Earth system models have considerably increased their spatial resolution to solve more complex problems and achieve more realistic solutions. However, this generates an enormous amount of model data which requires proper management. Some Earth system models use ineffici...

Clouds in observations are fractals: they show self-similarity across scales ranging from one to 1000 km. This includes individual storms and large-scale cloud structures typical of organised convection. It is not known whether global storm-resolving models reproduce th...

<p>Deep convection plays an important role in driving the large-scale circulation and the complex interaction between moist convection and the large-scale circulation regulates the thermodynamic structure of the tropical atmosphere.<span>&am...

Reducing the model spread in free-tropospheric relative humidity (RH) and its response to warming is a crucial step toward reducing the uncertainty in clear-sky climate sensitivity, a step that is hoped to be taken with recently developed global storm-resolving models (...

We analyze a multi-model ensemble at a convection-resolving resolution based on the DYAMOND models and a resolution ensemble based on the limited-area model COSMO over 40 days to study how tropical and subtropical marine low clouds are represented at a kilometer-scale r...

Plain Language Summary: The tropical temperature structure is determined by regions with deep convection, which is believed to be moist‐adiabatic. However, both models and observations show that the temperature deviates from moist‐adiabats. This is because convective pa...

Abstract We assess the ability of neural network emulators of physical parametrization schemes in numerical weather prediction models to aid in the construction of linearized models required by four‐dimensional variational (4D‐Var) data assimilation. Neural networks can...

We present a comprehensive inter-comparison of linear regression (LR), stochastic, and deep-learning approaches for reduced-order statistical emulation of ocean circulation. The reference dataset is provided by an idealized, eddy-resolving, double-gyre ocean circulation...

Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of established methods...

Abstract We assess the value of machine learning as an accelerator for the parameterization schemes of operational weather forecasting systems, specifically the parameterization of nonorographic gravity wave drag. Emulators of this scheme can be trained to produce stabl...