Student Seminar Series
Department of Atmospheric & Oceanic Sciences
a talk by
Assessing the predictability of Arctic minimum sea ice extent using observations: June solar radiation and late winter coastal divergence
Dramatic loss in Arctic sea ice extent (SIE) during the observation period has led to an increased interest in Arctic sea ice forecasting. Despite the climatological decreasing trend, minimum SIE has great interannual variability. Due to our limited understanding of sea ice dynamics and cost constraints, climate models lack the forecasting skill for Arctic SIE especially in large anomalous years. The project looks at two parameters with predictability for Arctic minimum SIE but with different lead-times: June reflected solar radiation (RSR) and late winter coastal divergence. June RSR reflects varying albedo conditions such as open water areas with melt onset. The positive albedo feedback mechanism leads to amplified melt of negative albedo anomalies by the end of melt season. Late winter coastal divergence forms thinner sea ice that is most likely to melt out by end of the following melt season. Consequently, the integrated area of late winter coastal divergence until spring gives predictability for September minimum SIE.
Results show that as a predictor, June RSR is analogous to June SIE, which reflects both thermodynamic and dynamic drivers of sea ice melt. Contrarily, wind-driven late winter coastal divergence is a dynamic mechanism that preconditions sea ice for melt. To make a parallel comparison with June RSR, we are interested in combining the predictability of winter dynamic preconditioning with spring thermodynamic effects like the Bering Strait ocean heat transport. The combined predictability increases the lead-time by one month than that of June RSR. The research contributes to better identifying the mechanisms that drive sea ice melt and increasing the lead-time for Arctic SIE forecasting.