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Reliable short-term sea ice forecasts are needed to support maritime operations in polar regions. While sea ice forecasts produced by physically based models still have limited accuracy, statistical post-processing techniques can be applied to reduce forecast errors. In this study, post-processing methods based on supervised machine learning have been developed for improving the skill of sea ice concentration forecasts from the TOPAZ4 prediction system for lead times from 1 to 10 d. The deep learning models use predictors from TOPAZ4 sea ice forecasts, weather forecasts, and sea ice concentration observations. Predicting the sea ice concentration for the next 10 d takes about 4 min (including data preparation), which is reasonable in an operational context. On average, the forecasts from the deep learning models have a root mean square error 41 % lower than TOPAZ4 forecasts and 29 % lower than forecasts based on persistence of sea ice concentration observations. They also significantly improve the forecasts for the location of the ice edges, with similar improvements as for the root mean square error. Furthermore, the impact of different types of predictors (observations, sea ice, and weather forecasts) on the predictions has been evaluated. Sea ice observations are the most important type of predictors, and the weather forecasts have a much stronger impact on the predictions than sea ice forecasts. [ABSTRACT FROM AUTHOR]

Reliable short-term sea ice forecasts are needed to support maritime operations in polar regions. While sea ice forecasts produced by physical-based models still have limited accuracy, statistical post-processing techniques (often called calibration) can be applied to reduce forecast errors. In this study, post-processing methods based on supervised machine learning have been developed for improving the skill of sea ice concentration forecasts from the TOPAZ4 prediction system for lead times from 1 to 10 days. The deep learning models use predictors from TOPAZ4 sea ice forecasts, weather forecasts, and sea ice concentration observations. On average, the forecasts from the deep learning models have a root mean square error 41 % lower than TOPAZ4 forecasts, and 29 % lower than forecasts based on persistence of the sea ice concentration observations. They also significantly improve the forecasts for the location of the ice edges, with similar improvements as for the root mean square error. Furthermore, the impact of different type of predictors (observations, sea ice and weather forecasts) on the predictions has been evaluated. Sea ice observations are the most important type of predictors, and the weather forecasts have a much stronger impact on the predictions than sea ice forecasts. [ABSTRACT FROM AUTHOR]

Global coupled climate models are generally capable of reproducing the observed trends in the globally averaged atmospheric temperature. However, the global models do not perform as well on regional scales. Here, we present results from a 20-year, high-resolution ocean model experiment for the Atlantic and Arctic Oceans. The atmospheric forcing is taken from the final 20 years of a twentieth-century control run with a coupled atmosphere–ocean general circulation model. The ocean model results from the regional ocean model are validated using observations of hydrography from repeat cruises in the Barents Sea. Validation is performed for average quantities and for probability distributions in space and time. The validation results reveal that, though the regional model is forced by a coupled global model that has a noticeable sea ice bias in the Barents Sea, the hydrography and its variability are reproduced with an encouraging quality. We attribute this improvement to the realistic transport of warm, salty waters into the Barents Sea in the regional model. These lateral fluxes in the ocean are severely underestimated by the global model. The added value with the regional model that we have documented here lends hope to advance the quality of oceanic climate change impact studies. [ABSTRACT FROM AUTHOR]