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Analyses of global climate models consistently show that clouds constitute the biggest source of uncertainty and instability in predictions.

New research on Frontera seeks to better incorporate clouds into global models by breaking models into two parts: a coarse-grained, lower-resolution (100km) planetary model and many small patches with 100 to 200 meter resolution.

These simulations can capture the physical processes and turbulent eddies involved in cloud formation and do not produce unwanted side-effects.

The research team is also exploring ‘climate invariant' machine learning approaches that incorporate physical knowledge of climate processes into machine learning algorithms.


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