A probabilistic deep learning methodology that learns from climate simulation big data offers advantageous seasonal forecasting skill and crucial climate model diagnosis information at a global scale.
A machine-learned correction enables an efficient coarse-grid global atmosphere model to better track the weather and time-mean precipitation of an expensive fine-grid ‘digital twin’ reference model.
A new way of representing microphysical uncertainty in convective-scale data assimilation reduces biases in model states and improves the accuracy of short-term precipitation forecasts.
A study of the impacts of radiative interactions with different cloud types on aggregation of rainstorms finds that interactions with high-clouds and water vapor are key.
Satellite data has been used to correct the aerosol loading and land surface albedo in several AeroCom models, which has improved shortwave flux biases between models and observations.
New metrics used to quantify errors in precipitation show that convection permitting simulations outperform coarser resolution simulations.
Contributions are invited to a new journal special collection on the use of new machine learning methodologies and applications of machine learning to Earth system modeling.