Abstract: This tutorial 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.
Markus Reichstein is Director of the Biogeochemical Integration Department at the Max-Planck-Institute for Biogeochemistry. His main research interests revolve around the response and feedback of ecosystems (vegetation and soils) to climatic variability with a Earth system perspective, considering coupled carbon, water and nutrient cycles. Of specific interest is the interplay of climate extremes with ecosystem and societal resilience. These topics are adressed via a model-data integration approach, combining data-driven machine learning with systems modelling of experimental, ground- and satellite-based observations.
Since 2013 Markus Reichstein is Professor for Global Geoecology at the FSU Jena, and founding Director at the Michael-Stifel-Center Jena for Data-driven and Simulation Science. He has been serving as lead author of the IPCC special report on Climate Extremes (SREX), as member of the German Commitee Future Earth on Sustainability Research, and the Thuringian Panel on Climate. Recent awards include the Piers J. Sellers Mid-Career Award by the American Geophysical Union (2018), an ERC Synergy Grant (2019) and the Gottfried Wilhelm Leibniz Preis (2020).