Advertisement

Regional Arctic sea–ice prediction: potential versus operational seasonal forecast skill

  • Mitchell Bushuk
  • Rym Msadek
  • Michael Winton
  • Gabriel Vecchi
  • Xiaosong Yang
  • Anthony Rosati
  • Rich Gudgel
Article

Abstract

Seasonal predictions of Arctic sea ice on regional spatial scales are a pressing need for a broad group of stakeholders, however, most assessments of predictability and forecast skill to date have focused on pan-Arctic sea–ice extent (SIE). In this work, we present the first direct comparison of perfect model (PM) and operational (OP) seasonal prediction skill for regional Arctic SIE within a common dynamical prediction system. This assessment is based on two complementary suites of seasonal prediction ensemble experiments performed with a global coupled climate model. First, we present a suite of PM predictability experiments with start dates spanning the calendar year, which are used to quantify the potential regional SIE prediction skill of this system. Second, we assess the system’s OP prediction skill for detrended regional SIE using a suite of retrospective initialized seasonal forecasts spanning 1981–2016. In nearly all Arctic regions and for all target months, we find a substantial skill gap between PM and OP predictions of regional SIE. The PM experiments reveal that regional winter SIE is potentially predictable at lead times beyond 12 months, substantially longer than the skill of their OP counterparts. Both the OP and PM predictions display a spring prediction skill barrier for regional summer SIE forecasts, indicating a fundamental predictability limit for summer regional predictions. We find that a similar barrier exists for pan-Arctic sea–ice volume predictions, but is not present for predictions of pan-Arctic SIE. The skill gap identified in this work indicates a promising potential for future improvements in regional SIE predictions.

Keywords

Sea ice Seasonal predictability Arctic 

Notes

Acknowledgements

This paper is dedicated to Walter Bushuk. We thank two anonymous reviewers for constructive comments which improved the manuscript. We also acknowledge Olga Sergienko and Hiroyuki Murakami for comments on a preliminary version of the manuscript. We thank Seth Underwood, Bill Hurlin, and Chris Blanton for assistance in setting up the model experiments. M. Bushuk was supported by NOAA’s Climate Program Office, Climate Variability and Predictability Program (Award GC15-504).

Supplementary material

382_2018_4288_MOESM1_ESM.pdf (747 kb)
Supplementary material 1 (pdf 747 KB)

References

  1. Anderson JL (2001) An ensemble adjustment Kalman filter for data assimilation. Mon Weather Rev 129(12):2884–2903CrossRefGoogle Scholar
  2. Bitz C, Holland M, Weaver A, Eby M (2001) Simulating the ice-thickness distribution in a coupled climate model. J Geophys Res Oceans 106(C2):2441–2463CrossRefGoogle Scholar
  3. Bitz C, Roe G (2004) A mechanism for the high rate of sea ice thinning in the Arctic Ocean. J Clim 17(18):3623–3632CrossRefGoogle Scholar
  4. 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 24:231–250CrossRefGoogle Scholar
  5. Blanchard-Wrigglesworth E, Barthélemy A, Chevallier M, Cullather R, Fučkar N, Massonnet F, Posey P, Wang W, Zhang J, Ardilouze C et al (2017) Multi-model seasonal forecast of Arctic sea–ice: forecast uncertainty at pan-Arctic and regional scales. Clim Dyn 49(4):1399–1410CrossRefGoogle Scholar
  6. Blanchard-Wrigglesworth E, Bitz C, Holland M (2011) Influence of initial conditions and climate forcing on predicting Arctic sea ice. Geophys Res Lett 38(18)Google Scholar
  7. Blanchard-Wrigglesworth E, Cullather R, Wang W, Zhang J, Bitz C (2015) Model forecast skill and sensitivity to initial conditions in the seasonal Sea Ice Outlook. Geophys Res Lett 42(19):8042–8048CrossRefGoogle Scholar
  8. Bretherton CS, Widmann M, Dymnikov VP, Wallace JM, Bladé I (1999) The effective number of spatial degrees of freedom of a time-varying field. J Clim 12(7):1990–2009CrossRefGoogle Scholar
  9. Bushuk M, Giannakis D (2015) Sea-ice reemergence in a model hierarchy. Geophys Res Lett 42:5337–5345CrossRefGoogle Scholar
  10. Bushuk M, Giannakis D (2017) The seasonality and interannual variability of Arctic sea–ice reemergence. J Clim 30:4657–4676CrossRefGoogle Scholar
  11. Bushuk M, Giannakis D, Majda AJ (2015) Arctic sea–ice reemergence: the role of large-scale oceanic and atmospheric variability. J Clim 28:5477–5509CrossRefGoogle Scholar
  12. Bushuk M, Msadek R, Winton M, Vecchi G, Gudgel R, Rosati A, Yang X (2017) Skillful regional prediction of Arctic sea ice on seasonal timescales. Geophys Res Lett 44Google Scholar
  13. Bushuk M, Msadek R, Winton M, Vecchi G, Gudgel R, Rosati A, Yang X (2017) Summer enhancement of Arctic sea–ice volume anomalies in the September-ice zone. J Clim 30:2341–2362CrossRefGoogle Scholar
  14. Cavalieri DJ, Parkinson CL, Gloersen P, Zwally HJ (1996) Sea ice concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1. NASA DAAC at the Natl. Snow and Ice Data Cent.  https://doi.org/10.5067/8GQ8LZQVL0VL
  15. Chen Z, Liu J, Song M, Yang Q, Xu S (2017) Impacts of assimilating satellite sea ice concentration and thickness on Arctic sea ice prediction in the NCEP Climate Forecast System. J Clim 30(21):8429–8446CrossRefGoogle Scholar
  16. 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)Google Scholar
  17. Chevallier M, Salas y Mélia D (2012) The role of sea ice thickness distribution in the Arctic sea ice potential predictability: a diagnostic approach with a coupled GCM. J Clim 25(8):3025–3038CrossRefGoogle Scholar
  18. Chevallier M, Salas y Mélia D, Voldoire A, Déqué M, Garric G (2013) Seasonal forecasts of the pan-Arctic sea ice extent using a GCM-based seasonal prediction system. J Clim 26(16):6092–6104CrossRefGoogle Scholar
  19. Collins M (2002) Climate predictability on interannual to decadal time scales: the initial value problem. Clim Dyn 19:671–692CrossRefGoogle Scholar
  20. Collow TW, Wang W, Kumar A, Zhang J (2015) Improving Arctic sea ice prediction using PIOMAS initial sea ice thickness in a coupled ocean–atmosphere model. Mon Weather Rev 143(11):4618–4630CrossRefGoogle Scholar
  21. Day J, Tietsche S, Hawkins E (2014) Pan-Arctic and regional sea ice predictability: initialization month dependence. J Clim 27(12):4371–4390CrossRefGoogle Scholar
  22. Day JJ, Goessling HF, Hurlin WJ, Keeley SP (2016) The Arctic predictability and prediction on seasonal-to-interannual timescales (APPOSITE) data set version 1. Geosci Model Dev 9(6):2255CrossRefGoogle Scholar
  23. Delworth TL, Broccoli AJ, Rosati A, Stouffer RJ, Balaji V, Beesley JA, Cooke WF, Dixon KW, Dunne J, Dunne K et al (2006) GFDL’s CM2 global coupled climate models. Part I: Formulation and simulation characteristics. J Clim 19(5):643–674CrossRefGoogle Scholar
  24. Delworth TL, Rosati A, Anderson W, Adcroft AJ, Balaji V, Benson R, Dixon K, Griffies SM, Lee HC, Pacanowski RC et al (2012) Simulated climate and climate change in the GFDL CM2. 5 high-resolution coupled climate model. J Clim 25(8):2755–2781CrossRefGoogle Scholar
  25. Deser C, Magnusdottir G, Saravanan R, Phillips A (2004) The effects of North Atlantic SST and sea ice anomalies on the winter circulation in CCM3. Part II: Direct and indirect components of the response. J Clim 17(5):877–889CrossRefGoogle Scholar
  26. Deser C, Walsh JE, Timlin MS (2000) Arctic sea ice variability in the context of recent atmospheric circulation trends. J Clim 13:617–633CrossRefGoogle Scholar
  27. Dirkson A, Merryfield WJ, Monahan A (2017) Impacts of sea ice thickness initialization on seasonal Arctic sea ice predictions. J Clim 30(3):1001–1017CrossRefGoogle Scholar
  28. Drobot SD (2007) Using remote sensing data to develop seasonal outlooks for Arctic regional sea–ice minimum extent. Remote Sens Environ 111(2–3):136–147CrossRefGoogle Scholar
  29. Drobot SD, Maslanik JA, Fowler C (2006) A long-range forecast of Arctic summer sea–ice minimum extent. Geophys Res Lett 33(10)Google Scholar
  30. 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):2519–2538Google Scholar
  31. Griffies S (2012) Elements of the modular ocean model (MOM), GFDL Ocean Group Technical Report. Tech. Rep. No. 7, NOAA/Geophysical Fluid Dynamics LaboratoryGoogle Scholar
  32. Griffies SM, Winton M, Donner LJ, Horowitz LW, Downes SM, Farneti R, Gnanadesikan A, Hurlin WJ, Lee HC, Liang Z et al (2011) The GFDL CM3 coupled climate model: characteristics of the ocean and sea ice simulations. J Clim 24(13):3520–3544CrossRefGoogle Scholar
  33. Guemas V, Chevallier M, Dqu M, Bellprat O, Doblas-Reyes F (2016) Impact of sea ice initialisation on sea ice and atmosphere prediction skill on seasonal timescales. Geophys Res Lett 43(8):3889–3896CrossRefGoogle Scholar
  34. Hawkins E, Tietsche S, Day JJ, Melia N, Haines K, Keeley S (2016) Aspects of designing and evaluating seasonal-to-interannual Arctic sea–ice prediction systems. Q J R Meteorol Soc 142(695):672–683CrossRefGoogle Scholar
  35. 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):1239–1253CrossRefGoogle Scholar
  36. Holland, M.M., Stroeve, J.: Changing seasonal sea ice predictor relationships in a changing arctic climate. Geophys Res Lett 38(18)Google Scholar
  37. Hunke E, Dukowicz J (1997) An elastic-viscous-plastic model for sea ice dynamics. J Phys Oceanogr 27(9):1849–1867CrossRefGoogle Scholar
  38. Jia L, Yang X, Vecchi G, Gudgel R, Delworth T, Fueglistaler S, Lin P, Scaife AA, Underwood S, Lin SJ (2017) Seasonal prediction skill of northern extratropical surface temperature driven by the stratosphere. J Clim 30(1):4463–4475CrossRefGoogle Scholar
  39. Jia L, Yang X, Vecchi GA, Gudgel RG, Delworth TL, Rosati A, Stern WF, Wittenberg AT, Krishnamurthy L, Zhang S et al (2015) Improved seasonal prediction of temperature and precipitation over land in a high-resolution GFDL climate model. J Clim 28(5):2044–2062CrossRefGoogle Scholar
  40. Johnson C, Bowler N (2009) On the reliability and calibration of ensemble forecasts. Mon Weather Rev 137(5):1717–1720CrossRefGoogle Scholar
  41. Jolliffe IT, Stephenson DB (2012) Forecast verification: a practitioner’s guide in atmospheric science, 2nd edn. WileyGoogle Scholar
  42. Jung T, Gordon ND, Bauer P, Bromwich DH, Chevallier M, Day JJ, Dawson J, Doblas-Reyes F, Fairall C, Goessling HF et al (2016) Advancing polar prediction capabilities on daily to seasonal time scales. Bull Am Meteorol Soc.  https://doi.org/10.1175/BAMS-D-14-00246.1
  43. Kapsch ML, Graversen RG, Economou T, Tjernström M (2014) The importance of spring atmospheric conditions for predictions of the Arctic summer sea ice extent. Geophys Res Lett 41(14):5288–5296CrossRefGoogle Scholar
  44. Kauker F, Kaminski T, Karcher M, Giering R, Gerdes R, Voßbeck M (2009) Adjoint analysis of the 2007 all time Arctic sea–ice minimum. Geophys Res Lett 36(3)Google Scholar
  45. 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):783–798CrossRefGoogle Scholar
  46. Krikken F, Schmeits M, Vlot W, Guemas V, Hazeleger W (2016) Skill improvement of dynamical seasonal Arctic sea ice forecasts. Geophys Res LettGoogle Scholar
  47. Kumar A, Peng P, Chen M (2014) Is there a relationship between potential and actual skill? Mon Weather Rev 142(6):2220–2227CrossRefGoogle Scholar
  48. Leutbecher M, Palmer TN (2008) Ensemble forecasting. J Comput Phys 227(7):3515–3539CrossRefGoogle Scholar
  49. Lin SJ (2004) A vertically Lagrangian finite-volume dynamical core for global models. Mon Weather Rev 132(10):2293–2307CrossRefGoogle Scholar
  50. Lindsay R, Zhang J, Schweiger A, Steele M (2008) Seasonal predictions of ice extent in the Arctic Ocean. J Geophys Res Oceans 113(C2)Google Scholar
  51. Martinson DG (1990) Evolution of the Southern Ocean winter mixed layer and sea ice: open ocean deepwater formation and ventilation. J Geophys Res Oceans 95(C7):11641–11654CrossRefGoogle Scholar
  52. Merryfield W, Lee WS, Wang W, Chen M, Kumar A (2013) Multi-system seasonal predictions of Arctic sea ice. Geophys Res Lett 40(8):1551–1556CrossRefGoogle Scholar
  53. Milly PC, Malyshev SL, Shevliakova E, Dunne KA, Findell KL, Gleeson T, Liang Z, Phillipps P, Stouffer RJ, Swenson S (2014) An enhanced model of land water and energy for global hydrologic and earth-system studies. J Hydrometeorol 15(5):1739–1761CrossRefGoogle Scholar
  54. Msadek R, Vecchi G, Winton M, Gudgel R (2014) Importance of initial conditions in seasonal predictions of Arctic sea ice extent. Geophys Res Lett 41(14):5208–5215CrossRefGoogle Scholar
  55. Murakami H, Vecchi GA, Delworth TL, Wittenberg AT, Underwood S, Gudgel R, Yang X, Jia L, Zeng F, Paffendorf K et al (2017) Dominant role of subtropical pacific warming in extreme Eastern Pacific hurricane seasons: 2015 and the future. J Clim 30(1):243–264CrossRefGoogle Scholar
  56. Murphy AH (1988) Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon Weather Rev 116(12):2417–2424CrossRefGoogle Scholar
  57. Owens WB, Lemke P (1990) Sensitivity studies with a sea ice-mixed layer-pycnocline model in the Weddell sea. J Geophys Res Oceans (1978–2012) 95(C6):9527–9538Google Scholar
  58. Palmer T, Buizza R, Hagedorn R, Lawrence A, Leutbecher M, Smith L (2006) Ensemble prediction: a pedagogical perspective. ECMWF Newslett 106:10–17Google Scholar
  59. Peterson KA, Arribas A, Hewitt H, Keen A, Lea D, McLaren A (2015) Assessing the forecast skill of Arctic sea ice extent in the GloSea4 seasonal prediction system. Clim Dyn 44(1–2):147–162CrossRefGoogle Scholar
  60. Petty AA, Schröder D, Stroeve J, Markus T, Miller J, Kurtz N, Feltham D, Flocco D (2017) Skillful spring forecasts of September Arctic sea ice extent using passive microwave sea ice observations. Earth’s Future 5(2):254–263CrossRefGoogle Scholar
  61. 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):4463–4472CrossRefGoogle Scholar
  62. Putman WM, Lin SJ (2007) Finite-volume transport on various cubed-sphere grids. J Comput Phys 227(1):55–78CrossRefGoogle Scholar
  63. Schröder D, Feltham DL, Flocco D, Tsamados M (2014) September Arctic sea-ice minimum predicted by spring melt-pond fraction. Nat Clim ChangeGoogle Scholar
  64. Schweiger A, Lindsay R, Zhang J, Steele M, Stern H, Kwok R (2011) Uncertainty in modeled Arctic sea ice volume. J Geophys Res Oceans 116(C8)Google Scholar
  65. Sigmond M, Fyfe J, Flato G, Kharin V, Merryfield W (2013) Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system. Geophys Res Lett 40(3):529–534CrossRefGoogle Scholar
  66. Sigmond M, Reader M, Flato G, Merryfield W, Tivy A (2016) Skillful seasonal forecasts of Arctic sea ice retreat and advance dates in a dynamical forecast system. Geophys Res Lett 43Google Scholar
  67. Stock CA, Pegion K, Vecchi GA, Alexander MA, Tommasi D, Bond NA, Fratantoni PS, Gudgel RG, Kristiansen T, OBrien TD et al (2015) Seasonal sea surface temperature anomaly prediction for coastal ecosystems. Prog Oceanogr 137:219–236CrossRefGoogle Scholar
  68. Stroeve J, Hamilton LC, Bitz CM, Blanchard-Wrigglesworth E (2014) Predicting September sea ice: ensemble skill of the SEARCH sea ice outlook 2008–2013. Geophys Res Lett 41(7):2411–2418CrossRefGoogle Scholar
  69. Sun L, Deser C, Tomas RA (2015) Mechanisms of stratospheric and tropospheric circulation response to projected Arctic sea ice loss. J Clim 28(19):7824–7845CrossRefGoogle Scholar
  70. 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):1035–1043CrossRefGoogle Scholar
  71. Tivy A, Howell SE, Alt B, Yackel JJ, Carrieres T (2011) Origins and levels of seasonal forecast skill for sea ice in Hudson Bay using Canonical Correlation Analysis. J Clim 24(5):1378–1395CrossRefGoogle Scholar
  72. Vecchi GA, Delworth T, Gudgel R, Kapnick S, Rosati A, Wittenberg AT, Zeng F, Anderson W, Balaji V, Dixon K et al (2014) On the seasonal forecasting of regional tropical cyclone activity. J Clim 27(21):7994–8016CrossRefGoogle Scholar
  73. Wang L, Ting M, Kushner P (2017) A robust empirical seasonal prediction of winter NAO and surface climate. Sci Rep 7(1):279CrossRefGoogle Scholar
  74. Wang L, Yuan X, Ting M, Li C (2016) Predicting summer Arctic sea ice concentration intraseasonal variability using a vector autoregressive model*. J Clim 29(4):1529–1543CrossRefGoogle Scholar
  75. Wang W, Chen M, Kumar A (2013) Seasonal prediction of Arctic sea ice extent from a coupled dynamical forecast system. Mon Weather Rev 141(4):1375–1394CrossRefGoogle Scholar
  76. Weigel AP, Liniger MA, Appenzeller C (2009) Seasonal ensemble forecasts: are recalibrated single models better than multimodels? Mon Weather Rev 137(4):1460–1479CrossRefGoogle Scholar
  77. Williams J, Tremblay B, Newton R, Allard R (2016) Dynamic preconditioning of the minimum September sea–ice extent. J Clim 29(16):5879–5891CrossRefGoogle Scholar
  78. Winton M (2000) A reformulated three-layer sea ice model. J Atmos Oceanic Technol 17(4):525–531CrossRefGoogle Scholar
  79. Yang X, Vecchi GA, Gudgel RG, Delworth TL, Zhang S, Rosati A, Jia L, Stern WF, Wittenberg AT, Kapnick S et al (2015) Seasonal predictability of extratropical storm tracks in GFDLs high-resolution climate prediction model. J Clim 28(9):3592–3611CrossRefGoogle Scholar
  80. Yeager SG, Karspeck AR, Danabasoglu G (2015) Predicted slowdown in the rate of Atlantic sea ice loss. Geophys Res Lett 42(24)Google Scholar
  81. Yuan X, Chen D, Li C, Wang L, Wang W (2016) Arctic sea ice seasonal prediction by a linear markov model. J Clim 29(22):8151–8173CrossRefGoogle Scholar
  82. Zhang J, Rothrock D (2003) Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates. Mon Weather Rev 131(5):845–861CrossRefGoogle Scholar
  83. Zhang S, Harrison M, Rosati A, Wittenberg A (2007) System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon Weather Rev 135(10):3541–3564CrossRefGoogle Scholar
  84. Zhang S, Rosati A (2010) An inflated ensemble filter for ocean data assimilation with a biased coupled GCM. Mon Weather Rev 138(10):3905–3931CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Geophysical Fluid Dynamics Laboratory, NOAAPrincetonUSA
  2. 2.University Corporation for Atmospheric ResearchBoulderUSA
  3. 3.CNRS/CERFACS, CECI UMR 5318ToulouseFrance
  4. 4.Department of GeosciencesPrinceton UniversityPrincetonUSA
  5. 5.Princeton Environmental InstitutePrinceton UniversityPrincetonUSA

Personalised recommendations