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Accurate and robust real-time prediction of September Arctic sea ice
Dmitri Kondrashov1, Ivan Sudakow2,3, Valerie Livina4
1Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California 90095, USA.
Abstract:
We describe the real-time forecasting of September 2024 Arctic sea ice extent using a theory-guided machine learning method based on data-adaptive harmonic decomposition and frequency-based nonlinear stochastic modeling, as part of the Sea Ice Outlook. Compared to standard statistical and machine learning models, this method adeptly accounts for non-linear behavior, effectively incorporates memory effects, and handles a wide range of time scale variations, from synoptic (stochastic-like) weather effects to low-frequency (red-noise like) variability, significantly enhancing the accuracy and reliability of sea ice prediction.
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