Atmosphere-Biosphere Coupling, Climate and Causality (ABC³)

Dr. Alexander J. Winkler

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Mission

The Atmosphere-Biosphere Coupling, Climate and Causality research group focuses on identifying feedbacks and causal links in the exchange of carbon, water and energy fluxes between the terrestrial biosphere and atmosphere. In particular, we aim to understand how this coupled system changes under rising atmospheric CO₂ concentration. We use models of varying complexity (conceptual models to fully coupled Earth system models) and combine our research with statistical analysis and modelling (machine learning to hybrid modelling) based on various Earth observation data streams.

Current key items of the ABC³ research group:

Methodological and Conceptual Approaches

• Exploring the application of modern machine learning / deep learning methods in Earth system research with a focus on obtaining interpretability to improve process understanding
• Developing hybrid-modelling approaches (i.e. theory-guided fusion of data-driven and mechanistic models) of land-atmosphere interactions

Overarching Research Objectives:

• Disentangling the different effects of rising atmospheric CO₂ concentration and other confounding factors in Earth observations and model simulations using causal inference
• Understanding the causal drivers of phenological changes in in-situ and satellite observations and identify the biogeophysical and biogeochemical feedbacks to the climate system
• Isolating and quantifying direct and indirect effects in the atmosphere-biosphere coupling in controlling anomalies in carbon fixation
• Determining constraints of key entities in the carbon cycle climate system by linking multi-model ensemble simulations and observational data

Available bachelor’s and master’s theses and internships within the ABC³ Research Group.

There are no topics or internships currently available.

Projects

How Land Carbon Dynamics Shape the Rise and Fall of Atmospheric CO₂ (PostPeak)

The carbon cycle determines how much anthropogenic carbon remains in the atmosphere and warms the planet. Beyond the emissions plateau, it shapes the atmospheric CO2 concentration—with persistent legacy effects and hysteresis. “PostPeak” posits the terrestrial carbon cycle as a key modulator of this transition and calls for precise dynamical predictions of climate responses to declining emissions.

Max-Planck Caltech Carnegie Columbia Center (MC³ 4 Earth)

The Max-Planck Caltech Carnegie Columbia Center (MC³ 4 Earth) Center aims to transform Earth system analysis and predictions, integrating advanced Earth observations and machine learning for deeper understanding and sustainable management of our planet, in particular the land.

ERC Synergy Grant USMILE

The European Research Council (ERC) Synergy Grant USMILE, awarded in 2019, aims to advance the understanding and modeling of the Earth system using machine learning (ML), one of the most important approaches in artificial intelligence (AI).

NFDI Consortium for Earth System Sciences (NFDI4Earth)

NFDI4Earth addresses digital needs of Earth System Sciences. Earth System scientists cooperate in international and interdisciplinary networks with the overarching aim to understand the functioning and interactions within the Earth system and address the multiple challenges of global change.

PhenoFeedbacks

The DFG-funded project “PhenoFeedbacks” addresses the effects and feedbacks of phenological changes in the climate system, addressing questions such as: How do changes in land surface phenology affect annual changes in biophysical properties such as land surface albedo, evapotranspiration, and total surface energy balance? What is the biophysical radiative forcing caused by phenological changes?

Special Project (ECMWF): OpenIFS Modeling of the Atmospheric Carbon Cycle

Together with research groups at the Barcelona Supercomputing Center (BSC) in Spain, Wageningen University & Research (WUR) in the Netherlands, the project aims to refine the OpenIFS model to be used for high-resolution transport modelling of CO₂.