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Enhanced vegetation productivity driven primarily by rate not duration of carbon uptake

Nature Climate Change volume 15, pages 560–568 (2025)Cite this article

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Abstract

Climate change is altering both the duration and the rate of carbon uptake in plants, thereby affecting terrestrial gross primary productivity (GPP). However, little is known about the relative strengths of these processes or underlying mechanisms. Here, using satellite and carbon-flux data, we show that the duration and mean daily rate of carbon uptake (GPPrate) have both increased in recent decades, enhancing total GPP with a rate of ~0.56% per year during the growing season across the Northern Hemisphere. Notably, the mean daily GPPrate, driven primarily by rising CO2 concentrations and temperatures, contributed ~65% to the changes in total GPP during the growing season over time, with higher contributions in early season (~83%) compared with late season (~55%). These findings highlight the importance of vegetation physiology in driving temporal changes in terrestrial GPP and suggest that the asymmetric changes in productivity across seasons will exacerbate under ongoing climate change.

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Fig. 1: Changes in vegetation carbon uptake during the entire GS over time.
Fig. 2: Changes in the duration and rate of carbon uptake during EGS and LGS over time.
Fig. 3: Contribution of the mean daily rate of carbon uptake (GPPrate) to the trend of total carbon uptake across the Northern Hemisphere.
Fig. 4: Relative influence of each driver on the trend of the mean daily rate of carbon uptake (GPPrate).
Fig. 5: Conceptual summary of the driving mechanisms of the total carbon uptake (GPPtotal) across the Northern Hemisphere.

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Data availability

All data used for our analyses are publicly available. The data for FluxSat GPP were obtained from the Oak Ridge National Lab Distributed Active Archive Center at https://daac.ornl.gov/VEGETATION/guides/FluxSat_GPP_FPAR.html. MODIS gross primary productivity (MODIS_GPP) was derived from the MOD17A2HGF (version 6.1) at https://lpdaac.usgs.gov/products/mod17a2hgfv061/. Carbon-flux data were downloaded from the AmeriFlux (https://ameriflux.lbl.gov), ICOS/European Flux (https://data.icos-cp.eu), FLUXNET2015 datasets (https://fluxnet.org/data/download-data/) and ChinaFLUX (https://www.nesdc.org.cn/). The MODIS land-cover map was obtained from the MCD12C1 version 6 data product at https://doi.org/10.5067/MODIS/MCD12C1.006. Climatic variables were derived from the ERA5-Land dataset at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview. All relevant data supporting the key findings of this study are available via Zenodo at https://doi.org/10.5281/zenodo.14949875 (ref. 51). Data were analysed using MATLAB (R2022b) and R version 4.2.3.

Code availability

The code used in this study is available via Zenodo at https://doi.org/10.5281/zenodo.14949875 (ref. 51).

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Acknowledgements

The work was supported by the National Science Fund for Distinguished Young Scholars (grant no. 42025101), the Key Program of the National Natural Science Foundation of China (grant no. 42430504), the National Key Research and Development Program of China (grant no. 2023YFF0805600) and the Fundamental Research Funds for the Central Universities (grant no. 2243300004). Z.L. was funded by the National Natural Science Foundation of China (grant no. 42301028) and the China Postdoctoral Science Foundation (grant no. 2023M730280).

Author information

Author notes

  1. Zunchi Liu

    Present address: Yanchi Research Station, School of Soil and Water Conservation, Beijing Forestry University, Beijing, China

Authors and Affiliations

  1. College of Water Sciences, Beijing Normal University, Beijing, China

    Zunchi Liu & Yongshuo H. Fu

  2. Laboratoire des Sciences du Climat et de l’Environnement, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France

    Philippe Ciais

  3. CREAF, Cerdanyola del Vallès, Barcelona, Spain

    Josep Peñuelas

  4. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Barcelona, Spain

    Josep Peñuelas

  5. Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, State Key Laboratory of Estuarine and Coastal Research, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China

    Jianyang Xia

  6. Research Center for Global Change and Complex Ecosystems, Institute of Eco-Chongming, East China Normal University, Shanghai, China

    Jianyang Xia

  7. State Key Laboratory of Earth Surface Processes and Hazards Risk Governance, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Sha Zhou

  8. Institute of Carbon Neutrality, Sino‐French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Yao Zhang

  9. Plants and Ecosystems, Department of Biology, University of Antwerp, Wilrijk, Belgium

    Yongshuo H. Fu

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Contributions

Y.H.F. designed the research. Z.L. performed the analysis. Y.H.F. and Z.L. drafted the paper and contributed to the interpretation of the results. Z.L., P.C., J.P., J.X., S.Z., Y.Z. and Y.H.F. discussed the design, methods and results and contributed to the writing of the paper.

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Correspondence to Yongshuo H. Fu.

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Extended data

Extended Data Fig. 1 Changes in total carbon uptake (GPPtotal) during early (EGS) and late (LGS) stages of the growing season for FluxSat GPP, Modis GPP, and Fluxnet data.

a–f, Percent change in GPPtotal during EGS (a–c) and LGS (d–f). The EGS is defined as the period from the start to the peak of carbon uptake when photosynthetic rates are highest, and the LGS is defined as the period from the peak to the end of carbon uptake. The changes in GPPtotal were derived using a two-sided Mann–Kendall test at a 95% confidence level. The black dots in a, b and c, d, and the dark color bar in e-f indicate significant (Sig.) changes at P < 0.05. The percentages of areas with positive (P) and negative (N) changes over the study period are shown, with significant percentages displayed in parentheses.

Extended Data Fig. 2 Contribution of the duration of carbon uptake to the trend of total carbon uptake for FluxSat GPP, Modis GPP, and Fluxnet data.

a–i, Contribution of the duration of carbon uptake to total carbon uptake during the entire growing season (GS, a–c), early growing season (EGS, d–f), and late growing season (LGS, g–i). The percentages of areas with positive (P) and negative (N) contributions are shown. The bar chart in each panel shows the frequency distribution of the corresponding contributions.

Extended Data Fig. 3 Contribution of the mean daily rate of carbon uptake (GPPrate) to the trend of total carbon uptake for Fluxnet data.

a–f, Contribution of the GPPrate to total carbon uptake during the entire growing season (GS, a), early growing season (EGS, b), and late growing season (LGS, c), and their combined effects (d–f). The combined effects represent duration combined with GPPrate jointly regulating total carbon uptake. For example, ‘− + +’ represents a negative contribution of the duration, a positive contribution of GPPrate, and a positive change in total carbon uptake over time. The percentages of areas with positive (P) and negative (N) contributions are shown in a–c.

Extended Data Fig. 4 Relative influence of each driver on the trend of the peak of carbon uptake (POS).

a–c, Relative influences of the biotic and abiotic factors on POS for FluxSat GPP (a), Modis GPP (b), and Fluxnet (c) data. A partial derivative-based approach was used to quantify the proportions of the contributions of the biotic and abiotic factors to POS. The biotic factors include GPP during early growing season (EGS) and the start of carbon uptake (SOS). The EGS GPP and the abiotic factors (soil moisture content, shortwave radiation, precipitation, and temperature) were calculated as the mean/sum values from the mean SOS to the mean peak of carbon uptake.

Extended Data Fig. 5 Change in the mean daily rate of carbon uptake (GPPrate) with the duration of carbon uptake during the entire growing season (GS).

a–c, Spatial patterns of pixel-level slopes between mean daily GPPrate and GS duration for FluxSat and Modis GPP data (a, b), and site-level correlations of mean daily GPPrate with GS duration for Fluxnet data (c). All correlations in a-c are significant at the 0.05 level based on the two-tailed t-test. The black dots in a, b indicate significant correlations at P < 0.05. The percentages of areas of positive correlation (P) and negative correlation (N) between GPPrate and GS duration are shown, and the significant percentages are displayed in parentheses.

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Liu, Z., Ciais, P., Peñuelas, J. et al. Enhanced vegetation productivity driven primarily by rate not duration of carbon uptake. Nat. Clim. Chang. 15, 560–568 (2025). https://doi.org/10.1038/s41558-025-02311-3

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