EGU2020-5440, updated on 10 Jan 2024
EGU General Assembly 2020
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Constraining uncertainty in projected gross primary production with machine learning

Manuel Schlund1, Veronika Eyring1,2, Gustau Camps-Valls3, Pierre Friedlingstein4, Pierre Gentine5,6, and Markus Reichstein7,8
Manuel Schlund et al.
  • 1Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Oberpfaffenhofen, Germany.
  • 2Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany.
  • 3Image Processing Laboratory (IPL), University of València, Valencia, Spain.
  • 4College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.
  • 5Department of Earth and Environmental Engineering, Columbia University, New York, NY 10027, USA.
  • 6Earth Institute and Data Science Institute, Columbia University, New York, NY 10027, USA.
  • 7Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany.
  • 8Michael-Stifel-Center Jena for Data-driven and Simulation Science, Jena, Germany.

By absorbing about one quarter of the total anthropogenic CO2 emissions, the terrestrial biosphere is an important carbon sink of Earth’s carbon cycle. A key metric of this process is the terrestrial gross primary production (GPP), which describes the biogeochemical production of energy by photosynthesis. Elevated atmospheric CO2 concentrations will increase GPP in the future (CO2 fertilization effect). However, projections from different Earth system models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) show a large spread in carbon cycle related quantities. In this study, we present a new supervised machine learning approach to constrain multi-model climate projections using observation-driven data. Our method based on Gradient Boosted Regression Trees handles multiple predictor variables of the present-day climate and accounts for non-linear dependencies. Applied to GPP in the representative concentration pathway RCP 8.5 at the end of the 21st century (2081–2100), the new approach reduces the “likely” range (as defined by the Intergovernmental Panel on Climate Change) of the CMIP5 multi-model projection of GPP to 161–203 GtC yr-1. Compared to the unweighted multi-model mean (148–224 GtC yr-1), this is an uncertainty reduction of 45%. Our new method is not limited to projections of the future carbon cycle, but can be applied to any target variable where suitable gridded data is available.

How to cite: Schlund, M., Eyring, V., Camps-Valls, G., Friedlingstein, P., Gentine, P., and Reichstein, M.: Constraining uncertainty in projected gross primary production with machine learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5440,, 2020.

This abstract will not be presented.