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http://bura.brunel.ac.uk/handle/2438/33544| Title: | Accurate and robust real-time prediction of September Arctic sea ice |
| Authors: | Kondrashov, D Sudakow, I Livina, V Yang, Q |
| Keywords: | nonlinear systems;cryosphere;geodesy;geographic location;machine learning;signal processing;harmonic analysis;operator theory;autocorrelation;stochastic processes |
| Issue Date: | 3-Feb-2026 |
| Publisher: | American Institute of Physics |
| Citation: | Kondrashov, D. et al. (2026) 'Accurate and robust real-time prediction of September Arctic sea ice', Chaos: An Interdisciplinary Journal of Nonlinear Science, 36 (2), 023110, pp. 1–16. doi: 10.1063/5.0295634. |
| 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. |
| Description: | Data Availability: The data that support the findings of this study are available from the corresponding author upon reasonable request. The NSIDC daily data by region is available at https://nsidc.org/sea-ice-today/sea-ice-tools . |
| URI: | https://bura.brunel.ac.uk/handle/2438/33544 |
| ISSN: | 1054-1500 |
| Other Identifiers: | ORCiD: Dmitri Kondrashov https://orcid.org/0000-0002-3471-7275 ORCiD: Ivan Sudakow https://orcid.org/0000-0003-2614-8794 ORCiD: Valerie Livina https://orcid.org/0000-0003-3759-9013 ORCiD: Qingping Yang https://orcid.org/0000-0002-2557-8752 |
| Appears in Collections: | Department of Mechanical and Aerospace Engineering Research Papers |
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|---|---|---|---|---|
| FullText.pdf | Copyright © 2026 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | 5.74 MB | Adobe PDF | View/Open |
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