PNNL

Abstract

Assessments of the effects of climate change typically require information at scales of 10 km or less. In regions with complex terrain, much of the spatial variability in climate (temperature, precipitation, and snow water) occurs on scales below 10 km. Since the typical global climate model simulation’s grid size is more than 200 km, it is necessary to develop models with much higher resolution. Unfortunately, no datasets currently produced are both highly accurate and provide data at a sufficiently high resolution. As a result, current global climate models are forced to ignore the important climate variations that occur below the 200 km scale. This predicament prompted the creation of a global hybrid dataset with information for precipitation, temperature, and relative humidity. The resulting dataset illustrated the importance of having high-resolution datasets and gives clear proof that regions with complex terrain require a fine resolution grid to give an accurate representation of their climatology. For example, the Andes Mountains in Chile cause a temperature shift of more than 25°C within the same area as a single 2.5° grid cell from the NCEP dataset. Fortunately the CRU, U.D., GPCP, and NCEP datasets, when hybridized, are able to provide both precision and satisfactory resolution with global coverage. This composite will enable the development of both high-resolution models and quality empirical downscaling methods--both of which are necessary for scientists to more accurately predict the effects of global climate change. Without accurate long-term forecasts, climatologists and policy makers will not have the tools they need to effectively reduce the negative effects human activity have on the earth.