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Drought amplifies warming-induced soil carbon loss in a decade-long experiment

Nature Climate Change (2026)Cite this article

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Abstract

A key uncertainty in understanding whether warming accelerates soil carbon (C) loss lies in how this response depends on other co-occurring environmental changes and the underlying mechanisms. Here we show that, in a 12-year grassland experiment, warming reduces soil C by 12.2% under drought but increases it by 6.7% under wet conditions. Such C losses during drought primarily result from the declines in mineral-associated organic C. These contrasting responses are closely linked to microbial processes: warming elevates microbial metabolic quotient under drought but suppresses it under wet conditions, accompanied by shifts in microbial community composition and C-degrading genes. Integrating these microbial metrics into an ecosystem model substantially improves predictions of soil C dynamics. These findings demonstrate the pivotal role of microbial processes in mediating soil C–climate feedbacks and underscore their critical importance for accurately projecting soil C dynamics in a warmer, potentially drier world.

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Fig. 1: A schematic diagram of study design and workflow.
Fig. 2: Effects of warming on soil C content and key biological functional variables under different precipitation levels.
Fig. 3: Responses of soil microbial communities and key functional genes to warming under different precipitation levels.
Fig. 4: Feedback mechanisms of soil microbial communities controlling soil C content under warming and altered precipitations.
Fig. 5: Simulation of soil C and microbial responses with ecosystem C models.

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Data availability

DNA sequences of 16S rRNA gene and ITS amplicons are available in NCBI Sequence Read Archive under project nos. PRJNA331185 and PRJNA1302016. GeoChip signal intensity data can be accessed through the URL (https://www.ou.edu/ieg/publications/data-sets). All other relevant data are available in Supplementary Information. Source data are provided with this paper.

Code availability

Statistical analyses and MEND model codes are available via Zenodo at https://doi.org/10.5281/zenodo.18396578 (ref. 78).

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Acknowledgements

We thank numerous previous and current members of the Institute for Environmental Genomics for their help in maintaining the experimental site. This work is supported by the US Department of Energy, Office of Science, Genomic Science Program under award no. DE-SC0004601 and DE-SC0010715 and the Office of the Vice President for Research at the University of Oklahoma to Junzhong Zhang.

Author information

Author notes

  1. These authors contributed equally: Xue Guo, Zhifeng Yang, Siyang Jian.

Authors and Affiliations

  1. State Key Laboratory of Regional and Urban Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Xue Guo

  2. Institute for Environmental Genomics, School of Biological Sciences, University of Oklahoma, Norman, OK, USA

    Xue Guo, Zhifeng Yang, Siyang Jian, Daliang Ning, Xuanyu Tao, Linwei Wu, Lauren Hale, Mengting Yuan, Xishu Zhou, Qian Li, Yuxiang Zhang, Mengying Zhao, Shun Han, Qiuting Zhang, Qun Gao, Huaqun Yin, Junzhong Zhang, Tianjiao Dai, Jonathan P. Michael, Reece D. V. Lennon, Zheng Shi, Liyou Wu & Jizhong Zhou

  3. School of Minerals Processing and Bioengineering, Central South University, Changsha, China

    Xue Guo, Xishu Zhou, Huaqun Yin & Xueduan Liu

  4. Institute of Ecology, College of Urban and Environmental Science, and State Key Laboratory for Vegetation Structure, Function and Construction (VegLab), Peking University, Beijing, China

    Linwei Wu

  5. Pacific Biosciences Research Center, University of Hawai’i at Mānoa, Honolulu, HI, USA

    Mengting Yuan

  6. School of Resources Environment and Safety Engineering, University of South China, Hengyang, China

    Qian Li

  7. School of Environment, Tsinghua University, Beijing, China

    Mengying Zhao

  8. State Key Laboratory of Water Resources Engineering and Management, and Institute for Water-Carbon Cycles and Carbon Neutrality, Wuhan University, Wuhan, China

    Gangsheng Wang

  9. Key Laboratory of Water and Sediment Sciences of Ministry of Education, State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, China

    Qun Gao

  10. Key Laboratory of Forest Disaster Warning and Control in Yunnan Province, Faculty of Forestry, Southwest Forestry University, Kunming, China

    Junzhong Zhang

  11. School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, China

    Tianjiao Dai

  12. Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Yunfeng Yang

  13. School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK, USA

    Jizhong Zhou

  14. School of Computer Sciences, University of Oklahoma, Norman, OK, USA

    Jizhong Zhou

  15. Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Jizhong Zhou

Authors
  1. Xue Guo
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  2. Zhifeng Yang
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  3. Siyang Jian
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Contributions

All authors contributed intellectual input and assistance to this study. The original concept and experimental strategy were developed by Jizhong Zhou. Field management was carried out by Liyou Wu, M.Y., Y.Z., Q.Z., Q.G., Junzhong Zhang, T.D., J.P.M., R.D.V.L. and H.Y. Sampling collections, DNA preparation and MiSeq sequencing analysis were carried out by X.Z., X.G., Q.L., S.H. and Linwei Wu. Soil chemical analysis was carried out by X.Z., L.H., X.G. and S.J. Various statistical analyses were carried out by X.G., S.J. and M.Z. Modelling analyses were carried out by Z.Y., G.W. and S.J. Assistance in data interpretation was provided by X.L., D.N., Z.S. and Y.Y. All data analysis and integration were guided by Jizhong Zhou. The manuscript was prepared by X.G., Z.Y. and Jizhong Zhou, with help from X.T. Considering their contributions to this study over the last 12 years, X.G., Z.Y. and S.J. were listed as co-first authors.

Corresponding author

Correspondence to Jizhong Zhou.

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The authors declare no competing interests.

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Nature Climate Change thanks Pablo García-Palacios 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 Effects of warming, altered precipitation, and clipping on total soil C content.

(a) Mean soil C content in response to warming, drought, wet and clipping from 2009 to 2020. The data of 552 samples were pooled across all measured years, and each bar shows the mean plus standard error (SE). There was a significant warming effect on soil C content (P < 0.050). Thus, the percent change by warming is labeled above the bars. (b) Warming effect on total soil C under different precipitation levels each year. The warming effect was assessed as (value under warming) minus (value under ambient temperature) at corresponding precipitation levels. The error bar represents mean ± S.E.M. (n = 8, 4 clipped+4 unclipped).

Source data

Extended Data Fig. 2 Soil carbon in POM and the correlation between soil C content and C in MAOM.

(a) Effects of warming on soil C in POM under different precipitation levels in 2020. Data were pooled across clipping treatments, and each bar shows the mean plus standard error (SE) (n = 8, 4 clipped+4 unclipped). (b) Correlation between soil C content and MAOM C in 2020. Coefficient of determination (r) and P values by the linear mixed-effects model are shown in the plot. Soil CMAOM: soil C in MAOM; Soil CPOM: soil C in POM.

Source data

Extended Data Fig. 3 Warming effects on C3 and C4 plant biomasses and microbial DNA yield under different precipitation levels.

(a, b) Mean C3 and C4 plant biomasses from 2009 to 2020. (c) Mean microbial DNA yield from 2009 to 2020. Data were pooled across clipping treatments and all measured years, and each bar shows the mean plus standard error (SE) (n = 92, (4 clipped+4 unclipped) × 11 years plus 4 clipped plots in 2009). Non-significant interactive effects of warming and altered precipitations were observed on C3 and C4 plant biomasses (P > 0.152), while there was a significant interactive effect on microbial biomass based on DNA yield by the linear mixed-effects models (P < 0.017). Thus, percent changes of DNA yield by warming are labeled above the bars under drought and wet conditions. P value of the two-sided paired t-test is labeled by ** P < 0.01.

Source data

Extended Data Fig. 4 Correlation of soil C content with plant and microbial biomasses.

(a–c) Correlations of soil C content with aboveground plant biomass, C3 and C4 plant biomasses from 2009 to 2020. (d, e) Correlations of soil C content with microbial biomass based on soil DNA yield and PLFA analyses from 2009 to 2020. The r and P values by the linear mixed-effects model were labeled in each plot (n = 552).

Extended Data Fig. 5 Responses of N cycling and P utilization genes to warming under different precipitation levels.

Relative changes of N cycling and P utilizing genes by warming under different precipitation levels from 2009 to 2020. Warming-induced changes of signal intensity detected by GeoChip 5.0 are presented as the differences of individual functional genes ((warmed-unwarmed)/unwarmed). Error bars represent standard error (n = 92, (4 clipped+4 unclipped) × 11 years plus 4 clipped plots in 2009). Statistical significance is indicated by *** P < 0.001, ** P < 0.01, and * P < 0.05.

Extended Data Fig. 6 Warming effects on microbial metabolic quotients under different precipitation levels.

(a) Mean microbial metabolic quotient (MQ) based on the ratio of heterotrophic respiration (Rh) and microbial DNA yields. Each bar shows the mean plus standard error (SE) averaged from 2010 to 2020 (n = 88, (4 clipped+4 unclipped) × 11 years). The warming × altered precipitation interactive effects are tested by the linear mixed-effects models. Percent changes in warmed plots relative to corresponding control plots are labeled above the bars. P values of the two-sided paired t-test are labeled by ** P < 0.01, and * P < 0.05. (b, c) Correlation of soil C content with microbial MQ based on microbial DNA yield or PLFA analysis. The r and P values by the linear mixed-effects model were labeled in each plot.

Source data

Extended Data Fig. 7 Model calibration for soil C pools, fluxes, microbial C, and gene abundance.

(a) Observed vs. Simulated soil C in MAOM under warming and altered precipitations. (b) Observed vs. Simulated soil C in POM under warming and altered precipitations. (c) Observed vs. Simulated microbial C under warming and altered precipitations. (d) Observed vs. Simulated Rt under warming and altered precipitations. (e) Observed hydrolytic enzyme-coding genes vs. Simulated hydrolytic enzyme concentrations under warming and altered precipitations. (f) Observed oxidative enzyme-coding genes vs. Simulated oxidative enzyme concentrations under warming and altered precipitations. The gene abundance and enzyme concentrations were rescaled to a range of 0-1 for unit-independent comparison. Given the differences in data types and sizes, the goodness-of-fit for model calibration for each treatment was indicated by Percent bias (PBIAS) for soil C in POM and MAOM, as well as microbial C, R2 for Rt, and Pearson correlation coefficient (r) for gene-enzyme relationships.

Source data

Extended Data Fig. 8 Parameter uncertainty determined by MEND calibration.

(a) Parameter uncertainty of each calibrated parameter. (b) Average parameter uncertainty for each treatment. To make the parameter uncertainty comparable to the initial range, standard deviation (SD) was calculated for calibrated parameter value ranges and divided by the SD of initial parameter ranges. The error bar represents mean ± S.E.M. (n = 15 calibrated parameters per treatment).

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Extended Data Fig. 9 Effects of microbial parametric changes on simulation bias.

(a) Simulation bias of soil C and microbial pools quantified by PBIAS under two parameter change conditions. (b) Simulation bias of Rh and Rt quantified by 1-R2 under two parameter change conditions: Microbial unchanged (all treatments using control microbial parameters) and Microbial changed (all treatments using calibrated parameters). (c) Simulation bias of oxidative and hydrolytic enzymes quantified by 1 - CORR (Pearson correlation) under two parameter change conditions. The error bar in subplot (a–c) represents mean ± S.E.M. (n = 5 treatments except Control condition). (d–f) Bias reduction when accounting for single parameter change. We compared the observations and simulations when each parameter was set as Control value. An increase in simulation bias highlights the significance of the detected parameter changes in minimizing bias. Due to the sample size and variable types, different metrics are used to quantify the simulation bias for each variable.

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Extended Data Fig. 10 Simulated soil C changes to warming under three precipitation levels.

(a–c) Simulated changes of surface (0-15 cm) soil C, MAOM, and POM. Model simulations were conducted under two conditions: with and without considering the changes of microbial parameters. The error bar represents mean ± S.E.M. (n = 11 yearly simulations). (d, e) Simulated soil C changes in MAOM and POM under multiple warming scenarios, where varying temperature increases were imposed on ambient temperatures across three precipitation levels. Plant, microbial parameters, and moisture were held constant across warming treatments to isolate the temperature effect only. The error bar represents mean ± S.E.M. (n = 11 yearly simulations).

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Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1–3, Tables 1–13 and Text A–F.

Reporting Summary (download PDF )

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Source Data (download XLSX )

Statistical source data for Figs. 2 and 5 and Extended Data Figs. 1–3 and 6–10.

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Guo, X., Yang, Z., Jian, S. et al. Drought amplifies warming-induced soil carbon loss in a decade-long experiment. Nat. Clim. Chang. (2026). https://doi.org/10.1038/s41558-026-02584-2

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