TY - JOUR
T1 - Dryness controls temperature-optimized gross primary productivity across vegetation types
AU - Wang, Bingxue
AU - Chen, Weinan
AU - Dai, Junhu
AU - Li, Zhaolei
AU - Fu, Zheng
AU - Sarmah, Sangeeta
AU - Luo, Yiqi
AU - Niu, Shuli
N1 - Funding Information:
This work is financially supported by the National Key Technology R & D Program of China (2018YFA 0606102), National Natural Science Foundation of China (31988102), and the International Collaboration Program of Chinese Academy of Sciences (131A11KYSB20180010) granted to S.N. We used the eddy covariance data of the FLUXNET community by the following networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, Swiss FluxNet and USCCC. The ERA-Interim reanalysis data were provided by ECMWF and processed by Laboratoire des sciences du climat et de l'environnement (LSCE). The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster and the AmeriFlux Management Project (with support by European Union H2020 projects and U.S. Department of Energy Office of Science, respectively), with contributions from the Carbon Dioxide Information Analysis Center, ICOS Ecosystem Thematic Centre, and OzFlux, ChinaFlux, and AsiaFlux offices.
Funding Information:
This work is financially supported by the National Key Technology R & D Program of China ( 2018YFA 0606102 ), National Natural Science Foundation of China ( 31988102 ), and the International Collaboration Program of Chinese Academy of Sciences ( 131A11KYSB20180010 ) granted to S.N. We used the eddy covariance data of the FLUXNET community by the following networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, Swiss FluxNet and USCCC. The ERA-Interim reanalysis data were provided by ECMWF and processed by Laboratoire des sciences du climat et de l'environnement (LSCE). The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster and the AmeriFlux Management Project (with support by European Union H2020 projects and U.S. Department of Energy Office of Science, respectively), with contributions from the Carbon Dioxide Information Analysis Center, ICOS Ecosystem Thematic Centre, and OzFlux, ChinaFlux, and AsiaFlux offices.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/8/15
Y1 - 2022/8/15
N2 - Temperature response of gross primary productivity (GPP) is a well-known property of ecosystem, but GPP at the optimum temperature (GPP_Topt) has not been fully discussed. Our understanding of how GPP_Topt responds to warming and water availability is highly limited. In this study, we analyzed data at 326 globally distributed eddy covariance sites (79oN-37oS), to identify controlling factors of GPP_Topt. Although GPP_Topt was significantly influenced by soil moisture, global solar radiation, mean annual temperature, and vapor pressure deficit in a non-linear pattern (R2 = 0.47), the direction and magnitude of these climate variables’ effects on GPP_Topt depend on the dryness index (DI), a ratio of potential evapotranspiration to precipitation. The spatial pattern showed that soil moisture did not affect GPP_Topt across energy-limited sites with DI < 1 while dominated GPP_Topt across water-limited sites with DI >1. The temporal pattern showed that GPP_Topt was lowered by warming or low precipitation in water-limited sites while energy-limited sites tended to maintain a stable GPP_Topt regardless of changes in air temperature. Vegetation types in humid climates tended to have higher GPP_Topt and were more likely to benefit from a warmer climate since it was not restricted by water conditions. This study highlights that the response of GPP_Topt to global warming depends on the dryness conditions, which explains the nonlinear control of water and temperature over GPP_Topt. Our finding is essential to realistic prediction of terrestrial carbon uptake under future climate and vegetation conditions.
AB - Temperature response of gross primary productivity (GPP) is a well-known property of ecosystem, but GPP at the optimum temperature (GPP_Topt) has not been fully discussed. Our understanding of how GPP_Topt responds to warming and water availability is highly limited. In this study, we analyzed data at 326 globally distributed eddy covariance sites (79oN-37oS), to identify controlling factors of GPP_Topt. Although GPP_Topt was significantly influenced by soil moisture, global solar radiation, mean annual temperature, and vapor pressure deficit in a non-linear pattern (R2 = 0.47), the direction and magnitude of these climate variables’ effects on GPP_Topt depend on the dryness index (DI), a ratio of potential evapotranspiration to precipitation. The spatial pattern showed that soil moisture did not affect GPP_Topt across energy-limited sites with DI < 1 while dominated GPP_Topt across water-limited sites with DI >1. The temporal pattern showed that GPP_Topt was lowered by warming or low precipitation in water-limited sites while energy-limited sites tended to maintain a stable GPP_Topt regardless of changes in air temperature. Vegetation types in humid climates tended to have higher GPP_Topt and were more likely to benefit from a warmer climate since it was not restricted by water conditions. This study highlights that the response of GPP_Topt to global warming depends on the dryness conditions, which explains the nonlinear control of water and temperature over GPP_Topt. Our finding is essential to realistic prediction of terrestrial carbon uptake under future climate and vegetation conditions.
KW - Dryness conditions
KW - Dryness index
KW - Energy-limitation
KW - Peak gross primary productivity
KW - Soil moisture
KW - Water-limitation
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U2 - 10.1016/j.agrformet.2022.109073
DO - 10.1016/j.agrformet.2022.109073
M3 - Article
AN - SCOPUS:85133280521
SN - 0168-1923
VL - 323
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
M1 - 109073
ER -