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Simulating biogenic volatile organic compound emissions in the Community Climate System Model
Author(s) -
Levis Samuel,
Wiedinmyer Christine,
Bonan Gordon B.,
Guenther Alex
Publication year - 2003
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2002jd003203
Subject(s) - environmental science , vegetation (pathology) , isoprene , biogeochemical cycle , climatology , climate model , atmospheric sciences , greenhouse gas , atmospheric model , climate change , atmosphere (unit) , meteorology , oceanography , geology , ecology , chemistry , organic chemistry , medicine , physics , pathology , copolymer , biology , polymer
The Community Climate System Model (CCSM) calculates terrestrial biogenic volatile organic compound (BVOC) emissions using an algorithm developed from field and laboratory observations. This algorithm is incorporated in CCSM, a coupled atmosphere, ocean, sea ice, and land model, as one step toward integrating biogeochemical processes in this model. CCSM is designed to easily incorporate more complex BVOC models in the present framework when such models become available. Two simulations are performed: a land‐only simulation driven with prescribed atmospheric data and satellite‐derived vegetation data and a fully coupled CCSM simulation with prognostic vegetation using CCSM's dynamic vegetation model. In both cases, warm and forested regions emit more BVOC than other regions, in agreement with observations. With prescribed vegetation, global terrestrial isoprene emissions of 507 Tg C per year compare well with other model simulations. With dynamic vegetation, BVOC emissions respond to varying climate and vegetation from year to year. The interannual variability of the simulated biogenic emissions can exceed 10% of the estimated annual anthropogenic emissions provided in the IPCC emission scenarios. We include BVOC emissions within the CCSM to ultimately reduce the simulated climate uncertainty due to natural processes in this model.

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