ALBERT

Description: Recently, downscaling global atmospheric model outputs (GCTM) for the USEPA Community Multiscale Air Quality (CMAQ) Initial (IC) and Boundary Conditions (BC) have become practical because of the rapid growth of computational technologies that allow global simulations to be completed within a reasonable time. The traditional method of generating IC/BC by profile data has lost its advocates due to the weakness of the limited horizontal and vertical variations found on the gridded boundary layers. Theoretically, high quality GCTM IC/BC should yield a better result in CMAQ. Unfortunately, several researchers have found that the outputs from GCTM IC/BC are not necessarily better than profile IC/BC due to the excessive transport of O3 aloft in GCTM IC/BC. In this paper, we intend to investigate the effects of using profile IC/BC and global atmospheric model data. In addition, we are suggesting a novel approach to resolve the existing issue in downscaling. In the study, we utilized the GEOS-Chem model outputs to generate time-varied and layer-varied IC/BC for year 2002 with the implementation of tropopause determining algorithm in the downscaling process (i.e., based on chemical (O3) tropopause definition). The comparison between the implemented tropopause approach and the profile IC/BC approach is performed to demonstrate improvement of considering tropopause. It is observed that without using tropopause information in the downscaling process, unrealistic O3 concentrations are created at the upper layers of IC/BC. This phenomenon has caused over-prediction of surface O3 in CMAQ. In addition, the amount of over-prediction is greatly affected by temperature and latitudinal location of the study domain. With the implementation of the algorithm, we have successfully resolved the incompatibility issues in the vertical layer structure between global and regional chemistry models to yield better surface O3 predictions than profile IC/BC for both summer and winter conditions. At the same time, it improved the vertical O3 distribution of CMAQ outputs. It is strongly recommended that the tropopause information should be incorporated into any two-way coupled global and regional models, where the tropospheric regional model is used, to solve the vertical incompatibility that exists between global and regional models. We have discovered that the previously published paper was not the latest version of the manuscript we intended to use. Some corrections made during the second ACPD reviewing process were not incorporated in the text. As a result, the figure numbers (i.e., figure number below the graph) were not referenced correctly in the manuscript. Therefore, we have decided to re-publish this paper as a corrigendum.

Description: Simulations of present and future average regional ozone and PM2.5 concentrations over the United States were performed to investigate the potential impacts of global climate change and emissions on regional air quality using CMAQ. Various emissions and climate conditions with different biogenic emissions and domain resolutions were implemented to study the sensitivity of future air quality trends from the impacts of changing biogenic emissions. A comparison of GEOS-Chem and CMAQ was performed to investigate the effect of downscaling on the prediction of future air quality trends. For ozone, the impacts of global climate change are relatively smaller when compared to the impacts of anticipated future emissions reduction, except for the Northeast area, where increasing biogenic emissions due to climate change have stronger positive effects (increases) to the regional ozone air quality. The combination effect from both climate change and emission reductions leads to approximately a 10 % or 5 ppbv decrease of the maximum daily average eight-hour ozone (MDA8) over the Eastern United States. For PM2.5, the impacts of global climate change have shown insignificant effect, where as the impacts of anticipated future emissions reduction account for the majority of overall PM2.5 reductions. The annual average 24-h PM2.5 of the future-year condition was found to be about 40 % lower than the one from the present-year condition, of which 60 % of its overall reductions are contributed to by the decrease of SO4 and NO3 particulate matters. Changing the biogenic emissions model increases the MDA8 ozone by about 5–10 % or 3–5 ppbv in the Northeast area. Conversely, it reduces the annual average PM2.5 by 5 % or 1.0 μg m−3 in the Southeast region.

Description: Recently, downscaling global atmospheric model outputs (GCTM) for the USEPA Community Multiscale Air Quality (CMAQ) Initial (IC) and Boundary Conditions (BC) have become practical because of the rapid growth of computational technologies that allow global simulations to be completed within a reasonable time. The traditional method of generating IC/BC by profile data has lost its advocates due to the weakness of the limited horizontal and vertical variations found on the gridded boundary layers. Theoretically, high quality GCTM IC/BC should yield a better result in CMAQ. Unfortunately, several researchers have found that the outputs from GCTM IC/BC are not necessarily better than profile IC/BC due to the excessive transport of O3 aloft in GCTM IC/BC. In this paper, we intend to investigate the effects of using profile IC/BC and global atmospheric model data. In addition, we are suggesting a novel approach to resolve the existing issue in downscaling. In the study, we utilized the GEOS-Chem model outputs to generate time-varied and layer-varied IC/BC for year 2002 with the implementation of tropopause determining algorithm in the downscaling process (i.e., based on chemical (O3) tropopause definition). The comparison between the implemented tropopause approach and the profile IC/BC approach is performed to demonstrate improvement of considering tropopause. It is observed that without using tropopause information in the downscaling process, unrealistic O3 concentrations are created at the upper layers of IC/BC. This phenomenon has caused over-prediction of surface O3 in CMAQ. In addition, the amount of over-prediction is greatly affected by temperature and latitudinal location of the study domain. With the implementation of the algorithm, we have successfully resolved the incompatibility issues in the vertical layer structure between global and regional chemistry models to yield better surface O3 predictions than profile IC/BC for both summer and winter conditions. At the same time, it improved the vertical O3 distribution of CMAQ outputs. It is strongly recommended that the tropopause information should be incorporated into any two-way coupled global and regional models, where the tropospheric regional model is used, to solve the vertical incompatibility that exists between global and regional models.

Description: We simulated elemental carbon (EC) and organic carbon (OC) aerosols over the Pearl River Delta (PRD) area of China and compared the results to seasonal surface measurements, with the aim of quantifying carbonaceous aerosol sources from a "top-down" perspective. Our regional model was driven by current-best estimates of PRD EC (39.5 Gg C yr−1) and OC (32.8 Gg C yr−1) emissions and included updated secondary organic aerosol formation pathways. The simulated annual mean EC and OC concentrations were 4.0 and 7.7 μg C m−3, respectively, lower than the observed annual mean EC and OC concentrations (4.5 and 13.1 μg C m−3, respectively). We used multiple regression to match the simulated EC against seasonal mean observations. The resulting top-down estimate for EC emission in the PRD area was 52.9 ± 8.0 Gg C yr−1. We estimated the OC emission in the PRD area to be 60.2 ± 10.3 Gg C yr−1, based on the top-down EC emission estimate and the primary OC / EC ratios derived from bottom-up statistics. Using these top-down emission estimates, the simulated average annual mean EC and OC concentrations were improved to 4.4 and 9.5 μg C m−3, respectively, closer to the observations. Secondary sources accounted for 42 % of annual mean surface OC in our top-down simulations, with biogenic VOCs being the most important precursors.

Description: In this paper, we evaluate a high-resolution chemistry transport model (CTM) (3 km x 3 km spatial resolution) with the new Hong Kong (HK) NO2 retrieval developed for the Ozone Monitoring Instrument (OMI) on-board the Aura satellite. The three-dimensional atmospheric chemistry was modelled in the Pearl River Delta (PRD) region in southern China by the Models-3 Community Multiscale Air Quality (CMAQ) modelling system from October 2006 to January 2007. In the HK NO2 retrieval, tropospheric air mass factors (AMF) were recalculated using high-resolution ancillary parameters of surface reflectance, NO2 profile shapes and aerosol profiles of which the latter two were taken from the CMAQ simulation. We also tested four different aerosol parametrizations. Ground level measurements by the PRD Regional Air Quality Monitoring (RAQM) network were used as additional independent measurements. The HK NO2 retrieval increases the NO2 vertical column densities (VCD) by (+31 ± 38) %, when compared to NASA's standard product (SP2), and reduces the mean bias (MB) between satellite and ground measurements by 26 percentage points from −41 to −15 %. The correlation coefficient r is low for both satellite datasets (r = 0.35) due to the high spatial variability of NO2 concentrations. The correlation between CMAQ and the RAQM network is low (r ≈ 0.3) and the model underestimates the NO2 concentrations in the north-western model domain (Foshan and Guangzhou). We compared the CMAQ NO2 time series of the two main plumes with our regional OMI NO2 product. The model overestimates the NO2 VCDs by about 15 % in Hong Kong and Shenzhen, while the correlation coefficient is satisfactory (r = 0.56). In Foshan and Guangzhou, the correlation is low (r = 0.37) and the model underestimates the VCDs strongly (MB = −40 %). In addition, we estimated that the OMI VCDs are also underestimated by about 10 to 20 % in Foshan and Guangzhou because of the influence of the model parameters on the AMF. In this study, we demonstrate that the HK OMI NO2 retrieval reduces the bias of the satellite measurements and thus the dataset can be used to study the magnitude of NO2 concentrations in a regional model. The low bias can be achieved if AMFs are recalculated with more accurate surface reflectance, aerosol profiles and NO2 profiles; only NO2 profiles have been replaced in earlier studies. Since unbiased concentrations are important, for example, in air pollution studies, the results of this paper can be very helpful in future model evaluation studies.

Description: Simulations of present and future average regional ozone and PM2.5 concentrations over the United States were performed to investigate the potential impacts of global climate change and emissions on regional air quality using CMAQ. Various emissions and climate conditions with different biogenic emissions and domain resolutions were implemented to study the sensitivity of future air quality trends from the impacts of changing biogenic emissions. A comparison of GEOS-Chem and CMAQ was performed to investigate the effect of downscaling on the prediction of future air quality trends. For ozone, the impacts of global climate change are relatively smaller when compared to the impacts of anticipated future emissions reduction, except for the Northeast area, where increasing biogenic emissions due to climate change have stronger positive effects (increases) to the regional ozone air quality. The combination effect from both climate change and emission reductions leads to approximately a 10% or 5 ppbv decrease of the maximum daily average eight-hour ozone (MDA8) over the Eastern United States. For PM2.5, the impacts of global climate change have shown insignificant effect, where as the impacts of anticipated future emissions reduction account for the majority of overall PM2.5 reductions. The annual average 24-h PM2.5 of the future-year condition was found to be about 40% lower than the one from the present-year condition, of which 60% of its overall reductions are contributed to by the decrease of SO4 and NO3 particulate matters. Changing the biogenic emissions model increases the MDA8 ozone by about 5–10% or 3–5 ppbv in the Northeast area. Conversely, it reduces the annual average PM2.5 by 5% or 1.0 μg/m3 in the Southeast region.

Description: Recent year, downscaling global atmospheric model outputs for the USEPA Community Multiscale Air Quality (CMAQ) Initial (IC) and Boundary Conditions (BC) have become practical because of the rapid growth of computational technologies that allow global simulations can be completed within a reasonable time and have better performance. The traditional method of generating IC/BC by profile data has lost its advocators due to the weakness of the limited horizontal and vertical variations found on the gridded boundary layers. In this paper, we are in effort to investigate the effects of using profile IC/BC and global atmospheric model data. We utilize the GEOS-Chem model outputs to generate time-varied and layer-varied IC/BC for year 2002 using our newly development of tropopause determining algorithm. The purpose of the study is to determine the tropopause effect to the downscaling process. From the results, we have found that without considering tropopause in the downscaling process created unrealistic O3 concentrations in IC/BC at the upper boundary conditions for regional tropospheric model. This phenomenon has caused over-prediction of surface O3 in CMAQ. And it is greatly affected by temperature and latitudinal location. With the implementation of our algorithm, we have successfully resolved the incompatibility issues in the vertical layer structure between global and regions chemistry models to yield better surface O3 predictions than profile IC/BC on both summer and winter conditions. At the same time, it improved the vertical O3 distribution of CMAQ outputs. The algorithm can be applied to a global atmospheric model which performs a reasonable outcome to determine the tropopause.

Description: The recent increase in spatial resolution for satellite instruments has made it feasible to study distributions of trace gas column densities on a regional scale. For this application a new gridding algorithm was developed to map measurements from the instrument's frame of reference (level 2) onto a longitude–latitude grid (level 3). The algorithm is designed for the Ozone Monitoring Instrument (OMI) and can easily be employed for similar instruments – for example, the upcoming TROPOspheric Monitoring Instrument (TROPOMI). Trace gas distributions are reconstructed by a continuous parabolic spline surface. The algorithm explicitly considers the spatially varying sensitivity of the sensor resulting from the instrument function. At the swath edge, the inverse problem of computing the spline coefficients is very sensitive to measurement errors and is regularised by a second-order difference matrix. Since this regularisation corresponds to the penalty term for smoothing splines, it similarly attenuates the effect of measurement noise over the entire swath width. Monte Carlo simulations are conducted to study the performance of the algorithm for different distributions of trace gas column densities. The optimal weight of the penalty term is found to be proportional to the measurement uncertainty and the width of the instrument function. A comparison with an established gridding algorithm shows improved performance for small to moderate measurement errors due to better parametrisation of the distribution. The resulting maps are smoother and extreme values are more accurately reconstructed. The performance improvement is further illustrated with high-resolution distributions obtained from a regional chemistry model. The new algorithm is applied to tropospheric NO2 column densities measured by OMI. Examples of regional NO2 maps are shown for densely populated areas in China, Europe and the United States of America. This work demonstrates that the newly developed gridding algorithm improves regional trace gas maps; its application could be very helpful for the study of satellite-derived trace gas distributions.

Description: In this paper, we present the custom Hong Kong NO2 retrieval (HKOMI) for the Ozone Monitoring Instrument (OMI) on board the Aura satellite which was used to evaluate a high-resolution chemistry transport model (CTM) (3 km × 3 km spatial resolution). The atmospheric chemistry transport was modelled in the Pearl River Delta (PRD) region in southern China by the Models-3 Community Multiscale Air Quality (CMAQ) modelling system from October 2006 to January 2007. In the HKOMI NO2 retrieval, tropospheric air mass factors (AMFs) were recalculated using high-resolution ancillary parameters of surface reflectance, a priori NO2 and aerosol profiles, of which the latter two were taken from the CMAQ simulation. We tested the influence of the ancillary parameters on the data product using four different aerosol parametrizations. Ground-level measurements by the PRD Regional Air Quality Monitoring (RAQM) network were used as additional independent measurements. The HKOMI retrieval increases estimated tropospheric NO2 vertical column densities (VCD) by (+31 ± 38)%, when compared to NASA's standard product (OMNO2-SP), and improves the normalized mean bias (NMB) between satellite and ground observations by 26 percentage points from −41 to −15%. The individual influences of the parameters are (+11.4 ± 13.4)% for NO2 profiles, (+11.0 ± 20.9)% for surface reflectance and (+6.0 ± 8.4)% for the best aerosol parametrization. The correlation coefficient r is low between ground and satellite observations (r = 0.35). The low r and the remaining NMB can be explained by the low model performance and the expected differences when comparing point measurements with area-averaged satellite observations. The correlation between CMAQ and the RAQM network is low (r ≈ 0.3) and the model underestimates the NO2 concentrations in the northwestern model domain (Foshan and Guangzhou). We compared the CMAQ NO2 time series of the two main plumes with our best OMI NO2 data set (HKOMI-4). The model overestimates the NO2 VCDs by about 15% in Hong Kong and Shenzhen, while the correlation coefficient is satisfactory (r = 0.56). In Foshan and Guangzhou, the correlation is low (r = 0.37) and the model underestimates the VCDs strongly (NMB = −40%). In addition, we estimated that the OMI VCDs are also underestimated by about 10 to 20% in Foshan and Guangzhou because of the influence of the model parameters on the AMFs. In this study, we demonstrate that the HKOMI NO2 retrieval reduces the bias of the satellite observations and how the data set can be used to study the magnitude of NO2 concentrations in a regional model at high spatial resolution of 3 × 3 km2. The low bias was achieved with recalculated AMFs using updated surface reflectance, aerosol profiles and NO2 profiles. Since unbiased concentrations are important, for example, in air pollution studies, the results of this paper can be very helpful in future model evaluation studies.

Description: Recent increase of spatial resolution for satellite instruments has it made feasible to study distributions of trace gas column densities on a regional scale. For this application a new gridding algorithm was developed to map measurements from the instrument's frame of reference (Level 2) onto a longitude-latitude grid (Level 3). The algorithm is designed for the Ozone Monitoring Instrument (OMI) and can be employed easily to similar instruments, for example, the upcoming TROPOspheric Monitoring Instrument (TROPOMI). Trace gas distributions are reconstructed by a continuous parabolic spline surface. The algorithm explicitly considers the spatially varying sensitivity of the sensor resulting from the instrument function. At the swath edge, the inverse problem of computing the spline coefficients is very sensitive to measurement errors and is regularised by a second-order difference matrix. Since this regularisation corresponds to the penalty term for smoothing splines, it similarly attenuates the effect of measurement noise over the entire swath width. Monte Carlo simulations are conducted to study the performance of the algorithm for different distributions of trace gas column densities. The optimal weight of the penalty term is found to be proportional to the measurement uncertainty and the width of the instrument function. A comparison with an established gridding algorithm shows improved performance for small to moderate measurement errors due to better parametrization of the distribution. The resulting maps are smoother and extreme values are more accurately reconstructed. The performance improvement is further illustrated with high-resolution distributions obtained from a regional chemistry model. The new algorithm is applied to tropospheric NO2 column densities measured by OMI. Examples of regional NO2 maps are shown for densely populated areas in China, Europe and the United States of America. This work demonstrates that the newly developed gridding algorithm improves regional trace gas maps; its application could be very helpful for the study of satellite-derived trace gas distributions.