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IMPACT OF USGS VEGETATION MAP ON GCM SIMULATIONS OVER THE UNITED STATES
Author(s) -
Fennessy M. J.,
Xue Y.
Publication year - 1997
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/1051-0761(1997)007[0022:iouvmo]2.0.co;2
Subject(s) - vegetation (pathology) , biosphere , biosphere model , environmental science , deciduous , climatology , precipitation , vegetation type , seasonality , atmospheric sciences , ecology , physical geography , geography , grassland , meteorology , geology , biology , medicine , pathology
A global atmospheric general circulation model (GCM) coupled with a biosphere model (SSiB, simplified simple biosphere model) was used to study the impact of vegetation on simulations over the United States during summer. Ensembles of 90‐d integrations were performed from early June initial conditions with different vegetation maps and different biophysical characteristics. Monthly and seasonal mean differences among these ensembles were analyzed. Incorporation of a new SiB vegetation map produced from the latest available data by the U.S. Geological Survey’s EROS (Earth Resources Observation Systems) Data Center has a significant impact on monthly and seasonal simulations of evaporation, surface air temperature, and precipitation over some regions of the United States. The impact is greater over the western half of the United States than over the eastern half, where moisture convergence plays a stronger role in the hydrological cycle. Systematic errors in the model simulations appear to be related to the use of a single crop vegetation type in SiB (simple biosphere model). Replacing the crops over the United States with broadleaf deciduous trees reduces the systematic errors. It appears that the strong seasonality of the SiB crop vegetation type makes it an unsuitable representative for crops in general. The importance of vegetation specification in monthly and seasonal predictions is emphasized.