My dissertation consists of three parts. Parts I and II are focused on the climate change impacts on meteorology and air quality conditions in California (CA), while Part III is focused on the source-receptor relationship. The WRF model is applied to dynamically downscaled PCM data, with a horizontal resolution of approximately 2.8°x2.8°, to 4km resolution under the Business as Usual (BAU) scenario. The dynamical downscaling method could retain the large-scale features of the global simulations with more meso-scale details. A seven year simulation is conducted for both present (2000∼2006) and future (2047∼2053) in order to avoid the El Niño related inter-annual variation. In order to assess the PCM data quality and estimate the simulation error inherited from the PCM data bias, the present seven year simulations are driven by NCEP's Global Forecast System (GFS) data with the same model configuration. Part I is focused on the comparisons of the present time climatology from the two sets of simulations and the driving global datasets (i.e., PCM vs. GFS), which illustrate that the biases of the downscaling results are mostly inherited from the driving GCM. The imprecise prediction for the location and strength of the Pacific Subtropical High (PSH) is a main source of the PCM data bias. The analysis also implies that using the simulation results driven by PCM data as the input of the air quality model will underrate the air pollution problems in CA. The regional averaged statistics of the downscaling results compared to observational data show that both the surface temperature and wind speed were overestimate for most times of the year, and WRF preformed better during summer than winter. The low summer PBLH in the San Joaquin Valley (SJV) is addressed, and two reasons causing this are the dominance of a high pressure system over the valley and, to a lesser extent, the valley wind at daytime during summer. Part II is focused on the future change of meteorology and air quality in CA and comparisons are made between future and present simulations driven by the PCM data. Both the duration and strength of stagnant events, during which most air pollution problems occur in SJV, are increased during summer and winter. The seven-year averaged spatial distribution of the air-pollution related meteorological variables, such as surface wind, temperature, PBLH, etc., indicate that the future summer ozone problem would be mitigated in the coast region of Los Angeles County (LAC), while both the summer ozone and winter particulate matter (PM) problem in SJV and other parts of the Southern California Air Basin (SoCAB) will be exacerbated in the future. The impact on the land-sea breeze, which plays a big role in California’s climate, is also explored in this part.
Part III of the thesis is to investigate the potential of applying a signal technique on the source-receptor relationship. This approach is more economical in terms of computational time and memory than the conventional tracer method. The signal technique was implemented into the WRF model, and an idealized supercell case and a real case in Turkey were used to investigate the potential of the technique. Emissions from different source locations were tagged with different frequencies, which were added onto the emitted pollutants, with a specific frequency from each location. The time series of the pollutant concentration collected at receptors were then projected onto the frequency space using the Fourier transform and short-time Fourier transform methods to identify the source locations. During the model integration, a particular constant tracer was also emitted from each pollutant source location to validate and evaluate the signal technique. Results show that the frequencies could be slightly shifted after signals were transported over a long distance and evident secondary frequencies (i.e., beats) could be generated due to nonlinear effects. Although these could potentially confuse the identification of signals released from source points, signals were still distinguishable in this study.
|Commitee:||Kleeman, Michael J., Wexler, Anthony S.|
|School:||University of California, Davis|
|School Location:||United States -- California|
|Source:||DAI-B 71/03, Dissertation Abstracts International|
|Subjects:||Climate Change, Atmospheric sciences, Environmental engineering|
|Keywords:||Climate change, Dynamical downscaling, SJV, Signal technique, SoCAB, Source-receptor relationship, WRF|
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