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Title: Environment-sensitivity functions for gross primary productivity in light use efficiency models

Abstract

The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of currentmore » models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products.« less

Authors:
 [1];  [1];  [1];  [2];  [3];  [4];  [1];  [5];  [6];  [1];  [7];  [8];  [1];  [9];  [10];  [11];  [12]
  1. Max Planck Institute for Biogeochemistry, Jena (Germany)
  2. Universite de Lorraine (France)
  3. Technical Univ. of Denmark, Lyngby (Denmark)
  4. Max Planck Institute for Biogeochemistry, Jena (Germany); Wageningen Univ. (Netherlands)
  5. Max Planck Institute for Biogeochemistry, Jena (Germany); Czech Academy of Sciences (CAS) (Czech Republic). Global Change Research Institute
  6. Univ. of Valencia (Spain)
  7. Univ. of Innsbruck (Austria)
  8. University of Technology Sydney (Australia)
  9. Univ. of Queensland (Australia); Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT (Australia)
  10. Agroscope, Zurich (Switzerland)
  11. Wageningen Univ. (Netherlands)
  12. Max Planck Institute for Biogeochemistry, Jena (Germany); Universidade Nova de Lisboa (Portugal)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). AmeriFlux
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1981531
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Agricultural and Forest Meteorology
Additional Journal Information:
Journal Volume: 312; Journal Issue: C; Journal ID: ISSN 0168-1923
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Carbon assimilation; Radiation use efficiency; Model comparison; Model equifinality; Diffuse fraction; Sensitivity formulations; Randomly sampled sites; Temporal scales

Citation Formats

Bao, Shanning, Wutzler, Thomas, Koirala, Sujan, Cuntz, Matthias, Ibrom, Andreas, Besnard, Simon, Walther, Sophia, Šigut, Ladislav, Moreno, Alvaro, Weber, Ulrich, Wohlfahrt, Georg, Cleverly, Jamie, Migliavacca, Mirco, Woodgate, William, Merbold, Lutz, Veenendaal, Elmar, and Carvalhais, Nuno. Environment-sensitivity functions for gross primary productivity in light use efficiency models. United States: N. p., 2021. Web. doi:10.1016/j.agrformet.2021.108708.
Bao, Shanning, Wutzler, Thomas, Koirala, Sujan, Cuntz, Matthias, Ibrom, Andreas, Besnard, Simon, Walther, Sophia, Šigut, Ladislav, Moreno, Alvaro, Weber, Ulrich, Wohlfahrt, Georg, Cleverly, Jamie, Migliavacca, Mirco, Woodgate, William, Merbold, Lutz, Veenendaal, Elmar, & Carvalhais, Nuno. Environment-sensitivity functions for gross primary productivity in light use efficiency models. United States. https://doi.org/10.1016/j.agrformet.2021.108708
Bao, Shanning, Wutzler, Thomas, Koirala, Sujan, Cuntz, Matthias, Ibrom, Andreas, Besnard, Simon, Walther, Sophia, Šigut, Ladislav, Moreno, Alvaro, Weber, Ulrich, Wohlfahrt, Georg, Cleverly, Jamie, Migliavacca, Mirco, Woodgate, William, Merbold, Lutz, Veenendaal, Elmar, and Carvalhais, Nuno. Fri . "Environment-sensitivity functions for gross primary productivity in light use efficiency models". United States. https://doi.org/10.1016/j.agrformet.2021.108708. https://www.osti.gov/servlets/purl/1981531.
@article{osti_1981531,
title = {Environment-sensitivity functions for gross primary productivity in light use efficiency models},
author = {Bao, Shanning and Wutzler, Thomas and Koirala, Sujan and Cuntz, Matthias and Ibrom, Andreas and Besnard, Simon and Walther, Sophia and Šigut, Ladislav and Moreno, Alvaro and Weber, Ulrich and Wohlfahrt, Georg and Cleverly, Jamie and Migliavacca, Mirco and Woodgate, William and Merbold, Lutz and Veenendaal, Elmar and Carvalhais, Nuno},
abstractNote = {The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products.},
doi = {10.1016/j.agrformet.2021.108708},
journal = {Agricultural and Forest Meteorology},
number = C,
volume = 312,
place = {United States},
year = {Fri Nov 19 00:00:00 EST 2021},
month = {Fri Nov 19 00:00:00 EST 2021}
}

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