Improving dynamical seasonal sea ice prediction in the Arctic with machine learning

 

Dynamical sea ice predictions have significant biases or systematic errors that are difficult to effectively remove. In this work, we introduce machine learning into the Norwegian Climate Prediction Model (NorCPM, a state-of-the-art dynamical prediction system) to improve Arctic sea ice predictions.

We build a statistics bias-correction methodology employing machine learning techniques. An artificial neural network is trained with NorCPM data. It is then used to predict sea ice concentration biases or systematic errors and correct them either in post-processing of the predictions (offline manner) or during the production of the prediction (online manner). We evaluate the outcomes by assessing sea ice extent (SIE) and comparing them against observational data. Our findings reveal that offline correction markedly reduces the prediction biases in summer (more than 30%), while online correction enhances the variability in sea ice predictions up to four months. These results underscore the potential of machine learning as a potent tool for refining the accuracy of Arctic sea ice seasonal predictions.