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 …

Predictions of the Earth system, such as weather forecasts and climate projections, require
models informed by observations at many levels. Some methods for integrating models and …

In this paper, we performed an analysis of the 500 most relevant scientific articles published
since 2018, concerning machine learning methods in the field of climate and numerical …

We assess the value of machine learning as an accelerator for the parameterization
schemes of operational weather forecasting systems, specifically the parameterization of …

Accurate prediction of precipitation intensity is crucial for both human and natural systems,
especially in a warming climate more prone to extreme precipitation. Yet, climate models fail …

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 …

Abstract Machine learning (ML) is increasing in popularity in the field of weather and climate
modelling. Applications range from improved solvers and preconditioners, to …

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 …

Climate variability and weather phenomena can cause extremes and pose significant risk to
society and ecosystems, making continued advances in our physical understanding of such …

Cloud microphysical processes occur at the smallest end of scales among cloud-related
processes and thus must be parameterized not only in large-scale global circulation models …