Abstract
The version of the Norwegian Climate Prediction Model (NorCPM) that only assimilates sea surface temperature (SST) with the Ensemble Kalman Filter has been used to investigate the seasonal to decadal prediction skill of regional Arctic sea ice extent (SIE). Based on a suite of NorCPM retrospective forecasts, we show that seasonal prediction of pan-Arctic SIE is skillful at lead times up to 12 months, which outperforms the anomaly persistence forecast. The SIE skill varies seasonally and regionally. Among the five Arctic marginal seas, the Barents Sea has the highest SIE prediction skill, which is up to 10–11 lead months for winter target months. In the Barents Sea, the skill during summer is largely controlled by the variability of solar heat flux and the skill during winter is mostly constrained by the upper ocean heat content/SST and also related to the heat transport through the Barents Sea Opening. Compared with several state-of-the-art dynamical prediction systems, NorCPM has comparable regional SIE skill in winter due to the improved upper ocean heat content. The relatively low skill of summer SIE in NorCPM suggests that SST anomalies are not sufficient to constrain summer SIE variability and further assimilation of sea ice thickness or atmospheric data is expected to increase the skill.
Highlights
Arctic sea ice has quickly retreated during recent decades (Stroeve et al 2014), which leads to large socioeconomic and climate impacts
We focus on the dynamical prediction of panArctic and regional Arctic sea ice extent (SIE) provided by the Norwegian Climate Prediction Model (NorCPM, Counillon et al 2016), which assimilates sea surface temperature (SST) anomalies with the Ensemble Kalman Filter (EnKF, Evensen 2003), an advanced data assimilation method
As sea ice prediction skill depends on initial states of the prediction system, we start with an assessment of the NorCPM reanalysis product
Summary
Arctic sea ice has quickly retreated during recent decades (Stroeve et al 2014), which leads to large socioeconomic and climate impacts. Sea ice predictions have been performed using empirical methods (e.g., Lindsay et al 2008) or coupled global climate models (e.g., Chevallier et al 2013; Bushuk et al 2017). The dynamic approaches are expected to show better performance in sea ice prediction (e.g., September SIE; Guemas et al 2016a). Previous studies based on retrospective predictions with coupled climate models have shown that prediction of detrended pan-Arctic sea ice extent (SIE) can be skillful at 1–5 (1–11) lead months for summer
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