User-driven downscaling: advances in data apportioning and analysis to augment adaptation planning

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Thursday, 21 January 2010: 2:00 PM
B211 (GWCC)
Edwin P. Maurer, Santa Clara University, Santa Clara, CA; and L. D. Brekke, T. Pruitt, K. D. White, E. Ochs, P. Duffy, and E. H. Girvetz

With the IPCC Fourth Assessment, the scientific case establishing anthropogenic influence for recently observed climate change is firmly established. The projections for future global climate impacts range from serious to disastrous, and many organizations and public agencies are now contemplating policies to adapt to a changed environment. As resource managers struggle with quantifying the range of possibilities for future climate and resulting impacts, one of the first barriers encountered is the difficulty in obtaining regional projections of future climate. This is especially the case for constructing multi-model ensembles, needed to characterize uncertainty and frame adaptation in a risk analysis context. We evaluate the use of a dataset of 112 monthly climate change projections to 2100, downscaled to a spatial resolution of about 12 km. This dataset was prepared initially for water managers to assess the range of uncertainty in regional climate projections under a variety of future greenhouse gas emissions pathways. From its release in November 2007 through July 2009, the dataset has provided over 500 unique users with 4TB of data via over 3500 downloads. A recently completed user survey illustrates the most useful characteristics of the dataset, where expansion is needed, and what shared data analysis tools may be desirable. We compare the usage of the data, whether for research, education, or resource management, and contrast the most important dataset characteristics for each usage type. We will also discuss a similar global dataset, at approximately 50km spatial resolution, which has provided nearly 2TB of data downloaded by over 80 users worldwide in four months. Both datasets are being coupled with a new online toolkit, the Climate Wizard, enabling online statistical data analysis. We will conclude with the implications of these findings on future climate dataset preparation and distribution, with the aim of providing the greatest utility to the resource management community for assessing potential future impacts and developing policies for adapting to climate change.