ALBERT

Description: The effect of global climate change on the annual average concentration of fine particulate matter (PM2.5) 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 PM2.5 concentrations in California but the absolute magnitude of the annual average PM2.5 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. 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 PM2.5 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 PM2.5 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 PM2.5 concentrations due to the effects of climate change in the present~study. The present study employs the highest spatial resolution (8 km) and the longest analysis windows (7 years) of any climate-air quality analysis conducted for California to date, but the results still have some degree of uncertainty. Most significantly, GCM calculations have inherent uncertainty that is not fully represented in the current study since a single GCM was used as the starting point for all calculations. The PCM results used in the current study predicted greater wintertime increases in air temperature over the Pacific Ocean than over land, further motivating comparison to other GCM results. Ensembles of GCM results are usually employed to build confidence in climate calculations. The current results provide a first data-point for the climate-air quality analysis that simultaneously employ the fine spatial resolution and long time scales needed to capture the behavior of climate-PM2.5 interactions in California. Future downscaling studies should follow up with a full ensemble of GCMs as their starting point, and include aerosol feedback effects on local meteorology.

Description: In this study, the Weather Research and Forecasting (WRF) model is coupled with the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA), a high-complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF, such as the popular NOAH model, are simple and lack the capability of representing the canopy structure. In contrast, ACASA is a complex multilayer land surface model with interactive canopy physiology and high-order turbulence closure that allows for an accurate representation of heat, momentum, water, and carbon dioxide fluxes between the land surface and the atmosphere. It allows for microenvironmental variables such as surface air temperature, wind speed, humidity, and carbon dioxide concentration to vary vertically within and above the canopy. Surface meteorological conditions, including air temperature, dew point temperature, and relative humidity, simulated by WRF-ACASA and WRF-NOAH are compared and evaluated with observations from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF-ACASA model not only maintains model accuracy but also properly accounts for the dominant biological and physical processes describing ecosystem–atmosphere interactions that are scientifically valuable. The different complexities of physical and physiological processes in the WRF-ACASA and WRF-NOAH models also highlight the impact of different land surface models on atmospheric and surface conditions.

Description: The source-oriented Weather Research and Forecasting chemistry model (SOWC) was modified to include warm cloud processes and applied to investigate how aerosol mixing states influence fog formation and optical properties in the atmosphere. SOWC tracks a 6-dimensional chemical variable (X, Z, Y, Size Bins, Source Types, Species) through an explicit simulation of atmospheric chemistry and physics. A source-oriented cloud condensation nuclei module was implemented into the SOWC model to simulate warm clouds using the modified two-moment Purdue Lin microphysics scheme. The Goddard shortwave and longwave radiation schemes were modified to interact with source-oriented aerosols and cloud droplets so that aerosol direct and indirect effects could be studied. The enhanced SOWC model was applied to study a fog event that occurred on 17 January 2011, in the Central Valley of California. Tule fog occurred because an atmospheric river effectively advected high moisture into the Central Valley and nighttime drainage flow brought cold air from mountains into the valley. The SOWC model produced reasonable liquid water path, spatial distribution and duration of fog events. The inclusion of aerosol–radiation interaction only slightly modified simulation results since cloud optical thickness dominated the radiation budget in fog events. The source-oriented mixture representation of particles reduced cloud droplet number relative to the internal mixture approach that artificially coats hydrophobic particles with hygroscopic components. The fraction of aerosols activating into CCN at a supersaturation of 0.5 % in the Central Valley decreased from 94 % in the internal mixture model to 80 % in the source-oriented model. This increased surface energy flux by 3–5 W m−2 and surface temperature by as much as 0.25 K in the daytime.

Description: For the first time, a decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution and daily time resolution has been conducted in California to provide air quality data for health effects studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O3, PM2.5, EC, OC, nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM2.5, EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95%, 100%, 71%, 73%, and 92% of the simulated months, respectively. The base dataset provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated one day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to over-predict wind speed during stagnation events, leading to under-predictions of high PM concentrations, usually in winter months. The WRF model also generally under-predicts relative humidity, resulting in less particulate nitrate formation especially during winter months. These issues will be improved in future studies. All model results included in the current manuscript can be downloaded free of charge at http://faculty.engineering.ucdavis.edu/kleeman/.

Description: A source-oriented representation of airborne particulate matter was added to the Weather Research & Forecasting (WRF) model with chemistry (WRF/Chem). The source-oriented aerosol separately tracks primary particles with different hygroscopic properties rather than instantaneously combining them into an internal mixture. The source-oriented approach avoids artificially mixing light absorbing black + brown carbon particles with materials such as sulfate that would encourage the formation of additional coatings. Source-oriented particles undergo coagulation and gas-particle conversion, but these processes are considered in a dynamic framework that realistically "ages" primary particles over hours and days in the atmosphere. The source-oriented WRF/Chem model more accurately predicts radiative feedbacks from anthropogenic aerosols compared to models that make internal mixing or other artificial mixing assumptions. A three-week stagnation episode (15 December 2000 to 6 January 2001) during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS) was chosen for the initial application of the new modeling system. Emissions were obtained from the California Air Resources Board. Gas-phase reactions were modeled with the SAPRC90 photochemical mechanism. Gas-particle conversion was modeled as a dynamic process with semi-volatile vapor pressures at the particle surface calculated using ISORROPIA. Source oriented calculations were performed for 8 particle size fractions ranging from 0.01–10 μm particle diameters with a spatial resolution of 4 km and hourly time resolution. Primary particles emitted from diesel engines, wood smoke, high sulfur fuel combustion, food cooking, and other anthropogenic sources were tracked separately throughout the simulation as they aged in the atmosphere. Results show that the source-oriented representation of particles with meteorological feedbacks in WRF/Chem changes the aerosol extinction coefficients, downward shortwave flux, and primary and secondary particulate matter concentrations relative to the internally mixed version of the model. Downward shortwave radiation predicted by source-oriented model is enhanced by 1% at ground level chiefly because diesel engine particles in the source-oriented mixture are not artificially coated with material that increases their absorption efficiency. The extinction coefficient predicted by the source-oriented WRF/Chem model is reduced by an average of ∼ 5–10% in the central valley with a maximum reduction of ∼ 20%. Particulate matter concentrations predicted by the source-oriented WRF/Chem model are ∼ 5–10% lower than the internally mixed version of the same model because increased solar radiation at the ground increases atmospheric mixing. All of these results stem from the mixing state of black carbon. The source-oriented model representation with realistic aging processes predicts that hydrophobic diesel engine particles remain largely uncoated over the +7 day simulation period, while the internal mixture model representation predicts significant accumulation of secondary nitrate and water on diesel engine particles. Similar results will likely be found in any air pollution stagnation episode that is characterized by significant particulate nitrate production.

Description: In this study, the Weather Research and Forecasting Model (WRF) is coupled with the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA), a high complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF are simple and lack the capability to simulate carbon dioxide, for example, the popular NOAH LSM. ACASA is a complex multilayer land surface model with interactive canopy physiology and full surface hydrological processes. It allows microenvironmental variables such as air and surface temperatures, wind speed, humidity, and carbon dioxide concentration to vary vertically. Simulations of surface conditions such as air temperature, dew point temperature, and relative humidity from WRF–ACASA and WRF–NOAH are compared with surface observation from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF–ACASA model not only maintains model accuracy, it also properly accounts for the dominant biological and physical processes describing ecosystem-atmosphere interactions that are scientifically valuable. The different complexities of physical and physiological processes in the WRF–ACASA and WRF–NOAH models also highlight the impacts of different land surface and model components on atmospheric and surface conditions.

Description: A source-oriented version of the Weather Research and Forecasting model with chemistry (SOWC, hereinafter) was developed. SOWC separately tracks primary particles with different hygroscopic properties rather than instantaneously combining them into an internal mixture. This approach avoids artificially mixing light absorbing black + brown carbon particles with materials such as sulfate that would encourage the formation of additional coatings. Source-oriented particles undergo coagulation and gas-particle conversion, but these processes are considered in a dynamic framework that realistically "ages" primary particles over hours and days in the atmosphere. SOWC more realistically predicts radiative feedbacks from anthropogenic aerosols compared to models that make internal mixing or other artificial mixing assumptions. A three-week stagnation episode (15 December 2000 to 6 January 2001) in the San Joaquin Valley (SJV) during the California Regional PM10 / PM2.5 Air Quality Study (CRPAQS) was chosen for the initial application of the new modeling system. Primary particles emitted from diesel engines, wood smoke, high-sulfur fuel combustion, food cooking, and other anthropogenic sources were tracked separately throughout the simulation as they aged in the atmosphere. Differences were identified between predictions from the source oriented vs. the internally mixed representation of particles with meteorological feedbacks in WRF/Chem for a number of meteorological parameters: aerosol extinction coefficients, downward shortwave flux, planetary boundary layer depth, and primary and secondary particulate matter concentrations. Comparisons with observations show that SOWC predicts particle scattering coefficients more accurately than the internally mixed model. Downward shortwave radiation predicted by SOWC is enhanced by ~1% at ground level chiefly because diesel engine particles in the source-oriented mixture are not artificially coated with material that increases their absorption efficiency. The extinction coefficient predicted by SOWC is reduced by an average of 0.012 km−1 (4.8%) in the SJV with a maximum reduction of ~0.2 km−1. Planetary boundary layer (PBL) height is increased by an average of 5.2 m (1.5%) with a~maximum of ~100 m in the SJV. Particulate matter concentrations predicted by SOWC are 2.23 μg m−3 (3.8%) lower than the average by the internally mixed version of the same model in the SJV because increased solar radiation at the ground increases atmospheric mixing. The changes in predicted meteorological parameters and particle concentrations identified in the current study stem from the mixing state of black carbon. The source-oriented model representation with realistic aging processes predicts that hydrophobic diesel engine particles remain largely uncoated over the +7 day simulation period, while the internal mixture model representation predicts significant accumulation of secondary nitrate and water on diesel engine particles. Similar results will likely be found in any air pollution stagnation episode that is characterized by significant particulate nitrate production. Future work should consider episodes where coatings are predominantly sulfate and/or secondary organic aerosol.

Description: In this paper, we present in-situ observations of processes occurring at the magnetopause and vicinity, including surface waves, oscillatory magnetospheric field lines, and flux transfer events, and coordinated observations at geosynchronous orbit by the GOES spacecraft, and on the ground by CANOPUS and 210° Magnetic Meridian (210MM) magnetometer arrays. On 7 February 2002, during a high-speed solar wind stream, the Polar spacecraft was skimming the magnetopause in a post-noon meridian plane for ~3h. During this interval, it made two short excursions and a few partial crossings into the magnetosheath and observed quasi-periodic cold ion bursts in the region adjacent to the magnetopause current layer. The multiple magnetopause crossings, as well as the velocity of the cold ion bursts, indicate that the magnetopause was oscillating with an ~6-min period. Simultaneous observations of Pc5 waves at geosynchronous orbit by the GOES spacecraft and on the ground by the CANOPUS magnetometer array reveal that these magnetospheric pulsations were forced oscillations of magnetic field lines directly driven by the magnetopause oscillations. The magnetospheric pulsations occurred only in a limited longitudinal region in the post-noon dayside sector, and were not a global phenomenon, as one would expect for global field line resonance. Thus, the magnetopause oscillations at the source were also limited to a localized region spanning ~4h in local time. These observations suggest that it is unlikely that the Kelvin-Helmholz instability and/or fluctuations in the solar wind dynamic pressure were the direct driving mechanisms for the observed boundary oscillations. Instead, the likely mechanism for the localized boundary oscillations was pulsed reconnection at the magnetopause occurring along the X-line extending over the same 4-h region. The Pc5 band pressure fluctuations commonly seen in high-speed solar wind streams may modulate the reconnection rate as an indirect cause of the observed Pc5 pulsations. During the same interval, two flux transfer events were also observed in the magnetosphere near the oscillating magnetopause. Their ground signatures were identified in the CANOPUS data. The time delays of the FTE signatures from the Polar spacecraft to the ground stations enable us to estimate that the longitudinal extent of the reconnection X-line at the magnetopause was ~43° or ~5.2 RE. The coordinated in-situ and ground-based observations suggest that FTEs are produced by transient reconnection taking place along a single extended X-line at the magnetopause, as suggested in the models by Scholer (1988) and Southwood et al. (1988). The observations from this study suggest that the reconnection occurred in two different forms simultaneously in the same general region at the dayside magnetopause: 1) continuous reconnection with a pulsed reconnection rate, and 2) transient reconnection as flux transfer events. Key words. Magnetospheric physics (Magnetopause, cusp and boundary layers; Magnetosphere-ionosphere interactions; MHD waves and instabilities)

Description: Standing Alfvén waves of 1.1 mHz (~15 min in period) were observed by the Cluster satellites in the mid-tail during 06:00-07:00 UT on 8 August 2003. Pulsations with the same frequency were also observed at several ground stations near Cluster's footpoint. The standing wave properties were determined from the electric and magnetic field measurements of Cluster. Data from the ground magnetometers indicated a latitudinal amplitude and phase structure consistent with the driven field line resonance (FLR) at 1.1 mHz. Simultaneously, quasi-periodic oscillations at different frequencies were observed in the post-midnight/early morning sector by GOES 12 (l0≈8.7), Polar (l0≈11-14) and Geotail (l0≈9.8). The 8 August 2003 event yields rare and interesting datasets. It provides, for the first time, coordinated in situ and ground-based observations of a very low frequency FLR in the mid-tail on stretched field lines.

Description: For the first time, a ~ decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution over populated regions and daily time resolution has been conducted for California to provide air quality data for health effect studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O3, PM2.5, elemental carbon (EC), organic carbon (OC), nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM2.5, EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95, 100, 71, 73, and 92% of the simulated months, respectively. The base data set provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated 1 day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to overpredict wind speed during stagnation events, leading to underpredictions of high PM concentrations, usually in winter months. The WRF model also generally underpredicts relative humidity, resulting in less particulate nitrate formation, especially during winter months. These limitations must be recognized when using data in health studies. All model results included in the current manuscript can be downloaded free of charge at http://faculty.engineering.ucdavis.edu/kleeman/ .