Enhancing sea ice concentration prediction with multi-task learning and conditional residual refinement

Sea ice covers the oceans in polar regions and is closely related to heat circulation between the Sun and the Earth, and the absolute amount of sea ice can represent climate change itself. The research aiming to prepare for future climate conditions by predicting sea ice concentration (SIC), the area ratio of the ocean covered by sea ice, is being actively conducted.

From a long-term perspective, sea ice is influenced by sea surface temperature (SST), 2 m air temperature (t2m), wind fields, and so on, and machine learning or deep learning techniques are used to predict SIC in order to leverage the correlations among variables. However, due to the characteristics of deep learning techniques, there are limitations in identifying how much each variable influences the SIC prediction results.

This study simultaneously predicts SIC, t2m, and SST through a multitask Transformer model, and the predicted t2m and SST are converted into a gate intensity map to correct the bias of SIC. Through this, we interpreted how atmospheric and oceanic environmental factors affected the SIC prediction results. In addition, by comparing the prediction results of SIC and environmental factors under conditions such as specific seasons and regions, where prediction is relatively unstable, we quantified the variable-specific weights under those conditions.

The gate intensity map used for SIC bias correction can itself be used as an uncertainty map, and expresses, as a spatial distribution, regions that are difficult for the deep learning model to predict. In addition, by comparing the impacts of each environmental factor by lead time, the contributions of variables can be identified at long-term and short-term prediction time points.