Machine Learning and Uncertainties in Climate Simulations

Sources of uncertainties can be of different nature in climate studies including model approximations from the true climate system, intra and inter model variability, subgrid errors, measurement errors, anthopogeneic forcing trajectories, and so on. This wokshop will investigate how to assess, model, and combine these uncertatinties wihin statistical and machine learning methods. This workshop will integrate sessions on stochastic weather simulators (SWGEN) in order to bridge the existing SWGEN community with ML researchers.