Stochastic methods are a crucial area in contemporary climate research and are
increasingly being used in comprehensive weather and climate prediction models as well as …

There is no more challenging problem in computational science than that of estimating, as
accurately as science and technology allows, the future evolution of Earth's climate; nor …

The last decade has seen the success of stochastic parameterizations in short-term, medium-
range, and seasonal forecasts: operational weather centers now routinely use stochastic …

Deep learning can accurately represent sub‐grid‐scale convective processes in climate
models, learning from high resolution simulations. However, deep learning methods usually …

Simple chaotic systems are useful tools for testing methods for use in numerical weather
simulations owing to their transparency and computational cheapness. The Lorenz system …

A conditional Gaussian framework for understanding and predicting complex multiscale
nonlinear stochastic systems is developed. Despite the conditional Gaussianity, such …

Land surfaces and soil conditions are key sources of climate predictability at the seasonal
time scale. In order to estimate how the initialization of the land surface affects the …

The parameterization of shallow cumuli across a range of model grid resolutions of kilometre‐
scales faces at least three major difficulties:(1) closure assumptions of conventional …

The representation of the Madden–Julian oscillation (MJO) is still a challenge for numerical
weather prediction and general circulation models (GCMs) because of the inadequate …

Convection influences climate and weather events over a wide range of spatial and
temporal scales. Therefore, accurate predictions of the time and location of convection and …