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

Zhao, Zhan; Chen, Shu Hua; Kleeman, Michael J.; Mahmud, Abdullah Al

doi:10.1175/2010jcli3850.1

Cited by 18 publications

(14 citation statements)

References 43 publications

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“…Except in August, EXP always provided slightly higher 10m wind speeds than CON. This general overestimation of 10m wind speed agrees with results from previous studies on WRF's performance in reproducing inversions and/or the wind field under stagnant or calm wind conditions [19] [73] [74].…”

Section: Windsupporting

confidence: 89%

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Assessment of the 2006-2012 Climatological Fields and Mesoscale Features from Regional Downscaling of CESM Data by WRF-Chem over Southeast Alaska

Mölders¹,

Bruyère²,

Gende³

et al. 2014

ACS

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This case study examined how well downscaling of Community Earth System Model (CESM) data can reproduce climatological conditions relevant for summer (JJA) air quality in Glacier Bay National Park. Climatology was determined from the meteorological results obtained by the Weather Research and Forecasting model inline coupled with chemistry (WRF-chem) when driven with CESM data of 2006-2012. The climatology of this experiment (EXP) was evaluated by climatology from gridded blended sea-wind speeds, CRU data, and 42 surface meteorology sites. The quality relative to known performance was assessed by comparison to climatology determined from WRF-chem control simulations driven with FNL analysis data (CON) in forecast mode. Compared to observations, the thermodynamic and dynamic performances of EXP showed similar shortcomings (dampened diurnal temperature range, overestimation of wind speed over land) as CON. Over water EXP wind-speed climatology JJA bias (simulated minus observed) was −0.7 m/s. With respect to the CRU data EXP biases in JJA 2m temperature, diurnal temperature range, relative humidity and accumulated precipitation were −1.1 K, −4.9 K, 13%, and 110 mm, respectively. The slightly warmer atmosphere in EXP compensated for deficiencies in the cloud schemes leading to better results for the number of wet days and accumulated precipitation than in CON. Downscaling captured known mesoscale responses important for regional climate in a similar way as CON. When using CESM forcing, lateral boundary effects expanded spatially farther into the domain N. Mölders et al. 590 than known for forcing by analysis data. Overall, climatologies obtained from downscaling for Southeast Alaska had similar skill than those derived from forecasts driven by analysis data.

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Section: Windsupporting

confidence: 89%

“…Since the analysis data based on observations small-scale impacts of roughness were indirectly included to a certain degree despite of the relatively coarse resolution. The low data density explains some of the systematic differences found in this and many other studies between simulated and observed wind fields [74] [77].…”

Section: Windcontrasting

confidence: 63%

Assessment of the 2006-2012 Climatological Fields and Mesoscale Features from Regional Downscaling of CESM Data by WRF-Chem over Southeast Alaska

Mölders¹,

Bruyère²,

Gende³

et al. 2014

ACS

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“…4a) suggests that these two sets of simulations predict dissimilar land-sea breezes. More details of landsea breeze in CA and its future changes will be explored in Part II of the study (Zhao et al 2011). During winter, PCM WRF simulations underestimated T2 for almost the entire analysis domain, except for over the Pacific Ocean adjacent to Southern CA and Mexico.…”

Section: Downscaling Results Analysismentioning

confidence: 99%

“…This phenomenon was studied and the possible reasons were explored. Part II of this study, which is presented in a separate paper (Zhao et al 2011), compares present and future simulations driven by PCM data to evaluate the impacts of climate change on meteorological conditions related to air quality, including land-sea breeze, in CA.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2011

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This 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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“…Of the total PM 2.5 mass, primary PM concentrations were only under-estimated by 12-66% while secondary PM concentrations were under-estimated by ∼18-73%. The majority of this under-prediction is likely caused by the excess ventilation produced by the PCM-WRF meteorological predictions that should be consistent in both present and future climate so that a minimum amount of net bias is introduced into the comparison (Zhao et al, 2010a). The present study is among the first to evaluate climate-air quality model performance over a climatologically relevant time-frame (7-year averages) using prognostic meteorology produced by a GCM.…”

Section: Discussionmentioning

confidence: 99%

Climate impact on airborne particulate matter concentrations in California using seven year analysis periods

Mahmud

Hixson

et al. 2010

Atmos. Chem. Phys.

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Abstract. The effect of global climate change on the annual average concentration of fine particulate matter (PM 2.5 ) in California was studied using a climate-air quality modeling system composed of global through regional models. Output from the NCAR/DOE Parallel Climate Model (PCM) generated under the "business as usual" global emissions scenario was downscaled using the Weather Research and Forecasting (WRF) model followed by air quality simulations using the UCD/CIT airshed model. The system represents major atmospheric processes acting on gas and particle phase species including meteorological effects on emissions, advection, dispersion, chemical reaction rates, gas-particle conversion, and dry/wet deposition. The air quality simulations were carried out for the entire state of California with a resolution of 8-km for the years 2000-2006 (present climate with present emissions) and 2047-2053 (future climate with present emissions). Each of these 7-year analysis periods was analyzed using a total of 1008 simulated days to span a climatologically relevant time period with a practical computational burden. The 7-year windows were chosen to properly account for annual variability with the added benefit that the air quality predictions under the present climate could be compared to actual measurements. The climate-air quality modeling system successfully predicted the spatial pattern of present climate PM 2.5 concentrations in California but the absolute magnitude of the annual average PM 2.5 concentrations were under-predicted by ∼4-39% in the major air basins. The majority of this under-prediction was caused by excess ventilation predicted by PCM-WRF that should be present to the same degree in the current and future time periods so that the net bias introduced into the comparison is minimized.Correspondence to: M. J. Kleeman (mjkleeman@ucdavis.edu) Surface temperature, relative humidity (RH), rain rate, and wind speed were predicted to increase in the future climate while the ultra violet (UV) radiation was predicted to decrease in major urban areas in the San Joaquin Valley (SJV) and South Coast Air Basin (SoCAB). These changes lead to a predicted decrease in PM 2.5 mass concentrations of ∼0.3-0.7 µg m −3 in the southern portion of the SJV and ∼0.3-1.1 µg m −3 along coastal regions of California including the heavily populated San Francisco Bay Area and the SoCAB surrounding Los Angeles. Annual average PM 2.5 concentrations were predicted to increase at certain locations within the SJV and the Sacramento Valley (SV) due to the effects of climate change, but a corresponding analysis of the annual variability showed that these predictions are not statistically significant (i.e. the choice of a different 7-year period could produce a different outcome for these regions). Overall, virtually no region in California outside of coastal + central Los Angeles, and a small region around the port of Oakland in the San Francisco Bay Area experienced a statistically significant change in annual average PM 2.5 concentra...

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

Cited by 18 publications

References 43 publications

Assessment of the 2006-2012 Climatological Fields and Mesoscale Features from Regional Downscaling of CESM Data by WRF-Chem over Southeast Alaska

Assessment of the 2006-2012 Climatological Fields and Mesoscale Features from Regional Downscaling of CESM Data by WRF-Chem over Southeast Alaska

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

Climate impact on airborne particulate matter concentrations in California using seven year analysis periods

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