The climate system is being increasingly accurately modeled at large scales by numerical models (coupled Atmosphere-Ocean Global Climate Models (AOGCM)). However, it remains necessary to use downscaling tools to describe climate on spatial scales suitable for impact analyses. Changes in near-surface wind speeds have long been acknowledged as having particular importance for climate change impacts on society via their impact on, for example, thermal comfort, structural design, feasibility of harnessing renewable energy resources, agriculture and forestry and soil erosion. However, tools have not yet been fully developed or evaluated which can be used to derive climatologies of near-surface wind speeds for historical, current or future conditions at the spatial scales necessary for application in impact analyses. This project will develop and apply physical (dynamical) and statistical (empirical) downscaling tools to AOGCM output to provide an assessment of the accuracy of various tools for downscaling wind climates, to generate projections of probability distributions of wind speeds for the contiguous USA and surrounding coastal waters, and to quantify uncertainties in those projections.
The research couples closely with the North American Regional Climate Change Assessment Program and the 4th Assessment Report of the Intergovernmental Panel on Climate Change process by utilizing climate model products generated within those programs. It is thus part of a larger endeavor designed to better understand and predict global and regional climates, and to disseminate climate information to a range of potential end-user communities. To facilitate this information transfer a digital atlas of current and possible future wind climates will be developed and made publicly accessible from a dynamic project WWW site hosted by Indiana University. The broader impacts of the research also include training and education of graduate and undergraduate students with a specific focus on students who are women or are from other groups that are traditionally under-represented in science disciplines.
