Data-driven equation discovery of a sea ice albedo parametrisations + Machine Learning insights…

On June 16, our Storms, Eddies and Science Hour featured EERIE project scientists Diajeng Atmojo (University of Bremen) and Simon Michel (University of Oxford) speaking about “Data-driven equation discovery of …

Characterizing Uncertainty in Deep Convection Triggering Using Explainable Machine Learning

Realistically representing deep atmospheric convection is important for accurate numerical weather and climate simulations. However, parameterizing where and when deep convection occurs (“triggering”) is a well-known source of model uncertainty. …

Deep learning based reconstructions of the Atlantic meridional overturning circulation confirm twenty-first century decline

Gaining knowledge of the past and present variations of the Atlantic meridional overturning circulation (AMOC) is crucial for the development of accurate future climate projections. The short range covered by …