Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth
and predicting how it might change in the future under ongoing anthropogenic forcing. In …

Progress within physical oceanography has been concurrent with the increasing
sophistication of tools available for its study. The incorporation of machine learning (ML) …

Leveraging artificial neural networks (ANNs) trained on climate model output, we use the
spatial pattern of historical temperature observations to predict the time until critical global …

While global warming has generally increased the occurrence of extreme precipitation, the
physical mechanisms by which climate change alters regional and local precipitation …

Convolutional neural networks (CNNs) have recently attracted great attention in geoscience
because of their ability to capture nonlinear system behavior and extract predictive …

The gap in resolution between existing global climate model output and that sought by
decision-makers drives an ongoing need for climate downscaling. Here we test the extent to …

The intensification of extreme precipitation under anthropogenic forcing is robustly projected
by global climate models, but highly challenging to detect in the observational record. Large …

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics
generates large datasets comprising hundreds to thousands of metabolites with complex …

Extreme events such as heat waves and cold spells, droughts, heavy rain, and storms are
particularly challenging to predict accurately due to their rarity and chaotic nature, and …

Machine learning (ML) algorithms are powerful tools to build models of clouds and climate
that are more faithful to the rapidly increasing volumes of Earth system data than commonly …