XCA3 - Feedback and uncertainty analysis

This cross-cutting activity (XCA) will coordinate and analyse a set of idealized experiments to quantify the feedback uncertainty associated with ESM2025’s model developments at the component and coupled system level. The experiments explore model parametric sensitivities and model uncertainties, including the potential of hybrid process-based/machine learning modelling approaches. XCA3 is tightly linked to developments in core theme 3, evaluations in XCA1 and the experimental settings in XCA2. Our ultimate goal is to get to a comprehensive understanding of the current uncertainties, including in feedbacks, in ESMs, and to contribute to a better sampling in the spread of possible future climate outcomes to support an informed, risk-based approach for realizing the Paris Agreement.

XCA Leader

Nuno Carvalhais

Nuno Carvalhais

Scientist/Group leader, Max Planck Institute for Biogeochemistry - Jena (Germany)

I have a PhD in Environmental Engineering from the Faculty of Sciences and Technology, New University of Lisbon, Portugal, on integrating multiple data streams, including EO, and modeling ecosystem carbon fluxes. I am leading the Model-Data Integration research group in the Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, since 2012. I am very interested in dynamics of terrestrial ecosystems, global biogeochemical cycles, model-data fusion approaches, model evaluation/development, remote sensing of vegetation, machine learning and hybrid modeling.


In ESM2025

Exploring machine learning and hybrid modeling approaches to maximize the information flow from observations to modeling particular land surface dynamics and explore its implications in a coupled Earth system modeling framework. Coordinate and synthesize results from the cross-cutting activity #3 on feedback and uncertainty analysis.