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Phosphorus Limitation on Carbon Sequestration in China under RCP8.5


doi: 10.1007/s00376-022-2195-y

  • Currently, there is a lack of understanding regarding carbon (C) sequestration in China arising as a result of phosphorus (P) limitation. In this study, a global land surface model (CABLE) was used to investigate the response of C uptake to P limitation after 1901. In China, P limitation resulted in reduced net primary production (NPP), heterotrophic respiration, and net ecosystem production (NEP) in both the 2030s and the 2060s. The reductions in NEP in the period 2061–70 varied from 0.32 Pg C yr−1 in China to 5.50 Pg C yr−1 at the global scale, translating to a decrease of 15.0% for China and 7.6% globally in the period 2061–70, relative to the changes including C and nitrogen cycles. These ranges reflect variations in the magnitude of P limitation on C uptake (or storage) at the regional and global scales. Both in China and at the global scale, these differences can be attributed to differences in soil nutrient controls on C uptake, or positive feedback between NPP and soil decomposition rates, or both. Our results highlight the strong ability of P limitation to influence the pattern, response, and magnitude of C uptake under future conditions (2030s–2060s), which may help to clarify the potential influence of P limitation when projecting C uptake in China.
    摘要: 目前,磷限制对中国和全球的碳汇和碳存储影响强度与差异缺乏系统地研究。本研究基于全球陆面模式CABLE,分析了1901年后磷限制对中国未来碳汇和碳存储变化的影响。在中国,磷限制引起了2030s和2060s的净初级生产力(NPP)、异养呼吸(HR)和净生态系统生产力(NEP)减少。相对于没有考虑磷循环过程,2061至2070年,中国与全球净初级生产力分别减少了0.32Pg C yr-1(或者15.0%)和5.50Pg C yr-1(或者7.6%)。这表明磷限制对中国碳汇的限制强度明显高于全球水平。土壤养分对碳吸收的影响差异、NPP和土壤分解速率之间的正反馈均会改变磷限制影响的强度。我们的研究强调了未来情景下磷限制对中国碳吸收有重要的影响。考虑磷限制的影响将有助于改善模式对中国碳汇评估与预估的能力。
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  • Figure 1.  Changes in NPP globally and for China from CABLE during 1901–2100. Difference in NPP for the globe (a) and China (b) from CMIP6 (black), with CABLE data for C–N cycles (blue) and C–N–P cycles (orange). CMIP6 model values covered historical conditions during 1901–2014, and SSP5-8.5 during 2015–2100. Gray shading indicates the range between the maximum and minimum CMIP6 values.

    Figure 2.  Changes in (a–c) China and (d–f) the global terrestrial ecosystem for net primary production (NPP, units: Pg C yr−1), heterotrophic respiration (HR, units: Pg C yr−1), and net ecosystem production (NEP, units: Pg C yr−1) from the 2020s to 2090s relative to the baseline values at 1901 as estimated using the C–N–P cycle or the C–N cycle. The horizontal lines represent the mean ± σ.

    Figure 3.  Variations of NPP from the 2020s to the 2090s as simulated by CABLE in response to varying (a, d) CO2, (b, e) climate, and (c, f) N deposition (Ndep) relative to the 1901 level for (a–c) the global terrestrial ecosystem and (d–f) China under the C–N cycle and the C–N–P cycle. The horizontal lines represent the mean ± σ.

    Figure 4.  Contributions of (a, d) plant pools, (b, e) soil pools, and (c, f) litter pools to C accumulated for (a–c) the global terrestrial ecosystem and (d–f) China under the C–N and C–N–P cycles. The horizontal lines represent the mean ± σ.

    Figure 5.  Contributions of P limitation to cumulative changes in (a) NEP, (b) NPP, and (c) HR, globally (blue bars) and for China (orange bars), from the 2020s to the 2090s, relative to that without P constraints. As we investigated how P limitations contribute to cumulative changes in each variable’s flux ($ {x}_{t} $), we calculated P nutrient contribution of each variable’s flux (ΔCP): ΔCP $ =\left(\sum _{t=1}^{n}{x}_{t}^{\mathrm{C}\mathrm{N}\mathrm{P}}-\sum _{t=1}^{n}{x}_{t}^{\mathrm{C}\mathrm{N}}\right)/\sum _{t=1}^{n}{x}_{t}^{\mathrm{C}\mathrm{N}} $.

    Figure 6.  China’s contributions to the global-scale changes in (a) NEP, (b) NPP, and (c) HR under the C–N cycle (blue bars) or C–N–P cycle (orange bars), from the 2020s to the 2090s. The horizontal lines represent the mean ± σ. As we investigated how China’s changes in variables ($ {x}_{t}^{\mathrm{C}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{a}} $) contributed to the changes globally ($ {x}_{t}^{\mathrm{G}\mathrm{l}\mathrm{o}\mathrm{b}\mathrm{e}} $), we calculated China’s contribution of variables (Δ$ {\mathrm{C}\mathrm{C}}_{t} $) to the global scale in both experiments with the C–N–P and C–N cycles: Δ$ {\mathrm{C}\mathrm{C}}_{t} $$ ={x}_{t}^{\mathrm{C}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{a}}/{x}_{t}^{\mathrm{G}\mathrm{l}\mathrm{o}\mathrm{b}\mathrm{e}} $.

    Figure 7.  (a) Annual decomposition rates (Ksoil; units: 0.01 d–1) as a result of P limitation from 1901 to 2100 and (b) annual net primary production (NPP) against Ksoil as estimated by CABLE for China (orange) and global terrestrial ecosystems (blue) from the C–N–P simulations.

    Table 1.  CABLE simulations used in this study under the C–N–P cycle and C–N cycle.

    SimulationCO2ClimateN deposition
    CN1/CNP1Time-varyingTime-varyingTime-varying
    CN2/CNP2Fixed at 1901Time-varyingTime-varying
    CN3/CNP3Time-varyingFixed at 1901Time-varying
    CN4/CNP4Time-varyingTime-varyingFixed at 1901
    DownLoad: CSV

    Table 2.  Details of the models from CMIP6 used in this study (Peng et al., 2021).

    Model nameSpatial resolutionLand componentFull N cycleFull P cycleFireReference
    ACCESS-ESM1.51.875° × 1.25°CABLEYesYesNoZiehn et al. (2020)
    BCC-CSM2-MR1.125° × 1.125°AVIM2NoNoNoWu et al. (2019)
    CanESM52.81° × 2.81°CLASS-CTEMNoNoNoArora and Scinocca (2016)
    CESM20.9° × 1.25°CLM5YesNoYesDanabasoglu et al. (2020)
    CNRM-ESM2-11.4° × 1.4°ISBA-CTRIPNoNoYesSéférian et al. (2016)
    IPSL-CM6A-LR2.5° × 1.3°ORCHIDEENoNoNoHourdin et al. (2020)
    UKESM1-0- LL1.875° × 1.25°JULES-ES1.0YesNoNoSellar et al. (2019)
    DownLoad: CSV
  • Alewell, C., B. Ringeval, C. Ballabio, D. A. Robinson, P. Panagos, and P. Borrelli, 2020: Global phosphorus shortage will be aggravated by soil erosion. Nature Communications, 11, 4546, https://doi.org/10.1038/s41467-020-18326-7.
    Andrews, M. B., and Coauthors, 2020: Historical simulations with HadGEM3-GC3.1 for CMIP6. Journal of Advances in Modeling Earth Systems, 12, e2019MS001995, https://doi.org/10.1029/2019MS001995.
    Arora, V. K., and J. F. Scinocca, 2016: Constraining the strength of the terrestrial CO2 fertilization effect in the Canadian Earth system model version 4.2 (CanESM4.2). Geoscientific Model Development, 9, 2357−2376, https://doi.org/10.5194/gmd-9-2357-2016.
    Arora, V. K., and Coauthors, 2020: Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models. Biogeosciences, 17, 4173−4222, https://doi.org/10.5194/bg-17-4173-2020.
    Brovkin, V., and D. Goll, 2015: Land unlikely to become large carbon source. Nature Geoscience, 8, 893, https://doi.org/10.1038/ngeo2598.
    Buendía, C., S. Arens, T. Hickler, S. I. Higgins, P. Porada, and A. Kleidon, 2014: On the potential vegetation feedbacks that enhance phosphorus availability-insights from a process-based model linking geological and ecological timescales. Biogeosciences, 11, 3661−3683, https://doi.org/10.5194/bg-11-3661-2014.
    Cao, L., G. Bala, K. Caldeira, R. Nemani, and G. Ban-Weiss, 2010: Importance of carbon dioxide physiological forcing to future climate change. Proceedings of the National Academy of Sciences of the United States of America, 107, 9513−9518, https://doi.org/10.1073/pnas.0913000107.
    Cleveland, C. C., and Coauthors, 2013: Patterns of new versus recycled primary production in the terrestrial biosphere. Proceedings of the National Academy of Sciences of the United States of America, 110, 12 733−12 737, https://doi.org/10.1073/pnas.1302768110.
    Cortés, J., M. D. Mahecha, M. Reichstein, R. B. Myneni, C. Chen, and A. Brenning, 2021: Where are global vegetation greening and browning trends significant. Geophys. Res. Lett., 48, e2020GL091496, https://doi.org/10.1029/2020GL091496.
    Cox, P. M., D. Pearson, B. B. Booth, P. Friedlingstein, C. Huntingford, C. D. Jones, and C. M. Luke, 2013: Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature, 494, 341−344, https://doi.org/10.1038/nature11882.
    Danabasoglu, G., and Coauthors, 2020: The community earth system model version 2 (CESM2). Journal of Advances in Modeling Earth Systems, 12, e2019MS001916, https://doi.org/10.1029/2019MS001916.
    Du, E. Z., and Coauthors, 2020: Global patterns of terrestrial nitrogen and phosphorus limitation. Nature Geoscience, 13, 221−226, https://doi.org/10.1038/s41561-019-0530-4.
    Fernández-Martínez, M., and Coauthors, 2019: Global trends in carbon sinks and their relationships with CO2 and temperature. Nature Climate Change, 9, 73−79, https://doi.org/10.1038/s41558-018-0367-7.
    Filippelli, G. M., 2008: The global phosphorus cycle: Past, present, and future. Elements, 4, 89−95, https://doi.org/10.2113/GSELEMENTS.4.2.89.
    Finzi, A. C., and Coauthors, 2006: Progressive nitrogen limitation of ecosystem processes under elevated CO2 in a warm-temperate forest. Ecology, 87, 15−25, https://doi.org/10.1890/04-1748.
    Fleischer, K., and Coauthors, 2019: Nitrogen deposition maintains a positive effect on terrestrial carbon sequestration in the 21st century despite growing phosphorus limitation at regional scales. Global Biogeochemical Cycles, 33, 810−824, https://doi.org/10.1029/2018GB005952.
    Friedlingstein, P., and Coauthors, 2006: Climate–carbon cycle feedback analysis: Results from the C4MIP model intercomparison. J. Climate, 19, 3337−3353, https://doi.org/10.1175/JCLI3800.1.
    Friedlingstein, P., M. Meinshausen, V. K. Arora, C. D. Jones, A. Anav, S. K. Liddicoat, and R. Knutti, 2013: Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Climate, 27, 511−526, https://doi.org/10.1175/JCLI-D-12-00579.1.
    Friedlingstein, P., and Coauthors, 2020: Global carbon budget 2020. Earth System Science Data, 12, 3269−3340, https://doi.org/10.5194/essd-12-3269-2020.
    Goll, D. S., and Coauthors, 2017: A representation of the phosphorus cycle for ORCHIDEE (revision 4520). Geoscientific Model Development, 10, 3745−3770, https://doi.org/10.5194/gmd-10-3745-2017.
    Hourdin, F., and Coauthors, 2020: LMDZ6A: The atmospheric component of the IPSL climate model with improved and better tuned physics. Journal of Advances in Modeling Earth Systems, 12, e2019MS001892, https://doi.org/10.1029/2019MS001892.
    Hurrell, J. W., and Coauthors, 2013: The Community Earth System Model: A framework for collaborative research. Bull. Amer. Meteorol. Soc., 94, 1339−1360.
    Kowalczyk, P., C. Balzer, G. Reichenauer, A. P. Terzyk, P. A. Gauden, and A. V. Neimark, 2016: Using in-situ adsorption dilatometry for assessment of micropore size distribution in monolithic carbons. Carbon, 103, 263−272, https://doi.org/10.1016/j.carbon.2016.02.080.
    Lamarque, J. F., and Coauthors, 2010: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: Methodology and application. Atmospheric Chemistry and Physics, 10, 7017−7039, https://doi.org/10.5194/acp-10-7017-2010.
    Lamarque, J. F., and Coauthors, 2013: Multi-model mean nitrogen and sulfur deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): Evaluation of historical and projected future changes. Atmospheric Chemistry and Physics, 13, 7997−8018, https://doi.org/10.5194/acp-13-7997-2013.
    Loveland, T., B. Reed, J. Brown, D. Ohlen, Z. Zhu, L. Yang, and J. Merchant, 2000: Development of a global land characteristics database and IGBP DISCover from 1 km AVHRR data. Int. J. Remote Sens., 21, 1303−1330, https://doi.org/10.1080/014311600210191.
    Maier, C. A., K. H. Johnsen, P. H. Anderson, S. Palmroth, D. Kim, H. R. McCarthy, and R. Oren, 2022: The response of coarse root biomass to long-term CO2 enrichment and nitrogen application in a maturing Pinus taeda stand with a large broadleaved component. Global Change Biology, 28, 1458−1476, https://doi.org/10.1111/gcb.15999.
    Mori, T., S. Ohta, S. Ishizuka, R. Konda, A. Wicaksono, J. Heriyanto, and A. Hardjono, 2010: Effects of phosphorus addition on N2O and NO emissions from soils of an Acacia mangium plantation. Soil Science & Plant Nutrition, 56, 782−788, https://doi.org/10.1111/j.1747-0765.2010.00501.x.
    Norby, R. J., J. M. Warren, C. M. Iversen, B. E. Medlyn, and R. E. McMurtrie, 2010: CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proceedings of the National Academy of Sciences of the United States of America, 107, 19 368−19 373, https://doi.org/10.1073/pnas.1006463107.
    Olson, D. M., et al., 2001: Terrestrial Ecoregions of the World: A New Map of Life on Earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience, 51(11), 933−938, https://doi.org/10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2.
    Peng, J., Y. P. Wang, B. Z. Houlton, L. Dan, B. Pak, and X. B. Tang, 2020: Global carbon sequestration is highly sensitive to model-based formulations of nitrogen fixation. Global Biogeochemical Cycles, 34, e2019GB006296, https://doi.org/10.1029/2019GB006296.
    Peng, J., L. Dan, F. Q. Yang, X. B. Tang, and D. Y. Wang, 2021: Global and regional estimation of carbon uptake using CMIP6 ESM compared with TRENDY ensembles at the centennial scale. J. Geophys. Res., 126, e2021JD035135, https://doi.org/10.1029/2021JD035135.
    Peng, J., Y. L. Wang, L. Dan, J. M. Feng, F. Q. Yang, X. B. Tang, Q. Z. Wu, and J. Tian, 2022: Overestimated terrestrial carbon uptake in the future owing to the lack of spatial variations CO2 in an earth system model. Earth's Future, 10, e2021EF002440, https://doi.org/10.1029/2021EF002440.
    Piao, S. L., and Coauthors, 2013: Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. Global Change Biology, 19, 2117−2132, https://doi.org/10.1111/gcb.12187.
    Qian, T. T., A. G. Dai, K. E. Trenberth, and K. W. Oleson, 2006: Simulation of global land surface conditions from 1948 to 2004. Part I: Forcing data and evaluations. Journal of Hydrometeorology, 7, 953−975, https://doi.org/10.1175/JHM540.1.
    Riahi, K., and Coauthors, 2011: RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climate Change, 109, 33−57, https://doi.org/10.1007/s10584-011-0149-y.
    Schillereff, D. N., R. C. Chiverrell, J. K. Sjöström, M. E. Kylander, J. F. Boyle, J. A. C. Davies, H. Toberman, and E. Tipping, 2021: Phosphorus supply affects long-term carbon accumulation in mid-latitude ombrotrophic peatlands. Communications Earth & Environment, 2, 241, https://doi.org/10.1038/s43247-021-00316-2.
    Séférian, R., and Coauthors, 2016: Development and evaluation of CNRM Earth system model – CNRM-ESM1. Geoscientific Model Development, 9, 1423−1453, https://doi.org/10.5194/gmd-9-1423-2016,2016.
    Sellar, A. A., and Coauthors, 2019: UKESM1: Description and evaluation of the U.K. Earth System Model. Journal of Advances in Modeling Earth Systems, 11, 4513−4558, https://doi.org/10.1029/2019MS001739.
    Seneviratne, S. I., and M. Hauser, 2020: Regional climate sensitivity of climate extremes in CMIP6 versus CMIP5 multimodel ensembles. Earth's Future, 8, e2019EF001474, https://doi.org/10.1029/2019EF001474.
    Shi, Z., S. Crowell, Y. Q. Luo, and B. Moore III, 2018: Model structures amplify uncertainty in predicted soil carbon responses to climate change. Nature Communications, 9, 2171, https://doi.org/10.1038/s41467-018-04526-9.
    Sponseller, R. A., M. J. Gundale, M. Futter, E. Ring, A. Nordin, T. Näsholm, and H. Laudon, 2016: Nitrogen dynamics in managed boreal forests: Recent advances and future research directions. Ambio, 45, 175−187, https://doi.org/10.1007/s13280-015-0755-4.
    Vitousek, P. M., S. Porder, B. Z. Houlton, and O. A. Chadwick, 2010: Terrestrial phosphorus limitation: Mechanisms, implications, and nitrogen-phosphorus interactions. Ecological Applications, 20, 5−15, https://doi.org/10.1890/08-0127.1.
    Wang, Y. P., B. Z. Houlton, and C. B. Field, 2007: A model of biogeochemical cycles of carbon, nitrogen, and phosphorus including symbiotic nitrogen fixation and phosphatase production. Global Biogeochemical Cycles, 21, GB1018, https://doi.org/10.1029/2006GB002797.
    Wang, Y. P., R. M. Law, and B. Pak, 2010: A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere. Biogeosciences, 7, 2261−2282, https://doi.org/10.5194/bg-7-2261-2010.
    Wang, Y. P., X. J. Lu, I. J. Wright, Y. J. Dai, P. J. Rayner, and P. B. Reich, 2012: Correlations among leaf traits provide a significant constraint on the estimate of global gross primary production. Geophys. Res. Lett., 39, L19405, https://doi.org/10.1029/2012GL053461.
    Wang, Z. N., and Coauthors, 2020: Coupling of phosphorus processes with carbon and nitrogen cycles in the dynamic land ecosystem model: Model structure, parameterization, and evaluation in tropical forests. Journal of Advances in Modeling Earth Systems, 12, e2020MS002123, https://doi.org/10.1029/2020MS002123.
    Wieder, W. R., C. C. Cleveland, W. K. Smith, and K. Todd-Brown, 2015: Future productivity and carbon storage limited by terrestrial nutrient availability. Nature Geoscience, 8, 441−444, https://doi.org/10.1038/ngeo2413.
    Wu, T. W., and Coauthors, 2019: The Beijing climate center climate system model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geoscientific Model Development, 12, 1573−1600, https://doi.org/10.5194/gmd-12-1573-2019.
    Zhang, Q., A. J. Pitman, Y. P. Wang, Y. J. Dai, and P. J. Lawrence, 2013: The impact of nitrogen and phosphorous limitation on the estimated terrestrial carbon balance and warming of land use change over the last 156 yr. Earth System Dynamics, 4, 333−345, https://doi.org/10.5194/esd-4-333-2013.
    Zhang, Q., Y. P. Wang, R. J. Matear, A. J. Pitman, and Y. J. Dai, 2014: Nitrogen and phosphorous limitations significantly reduce future allowable CO2 emissions. Geophys. Res. Lett., 41, 632−637, https://doi.org/10.1002/2013GL058352.
    Zhang, X. Z., P. J. Rayner, Y. P. Wang, J. D. Silver, X. J. Lu, B. Pak, and X. G. Zheng, 2016: Linear and nonlinear effects of dominant drivers on the trends in global and regional land carbon uptake: 1959 to 2013. Geophys. Res. Lett., 43, 1607−1614, https://doi.org/10.1002/2015GL067162.
    Zhang, X. Z., and Coauthors, 2021: A small climate-amplifying effect of climate-carbon cycle feedback. Nature Communications, 12, 2952, https://doi.org/10.1038/s41467-021-22392-w.
    Ziehn, T., and Coauthors, 2020: The australian earth system model: ACCESS-ESM1.5. Journal of Southern Hemisphere Earth Systems Science, 70, 193−214, https://doi.org/10.1071/ES19035.
  • [1] REN Guoyu, DING Yihui, ZHAO Zongci, ZHENG Jingyun, WU Tongwen, TANG Guoli, XU Ying, 2012: Recent Progress in Studies of Climate Change in China, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 958-977.  doi: 10.1007/s00376-012-1200-2
    [2] XU Yongfu, HUANG Yao, LI Yangchun, 2012: Summary of Recent Climate Change Studies on the Carbon and Nitrogen Cycles in the Terrestrial Ecosystem and Ocean in China, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 1027-1047.  doi: 10.1007/s00376-012-1206-9
    [3] ZHANG Wen, HUANG Yao, SUN Wenjuan, YU Yongqiang, 2007: Simulating Crop Net Primary Production in China from 2000 to 2050 by Linking the Crop-C model with a FGOALS's Model Climate Change Scenario, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 845-854.  doi: 10.1007/s00376-007-0845-8
    [4] Ting WEI, Wenjie DONG, Qing YAN, Jieming CHOU, Zhiyong YANG, Di TIAN, 2016: Developed and Developing World Contributions to Climate System Change Based on Carbon Dioxide, Methane and Nitrous Oxide Emissions, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 632-643.  doi: 10.1007/s00376-015-5141-4
    [5] Dai Xiaosu, Ding Yihui, 1994: A Modeling Study of Climate Change and Its Implication for Agriculture in China Part II: The Implication of Climate Change for Agriculture in China, ADVANCES IN ATMOSPHERIC SCIENCES, 11, 499-506.  doi: 10.1007/BF02658171
    [6] Gao Ge, Huang Chaoying, 2001: Climate Change and Its Impact on Water Resources in North China, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 718-732.  doi: 10.1007/BF03403497
    [7] DING Yihui, REN Guoyu, ZHAO Zongci, XU Ying, LUO Yong, LI Qiaoping, ZHANG Jin, 2007: Detection, Causes and Projection of Climate Change over China: An Overview of Recent Progress, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 954-971.  doi: 10.1007/s00376-007-0954-4
    [8] BUHE Cholaw, Ulrich CUBASCH, LIN Yonghui, JI Liren, 2003: The Change of North China Climate in Transient Simulations Using the IPCC SRES A2 and B2 Scenarios with a Coupled Atmosphere-Ocean General Circulation Model, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 755-766.  doi: 10.1007/BF02915400
    [9] Xiujing YU, Guoyu REN, Panfeng ZHANG, Jingbiao HU, Ning LIU, Jianping LI, Chenchen ZHANG, 2020: Extreme Temperature Change of the Last 110 Years in Changchun, Northeast China, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 347-358.  doi: 10.1007/s00376-020-9165-z
    [10] JIANG Dabang, 2008: Projected Potential Vegetation Change in China under the SRES A2 and B2 Scenarios, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 126-138.  doi: 10.1007/s00376-008-0126-1
    [11] XIAO Cunde, QIN Dahe, YAO Tandong, DING Yongjian, LIU Shiyin, ZHAO Lin, LIU Yujie, 2008: Progress on Observation of Cryospheric Components and Climate-Related Studies in China, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 164-180.  doi: 10.1007/s00376-008-0164-8
    [12] Zhiding ZHANG, Xu YUE, Hao ZHOU, Jun ZHU, Yadong LEI, Chenguang TIAN, 2024: Simulation of the Ecosystem Productivity Responses to Aerosol Diffuse Radiation Fertilization Effects over the Pan-Arctic during 2001–19, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 84-96.  doi: 10.1007/s00376-023-2329-x
    [13] Weile WANG, Ramakrishna NEMANI, 2016: Dynamic Responses of Atmospheric Carbon Dioxide Concentration to Global Temperature Changes between 1850 and 2010, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 247-258.  doi: 10.1007/s00376-015-5090-y
    [14] ZHANG Lixia* and ZHOU Tianjun, , 2014: An Assessment of Improvements in Global Monsoon Precipitation Simulation in FGOALS-s2, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 165-178.  doi: 10.1007/s00376-013-2164-6
    [15] HAN Zuoqiang, YAN Zhongwei*, LI Zhen, LIU Weidong, and WANG Yingchun, 2014: Impact of Urbanization on Low-Temperature Precipitation in Beijing during 19602008, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 48-56.  doi: 10.1007/s00376-013-2211-3
    [16] LIAO Hong, CHANG Wenyuan, YANG Yang, 2015: Climatic Effects of Air Pollutants over China: A Review, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 115-139.  doi: 10.1007/s00376-014-0013-x
    [17] MA Jianzhong, GUO Xueliang, ZHAO Chunsheng, ZHANG Yijun, HU Zhijin, 2007: Recent Progress in Cloud Physics Research in China, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 1121-1137.  doi: 10.1007/s00376-007-1121-7
    [18] Yitian QIAN, Pang-Chi HSU, Chi-Han CHENG, 2017: Changes in Surface Energy Partitioning in China over the Past Three Decades, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 635-649.  doi: 10.1007/s00376-016-6194-8
    [19] Qian Weihong, Zhang Henian, Zhu Yafen, Dong-Kyou Lee, 2001: lnterannual and lnterdecadal Variability of East Asian Acas and Their Impact on Temperature of China in Winter Season for the Last Century, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 511-523.  doi: 10.1007/s00376-001-0041-1
    [20] Yujie WANG, Botao ZHOU, Dahe QIN, Jia WU, Rong GAO, Lianchun SONG, 2017: Changes in Mean and Extreme Temperature and Precipitation over the Arid Region of Northwestern China: Observation and Projection, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 287-305.  doi: 10.1007/s00376-016-6160-5

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Manuscript received: 28 July 2022
Manuscript revised: 09 November 2022
Manuscript accepted: 23 November 2022
通讯作者: 陈斌, bchen63@163.com
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Phosphorus Limitation on Carbon Sequestration in China under RCP8.5

    Corresponding author: Jing PENG, pengjing@tea.ac.cn
  • 1. CAS Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 2. Laboratory of Cloud-Precipitation Physics and Severe Storms (LACS), Institute of Atmospheric Physics, Beijing 100029, China

Abstract: Currently, there is a lack of understanding regarding carbon (C) sequestration in China arising as a result of phosphorus (P) limitation. In this study, a global land surface model (CABLE) was used to investigate the response of C uptake to P limitation after 1901. In China, P limitation resulted in reduced net primary production (NPP), heterotrophic respiration, and net ecosystem production (NEP) in both the 2030s and the 2060s. The reductions in NEP in the period 2061–70 varied from 0.32 Pg C yr−1 in China to 5.50 Pg C yr−1 at the global scale, translating to a decrease of 15.0% for China and 7.6% globally in the period 2061–70, relative to the changes including C and nitrogen cycles. These ranges reflect variations in the magnitude of P limitation on C uptake (or storage) at the regional and global scales. Both in China and at the global scale, these differences can be attributed to differences in soil nutrient controls on C uptake, or positive feedback between NPP and soil decomposition rates, or both. Our results highlight the strong ability of P limitation to influence the pattern, response, and magnitude of C uptake under future conditions (2030s–2060s), which may help to clarify the potential influence of P limitation when projecting C uptake in China.

摘要: 目前,磷限制对中国和全球的碳汇和碳存储影响强度与差异缺乏系统地研究。本研究基于全球陆面模式CABLE,分析了1901年后磷限制对中国未来碳汇和碳存储变化的影响。在中国,磷限制引起了2030s和2060s的净初级生产力(NPP)、异养呼吸(HR)和净生态系统生产力(NEP)减少。相对于没有考虑磷循环过程,2061至2070年,中国与全球净初级生产力分别减少了0.32Pg C yr-1(或者15.0%)和5.50Pg C yr-1(或者7.6%)。这表明磷限制对中国碳汇的限制强度明显高于全球水平。土壤养分对碳吸收的影响差异、NPP和土壤分解速率之间的正反馈均会改变磷限制影响的强度。我们的研究强调了未来情景下磷限制对中国碳吸收有重要的影响。考虑磷限制的影响将有助于改善模式对中国碳汇评估与预估的能力。

    • Projection of terrestrial carbon (C) uptake in the 21st century remains highly uncertain (Piao et al., 2013; Friedlingstein et al., 2020; Peng et al., 2020). The lack of understanding on how nutrient availability affects plant productivity and microbial function is one of the main sources of this uncertainty (Peng et al., 2020). The sixth phase of the Coupled Model Intercomparison Project (CMIP6), whose models are capable of representing the terrestrial C biogeochemistry in their simulations of 21st century climate, projects that although heterotrophic respiration is heightened under climate warming, there would be an increase in net terrestrial ecosystem C accumulation if carbon dioxide (CO2) fertilization stimulates net primary production (NPP) without considering nutrient regulation, as predicted by current models (Arora et al., 2020; Peng et al., 2021). Recent studies have clearly shown that nitrogen (N) dynamics imposes a significant limitation on CO2 fertilization (Norby et al., 2010; Maier et al., 2022). In addition, phosphorus (P) limitation is prevalent throughout the terrestrial biosphere and is expected to increase in the future (Wieder et al., 2015; Schillereff et al., 2021). Currently, less than one-fifth of the participating CMIP6 models include N dynamics (Arora et al., 2020). In addition, most models involved in CMIP6, in the Shared Socioeconomic Pathway (SSP) experiments, do not include a full P cycle (Andrews et al., 2020; Arora et al., 2020; Peng et al., 2021). However, the magnitude and pattern of changes in terrestrial C accumulation suggest increased dependence on nutrient availability in the future (Wieder et al., 2015).

      Nutrient limitation on C accumulation has been assessed based on in situ observational methods (Du et al., 2020) or theoretical frameworks (Wang et al., 2010), where the latter assumes that soil mineralization liberates C and N in a specific ratio, and 100% of new nutrient inputs are plant-available (Wieder et al., 2015). Global N and P limitation was estimated using the ratio of site-averaged leaf N and P resorption efficiencies across 106 and 53 sites, respectively (Du et al., 2020). However, these sites were not uniform across the world; they were mainly concentrated in eastern Asia, eastern North America, Europe, and South America, and there were few sites in Africa and the northern Eurasian continent. This may lead to difficulties in determining any uniform and continuous nutrient limitation on C storage. In addition, in a CMIP5-based study, a global state-of-the-art coupled carbon–climate model was used to evaluate how simulated CO2 fertilization effects could be constrained by nutrient availability (Wieder et al., 2015). However, nutrient limitations were likely overestimated because of the lack of inclusion of several feedback processes (Brovkin and Goll, 2015). For example, the feedbacks of changes in the mineralization of nutrients from soil organic matter due to warming, increasing CO2 concentrations, and increased N deposition, were absent. To address this problem, a terrestrial model is needed that includes these feedbacks when using N and P dynamics.

      Spatially, different types of representative biochemical cycles may have different effects on patterns of C sequestration in terrestrial biomes when using Earth system models (ESMs) (Peng et al., 2021). Currently, the ability of the terrestrial ecosystem to absorb atmospheric CO2 may be overestimated by the C-cycle predictions of CMIP5 models (Wieder et al., 2015; Seneviratne and Hauser, 2020; Zhang et al., 2021). In these processes, nutrient limitations are critical for predicting climate–C feedbacks. By overlaying projections with global maps of major terrestrial biomes (Olson et al., 2001) (Olson et al., 2001), N limitation was shown to occur relatively more frequently in boreal forests, tundra, and temperate coniferous forests (Sponseller et al., 2016). Conversely, P is more limited in tropical and subtropical forests than other plant functional types (Mori et al., 2010). In general, the potential NPP might be ultimately determined by P-limitation, especially in tropical forests (Wang et al., 2020). The results of previous studies suggest that nutrient availability may be insufficient to meet the projected nutrient requirements for plant productivity in the future, and thus the effect of increasing CO2 concentrations on plant growth could be restricted, especially at regional scales.

      Terrestrial P limitation occurs when biological demand for nutrients such as P exceeds their soil-based supply, which ultimately depends on nutrient availability (Zhang et al., 2014; Du et al., 2020). For now, these limits remain highly uncertain (Filippelli, 2008; Wieder et al., 2015; Schillereff et al., 2021). Taking into account the P constraints, the estimates of global NPP in 2100 would be reduced by ~6% (Zhang et al., 2014). Under future conditions, P limitation is expected to broadly reduce terrestrial C uptake, leading to further losses of terrestrial C (Alewell et al., 2020; Schillereff et al., 2021). However, the strength of this P limitation is thought to be exaggerated because of a lack of inclusion of dynamic processes that respond positively to warming, such as the soil N mineralization rate and P mineralization rate, in projection models (Finzi et al., 2006; Vitousek et al., 2010). This also indicates that the strength of the influence of nutrient availability on plant photosynthesis and C accumulation remains highly uncertain, especially at the regional scale.

      China is a hotspot of nutrient limitation (Wieder et al., 2015); however, the extent to which P availability may limit the fertilization effect of CO2 and strength of the terrestrial C sink remains unclear (Cao et al., 2010; Cox et al., 2013; Fernández-Martínez et al., 2019; Fleischer et al., 2019; Cortés et al., 2021). In particular, the climatic environments have undergone drastic changes in this region, such as rapid increases in CO2 concentration and atmospheric N deposition (Fernández-Martínez et al., 2019; Peng et al., 2020, 2022). It is possible to change vegetation nutrient allocation strategies or C and nutrient allocation patterns in the leaf, stem, and root, but how these changes affect local C uptake remains poorly understood.

      The Community Atmosphere Biosphere Land Exchange model (CABLE) takes into account C, N, and P dynamics. Changes in global C uptake can be simulated by combining dynamic N and P cycles (Buendía et al., 2014; Goll et al., 2017). Previous studies have estimated the impacts of P limitation on historical and future C uptake globally (Wang et al., 2010; Zhang et al., 2013, 2014). Simulations from CABLE show that the magnitude of P limitation at the regional scale differs substantially from the global level (Wang et al., 2010; Zhang et al., 2014). However, there is no clear understanding of the potential control of P dynamics on C uptake in China and the possible reasons for these responses in the critical periods under China’s C-neutral strategy.

      In this study, we implemented dominant biochemical cycles including C–N and C–N–P elements into CABLE to examine the sensitivity of China’s C uptake and assess how the availability of P could constrain C uptake in response to increasing CO2 concentrations, climate change, and atmospheric N deposition. Changes in C fluxes and C accumulation were investigated with reference to C–N and C–N–P cycles. The simulations including C–N or C–N–P interactions were based on the theory developed in studies by Wang et al. (2007). We address three principal questions in this paper, as follows: (1) how the estimated C uptake using the two different biochemical cycles differs under future climate conditions for China; (2) how C uptake responds differently to changes in climate, atmospheric CO2 and N deposition when using the two biochemical cycle types; and (3) what the implications of P limitation are when using the two different biochemical cycles on terrestrial C uptake from the 2020s to 2090s in China, as compared with the global level. These findings will be useful towards improving projections of China’s C uptake under future C-neutral pathways.

    2.   Methods and simulations
    • Net ecosystem production (NEP) was used to represent C uptake and was calculated as follows:

      where NPP is the net primary production and HR is the heterotrophic respiration, both in Pg C yr−1, at the regional and global scales.

      The C accumulation after 1901 was calculated as:

      where Ct is the sum of NEP in each year i (i = 1, 2…t) and the unit of NEP is Pg C yr−1.

      In this study, we estimated the P limitation using two different biogeochemical cycles: the C–N cycle and C–N–P cycle. In the C–N–P experiment, we assumed that increases in C uptake and storage were limited by P. As we investigated how a new biochemical cycle changed the C uptake and C storage, the changes of each variable (Δx) due to the inclusion of P were calculated as the difference between the C–N–P and C–N experiments:

      where $ {x}_{t}^{\mathrm{C}\mathrm{N}} $ represents the change in each variable when using the C–N cycle (i.e., without the P cycle) t times relative to the baseline value in 1901, and $ {x}_{t}^{\mathrm{C}\mathrm{N}\mathrm{P}} $ is the same but when using the C–N–P cycle (i.e., considering the effect of P), relative to the initial values (units: Pg C yr−1).

      The magnitude of P limitation was calculated as:

      where $ {\mathrm{f}}_{t}^{\mathrm{P}} $ represents the magnitude of P limitation at the tth time and the unit is %.

      The above formulae are based on different biogeochemical cycles. We assumed that the N:P ratios were constant for all pools under the C–N cycle but that the C:N:P ratios differed among different pools under the C–N–P cycle. When higher and negative scores ($ {\mathrm{f}}_{t}^{\mathrm{P}} $) were obtained, it implied that there were greater P restrictions on C uptake. Conversely, smaller or positive scores were characterized as low or no nutritional restriction.

    • In this study, we used version 2.0 of CABLE, including a global biogeochemical model (CASA-CNP) (Wang et al., 2010). The biogeochemical model predicts both canopy leaf area index and maximal leaf carboxylation rate (vcmax), which are not specified in advance. The effects of nutrient limitation on the terrestrial C cycle under different representative concentration pathways have previously been evaluated using this version of CABLE (Zhang et al., 2013, 2014; Peng et al., 2020).

      We employed each of the two biochemical cycles in CABLE (version 2.0), including interactive C and N cycles (C–N cycle), reported previously by Peng et al. (2020), and fully cooperative C, N, and P cycles (C–N–P cycle), for simulating C accumulation in China. The model includes three plant, three litter, and three soil pools for the C, N, and P cycles. The method by which CABLE calculates nutrient limitations has been reported previously (Zhang et al., 2013). There are five submodels in this model: radiation, canopy micrometeorology, surface flux, soil and snow, and biogeochemical cycles (Zhang et al., 2013). Good performance has been shown in estimating vegetation productivity and sizes of C pools, as compared with observations from eddy flux measurements or other process-based land surface models (Wang et al., 2007, 2012; Zhang et al., 2016; Peng et al., 2020).

      There were eight types of meteorological inputs from 1901 to 2100: temperature, specific humidity, air pressure, downward solar radiation, downward longwave radiation, rainfall, snowfall, and wind speed. The meteorological variables from 1901 to 2005 were generated from the meteorological variables of GCP-TRENDY data. Using the method as reported by Qian et al. (2006), the GCP-TRENDY meteorological datasets were interpolated from six-hourly to hourly at a 1.9° (latitude) by 2.5° (longitude) spatial resolution. From 2006 to 2100, using the same spatial resolution, hourly meteorological variables were generated from the daily corresponding variables using CESM version 1.0 under the most severe Representative Concentration Pathway (RCP) scenario (RCP8.5; Riahi et al., 2011).

      For each simulation, we spun up the model by recycling the meteorological forcing for 1901 to 1910 until all C and N pools reached equilibrium. If the difference in any pool size between two successive cycles was <0.01%, it was judged to be in equilibrium for the model. These equilibrium values of all state variables were used as the initial conditions to perform the simulations for the period 1901–2100. In addition, for all simulations, the vegetation cover for the 1990s was used based on the land cover classification of the International Geosphere Biosphere Program (IGBP) data at a 0.5° by 0.5° spatial resolution (Loveland et al., 2000). Subsequently, it was interpolated into CABLE plant functional types by Kowalczyk et al. (2016). Thus, in this study, we were unable to account for the impacts due to changes in land use (Peng et al., 2020).

      To assess the impacts of climate change, atmospheric CO2, and N deposition on modeling terrestrial C uptake in China, we performed four different simulations using CABLE, based on each of the two different biochemical cycles (Table 1). The simulations were as follows: simulations CN1 and CNP1 included atmospheric CO2, climate, and N deposition, which varied over time; simulations CN2 and CNP2 fixed atmospheric CO2 at the 1901 level of about 295.4 ppm but allowed climate and N deposition to vary over time; simulations CN3 and CNP3 fixed the climate at the 1901 level but allowed atmospheric CO2 and N deposition to vary over time; and simulations CN4 and CNP4 used the 1901 N deposition of 20 Tg N yr−1 (Lamarque et al., 2010, 2013) but allowed atmospheric CO2 and climate to vary over time for all model years. The experiments for each cycle (C–N and C–N–P) are named CN and CNP, respectively. The terrestrial C uptake differences between the CNP and CN simulations were used to examine the effect of P limitation.

      SimulationCO2ClimateN deposition
      CN1/CNP1Time-varyingTime-varyingTime-varying
      CN2/CNP2Fixed at 1901Time-varyingTime-varying
      CN3/CNP3Time-varyingFixed at 1901Time-varying
      CN4/CNP4Time-varyingTime-varyingFixed at 1901

      Table 1.  CABLE simulations used in this study under the C–N–P cycle and C–N cycle.

      Under historical and future conditions from 1901 to 2100, relative to initial conditions in 1901, the differences in NPP were estimated for simulations CN1 and CNP1. We also compared our results with results from CMIP6, globally and for China. The CMIP6 models used were ACCESS-ESM, BCC-CSM2-MR, CanESM5, CLASS-CTEM, CESM2, CNRM-ESM2-1, IPSL-CM6A-LR, and UKESM1-0- LL. Further details are provided in Table 2.

      Model nameSpatial resolutionLand componentFull N cycleFull P cycleFireReference
      ACCESS-ESM1.51.875° × 1.25°CABLEYesYesNoZiehn et al. (2020)
      BCC-CSM2-MR1.125° × 1.125°AVIM2NoNoNoWu et al. (2019)
      CanESM52.81° × 2.81°CLASS-CTEMNoNoNoArora and Scinocca (2016)
      CESM20.9° × 1.25°CLM5YesNoYesDanabasoglu et al. (2020)
      CNRM-ESM2-11.4° × 1.4°ISBA-CTRIPNoNoYesSéférian et al. (2016)
      IPSL-CM6A-LR2.5° × 1.3°ORCHIDEENoNoNoHourdin et al. (2020)
      UKESM1-0- LL1.875° × 1.25°JULES-ES1.0YesNoNoSellar et al. (2019)

      Table 2.  Details of the models from CMIP6 used in this study (Peng et al., 2021).

      New emission scenarios in CMIP6 driven by different socioeconomic models, i.e., the SSPs, have replaced the RCPs used previously in CMIP5. Among the scenarios, SSP5-8.5 and RCP8.5 are the most severe in terms of emissions. We chose CMIP6 under SSP5-8.5 instead of CMIP5 under RCP8.5, and this choice did not influence our conclusions. The reason was because of the focus in this study on the impacts of a new biogeochemical cycle on C uptake and C storage in China, which is an aspect better represented in CMIP6 models. Correspondingly, we used CMIP6 outputs to check whether our results fell within a reasonable range.

    3.   Results
    • By the end of the century, CMIP5 models simulated global NPP changes ranging from 10.9 Pg C yr−1 to 59.9 Pg C yr−1, relative to pre-industrial levels (Wieder et al., 2015), while CMIP6 simulated global NPP changes ranging from 10.4 Pg C yr−1 to 75.8 Pg C yr−1. In this study, four out of seven models from CMIP6 did not include the N cycle, and six out of seven did not include the P cycle. At the same time, when the N cycle was considered, CABLE simulated a greater NPP of 30.1 Pg C yr−1, compared to the pre-industrial level; and when the P cycle was simulated, the increase in NPP relative to pre-industrial levels was estimated to be 33.7 Pg C yr−1. It should be noted that the NPP simulated by CABLE consistently fell into the ranges of CMIP5 and CMIP6.

      There is large uncertainty in the differences in NPP changes predicted by CMIP6 after 1901. Changes in NPP from CMIP6 ranged from 9.6 Pg C yr−1 to 31.9 Pg C yr−1 by the 2030s and from 12.0 Pg C yr−1 to 53.6 Pg C yr−1 in the period 2061–70 globally. The differences in NPP simulated by CABLE by the two different biogeochemical cycles were higher than the CMIP6 ensemble mean before 2065 globally. For China, excellent agreement between the two different NPP estimates was found before 2068 (Fig. 1b).

      Figure 1.  Changes in NPP globally and for China from CABLE during 1901–2100. Difference in NPP for the globe (a) and China (b) from CMIP6 (black), with CABLE data for C–N cycles (blue) and C–N–P cycles (orange). CMIP6 model values covered historical conditions during 1901–2014, and SSP5-8.5 during 2015–2100. Gray shading indicates the range between the maximum and minimum CMIP6 values.

      After 2068, CABLE predicted a lower NPP than the CMIP6 ensemble mean. Three out of the seven ESMs from CMIP6 used in this study do not include the N cycle, while the current version of CABLE contains both N and P cycles. Although lower NPP was estimated by CABLE, especially after 2068, as compared with the CMIP6 ensemble mean, it still fell into the range between the minimum and maximum of CMIP6. Notably, the latter’s estimates of China’s NPP also remained uncertain, ranging from 0.8 Pg C yr−1 to 2.2 Pg C yr−1 by the 2030s, from 1.2 Pg C yr−1 to 3.7 Pg C yr−1 in the period 2061–70, and from 1.3 Pg C yr−1 to 5.7 Pg C yr−1 in the last decade of this century. It is important to note that the differences between the experiments of CABLE and CMIP6 are not equivalent to the effects of P limitation; they also include the effects of many other factors, such as model structure, parameterization, and forcing datasets.

    • The business-as-usual C emissions (RCP8.5) scenario is estimated to bring an increase in global average surface temperature of about 5.7°C (Hurrell et al., 2013), a rise in atmospheric CO2 concentration of about 634 ppmv (see Fig. 2b), and a growth of global N deposition of about 40 Tg N yr−1 [Fig. 2c in Peng et al. (2020)].

      Figure 2.  Changes in (a–c) China and (d–f) the global terrestrial ecosystem for net primary production (NPP, units: Pg C yr−1), heterotrophic respiration (HR, units: Pg C yr−1), and net ecosystem production (NEP, units: Pg C yr−1) from the 2020s to 2090s relative to the baseline values at 1901 as estimated using the C–N–P cycle or the C–N cycle. The horizontal lines represent the mean ± σ.

      The estimated differences in China’s NPP by the 2030s using the C–N cycle and C–N–P cycle were 1.6 Pg C yr−1 and 1.4 Pg C yr−1, respectively (Fig. 2). The former was close to the ensemble mean of 1.5 ± 0.5 Pg C yr−1 (mean ± 1σ) simulated by CMIP6 in China (Fig. 1b). Globally, the annual difference in NPP from 2030 to 2040 was 18.1 ± 8.5 Pg C yr−1 from CMIP6, as compared to 25.6 Pg C yr−1 and 24.0 Pg C yr−1 for the C–N cycle and C–N–P cycle, respectively. By the period 2061–70, NPP in China increased by 2.5 ± 0.9 Pg C yr−1 using CMIP6 and by 2.3 Pg C yr−1 using CABLE under the C–N cycle and 2.0 Pg C yr−1 under the C–N–P cycle. Globally, during the same period, the increase in NPP was estimated to be 29.6 ± 16.3 Pg C yr−1 from CMIP6, and 32.1 Pg C yr−1 under the C–N cycle and 29.4 Pg C yr−1 under the C–N–P cycle.

      China also showed an increase in HR after 1901 for both the C–N and C–N–P cycles (Fig. 2). During 2031 to 2040, HR increased by 1.2 Pg C yr−1 for HR under the C–N cycle, relative to 1901. An increase of 1.0 Pg C yr−1 in China was shown under the C–N–P cycle. For the whole global terrestrial ecosystem, HR increased during the same period by 18.0 Pg C yr−1 and 17.2 Pg C yr−1 under the C–N cycle and C–N–P cycle, respectively. The contributions of P limitation to HR were greater for China (16.7%) than for the global terrestrial ecosystem (4.4%) (Fig. 2b). The rate of increase in HR during 2061–70 was estimated to be 2.0 Pg C yr−1 and 1.7 Pg C yr−1 for China under the C–N and C–N–P cycles, respectively. For the global terrestrial ecosystem, it was estimated to be 26.6 Pg C yr−1 and 25.0 Pg C yr−1 under the C–N and C–N–P cycles, respectively. Overall, P limitation contributed −6.8% of the changes in HR globally or up to −14.2% for China by the period 2061–70, relative to the level under the C–N cycle.

      NEP varied considerably between the two biochemical cycles at both regional and global scales (Fig. 2c). From the 2020s to 2090s, CABLE predicted an increase in NEP from 0.32 Pg C yr−1 in the 2020s to a peak at 0.39 Pg C yr−1 by 2050 for China using the C–N cycle. NEP under the C–N cycle was higher than that under the C–N–P cycle by the 2030s and 2060s. Relative to the 1901 level, CABLE predicted an increase in NEP from 0.32 Pg C yr−1 and 0.26 Pg C yr−1 by the 2020s to a peak at 0.40 Pg C yr−1 and 0.32 Pg C yr−1 by the 2050s for both the C–N and C–N–P cycles.

      In contrast, after the 2050s, the simulated NEP increased from 0.32 Pg C yr−1 and 0.26 Pg C yr−1 by the 2060s to 0.21 Pg C yr−1 and 0.15 Pg C yr−1 by the 2090s under the C–N and C–N–P cycles, respectively. Globally, the change in NEP by the 2030s was 7.63 Pg C yr−1 and 6.86 Pg C yr−1 under the C–N cycle and C–N–P cycle, respectively, and by 5.50 Pg C yr−1 and 4.38 Pg C yr−1, respectively, by the 2060s. Under future conditions, the additional reductions in NEP in China and the global terrestrial ecosystem due to P limitations by the 2030s were estimated to be −15.1% and −7.2%, respectively, and −15.0% and −7.6% by the 2060s, relative to the changes in NEP under the C–N cycle after 1901.

    • The responses of NPP to changes in climate, atmospheric CO2, and N deposition were estimated under the two different biogeochemical cycles (Fig. 3). The C accumulated for China was predicted to increase relative to 1901 levels from the 2020s to 2090s as atmospheric CO2 concentrations increase (Figs. 3a and d). NPP variations were greater under the C–N cycle than under the C–N–P cycle by about 0.13 Pg C by the 2030s and 0.17 Pg C by the 2060s. Globally, additional increases of NPP under the C–N–P cycle were also less than those under the C–N cycle after 1901.

      Figure 3.  Variations of NPP from the 2020s to the 2090s as simulated by CABLE in response to varying (a, d) CO2, (b, e) climate, and (c, f) N deposition (Ndep) relative to the 1901 level for (a–c) the global terrestrial ecosystem and (d–f) China under the C–N cycle and the C–N–P cycle. The horizontal lines represent the mean ± σ.

      In contrast, the responses of changes in NPP to climate change simulated by the two different types of biogeochemical cycles were different from the atmospheric CO2 concentrations in both direction and magnitude (Figs. 3b and 3e). Decreases in NPP were predicted for both types of biochemical cycles in China and globally. For China, NPP was estimated to be reduced by about 0.56 Pg C and 0.61 Pg C owing to climate change by the 2030s under the C–N and C–N–P cycles, respectively, and by up to 0.66 Pg C under the C–N cycle and 0.76 Pg C under the C–N–P cycle by the 2060s. At the global scale, NPP after 1901 was also projected to decrease under the two cycles in the future. By the 2060s, climate change resulted in variations of NPP under the C–N cycle versus C–N–P cycle by −16.4% under the C–N cycle and −19.2% under the C–N–P cycle for China, with global values of −22.9% and −24.7%, respectively.

      Climate change and atmospheric CO2 had the greatest impact on NPP in different biochemical cycles (Figs. 3c and 3f), and the absolute effect of N deposition was relatively small. However, P limitation resulted in smaller increases in NPP in response to N deposition, as compared with that under the C–N cycle. After 1901, the change in NPP for China in response to atmospheric N deposition increased by 0.21 Pg C under the C–N cycle and 0.18 Pg C under the C–N–P cycle by the 2030s, and by 0.27 Pg C under the C–N cycle and 0.26 Pg C under the C–N–P cycle by the 2060s. Meanwhile, the simulated global NPP in response to N deposition increased from 1.55 Pg C in the 2030s to 1.66 Pg C in the 2060s under the C–N cycle, and by 1.39 Pg C in the 2030s to 1.80 Pg C in the 2060s under the C–N–P cycle.

    • As shown in Fig. 4, the simulated contributions of changes in C plant pools to changes in the sum of plant, soil, and litter pools increased by 45.0% under the C–N cycle and 47.3% under the C–N–P cycle in China during the 2030s (Fig. 4), and by 47.8% and 50.4% during the 2060s, respectively. Consequently, contributions of C plant pools to total C pools under the C–N–P cycle were estimated with much smaller increases than those under the C–N cycle (i.e., <0.7%) at the global scale by the 2030s. Likewise, increases of 56.9% and 57.5% in the global contributions of C plant pools by the 2060s were predicted under the C–N and C–N–P cycles, respectively. China is mainly located in the middle and high latitudes of the Northern Hemisphere, which are regions limited by P and N. Plant growth in China can thus be considered more restricted compared with the global level, and therefore the C accumulated in plant pools could be faced with limited nutrient availability. Therefore, the contribution of plant pools in China was smaller than that of the global level under both the C–N and C–N–P cycles.

      Figure 4.  Contributions of (a, d) plant pools, (b, e) soil pools, and (c, f) litter pools to C accumulated for (a–c) the global terrestrial ecosystem and (d–f) China under the C–N and C–N–P cycles. The horizontal lines represent the mean ± σ.

      In contrast, the contribution of soil C pools at the global scale was predicted to be smaller than that for China. Globally, the contributions of C soil pools from 2030 to 2040 were 32.2% and 31.9% under the C–N and C–N–P cycles, respectively, and about 31.1% and 30.7% by the 2060s. In contrast, the contributions of C soil pools were predicted to be relatively smaller under the C–N–P cycle than those under the C–N cycle, by about −1.3% in the 2030s and about −1.7% in the 2060s. For China and globally, the contributions of C litter pools were less than those of C plant or soil pools. Both China and the global terrestrial ecosystem showed simulated decreases in the contributions of soil and litter pools as a result of the increased contributions in plant pools under the C–N–P cycle, as compared with those under the C–N cycle.

    4.   Discussion
    • In this study, we asked two key questions: How important is the role of P limitation in determining the pattern and magnitude of C uptake in China? And how might it respond to global change in the 21st century?

      Globally, the estimated P limitation simulated by CABLE agreed with the recent estimate by Wieder et al. (2015). In addition, we found that P limitation contributed more to C uptake for China than for the global terrestrial ecosystem. This indicates that there is variation in the effect of P limitation between the global and the regional scale.

      Considering P limitation, increases in C accumulated for China would be decreased by 10.8% in the 2030s and by 11.6% during the 2060s, as compared with the case without P nutrient control (Fig. 5). In comparison, P limitation at the global scale contributed less to reducing accumulated C, by 3.6% during the 2030s and by 4.6% during the 2060s. Therefore, P limitation contributed more to reducing C accumulation in China than it did across the global terrestrial ecosystem. The fractions of plant pools in China using the C–N–P cycle were considerably greater than those using the C–N cycle, which would enhance depletion of soil organic matter pools and thereby maintain a larger level of plant growth. Moreover, P limitation made a stronger negative contribution to NPP in China than it did across the global terrestrial ecosystem (Fig. 5b). As a result, we suggest that there is considerable C uptake reduction due to P limitation in China.

      Figure 5.  Contributions of P limitation to cumulative changes in (a) NEP, (b) NPP, and (c) HR, globally (blue bars) and for China (orange bars), from the 2020s to the 2090s, relative to that without P constraints. As we investigated how P limitations contribute to cumulative changes in each variable’s flux ($ {x}_{t} $), we calculated P nutrient contribution of each variable’s flux (ΔCP): ΔCP $ =\left(\sum _{t=1}^{n}{x}_{t}^{\mathrm{C}\mathrm{N}\mathrm{P}}-\sum _{t=1}^{n}{x}_{t}^{\mathrm{C}\mathrm{N}}\right)/\sum _{t=1}^{n}{x}_{t}^{\mathrm{C}\mathrm{N}} $.

      Our study was based on the assumption that P can limit vegetation growth and thereby reduce C sequestration in the C–N–P cycle because of the widespread phenomenon of insufficient P input. It has been shown that accelerated cycling of soil nutrient pools in the future can induce an increase in NPP and C uptake, alleviating the negative feedback of P limitation (Zhang et al., 2014; Wieder et al., 2015). On the one hand, warming leads to accelerated N or P mineralization in soil pools, increasing NPP or C accumulation (Wieder et al., 2015). On the other hand, subsurface C allocations correspondingly increase in response to elevated CO2 concentrations to achieve enhanced C uptake (Cleveland et al., 2013).

      Conversely, our simulation results showed that the contribution of P limitation in China increased from the 2020s to the 2090s. The amount of reduction in C uptake due to P limitation was estimated to be more than 10% of C accumulated from the 2030s to the 2090s for China. Using the C–N–P cycle, CABLE estimated double the decrease of cumulative NEP in China due to P limitation relative to the global level. The difference between China and the global scale largely resulted from the cumulative NPP (Fig. 5). Negative contributions of P limitation to cumulative NPP were estimated to be −11.8% by the 2030s and −12.2% by the 2060s in China, as compared with −4.4% by the 2030s and −5.4% by the 2060s globally. As a result, P limitation resulted in a reduction of China’s contribution to global NEP, by −1.1% in the 2030s and by −2.1% in the 2060s (Fig. 6).

      Figure 6.  China’s contributions to the global-scale changes in (a) NEP, (b) NPP, and (c) HR under the C–N cycle (blue bars) or C–N–P cycle (orange bars), from the 2020s to the 2090s. The horizontal lines represent the mean ± σ. As we investigated how China’s changes in variables ($ {x}_{t}^{\mathrm{C}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{a}} $) contributed to the changes globally ($ {x}_{t}^{\mathrm{G}\mathrm{l}\mathrm{o}\mathrm{b}\mathrm{e}} $), we calculated China’s contribution of variables (Δ$ {\mathrm{C}\mathrm{C}}_{t} $) to the global scale in both experiments with the C–N–P and C–N cycles: Δ$ {\mathrm{C}\mathrm{C}}_{t} $$ ={x}_{t}^{\mathrm{C}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{a}}/{x}_{t}^{\mathrm{G}\mathrm{l}\mathrm{o}\mathrm{b}\mathrm{e}} $.

      In summary, higher magnitudes of P limitation were generated in China compared with those at the global scale. This difference in China and globally can be partly explained by an emergent positive feedback of soil C decomposition rates to NPP. Specifically, the soil decomposition rates in China using CABLE were smaller than the global level (Fig. 7a), which in turn was not conducive to vegetation obtaining more nutrients and increasing the NPP. This feedback loop translated into a quasi-linear weak rise in NPP (Fig. 7b). At the global scale, the increase in NPP relative to the soil decomposition rate showed a rapid, near exponential increase globally and was considerably larger than that in China. However, higher atmospheric N deposition rates occur over China, thereby reducing the adverse effects of N nutrients, which is less pronounced than that of CO2 or climate change. However, as a result of the positive feedback between NPP and soil decomposition rates, the slope of NPP to soil decomposition rates for China was significantly smaller than that at the global level (Fig. 7b).

      Figure 7.  (a) Annual decomposition rates (Ksoil; units: 0.01 d–1) as a result of P limitation from 1901 to 2100 and (b) annual net primary production (NPP) against Ksoil as estimated by CABLE for China (orange) and global terrestrial ecosystems (blue) from the C–N–P simulations.

    • Carbon neutrality is the generation of a state of equilibrium between C emissions and sequestration. Carbon emissions mainly arise from fossil fuel combustion in industry, transportation, and construction, and from land-use changes. The magnitude of C sequestration primarily depends on terrestrial and marine ecosystems, accounting for about ~52% of the total C emissions during 2010–19 (Friedlingstein et al., 2020). The state of C neutrality can be approximated by net anthropogenic CO2 emissions being zero. In particular, C sequestration is one of the two most critical processes necessary to achieve C neutrality, and combines maximizing terrestrial ecosystem C sinks and uptake of industrial C sequestration technologies, such as Carbon Capture, Utilization and Storage. Increasing terrestrial C sinks can be considered a natural climate solution, and is one of the most advanced ways to mitigate climate change.

      Currently, across China, there is no clear understanding of C uptake reduction as a result of P restriction under future conditions. According to the result of the current study, from the 2020s to 2090s, P limitation could result in decreases in C uptake varying from 14.7% to 15.5% for China and from 7.2% to 8.2% globally. Hence, it could indicate that offsetting C emissions such as fossil fuels in China by the 2060s is ~7.4% more difficult than the global average level when P limitation is considered.

    • Fertilization is well known to alter plant responses to N or P in nature, and this temporally and spatially varying availability of N or P nutrients could have a major impact on estimated C uptake. However, the current version of CABLE does not take these processes into account. For crops, a fixed ratio of N and P fertilizers is evenly applied in CABLE. In our simulations, the limiting impact of N or P elements on C accumulation may have been overestimated if different fertilization ratios were included, as changes in fertilizer use were not considered in this study. However, China has the highest rate of chemical fertilizer use worldwide. Future studies should examine the optimal rate of chemical fertilizer use to improve local C uptake.

      Finally, it is well known that the terrestrial C cycle uncertainty in ESMs is far greater than that of the climate system (Friedlingstein et al., 2006, 2013, 2020; Arora et al., 2020). Here, we used simulated results based on only a single model and did not include multimodel comparisons. At present, the uncertainty is mostly based on the results from multimodel simulations, giving the upper and lower threshold ranges of C sequestration. It is difficult to quantify the uncertainty in C accumulation in response to other biogeochemical cycles using a single model. Future research needs to be based on the same framework to further explore the pattern and magnitude of C sequestration in response to the N or P cycle, using different models in response to nutrient availability.

      Recent studies have revealed that model parameters, structures, and their interactions can make a significant contribution to some ecosystems (Shi et al., 2018), which was not considered in this study. Diverse model structures and parameters could be necessary to increase confidence in estimation of C uptake and C storage. Such different model structures and parameters might regulate the stress of the P constraints on C uptake and storage. This needs to be investigated in the future.

    5.   Conclusion
    • This study compared two different biochemical cycles for estimating future C uptake in China and globally. Together with increasing CO2 concentrations, China’s C uptake was clearly influenced by P limitation by the 2030s and 2060s, respectively. Moreover, P limitation also impacted variations in NPP and HR, which could control the C sink in China. This study attempted to quantify the consequences of P limitation on C cycles in China, compared with those globally, in the 2030s and 2060s, respectively. Our results showed that P limitation has the potential to significantly alter C uptake by the 2030s, and considerably by the 2060s. In particular, the C uptake as a result of P limitation was projected to reduce by 15.1% in the 2030s and by 15.0% in the 2060s over China, and by 7.2% in the 2030s and 7.6% in the 2060s globally. In China, we identified greater reductions in C uptake due to P limitation, as compared with the global levels, which could make it harder for China to achieve the goals of its C neutral strategy.

      Acknowledgements. We thank National Key Research and Development Program of China (Grant No. 2018YFA0606004), the National Natural Science Foundation of China (Grant Nos. 41975112, 42175142, 42175013, and 42141017) for supporting our study.

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