ArticleScience advances2024
Climate-invariant machine learning.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Predicting regional gray swans via translocation: AI weather models and Dubai's unprecedented 2024 rainfall.Science advances · 2026Article
- Multi-Decadal Dynamics of Wetland Methane Emissions Revealed by Knowledge-Guided Machine Learning.Global change biology · 2026Article
- A weakened diurnal weather constraint leads to longer burning hours in North America.Science advances · 2026Article
- Projection of ENSO using observation-informed deep learning.Nature communications · 2025Article
- Can AI weather models predict out-of-distribution gray swan tropical cyclones?Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Artificial intelligence for modeling and understanding extreme weather and climate events.Nature communications · 2025Review
- Predicting Cloud-To-Ground Lightning in the Western United States From the Large-Scale Environment Using Explainable Neural Networks.Journal of geophysical research. Atmospheres : JGR · 2024Article
- Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments With Global AI-Based Weather Models.Geophysical research letters · 2024Article
- Article
- Monthly climate prediction using deep convolutional neural network and long short-term memory.Scientific reports · 2024Article
- Climate-invariant machine learning.Science advances · 2024Article
- Learning PDE to Model Self-Organization of Matter.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations but tend to extrapolate poorly to climate regimes that they were not trained on. To get the best of the physical and statistical worlds, we propose a framework, termed "climate-invariant" ML, incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.
Identifiers
What OpenQuestion holds
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.