An assessment of regional sea ice predictability in the Arctic ocean
Arctic sea ice plays a central role in the Earth’s climate. Changes in the sea ice on seasonal-to-interannual timescales impact ecosystems, populations and a growing number of stakeholders. A prerequisite for achieving better sea ice predictions is a better understanding of the underlying mechanisms of sea ice predictability. Previous studies have shown that sea ice predictability depends on the predictand (area, extent, volume), region, and the initial and target dates. Here we investigate seasonal-to-interannual sea ice predictability in so-called “perfect-model” 3-year-long experiments run with six global climate models initialized in early July. Consistent with previous studies, robust mechanisms for reemergence are highlighted, i.e. increases in the autocorrelation of sea ice properties after an initial loss. Similar winter sea ice extent reemergence is found for HadGEM1.2, GFDL-CM3 and E6F, while a long sea ice volume persistence is confirmed for all models. The comparable predictability characteristics shown by some of the peripheral regions of the Atlantic side illustrate that robust similarities can be found even if models have distinct sea ice states. The analysis of the regional sea ice predictability in EC-Earth2.3 demonstrates that Arctic basins can be classified according to three distinct regimes. The central Arctic drives most of the pan-Arctic sea ice volume persistence. In peripheral seas, we find predictability for the sea ice area in winter but low predictability throughout the rest of the year, due to the particularly unpredictable sea ice edge location. The Labrador Sea stands out among the considered regions, with sea ice predictability extending up to 1.5 years if the oceanic conditions upstream are known.
KeywordsSea ice Regional Arctic Predictability
We thank Jonathan Day and Steffen Tietsche for providing the data for the ocean heat transport into the Arctic; Nicolau Manubens, Javier Vegas-Regidor and Pierre-Antoine Bretonnière for the technical support; Pablo Ortega for useful comments on the pre-submission draft. We thank Javier García-Serrano for useful discussions regarding this study and Alasdair Hunter for the revision of the English. We give thanks to two anonymous reviewers for their insightful comments that improved the manuscript. The R-package s2dverification was used for processing the data and calculating different scores (Manubens et al. 2018). We acknowledge the Ariane tool and its creators (http://stockage.univ-brest.fr/~grima/Ariane/). We also thank the projects APPLICATE (H2020 GA 727862), INTAROS (H2020 GA 727890), the programme Copernicus and the fellowships Ramón y Cajal (MINECO) and Formación de Profesorado Universitario (FPU; Ministerio de Educación, Cultura y Deporte) for funding this work.
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