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An assessment of regional sea ice predictability in the Arctic ocean

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Abstract

Arctic sea ice plays a central role in the Earth’s climate. Changes in the sea ice on seasonal-to-interannual timescales impact ecosystems, populations and a growing number of stakeholders. A prerequisite for achieving better sea ice predictions is a better understanding of the underlying mechanisms of sea ice predictability. Previous studies have shown that sea ice predictability depends on the predictand (area, extent, volume), region, and the initial and target dates. Here we investigate seasonal-to-interannual sea ice predictability in so-called “perfect-model” 3-year-long experiments run with six global climate models initialized in early July. Consistent with previous studies, robust mechanisms for reemergence are highlighted, i.e. increases in the autocorrelation of sea ice properties after an initial loss. Similar winter sea ice extent reemergence is found for HadGEM1.2, GFDL-CM3 and E6F, while a long sea ice volume persistence is confirmed for all models. The comparable predictability characteristics shown by some of the peripheral regions of the Atlantic side illustrate that robust similarities can be found even if models have distinct sea ice states. The analysis of the regional sea ice predictability in EC-Earth2.3 demonstrates that Arctic basins can be classified according to three distinct regimes. The central Arctic drives most of the pan-Arctic sea ice volume persistence. In peripheral seas, we find predictability for the sea ice area in winter but low predictability throughout the rest of the year, due to the particularly unpredictable sea ice edge location. The Labrador Sea stands out among the considered regions, with sea ice predictability extending up to 1.5 years if the oceanic conditions upstream are known.

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References

  • Årthun M, Eldevik T, Smedsrud LH, Skagseth Ø, Ingvaldsen R (2012) Quantifying the influence of atlantic heat on Barents Sea ice variability and retreat. J Clim 25(13):4736–4743

    Article  Google Scholar 

  • Bhatt US, Walker DA, Walsh JE, Carmack EC, Frey KE, Meier WN, Moore SE, Parmentier F-JW, Post E, Romanovsky VE (2014) Implications of Arctic sea ice decline for the earth system. Ann Rev Environ Resour 39:57–89

    Article  Google Scholar 

  • Bitz C, Holland M, Hunke E, Moritz R (2005) Maintenance of the sea-ice edge. J Clim 18(15):2903–2921

    Article  Google Scholar 

  • Blanchard-Wrigglesworth E, Armour KC, Bitz CM, DeWeaver E (2011) Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations. J Clim 24(1):231–250

    Article  Google Scholar 

  • Blanchard-Wrigglesworth E, Barthélemy A, Chevallier M, Cullather R, Fučkar N, Massonnet F, Posey P, Wang W, Zhang J, Ardilouze C (2016) Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales. Clim Dyn 49(4):1399–1410

    Article  Google Scholar 

  • Blanke B, Arhan M, Madec G, Roche S (1999) Warm water paths in the equatorial Atlantic as diagnosed with a general circulation model. J Phys Oceanogr 29(11):2753–2768

    Article  Google Scholar 

  • Blanke B, Raynaud S (1997) Kinematics of the pacific equatorial undercurrent: an Eulerian and Lagrangian approach from GCM results. J Phys Oceanogr 27(6):1038–1053

    Article  Google Scholar 

  • Boer GJ (2004) Long time-scale potential predictability in an ensemble of coupled climate models. Clim Dyn 23(1):29–44

    Article  Google Scholar 

  • Bushuk M, Msadek R, Winton M, Vecchi G, Yang X, Rosati A, Gudgel R (2018) Regional Arctic sea–ice prediction: potential versus operational seasonal forecast skill. Clim Dyn. https://doi.org/10.1007/s00382-018-4288-y

    Article  Google Scholar 

  • Bushuk M, Giannakis D, Majda AJ (2015) Arctic sea ice reemergence: the role of large-scale oceanic and atmospheric variability. J Clim 28(14):5477–5509

    Article  Google Scholar 

  • Cheng W, Blanchard-Wrigglesworth E, Bitz CM, Ladd C, Stabeno PJ (2016) Diagnostic sea ice predictability in the pan-Arctic and US Arctic regional seas. Geophys Res Lett 43(22):11688–11696

    Article  Google Scholar 

  • Collins M (2002) Climate predictability on interannual to decadal time scales: the initial value problem. Clim Dyn 19(8):671–692

    Article  Google Scholar 

  • Day J, Tietsche S, Collins M, Goessling H, Guemas V, Guillory A, Hurlin W, Ishii M, Keeley S, Matei D et al (2016) The Arctic predictability and prediction on seasonal-to-interannual timescales (apposite) data set. Geosci Model Dev 8(10):2255–2270

    Article  Google Scholar 

  • Day J, Tietsche S, Hawkins E (2014) Pan-Arctic and regional sea ice predictability: initialization month dependence. J Clim 27(12):4371–4390

    Article  Google Scholar 

  • Deser C, Tomas R, Alexander M, Lawrence D (2010) The seasonal atmospheric response to projected arctic sea ice loss in the late twenty-first century. J Clim 23(2):333–351

    Article  Google Scholar 

  • Donner LJ, Wyman BL, Hemler RS, Horowitz LW, Ming Y, Zhao M, Golaz J-C, Ginoux P, Lin S-J, Schwarzkopf MD (2011) The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J Clim 24(13):3484–3519

    Article  Google Scholar 

  • Francis JA, Hunter E (2007) Drivers of declining sea ice in the arctic winter: a tale of two seas. Geophys Res Lett 34(17):L17503. https://doi.org/10.1029/2007GL030995

    Article  Google Scholar 

  • Francis JA, Vavrus SJ (2012) Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophys Res Lett 39(6):L06801. https://doi.org/10.1029/2012GL051000

    Article  Google Scholar 

  • Germe A, Chevallier M, y Mélia DS, Sanchez-Gomez E, Cassou C (2014) Interannual predictability of Arctic sea ice in a global climate model: regional contrasts and temporal evolution. Clim Dyn 43(9–10):2519–2538

    Article  Google Scholar 

  • Goessling HF, Tietsche S, Day JJ, Hawkins E, Jung T (2016) Predictability of the Arctic sea ice edge. Geophys Res Lett 43(4):1642–1650

    Article  Google Scholar 

  • Goosse H, Arzel O, Bitz CM, de Montety A, Vancoppenolle M (2009) Increased variability of the Arctic summer ice extent in a warmer climate. Geophys Res Lett 36(23):L23702. https://doi.org/10.1029/2009GL040546

    Article  Google Scholar 

  • Griffies SM, Winton M, Donner LJ, Horowitz LW, Downes SM, Farneti R, Gnanadesikan A, Hurlin WJ, Lee H-C, Liang Z (2011) The GFDL CM3 coupled climate model: characteristics of the ocean and sea ice simulations. J Clim 24(13):3520–3544

    Article  Google Scholar 

  • Guemas V, Doblas-Reyes FJ, Mogensen K, Keeley S, Tang Y (2014) Ensemble of sea ice initial conditions for interannual climate predictions. Clim Dyn 43(9–10):2813–2829

    Article  Google Scholar 

  • Hassol SJ (2004) Impacts of a warming Arctic—Arctic climate impact assessment. Cambridge University Press, Cambridge

    Google Scholar 

  • Hazeleger W, Wang X, Severijns C, Ştefănescu S, Bintanja R, Sterl A, Wyser K, Semmler T, Yang S, Van den Hurk B (2012) EU-earth U2. 2: description and validation of a new seamless earth system prediction model. Clim Dyn 39(11):2611–2629

    Article  Google Scholar 

  • Holland MM, Bailey DA, Vavrus S (2011) Inherent sea ice predictability in the rapidly changing Arctic environment of the community climate system model, version 3. Clim Dyn 36(7–8):1239–1253

    Article  Google Scholar 

  • Johns TC, Durman CF, Banks HT, Roberts MJ, McLaren AJ, Ridley JK, Senior CA, Williams K, Jones A, Rickard G (2006) The new hadley centre climate model (HADGEM1): evaluation of coupled simulations. J Clim 19(7):1327–1353

    Article  Google Scholar 

  • Jungclaus J, Fischer N, Haak H, Lohmann K, Marotzke J, Matei D, Mikolajewicz U, Notz D, Storch J (2013) Characteristics of the ocean simulations in the Max Planck institute ocean model (MPIOM) the ocean component of the MPI-earth system model. J Adv Model Earth Syst 5(2):422–446

    Article  Google Scholar 

  • Koenigk T, Mikolajewicz U (2009) Seasonal to interannual climate predictability in mid and high northern latitudes in a global coupled model. Clim Dyn 32(6):783

    Article  Google Scholar 

  • Liu J, Curry JA, Wang H, Song M, Horton RM (2012) Impact of declining Arctic sea ice on winter snowfall. Proc Natl Acad Sci 109(11):4074–4079

    Article  Google Scholar 

  • Manubens N, Caron L-P, Hunter A, Bellprat O, Exarchou E, Fučkar NS, Garcia-Serrano J, Massonnet F, Ménégoz M, Sicardi V (2018) An R package for climate forecast verification. Environ Model Softw 103:29–42

    Article  Google Scholar 

  • Nakanowatari T, Sato K, Inoue J (2014) Predictability of the Barents Sea ice in early winter: remote effects of oceanic and atmospheric thermal conditions from the North Atlantic. J Clim 27(23):8884–8901

    Article  Google Scholar 

  • Notz D, Haumann FA, Haak H, Jungclaus JH, Marotzke J (2013) Arctic sea-ice evolution as modeled by Max Planck Institute for meteorology’s Earth system model. J Adv Model Earth Syst 5(2):173–194

    Article  Google Scholar 

  • Onarheim IH, Eldevik T, Årthun M, Ingvaldsen RB, Smedsrud LH (2015) Skillful prediction of Barents Sea ice cover. Geophys Res Lett 42(13):5364–5371

    Article  Google Scholar 

  • Pohlmann H, Botzet M, Latif M, Roesch A, Wild M, Tschuck P (2004) Estimating the decadal predictability of a coupled AOGCM. J Clim 17(22):4463–4472

    Article  Google Scholar 

  • Schlichtholz P (2011) Influence of oceanic heat variability on sea ice anomalies in the Nordic seas. Geophys Res Lett 38(5):L05705. https://doi.org/10.1029/2010GL045894

    Article  Google Scholar 

  • Serreze MC, Barry RG (2011) Processes and impacts of Arctic amplification: a research synthesis. Glob Planety Change 77(1):85–96

    Article  Google Scholar 

  • Shaffrey LC, Stevens I, Norton W, Roberts M, Vidale PL, Harle J, Jrrar A, Stevens D, Woodage MJ, Demory M-E (2009) UK HIGEM: the new UK high-resolution global environment model—model description and basic evaluation. J Clim 22(8):1861–1896

    Article  Google Scholar 

  • Sidorenko D, Rackow T, Jung T, Semmler T, Barbi D, Danilov S, Dethloff K, Dorn W, Fieg K, Gößling HF (2015) Towards multi-resolution global climate modeling with ECHAM6–FESOM. Part I: model formulation and mean climate. Clim Dyn 44(3–4):757–780

    Article  Google Scholar 

  • Sorteberg A, Kvingedal B (2006) Atmospheric forcing on the Barents Sea winter ice extent. J Clim 19(19):4772–4784

    Article  Google Scholar 

  • Stammerjohn S, Massom R, Rind D, Martinson D (2012) Regions of rapid sea ice change: an inter-hemispheric seasonal comparison. Geophys Res Lett 39(20):L06501. https://doi.org/10.1029/2012GL050874

    Article  Google Scholar 

  • Tietsche S, Day J, Guemas V, Hurlin W, Keeley S, Matei D, Msadek R, Collins M, Hawkins E (2014) Seasonal to interannual Arctic sea ice predictability in current global climate models. Geophys Res Lett 41(3):1035–1043

    Article  Google Scholar 

  • Tietsche S, Hawkins E, Day JJ (2016) Atmospheric and oceanic contributions to irreducible forecast uncertainty of Arctic surface climate. J Clim 29(1):331–346

    Article  Google Scholar 

  • Vihma T (2014) Effects of Arctic sea ice decline on weather and climate: a review. Surv Geophys 35(5):1175–1214

    Article  Google Scholar 

  • Watanabe M, Suzuki T, O’ishi R, Komuro Y, Watanabe S, Emori S, Takemura T, Chikira M, Ogura T, Sekiguchi M (2010) Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J Clim 23(23):6312–6335

    Article  Google Scholar 

  • Wouters B, Hazeleger W, Drijfhout S, Oldenborgh G, Guemas V (2013) Multiyear predictability of the North Atlantic subpolar gyre. Geophys Res Lett 40(12):3080–3084

    Article  Google Scholar 

  • Yang S, Christensen JH (2012) Arctic sea ice reduction and European cold winters in CMIP5 climate change experiments. Geophys Res Lett 39(20):L20707. https://doi.org/10.1029/2012GL053338

    Article  Google Scholar 

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Acknowledgements

We thank Jonathan Day and Steffen Tietsche for providing the data for the ocean heat transport into the Arctic; Nicolau Manubens, Javier Vegas-Regidor and Pierre-Antoine Bretonnière for the technical support; Pablo Ortega for useful comments on the pre-submission draft. We thank Javier García-Serrano for useful discussions regarding this study and Alasdair Hunter for the revision of the English. We give thanks to two anonymous reviewers for their insightful comments that improved the manuscript. The R-package s2dverification was used for processing the data and calculating different scores (Manubens et al. 2018). We acknowledge the Ariane tool and its creators (http://stockage.univ-brest.fr/~grima/Ariane/). We also thank the projects APPLICATE (H2020 GA 727862), INTAROS (H2020 GA 727890), the programme Copernicus and the fellowships Ramón y Cajal (MINECO) and Formación de Profesorado Universitario (FPU; Ministerio de Educación, Cultura y Deporte) for funding this work.

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Authors and Affiliations

  1. Barcelona Supercomputing Center (Centro Nacional de Supercomputación), Nexus II-Planta 1 C/ Jordi Girona, 29, 08034, Barcelona, Spain

    Rubén Cruz-García & Virginie Guemas

  2. Centre National de Recherches Météorologiques/Groupe d’Etude de l’Atmosphère Météorologique, Météo-France, CNRS, Toulouse, France

    Matthieu Chevallier

  3. Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium

    François Massonnet

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  1. Rubén Cruz-García
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  2. Virginie Guemas
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Correspondence to Rubén Cruz-García.

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Cruz-García, R., Guemas, V., Chevallier, M. et al. An assessment of regional sea ice predictability in the Arctic ocean. Clim Dyn 53, 427–440 (2019). https://doi.org/10.1007/s00382-018-4592-6

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