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

In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to
combat global climate change. We identify two crucial opportunities that AI offers in this …

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 …

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 …

Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …

Recent progress in machine learning (ML) inspires the idea of improving (or learning) earth
system models directly from the observations. Earth sciences already use data assimilation …

In September 2019, a workshop was held to highlight the growing area of applying machine
learning techniques to improve weather and climate prediction. In this introductory piece, we …

In this paper, the performance of three machine-learning methods for predicting short-term
evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz …

Spatiotemporal forecasting (STF) extends traditional time series forecasting or spatial
interpolation problem to space and time dimensions. Here, we review the statistical, physical …