Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction

Building upon the previously introduced MVGC and PCMCI+ algorithms, we applied these methods to identify key causal variables of Arctic sea ice dynamics. For both daily and monthly datasets, MVGC identified all variables except Sea Surface T emperature (SST) as causal features. This result underscores the broad influence of atmospheric and oceanic variables on Arctic sea ice. PCMCI+, known for its robustness in handling high-dimensional and autocorrelated time series data, provided a more refined identification of causal features. For the daily dataset, PCMCI+ highlighted longwave radiation, snowfall, sea surface salinity (SSS), surface pressure, and SIE itself as the primary causal factors. For the monthly dataset, the identified causal features were longwave radiation, SST, and SIE . These results suggest temporal and spatial differences in the causal relationships influencing SIE dynamics across daily and monthly timescales. Figure 4 shows the causal graphs generated by PCMCI+ for daily and monthly datasets, highlighting the direct causal influences of key variables on Arctic SIE. The identified features guided the selection of input variables for the GRU-LSTM model, ensuring that the model leveraged causally significant information for prediction.