Summer Arctic Sea Ice Intra-Seasonal Predictability Using a Vector Auto-Regressive Model

Thursday, 18 December 2014
Lei Wang, Lamont -Doherty Earth Observatory, Palisades, NY, United States, Xiaojun Yuan, Lamont-Doherty Earth Observ, Palisades, NY, United States and Mingfang Ting, Lamont Doherty Earth Observ, Palisades, NY, United States
Recent Arctic sea ice changes have important societal and economic impacts: the accelerated melting of Arctic sea ice in summer provides new fishery opportunities and increases the feasibility of trans-Arctic shipping, yet it may also lead to adverse effects on the Arctic ecosystem, weather and climate. Understanding the predictability of Arctic sea ice melting is thus an important task. A Vector Auto-Regressive (VAR) model is evaluated for predicting the summer time (May through September) daily Arctic sea ice concentrations. The intra-seasonal forecast skill of the Arctic sea ice is assessed using 1979-2012 satellite data provided by the National Snow & Ice Data Center (NSIDC). The cross-validated forecast skill of the VAR model is superior over persistence and climatological seasonal cycle for a lead-time of 15~60 days, especially over marginal seas. In addition to capturing the general seasonal melt of sea ice, the VAR model is also able to capture the interannual variability of the melting, from partial melt of the marginal sea ice in the beginning of the period to almost a complete melt in the later years. While the detailed mechanism leading to the high predictability of intra-seasonal sea ice concentration needs to be further examined, the study reveals for the first time that Arctic sea ice concentration can be predicted statistically with reasonable skills at the intra-seasonal time scales.