Dr. Alexander Winkler
Research Group Leader at the Max Planck Institute for Biogeochemistry Jena
Group: Atmosphere-Biosphere-Coupling, Climate and Causality (ABC3)
Alex is an Earth system scientist specializing in the intricate interplay of the atmosphere and the biosphere, and associated climate feedbacks. He completed his PhD in Geoscience with an emphasis on Earth system modeling at the Max-Planck-Institute for Meteorology and the University of Hamburg in 2019. Subsequently, he undertook a Postdoctoral role within the “Climate, Climatic Change, and Society” (CLICCS) Cluster of Excellence. In 2020, he joined the Max-Planck-Institute for Biogeochemistry, Jena, as part of the ERC Synergy Grant “Understanding and Modelling the Earth System with Machine Learning (USMILE).” In 2021, Alex initiated the establishment of the Research Group “Atmosphere-Biosphere Coupling, Climate, and Causality” at the MPI for Biogeochemistry. He received the Feodor-Lynen Fellowship from the Alexander-von-Humboldt foundation, facilitating collaborative research at the Scripps Institution for Oceanography, University of California, San Diego.
His research revolves around discerning feedback loops and causal connections governing the exchange of CO2, water, and energy between the land biosphere and atmosphere, particularly under the context of rising atmospheric CO2 levels. His research employs an array of models, ranging from basic conceptual frameworks to complex Earth system models. Methodologically, Alex emphasizes the application of machine learning to Earth system research, prioritizing interpretability for enhanced process understanding through harnessing a diverse spectrum of Earth observation data. Specifically, he pioneers hybrid models that fuse data-driven and mechanistic approaches in Earth system modeling.
The current overarching research objectives are:
- Dissecting the distinct impacts of rising atmospheric CO2 concentrations and other confounding factors in both Earth observations and model simulations using causal inference.
- Deciphering the causal drivers behind shifts in phenological patterns through both in-situ and satellite observations, while identifying biogeophysical and biogeochemical feedbacks within the climate system.
- Reducing uncertainty in key entities in the carbon cycle climate system by bridging multi-model ensemble simulations with observational data.