Change in ozone air pollution over Chicago associated with global climate change

American Geophysical Union (AGU) - Tập 113 Số D22 - 2008
Tracey Holloway1, Scott N. Spak1, Daniel W. Barker1, Matthew Bretl1, C. C. Moberg1, Katharine Hayhoe2,3, Jeff Van Dorn2, Donald J. Wuebbles4
1Center for Sustainability and the Global Environment, Nelson Institute for Environmental Studies University of Wisconsin—Madison Madison Wisconsin USA
2ATMOS Research and Consulting Lubbock Texas USA
3Department of Geosciences, Texas Tech University, Lubbock, Texas, USA
4Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

Tóm tắt

This study uses statistical downscaling to estimate the impact of future climate change on air quality. We employ historical observations of surface ozone (O3) over the Chicago area, large‐scale climate variables from the National Center for Environmental Protection (NCEP) reanalysis data, and climate projections from three GCMs (GFDL, PCM, and HadCM3), driven by two SRES emission scenarios (A1FI and B1 for GFDL and PCM; A2 and B1 for HadCM3). This approach calculates historic relationships between meteorology and O3, and considers how future meteorology would affect ground‐level O3 if these relationships remain constant. Ozone mixing ratios over Chicago are found to be most sensitive to surface temperature, horizontal surface winds, surface relative humidity, incoming solar radiation, and cloud cover. Considering the change in O3 due to global climate change alone, summertime (June, July, and August) mean mixing ratios over Chicago are projected to increase by 6–17 ppb by the end of the century, depending on assumptions about global economic growth and choice of GCM. Changes are greater under higher climate emissions scenarios and more sensitive climate models (e.g. 24 ppb for GFDL A1FI as compared to 2 ppb for PCM B1). However, this approach does not take into account changes in O3‐precursor emissions nor changes in local and lake‐effect meteorology, which could combine with climate change to either enhance or diminish the projected change in local mixing ratios. Statistical downscaling is performed with the Statistical DownScaling Model (SDSM v. 4.1, a publicly available scientific analysis and decision‐support tool.

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