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<p>The operational production of skillful long-range forecasts of Arctic sea ice  has the potential to be very useful when integrated into the planning of Arctic  operations by the U.S. Navy and other organizations. We investigated the  potential for predicting October sea ice concentration (SIC) in the Beaufort Sea at  lead times of one to five months. We used SIC data for 1979-2007 to  statistically and dynamically analyze atmospheric and oceanic processes  associated with variations of SIC in the Beaufort Sea. We also conducted  correlation analyses to identify climate system variables for use as predictors of  SIC. We developed linear regression models for predicting SIC based on  multiple predictors. We tested these models by generating hindcasts of October  SIC for 1979-2007 based on several combinations of predictors. We found two  key predictors of October SIC in the Beaufort Sea at leads of one to five  months--antecedent SIC in the Beaufort Sea and sea surface temperature (SST)  in the Caribbean Sea in the preceding May-September period. Both of these  predictors showed a consistent and statistically significant relationship with  October SIC at all lead times. Both are also dynamically reasonable predictors,  given the role of antecedent ice conditions, and of the Arctic Oscillation and  North Atlantic Oscillation in influencing basin scale SSTs. Our hindcast  verification metrics show that a linear regression model based on these two  predictors produces skillful forecasts of SIC at leads of one to five months.  Based on these results, we issued a forecast on 01 June 2010 for SIC in the  Beaufort Sea in October 2010. We also identified and conducted mult-year,  linear regression hindcasts using several other predictors (e.g., low level air  temperature, low level winds, and upper ocean temperature) that proved useful  at various lead times. Our results indicate a significant potential for improving  long range forecasts in support of Arctic operations by the U.S. Navy and other  organizations.
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<br>Subjects: Meteorology; Climatology; Oceanography</div>
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Stone, Megan M.</div>
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