Ge J., Qi J., Lofgren B.M., Moore N., Torbick N., Olson J.M.
Department of Geography, Center for Global Change and Earth Observations, Michigan State University, 116 Geography Building, East Lansing, MI 48824-1115, United States; NOAA, Great Lakes Environmental Research Laboratory, 2205 Commonwealth Boulevard, Ann Arbor, MI 48105-2945, United States; Department of Geography, Michigan State University, 116 Geography Building, East Lansing, MI 48824-1115, United States; International Livestock Research Institute, Nairobi, Kenya
Ge, J., Department of Geography, Center for Global Change and Earth Observations, Michigan State University, 116 Geography Building, East Lansing, MI 48824-1115, United States; Qi, J., Department of Geography, Center for Global Change and Earth Observations, Michigan State University, 116 Geography Building, East Lansing, MI 48824-1115, United States; Lofgren, B.M., NOAA, Great Lakes Environmental Research Laboratory, 2205 Commonwealth Boulevard, Ann Arbor, MI 48105-2945, United States; Moore, N., Department of Geography, Center for Global Change and Earth Observations, Michigan State University, 116 Geography Building, East Lansing, MI 48824-1115, United States; Torbick, N., Department of Geography, Center for Global Change and Earth Observations, Michigan State University, 116 Geography Building, East Lansing, MI 48824-1115, United States; Olson, J.M., Department of Geography, Michigan State University, 116 Geography Building, East Lansing, MI 48824-1115, United States, International Livestock Research Institute, Nairobi, Kenya
Land use/cover change has been recognized as a key component in global change. Various land cover data sets, including historically reconstructed, recently observed, and future projected, have been used in numerous climate modeling studies at regional to global scales. However, little attention has been paid to the effect of land cover classification accuracy on climate simulations, though accuracy assessment has become a routine procedure in land cover production community. In this study, we analyzed the behavior of simulated precipitation in the Regional Atmospheric Modeling System (RAMS) over a range of simulated classification accuracies over a 3 month period. This study found that land cover accuracy under 80% had a strong effect on precipitation especially when the land surface had a greater control of the atmosphere. This effect became stronger as the accuracy decreased. As shown in three follow-on experiments, the effect was further influenced by model parameterizations such as convection schemes and interior nudging, which can mitigate the strength of surface boundary forcings. In reality, land cover accuracy rarely obtains the commonly recommended 85% target. Its effect on climate simulations should therefore be considered, especially when historically reconstructed and future projected land covers are employed. Copyright 2007 by the American Geophysical Union.