Abstract
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.
Highlights
Sea ice is an early indicator of climate change and an amplifier of climatic perturbations (e.g., Serreze and Barry, 2011; Vihma, 2014)
Ocean and the surrounding North Atlantic and Pacific Oceans and investigate how understanding of the regional-scale mechanisms helps to clarify the predictability at the pan-Arctic scale
The potential predictability was estimated by measuring the growth of the ensemble spread in the idealized predictions, and comparing it to the natural variability derived from the control experiment
Summary
Sea ice is an early indicator of climate change and an amplifier of climatic perturbations (e.g., Serreze and Barry, 2011; Vihma, 2014). Sea ice predictability has been assessed in various frameworks, including idealized perfect-model experiments. In such experiments, model simulations are used as a surrogate for the real climate, to estimate the extent to which the model can predict itself. Potential predictability is a measure of the amplification of those perturbations, i.e. the fraction of the signal which is inherently not predictable. Such experiments using state-of-the-art models provide an indication of the maximum level of skill that could be achieved in real predictions if all the observations required to initialize the predictions were available, and if all processes were perfectly represented by the models
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