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Arctic sea ice plays integral roles in both polar and global environmentalsystems, notably ecosystems, communities, and economies. As sea ice continuesto decline due to climate change, it has become imperative to accuratelypredict the future of sea ice extent (SIE). Using datasets of Arcticmeteorological and SIE variables spanning 1979 to 2021, we proposearchitectures capable of processing multivariate time series and spatiotemporaldata. Our proposed framework consists of ensembled stacked Fourier Transformsignals (FFTstack) and Gradient Boosting models. In FFTstack, grid searchiteratively detects the optimal combination of representative FFT signals, aprocess that improves upon current FFT implementations and deseasonalizers. Anoptimized Gradient Boosting Regressor is then trained on the residual of theFFTstack output. Through experiment, we found that the models trained on bothmultivariate and spatiotemporal time series data performed either similar to orbetter than models in existing research. In addition, we found that integrationof FFTstack improves the performance of current multivariate time seriesmodels. We conclude that the high flexibility and performance of thismethodology have promising applications in guiding future adaptation,resilience, and mitigation efforts in response to Arctic sea ice retreat.

Integrating Fourier Transform and Residual Learning for Arctic Sea Ice Forecasting