman-driven ocean heat transport. J. Climate, 34, 44494462, https://doi.org/10.1175/JCLI-D-20-0544.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Letterly, A., J. Key, and Y. Liu, 2016: The influence of winter cloud on summer sea ice in the Arctic. J. Geophys. Res., 121, 21782187, https://doi.org/10.1002/2015JD024316.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Light, B., D. K. Perovich, M. A. Webster, C. Polashenski, and R. Dadic, 2015: Optical properties of melting first-year Arctic sea ice. J. Geophys. Res. Oceans, 120, 76577675, https://doi.org/10.1002/2015JC011163.

    • 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
  • Liu, J., M. Song, R. M. Horton, and Y. Hu, 2015: Revisiting the potential of melt pond fraction as a predictor for the seasonal Arctic sea ice extent minimum. Environ. Res. Lett., 10, 054017, https://doi.org/10.1088/1748-9326/10/5/054017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., and J. R. Key, 2014: Less winter cloud aids summer 2013 Arctic sea ice return from 2012 minimum. Environ. Res. Lett., 9, 044002, https://doi.org/10.1088/1748-9326/9/4/044002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., and et al. , 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.

    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., and et al. , 2020: Toward a consistent definition between satellite and model clear-sky radiative fluxes. J. Climate, 33, 6175, https://doi.org/10.1175/JCLI-D-19-0381.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., B. A. Wielicki, D. R. Doelling, G. L. Smith, D. F. Keyes, S. Kato, N. Manalo-Smith, and T. Wong, 2009: Toward optimal closure of the Earth’s top-of-atmosphere radiation budget. J. Climate, 22, 748766, https://doi.org/10.1175/2008JCLI2637.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maslanik, J. A., C. Fowler, J. Stroeve, S. Drobot, J. Zwally, D. Yi, and W. Emery, 2007: A younger, thinner Arctic ice cover: Increased potential for rapid, extensive sea-ice loss. Geophys. Res. Lett., 34, L24501, https://doi.org/10.1029/2007GL032043.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maslanik, J. A., J. Stroeve, C. Fowler, and W. Emery, 2011: Distribution and trends in Arctic sea ice age through spring 2011. Geophys. Res. Lett., 38, L13502, https://doi.org/10.1029/2011GL047735.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meier, W. N., M. Savoie, and S. Mallory, 2011: CDR climate algorithm and theoretical basis document: Sea ice concentration, rev. 6th ed. F. Fetterer and A. Windnagel, Eds., NOAA NCDC CDR Program.

  • Meier, W. N., F. Fetterer, M. Savoie, S. Mallory, R. Duerr, and J. Stroeve, 2017: NOAA/NSIDC climate data record of passive microwave sea ice concentration, version 3 [Daily SIC]. National Snow and Ice Data Center, accessed May 2019, https://doi.org/10.7265/N59P2ZTG.

    • Crossref
    • Export Citation
  • Miles, M. W., and R. G. Barry, 1998: A 5-year satellite climatology of winter sea ice leads in the western Arctic. J. Geophys. Res., 103, 21 72321 734, https://doi.org/10.1029/98JC01997.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morales Maqueda, M. A., A. J. Willmott, and N. R. T. Biggs, 2004: Polynya dynamics: A review of observations and modeling. Rev. Geophys., 42, 1, https://doi.org/10.1029/2002RG000116.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Msadek, R., G. A. Vecchi, M. Winton, and R. G. Gudgel, 2014: Importance of initial conditions in seasonal predictions of Arctic sea ice extent. Geophys. Res. Lett., 41, 52085215, https://doi.org/10.1002/2014GL060799.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newton, R., S. Pfirman, B. Tremblay, and P. DeRepentigny, 2017: Increasing transnational sea-ice exchange in a changing Arctic Ocean. Earth’s Future, 5, 633647, https://doi.org/10.1002/2016EF000500.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nikolaeva, A. J., and N. P. Sesterikov, 1970: A method of calculation of ice conditions (on the example of the Laptev Sea). Ice Forecasting Techniques for the Arctic Seas. B. A. Krutskih, Z. M. Gudkovic, and A. L. Sokolov, Eds., Amerind Publishing, 150–230.

  • Onarheim, I., T. Eldevik, L. Smedsrud, and J. Stroeve, 2018: Seasonal and regional manifestation of Arctic sea ice loss. J. Climate, 31, 49174932, https://doi.org/10.1175/JCLI-D-17-0427.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parkinson, C. L., and J. C. Comiso, 2013: On the 2012 record low Arctic sea ice cover: Combined impact of preconditioning and an August storm. Geophys. Res. Lett., 40, 13561361, https://doi.org/10.1002/grl.50349.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peng, G., W. N. Meier, D. Scott, and M. Savoie, 2013: A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring. Earth Syst. Sci. Data, 5, 311318, https://doi.org/10.5194/essd-5-311-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perovich, D. K., and C. Polashenski, 2012: Albedo evolution of seasonal Arctic sea ice. Geophys. Res. Lett., 39, L08501, https://doi.org/10.1029/2012GL051432.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perovich, D. K., J. A. Richter-Menge, K. F. Jones, and B. Light, 2008: Sunlight, water, and ice: Extreme Arctic sea ice melt during the summer of 2007. Geophys. Res. Lett., 35, L11501, https://doi.org/10.1029/2008GL034007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Preußer, A., K. I. Ohshima, K. Iwamoto, S. Willmes, and G. Heinemann, 2019: Retrieval of wintertime sea ice production in Arctic polynyas using thermal infrared and passive microwave remote sensing data. J. Geophys. Res. Oceans, 124, 55035528, https://doi.org/10.1029/2019JC014976.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rigor, I. G., and J. M. Wallace, 2004: Variations in the age of Arctic sea-ice and summer sea-ice extent. Geophys. Res. Lett., 31, L09401, https://doi.org/10.1029/2004GL019492.

    • 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
  • Rösel, A., L. Kaleschke, and G. Birnbaum, 2012: Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network. Cryosphere, 6, 431446, https://doi.org/10.5194/tc-6-431-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schröder, D., D. Feltham, D. Flocco, and M. Tsamados, 2014: September Arctic sea-ice minimum predicted by spring melt-pond fraction. Nat. Climate Change, 4, 353357, https://doi.org/10.1038/nclimate2203.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., M. M. Holland, and J. Stroeve, 2007: Perspectives on the Arctic’s shrinking sea-ice cover. Science, 315, 15331536, https://doi.org/10.1126/science.1139426.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., A. D. Crawford, J. C. Stroeve, A. P. Barrett, and R. A. Woodgate, 2016: Variability, trends, and predictability of seasonal sea ice retreat and advance in the Chukchi Sea. Geophys. Res. Lett., 121, 73087325, https://doi.org/10.1002/2016JC011977.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmonds, I., and I. Rudeva, 2012: The great Arctic cyclone of August 2012. Geophys. Res. Lett., 39, L23709, https://doi.org/10.1029/2012GL054259.

  • Spreen, G., R. Kwok, and D. Menemenlis, 2011: Trends in Arctic sea ice drift and role of wind forcing: 1992–2009. Geophys. Res. Lett., 38, L19501, https://doi.org/10.1029/2011GL048970.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Staalesen, A., 2017: 100 sailors trapped in ice near Arctic outpost. The Barents Observer, 7 February 2017, https://thebarentsobserver.com/en/arctic/2017/02/100-sailors-trapped-ice-near-arctic-outpost.

  • Steele, M., W. Ermold, and J. Zhang, 2008: Arctic Ocean surface warming trends over the past 100 years. Geophys. Res. Lett., 35, L02614, https://doi.org/10.1029/2007GL031651.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steffen, K., J. Key, D. Cavalieri, J. Comiso, P. Gloersen, K. S. Germain, and I. Rubinstein, 1992: The estimation of geophysical parameters using passive microwave algorithms. Microwave Remote Sensing of Sea Ice, Geophys. Monogr., Vol. 68, Amer. Geophys. Union, 201–231.

    • Crossref
    • Export Citation
  • Stephen, K., 2018: Societal impacts of a rapidly changing Arctic. Curr. Climate Change, 4, 223237, https://doi.org/10.1007/s40641-018-0106-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stroeve, J. C., A. B. V. Kattsov, M. Serreze, T. T. Pavlova, M. Holland, and W. N. Meier, 2012: Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophys. Res. Lett., 39, L16502, https://doi.org/10.1029/2012GL052676.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stroeve, J. C., L. C. 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, https://doi.org/10.1002/2014GL059388.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tschudi, M. A., J. A. Maslanik, and D. K. Perovich, 2008: Derivation of melt pond coverage on Arctic sea ice using MODIS observation. Remote Sens. Environ., 112, 26052614, https://doi.org/10.1016/j.rse.2007.12.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tschudi, M. A., W. N. Meier, and J. S. Stewart, 2019a: An enhancement to sea ice motion and age products. Cryosphere Discuss., 2019, 129, https://doi.org/10.5194/tc-2019-40.

    • Search Google Scholar
    • Export Citation
  • Tschudi, M. A., W. N. Meier, J. S. Stewart, C. Fowler, and J. A. Maslanik, 2019b: Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, version 4. Subset used: 25 October 1978–31 December 2019. NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed October 2019, https://doi.org/10.5067/INAWUWO7QH7B.

    • Crossref
    • Export Citation
  • Wang, J., J. Zhang, E. Watanabe, M. Ikeda, K. Mizobata, J. Walsh, X. Bai, and B. Wu, 2009: Is the dipole anomaly a major driver to record lows in Arctic summer sea ice extent? Geophys. Res. Lett., 36, L05706, https://doi.org/10.1029/2008GL036706.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, J., B. Tremblay, R. Newton, and R. Allard, 2016: Dynamic preconditioning of the minimum September sea-ice extent. J. Climate, 29, 58795891, https://doi.org/10.1175/JCLI-D-15-0515.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Willmes, S., S. Adams, D. Schröder, and G. Heinemann, 2011: Spatio-temporal variability of polynya dynamics and ice production in the Laptev Sea between the winters of 1979/80 and 2007/08. Polar Res., 30, 5971, https://doi.org/10.3402/polar.v30i0.5971.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woodgate, R. A., 2018: Increases in the Pacific inflow to the Arctic from 1990 to 2015 and insights into seasonal trends and driving mechanisms from year-round Bering Strait mooring data. Prog. Oceanogr., 160, 124154, https://doi.org/10.1016/j.pocean.2017.12.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woodgate, R. A., T. Weingartner, and R. Lindsay, 2010: The 2007 Bering Strait oceanic heat flux and anomalous Arctic sea-ice retreat. Geophys. Res. Lett., 37, L01602, https://doi.org/10.1029/2009GL041621.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhan, Y., and R. Davies, 2017: September Arctic sea ice extent indicated by June reflected solar radiation. J. Geophys. Res., 122, 21942202, https://doi.org/10.1002/2016JD025819.

    • Crossref
    • Search Google Scholar
    • Export Citation
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A Regional Seasonal Forecast Model of Arctic Minimum Sea Ice Extent: Reflected Solar Radiation versus Late Winter Coastal Divergence

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  • 1 a Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada
  • | 2 b Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York
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Abstract

Thinning sea ice cover in the Arctic is associated with larger interannual variability in the minimum sea ice extent (SIE). The current generation of forced or fully coupled models, however, has difficulty predicting SIE anomalies from the long-term trend, highlighting the need to better identify the mechanisms involved in the seasonal evolution of sea ice cover. One such mechanism is coastal divergence (CD), a proxy for ice thickness anomalies based on late winter ice motion, quantified using Lagrangian ice tracking. CD gains predictive skill through the positive feedback of surface albedo anomalies, mirrored in reflected solar radiation (RSR), during melt season. Exploring the dynamic and thermodynamic contributions to minimum SIE predictability, RSR, initial SIE (iSIE), and CD are compared as predictors using a regional seasonal sea ice forecast model for 1 July, 1 June, and 1 May forecast dates for all Arctic peripheral seas. The predictive skill of June RSR anomalies mainly originates from open water fraction at the surface; that is, June iSIE and June RSR have equal predictive skill for most seas. The finding is supported by the surprising positive correlation found between June melt pond fraction (MPF) and June RSR in all peripheral seas: MPF anomalies indicate the presence of ice or open water, which is key to creating minimum SIE anomalies. This contradicts models that show correlation between melt onset, MPF, and the minimum SIE. A hindcast model shows that for a 1 May forecast, CD anomalies have better predictive skill than RSR anomalies for most peripheral seas.

© 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: Rachel Kim, rachel.h.kim@mail.mcgill.ca

Abstract

Thinning sea ice cover in the Arctic is associated with larger interannual variability in the minimum sea ice extent (SIE). The current generation of forced or fully coupled models, however, has difficulty predicting SIE anomalies from the long-term trend, highlighting the need to better identify the mechanisms involved in the seasonal evolution of sea ice cover. One such mechanism is coastal divergence (CD), a proxy for ice thickness anomalies based on late winter ice motion, quantified using Lagrangian ice tracking. CD gains predictive skill through the positive feedback of surface albedo anomalies, mirrored in reflected solar radiation (RSR), during melt season. Exploring the dynamic and thermodynamic contributions to minimum SIE predictability, RSR, initial SIE (iSIE), and CD are compared as predictors using a regional seasonal sea ice forecast model for 1 July, 1 June, and 1 May forecast dates for all Arctic peripheral seas. The predictive skill of June RSR anomalies mainly originates from open water fraction at the surface; that is, June iSIE and June RSR have equal predictive skill for most seas. The finding is supported by the surprising positive correlation found between June melt pond fraction (MPF) and June RSR in all peripheral seas: MPF anomalies indicate the presence of ice or open water, which is key to creating minimum SIE anomalies. This contradicts models that show correlation between melt onset, MPF, and the minimum SIE. A hindcast model shows that for a 1 May forecast, CD anomalies have better predictive skill than RSR anomalies for most peripheral seas.

© 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: Rachel Kim, rachel.h.kim@mail.mcgill.ca
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