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    The Impact of Climate Change on Air Quality–Related Meteorological Conditions in California. Part I: Present Time Simulation Analysis

    Source: Journal of Climate:;2011:;volume( 024 ):;issue: 013::page 3344
    Author:
    Zhao, Zhan
    ,
    Chen, Shu-Hua
    ,
    Kleeman, Michael J.
    ,
    Tyree, Mary
    ,
    Cayan, Dan
    DOI: 10.1175/2011JCLI3849.1
    Publisher: American Meteorological Society
    Abstract: his study investigates the impacts of climate change on meteorology and air quality conditions in California by dynamically downscaling Parallel Climate Model (PCM) data to high resolution (4 km) using the Weather Research and Forecast (WRF) model. This paper evaluates the present years? (2000?06) downscaling results driven by either PCM or National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) reanalysis data. The analyses focused on the air quality?related meteorological variables, such as planetary boundary layer height (PBLH), surface temperature, and wind. The differences of the climatology from the two sets of downscaling simulations and the driving global datasets were compared, which illustrated that most of the biases of the downscaling results were inherited from the driving global climate model (GCM). The downscaling process added mesoscale features but also introduced extra biases into the driving global data. The main source of bias in the PCM data is an imprecise prediction of the location and strength of the Pacific subtropical high (PSH). The analysis implied that using simulation results driven by PCM data as the input for air quality models will likely underestimate air pollution problems in California. Regional-averaged statistics of the downscaling results were estimated for two highly polluted areas, the South Coast Air Basin (SoCAB) and the San Joaquin Valley (SJV), by comparing to observations. The simulations driven by GFS data overestimated surface temperature and wind speed for most of the year, indicating that WRF has systematic errors in these two regions. The simulation matched the observations better during summer than winter in terms of bias. WRF has difficulty reproducing weak surface wind, which normally happens during stagnation events in these two regions. The shallow summer PBLH in the Central Valley is caused by the dominance of high pressure systems over the valley and the strong valley wind during summer. The change of meteorology and air quality in California due to climate change will be explored in Part II of this study, which compares the future (2047?53) and present (2000?06) simulation results driven by PCM data and is presented in a separate paper.
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      The Impact of Climate Change on Air Quality–Related Meteorological Conditions in California. Part I: Present Time Simulation Analysis

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4213723
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    contributor authorZhao, Zhan
    contributor authorChen, Shu-Hua
    contributor authorKleeman, Michael J.
    contributor authorTyree, Mary
    contributor authorCayan, Dan
    date accessioned2017-06-09T16:39:49Z
    date available2017-06-09T16:39:49Z
    date copyright2011/07/01
    date issued2011
    identifier issn0894-8755
    identifier otherams-71792.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4213723
    description abstracthis study investigates the impacts of climate change on meteorology and air quality conditions in California by dynamically downscaling Parallel Climate Model (PCM) data to high resolution (4 km) using the Weather Research and Forecast (WRF) model. This paper evaluates the present years? (2000?06) downscaling results driven by either PCM or National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) reanalysis data. The analyses focused on the air quality?related meteorological variables, such as planetary boundary layer height (PBLH), surface temperature, and wind. The differences of the climatology from the two sets of downscaling simulations and the driving global datasets were compared, which illustrated that most of the biases of the downscaling results were inherited from the driving global climate model (GCM). The downscaling process added mesoscale features but also introduced extra biases into the driving global data. The main source of bias in the PCM data is an imprecise prediction of the location and strength of the Pacific subtropical high (PSH). The analysis implied that using simulation results driven by PCM data as the input for air quality models will likely underestimate air pollution problems in California. Regional-averaged statistics of the downscaling results were estimated for two highly polluted areas, the South Coast Air Basin (SoCAB) and the San Joaquin Valley (SJV), by comparing to observations. The simulations driven by GFS data overestimated surface temperature and wind speed for most of the year, indicating that WRF has systematic errors in these two regions. The simulation matched the observations better during summer than winter in terms of bias. WRF has difficulty reproducing weak surface wind, which normally happens during stagnation events in these two regions. The shallow summer PBLH in the Central Valley is caused by the dominance of high pressure systems over the valley and the strong valley wind during summer. The change of meteorology and air quality in California due to climate change will be explored in Part II of this study, which compares the future (2047?53) and present (2000?06) simulation results driven by PCM data and is presented in a separate paper.
    publisherAmerican Meteorological Society
    titleThe Impact of Climate Change on Air Quality–Related Meteorological Conditions in California. Part I: Present Time Simulation Analysis
    typeJournal Paper
    journal volume24
    journal issue13
    journal titleJournal of Climate
    identifier doi10.1175/2011JCLI3849.1
    journal fristpage3344
    journal lastpage3361
    treeJournal of Climate:;2011:;volume( 024 ):;issue: 013
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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