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Machine-learning-model-data integration for a better understanding of the Earth System

This presentation will give an overview about how Machine learning can support Earth System Science. First I present an overview of the key challenges in this field of science, which addresses the complex interplay between e.g. hydrosphere, biosphere, atmosphere and cryosphere, with emphasis on the carbon cycle and climate feedbacks. This will be complemented by four examples on 1) how to infer global carbon fluxes from sparse observations, 2) how to quantify uncertainties therein including extrapolation, 3) how to model landscapes, i.e. the spatial arrangement of elements, 4) how address dynamic effects as expressed in time-series and spatio-temporal data.