ArticleNature communications2021
Seasonal Arctic sea ice forecasting with probabilistic deep learning.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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Who cites it
15 citing papers in PubMed.
- Adversarial dynamical systems characterize when data-driven learning succeeds or fails.Nature communications · 2026Article
- AI for atmosphere-ocean sciences: advancements, challenges and ways forward.National science review · 2026Review
- Advancing global sea ice prediction capabilities using a fully coupled climate model with integrated machine learning.Science advances · 2026Article
- Accurate Mediterranean Sea forecasting via graph-based deep learning.Scientific reports · 2025Article
- Agricultural land-use system management: Research progress and perspectives.Fundamental research · 2025Review
- Unicorn: U-Net for sea ice forecasting with convolutional neural ordinary differential equations.Scientific reports · 2025Article
- Data-driven uncertainty-aware forecasting of sea ice conditions in the gulf of Ob based on satellite radar imagery.Scientific reports · 2025Article
- Article
- Chromatic forecasting hydrogels for anti-icing applications.Nature communications · 2025Article
- Artificial intelligence for geoscience: Progress, challenges, and perspectives.Innovation (Cambridge (Mass.)) · 2024Review
- Increased methane emissions from oil and gas following the Soviet Union's collapse.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Unsupervised feature extraction of aerial images for clustering and understanding hazardous road segments.Scientific reports · 2023Article
- Newly reconstructed Arctic surface air temperatures for 1979-2021 with deep learning method.Scientific data · 2023Article
- A deep-learning estimate of the decadal trends in the Southern Ocean carbon storage.Nature communications · 2022Article
- Seasonal Arctic sea ice forecasting with probabilistic deep learning.Nature communications · 2021Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.