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

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 …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

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 …

In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …

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

Understanding how turbulence leads to the enhanced irreversible transport of heat and
other scalars such as salt and pollutants in density-stratified fluids is a fundamental and …

The resolution of climate models is limited by computational cost. Therefore, we must rely on
parameterizations to represent processes occurring below the scale resolved by the models …

Neural networks have become increasingly prevalent within the geosciences, although a
common limitation of their usage has been a lack of methods to interpret what the networks …

Neural networks can emulate nonlinear physical systems with high accuracy, yet they may
produce physically inconsistent results when violating fundamental constraints. Here, we …