Implications of global climate change on California's regional PM pollution during 1951-2008

Air pollution in California's Central Valley is largely attributed to local weather conditions, which are in turn driven by synoptic weather systems prevailing over the southwestern United States. These synoptic systems are a part of a larger circulation pattern with complex interactions amongst its various components. An air pollution episode in Central California could thus be a result of complex interactions between the various components of a large scale circulation. Accelerated changes in global circulation patterns in the recent past have altered the local climate in the Central Valley region. The implications of such changes could be to alter the frequency of meteorological conditions favorable to the build-up of pollutants and increased air pollution episodes in the valley. There has been a good deal of interest in understanding the effects of climate change on regional air quality and such efforts have involved conducting climate change simulations using general circulation models (GCM's) and the results obtained are currently subject to significant uncertainties. The scope of data analysis methods to study the response of air quality to these climatological changes is limited due to the lack of good air quality and meteorological datasets. Though multivariate data modeling techniques are not prognostic in nature, the analysis of historical databases of climatological data can provide useful insight into such changes in the recent past and help predict these trends in future. Analysis of voluminous weather datasets can reveal low frequency trends in air quality otherwise impossible to determine owing to lack of data. Particulate matter (PM) data for various sites located in the Central Valley is limited to the period between 1999--2008. The limited dataset is used to identify a unique set of days during the PM season each year (November-February) as severe and widespread exceedances using objective selection criteria. The daily averaged NCEP/NCAR reanalysis 500-hPa geopotential height weather maps for the target exceedance set are then used to identify areas on the contour maps which significantly differ from the overall seasonal mean by performing a statistical significance test at each data point for an expanded spatial domain (20°S to 75°N latitude and 100°E to 350°E longitude). Data points with significant differences were found to exist and correspond to key weather events in the northern hemisphere. The reanalysis weather maps are analyzed using 2-D wavelet transforms to characterize variable gradients and the spatial extent of each of the interacting components. This transformation enables the segregation of significant events on the contour maps and identifies relevant scales which contain meaningful information. The wavelet details are then analyzed at chosen scales for a comprehensive multiscale analysis of the reanalysis weather maps. The wavelet coefficients at each scale are subject to a significance test to identify a reduced set of statistically significant coefficients. Such dimension reduction is essential owing to a large number of variables and availability of limited target samples. The principal step of the proposed methodology is to then scan historical database of the daily averaged NCEP/NCAR reanalysis weather maps (1951-2008) for similar events conducive to poor air quality in the past. Each day from a PM season in the past is compared to the identified exceedance set by first performing wavelet decomposition and then testing for similarity of chosen coefficients at the relevant scales. The matches returned by the algorithm are days having large scale weather conditions in the expanded domain which are conducive to widespread and in certain cases severe exceedances in central California. The variation in the frequency of such events during the study period (1951-2008) is then examined to see the effects of the changing climate on central California's PM pollution potential for the past six decades. The matches returned by the algorithm have large scale weather conditions in the expanded domain conducive to widespread and in certain cases severe PM exceedances in California's Central Valley. The correlation patterns between sea surface temperature anomaly indices in the Pacific Basin and identified matches are used to understand the response wintertime air quality in California's Central valley to ENSO variability (a sub-decadal scale global atmospheric pattern). The projected warming trends for sea surface temperatures are expected to influence PM response in Central Valley consistent with results obtained earlier.