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

Journal Article · 27 September 2018 · Climate Dynamics

DOI: https://doi.org/10.1007/s00382-018-4459-x · OSTI ID:1480002

Comeau, Darin  ^[1]; Giannakis, Dimitrios ^[2]; Zhao, Zhizhen ^[3]; Majda, Andrew J. ^[2]

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1. New York Univ. (NYU), NY (United States). Center for Atmosphere Ocean Science. Courant Inst. of Mathematical Sciences; Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2. New York Univ. (NYU), NY (United States). Center for Atmosphere Ocean Science. Courant Inst. of Mathematical Sciences
3. Univ. of Illinois, Urbana, IL (United States). Dept. of Electrical and Computer Engineering. Coordinated Science Lab.

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. 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. Finally, 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.

• Research Organization: Los Alamos National Lab. (LANL), Los Alamos, NM (United States); New York Univ. (NYU), NY (United States); Univ. of Illinois, Urbana, IL (United States)
• Sponsoring Organization: National Science Foundation (NSF) (United States); Office of Naval Research (ONR) (United States); USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
• Grant/Contract Number: AC52-06NA25396
• OSTI ID: 1480002
• Report Number(s): LA-UR--18-25886
• Journal Information: Climate Dynamics, Journal Name: Climate Dynamics Journal Issue: 9 Vol. 52; ISSN 0930-7575
• Publisher: Springer-VerlagCopyright Statement
• Country of Publication: United States
• Language: English