Seasonal Arctic sea ice forecasting with probabilistic deep learning
Accurate seasonal forecasts of sea ice are highly valuable, particularly in the context of sea ice loss due to global warming. A new machine learning tool for sea ice forecasting offers a substantial increase in accuracy over current physics-based dynamical model predictions.
Published in: | Nature Communications |
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Main Authors: | , , , , , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Nature Portfolio
2021
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Subjects: | |
Online Access: | https://doi.org/10.1038/s41467-021-25257-4 https://doaj.org/article/5d37269ed2734f7bac2303b89ff00149 |
Summary: | Accurate seasonal forecasts of sea ice are highly valuable, particularly in the context of sea ice loss due to global warming. A new machine learning tool for sea ice forecasting offers a substantial increase in accuracy over current physics-based dynamical model predictions. |
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