Development of Stochastic Seasonal Prediction Model of Arctic Sea Ice Concentration

Abstract:

The Arctic sea ice extent has dramatically decreased in recent decades, which has become one of the most distinct signals of the continuing climate change. Recently, a development of sea ice prediction models has been extensively studied, but the predictability for three months ahead or longer remains poor due to the complexity of the Arctic climate system. Here we show a new and simple seasonal prediction of the Arctic sea ice in summer. Since the thermodynamic process is important in the sea ice melting, our focus is to predict the summer Sea Ice Concentration (SIC) by using the observed total-sky Net Radiant Flux (NRF) at the top of the atmosphere that indicates the heat input into the open sea and sea ice surface. In the observation, SIC and NRF have a strong lagged correlation, that is, a decrease in SIC anomalies in late summer/early autumn (August-September-October) was followed by the increase in NRF in June (correlation coefficient, r ≈ -0.83 with a lag of 2 to 4 months). Thus, NRF in June can be used as a major predictor of SIC in the prediction model. The prediction model is based on the Markovian stochastic method characterized by a transition matrix describing the probabilities of particular transitions from NRF to SIC anomalies. As results, the area-averaged Arctic SIC predicted by the model is found to be more closer to the observation than that by the linear regression model. Moreover, the model proved considerably good results in regions, specifically Beaufort Sea and East Siberian Sea, although it has a limitation of predicting the extreme SIC anomalies in a regional scale. The results suggest that the present stochastic model designed with simplicity astonishingly has the great advantage that up to 4-months-ahead prediction of Arctic SIC in summer is possible with accuracy.