Ea SM-3: Collaborative Res: Surface-induced Forcing and Decadal Variability and Change of the East Asian Climate, Surface Hydrology & Agriculture-A Modeling and Data Approach

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This project will contribute to identifying the attribution of East Asian (EA) precipitation changes to anthropogenic and natural drivers and the effects of climate variability on the ecosystem and water resources. The Tibetan Plateau is the source of major Asian river systems that support more than a billion people downstream. The results from this study will provide useful information for surface hydrology and agriculture, critical for a populous and economically vibrant part of the world. The tools for this EA study can be used for global and other regional studies and the state-of-the-art models developed in this project will be released to the research community. This project will also contribute to a number of educational outreach activities. A fully-coupled Atmospheric/Ocean GCM/biophysical and biogeochemical models/dynamic vegetation model to: examine the improvements in predictions of EA climate variability and change due to surface-induced forcings, including land use and land cover change (LULCC) and dust and black carbon effects and their two-way interactions in reference to predictions in which one or more of those processes and interactions are neglected; to evaluate and attribute the role played in the EA climate variability by these factors and mechanisms; to examine the effects of those processes on future projections of that regional climate; and to estimate uncertainty through a set of experiments. Data from different sources, especially from observations in the Tibetan Plateau (TP), will be applied to evaluate model predictions. In addition, a regional climate model with high resolution will be applied to assess the regional details of effects of surface-atmosphere interactions on the climate and ecosystem of EA, the TP in particular. Through the representation of key non-linear feedback processes in the EaSM, this project has the potential to substantially improve predictive capabilities and to further explain how different Earth processes and human activities have contributed to EA climate variability and changes over much of the last century.