Heat waves (HWs) can cause large socioeconomic and environmental impacts. The
observed increases in their frequency, intensity and duration are projected to continue with …

Anthropogenic climate change is decreasing seasonal snowpacks globally, with potentially
catastrophic consequences on water resources, given the long-held reliance on snowpack …

Most state-of-the-art approaches for weather and climate modeling are based on physics-
informed numerical models of the atmosphere. These approaches aim to model the non …

Machine learning (ML) provides novel and powerful ways of accurately and efficiently
recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio …

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 …

Earth system modelling (ESM) is essential for understanding past, present and future Earth
processes. Deep learning (DL), with the data-driven strength of neural networks, has …

In the context of science, the well-known adage" a picture is worth a thousand words" might
well be" a model is worth a thousand datasets." In this manuscript we introduce the SciML …

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 …

Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

General circulation models (GCMs) are the foundation of weather and climate prediction,.
GCMs are physics-based simulators that combine a numerical solver for large-scale …