Summer predictions of Arctic sea ice edge in multi‐model seasonal re‐forecasts

• Published in: Climate Dynamics
• Main Authors: Batté, Lauriane, Välisuo, Ilona, Chevallier, Matthieu, Acosta Navarro, Juan C., Ortega, Pablo, Smith, Doug
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Zenodo 2020
• Subjects: Seasonal forecasting; Sea ice; Arctic; Climate prediction; Chukchi; Climate change; Kara-Laptev; laptev
• Online Access: https://doi.org/10.1007/s00382-020-05273-8

In this study, the forecast quality of 1993–2014 summer seasonal predictions of five global coupled models, of which three are operational seasonal forecasting systems contributing to the Copernicus Climate Change Service (C3S), is assessed for Arctic sea ice. Beyond the Pan-Arctic sea ice concentration and extent deterministic re-forecast assessments, we use sea ice edge error metrics such as the Integrated Ice Edge Error (IIEE) and Spatial Probability Score (SPS) to evaluate the advantages of a multi-model approach. Skill in forecasting the September sea ice minimum from late April to early May start dates is very limited, and only one model shows significant correlation skill over the period when removing the linear trend in total sea ice extent. After bias and trend-adjusting the sea ice concentration data, we find quite similar results between the different systems in terms of ice edge forecast errors. The highest values of September ice edge error in the 1993–2014 period are found for the sea ice minima years (2007 and 2012), mainly due to a clear overestimation of the total extent. Further analyses of deterministic and probabilistic skill over the Barents–Kara, Laptev–East Siberian and Beaufort–Chukchi regions provide insight on differences in model performance. For all skill metrics considered, the multi-model ensemble, whether grouping all five systems or only the three operational C3S systems, performs among the best models for each forecast time, therefore confirming the interest of multi-system initiatives building on model diversity for providing the best forecasts.