EGU24-555, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Towards understanding dominant controls on Earth system processes across observations and models

Robert Reinecke1,2, Francesca Pianosi3,4, and Thorsten Wagener2
Robert Reinecke et al.
  • 1Johannes Gutenberg-University Mainz, Institute of Geography, Mainz, Germany (
  • 2University of Potsdam, Institute for Environmental Science and Geography, Potsdam, Germany
  • 3Department of Civil Engineering, University of Bristol, Bristol, UK
  • 4Cabot Institute, University of Bristol, Bristol, UK

We are in a simultaneous state of exuberance and starvation of Earth system data. Model ensembles of increasing complexity provide petabytes of output, while remote sensing products offer terabytes of new data every day. On the other hand, we still lack data on some key processes that are more challenging to observe, like groundwater recharge, or only from particular regions of the world (often regions already heavily impacted by anthropogenic change). This leaves us with highly imbalanced datasets. Our ability to produce and collect mountains of data contrasts with our progress in improving scientific process understanding. How can we harness model simulations and data alike to enhance our knowledge and test scientific hypotheses about process relationships despite data gaps and poorly known biases in modelled and observational datasets? Our talk discusses methods to approach this problem while being agnostic to the data source (model simulations or observations). We present a new approach to interrogate a given dataset and identify correlational and possibly causal relationships between its variables. We test the method on an ensemble of complex global hydrological model simulations and observations from the ISIMIP experiments, and demonstrate its usefulness and limitations. We show that our approach can provide powerful insights into dominant process controls while scaling with large amounts of data.

How to cite: Reinecke, R., Pianosi, F., and Wagener, T.: Towards understanding dominant controls on Earth system processes across observations and models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-555,, 2024.