Predicting the impact of climate change on severe Beijing winter haze events using extreme value theory
Abstract
We use extreme value theory to develop point process (PP) statistical models relating the probability of extreme winter haze events in Beijing to local meteorological variables. The models are trained with the 2009-2017 record of fine particulate matter concentrations (PM2.5) from the US embassy. We find that the combination of 850 hPa meridional wind velocity (V850) and relative humidity (RH) can successfully predict days when daily mean PM2.5 will exceed 300 μg m-3 (>95th percentile of the frequency distribution) as well as higher thresholds. Other models including additional meteorological predictors are not as successful. We apply the PP models to mid-21st century meteorological projections generated by the CMIP5 ensemble of climate models under two climate forcing scenarios (RCP8.5 and RCP4.5). We find that the frequency of severe haze events in Beijing is more likely to decrease as a result of climate change, driven mainly by a decrease in RH. This is consistent with the general expectation that greenhouse climate forcing should decrease RH in China. Our results are in contrast to previous studies that found increases in haze frequency as a result of climate change but did not include RH as a meteorological predictor.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.A21C..01P
- Keywords:
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- 0305 Aerosols and particles;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 0322 Constituent sources and sinks;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 0345 Pollution: urban and regional;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 0368 Troposphere: constituent transport and chemistry;
- ATMOSPHERIC COMPOSITION AND STRUCTURE