Bright Prospects for Arctic Sea Ice Prediction on Subseasonal Time Scales

Abstract

With retreating sea ice and increasing human activities in the Arctic come a growing need for reliable sea ice forecasts up to months ahead. We exploit the subseasonal-to-seasonal prediction database and provide the first thorough assessment of the skill of operational forecast systems in predicting the location of the Arctic sea ice edge on these time scales. We find large differences in skill between the systems, with some showing a lack of predictive skill even at short weather time scales and the best producing skillful forecasts more than 1.5 months ahead. This highlights that the area of subseasonal prediction in the Arctic is in an early stage but also that the prospects are bright, especially for late summer forecasts. To fully exploit this potential, it is argued that it will be imperative to reduce systematic model errors and develop advanced data assimilation capacity. Plain Language SummaryThe need for reliable forecasts for the sea ice evolution from weeks to months in advance has substantially grown in the last decade. Sea ice forecasts are of critical importance to manage the opportunities and risks that come with increasing socioeconomic activities in the rapidly changing Arctic, which, despite the reduction of the sea ice cover, remains an extreme environment. The position of the sea ice edge is a key parameter for potential forecast users, such as Arctic mariners. However, little is known about the ability of current operational subseasonal forecast systems to predict the evolution of the ice edge. Therefore, we assess for the first time the skill of state-of-the-art forecast systems, using a new verification metric that quantifies the accuracy of the ice edge position in a meaningful way. Our results demonstrate that subseasonal sea ice predictions are in an early stage, although skillful predictions 1.5 months ahead are already possible. We argue that relatively modest investments into reducing initial state and model errors will lead to major returns in predictive skill.

A Year‐Round Subseasonal‐to‐Seasonal Sea Ice Prediction Portal

“…Overall, the MME forecast of SIC was the most skillful forecast at lead times up to 4 weeks and had similar skill to the best performing models at longer lead times (Figure 1), highlighting the advantage of a forecast database with many models such as ours as has been found in other studies of seasonal predictability (Hagedorn et al, 2005). This is considerably longer than the 1.5 months for individual models found by Zampieri et al (2018), which may be explained by the large number of models in the MME of our study or the shorter study period used here (January-November 2018). The MME skill might be improved through nonuniform weighting of models, which was not attempted here due the short study period (11 months).…”

Predictability of Antarctic Sea Ice Edge on Subseasonal Time Scales

“…The availability of comprehensive sets of both reforecasts and real‐time forecasts allows for a robust assessment of the forecast skill over a relatively long time period (>10 years), covering the whole seasonal cycle. Here, we extend the analysis by Zampieri et al () for the Arctic, to Antarctica, addressing the two following guiding questions: Are fully coupled forecasting systems in the Antarctic better than observation‐based benchmark forecasts in predicting the sea ice edge? Does the predictive skill of dynamical forecast systems differ between the two hemispheres? …”

An Intercomparison of Verification Scores for Evaluating the Sea Ice Edge Position in Seasonal Forecasts

“…In this study, the position of the sea ice edge in the ECMWF SEAS5 retrospective forecasts has been evaluated using various verification scores. The SPS is correlated to the length of the ice edge (Goessling & Jung, ; Zampieri et al, ) and is therefore not suitable for analyzing the seasonal variation of the forecast errors without normalization. The SPS length and the MHD are more relevant verification scores for comparing the forecast errors during different seasons.…”

“…Due to the different units of the evaluated verification scores (area for the SPS and distance for the MHD), we introduce the SPS length as the ratio of the SPS area to the ice edge length. It is worth noting that the SPS is strongly influenced by the ice edge length (see Figure S2; Goessling & Jung, ; Zampieri et al, ) and is therefore less relevant than the SPS length for analyzing the seasonal variation of the forecast errors. For calculating the SPS length , the length of the ice edge has been defined as the mean value of the ice edge lengths from the two data sets (observations and forecasts).…”