Simulations of tropical ocean convection help distinguish climate effects resulting from large-scale changes in atmospheric circulation from those resulting from higher temperatures.
New methods that help researchers understand the decision-making processes of neural networks could make the machine learning tool more applicable for the geosciences.
Researchers apply a superparameterization technique to boost the accuracy and efficiency of climate predictions generated by the Energy Exascale Earth System Model.
A new study shows that models that reproduce moisture on land are better at accurately recreating cumulus cloud behavior.
Climate models struggle to accurately portray clouds because the models cannot resolve the scales at which clouds form. A new study demonstrates a potential fix for the problem.
A pair of revisions to the Energy Exascale Earth System Model improves its ability to capture late afternoon and nocturnal rainfall as well as the timing and movement of convection.
Simulations that test different approaches to modeling radiation suggest a commonly used scheme fails to fully capture changes in midlatitude circulation associated with climate change.
New modeling casts doubt on the suitability of running experiments with fixed sea surface temperatures to understand the effects of cloud aggregation on Earth’s climate.
Simulating the dynamic nature of plant root profiles in Earth system models improves the representation of the carbon and water cycles.
An evolving set of tools helps land surface model developers optimize the realism of their parameterizations for the next generation of weather and climate models.