A new genealogy based on similarities in the computer codes of different climate models could improve studies that combine projections from multiple models.
A new Bayesian approach is used for the estimation and uncertainty quantification of unobservable parameters required to model tracer evolution in ocean sediment transport and tracer concentrations.
A new study couples an emission and transport scheme of pollen from vegetation, and explores pollen’s evolution in different atmospheric conditions and its impacts on clouds and precipitation.
A new non-column based spectral element implementation of cloud microphysics enables full 3D flexibility in computing clouds and improves computational efficiency.
By bringing together multiple data sources a new statistical method aims to improve the accuracy with which we might predict future ice melt in Greenland.
For the first time, a neural network parameterization of subgrid momentum transport is developed by training on a coarse-grained high-resolution atmospheric simulation.
Experiments in a cloud chamber have provided valuable insights into microphysical processes and will get more realistic as the height of the chamber increases.
Automated Machine Learning liberates domain scientists from selecting learners and hyperparameters and discovers the importance of atmospheric trace gases for improving surface PM2.5 estimates.
Climate models have many persistent and systematic biases, but a new study shows that allowing for a physical rather than statistical representation of energy transport reduces one of them.
A new computational method enables finding steady-state distributions of tracers in ocean circulation models, opening opportunities for physical and biogeochemical insight.