{"author":{"name":"Maria Conchita Agana Navarro, Geng Li, Theo Wolf, Maria Perez-Ortiz","slug":"maria-conchita-agana-navarro-geng-li-theo-wolf-maria-perez-ortiz","article_count":1,"latest_published_at":"2026-04-23T13:19:14.845+00:00","profile_url":"https://vff.ai/authors/maria-conchita-agana-navarro-geng-li-theo-wolf-maria-perez-ortiz","api_url":"https://vff.ai/api/authors/maria-conchita-agana-navarro-geng-li-theo-wolf-maria-perez-ortiz"},"articles":[{"slug":"assessing-the-robustness-of-climate-foundation-models-under-no-analog-distributi","title":"Climate Foundation Models Falter on No-Analog Futures","url":"https://vff.ai/article/2026/04/23/assessing-the-robustness-of-climate-foundation-models-under-no-analog-distributi","content_type":"research_summary","summary":"Researchers benchmarked three machine learning climate models, including the ClimaX foundation model, to assess their robustness when predicting climate states outside their historical training data. Testing on temporal extrapolation (2015-2023) and cross-scenario forcing shifts revealed a critical trade-off: while ClimaX achieved the lowest absolute error, it showed higher sensitivity to distribution shifts, with precipitation errors increasing up to 8.44% under extreme scenarios. The findings highlight that even high-capacity foundation models struggle with no-analog climate conditions and underscore the need for scenario-aware training and rigorous out-of-distribution evaluation protocols.","published_at":"2026-04-23T13:19:14.845+00:00","updated_at":"2026-05-07T02:19:52.375108+00:00","source":{"url":"https://arxiv.org/abs/2603.23043","name":"ArXiv (cs.AI)"},"featured_image":{"url":"https://news.mit.edu/sites/default/files/images/201902/3Q-Paul-OGorman-Machine-Learning-Climate-Modeling-world-clouds-NASA-MIT-00.jpg","alt":null},"categories":[{"name":"Research","slug":"research"},{"name":"AI Safety & Alignment","slug":"ai-safety-alignment"},{"name":"Data & Training","slug":"data-training"}]}]}