Observed and predicted sensitivities of extreme surface ozone to meteorological drivers in three US cities. (March 2018)

• Record Type: Journal Article
• Authors: Fix, Miranda J.; Cooley, Daniel; Hodzic, Alma; Gilleland, Eric; Russell, Brook T.; Porter, William C.; Pfister, Gabriele G.
• Is Part Of: Atmospheric environment. Volume 176(2018)
• Page Start: 292
• Page End: 300
• Publication Date: 2018-03
• Subjects: Surface ozone -- Meteorological variables -- Quantile regression -- Extreme value theory; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2017.12.036
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: We conduct a case study of observed and simulated maximum daily 8-h average (MDA8) ozone (O3 ) in three US cities for summers during 1996–2005. The purpose of this study is to evaluate the ability of a high resolution atmospheric chemistry model to reproduce observed relationships between meteorology and high or extreme O3 . We employ regional coupled chemistry-transport model simulations to make three types of comparisons between simulated and observational data, comparing (1) tails of the O3 response variable, (2) distributions of meteorological predictor variables, and (3) sensitivities of high and extreme O3 to meteorological predictors. This last comparison is made using two methods: quantile regression, for the 0.95 quantile of O3, and tail dependence optimization, which is used to investigate even higher O3 extremes. Across all three locations, we find substantial differences between simulations and observational data in both meteorology and meteorological sensitivities of high and extreme O3 . Highlights: Framework proposed to compare meteorological sensitivities of extreme surface ozone. High resolution, regional scale NRCM-Chem simulations compared to observational data. Quantile regression and extreme value analysis applied to case study of 3 US cities. Substantial differences found in both meteorology and meteorological sensitivities.