Work

Accurate and robust real-time prediction of September Arctic sea ice

Year: 2026

Type: article

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.

Source: Chaos An Interdisciplinary Journal of Nonlinear Science

DOI: 10.1063/5.0295634

Authors: Dmitri Kondrashov, Ivan Sudakow, Valerie Livina, Qingping Yang

Institutions: University of California, Los Angeles, The Open University, Search for Extraterrestrial Intelligence, National Physical Laboratory, Brunel University of London

Language: English

Topic: Arctic and Antarctic ice dynamics

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, London Mathematical Society

Awards: Institute for Mathematical and Statistical Innovation, Collaborative Research: Advancing predictive understanding of summertime Arctic sea ice cover, G7540469058