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Robustness of Arctic sea-ice predictability in GCMs

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Abstract

General circulation models have been amply used to quantify Arctic sea-ice predictability. While models share some common aspects of predictability loss with increasing forecast lead time, there is significant model spread in the magnitude and timing of predictability loss. Here we show that inter-model differences in predictability are linked to inter-model differences in the persistence timescales of sea-ice anomalies that are unique to each model, with models that exhibit longer persistence having higher potential predictability. Given this result and previous work showing that in a single model control simulation the magnitude of persistence fluctuates between multi-annual periods of high and low persistence, we assess whether initial-value predictability is dependent on the persistence state of the initial conditions. We find that predictability is not clearly impacted by the persistence state of the initial conditions, suggesting that predictability may be robust within a constant climate mean state.

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References

  • Blanchard-Wrigglesworth E, Armour K, Bitz CM, deWeaver E (2011) Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations. J Clim. https://doi.org/10.1175/2010JCLI3775.1

    Google Scholar 

  • Blanchard-Wrigglesworth E, Bitz CM, Holland MH (2011) Influence of initial conditions and climate forcing on predicting Arctic sea ice. Geophys Res Lett. https://doi.org/10.1029/2011GL048807

    Google Scholar 

  • 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):8042–8048

    Article  Google Scholar 

  • Bushuk M, Giannakis D (2015) Sea-ice reemergence in a model hierarchy. Geophys Res Lett 42:5337–5345

    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 28:5477–5509

    Article  Google Scholar 

  • 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 44:4953–4964. https://doi.org/10.1002/2017GL073155

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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):2255

    Article  Google Scholar 

  • Fetterer F, Knowles K, Meier W, Savoie M (2002) Sea ice index. https://nsidc.org/data/docs/noaa/g02135_seaice_index/ (updated 2016)

  • Gent P, Danabasoglu G, Donner L, Holland M, Hunke E, Jayne S, Lawrence D, Neale R, Rasch P, Vertenstein M, Worley P, Yang ZL, Zhang M (2011) The community climate system model version 4. Jo Clim 24(19):4973–4991

    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 

  • Guemas V, Blanchard-Wrigglesworth E, Chevallier M, Day JJ, Déqué M, Doblas-Reyes FJ, Fučkar NS, Germe A, Hawkins E, Keeley S, Koenigk T (2016) A review on Arctic sea-ice predictability and prediction on seasonal to decadal time-scales. Quart J R Meteorol Soc 142(695):546–561

    Article  Google Scholar 

  • Hebert DA, Allard RA, Metzger EJ, Posey PG, Preller RH, Wallcraft AJ, Phelps MW, Smedstad OM (2015) Short-term sea ice forecasting: an assessment of ice concentration and ice drift forecasts using the us navy’s arctic cap nowcast/forecast system. J Geophys Res: Oceans 120(12):8327–8345

    Article  Google Scholar 

  • Holland MM, Bailey DA, Vavrus S (2010) Inherent sea ice predictability in the rapidly changing Arctic environment of the Community Climate System Model, version 3. Clim Dyn. https://doi.org/10.1007/s00382-010-0792-4

    Google Scholar 

  • Holland MM, Blanchard-Wrigglesworth E, Kay J, Vavrus S (2013) Initial-value predictability of Antarctic sea ice in the Community Climate System Model 3. Geophys Res Lett 40(10):2121–2124

    Article  Google Scholar 

  • Jung T, Gordon ND, Bauer P, Bromwich DH, Chevallier M, Day JJ, Dawson J, Doblas-Reyes F, Fairall C, Goessling HF (2016) Advancing polar prediction capabilities on daily to seasonal time scales. Bull Am Meteorol Soc 97(9):1631–1647

    Article  Google Scholar 

  • Kleeman R (2002) Measuring dynamical prediction utility using relative entropy. J Atmos Sci 59:2057–2072

    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. https://doi.org/10.1007/s00382-008-0419-1

    Google Scholar 

  • Kumar A, Peng P, Chen M (2014) Is there a relationship between potential and actual skill? Mon Weather Rev 142(6):2220–2227

    Article  Google Scholar 

  • Merryfield W, Lee WS, Wang W, Chen M, Kumar A (2013) Multi-system seasonal predictions of Arctic sea ice. Geophys Res Lett 40(8):1551–1556

    Article  Google Scholar 

  • 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):5208–5215

    Article  Google Scholar 

  • Peterson KA, Arribas A, Hewitt HT, Keen AB, Lea DJ, McLaren AJ (2015) Assessing the forecast skill of Arctic sea ice extent in the GloSea4 seasonal prediction system. Clim Dyn 44(1–2):147–162

    Article  Google Scholar 

  • 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):254–263

    Article  Google Scholar 

  • Schröder D, Feltham DL, Flocco D, Tsamados M (2014) September Arctic sea-ice minimum predicted by spring melt-pond fraction. Nat Clim Change 4(5):353

    Article  Google Scholar 

  • 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):529–534

    Article  Google Scholar 

  • Tietsche S, Day JJ, Guemas V, Hurlin WJ, Keeley SPE, 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 

  • Tietsche S, Notz D, Jungclaus JH, Marotzke J (2013) Predictability of large interannual Arctic sea-ice anomalies. Clim Dyn 41(9–10):2511–2526

    Article  Google Scholar 

  • 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):1375–1394

    Article  Google Scholar 

  • 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):8151–8173

    Article  Google Scholar 

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Acknowledgements

We thank Cecilia Bitz and Dimitrios Giannakis for discussions on the work presented, Jonny Day for help with accessing the APPOSITE data, and Jen Kay, David Bailey and NCAR’s Polar Climate Working Group for assisting with computing resources. EBW was supported by the Office of Naval Research grants N00014-13-1-0793 and N00014-17-1-2986, and MB was supported by NOAA’s Climate Program Office, Climate Variability and Predictability Program (award GC15-504).

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Authors and Affiliations

  1. Department of Atmospheric Sciences, University of Washington, Seattle, WA, 98195-1640, USA

    E. Blanchard-Wrigglesworth

  2. Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, NJ, USA

    M. Bushuk

  3. University Corporation for Atmospheric Research, Boulder, CO, USA

    M. Bushuk

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  1. E. Blanchard-Wrigglesworth
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  2. M. Bushuk
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Correspondence to E. Blanchard-Wrigglesworth.

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Blanchard-Wrigglesworth, E., Bushuk, M. Robustness of Arctic sea-ice predictability in GCMs. Clim Dyn 52, 5555–5566 (2019). https://doi.org/10.1007/s00382-018-4461-3

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