Abstract
Terrestrial ecosystems are an important part of Earth systems, and they are undergoing remarkable changes in response to global warming. This study investigates the response of the terrestrial vegetation distribution and carbon fluxes to global warming by using the new dynamic global vegetation model in the second version of the Chinese Academy of Sciences (CAS) Earth System Model (CAS-ESM2). We conducted two sets of simulations, a present-day simulation and a future simulation, which were forced by the present-day climate during 1981–2000 and the future climate during 2081–2100, respectively, as derived from RCP8.5 outputs in CMIP5. CO2 concentration is kept constant in all simulations to isolate CO2-fertilization effects. The results show an overall increase in vegetation coverage in response to global warming, which is the net result of the greening in the mid-high latitudes and the browning in the tropics. The results also show an enhancement in carbon fluxes in response to global warming, including gross primary productivity, net primary productivity, and autotrophic respiration. We found that the changes in vegetation coverage were significantly correlated with changes in surface air temperature, reflecting the dominant role of temperature, while the changes in carbon fluxes were caused by the combined effects of leaf area index, temperature, and precipitation. This study applies the CAS-ESM2 to investigate the response of terrestrial ecosystems to climate warming. Even though the interpretation of the results is limited by isolating CO2-fertilization effects, this application is still beneficial for adding to our understanding of vegetation processes and to further improve upon model parameterizations.
摘要
陆地生态系统是地球系统的重要组成部分,由于全球变暖,陆地生态系统正经历着显著的变化。本文利用中国科学院地球系统模式第二版(CAS-ESM2)中的新版全球植被动力学模型,研究了陆地植被分布和碳通量对未来全球变暖的响应变化。本文做了两组试验:当代试验和未来试验。它们分别对应1981–2000年的当代气候和CMIP5 RCP8.5模拟的2081–2100年的未来气候。同时,为排除CO2施肥效应的影响,所有模拟试验中CO2浓度都设为恒定值。模拟结果表明,全球变暖将导致植被覆盖度总体增加,这是中高纬度地区植被增加和热带地区植被减少的综合结果;另外,碳通量(包括总初级生产力、净初级生产力和自养呼吸)也将随全球变暖有所增加。研究发现,植被覆盖度的变化与地表气温的变化存在显著的相关性,这表明温度对未来植被分布起主导作用,而碳通量的变化则是叶面积指数、温度和降水共同作用的结果。本文应用CAS-ESM2研究陆地生态系统对未来气候变暖的响应,尽管剔除CO2施肥效应会对研究结果产生一定的限制,但本文研究仍有利于更好地理解植被过程,并进一步改进模型的参数化
Similar content being viewed by others
References
Alo, C. A., and G. L. Wang, 2008: Potential future changes of the terrestrial ecosystem based on climate projections by eight general circulation models. J. Geophys. Res., 113, G01004, https://doi.org/10.1029/2007JG000528.
Anav, A., and Coauthors, 2015: Spatiotemporal patterns of terrestrial gross primary production: A review. Rev. Geophys., 53(3), 785–818, https://doi.org/10.1002/2015RG000483.
Andreu-Hayles, L., R. D’Arrigo, K. J. Anchukaitis, P. S. A. Beck, D. Frank, and S. Goetz, 2011: Varying boreal forest response to arctic environmental change at the Firth River, Alaska. Environmental Research Letters, 6(4), 045503, https://doi.org/10.1088/1748-9326/6/4/045503.
Arora, V. K., and Coauthors, 2020: Carbon-concentration and carbon-climate feedbacks in CMIP6 models and their comparison to CMIP5 models. Biogeosciences, 17(16), 4173–4222, https://doi.org/10.5194/bg-17-4173-2020.
Bi, J., L. Xu, A. Samanta, Z. C. Zhu, and R. Myneni, 2013: Divergent Arctic-boreal vegetation changes between North America and Eurasia over the past 30 years. Remote Sensing, 5(5), 2093–2112, https://doi.org/10.3390/rs5052093.
Blok, D., M. M. P. D. Heijmans, G. Schaepman-Strub, A. V. Kononov, T. C. Maximov, and F. Berendse, 2010: Shrub expansion may reduce summer permafrost thaw in Siberian tundra. Global Change Biology, 16(4), 1296–1305, https://doi.org/10.1111/j.1365-2486.2009.02110.x.
Brovkin, V., T. Raddatz, C. H. Reick, M. Claussen, and V. Gayler, 2009: Global biogeophysical interactions between forest and climate. Geophys. Res. Lett., 36(7), L07405, https://doi.org/10.1029/2009GL037543.
Cao, X. Y., F. Tian, A. Dallmeyer, and U. Herzschuh, 2019: Northern hemisphere biome changes (>30°N) since 40 cal ka BP and their driving factors inferred from model-data comparisons. Quaternary Science Reviews, 220, 291–309, https://doi.org/10.1016/j.quascirev.2019.07.034.
Clark, D. A., S. C. Piper, C. D. Keeling, and D. B. Clark, 2003: Tropical rain forest tree growth and atmospheric carbon dynamics linked to interannual temperature variation during 1984–2000. Proceedings of the National Academy of Sciences of the United States of America, 100(10), 5852–5857, https://doi.org/10.1073/pnas.0935903100.
Corlett, R. T., 2011: Impacts of warming on tropical lowland rainforests. Trends in Ecology & Evolution, 26(11), 606–613, https://doi.org/10.1016/j.tree.2011.06.015.
Cramer, W., and Coauthors, 2001: Global response of terrestrial ecosystem structure and function to CO2 and climate change: Results from six dynamic global vegetation models. Global Change Biology, 7(4), 357–373, https://doi.org/10.1046/j.1365-2486.2001.00383.x.
De Kauwe, M. G., and Coauthors, 2014: Where does the carbon go? A model-data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites. New Phytologist, 203(3), 883–899, https://doi.org/10.1111/nph.12847.
Diffenbaugh, N. S., and C. B. Field, 2013: Changes in ecologically critical terrestrial climate conditions. Science, 341(6145), 486–492, https://doi.org/10.1126/science.1237123.
Doughty, C. E., and M. L. Goulden, 2008: Are tropical forests near a high temperature threshold. J. Geophys. Res., 113, G00B07, https://doi.org/10.1029/2007JG000632.
Eric Dusenge, M., D. Galvao Duarte, and D. A. Way, 2019: Plant carbon metabolism and climate change: Elevated CO2 and temperature impacts on photosynthesis, photorespiration and respiration. New Phytologist, 221, 32–49, https://doi.org/10.1111/nph.15283.
Falloon, P. D., R. Dankers, R. A. Betts, C. D. Jones, B. B. B. Booth, and F. H. Lambert, 2012: Role of vegetation change in future climate under the A1B scenario and a climate stabilisation scenario, using the HadCM3C Earth system model. Biogeosciences, 9(11), 4739–4756, https://doi.org/10.5194/bg-9-4739-2012.
Fan, Z. M., and B. Fan, 2019: Shifts of the mean centers of potential vegetation ecosystems under future climate change in Eurasia. Forests, 10(10), 873, https://doi.org/10.3390/f10100873.
Field, C. B., L. D. Mortsch, M. Brklacich, D. L. Forbes, P. Kovacs, J. A. Patz, S. W. Running, and M. J. Scott, 2007: North America. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M. L. Parry et al., Eds., Cambridge University Press.
Fraser, R. H., I. Olthof, M. Carrière, A. Deschamps, and D. Pouliot, 2011: Detecting long-term changes to vegetation in northern Canada using the Landsat satellite image archive. Environmental Research Letters, 6, 045502, https://doi.org/10.1088/1748-9326/6/4/045502.
Frost, G. V., and H. E. Epstein, 2014: Tall shrub and tree expansion in Siberian tundra ecotones since the 1960s. Global Change Biology, 20(4), 1264–1277, https://doi.org/10.1111/gcb.12406.
Gang, C. C., and Coauthors, 2017: Modeling the dynamics of distribution, extent, and NPP of global terrestrial ecosystems in response to future climate change. Global and Planetary Change, 148, 153–165, https://doi.org/10.1016/j.gloplacha.2016.12.007.
Gao, D. D., L. Dan, G. Z. Fan, J. Peng, X. J. Yang, F. Q. Yang, and Y. Y. Li, 2019: Spatial and temporal variations of net primary productivity at century scale in earth system models and its relationship with climate. Climatic and Environmental Research, 24(6), 663–677, https://doi.org/10.3878/j.issn.1006-9585.2018.18052.
Giorgi, F., and R. Francisco, 2000: Uncertainties in regional climate change prediction: A regional analysis of ensemble simulations with the HADCM2 coupled AOGCM. Climate Dyn., 16(2), 169–182, https://doi.org/10.1007/PL00013733.
Gurney, K. R., and Coauthors, 2004: Transcom 3 inversion inter-comparison: Model mean results for the estimation of seasonal carbon sources and sinks. Global Biogeochemical Cycles, 18, GB1010, https://doi.org/10.1029/2003GB002111.
Hawkins, L. R., and Coauthors, 2019: Parametric sensitivity of vegetation dynamics in the TRIFFID model and the associated uncertainty in projected climate change impacts on Western U. S. forests. Journal of Advances in Modeling Earth Systems, 11(8), 2787–2813, https://doi.org/10.1029/2018MS001577.
Horvath, P., H. Tang, R. Halvorsen, F. Stordal, L. M. Tallaksen, T. K. Berntsen, and A. Bryn, 2021: Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models. Biogeosciences, 18(1), 95–112, https://doi.org/10.5194/bg-18-95-2021.
Hu, L., W. J. Fan, W. P. Yuan, H. Z. Ren, and Y. K. Cui, 2021: Spatiotemporal variation of vegetation productivity and its feedback to climate change in northeast china over the last 30 years. Remote Sensing, 13(5), 951, https://doi.org/10.3390/rs13050951.
Huang, M. T., and Coauthors, 2019: Air temperature optima of vegetation productivity across global biomes. Nature Ecology & Evolution, 3(5), 772–779, https://doi.org/10.1038/s41559-019-0838-x.
Jung, M., and Coauthors, 2007: Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models. Global Biogeochemical Cycles, 21(4), https://doi.org/10.1029/2006GB002915.
Kang, S., and E. A. B. Eltahir, 2018: North China plain threatened by deadly heatwaves due to climate change and irrigation. Nature Communications, 9, 2894, https://doi.org/10.1038/s41467-018-05252-y.
Keenan, T. F., and W. J. Riley, 2018: Greening of the land surface in the world’s cold regions consistent with recent warming. Nature Climate Change, 8, 825–828, https://doi.org/10.1038/s41558-018-0258-y.
Knorr, W., and M. Heimann, 2001: Uncertainties in global terrestrial biosphere modeling: 1. A comprehensive sensitivity analysis with a new photosynthesis and energy balance scheme. Global Biogeochemical Cycles, 15(1), 207–225, https://doi.org/10.1029/1998GB001059.
Kreplin, H. N., C. S. S. Ferreira, G. Destouni, S. D. Keesstra, L. Salvati, and Z. Kalantari, 2021: Arctic wetland system dynamics under climate warming. Wiley Interdisciplinary Reviews, 8(4), e1526, https://doi.org/10.1002/wat2.1526.
Kumar, D., and S. Scheiter, 2019: Biome diversity in South Asia-How can we improve vegetation models to understand global change impact at regional level. Science of the Total Environment, 671, 1001–1016, https://doi.org/10.1016/j.scitotenv.2019.03.251.
Li, F., X. D. Zeng, and S. Levis, 2012: A process-based fire parameterization of intermediate complexity in a Dynamic Global Vegetation Model. Biogeosciences, 9(11), 2761–2780, https://doi.org/10.5194/bg-9-2761-2012.
Liu, L. B., S. S. Peng, A. Aghakouchak, Y. Y. Huang, Y. Li, D. H. Qin, A. L. Xie, and S. C. Li, 2018: Broad consistency between satellite and vegetation model estimates of net primary productivity across global and regional scales. J. Geophys. Res., 123(12), 3603–3616, https://doi.org/10.1029/2018JG004760.
Liu, W. G., G. L. Wang, M. Yu, H. S. Chen, Y. L. Jiang, M. J. Yang, and Y. Shi, 2020: Projecting the future vegetation-climate system over East Asia and its RCP-dependence. Climate Dyn., 55(9), 2725–2742, https://doi.org/10.1007/s00382-020-05411-2.
Liu, Y., Y. K. Xue, G. MacDonald, P. Cox, and Z. Q. Zhang, 2019: Global vegetation variability and its response to elevated CO2, global warming, and climate variability-a study using the offline SSiB4/TRIFFID model and satellite data. Earth System Dynamics, 10(1), 9–29, https://doi.org/10.5194/esd-10-9-2019.
Mackay, A., 2008: Climate change 2007: Impacts, adaptation and vulnerability. contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Journal of Environmental Quality, 37(6), 2407, https://doi.org/10.2134/jeq2008.0015br.
Madani, N., and Coauthors, 2020: Recent amplified global gross primary productivity due to temperature increase is offset by reduced productivity due to water constraints. AGU Advances, 1(4), e2020AV000180, https://doi.org/10.1029/2020AV000180.
Mahowald, N., F. Lo, Y. Zheng, L. Harrison, C. Funk, D. Lombardozzi, and C. Goodale, 2016: Projections of leaf area index in earth system models. Earth System Dynamics, 7(1), 211–229, https://doi.org/10.5194/esd-7-211-2016.
Mao, J. F., and Coauthors, 2016: Human-induced greening of the northern extratropical land surface. Nature Climate Change, 6(10), 959–963, https://doi.org/10.1038/nclimate3056.
McGuire, A. D., and Coauthors, 2001: Carbon balance of the terrestrial biosphere in the twentieth century: Analyses of CO2, climate and land use effects with four process-based ecosystem models. Global Biogeochemical Cycles, 15(1), 183–206, https://doi.org/10.1029/2000gb001298.
Mekonnen, Z. A., and Coauthors, 2021: Arctic tundra shrubification: A review of mechanisms and impacts on ecosystem carbon balance. Environmental Research Letters, 16(5), 053001, https://doi.org/10.1088/1748-9326/abf28b.
Myers-Smith, I. H., and Coauthors, 2011: Shrub expansion in tundra ecosystems: Dynamics, impacts and research priorities. Environmental Research Letters, 6(4), 045509, https://doi.org/10.1088/1748-9326/6/4/045509.
Myers-Smith, I. H., and Coauthors, 2020: Complexity revealed in the greening of the Arctic. Nature Climate Change, 10(2), 106–117, https://doi.org/10.1038/s41558-019-0688-1.
Nemani, R. R., C. D. Keeling, H. Hashimoto, W. M. Jolly, S. C. Piper, C. J. Tucker, R. B. Myneni, and S. W. Running, 2003: Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300(5625), 1560–1563, https://doi.org/10.1126/science.1082750.
Piao, S. L., P. Friedlingstein, P. Ciais, N. Viovy, and J. Demarty, 2007: Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Global Biogeochemical Cycles, 21(3), https://doi.org/10.1029/2006GB002888.
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(7), 2117–2132, https://doi.org/10.1111/gcb.12187.
Piao, S. L., and Coauthors, 2020: Characteristics, drivers and feedbacks of global greening. Nature Reviews Earth & Environment, 1, 14–27, https://doi.org/10.1038/s43017-019-0001-x.
Prentice, I. C., and Coauthors, 2007: Dynamic global vegetation modeling: Quantifying terrestrial ecosystem responses to large-scale environmental change. Terrestrial Ecosystems in a Changing World, J. G. Canadell et al., Eds., Springer, 175–192.
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(5), 953–975, https://doi.org/10.1175/JHM540.1.
Quillet, A., C. H. Peng, and M. Garneau, 2010: Toward dynamic global vegetation models for simulating vegetation-climate interactions and feedbacks: Recent developments, limitations, and future challenges. Environmental Reviews, 18, 333–353, https://doi.org/10.1139/A10-016.
Raddatz, T. J., and Coauthors, 2007: Will the tropical land biosphere dominate the climate-carbon cycle feedback during the twenty-first century. Climate Dyn., 29(6), 565–574, https://doi.org/10.1007/s00382-007-0247-8.
Schaphoff, S., C. P. O. Reyer, D. Schepaschenko, D. Gerten, and A. Shvidenko, 2016: Tamm review: Observed and projected climate change impacts on Russia’s forests and its carbon balance. Forest Ecology and Management, 361, 432–444, https://doi.org/10.1016/j.foreco.2015.11.043.
Scheiter, S., L. Langan, and S. L. Higgins, 2013: Next-generation dynamic global vegetation models: Learning from community ecology. New Phytologist, 198(3), 957–969, https://doi.org/10.1111/nph.12210.
Scheiter, S., and Coauthors, 2020: Climate change promotes transitions to tall evergreen vegetation in tropical Asia. Global Change Biology, 26(9), 5106–5124, https://doi.org/10.1111/gcb.15217.
Shafer, S. L., P. J. Bartlein, E. M. Gray, and R. T. Pelltier, 2015: Projected future vegetation changes for the Northwest United States and Southwest Canada at a fine spatial resolution using a dynamic global vegetation model. PLoS One, 10(10), e0138759, https://doi.org/10.1371/journal.pone.0138759.
Shiyatov, S. G., M. M. Terent’ev, and V. V. Fomin, 2005: Spatiotemporal dynamics of forest-tundra communities in the polar urals. Russian Journal of Ecology, 36(2), 69–75, https://doi.org/10.1007/s11184-005-0051-9.
Sitch, S., and Coauthors, 2008: Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five dynamic global vegetation models (DGVMs). Global Change Biology, 14(9), 2015–2039, https://doi.org/10.1111/j.1365-2486.2008.01626.x.
Smith, B., D. Wårlind, A. Arneth, T. Hickler, P. Leadley, J. Siltberg, and S. Zaehle, 2014: Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences, 11(7), 2027–2054, https://doi.org/10.5194/bg-11-2027-2014.
Song, X., X. D. Zeng, J. W. Zhu, and P. Shao, 2016: Development of an establishment scheme for a DGVM. Adv. Atmos. Sci., 33, 829–840, https://doi.org/10.1007/s00376-016-5284-y.
Speed, J. D. M., S. J. Woodin, H. Tømmervik, and R. Van Der Wal, 2010: Extrapolating herbivore-induced carbon loss across an arctic landscape. Polar Biology, 33(6), 789–797, https://doi.org/10.1007/s00300-009-0756-5.
Sturm, M., C. Racine, and K. Tape, 2001: Increasing shrub abundance in the Arctic. Nature, 411(6837), 546–547, https://doi.org/10.1038/35079180.
Sulman, B. N., E. Shevliakova, E. R. Brzostek, S. N. Kivlin, S. Malyshev, D. N. L. Menge, and X. Zhang, 2019: Diverse mycorrhizal associations enhance terrestrial C storage in a global model. Global Biogeochemical Cycles, 33(4), 501–523, https://doi.org/10.1029/2018GB005973.
Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93(4), 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1.
Tharammal, T., G. Bala, D. Narayanappa, and R. Nemani, 2019: Potential roles of CO2 fertilization, nitrogen deposition, climate change, and land use and land cover change on the global terrestrial carbon uptake in the twenty-first century. Climate Dyn., 52, 4393–4406, https://doi.org/10.1007/s00382-018-4388-8.
Tømmervik, H., and B. C. Forbes, 2020: Focus on recent, present and future Arctic and boreal productivity and biomass changes. Environmental Research Letters, 15(8), 080201, https://doi.org/10.1088/1748-9326/ab79e3.
Vickers, H., K. A. Høgda, S. Solbø, S. R. Karlsen, H. Tømmervik, R. Aanes, and B. B. Hansen, 2016: Changes in greening in the high arctic: Insights from a 30 year AVHRR max NDVI dataset for Svalbard. Environmental Research Letters, 11(10), 105004, https://doi.org/10.1088/1748-9326/11/10/105004.
Walker, M. D., and Coauthors, 2006: Plant community responses to experimental warming across the tundra biome. Proceedings of the National Academy of Sciences of the United States of America, 103(5), 1342–1346, https://doi.org/10.1073/pnas.0503198103.
Wibowo, A., M. M. Yusoff, T. A. Adura, A. Wibowo, Supriatna, and L. H. Zaini, 2020: Spatial model of air surface temperature using Landsat 8 TIRS. IOP Conference Series: Earth and Environmental Science, 500, 012009, https://doi.org/10.1088/1755-1315/500/1/012009.
Woodward, F. I., and B. G. Williams, 1987: Climate and plant distribution at global and local scales. Vegetatio, 69, 189–197, https://doi.org/10.1007/BF00038700.
Woodward, F. I., and M. R. Lomas, 2004: Vegetation dynamics-simulating responses to climatic change. Biological Reviews, 79(3), 643–670, https://doi.org/10.1017/S1464793103006419.
Wu, L., T. Kato, H. Sato, T. Hirano, and T. Yazaki, 2019: Sensitivity analysis of the typhoon disturbance effect on forest dynamics and carbon balance in the future in a cool-temperate forest in northern Japan by using SEIB-DGVM. Forest Ecology and Management, 451, 117529, https://doi.org/10.1016/j.foreco.2019.117529.
Xue, Y. K., F. De Sales, R. Vasic, C. R. Mechoso, A. Arakawa, and S. Prince, 2010: Global and seasonal assessment of interactions between climate and vegetation biophysical processes: A GCM study with different land-vegetation representations. J. Climate, 23(6), 1411–1433, https://doi.org/10.1175/2009JCLI3054.1.
Yao, R., L. C. Wang, X. Huang, X. X. Chen, and Z. J. Liu, 2019: Increased spatial heterogeneity in vegetation greenness due to vegetation greening in mainland China. Ecological Indicators, 99, 240–250, https://doi.org/10.1016/j.ecolind.2018.12.039.
Yin, Y. H., D. Y. Ma, and S. H. Wu, 2018: Climate change risk to forests in China associated with warming. Scientific Reports, 8, 493, https://doi.org/10.1038/s41598-017-18798-6.
Yu, J. J., P. Berry, B. P. Guillod, and T. Hickler, 2021: Climate change impacts on the future of forests in Great Britain. Frontiers in Environmental Science, 9, 640530, https://doi.org/10.3389/fenvs.2021.640530.
Yu, M., G. L. Wang, D. Parr, and K. F. Ahmed, 2014: Future changes of the terrestrial ecosystem based on a dynamic vegetation model driven with RCP8.5 climate projections from 19 GCMs. Climatic Change, 127(2), 257–271, https://doi.org/10.1007/s10584-014-1249-2.
Yu, M., G. L. Wang, and H. S. Chen, 2016: Quantifying the impacts of land surface schemes and dynamic vegetation on the model dependency of projected changes in surface energy and water budgets. Journal of Advances in Modeling Earth Systems, 8(1), 370–386, https://doi.org/10.1002/2015MS000492.
Zeng, X. D., 2010: Evaluating the dependence of vegetation on climate in an improved dynamic global vegetation model. Adv. Atmos. Sci., 27(5), 977–991, https://doi.org/10.1007/s00376-009-9186-0.
Zeng, X. D., X. B. Zeng, and M. Barlage, 2008: Growing temperate shrubs over arid and semiarid regions in the community land model-dynamic global vegetation model. Global Biogeochemical Cycles, 22, GB3003, https://doi.org/10.1029/2007GB003014.
Zeng, X. D., F. Li, and X. Song, 2014: Development of the IAP dynamic global vegetation model. Adv. Atmos. Sci., 31, 505–514, https://doi.org/10.1007/s00376-013-3155-3.
Zeng, Z. Z., and Coauthors, 2018: Global terrestrial stilling: Does Earth’s greening play a role. Environmental Research Letters, 13(12), 124013, https://doi.org/10.1088/1748-9326/aaea84.
Zhang, H., M. H. Zhang, and Q.-C. Zeng, 2013: Sensitivity of simulated climate to two atmospheric models: Interpretation of differences between dry models and moist models. Mon. Wea. Rev., 141(5), 1558–1576, https://doi.org/10.1175/MWR-D-11-00367.1.
Zhang, H., and Coauthors, 2020: Description and climate simulation performance of CAS-ESM Version 2. Journal of Advances in Modeling Earth Systems, 12(12), e2020MS002210, https://doi.org/10.1029/2020MS002210.
Zhang, K., and Coauthors, 2015: The fate of Amazonian ecosystems over the coming century arising from changes in climate, atmospheric CO2- and land use. Global Change Biology, 21(7), 2569–2587, https://doi.org/10.1111/gcb.12903.
Zhu, J. W., and X. D. Zeng, 2015: Comprehensive study on the influence of evapotranspiration and albedo on surface temperature related to changes in the leaf area index. Adv. Atmos. Sci., 32(7), 935–942, https://doi.org/10.1007/s00376-014-4045-z.
Zhu, J. W., and X. D. Zeng, 2017: Influences of the seasonal growth of vegetation on surface energy budgets over middle to high latitudes. International Journal of Climatology, 37(12), 4251–4260, https://doi.org/10.1002/joc.5068.
Zhu, J. W., M. H. Zhang, Y. Zhang, X. D. Zeng, and X. M. Xiao, 2018a: Response of tropical terrestrial gross primary production to the super El Niño event in 2015. J. Geophys. Res., 123(10), 3193–3203, https://doi.org/10.1029/2018JG004571.
Zhu, J. W., and Coauthors, 2018b: Evaluation of the new dynamic global vegetation model in CAS-ESM. Adv. Atmos. Sci., 35(6), 659–670, https://doi.org/10.1007/s00376-017-7154-7.
Zhu, Z. C., and Coauthors, 2016: Greening of the Earth and its drivers. Nature Climate Change, 6(8), 791–795, https://doi.org/10.1038/nclimate3004.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 41705070), the Major Program of the National Natural Science Foundation of China (Grant No. 41991282) and the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (EarthLab).
Author information
Authors and Affiliations
Corresponding author
Additional information
Article Highlights
• The projected vegetation coverage and carbon fluxes show an overall increase under global warming.
• Surface air temperature is the dominant driver of changes in vegetation distribution.
• Changes in carbon fluxes are caused by the combined effects of leaf area index, temperature, and precipitation.
This paper is a contribution to the special issue on Carbon Neutrality: Important Roles of Renewable Energies, Carbon Sinks, NETs, and non-CO2 GHGs
Electronic Supplementary Material to
376_2021_1138_MOESM1_ESM.pdf
Changes in Global Vegetation Distribution and Carbon Fluxes in Response to Global Warming: Simulated Results from IAP-DGVM in CAS-ESM2
Rights and permissions
About this article
Cite this article
Gao, X., Zhu, J., Zeng, X. et al. Changes in Global Vegetation Distribution and Carbon Fluxes in Response to Global Warming: Simulated Results from IAP-DGVM in CAS-ESM2. Adv. Atmos. Sci. 39, 1285–1298 (2022). https://doi.org/10.1007/s00376-021-1138-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00376-021-1138-3