Regional and Global Impacts of Land Cover Change and Sea Surface Temperature Anomalies

• Main Authors: Kirsten L. Findell, Andrew J. Pitman, Matthew, H. England, Philip J. Pegion
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Published: 2009
• Subjects: North Atlantic; North Atlantic oscillation
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.685.1677; http://web.science.unsw.edu.au/%7Ematthew/Findell_et_al_appeared.2009.pdf

The atmospheric and land components of the Geophysical Fluid Dynamics Laboratory’s (GFDL’s) Cli-mate Model version 2.1 (CM2.1) is used with climatological sea surface temperatures (SSTs) to investigate the relative climatic impacts of historical anthropogenic land cover change (LCC) and realistic SST anom-alies. The SST forcing anomalies used are analogous to signals induced by El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the background global warming trend. Coherent areas of LCC are represented throughout much of central and eastern Europe, northern India, southeastern China, and on either side of the ridge of the Appalachian Mountains in North America. Smaller areas of change are present in various tropical regions. The land cover changes in the model are almost exclusively a conversion of forests to grasslands. Model results show that, at the global scale, the physical impacts of LCC on temperature and rainfall are less important than large-scale SST anomalies, particularly those due to ENSO. However, in the regions where the land surface has been altered, the impact of LCC can be equally or more important than the SST forcing patterns in determining the seasonal cycle of the surface water and energy balance. Thus, this work provides a context for the impacts of LCC on climate: namely, strong regional-scale impacts that can sig-nificantly change globally averaged fields but that rarely propagate beyond the disturbed regions. This suggests that proper representation of land cover conditions is essential in the design of climate model experiments, particularly if results are to be used for regional-scale assessments of climate change impacts. 1.