Skip to main content

Predictability of large interannual Arctic sea-ice anomalies

Abstract

In projections of twenty-first century climate, Arctic sea ice declines and at the same time exhibits strong interannual anomalies. Here, we investigate the potential to predict these strong sea-ice anomalies under a perfect-model assumption, using the Max-Planck-Institute Earth System Model in the same setup as in the Coupled Model Intercomparison Project Phase 5 (CMIP5). We study two cases of strong negative sea-ice anomalies: a 5-year-long anomaly for present-day conditions, and a 10-year-long anomaly for conditions projected for the middle of the twenty-first century. We treat these anomalies in the CMIP5 projections as the truth, and use exactly the same model configuration for predictions of this synthetic truth. We start ensemble predictions at different times during the anomalies, considering lagged-perfect and sea-ice-assimilated initial conditions. We find that the onset and amplitude of the interannual anomalies are not predictable. However, the further deepening of the anomaly can be predicted for typically 1 year lead time if predictions start after the onset but before the maximal amplitude of the anomaly. The magnitude of an extremely low summer sea-ice minimum is hard to predict: the skill of the prediction ensemble is not better than a damped-persistence forecast for lead times of more than a few months, and is not better than a climatology forecast for lead times of two or more years. Predictions of the present-day anomaly are more skillful than predictions of the mid-century anomaly. Predictions using sea-ice-assimilated initial conditions are competitive with those using lagged-perfect initial conditions for lead times of a year or less, but yield degraded skill for longer lead times. The results presented here suggest that there is limited prospect of predicting the large interannual sea-ice anomalies expected to occur throughout the twenty-first century.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  • Arzel O, Fichefet T, Goosse H (2005) Sea ice evolution over the 20th and 21st centuries as simulated by current AOGCMs. Ocean Model 12:401–415. doi:10.1016/j.ocemod.2005.08.002

    Article  Google Scholar 

  • Bader J, Mesquita MDS, Hodges KI, Keenlyside N, Østerhus S, Miles M (2011) A review on northern hemisphere sea-ice, storminess and the North Atlantic Oscillation: observations and projected changes. Atmospheric Research 101(4):809–834. doi:10.1016/j.atmosres.2011.04.007

    Article  Google Scholar 

  • Bengtsson L, Semenov VA, Johannessen OM (2004) The early twentieth-century warming in the Arctic—a possible mechanism. J Clim 17:4045–4057. doi:10.1175/1520-0442(2004)017<4045:TETWIT>2.0.CO;2

    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 24:231–250. doi:10.1175/2010JCLI3775.1

    Article  Google Scholar 

  • Blanchard-Wrigglesworth E, Bitz CM, Holland MM (2011) Influence of initial conditions and climate forcing on predicting Arctic sea ice. Geophys Res Lett 38:L18,503. doi:10.1029/2011GL048807

    Article  Google Scholar 

  • Boé JL, Hall A, Qu X (2009) September sea-ice cover in the Arctic Ocean projected to vanish by 2100. Nat Geosci 2(5):341–343

    Article  Google Scholar 

  • Box GEP, Jenkins GM, Reinsel GC (2008) Time series analysis: forecasting and control. Wiley series in probability and statistics. Wiley, New York

  • Branstator G, Teng HY (2010) Two limits of initial-value decadal predictability in a CGCM. J Clim 23(23):6292–6311. doi:10.1175/2010JCLI3678.1

    Article  Google Scholar 

  • Brovkin V, Raddatz T, Reick CH, Claussen M, Gayler V (2009) Global biogeophysical interactions between forest and climate. Geophys Res Lett 36:L07,405. doi:10.1029/2009GL037543

    Article  Google Scholar 

  • Francis JA, Vavrus SJ (2012) Evidence linking arctic amplification to extreme weather in mid-latitudes. Geophys Res Lett 39:L06,801. doi:10.1029/2012GL051000

    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:L23,702. doi:10.1029/2009GL040546

    Article  Google Scholar 

  • Hermanson L, Sutton RT (2009) Case studies in interannual to decadal climate predictability. Clim Dyn. doi:10.1007/s00382-009-0672-y

  • Hibler WD III (1979) A dynamic thermodynamic sea ice model. J Phys Oceanogr 9(4):815–846

    Article  Google Scholar 

  • Holland MM, Bitz CM, Tremblay B (2006) Future abrupt reductions in the summer Arctic sea ice. Geophys Res Lett 33:L23,503. doi:10.1029/2006GL028024

    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 36:1239–1253. doi:10.1007/s00382-010-0792-4

    Article  Google Scholar 

  • Honda M, Inoue J, Yamane S (2009) Influence of low Arctic sea-ice minima on anomalously cold Eurasian winters. Geophys Res Lett 36:L08,707. doi:10.1029/2008GL037079

    Article  Google Scholar 

  • Jolliffe, IT, Stephenson, DB (eds) (2012) Forecast verification: a practicioner’s guide in atmospheric science. Wiley-Blackwell, Oxford

    Google Scholar 

  • Jungclaus JH, Fischer N, Haak H, Lohmann K, Marotzke J, Matei D, Mikolajewicz U, Notz D, von Storch J (2013) Characteristics of the ocean simulations in MPIOM, the ocean component of the MP Earth System Model. J Adv Model Earth Syst (accepted)

  • 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:L03,707

    Article  Google Scholar 

  • Kay JE, Holland MM, Jahn A (2011) Inter-annual to multi-decadal Arctic sea ice extent trends in a warming world. Geophys Res Lett 38:L15,708. doi:10.1029/2011GL048008

    Article  Google Scholar 

  • Koenigk T, Mikolajewicz U (2008) Seasonal to interannual climate predictability in mid and high northern latitudes in a global coupled model. Clim Dyn 32(6):783–798. doi:10.1007/s00382-008-0419-1

    Article  Google Scholar 

  • Marsland SJ, Haak H, Jungclaus JH, Latif M, Röske F (2003) The Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear coordinates. Ocean Model 5:91–127

    Article  Google Scholar 

  • Matei D, Baehr J, Jungclaus JH, Haak H, Muller WA, Marotzke J (2012) Multiyear prediction of monthly mean atlantic meridional overturning circulation at 26.5 degrees N. Science 335(6064):76–79. doi:10.1126/science.1210299

    Article  Google Scholar 

  • Mauritsen T, et al (2012) Tuning the climate of a global model. J Adv Model Earth Syst. doi:10.1029/2012MS000154

  • Meinshausen M, Smith SJ, Calvin K, Daniel JS, Kainuma MLT, Lamarque J, Matsumoto K, Montzka SA, Raper SCB, Riahi K, Thomson A, Velders GJM, van Vuuren DPP (2011) The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Chang 109(1):213–241 URL http://www.scopus.com

    Google Scholar 

  • Notz D (2009) The future of ice sheets and sea ice: between reversible retreat and unstoppable loss. Proc Nat Acad Sci USA 106(49):20,590–20,595. doi:10.1073/pnas.0902356106

    Article  Google Scholar 

  • Notz D, Haumann FA, Haak H, Jungclaus JH, Marotzke J (2013) Arctic sea-ice evolution as modeled by MPI-ESM. J Adv Model Earth Syst (accepted) doi:10.1002/jame.20016, URL http://onlinelibrary.wiley.com/doi/10.1002/jame.20016/abstract

  • Ogi M, Rigor IG, McPhee MG, Wallace JM (2008) Summer retreat of Arctic sea ice: role of summer winds. Geophys Res Lett 35(24):L24,701. doi:10.1029/2008GL035672

    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 17:4463–4472

    Article  Google Scholar 

  • Raddatz TJ, Reick CH, Knorr W, Kattge J, Roeckner E, Schnur R, Schnitzler KG, Wetzel P, Jungclaus J (2007) Will the tropical land biosphere dominate the climate–carbon cycle feedback during the twenty-first century. Clim Dyn 29(6):565–574. doi:10.1007/s00382-007-0247-8

    Article  Google Scholar 

  • Roeckner E, Buml G, Bonaventura L, Brokopf R, Esch M, Giorgetta M, Hagemann S, Kirchner I, Manzini LKE, Rhodin A, Schlese U, Schulzweida U, Tompkins A (2003) The atmospheric general circulation model ECHAM5. Tech. Rep. 349, Max Planck Institute for Meteorology, Hamburg

  • Semtner AJ (1976) A model for the thermodynamic growth of sea ice in numerical investigations of climate. J Phys Oceanogr 6:379–389

    Article  Google Scholar 

  • Shepherd TG, Arblaster JM, Bitz CM, Furevik T, Goosse H, Kattsov VM, Marshall J, Ryabinin V, Walsh JE (2011) Report on WCRP workshop on seasonal to multi-decadal predictability of polar climate (Bergen, Norway, 25–29 October 2010). SPARC Newsletter no. 36:11–19, URL http://www.atmosp.physics.utoronto.ca/SPARC/sparcnewsletter36.pdf

  • Stevens B, Giorgetta M, Esch M, Mauritsen T, Crueger T, Rast S, Salzmann M, Schmidt H, Bader J, Block K, Brokopf R, Fast I, Kinne S, Kornblueh L, Lohmann U, Pincus R, Reichler T, Roeckner E (2013) The atmospheric component of the MPI-M earth system model: ECHAM6. J Adv Model Earth Syst. doi:10.1002/jame.20015, URL http://onlinelibrary.wiley.com/doi/10.1002/jame.20015/abstract

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteor Soc 93(4):485–498. doi:10.1175/BAMS-D-11-00094.1

    Article  Google Scholar 

  • Tietsche S, Notz D, Jungclaus JH, Marotzke J (2011) Recovery mechanisms of Arctic summer sea ice. Geophys Res Lett 38:L02,707. doi:10.1029/2010GL045698

    Article  Google Scholar 

  • Tietsche S, Notz D, Jungclaus JH, Marotzke J (2013) Assimilation of sea-ice concentration in a global climate model—physical and statistical aspects. Ocean Sci 9:19–36 doi:10.5194/os-9-19-2013, URL http://www.ocean-sci.net/9/19/2013/os-9-19-2013.html

  • von Storch H, Zwiers FW (1999) Statistical analysis in climate research. Cambridge University Press, Cambridge

    Google Scholar 

  • Wetzel P, Winguth A, Maier-Reimer E (2005) Sea-to-air CO2 flux from 1948 to 2003: a model study. Global Biogeochemical Cycles 19(2):GB2005. doi:10.1029/2004GB002339

    Article  Google Scholar 

  • Woodgate RA, Weingartner T, Lindsay R (2010) The 2007 Bering Strait oceanic heat flux and anomalous Arctic sea-ice retreat. Geophys Res Lett 37:L01,602. doi:10.1029/2009GL041621

    Article  Google Scholar 

Download references

Acknowledgments

We thank all colleagues in Hamburg developing MPI-ESM and performing the CMIP5 simulations for technical support of this study at an early stage of model dissemination. We also thank two anonymous reviewers for thoughtful comments that helped to improve the manuscript. This work was supported by the Max Planck Society for the Advancement of Science and the International Max Planck Research School on Earth System Modelling. All simulations were performed at the German Climate Computing Center (DKRZ) in Hamburg, Germany.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Steffen Tietsche.

Electronic supplementary material

Below is the link to the electronic supplementary material.

382_2013_1698_MOESM1_ESM.eps

Supplementary material 1. Present-day case study: Characterization of the distribution of annual mean sea-ice extent predicted by the ensembles (compare Figure 8 of the manuscript). The reference run is given by the solid line with filled circles. The box plots have the ensemble median as their central value, the upper and lower quartile as box boundaries, and the ensemble minimum/maximum as whiskers. The climatological mean is indicated by a thick dashed line, and the climatological standard deviation by thin dashed lines. The title of the subplots denotes the prediction experiment shown: “LP” for lagged-perfect initial conditions or “SA” for sea-ice assimilated initial conditions, followed by the start month of the prediction experiment. (EPS 316 kb)

382_2013_1698_MOESM2_ESM.eps

Supplementary material 2. Mid-century case study: Characterization of the distribution of annual mean sea-ice extent predicted by the ensembles (compare Figure 8 of the manuscript). The reference run is given by the solid line with filled circles. The box plots have the ensemble median as their central value, the upper and lower quartile as box boundaries, and the ensemble minimum/maximum as whiskers. The climatological mean is indicated by a thick dashed line, and the climatological standard deviation by thin dashed lines. The title of the subplots denotes the prediction experiment shown: ``LP'' for lagged-perfect initial conditions or ``SA'' for sea-ice assimilated initial conditions, followed by the start month of the prediction experiment. (EPS 398 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Tietsche, S., Notz, D., Jungclaus, J.H. et al. Predictability of large interannual Arctic sea-ice anomalies. Clim Dyn 41, 2511–2526 (2013). https://doi.org/10.1007/s00382-013-1698-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-013-1698-8

Keywords

  • Lead Time
  • Ensemble Prediction
  • Ensemble Spread
  • Predictive Skill
  • Interannual Anomaly