Max Planck Gesellschaft

Machine Learning & Data Science

Earth system models are the basis for understanding and projecting climate change. Combining machine learning with physical models of the atmosphere and land will improve climate models and the way how we can analyze and interpret complex Earth system data.

Activities & collaborations

ERC Synergy Grant “Understanding and Modelling the Earth System with Machine Learning” (USMILE)

Earth System Data Lab

Information from diverse and heterogeneous data streams must be interpreted jointly to understand complex extreme event impacts such as e.g. the Russian Heatwave 2010.

Methodological challenges

… more about causal discovery in the complex Earth system

Runge, J. et al. (2019). Inferring causation from time series in Earth system sciences. doi:10.1038/s41467-019-10105-3

Ellis Society

Machine Learning for Earth and Climate Sciences

Model and understand the Earth system with Machine Learning and Process Understanding


Machine learning challenges

… more about ML challenges for Earth System Sciences

Reichstein, M. et al. (2019). Deep learning and process understanding for data-driven Earth system science. doi:10.1038/s41586-019-0912-1

Key publications

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., Prabhat (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204. doi:10.1038/s41586-019-0912-1

Camps-Valls, G., Sejdinovic, D., Runge, J., Reichstein, M. (2019). A perspective on Gaussian processes for Earth observation. National Science Review, 6(4), 616-618. doi:10.1093/nsr/nwz028

Kraft, B., Jung, M., Körner, M., Requena Mesa, C., Cortés, J., Reichstein, M. (2019). Identifying dynamic memory effects on vegetation state using recurrent neural networks. Frontiers in Big Data, 2: 31. doi:10.3389/fdata.2019.00031

Talks, videos & interviews

Interview with Markus Reichstein in BR2 radio series "IQ - Science and Research" (German), 17.01.2019

Stockholm Seminar with Markus Reichstein, 21.08.2019

Latest Thinking video with Markus Reichstein, 04.06.2020

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