Constraining Uncertainty in Projected Gross Primary Production With Machine Learning

Media type: E-Article

Title: Constraining Uncertainty in Projected Gross Primary Production With Machine Learning

Contributor: Schlund, Manuel [Author]; Eyring, Veronika [Author]; Camps‐Valls, Gustau [Author]; Friedlingstein, Pierre [Author]; Gentine, Pierre [Author]; Reichstein, Markus [Author]; 1 Deutsches Zentrum für Luft‐ und Raumfahrt (DLR) Institut für Physik der Atmosphäre Oberpfaffenhofen Germany [Author]; 3 Image Processing Laboratory (IPL) Universitat de València València Spain [Author]; 4 College of Engineering, Mathematics and Physical Sciences University of Exeter Exeter UK [Author]; 6 Department of Earth and Environmental Engineering Columbia University New York NY USA [Author]; 8 Department of Biogeochemical Integration Max Planck Institute for Biogeochemistry Jena Germany [Author]

imprint: GEO-LEOe-docs (FID GEO), 2020-11-21

Language: English

DOI: https://doi.org/10.23689/fidgeo-4207

Keywords: future climate projections ; modeling

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Description: The terrestrial biosphere is currently slowing down global warming by absorbing about 30% of human emissions of carbon dioxide (CO2). The largest flux of the terrestrial carbon uptake is gross primary production (GPP) defined as the production of carbohydrates by photosynthesis. Elevated atmospheric CO2 concentration is expected to increase GPP (“CO2 fertilization effect”). However, Earth system models (ESMs) exhibit a large range in simulated GPP projections. In this study, we combine an existing emergent constraint on CO2 fertilization with a machine learning approach to constrain the spatial variations of multimodel GPP projections. In a first step, we use observed changes in the CO2 seasonal cycle at Cape Kumukahi to constrain the global mean GPP at the end of the 21st century (2091–2100) in Representative Concentration Pathway 8.5 simulations with ESMs participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) to 171 ± 12 Gt C yr−1, compared to the unconstrained model range of 156–247 Gt C yr−1. In a second step, we use a machine learning model to constrain gridded future absolute GPP and gridded fractional GPP change in two independent approaches. For this, observational data are fed into the machine learning algorithm that has been trained on CMIP5 data to learn relationships between present‐day physically relevant diagnostics and the target variable. In a leave‐one‐model‐out cross‐validation approach, the machine learning model shows superior performance to the CMIP5 ensemble mean. Our approach predicts an increased GPP change in northern high latitudes compared to regions closer to the equator. ; Plain Language Summary: About a quarter of human emissions of carbon dioxide (CO2) is absorbed by vegetation and soil on the Earth's surface and hence does not contribute to global warming caused by CO2 in the atmosphere. Thus, in order to better define the remaining carbon budgets left to meet particular warming targets like the 1.5°C of the Paris Agreement, it is important to accurately ...

Access State: Open Access

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