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Reduced water loss rather than increased photosynthesis controls CO2-enhanced water-use efficiency

Nature Ecology & Evolution volume 9, pages 1571–1584 (2025)Cite this article

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Abstract

Numerous leaf-level experiments suggest that plant intrinsic water-use efficiency (iWUE) increases under elevated CO2 because of reduced stomatal conductance and enhanced photosynthesis. However, it remains elusive whether this response can be extrapolated to the ecosystem scale, because confounding factors and compensating feedbacks are often involved in ecosystem iWUE variations. Here we develop a machine learning-based framework to disentangle the ecosystem-scale CO2 effects on iWUE and its two components, canopy conductance (Gc) and gross primary productivity (GPP), based on global networks of long-term eddy covariance observations. Our results show widespread CO2-induced enhancement of iWUE across diverse ecosystems, driven predominantly by Gc reduction rather than GPP stimulation. Moreover, three divergent response types are identified across the studied ecosystems, based on the strength and significance of CO2-driven Gc reduction and GPP enhancement, indicating spatially non-uniform responses to rising CO2. Nutrient supply, water availability and biome types are found to be critical factors regulating this spatial heterogeneity. Overall, our study provides observational insights into ecosystem-scale CO2 fertilization effects. Such understandings are essential to inform terrestrial biosphere models for better projections of carbon and water cycles given the intensified changing climate in a CO2-rich future.

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Fig. 1: Conceptual diagram of this study.
Fig. 2: Overview of CO2 effects across 63 EC sites.
Fig. 3: Three dominant response patterns to rising CO2 across 63 EC sites.
Fig. 4: Characteristics of CO2 effects across different PFTs.
Fig. 5: Dependence of CO2 effects on nutrient and water availability.

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Data availability

All datasets used in this study are available as follows. The FLUXNET2015 EC dataset is available at https://fluxnet.org/data/fluxnet2015-dataset/; the AmeriFlux FLUXNET EC dataset is available at https://ameriflux.lbl.gov/data/flux-data-products/fluxnet-publish/; the AmeriFlux BASE EC dataset is available at https://ameriflux.lbl.gov/data/flux-data-products/base-publish/; the ICOS EC dataset is available at https://www.icos-cp.eu/data-products/2G60-ZHAK; the OzFlux EC dataset is available at https://data.ozflux.org.au/portal/home.jspx; the CASM dataset is available via Zenodo at https://doi.org/10.5281/zenodo.7072511 (ref. 125); the MODIS LAI dataset is available at https://lpdaac.usgs.gov/products/mcd15a3hv006/; the LCSIF NIRv dataset is available via Zenodo at https://doi.org/10.5281/zenodo.14568024 (ref. 126); the CAMS CO2 dataset is available at https://ads.atmosphere.copernicus.eu/datasets/cams-global-greenhouse-gas-inversion?tab=overview; the nitrogen and phosphorous resorption efficiency dataset is available at https://pan.bnu.edu.cn/l/KnHzUj; the global leaf nitrogen and phosphorous concentration dataset is available at https://isp.uv.es/code/try.html; and the CMIP6 simulations are available at https://aims2.llnl.gov/search/cmip6/. The data needed to reproduce the main findings of this study are publicly available via GitHub at https://github.com/Weiwei047/iWUE_CO2. Source data are provided with this paper.

Code availability

The code necessary to reproduce the main findings of this study are publicly available via GitHub at https://github.com/Weiwei047/iWUE_CO2.

References

  1. Friedlingstein, P. et al. Global carbon budget 2022. Earth Syst. Sci. Data 14, 4811–4900 (2022).

    Article  Google Scholar 

  2. IPCC: Summary for Policymakers. In Climate Change 2023: Synthesis Report (eds Lee, H. et al.) (IPCC, 2023).

  3. Mathias, J. M. & Thomas, R. B. Global tree intrinsic water use efficiency is enhanced by increased atmospheric CO2 and modulated by climate and plant functional types. Proc. Natl Acad. Sci. USA 118, e2014286118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Guerrieri, R. et al. Disentangling the role of photosynthesis and stomatal conductance on rising forest water-use efficiency. Proc. Natl Acad. Sci. USA 116, 16909–16914 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Condon, A. G., Richards, R., Rebetzke, G. & Farquhar, G. Improving intrinsic water‐use efficiency and crop yield. Crop Sci. 42, 122–131 (2002).

    PubMed  Google Scholar 

  6. Saugier, B., Ehleringer, J. R., Hall, A. E. & Farquhar, G. D. Stable Isotopes and Plant Carbon–Water Relations (Elsevier, 2012).

  7. Lemordant, L., Gentine, P., Swann, A. S., Cook, B. I. & Scheff, J. Critical impact of vegetation physiology on the continental hydrologic cycle in response to increasing CO2. Proc. Natl Acad. Sci. USA 115, 4093–4098 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kooperman, G. J. et al. Plant physiological responses to rising CO2 modify simulated daily runoff intensity with implications for global‐scale flood risk assessment. Geophys. Res. Lett. 45, 12457–12466 (2018).

    Article  Google Scholar 

  9. Drake, B. G., Gonzàlez-Meler, M. A. & Long, S. P. More efficient plants: a consequence of rising atmospheric CO2? Annu. Rev. Plant Biol. 48, 609–639 (1997).

    Article  CAS  Google Scholar 

  10. Ainsworth, E. A. & Rogers, A. The response of photosynthesis and stomatal conductance to rising [CO2]: mechanisms and environmental interactions. Plant Cell Environ. 30, 258–270 (2007).

    Article  CAS  PubMed  Google Scholar 

  11. Morison, J. I. Sensitivity of stomata and water use efficiency to high CO2. Plant Cell Environ. 8, 467–474 (1985).

    Article  Google Scholar 

  12. Cernusak, L. A. et al. Robust response of terrestrial plants to rising CO2. Trends Plant Sci. 24, 578–586 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Saxe, H., Ellsworth, D. S. & Heath, J. Tree and forest functioning in an enriched CO2 atmosphere. New Phytol. 139, 395–436 (1998).

    Article  Google Scholar 

  14. Kimball, B., Mauney, J., Nakayama, F. & Idso, S. Effects of increasing atmospheric CO2 on vegetation. Vegetatio 104, 65–75 (1993).

    Article  Google Scholar 

  15. Wand, S. J., Midgley, G. F., Jones, M. H. & Curtis, P. S. Responses of wild C4 and C3 grass (Poaceae) species to elevated atmospheric CO2 concentration: a meta-analytic test of current theories and perceptions. Glob. Change Biol. 5, 723–741 (1999).

    Article  Google Scholar 

  16. Nowak, R. S., Ellsworth, D. S. & Smith, S. D. Functional responses of plants to elevated atmospheric CO2–do photosynthetic and productivity data from FACE experiments support early predictions? New Phytol. 162, 253–280 (2004).

    Article  Google Scholar 

  17. Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. New Phytologist 229, 2413–2445 (2021).

    Article  CAS  PubMed  Google Scholar 

  18. Way, D. A., Cook, A. & Rogers, A. The effects of rising CO2 concentrations on terrestrial systems: scaling it up. New Phytologist 229, 2383–2385 (2021).

    Article  PubMed  Google Scholar 

  19. Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–372 (2005).

    Article  PubMed  Google Scholar 

  20. Norby, R. J. et al. Model-data synthesis for the next generation of forest free-air CO2 enrichment (FACE) experiments. New Phytol. 209, 17–28 (2016).

    Article  CAS  PubMed  Google Scholar 

  21. Baldocchi, D. ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Aust. J. Bot. 56, 1–26 (2008).

    Article  CAS  Google Scholar 

  22. Lavergne, A. et al. Observed and modelled historical trends in the water‐use efficiency of plants and ecosystems. Glob. Change Biol. 25, 2242–2257 (2019).

    Article  Google Scholar 

  23. Knauer, J. et al. The response of ecosystem water-use efficiency to rising atmospheric CO2 concentrations: sensitivity and large-scale biogeochemical implications. New Phytol. 213, 1654–1666 (2017).

    Article  CAS  PubMed  Google Scholar 

  24. Baldocchi, D., Ma, S. & Verfaillie, J. On the inter-and intra-annual variability of ecosystem evapotranspiration and water use efficiency of an oak savanna and annual grassland subjected to booms and busts in rainfall. Glob. Change Biol. 27, 359–375 (2021).

    Article  CAS  Google Scholar 

  25. Baldocchi, D. D. How eddy covariance flux measurements have contributed to our understanding of global change biology. Glob. Change Biol. 26, 242–260 (2020).

    Article  Google Scholar 

  26. Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).

    Article  CAS  PubMed  Google Scholar 

  27. Mastrotheodoros, T. et al. Linking plant functional trait plasticity and the large increase in forest water use efficiency. J. Geophys. Res. 122, 2393–2408 (2017).

    Article  Google Scholar 

  28. Ueyama, M. et al. Inferring CO2 fertilization effect based on global monitoring land-atmosphere exchange with a theoretical model. Environ. Res. Lett. 15, 084009 (2020).

    Article  CAS  Google Scholar 

  29. Sun, Z. et al. Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO2 trends. Sci. Total Environ. 668, 696–713 (2019).

    Article  CAS  PubMed  Google Scholar 

  30. Wang, M., Chen, Y., Wu, X. & Bai, Y. Forest-type-dependent water use efficiency trends across the Northern Hemisphere. Geophys. Res. Lett. 45, 8283–8293 (2018).

    Article  Google Scholar 

  31. Fernández-Martínez, M. et al. Atmospheric deposition, CO2, and change in the land carbon sink. Sci. Rep. 7, 9632 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Chen, C., Riley, W. J., Prentice, I. C. & Keenan, T. F. CO2 fertilization of terrestrial photosynthesis inferred from site to global scales. Proc. Natl Acad. Sci. USA 119, e2115627119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Cai, W. & Prentice, I. C. Recent trends in gross primary production and their drivers: analysis and modelling at flux-site and global scales. Environ. Res. Lett. 15, 124050 (2020).

    Article  Google Scholar 

  34. Barton, C. V. et al. Effects of elevated atmospheric [CO2] on instantaneous transpiration efficiency at leaf and canopy scales in Eucalyptus saligna. Glob. Change Biol. 18, 585–595 (2012).

    Article  Google Scholar 

  35. Frank, D. C. et al. Water-use efficiency and transpiration across European forests during the Anthropocene. Nat. Clim. Change 5, 579–583 (2015).

    Article  CAS  Google Scholar 

  36. Peñuelas, J., Canadell, J. G. & Ogaya, R. Increased water‐use efficiency during the 20th century did not translate into enhanced tree growth. Glob. Ecol. Biogeogr. 20, 597–608 (2011).

    Article  Google Scholar 

  37. Saurer, M., Siegwolf, R. T. & Schweingruber, F. H. Carbon isotope discrimination indicates improving water‐use efficiency of trees in northern Eurasia over the last 100 years. Glob. Change Biol. 10, 2109–2120 (2004).

    Article  Google Scholar 

  38. Ueyama, M. et al. Optimization of a biochemical model with eddy covariance measurements in black spruce forests of Alaska for estimating CO2 fertilization effects. Agric. For. Meteorol. 222, 98–111 (2016).

    Article  Google Scholar 

  39. Drake, J. E. et al. Trees tolerate an extreme heatwave via sustained transpirational cooling and increased leaf thermal tolerance. Glob. Change Biol. 24, 2390–2402 (2018).

    Article  Google Scholar 

  40. Krich, C. et al. Decoupling between ecosystem photosynthesis and transpiration: a last resort against overheating. Environ. Res. Lett. 17, 044013 (2022).

    Article  CAS  Google Scholar 

  41. De Kauwe, M. G. et al. Examining the evidence for decoupling between photosynthesis and transpiration during heat extremes. Biogeosciences 16, 903–916 (2019).

    Article  Google Scholar 

  42. Blonder, B. W. et al. Plant water use theory should incorporate hypotheses about extreme environments, population ecology, and community ecology. New Phytol. 238, 2271–2283 (2023).

    Article  PubMed  Google Scholar 

  43. Zhan, C. et al. Estimating the CO2 fertilization effect on extratropical forest productivity from flux-tower observations. JGR Biosciences 129, e2023JG007910 (2024).

    Article  CAS  Google Scholar 

  44. Launiainen, S. et al. Does growing atmospheric CO2 explain increasing carbon sink in a boreal coniferous forest? Glob. Change Biol. 28, 2910–2929 (2022).

    Article  CAS  Google Scholar 

  45. Liu, F., Wang, X., Wang, C. & Zhang, Q. Environmental and biotic controls on the interannual variations in CO2 fluxes of a continental monsoon temperate forest. Agric. For. Meteorol. 296, 108232 (2021).

    Article  Google Scholar 

  46. Tamrakar, R., Rayment, M. B., Moyano, F., Mund, M. & Knohl, A. Implications of structural diversity for seasonal and annual carbon dioxide fluxes in two temperate deciduous forests. Agric. For. Meteorol. 263, 465–476 (2018).

    Article  Google Scholar 

  47. Desai, A. R. et al. Drivers of decadal carbon fluxes across temperate ecosystems. J. Geophys. Res. 127, e2022JG007014 (2022).

    Article  Google Scholar 

  48. Pan, L. et al. Interannual variations and trends of gross primary production and transpiration of four mature deciduous broadleaf forest sites during 2000–2020. Remote Sens. Environ. 304, 114042 (2024).

    Article  Google Scholar 

  49. Yang, C. & Lei, H. Climate and management impacts on crop growth and evapotranspiration in the North China Plain based on long-term eddy covariance observation. Agric. For. Meteorol. 325, 109147 (2022).

    Article  Google Scholar 

  50. Gardner, A. et al. Optimal stomatal theory predicts CO2 responses of stomatal conductance in both gymnosperm and angiosperm trees. New Phytol. 237, 1229–1241 (2023).

    Article  PubMed  Google Scholar 

  51. Seneviratne, S. I. et al. Impact of soil moisture–climate feedbacks on CMIP5 projections: first results from the GLACE-CMIP5 experiment. Geophys. Res. Lett. 40, 5212–5217 (2013).

    Article  Google Scholar 

  52. Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Saurer, M. et al. Spatial variability and temporal trends in water-use efficiency of European forests. Glob. Change Biol. 20, 3700–3712 (2014).

    Article  Google Scholar 

  54. Franks, P. J. et al. Sensitivity of plants to changing atmospheric CO2 concentration: from the geological past to the next century. New Phytol. 197, 1077–1094 (2013).

    Article  CAS  PubMed  Google Scholar 

  55. Adams, M. A., Buckley, T. N. & Turnbull, T. L. Diminishing CO2-driven gains in water-use efficiency of global forests. Nat. Clim. Change 10, 466–471 (2020).

    Article  CAS  Google Scholar 

  56. Yang, J. et al. Low sensitivity of gross primary production to elevated CO2 in a mature eucalypt woodland. Biogeosciences 17, 265–279 (2020).

    Article  CAS  Google Scholar 

  57. Field, C. B., Jackson, R. B. & Mooney, H. A. Stomatal responses to increased CO2: implications from the plant to the global scale. Plant Cell Environ. 18, 1214–1225 (1995).

    Article  Google Scholar 

  58. Wullschleger, S. D., Gunderson, C., Hanson, P., Wilson, K. & Norby, R. Sensitivity of stomatal and canopy conductance to elevated CO2 concentration–interacting variables and perspectives of scale. New Phytol. 153, 485–496 (2002).

    Article  CAS  PubMed  Google Scholar 

  59. Andreu-Hayles, L. et al. Long tree‐ring chronologies reveal 20th century increases in water‐use efficiency but no enhancement of tree growth at five Iberian pine forests. Glob. Change Biol. 17, 2095–2112 (2011).

    Article  Google Scholar 

  60. Van Der Sleen, P. et al. No growth stimulation of tropical trees by 150 years of CO2 fertilization but water-use efficiency increased. Nat. Geosci. 8, 24–28 (2015).

    Article  Google Scholar 

  61. Groenendijk, P. et al. No evidence for consistent long-term growth stimulation of 13 tropical tree species: results from tree-ring analysis. Glob. Change Biol. 21, 3762–3776 (2015).

    Article  Google Scholar 

  62. Xu, X. et al. Constraining long-term model predictions for woody growth using tropical tree rings. Glob. Change Biol. 30, e17075 (2024).

    Article  Google Scholar 

  63. Villarreal, S. & Vargas, R. Representativeness of FLUXNET sites across Latin America. J. Geophys. Res. 126, e2020JG006090 (2021).

    Article  Google Scholar 

  64. Ichii, K. et al. New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression. J. Geophys. Res. 122, 767–795 (2017).

    Article  CAS  Google Scholar 

  65. Ehleringer, J. & Björkman, O. Quantum yields for CO2 uptake in C3 and C4 plants: dependence on temperature, CO2, and O2 concentration. Plant Physiol. 59, 86–90 (1977).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Rogers, A. et al. Acclimation of photosynthesis to elevated CO2 under low-nitrogen nutrition is affected by the capacity for assimilate utilization. Perennial ryegrass under free-air CO2 enrichment. Plant Physiol. 118, 683–689 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Stitt, M. & Krapp, A. The interaction between elevated carbon dioxide and nitrogen nutrition: the physiological and molecular background. Plant Cell Environ. 22, 583–621 (1999).

    Article  CAS  Google Scholar 

  68. Farquhar, G. D., von Caemmerer, S. V. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).

    Article  CAS  PubMed  Google Scholar 

  69. Long, S. Modification of the response of photosynthetic productivity to rising temperature by atmospheric CO2 concentrations: has its importance been underestimated? Plant Cell Environ. 14, 729–739 (1991).

    Article  CAS  Google Scholar 

  70. Arora, V. K. et al. Carbon-concentration and carbon-climate feedbacks in CMIP6 models and their comparison to CMIP5 models. Biogeosciences 17, 4173–4222 (2019).

    Article  Google Scholar 

  71. Gimeno, T. E. et al. Conserved stomatal behaviour under elevated CO2 and varying water availability in a mature woodland. Funct. Ecol. 30, 700–709 (2016).

    Article  Google Scholar 

  72. Vicente-Serrano, S. M. et al. The uncertain role of rising atmospheric CO2 on global plant transpiration. Earth Sci. Rev. 230, 104055 (2022).

    Article  CAS  Google Scholar 

  73. Trugman, A. T. & Anderegg, L. D. Source vs sink limitations on tree growth: from physiological mechanisms to evolutionary constraints and terrestrial carbon cycle implications. New Phytol. 245, 966–981 (2025).

    Article  PubMed  Google Scholar 

  74. Fleischer, K. & Terrer, C. Estimates of soil nutrient limitation on the CO2 fertilization effect for tropical vegetation. Glob. Change Biol. 28, 6366–6369 (2022).

    Article  CAS  Google Scholar 

  75. Fatichi, S., Pappas, C., Zscheischler, J. & Leuzinger, S. Modelling carbon sources and sinks in terrestrial vegetation. New Phytol. 221, 652–668 (2019).

    Article  CAS  PubMed  Google Scholar 

  76. Reed, S. C., Yang, X. & Thornton, P. E. Incorporating phosphorus cycling into global modeling efforts: a worthwhile, tractable endeavor. New Phytol. 208, 324–329 (2015).

    Article  CAS  PubMed  Google Scholar 

  77. Kolby Smith, W. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306–310 (2016).

    Article  CAS  Google Scholar 

  78. Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).

    Article  PubMed  Google Scholar 

  79. Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Change 9, 684–689 (2019).

    Article  CAS  Google Scholar 

  80. Lian, X. et al. Multifaceted characteristics of dryland aridity changes in a warming world. Nat. Rev. Earth Environ. 2, 232–250 (2021).

    Article  Google Scholar 

  81. Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, 1023–1027 (2016).

    Article  CAS  Google Scholar 

  82. Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R. & Donohue, R. J. Hydrologic implications of vegetation response to elevated CO2 in climate projections. Nat. Clim. Change 9, 44–48 (2019).

    Article  Google Scholar 

  83. Betts, R. A. et al. Projected increase in continental runoff due to plant responses to increasing carbon dioxide. Nature 448, 1037–1041 (2007).

    Article  CAS  PubMed  Google Scholar 

  84. Lian, X. et al. Partitioning global land evapotranspiration using CMIP5 models constrained by observations. Nat. Clim. Change 8, 640–646 (2018).

    Article  Google Scholar 

  85. Swann, A. L., Hoffman, F. M., Koven, C. D. & Randerson, J. T. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc. Natl Acad. Sci. USA 113, 10019–10024 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Fowler, M. D., Kooperman, G. J., Randerson, J. T. & Pritchard, M. S. The effect of plant physiological responses to rising CO2 on global streamflow. Nat. Clim. Change 9, 873–879 (2019).

    Article  CAS  Google Scholar 

  87. Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Baldocchi, D., Novick, K., Keenan, T. & Torn, M. AmeriFlux: its impact on our understanding of the ‘breathing of the biosphere’, after 25 years. Agric. For. Meteorol. 348, 109929 (2024).

    Article  Google Scholar 

  89. Chu, H. et al. AmeriFlux BASE data pipeline to support network growth and data sharing. Sci. Data 10, 614 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Rebmann, C. et al. ICOS eddy covariance flux-station site setup: a review. Int. Agrophys. 32, 471–494 (2018).

    Article  Google Scholar 

  91. Beringer, J. et al. Bridge to the future: important lessons from 20 years of ecosystem observations made by the OzFlux network. Glob. Change Biol. 28, 3489–3514 (2022).

    Article  CAS  Google Scholar 

  92. Zhou, S., Yu, B., Zhang, Y., Huang, Y. & Wang, G. Partitioning evapotranspiration based on the concept of underlying water use efficiency. Water Resour. Res. 52, 1160–1175 (2016).

    Article  Google Scholar 

  93. Li, X. et al. A simple and objective method to partition evapotranspiration into transpiration and evaporation at eddy-covariance sites. Agric. For. Meteorol. 265, 171–182 (2019).

    Article  Google Scholar 

  94. Lin, C. et al. Diel ecosystem conductance response to vapor pressure deficit is suboptimal and independent of soil moisture. Agric. For. Meteorol. 250, 24–34 (2018).

    Article  Google Scholar 

  95. NOAA Global Monitoring Laboratory. Cooperative Global Air Sampling Network gml.noaa.gov/dv/iadv/index.php (2025).

  96. Peuch, V.-H. et al. The Copernicus Atmosphere Monitoring Service: from research to operations. Bull. Am. Meteorol. Soc. 103, E2650–E2668 (2022).

    Article  Google Scholar 

  97. Skulovich, O. & Gentine, P. A long-term consistent artificial intelligence and remote sensing-based soil moisture dataset. Sci. Data 10, 154 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Stocker, B. D. et al. Quantifying soil moisture impacts on light use efficiency across biomes. New Phytol. 218, 1430–1449 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Restrepo-Acevedo, A. M. et al. PSInet: a new global water potential network. Tree Physiol. 44, tpae110 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Verger, A., Baret, F. & Weiss, M. Near real-time vegetation monitoring at global scale. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7, 3473–3481 (2014).

    Article  Google Scholar 

  101. Fang, J. et al. A long-term reconstruction of a global photosynthesis proxy over 1982–2023. Sci. Data 12, 372 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Dechant, B. et al. NIRVP: a robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales. Remote Sens. Environ. 268, 112763 (2022).

    Article  Google Scholar 

  103. Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).

    Article  CAS  Google Scholar 

  104. Moreno-Martínez, Á. et al. A methodology to derive global maps of leaf traits using remote sensing and climate data. Remote Sens. Environ. 218, 69–88 (2018).

    Article  Google Scholar 

  105. Kattge, J. et al. TRY – a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).

    Article  Google Scholar 

  106. Koerselman, W. & Meuleman, A. F. The vegetation N:P ratio: a new tool to detect the nature of nutrient limitation. J. Appl. Ecol. 33, 1441–1450 (1996).

    Article  Google Scholar 

  107. Lasslop, G. et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Glob. Change Biol. 16, 187–208 (2010).

    Article  Google Scholar 

  108. Wutzler, T. et al. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosciences 15, 5015–5030 (2018).

    Article  CAS  Google Scholar 

  109. Wang, S., Hoch, G., Grun, G. & Kahmen, A. Water loss after stomatal closure: quantifying leaf minimum conductance and minimal water use in nine temperate European tree species during a severe drought. Tree Physiol. 44, tpae027 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  110. Zailaa, J., Scoffoni, C. & Brodersen, C. R. Stomatal closure as a driver of minimum leaf conductance declines at high temperature and vapor pressure deficit in Quercus. Plant Physiol. 197, kiae551 (2025).

    Article  CAS  Google Scholar 

  111. Leuning, R. A critical appraisal of a combined stomatal-photosynthesis model for C3 plants. Plant Cell Environ. 18, 339–355 (1995).

    Article  CAS  Google Scholar 

  112. Way, D. A., Oren, R., Kim, H. S. & Katul, G. G. How well do stomatal conductance models perform on closing plant carbon budgets? A test using seedlings grown under current and elevated air temperatures. J. Geophys. Res. 116, G04031 (2011).

    Google Scholar 

  113. Medlyn, B. E. et al. Stomatal conductance of forest species after long-term exposure to elevated CO2 concentration: a synthesis. New Phytol. 149, 247–264 (2001).

    Article  CAS  PubMed  Google Scholar 

  114. Limousin, J. M. et al. Regulation and acclimation of leaf gas exchange in a piñon–juniper woodland exposed to three different precipitation regimes. Plant Cell Environ. 36, 1812–1825 (2013).

    Article  CAS  PubMed  Google Scholar 

  115. Launiainen, S., Katul, G. G., Kolari, P., Vesala, T. & Hari, P. Empirical and optimal stomatal controls on leaf and ecosystem level CO2 and H2O exchange rates. Agric. For. Meteorol. 151, 1672–1689 (2011).

    Article  Google Scholar 

  116. Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).

    Article  Google Scholar 

  117. Dang, Q.-L., Margolis, H. A. & Collatz, G. J. Parameterization and testing of a coupled photosynthesis–stomatal conductance model for boreal trees. Tree Physiol. 18, 141–153 (1998).

    Article  PubMed  Google Scholar 

  118. Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (eds Krishnapuram, B. et al.) 785–794 (ACM, 2016).

  119. Deng, Y. et al. Divergent seasonal responses of carbon fluxes to extreme droughts over China. Agric. For. Meteorol. 328, 109253 (2023).

    Article  Google Scholar 

  120. Ma, R. et al. Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data. Geosci. Model. Dev. 15, 6637–6657 (2022).

    Article  CAS  Google Scholar 

  121. Bennington, V., Galjanic, T. & McKinley, G. A. Explicit physical knowledge in machine learning for ocean carbon flux reconstruction: the pCO2-residual method. J. Adv. Model. Earth Syst. 14, e2021MS002960 (2022).

    Article  Google Scholar 

  122. Bergstra, J., Komer, B., Eliasmith, C., Yamins, D. & Cox, D. D. Hyperopt: a python library for model selection and hyperparameter optimization. Comput. Sci. Discov. 8, 014008 (2015).

    Article  Google Scholar 

  123. Guo, Z. & Dirmeyer, P. A. Interannual variability of land–atmosphere coupling strength. J. Hydrometeorol. 14, 1636–1646 (2013).

    Article  Google Scholar 

  124. Brando, P. M. et al. Seasonal and interannual variability of climate and vegetation indices across the Amazon. Proc. Natl Acad. Sci. USA 107, 14685–14690 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Skulovich, O. & Gentine, P. CASM: a long-term Consistent Artificial-intelligence based Soil Moisture dataset based on machine learning and remote sensing. Zenodo https://doi.org/10.5281/zenodo.7072511 (2022).

  126. Fang, J. et al. Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 1982–2000 (version 3.2). Zenodo https://doi.org/10.5281/zenodo.14568024 (2025).

  127. McKnight, P. E. & Najab, J. in The Corsini Encyclopedia of Psychology (eds Weiner, I. B. & Craighead, W. E.) 1-1 (Wiley, 2010).

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Acknowledgements

This study received funding from the European Research Council (ERC) Synergy Grant ‘Understanding and modeling the Earth System with Machine Learning’ (USMILE) under the Horizon 2020 research and innovation programme (Grant agreement No. 855187). It also received funding from the National Science Foundation Science and Technology Center, Learning the Earth with Artificial intelligence and Physics, LEAP (AGS-2019625). W.Z., A.J.W. and P.G. acknowledge support from the Max Planck-Caltech-Carnegie-Columbia MC3 4 Earth Center, funded by the Max Planck Foundation. X.L. and P.G. acknowledge support from the LEMONTREE project (Land Ecosystem Models based on New Theory, obseRvations and ExperimEnts), funded through the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures programme. C.Z. acknowledges support from the International Max Planck Research School (IMPRS). A.J.W. acknowledges funding from the Alexander‐von‐Humboldt Foundation. We thank J. Fang for his insightful comments, and J. S. Dukes, A. M. Michalak, W. Sun and J. Wen for valuable discussions during W.Z.’s visit to the Carnegie Institution for Science. We also thank O. Skulovich and S. Jeong for preparing soil moisture and LAI datasets. This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, Fluxnet-Canada, ICOS and OzFlux-TERN. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of the OzFlux office.

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Authors and Affiliations

  1. Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA

    Weiwei Zhan, Xu Lian, Jiangong Liu, Jisu Han, Yu Huang, Hao Yang & Pierre Gentine

  2. Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Xu Lian

  3. Max Planck Institute for Biogeochemistry, Jena, Germany

    Chunhui Zhan & Alexander J. Winkler

  4. European Laboratory for Learning and Intelligent Systems (ELLIS) Unit Jena, Jena, Germany

    Alexander J. Winkler

  5. LEAP Science and Technology Center, School of Engineering and Applied Sciences, Climate School, Columbia University, New York, NY, USA

    Pierre Gentine

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  1. Weiwei Zhan
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Contributions

W.Z. and P.G. conceived the study. W.Z., X.L., J.L., and P.G. contributed to early-stage discussions. W.Z. and J.H. collected and preprocessed the data. W.Z. performed the analysis, led the result interpretation and drafted the initial manuscript. Y.H., H.Y., C.Z. and A.J.W. contributed to the development and discussion of the methods. All co-authors reviewed the results and contributed to the writing and revision of the manuscript.

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Correspondence to Weiwei Zhan.

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Extended data

Extended Data Fig. 1 Site example and aggregated results for the differencing method.

Panels a-c show the isolated CO2 effects for iWUE, Gc, and GPP at an example site, illustrating the quantification of \(\beta\) and \({\rm{SNR}}\) based on actual data. Dark solid lines represent the regression lines fitted between CO2 and \(\delta\) (that is, the difference between the with-CO2 model \({f}_{2}\) and the without-CO2 model \({f}_{1}\)). Shaded areas around the zero horizontal lines (shown as dark dashed lines) indicate the standard deviation of the detrended \(\delta\), which serves as the denominator in the SNR calculation (equation 4 in the main text). Panel d shows the aggregated \(\beta\) values across 63 EC sites. Each row corresponds to a target variable (Gc, GPP, and iWUE), and each column represents an EC site. The sites are categorized into three groups, denoted by different font colours, based on their divergent response patterns (the joint-response pattern, Gc-only pattern, and minimal-response pattern). The colourmap for Gc is inverted relative to GPP and iWUE, so that more bluish colours consistently represent stronger CO2 effects.

Source data

Extended Data Fig. 2 Site example and aggregated results for the perturbation method.

Panels a-c show the isolated CO2 effects for iWUE, Gc, and GPP at an example site, illustrating the quantification of \(\Delta {{\rm{Y}}}_{{\rm{CO}}2}\) based on actual data. Black scatters and lines represent the control experiment (CTL), while colour-coded scatters and lines represent the mute_CO2 experiment. The scatters represent the annual values. The annual values of iWUE and Gc are obtained by taking the median, while annual GPP is calculated by taking the sum over the growing season. For inter-site comparison, original annual values are normalized by the site-specific mean and represented as percentage changes relative to the site mean. Solid lines represent the long-term trends derived from annual data. The difference in trend slopes between CTL and mute_CO2 experiments is calculated as \(\Delta {{\rm{Y}}}_{{\rm{CO}}2}\). Panel d shows the aggregated \(\Delta {{\rm{Y}}}_{{\rm{CO}}2}\) values across 63 EC sites. Each row corresponds to a target variable (Gc, GPP, and iWUE), and each column represents an EC site. The sites are categorized into three groups, denoted by different font colours, based on their divergent response patterns (the joint-response pattern, Gc-only pattern, and minimal-response pattern). The colourmap for Gc is inverted relative to GPP and iWUE, so that more bluish colours consistently represent stronger CO2 effects.

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Extended Data Fig. 3 Violin plots illustrating the distributions of the β factor for iWUE, GPP, and Gc.

a. \(\beta\) distributions for direct CO2 effects. b. \(\beta\) distributions for total CO2 effects, which include both direct and indirect CO2 effects. The shape of each violin represents the probability density of data at different \(\beta\) values, calculated using kernel density estimation. Broader sections of the violin indicate higher data density. The error bar within each violin represents the data range, with the two ends corresponding to the minimum and maximum values, respectively. The black-edged circle within each error bar represents the median \(\beta\) (\(\widetilde{\beta }\)) across 63 sites, with specific median value annotated below each circle. Semi-transparent scatter points overlaid on each violin represent the individual \(\beta\) values at each EC site.

Source data

Extended Data Fig. 4 Site-specific results for direct and total CO2 effects, along with the trend slope of peak leaf area index.

Peak leaf area index (LAIpeak) is defined as the 95th percentile of LAI for each year. Cross symbols (×) denote sites with significant LAIpeak trends (P < 0.05), based on a two-sided Mann-Kendall trend test. Dot (•) symbols indicate sites with detectable CO2 effects. The CO2 effect is considered detectable when \(\left|{\rm{SNR}}\right|\ge 1\), where SNR measures the relative strength of CO2 effects (signal) against inherent variability in data (noise). Red boxes highlight the seven sites that support the canopy size affects leaf physiology hypothesis, specifically, they (1) exhibit an increasing trend in the annual peak LAI, (2) show a notable direct CO2 effect on GPP (to ensure the enhanced LAIpeak is more likely driven by CO2 rather than other factors), and (3) exhibit a less responsive GPP enhancement than Gc reduction (\(\left|{{{\upbeta }}}_{{\rm{GPP}},{\rm{total}}}\right| < \left|{{{\upbeta }}}_{{\rm{Gc}},{\rm{total}}}\right|\), where the ‘total’ subscript indicates total CO2 effects).

Source data

Extended Data Fig. 5 Comparison between EC observations and CMIP6 simulations.

a. Comparison of \({\beta }_{{\rm{iWUE}}}\). The horizontal dashed line indicates the spatial median \({\beta }_{{\rm{iWUE}}}\) (\({\widetilde{\beta }}_{{\rm{iWUE}}}\)) across 63 sites, derived from EC observations. Bars represent \({\widetilde{\beta }}_{{\rm{iWUE}}}\) derived from CMIP6 model ensembles, obtained by first averaging across CMIP6 models at each site and then calculating the spatial median across sites. Markers overlaid on the bars represent the individual \({\widetilde{\beta }}_{{\rm{iWUE}}}\) from each CMIP6 model. Error bars indicate the standard deviation of \({\widetilde{\beta }}_{{\rm{iWUE}}}\) across models. b. \(\beta\) comparison for direct CO2 effects on GPP and Gc. c. \(\beta\) comparison for total CO2 effects on GPP and Gc. Diagonal lines in b and c represent regimes in which GPP and Gc responses are proportionally balanced (\(\left|{\beta }_{{\rm{GPP}}}\right|=\left|{\beta }_{{\rm{Gc}}}\right|\)), indicating that CO2-enhanced iWUE is jointly driven by GPP stimulation and Gc reduction (referred to as the joint-driven regime). Regimes above the diagonal line indicate that CO2-enhanced iWUE is dominantly driven by increased GPP (\(\left|{\beta }_{{\rm{GPP}}}\right| > \left|{\beta }_{{\rm{Gc}}}\right|\), referred to as the GPP-dominance regime), whereas areas below the line represent the Gc-dominance regime (\(\left|{\beta }_{{\rm{Gc}}}\right| > \left|{\beta }_{{\rm{GPP}}}\right|\)), in which Gc reduction dominantly drives CO2-enhanced iWUE.

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Supplementary information

Supplementary Information

Supplementary Texts 1–7, Tables 1–3 and Figs. 1–21.

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Zhan, W., Lian, X., Liu, J. et al. Reduced water loss rather than increased photosynthesis controls CO2-enhanced water-use efficiency. Nat Ecol Evol 9, 1571–1584 (2025). https://doi.org/10.1038/s41559-025-02761-0

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