Gross primary production (GPP) by boreal forests is highly sensitive to environmental changes. However, GPP simulated by land surface models (LSMs) remains highly uncertain due to the lack of direct photosynthesis observations at large scales. Carbonyl sulfide (COS) has emerged as a promising proxy to improve the representation of GPP in LSMs. Because COS is absorbed by vegetation following the same diffusion pathway as CO2 during photosynthesis and not emitted back to the atmosphere, incorporating a mechanistic representation of vegetation COS uptake in LSMs allows using COS observations to refine GPP representation. Here, we perform ecosystem COS flux and GPP data assimilations to constrain the COS- and GPP-related parameters in the ORCHIDEE LSM for boreal evergreen needleleaf forests (BorENF). Assimilating ecosystem COS fluxes at Hyytiälä forest increases the simulated net ecosystem COS uptake by 14%. This increase largely results from changes in the internal conductance to COS, highlighting the need to improve the representation of COS internal diffusion and consumption. Moreover, joint assimilation of ecosystem COS flux and GPP at Hyytiälä improves the simulated latent heat flux, contrary to the GPP-only data assimilation, which fails to do so. Finally, we scaled this assimilation framework up to the boreal region and find that the joint assimilation of COS at Hyytiälä and GPP fluxes at 10 BorENF sites increases the modeled vegetation COS uptake up to 18%, but not GPP. Therefore, this study encourages the use of COS flux observations to inform GPP and latent heat flux representations in LSMs.
The authors are very grateful to everyone who participated in field data collection used in this study. We warmly thank X. Wang-Faivre and C. Bacour for formatting and sharing the global FLUXCOM, GLEAM, and FLUXSAT products. We would also like to thank the editor and the three reviewers, Mary Whelan and two anonymous referees, for their insightful comments and suggestions that have helped us improve the manuscript. This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California - Berkeley, University of Virginia. The FLUXNET data can be downloaded at https://fluxnet.org. Finally, we would like to acknowledge the computer team at LSCE for the computational resources provided. This research has been mainly supported by the 4C project of the European Commission's Horizon 2020 framework programme (Grant 821003). The eddy covariance measurements at Hyytiälä were supported by ICOS-Finland (319871) by the University of Helsinki.
The authors are very grateful to everyone who participated in field data collection used in this study. We warmly thank X. Wang‐Faivre and C. Bacour for formatting and sharing the global FLUXCOM, GLEAM, and FLUXSAT products. We would also like to thank the editor and the three reviewers, Mary Whelan and two anonymous referees, for their insightful comments and suggestions that have helped us improve the manuscript. This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE‐FG02‐04ER63917 and DE‐FG02‐04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet‐Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS‐Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO‐GTOS‐TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California ‐ Berkeley, University of Virginia. The FLUXNET data can be downloaded at https://fluxnet.org . Finally, we would like to acknowledge the computer team at LSCE for the computational resources provided. This research has been mainly supported by the 4C project of the European Commission's Horizon 2020 framework programme (Grant 821003). The eddy covariance measurements at Hyytiälä were supported by ICOS‐Finland (319871) by the University of Helsinki.