Improving regional climate modeling in East Africa using remote sensing products

Accurate representation of the land surface in regional climate models is becoming unprecedentedly crucial as numerous studies are now focused to simulate the influences of human modification of the Earth's surface on regional and global climate. The unique advantages of remote sensing technique in monitoring the land surface have been recognized for decades. The climate modeling community, however, has yet to fully utilize this technique, especially the recently developed remote sensing products which have been proven to be more suitable for global change studies. The objectives of this study are to improve the representation of the land surface and to investigate the impacts of land cover classification accuracy on regional climate modeling in East Africa.;Several land cover datasets from different sources now exist in almost any region of the world. A new statistical measure Q is developed to evaluate the land cover classification specifically for the purpose of climate modeling. In terms of this Q measure, Global Land Cover 2000 (GLC2000) is ranked the best among four land cover datasets.;To better represent the land surface newly developed MODIS Leaf Area Index (LAI) and Vegetation Fractional Cover (VFC) imageries are incorporated directly in the Regional Atmospheric Modeling System (RAMS). The default land cover dataset is updated by GLC2000 as well. The impact is examined by comparing the model simulated land surface temperature (LST) and precipitation with MODIS LST and precipitation from the Tropical Rainfall Measuring Mission (TRMM) satellite. This study finds that the incorporation of MODIS LAI and VFC greatly improves the spatial and temporal characteristics of LST. The precipitation, however, is less sensitive to the improved land surface conditions.;The uncertainty originating from the land surface and its propagation need to be examined to truly improve the representation of the land surface in climate models. This study focuses on the land cover classification accuracy, which is the first such investigation. This study finds that classification accuracy under 80% has significant impact on simulated precipitation, especially when the land surface has a greater control of the overlying atmosphere. As the accuracy worsens, the effect becomes much stronger. In remote sensing community, an 85% overall accuracy has been brought up as a guideline of classification quality control. This study shows that this accuracy target can indeed satisfy the requirement of climate modeling in the East Africa region. In reality, however, the classification accuracy can be much lower as historically reconstructed and future projected land cover datasets are extensively used in many climate modeling studies.