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Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting

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Abstract

Predicting Arctic sea ice extent is a notoriously difficult forecasting problem, even for lead times as short as one month. Motivated by Arctic intraannual variability phenomena such as reemergence of sea surface temperature and sea ice anomalies, we use a prediction approach for sea ice anomalies based on analog forecasting. Traditional analog forecasting relies on identifying a single analog in a historical record, usually by minimizing Euclidean distance, and forming a forecast from the analog’s historical trajectory. Here an ensemble of analogs is used to make forecasts, where the ensemble weights are determined by a dynamics-adapted similarity kernel, which takes into account the nonlinear geometry on the underlying data manifold. We apply this method for forecasting pan-Arctic and regional sea ice area and volume anomalies from multi-century climate model data, and in many cases find improvement over the benchmark damped persistence forecast. Examples of success include the 3–6 month lead time prediction of Arctic sea ice area, the winter sea ice area prediction of some marginal ice zone seas, and the 3–12 month lead time prediction of sea ice volume anomalies in many central Arctic basins. We discuss possible connections between KAF success and sea ice reemergence, and find KAF to be successful in regions and seasons exhibiting high interannual variability.

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Acknowledgements

The research of Andrew Majda and Dimitrios Giannakis is partially supported by ONR MURI Grant 25-74200-F7112. Darin Comeau was supported as a postdoctoral fellow through this Grant. Dimitrios Giannakis and Zhizhen Zhao are partially supported by NSF Grant DMS-1521775. Dimitrios Giannakis also acknowledges support from ONR Grant N00014-14-1-0150. Darin Comeau also acknowledges additional support from Regional and Global Climate Modeling program of the US Department of Energy Office of Science, as a contribution to the HiLAT Project. We thank Mitch Bushuk for helpful discussions. We also thank two anonymous reviewers for their helpful comments in reviewing this manuscript.

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  1. Darin Comeau

    Present address: Climate, Ocean, and Sea Ice Modeling Group, Computational Physics and Methods Group (CCS-2), Los Alamos National Laboratory, Los Alamos, NM, USA

Authors and Affiliations

  1. Center for Atmosphere Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA

    Darin Comeau, Dimitrios Giannakis & Andrew J. Majda

  2. Department of Electrical and Computer Engineering, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Zhizhen Zhao

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  1. Darin Comeau
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Correspondence to Darin Comeau.

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Comeau, D., Giannakis, D., Zhao, Z. et al. Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting. Clim Dyn 52, 5507–5525 (2019). https://doi.org/10.1007/s00382-018-4459-x

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