Klaus Hasselmann's revolutionary intuition in climate science was to use the stochasticity
associated with fast weather processes to probe the slow dynamics of the climate system …

Subgrid parameterizations of mesoscale eddies continue to be in demand for climate
simulations. These subgrid parameterizations can be powerfully designed using physics …

There is growing interest in discovering interpretable, closed‐form equations for subgrid‐
scale (SGS) closures/parameterizations of complex processes in Earth systems. Here, we …

Turbulence closure modeling using (ML) is at an early crossroads. The extraordinary
success of ML in a variety of challenging fields had given rise to an expectation of similar …

There is growing interest in discovering interpretable, closed-form equations for subgrid-
scale (SGS) closures/parameterizations of complex processes in Earth systems. Here, we …

The present work proposes a novel transformer-based convolutional neural network
(TransCNN) method to effectively model the inverse energy cascade in two dimensional …

Global climate models (GCMs) are the main tools for understanding and predicting climate
change. However, due to limited numerical resolutions, these models suffer from major …

Constructing sparse, effective reduced-order models (ROMs) for high-dimensional
dynamical data is an active area of research in applied sciences. In this work, we study an …

We present a novel machine learning approach to reduce the dimensionality of state
variables in stratified turbulent flows governed by the Navier–Stokes equations in the …

Different approaches to using data-driven methods for subgrid-scale closure modeling have
emerged recently. Most of these approaches are data-hungry, and lack interpretability and …