The global distribution of the optimum air temperature for ecosystem-level gross primary productivity (\({\it{T}}_{{\mathrm{opt}}}^{{\mathrm{eco}}}\)) is poorly understood, despite its importance for ecosystem carbon uptake under future warming. We provide empirical evidence for the existence of such an optimum, using measurements of in situ eddy covariance and satellite-derived proxies, and report its global distribution. \(T_{\mathrm{opt}}^{\mathrm{eco}}\) is consistently lower than the physiological optimum temperature of leaf-level photosynthetic capacity, which typically exceeds 30 °C. The global average \(T_{\mathrm{opt}}^{\mathrm{eco}}\) is estimated to be 23 ± 6 °C, with warmer regions having higher \(T_{\mathrm{opt}}^{\mathrm{eco}}\) values than colder regions. In tropical forests in particular, \(T_{\mathrm{opt}}^{\mathrm{eco}}\) is close to growing-season air temperature and is projected to fall below it under all scenarios of future climate, suggesting a limited safe operating space for these ecosystems under future warming.

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This study was supported by the Strategic Priority Research Program (A) of the Chinese Academy of Sciences (Grant No. XDA20050101), the National Natural Science Foundation of China (41530528) and the National Key R&D Program of China (2017YFA0604702). This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US 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 and USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-CO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada, and the 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 and University of Virginia. P.C., J.P. and I.A.J. would like to acknowledge the financial support from the European Research Council Synergy Grant No. ERC-SyG-2013-610028 IMBALANCE-P. P.C. was also supported by the French Agence Nationale de la Recherche Convergence Lab Changement climatique et usage des terres (CLAND). I.A.J. acknowledges the Methusalem funding of the Flemish Community through the Research Council of the University of Antwerp. T.F.K. was supported by the NASA Terrestrial Ecology Program IDS Award NNH17AE86I. J.M. and X.S. are supported by the Terrestrial Ecosystem Science Scientific Focus Area project funded through the Terrestrial Ecosystem Science Program in the Climate and Environmental Sciences Division of the Biological and Environmental Research Program in the US Department of Energy Office of Science. Oak Ridge National Laboratory is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725. M.C. was supported by a grant overseen by the French National Research Agency (ANR) as part of the Investissements d’Avenir program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE).

Author information


  1. Sino-French Institute for Earth System Science, Peking University, Beijing, China

    • Mengtian Huang
    • , Shilong Piao
    • , Xuhui Wang
    • , Shushi Peng
    • , Kai Wang
    • , Mengdi Gao
    • , Yue He
    •  & Yongwen Liu
  2. Key Laboratory of Alpine Ecology and Biodiversity, Chinese Academy of Sciences, Beijing, China

    • Shilong Piao
    •  & Tao Wang
  3. Center for Excellence in Tibetan Earth Science, Chinese Academy of Sciences, Beijing, China

    • Shilong Piao
    •  & Tao Wang
  4. Laboratoire des Sciences du Climat et de l’Environnement, Gif-sur-Yvette, France

    • Philippe Ciais
  5. Centre for Research on Ecology and Forestry Applications, Barcelona, Spain

    • Josep Peñuelas
  6. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Spain

    • Josep Peñuelas
  7. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    • Trevor F. Keenan
  8. Department of Environmental Science Policy and Management, UC Berkeley, Berkeley, CA, USA

    • Trevor F. Keenan
  9. Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA

    • Joseph A. Berry
  10. Climate Change Science Institute and Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    • Jiafu Mao
    •  & Xiaoying Shi
  11. European Commission, Joint Research Centre (JRC), Ispra, Italy

    • Ramdane Alkama
    •  & Alessandro Cescatti
  12. Université de Lorraine, INRA, AgroParisTech, UMR Silva, Nancy, France

    • Matthias Cuntz
  13. CAVElab Computational and Applied Vegetation Ecology, Ghent University, Gent, Belgium

    • Hannes De Deurwaerder
    •  & Hans Verbeeck
  14. Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA

    • Yiqi Luo
  15. Department of Earth and Environment, Boston University, Boston, MA, USA

    • Ranga B. Myneni
  16. Key Laboratory of Ecosystem Network Observation and Modeling, Chinese Academy of Sciences, Beijing, China

    • Shuli Niu
  17. School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou, China

    • Wenping Yuan
  18. Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA

    • Jin Wu
  19. School of Biological Sciences, University of Hong Kong, Pokfulam, Hong Kong

    • Jin Wu
  20. Centre of Excellence – Plants and Vegetation Ecology, University of Antwerp, Wilrijk, Belgium

    • Ivan A. Janssens


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S. Piao designed the research. M. H. performed the analysis. S. Piao drafted the paper. All authors contributed to the interpretation of the results and the text.

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The authors declare no competing interests.

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Correspondence to Shilong Piao.

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