Data-driven Earth system modelling
Prof. Niklas Boers
Technical University of Munich, Germany
邀请人/报告会主持人 ： 潘宝祥 副研究员
The Earth’s climate is a highly complex system and as such many processes and phenomena cannot be resolved in terms of fundamental physical equations. While there is a long history of complementary, data-driven modelling approaches, recent developments in deep learning promise substantial advances. Here I present some recent results on machine learning approaches to address several challenges in nonlinear Earth system dynamics, including the prediction of climate phenomena such as monsoon onsets or ENSO, hybrid modelling combining process-based with deep learning model components, as well as bias correction and downscaling of Earth system models.
Niklas Boers is Professor of Earth System Modelling, Technical University of Munich (TUM) and Leader of the Future Lab 'Artificial Intelligence in the Anthropocene' at the Potsdam Institute for Climate Impact Research (PIK). He is also associate coordinator of the Horizon 2020 project 'Tipping Points in the Earth System'.
Niklas Boers works on theoretical questions of Earth system science with focus on the analysis, modelling, and prediction of extreme events and abrupt transitions (‘tipping points’). In his research, he develops methods rooted in Mathematics and Theoretical Physics, in particular Complexity Science and Machine Learning, to combine process-based and data-driven models. His work finds applications in climate dynamics, paleoclimatology, and in the context of anthropogenic climate change.
Niklas Boers studied Physics and Mathematics at Ludwig Maximilian University of Munich and TUM and obtained his PhD in Theoretical Physics from the Humboldt University of Berlin. Thereafter he worked on different topics in theoretical Earth system dynamics at the Potsdam Institute for Climate Impact Research, Ecole Normale Supérieure in Paris, Imperial College London, and Freie Universität Berlin. 2021 Niklas Boers was appointed Professor of Earth system modelling at TUM.