Seasonal to interannual Arctic sea ice predictability in current global climate models

• Published in: Geophysical research letters Vol. 41; no. 3; pp. 1035 - 1043
• Main Authors: Tietsche, S., Day, J. J., Guemas, V., Hurlin, W. J., Keeley, S. P. E., Matei, D., Msadek, R., Collins, M., Hawkins, E.
• Format: Journal Article
• Language: English
• Published: Washington Blackwell Publishing Ltd 16.02.2014; John Wiley & Sons, Inc
• Subjects: Advection; Amplification; Arctic; Arctic climates; Arctic Ocean; Arctic sea ice; Climate; Climate models; Coasts; Errors; Forecasting; Global climate; Global climate models; Growth rate; Ice; Ice cover; Ice edge; Ice thickness; Marine; Mathematical models; Polar environments; Predictions; Robustness; Sea ice; Sea ice concentrations; Sea ice thickness; seasonal to decadal predictability; State of the art; Temperature; Winter; Arctic Ocean; PN, Arctic Ocean
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1002/2013GL058755

We establish the first intermodel comparison of seasonal to interannual predictability of present‐day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state‐of‐the‐art global climate models. For Arctic sea ice extent and volume, there is potential predictive skill for lead times of up to 3 years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate. Key Points Arctic sea ice is potentially predictable for up to three years in current GCMs Potential prediction errors are amplified at the coasts of the Arctic ocean Advective processes are very important for spatial error patterns