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Using the Lund-Potsdam-Jena model to understand the different responses of three woody plants to land use in China

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Abstract

In this study, the approach of conditional nonlinear optimal perturbation related to initial perturbation (CNOP-I) was employed to investigate the maximum variations in plant amount for three main woody plants (a temperate broadleaved evergreen, a temperate broadleaved summergreen, and a boreal needleleaved evergreen) in China. The investigation was conducted within a certain range of land use intensity using a state-of-the-art Lund-Potsdam-Jena dynamic global vegetation model (LPJ DGVM). CNOP-I represents a class of deforestation and can be considered a type of land use with respect to the initial perturbation. When deforestation denoted by the CNOP-I has the same intensity for all three plants, the variation in plant amount of the boreal needleleaved evergreen in northern China is greater than the variation in plant amount of both the temperate broadleaved evergreen and temperate broadleaved summergreen in southern China. As deforestation intensity increases, the plant amount variation in the three woody plant functional types carbon changes, in a nonlinear fashion. The impact of land use on plant functional types is minor because the interaction between climate condition and land use is not considered in the LPJ model. Finally, the different impacts of deforestation on net primary production of the three plant functional types were analyzed by modeling gross primary production and autotrophic respiration. Our results suggest that the CNOP-I approach is a useful tool for exploring the nonlinear and different responses of terrestrial ecosystems to land use.

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References

  • Beer, C., W. Lucht, D. Gerten, K. Thonicke, and C. Schmullius, 2007: Effects of soil freezing and thawing on vegetation carbon density in Siberia: A modeling analysis with the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). Global Biogeochemical Cycles, 21, GB1012, doi:10.1029/2006GB002760.

    Article  Google Scholar 

  • Bernoux, M., M. C. S. Carvalho, B. Volkoff, and C. C. Cerri, 2001: CO2 emission from mineral soils following land-cover change in Brazil. Global Change Biology, 7, 779–787.

    Article  Google Scholar 

  • Betts, A. K., and J. H. Ball, 1997: Albedo over the boreal forest. J. Geophys. Res., 102, 28901–28909, doi: 10.1029/96JD03876.

    Article  Google Scholar 

  • Birgin, E. G., J. M. Martinez, and M. Raydan, 2000: Nonmonotone spectral projected gradient methods for convex sets. SIAM Journal on Optimization, 10, 1196–1211.

    Article  Google Scholar 

  • Bonan, G., S. Levis, S. Sitch, M. Vertenstein, and K. Oleson, 2003: A dynamic global vegetation model for use with climate models: Concepts and description of simulated vegetation dynamics. Global Change Biology, 9, 1543–1566.

    Article  Google Scholar 

  • Brovkin, V., S. Sitch, von W. Bloh, M. Claussen, E. Bauer, and W. Cramer, 2004: Role of land cover changes for atmospheric CO2 increase and climate change during the last 150 years. Global Change Biology, 10, 1253–1266, doi: 10.1111/j.1365-2486.2004.00812.x.

    Article  Google Scholar 

  • Claussen, M., V. Brovkin, and A. Ganopolski, 2001: Biogeophysical versus biogeochemical feedbacks of largescale land cover change. Geophys. Res. Lett., 28, 1011–1014, doi: 10.1029/2000GL012471.

    Article  Google Scholar 

  • Conant, R. T., and K. Paustian, 2002: Potential soil carbon sequestration in overgrazed grassland ecosystems. Global Biogeochemical Cycles, 16, 1143, doi: 10.1029/200GB001661.

    Article  Google Scholar 

  • Conant, R. T., K. Paustian, and E. T. Elliott, 2001: Grassland management and conversion into grassland: Effects on soil carbon. Ecological Applications, 11, 343–355.

    Article  Google Scholar 

  • Cramer, W., and Coauthors, 2001: Global responses of terrestrial ecosystem structure and function to CO2 and climate change: Results from six dynamic global vegetation models. Global Change Biology, 7, 357–373.

    Article  Google Scholar 

  • Dan, L., J. Ji, and Y. He, 2007: Use of ISLSCP II data to intercompare and validate the terrestrial net primary production in a land surface model coupled to a general circulation model. J. Geophys. Res., 112, D02S90, doi: 10.1029/2006JD007721.

    Article  Google Scholar 

  • DeFries, R. S., C. B. Field, I. Fung, G. J. Collatz, and L. Bounoua, 1999: Combining satellite data and biogeochemical models to estimate global effects of humaninduced land cover change on carbon emissions and primary productivity. Global Biogeochemical Cycles, 13, 803–815, doi: 10.1029/1999GB900037.

    Article  Google Scholar 

  • Fang, J. Y., A. P. Chen, C. H. Peng, S. Q. Zhao, and L. J. Ci, 2001: Changes in forest biomass carbon storage in China between 1949 and 1998. Science, 292, 2320–2322.

    Article  Google Scholar 

  • Foley, J. A., and Coauthors, 2005: Global Consequences of Land Use. Science, 309, 570–574.

    Article  Google Scholar 

  • Gao, Q., X. B. Li, and X. S. Yang, 2003: Responses of vegetation and primary production in north-south transect of eastern China to global change under land use constraint. Acta Botanica Sinica, 45, 1274–1284.

    Google Scholar 

  • Ge, Q. S., J. H. Dai, F. N. He, Y. Pan, and M. M. Wang, 2008: Land use changes and their relations with carbon cycles over the past 300 a in China. Science in China (D), 51, 871–884.

    Article  Google Scholar 

  • Global Carbon Project, 2003: Science framework and implementation. Earth System Science Partnership (IGBP, IHDP, WCRP, DIVERSITAS) Report No. 1, Global Carbon Project Report No. 1, Canberra, 69pp.

  • Global Land Project, 2005: Science plan and implementation itrategy. IGBP Report No. 53/IHDP Report No. 19, IGBP Secretariat, Stockholm, 64pp.

    Google Scholar 

  • Houghton, R. A., 1995: Land-use change and the carbon cycle. Global Change Biology, 1, 275–287.

    Article  Google Scholar 

  • Houghton, R. A., and J. L. Hackler, 2003: Sources and sinks of carbon from land-use change in China. Global Biogeochemical Cycles, 17, 1034, doi: 10.1029/2002GB001970.

    Article  Google Scholar 

  • Jiang, Z. N., and M. Mu, 2009: A comparisons study of the methods of conditional nonlinear optimal perturbations and singular vectors in ensemble prediction. Adv. Atmos. Sci., 26, 465–470, doi: 10.1007/s00376-009-0465-6.

    Article  Google Scholar 

  • 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, GB4021, doi: 10.1029/2006GB002915.

    Article  Google Scholar 

  • Kicklighter, D. W., and Coauthor, 1999: A first-order analysis of the potential role of CO2 fertilization to affect the global carbon budget: A comparison of four terrestrial biosphere models. Tellus B, 51, 343–366.

    Article  Google Scholar 

  • Lambin, E. F., and H. J. Geist, Eds., 2006: Land-Use and Land-Cover Change. Vol. 18, Local Processes and Global Impacts, Global Change-The IGBP Series, Springer-Verlag, Berlin, 222pp.

    Google Scholar 

  • Mitchell, T. D., and P. D. Jones, 2005: An improved method of constructing a database of monthly climate observations and associated high-resolution grids. International Journal of Climatology, 25, 693–712.

    Article  Google Scholar 

  • Mu, M., and B. Wang, 2007: Nonlinear instability and sensitivity of a theoretical grassland ecosystem to finite-amplitude perturbations. Nonlinear Processes in Geophysics, 14, 409–423.

    Article  Google Scholar 

  • Mu, M., and Z. N. Jiang, 2008: A new approach to the generation of initial perturbations for ensemble prediction: Conditional nonlinear optimal perturbation. Chinese Science Bulletin, 53, 2062–2068.

    Article  Google Scholar 

  • Mu, M., W. S. Duan, and B. Wang, 2003: Conditional nonlinear optimal perturbation and its applications. Nonlinear Processes in Geophysics, 10, 493–501.

    Article  Google Scholar 

  • Mu, M., L. Sun, and H. A. Dijikstra, 2004: The sensitivity and stability of the ocean’s thermohaline circulation to finite amplitude perturbations. J. Phys. Oceanogr., 34, 2305–2315.

    Article  Google Scholar 

  • Mu, M., F. Zhou, and H. Wang, 2009: A method for identifying the sensitive areas in targeted observations for tropical cyclone prediction: Conditional nonlinear optimal perturbation. Mon. Wea. Rev., 137, 1623–1639.

    Article  Google Scholar 

  • Mu, M., W. S. Duan, Q. Wang, and R. Zhang, 2010: An extension of conditional nonlinear optimal perturbation approach and its applications. Nonlinear Processes in Geophysics, 17, 211–220, doi: 10.5194/npg-17-211-2010.

    Article  Google Scholar 

  • Ni, J., and X. S. Zhang, 2000: Climate variability, ecological gradient and the Northeast China Transect (NSTEC). Journal of Arid Environments, 46, 313–325.

    Article  Google Scholar 

  • Petit, C. C., and E. F. Lambin, 2002: Long-term land-cover changes in the Belgian Ardennes (1775–1929): Model-based reconstruction vs. historical maps. Global Change Biology, 8, 616–630.

    Article  Google Scholar 

  • Piao, S., J. Fang, L. Zhou, K. Tan, and S. Tao, 2007: Changes in biomass carbon stocks in China’s grasslands between 1982 and 1999. Global Biogeochemical Cycles, 21, GB2002, doi: 10.1029/2005GB002634.

    Article  Google Scholar 

  • Prentice, I. C., W. Cramer, S. P. Harrison, R. Leemans, R. A. Monserud, and A. M. Solomon, 1992: A global biome model based on plant physiology and dominance, soil properties and climate. Journal of Biogeography, 19, 117–134.

    Article  Google Scholar 

  • Quaife, T., S. Quegan, M. Disney, P. Lewis, M. Lomas, and F. I. Woodward, 2008: Impact of land cover uncertainties on estimates of biospheric carbon fluxes. Global Biogeochemical Cycles, 22, GB4016, doi: 10.1029/2007GB003097.

    Article  Google Scholar 

  • Sitch, S., and Coauthor, 2003: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology, 9, 161–185.

    Article  Google Scholar 

  • Sitch, S., V. Brovkin, W. von Bloh, D. van Vuuren, B. Eickhout, and A. Ganopolski, 2005: Impacts of future land cover changes on atmospheric CO2 and climate. Global Biogeochemical Cycles, 19, GB2013, doi: 10.1029/2004GB002311.

    Article  Google Scholar 

  • Solomon, D., and Coauthor, 2007: Long-term impacts of anthropogenic perturbations on the dynamics and speciation of organic carbon in tropical forest and subtropical grassland ecosystems. Global Change Biology, 13, 511–530.

    Article  Google Scholar 

  • Sun, G. D., and M. Mu, 2009: Nonlinear feature of the abrupt transitions between multiple equilibria states of an ecosystem model. Adv. Atmos. Sci., 26, 293–304, doi: 10.1007/s00376-009-0293-8.

    Article  Google Scholar 

  • Vitousek, P. M., H. A. Mooney, J. Lubchenco, and J. M. Melillo, 1997: Human domination of earth’s ecosystems. Science, 277, 494–499.

    Article  Google Scholar 

  • Wen, X. Y., S. W. Wang, J. H. Zhu, and D. Viner, 2006: An overview of China climate change over the 20th century using UK UEA/CRU high resolution grid data. Chinese J. Atmos. Sci., 30, 894–904. (in Chinese)

    Google Scholar 

  • Wu, H. B., Z. T. Guo, and C. H. Peng, 2003: Land use induced changes of organic carbon storage in soils of China. Global Change Biology, 9, 305–315.

    Article  Google Scholar 

  • Zobler, L., 1986: A world soil file for global climate modelling. NASA Technical Memorandum, 87802, 32pp.

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Authors and Affiliations

  1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

    Guodong Sun  (孙国栋) & Mu Mu  (穆 穆)

  2. Key Laboratory of Ocean Circulation and Wave, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China

    Mu Mu  (穆 穆)

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  1. Guodong Sun  (孙国栋)
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  2. Mu Mu  (穆 穆)
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Correspondence to Guodong Sun  (孙国栋).

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Sun, G., Mu, M. Using the Lund-Potsdam-Jena model to understand the different responses of three woody plants to land use in China. Adv. Atmos. Sci. 30, 515–524 (2013). https://doi.org/10.1007/s00376-012-2011-1

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  • DOI: https://doi.org/10.1007/s00376-012-2011-1

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