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Climate Dynamics

, Volume 41, Issue 9–10, pp 2511–2526 | Cite as

Predictability of large interannual Arctic sea-ice anomalies

  • Steffen TietscheEmail author
  • Dirk Notz
  • Johann H. Jungclaus
  • Jochem Marotzke
Article

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.

Keywords

Lead Time Ensemble Prediction Ensemble Spread Predictive Skill Interannual Anomaly 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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.

Supplementary material

382_2013_1698_MOESM1_ESM.eps (316 kb)
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 (398 kb)
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)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Steffen Tietsche
    • 1
    • 2
    • 3
    Email author
  • Dirk Notz
    • 1
  • Johann H. Jungclaus
    • 1
  • Jochem Marotzke
    • 1
  1. 1.Max Planck Institute for MeteorologyHamburgGermany
  2. 2.International Max Planck Research School on Earth System ModellingHamburgGermany
  3. 3.NCAS-ClimateUniversity of ReadingReadingUK

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