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Elevated CO2 levels promote both carbon and nitrogen cycling in global forests

Nature Climate Change (2024)Cite this article

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Abstract

Forests provide vital ecosystem services, particularly as carbon sinks for nature-based climate solutions. However, the impact of elevated atmospheric carbon dioxide (CO2) levels on carbon and nitrogen interactions of forests remains poorly quantified. We integrate experimental observations and biogeochemical models to elucidate the synergies between enhanced nitrogen and carbon cycling in global forests under elevated CO2. Elevated CO2 alone increases net primary productivity (+27%; 95% CI: 23–31%) and leaf C/N ratio (+26%; 95% CI: 16–39%), while stimulating biological nitrogen fixation (+25%; 95% CI: 0–56%) and nitrogen use efficiency (+32%; 95% CI: 5–65%) according to a global meta-analysis. Under the elevated CO2 middle-road scenario for 2050, the forest carbon sink is projected to increase by 0.28 billion tonnes (PgC yr−1), with reactive nitrogen loss decreasing by 8 Tg yr−1 relative to the baseline. The monetary impact assessment of the elevated CO2 impact on forests represents a societal value of US$271 billion.

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Fig. 1: Global eCO2 experimental sites and eCO2 impact on carbon and nitrogen variables in global forests.
Fig. 2: Carbon sink and nitrogen budgets of global forests and their changes between eCO2 SSP2–4.5 middle-road scenario and baseline scenario in 2050.
Fig. 3: Nitrogen flows in global forests and changes due to elevated CO2 relative to baseline for 2050.
Fig. 4: Time series of carbon and nitrogen budgets in global forests over the period 2000–2050 under baseline and elevated CO2 scenarios.
Fig. 5: Impact assessment of elevated atmospheric CO2 levels as a single factor on forests under the elevated CO2 middle-road scenario (SSP2–4.5) relative to the baseline in 2050.

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Data availability

All data supporting the findings of this study are openly available, and their sources are detailed in the Methods and Supplementary Information. A global database of elevated CO2 simulation experiments was established by extracting data from site-based manipulation studies. Climate data for our study sites were sourced from the WorldClim database (https://worldclim.org/data/index.html#). Soil data were obtained primarily from publications or supplemented with data from the Global Land Data Assimilation System (GLDAS) (https://ldas.gsfc.nasa.gov/gldas/soils). Future forest areas under different socioeconomic pathways were projected using the Global Change Analysis Model. National carbon prices were obtained from a World Carbon Pricing Database, while fertilizer price data were sourced from the UN Comtrade Database (https://comtrade.un.org/). Source data are provided with this paper and are available via Zenodo at https://doi.org/10.5281/zenodo.10731367 (ref. 73).

Code availability

The code used in the analysis is available online and can be accessed in the Zenodo repository73.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (42325707 and 42261144001, B.G.), National Key Research and Development Project of China (2022YFE0138200, B.G.) and Postdoctoral Fellowship Program of China Postdoctoral Science Foundation (CPSF) (GZC20232311, J.C.). We thank H. Chen for assistance in meta-data collection.

Author information

Authors and Affiliations

  1. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China

    Jinglan Cui, Miao Zheng, Xiuming Zhang, Ziyue Qiu, Jianming Xu & Baojing Gu

  2. Policy Simulation Laboratory, Zhejiang University, Hangzhou, China

    Jinglan Cui

  3. Center for Earth System Science and Global Sustainability, Schiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA, USA

    Zihao Bian, Naiqing Pan & Hanqin Tian

  4. School of Geography, Nanjing Normal University, Nanjing, China

    Zihao Bian

  5. Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Zhejiang University, Hangzhou, China

    Jianming Xu

  6. Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, Zhejiang University, Hangzhou, China

    Baojing Gu

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Contributions

B.G. and J.C. designed the study. J.C. analysed the data and wrote the first draft of the paper. All authors contributed to the discussion and revision of the paper. M.Z. provided support for meta-data collection and visualization. Z.B., N.P. and H.T. provided modelling support for DLEM. X.Z. provided support for CHANS model and impact assessment. Z.Q. provided support for meta-data collection. J.X. contributed to the discussion of the study.

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Correspondence to Baojing Gu.

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Nature Climate Change thanks César Terrer, Kevin Van Sundert and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Modelling framework.

We establish a database of elevated atmospheric CO2 (eCO2) experiments conducted across forest sites. A global synthesis is then performed to quantify the impacts of eCO2 on nitrogen and carbon cycles. We utilize the Dynamic Land Ecosystem Model (DLEM) and the Coupled Human and Natural Systems (CHANS) model to simulate global forest carbon and nitrogen budgets under multiple scenarios. Finally, the carbon and nitrogen budgets are fed into the impact assessment module, facilitating the monetization of elevated CO2 impacts on forest assets.

Extended Data Fig. 2 Relationship between the response ratio to eCO2 for selected variables and moderators.

(a) Response ratio of NPP against the squared mean annual precipitation (MAP) in FACE Experiments. (b) Response ratio of BNF against soil C:N ratio. (c) Response ratio of DOC against MAP. (d) Response ratio of Rs against MAP in FACE Experiments. The regression lines illustrate the mean response of observations using mixed-effects meta-regression models. The gray shading denotes the 95% confidence intervals for the mean. The sizes of bubbles in the scatter plots are proportional to the weights of response ratios for each individual observation.

Source data

Extended Data Fig. 3 Relationship between the response ratio to eCO2 of selected variables and manipulation magnitude (ΔCO2).

Regressions between NUE (a), WUE (b), N2O (c) against ΔCO2. The regression lines illustrate the mean response of observations using mixed-effects meta-regression models. The gray shading denotes the 95% confidence intervals for the mean. The sizes of bubbles in the scatter plots are proportional to the weights of response ratios for each individual observation.

Source data

Extended Data Fig. 4 The nitrogen accumulation of global forests and the changes between eCO2 SSP2-4.5 middle-road scenario and baseline scenario in 2050.

Nitrogen accumulation in baseline scenario (a), eCO2 scenario (b), and Δaccumulation (eCO2-induced change) (c). NUE in baseline scenario (d), eCO2 scenario (e), and ΔNUE (eCO2-induced change) (f). Values in the legend reflect the average annual N budget from forests within a grid cell (0.5° × 0.5°). The base map is from GADM data.

Source data

Extended Data Fig. 5 The eCO2 impact on carbon sink and uncertainties in global forests under eCO2 SSP2-4.5 middle-road scenario for 2050.

(a) The eCO2–induced changes in carbon sink per square meter of forest area. (b) The absolute uncertainties of ΔC sink in Fig. 2 based on the standard deviations of the model ensembles derived from Monte Carlo simulations. Values of uncertainties in the legend reflect the annual values from forests within one grid cell (0.5° × 0.5°). The base map is from GADM data.

Source data

Extended Data Fig. 6 The nitrogen input of global forests and the changes between eCO2 SSP2-4.5 middle-road scenario and baseline scenario in 2050.

Biological nitrogen fixation (BNF) in baseline scenario (a), eCO2 scenario (b), and ΔBNF (eCO2-induced change) (c); nitrogen deposition in baseline scenario (d), eCO2 scenario (e), and ΔDeposition (f); nitrogen fertilizer in baseline scenario (g), eCO2 scenario (h), and Δfertilizer (i). Values in the legend reflect the average annual nitrogen budget from forests within a grid cell (0.5° × 0.5°). The base map is from GADM data.

Source data

Extended Data Fig. 7 Uncertainties of eCO2–induced changes in nitrogen budgets in global forests under eCO2 SSP2-4.5 middle-road scenario for 2050.

(a) ΔN input; (b) ΔN products; (c) ΔNr loss. The absolute uncertainties are based on the standard deviations of the model ensembles derived from Monte Carlo simulations. Values in the legend reflect the annual values from forest within a grid cell (0.5° × 0.5°). The base map is from GADM data.

Source data

Extended Data Fig. 8 The reactive nitrogen loss of global forests and the changes due to elevated CO2 under eCO2 middle road scenario (SSP2-4.5) relative to baseline scenario in 2050.

NH3 emissions in baseline scenario (a), eCO2 scenario (b), and ΔNH3 (eCO2-induced change) (c); N2O emissions in baseline scenario (d), eCO2 scenario (e), and ΔN2O (f); NOx emissions in baseline scenario (g), eCO2 scenario (h), and ΔNOx (i); NO3− in baseline scenario (j), eCO2 scenario (k), and ΔNO3− (l). Values in the legend reflect the average annual nitrogen budget from forest within a grid cell (0.5° × 0.5°). The base map is from GADM data.

Source data

Extended Data Fig. 9 A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram of our meta-analysis.

It delineates the flow of information, indicating the number of relevant publications at various stages of the meta-analysis process, encompassing ‘Identification’, ‘Screening & Eligibility’, and ‘Included’.

Supplementary information

Supplementary Information

Supplementary Figs. 1–6, Tables 1–3, Discussion, Terminology used in the study and references.

Reporting Summary

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Cui, J., Zheng, M., Bian, Z. et al. Elevated CO2 levels promote both carbon and nitrogen cycling in global forests. Nat. Clim. Chang. (2024). https://doi.org/10.1038/s41558-024-01973-9

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