Climate Dynamics

, Volume 22, Issue 5, pp 461–479 | Cite as

Natural and anthropogenic climate change: incorporating historical land cover change, vegetation dynamics and the global carbon cycle

  • H. D. MatthewsEmail author
  • A. J. Weaver
  • K. J. Meissner
  • N. P. Gillett
  • M. Eby


This study explores natural and anthropogenic influences on the climate system, with an emphasis on the biogeophysical and biogeochemical effects of historical land cover change. The biogeophysical effect of land cover change is first subjected to a detailed sensitivity analysis in the context of the UVic Earth System Climate Model, a global climate model of intermediate complexity. Results show a global cooling in the range of –0.06 to –0.22 °C, though this effect is not found to be detectable in observed temperature trends. We then include the effects of natural forcings (volcanic aerosols, solar insolation variability and orbital changes) and other anthropogenic forcings (greenhouse gases and sulfate aerosols). Transient model runs from the year 1700 to 2000 are presented for each forcing individually as well as for combinations of forcings. We find that the UVic Model reproduces well the global temperature data when all forcings are included. These transient experiments are repeated using a dynamic vegetation model coupled interactively to the UVic Model. We find that dynamic vegetation acts as a positive feedback in the climate system for both the all-forcings and land cover change only model runs. Finally, the biogeochemical effect of land cover change is explored using a dynamically coupled inorganic ocean and terrestrial carbon cycle model. The carbon emissions from land cover change are found to enhance global temperatures by an amount that exceeds the biogeophysical cooling. The net effect of historical land cover change over this period is to increase global temperature by 0.15 °C.


Land Cover Change Aerosol Optical Depth Surface Albedo Land Surface Model Atmospheric Carbon Dioxide 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors wish to thank E. Wiebe, T. Ewen and S. Turner for assistance, advice, and editorial comments as well as M. Claussen and B. Govindasamy for their thoughtful and useful suggestions. Funding support from the Climate Variability and Predictability Research Program (CLIVAR), the Canadian Foundation for Climate and Atmospheric Studies (CFCAS) and the National Science and Engineering Research Council (NSERC) is gratefully acknowledged.


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Copyright information

© Springer-Verlag  2004

Authors and Affiliations

  • H. D. Matthews
    • 1
    Email author
  • A. J. Weaver
    • 1
  • K. J. Meissner
    • 1
  • N. P. Gillett
    • 1
  • M. Eby
    • 1
  1. 1.School of Earth and Ocean Sciences, University of Victoria, PO Box 3055, Victoria, BC, V8W 3P6, Canada

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