Using timing of ice retreat to predict timing of fall freeze‐up in the Arctic

Abstract

Reliable forecasts of the timing of sea ice advance are needed in order to reduce risks associated with operating in the Arctic as well as planning of human and environmental emergencies. This study investigates the use of a simple statistical model relating the timing of ice retreat to the timing of ice advance, taking advantage of the inherent predictive power supplied by the seasonal ice‐albedo feedback and ocean heat uptake. Results show that using the last retreat date to predict the first advance date is applicable in some regions, such as Baffin Bay and the Laptev and East Siberian seas, where a predictive skill is found even after accounting for the long‐term trend in both variables. Elsewhere, in the Arctic, there is some predictive skills depending on the year (e.g., Kara and Beaufort seas), but none in regions such as the Barents and Bering seas or the Sea of Okhotsk. While there is some suggestion that the relationship is strengthening over time, this may reflect that higher correlations are expected during periods when the underlying trend is strong.

Mechanisms influencing seasonal to inter-annual prediction skill of sea ice extent in the Arctic Ocean in MIROC

“…Under such maritime activities in the Arctic Ocean, forecasts of the local sea ice distribution rather than the total sea ice extent become of greater interest for marine users. Recent studies have reported the forecast skills of the retreat and advance dates of the sea ice distribution based on statistical methods (e.g., Stroeve et al, 2016;Wang et al, 2016) as well as a dynamical forecast system Bushuk et al, 2017). In the present study, our hindcasts could not reproduce precise seaice edges from summer to fall.…”

Investigating the local-scale influence of sea ice on Greenland surface melt

“…The link between MO and the timing of ice retreat has already been established, with correlations between the detrended melt onset and detrended ice retreat dates greater than 0.4 (see Fig. S10; Stroeve et al, 2016).…”

Simple Statistical Probabilistic Forecasts of the Winter NAO

“…First the trend, up to 60% of which is a likely consequence of greenhouse gas forcing and increased Arctic amplification, with the remainder being due to internal variability (Kay et al 2011;Stroeve et al 2012). Second, there is the interannual variability of autumn sea ice coverage, which can be influenced by initial ice conditions, summer weather conditions, and storms in the Arctic, which can affect the amount of solar radiation absorbed by the ocean, and poleward atmospheric moisture and heat fluxes and oceanic heat fluxes Park et al 2015;Stroeve et al 2016). It is possible that some of these factors influencing sea ice interannual variability, such as shifts in the Gulf Stream, may themselves be effective predictors of the NAO, and the sea ice in fact modulates such a signal (e.g., Sato et al 2014).…”