Air Quality Model Evaluation International Initiative Research Articles

Modelling black carbon absorption of solar radiation: combining external and internal mixing assumptions.

An accurate simulation of the absorption properties is key for assessing the radiative effects of aerosol on meteorology and climate. The representation of how chemical species are mixed inside the particles (the mixing state) is one of the major uncertainty factors in the assessment of these effects. Here we compare aerosol optical properties simulations over Europe and North America, coordinated in the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII), to 1 year of AERONET sunphotometer retrievals, in an attempt to identify a mixing state representation that better reproduces the observed single scattering albedo and its spectral variation. We use a single post-processing tool (FlexAOD) to derive aerosol optical properties from simulated aerosol speciation profiles, and focus on the absorption enhancement of black carbon when it is internally mixed with more scattering material, discarding from the analysis scenes dominated by dust.We found that the single scattering albedo at 440 nm (ω0,440) is on average overestimated (underestimated) by 3–5 % when external (core-shell internal) mixing of particles is assumed, a bias comparable in magnitude with the typical variability of the quantity. The (unphysical) homogeneous internal mixing assumption underestimates ω0,440 by ~ 14 %. The combination of external and core-shell configurations (partial internal mixing), parameterized using a simplified function of air mass aging, reduces the ω0,440 bias to −1/−3 %. The black carbon absorption enhancement (Eabs) in core-shell with respect to the externally mixed state is in the range 1.8–2.5, which is above the currently most accepted upper limit of ~ 1.5. The partial internal mixing reduces Eabs to values more consistent with this limit. However, the spectral dependence of the absorption is not well reproduced, and the absorption Ångström exponent is overestimated by 70–120 %. Further testing against more comprehensive campaign data, including a full characterization of the aerosol profile in terms of chemical speciation, mixing state, and related optical properties, would help in putting a better constraint on these calculations.

Evaluating cloud properties in an ensemble of regional online coupled models against satellite observations

Abstract. Online coupled meteorology–chemistry models permit the description of the aerosol–radiation (ARI) and aerosol–cloud interactions (ACIs). The aim of this work is to assess the representation of several cloud properties in regional-scale coupled models when simulating the climate–chemistry–cloud–radiation system. The evaluated simulations are performed under the umbrella of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 and include ARI+ACI interactions. Model simulations are evaluated against observational data from the European Space Agency (ESA) Cloud_cci project. The results show an underestimation (overestimation) of cloud fraction (CF) over land (sea) areas by the models. Lower bias values are found in the ensemble mean. Cloud optical depth (COD) and cloud ice water path (IWP) are generally underestimated over the whole European domain. The cloud liquid water path (LWP) is broadly overestimated. The temporal correlation suggests a generally positive correlation between models and satellite observations. Finally, CF gives the best spatial variability representation, whereas COD, IWP, and LWP show less capacity. The differences found can be attributed to differences in the microphysics schemes used; for instance, the number of ice hydrometeors and the prognostic/diagnostic treatment of the LWP are relevant.

Evaluation and uncertainty estimation of the impact of air quality modelling on crop yields and premature deaths using a multi-model ensemble

This study promotes the critical use of air pollution modelling results for health and agriculture impacts, with the primary goal of providing more reliable estimates to decision makers. To date, the accuracy of air quality (AQ) models and the effects of model-to-model result variability (which we will refer to as model uncertainty) on impact assessment studies have been often ignored, thus undermining the robustness of the information used in the decision making process and the confidence in the results obtained.A suite of twelve PM2.5 and ozone concentration fields produced by regional-scale chemistry transport Air Quality (AQ) models during the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) has been used to calculate the impact of air pollution on premature deaths and crop yields. An innovative technique is applied to bias-adjust the models to available observations.The model results for ozone and PM2.5 are combined in a multi-model (MM) ensemble, which is used to estimate the damage and economic cost to human health and crop yields, as well as the associated uncertainties. The MM ensemble quantifies directly the uncertainty introduced by AQ models into the air pollution impact assessment chain, while the indirect use of experimental information through a bias adjustment, reduces the uncertainty in the ozone and PM2.5 fields and subsequently the uncertainty of the final impact assessment and cost valuation.The analysis over the European countries analysed in this study shows a mean number of premature deaths due to exposure to PM2.5 and ozone of approximately 370,000 (inter-quantile range between 260,000 and 415,000) and a relative yield loss of approximately 7% to 9% (depending on the exposure metrics used, for wheat and maize together). Furthermore, the results indicate that a reduction in the uncertainty of the modelled ozone by 61% and by 80% (depending on the aggregation metric used) and by 46% for PM2.5, produces a reduction in the uncertainty in premature mortality and crop loss of >60%, and of an equivalent percentage in the final uncertainty of cost valuation, providing decision makers with more accurate estimations for more targeted interventions.

Impacts of different characterizations of large-scale background on simulated regional-scale ozone over the continental United States.

This study analyzes simulated regional-scale ozone burdens both near the surface and aloft, estimates process contributions to these burdens, and calculates the sensitivity of the simulated regional-scale ozone burden to several key model inputs with a particular emphasis on boundary conditions derived from hemispheric or global-scale models. The Community Multiscale Air Quality (CMAQ) model simulations supporting this analysis were performed over the continental US for the year 2010 within the context of the Air Quality Model Evaluation International Initiative (AQMEII) and Task Force on Hemispheric Transport of Air Pollution (TF-HTAP) activities. CMAQ process analysis (PA) results highlight the dominant role of horizontal and vertical advection on the ozone burden in the mid-to-upper troposphere and lower stratosphere. Vertical mixing, including mixing by convective clouds, couples fluctuations in free-tropospheric ozone to ozone in lower layers. Hypothetical bounding scenarios were performed to quantify the effects of emissions, boundary conditions, and ozone dry deposition on the simulated ozone burden. Analysis of these simulations confirms that the characterization of ozone outside the regional-scale modeling domain can have a profound impact on simulated regional-scale ozone. This was further investigated by using data from four hemispheric or global modeling systems (Chemistry - Integrated Forecasting Model (C-IFS), CMAQ extended for hemispheric applications (H-CMAQ), the Goddard Earth Observing System model coupled to chemistry (GEOS-Chem), and AM3) to derive alternate boundary conditions for the regional-scale CMAQ simulations. The regional-scale CMAQ simulations using these four different boundary conditions showed that the largest ozone abundance in the upper layers was simulated when using boundary conditions from GEOS-Chem, followed by the simulations using C-IFS, AM3, and H-CMAQ boundary conditions, consistent with the analysis of the ozone fields from the global models along the CMAQ boundaries. Using boundary conditions from AM3 yielded higher springtime ozone columns burdens in the middle and lower troposphere compared to boundary conditions from the other models. For surface ozone, the differences between the AM3-driven CMAQ simulations and the CMAQ simulations driven by other large-scale models are especially pronounced during spring and winter where they can reach more than 10 ppb for seasonal mean ozone mixing ratios and as much as 15 ppb for domain-averaged daily maximum 8 h average ozone on individual days. In contrast, the differences between the C-IFS-, GEOS-Chem-, and H-CMAQ-driven regional-scale CMAQ simulations are typically smaller. Comparing simulated sur face ozone mixing ratios to observations and computing seasonal and regional model performance statistics revealed that boundary conditions can have a substantial impact on model performance. Further analysis showed that boundary conditions can affect model performance across the entire range of the observed distribution, although the impacts tend to be lower during summer and for the very highest observed percentiles. The results are discussed in the context of future model development and analysis opportunities.

Source-sector contributions to European ozone and fine PM in 2010 using AQMEII modeling data

Abstract. Source apportionment modeling provides valuable information on the contributions of different source sectors and/or source regions to ozone (O3) or fine particulate matter (PM2.5) concentrations. This information can be useful in designing air quality management strategies and in understanding the potential benefits of reducing emissions from a particular source category. The Comprehensive Air quality Model with Extensions (CAMx) offers unique source attribution tools, called the Ozone and Particulate Source Apportionment Technology (OSAT/PSAT), which track source contributions. We present results from a CAMx source attribution modeling study for a summer month and a winter month using a recently evaluated European CAMx modeling database developed for Phase 3 of the Air Quality Model Evaluation International Initiative (AQMEII). The contributions of several source sectors (including model boundary conditions of chemical species representing transport of emissions from outside the modeling domain as well as initial conditions of these species) to O3 or PM2.5 concentrations in Europe were calculated using OSAT and PSAT, respectively. A 1-week spin-up period was used to reduce the influence of initial conditions. Evaluation focused on 16 major cities and on identifying source sectors that contributed above 5 %. Boundary conditions have a large impact on summer and winter ozone in Europe and on summer PM2.5, but they are only a minor contributor to winter PM2.5. Biogenic emissions are important for summer ozone and PM2.5. The important anthropogenic sectors for summer ozone are transportation (both on-road and non-road), energy production and conversion, and industry. In two of the 16 cities, solvent and product also contributed above 5 % to summertime ozone. For summertime PM2.5, the important anthropogenic source sectors are energy, transportation, industry, and agriculture. Residential wood combustion is an important anthropogenic sector in winter for PM2.5 over most of Europe, with larger contributions in central and eastern Europe and the Nordic cities. Other anthropogenic sectors with large contributions to wintertime PM2.5 include energy, transportation, and agriculture.

Evaluation and error apportionment of an ensemble of atmospheric chemistry transport modeling systems: multivariable temporal and spatial breakdown.

Through the comparison of several regional-scale chemistry transport modeling systems that simulate meteorology and air quality over the European and North American continents, this study aims at (i) apportioning error to the responsible processes using timescale analysis, (ii) helping to detect causes of model error, and (iii) identifying the processes and temporal scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition, and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overallsense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance, and covariance) can help assess the nature and quality of the error. Each of the error components is analyzed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intraday) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emission and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high interdependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. This will require evaluaion methods that are able to frame the impact on error of processes, conditions, and fluxes at the surface. For example, error due to emission and boundary conditions is dominant for primary species (CO, particulate matter (PM)), while errors due to meteorology and chemistry are most relevant to secondary species, such as ozone. Some further aspects emerged whose interpretation requires additional consideration, such as the uniformity of the synoptic error being region- and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.

Technical note: Coordination and harmonization of the multi-scale, multi-model activities HTAP2, AQMEII3, and MICS-Asia3: simulations, emission inventories, boundary conditions, and model output formats.

We present an overview of the coordinated global numerical modelling experiments performed during 2012–2016 by the Task Force on Hemispheric Transport of Air Pollution (TF HTAP), the regional experiments by the Air Quality Model Evaluation International Initiative (AQMEII) over Europe and North America, and the Model Intercomparison Study for Asia (MICS-Asia). To improve model estimates of the impacts of intercontinental transport of air pollution on climate, ecosystems, and human health and to answer a set of policy-relevant questions, these three initiatives performed emission perturbation modelling experiments consistent across the global, hemispheric, and continental/regional scales. In all three initiatives, model results are extensively compared against monitoring data for a range of variables (meteorological, trace gas concentrations, and aerosol mass and composition) from different measurement platforms (ground measurements, vertical profiles, airborne measurements) collected from a number of sources. Approximately 10 to 25 modelling groups have contributed to each initiative, and model results have been managed centrally through three data hubs maintained by each initiative. Given the organizational complexity of bringing together these three initiatives to address a common set of policy-relevant questions, this publication provides the motivation for the modelling activity, the rationale for specific choices made in the model experiments, and an overview of the organizational structures for both the modelling and the measurements used and analysed in a number of modelling studies in this special issue.

Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data

Abstract. Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism). Multi-model ensembles can improve the forecast skill, provided that certain mathematical conditions are fulfilled. In this work, four ensemble methods were applied to two different datasets, and their performance was compared for ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM10). Apart from the unconditional ensemble average, the approach behind the other three methods relies on adding optimum weights to members or constraining the ensemble to those members that meet certain conditions in time or frequency domain. The two different datasets were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). The methods are evaluated against ground level observations collected from the EMEP (European Monitoring and Evaluation Programme) and AirBase databases. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill over the single models and the ensemble mean. Verification statistics show that the deterministic models simulate better O3 than NO2 and PM10, linked to different levels of complexity in the represented processes. The unconditional ensemble mean achieves higher skill compared to each station's best deterministic model at no more than 60 % of the sites, indicating a combination of members with unbalanced skill difference and error dependence for the rest. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31 % compared to using the full ensemble in an unconditional way. The skill improvements were higher for O3 and lower for PM10, associated with the extent of potential changes in the joint distribution of accuracy and diversity in the ensembles. The skill enhancement was superior using the weighting scheme, but the training period required to acquire representative weights was longer compared to the sub-selecting schemes. Further development of the method is discussed in the conclusion.

Error apportionment for atmospheric chemistry-transport models – a new approach to model evaluation

Abstract. In this study, methods are proposed to diagnose the causes of errors in air quality (AQ) modelling systems. We investigate the deviation between modelled and observed time series of surface ozone through a revised formulation for breaking down the mean square error (MSE) into bias, variance and the minimum achievable MSE (mMSE). The bias measures the accuracy and implies the existence of systematic errors and poor representation of data complexity, the variance measures the precision and provides an estimate of the variability of the modelling results in relation to the observed data, and the mMSE reflects unsystematic errors and provides a measure of the associativity between the modelled and the observed fields through the correlation coefficient. Each of the error components is analysed independently and apportioned to resolved processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) and as a function of model complexity.The apportionment of the error is applied to the AQMEII (Air Quality Model Evaluation International Initiative) group of models, which embrace the majority of regional AQ modelling systems currently used in Europe and North America.The proposed technique has proven to be a compact estimator of the operational metrics commonly used for model evaluation (bias, variance, and correlation coefficient), and has the further benefit of apportioning the error to the originating timescale, thus allowing for a clearer diagnosis of the processes that caused the error.

Funcionamiento del Sistema WRF-CMAQ para Bulgaria con tres subdominios en Europa

The air quality modelling system WRF-CMAQ running at the National Institute of Meteorology and Hydrology (NIMH) in Sofia was applied to the European domain for the year 2010 in the frame of the Air Quality Model Evaluation International Initiative (AQMEII), Phase 2. The model system was set up for a domain of 5000x5000 km2 size with horizontal resolution of 25 km. The models’ options used and the emission input are briefly outlined. The model performance was investigated based on graphical plots and statistical indexes obtained by the web-based model evaluation platform ENSEMBLE. A preliminary operational model evaluation for ozone and particulate matter was conducted, comparing simulated and observed concentrations at ground level in three sub-domains of Europe. The analysis shows model overestimation for ozone and model underestimation for particulate matter. The best statistical indicators are for ozone concentrations during summer, when comparing data for EMEP stations in the EU domain. The worse results are for PM10 winter concentration in the region of the Balkan countries.

Application of WRF/Chem over North America under the AQMEII Phase 2 – Part 2: Evaluation of 2010 application and responses of air quality and meteorology–chemistry interactions to changes in emissions and meteorology from 2006 to 2010

Abstract. The Weather Research and Forecasting model with Chemistry (WRF/Chem) simulation with the 2005 Carbon Bond (CB05) gas-phase mechanism coupled to the Modal for Aerosol Dynamics for Europe (MADE) and the volatility basis set approach for secondary organic aerosol (SOA) are conducted over a domain in North America for 2006 and 2010 as part of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 project. Following the Part 1 paper that focuses on the evaluation of the 2006 simulations, this Part 2 paper focuses on a comparison of model performance in 2006 and 2010 as well as analysis of the responses of air quality and meteorology–chemistry interactions to changes in emissions and meteorology from 2006 to 2010. In general, emissions for gaseous and aerosol species decrease from 2006 to 2010, leading to a reduction in gaseous and aerosol concentrations and associated changes in radiation and cloud variables due to various feedback mechanisms. WRF/Chem is able to reproduce most observations and the observed variation trends from 2006 to 2010, despite its slightly worse performance than WRF that is likely due to inaccurate chemistry feedbacks resulting from less accurate emissions and chemical boundary conditions (BCONs) in 2010. Compared to 2006, the performance for most meteorological variables in 2010 gives lower normalized mean biases but higher normalized mean errors and lower correlation coefficients. The model also shows poorer performance for most chemical variables in 2010. This could be attributed to underestimations in emissions of some species, such as primary organic aerosol in some areas of the US in 2010, and inaccurate chemical BCONs and meteorological predictions. The inclusion of chemical feedbacks in WRF/Chem reduces biases in meteorological predictions in 2010; however, it increases errors and weakens correlations comparing to WRF simulations. Sensitivity simulations show that the net changes in meteorological variables from 2006 to 2010 are mostly influenced by changes in meteorology and those of ozone and fine particulate matter are influenced to a large extent by emissions and/or chemical BCONs and to a lesser extent by changes in meteorology. Using a different set of emissions and/or chemical BCONs helps improve the performance of individual variables, although it does not improve the degree of agreement with observed interannual trends. These results indicate a need to further improve the accuracy and consistency of emissions and chemical BCONs, the representations of SOA and chemistry–meteorology feedbacks in the online-coupled models.

Sensitivity of feedback effects in CBMZ/MOSAIC chemical mechanism

To investigate the impact of the aerosol effects on meteorological variables and pollutant concentrations two simulations with the WRF-Chem model have been performed over Europe for year 2010. We have performed a baseline simulation without any feedback effects and a second simulation including the direct as well as the indirect aerosol effect. The paper describes the full configuration of the model, the simulation design, special impacts and evaluation. Although low aerosol particle concentrations are detected, the inclusion of the feedback effects results in an increase of solar radiation at the surface over cloudy areas (North-West, including the Atlantic) and decrease over more sunny locations (South-East). Aerosol effects produce an increase of the water vapor and decrease the planet boundary layer height over the whole domain except in the Sahara area, where the maximum particle concentrations are detected. Significant ozone concentrations are found over the Mediterranean area. Simulated feedback effects between aerosol concentrations and meteorological variables and on pollutant distributions strongly depend on the aerosol concentrations and the clouds. Further investigations are necessary with higher aerosol particle concentrations. WRF-Chem variables are evaluated using available hourly observations in terms of performance statistics. Standardized observations from the ENSEMBLE system web-interface were used. The research was developed under the second phase of Air Quality Model Evaluation International Initiative (AQMEII). WRF-Chem demonstrates its capability in capturing temporal and spatial variations of the major meteorological variables and pollutants, except the wind speed over complex terrain. The wind speed bias may affect the accuracy in the chemical predictions (NO2, SO2). The analysis of the correlations between simulated data sets and observational data sets indicates that the simulation with aerosol effects performs slightly better. These results indicate potential importance of the aerosol feedback effects and an urgent need to further improve the representations in current atmospheric models to reduce uncertainties at all scales.

Assessment of the MACC reanalysis and its influence as chemical boundary conditions for regional air quality modeling in AQMEII-2

The Air Quality Model Evaluation International Initiative (AQMEII) has now reached its second phase which is dedicated to the evaluation of online coupled chemistry-meteorology models. Sixteen modeling groups from Europe and five from North America have run regional air quality models to simulate the year 2010 over one European and one North American domain. The MACC re-analysis has been used as chemical initial (IC) and boundary conditions (BC) by all participating regional models in AQMEII-2. The aim of the present work is to evaluate the MACC re-analysis along with the participating regional models against a set of ground-based measurements (O3, CO, NO, NO2, SO2, SO42−) and vertical profiles (O3 and CO). Results indicate different degrees of agreement between the measurements and the MACC re-analysis, with an overall better performance over the North American domain. The influence of BC on regional air quality simulations is analyzed in a qualitative way by contrasting model performance for the MACC re-analysis with that for the regional models. This approach complements more quantitative approaches documented in the literature that often have involved sensitivity simulations but typically were limited to only one or only a few regional scale models. Results suggest an important influence of the BC on ozone for which the underestimation in winter in the MACC re-analysis is mimicked by the regional models. For CO, it is found that background concentrations near the domain boundaries are rather close to observations while those over the interior of the two continents are underpredicted by both MACC and the regional models over Europe but only by MACC over North America. This indicates that emission differences between the MACC re-analysis and the regional models can have a profound impact on model performance and points to the need for harmonization of inputs in future linked global/regional modeling studies.

Simulation of European air quality by WRF–CMAQ models using AQMEII-2 infrastructure

The air quality modeling system WRF–CMAQ was applied to the European domain for the year 2010 in the frame of the Air Quality Model Evaluation International Initiative (AQMEII), Phase 2. The model system was set up for a domain of 5000×5000 km2 size with horizontal resolution of 25km. The emissions at European level were available through AQMEII and further processed in a way to feed the chemistry transport model CMAQ. The meso-meteorological model WRF was driven by NCEP GFS data with 1°×1° resolution. The chemical boundary conditions were extracted from MACC global simulation data. Model performance was investigated by means of AQMEII-2 web based evaluation platform and the monitoring data gathered for this activity. A preliminary model evaluation for ozone, nitrogen dioxide and particulate matter was conducted. The statistical analysis was based on comparison between simulated and observed concentrations at different type of surface stations in the EU wide domain (rural, urban, suburban), as well as for selected four cities. Model performance was characterized by overestimation for ozone and underestimation for the other pollutants. The relative statistical indicators were discussed also in view of recently published performance criteria.

Sensitivity analysis of the microphysics scheme in WRF-Chem contributions to AQMEII phase 2

The parameterization of cloud microphysics is a crucial part of fully-coupled meteorology-chemistry models, since microphysics governs the formation, growth and dissipation of hydrometeors and also aerosol cloud interactions. The main objective of this study, which is based on two simulations for Europe contributing to Phase 2 of the Air Quality Model Evaluation International Initiative (AQMEII) is to assess the sensitivity of WRF-Chem to the selection of the microphysics scheme. Two one-year simulations including aerosol cloud interactions with identical physical-chemical parameterizations except for the microphysics scheme (Morrison –MORRAT vs Lin –LINES) are compared. The study covers the difference between the simulations for two three-month periods (cold and a warm) during the year 2010, allowing thus a seasonal analysis. Overall, when comparing to observational data, no significant benefits from the selection of the microphysical schemes can be derived from the results. However, these results highlight a marked north-south pattern of differences, as well as a decisive impact of the aerosol pollution on the results. The MORRAT simulation resulted in higher cloud water mixing ratios over remote areas with low CCN concentrations, whereas the LINES simulation yields higher cloud water mixing ratios over the more polluted areas. Regarding the droplet number mixing ratio, the Morrison scheme was found to yield higher values both during winter and summer for nearly the entire model domain. As smaller and more numerous cloud droplets are more effective in scattering shortwave radiation, the downwelling shortwave radiation flux at surface was found to be up to 30 W m−2 lower for central Europe for the MORRAT simulation as compared to the simulation using the LINES simulation during wintertime. Finally, less convective precipitation is simulated over land with MORRAT during summertime, while no almost difference was found for the winter. On the other hand, non-convective precipitation was up to 4 mm lower during wintertime over Italy and the Balkans for the case of including Lin microphysics as compared to the MORRAT simulation.

Impact of NOx emissions on air quality simulations with the Bulgarian WRF-CMAQ modelling system

The WRF-CMAQ modelling system was applied for air quality simulation over Europe in the frame of the air quality model evaluation international initiative (AQMEII), phase 2. The models were run for the year 2010 for a domain of 5,000 × 5,000 km² with a horizontal grid resolution of 25 km. A preliminary evaluation of model results vs. EU surface measurements was conducted using JRC web-based ENSEMBLE platform. Model performance was characterised by overestimation for ozone and underestimation for NO2. Trying to understand this fact, another set of NOx emissions, a more complete one, was prepared and the simulations were repeated for the entire year (BG1 and BG2 sets). The increase of NOx emissions with 30% slightly increase the quality of the simulation - the overestimation of O3 decreases by a few percent and NO2 concentrations increase and reach observed levels at rural sites, while they still remain underpredicted at urban sites.

Annual application and evaluation of the online coupled WRF–CMAQ system over North America under AQMEII phase 2

We present an application of the online coupled Weather Research and Forecasting–Community Multiscale Air Quality (WRF–CMAQ) modeling system to two annual simulations over North America performed under Phase 2 of the Air Quality Model Evaluation International Initiative (AQMEII). Operational evaluation shows that model performance is comparable to earlier annual applications of the uncoupled WRF/CMAQ modeling system Results also indicate that factors such as changes in the underlying emissions inventory and chemical boundary conditions likely exert a larger influence on overall model performance than feedback effects. A comparison of the simulated Aerosol Optical Depth (AOD) against observations reveals a tendency toward underprediction in all seasons despite a general overprediction of PM2.5 during wintertime. Summertime sensitivity simulations without feedback effects are used to quantify the average impact of the simulated direct feedback effect on temperature, PBL heights, ozone and PM2.5 concentrations. Model results for 2006 and 2010 are analyzed to compare modeled changes between these years to those seen in observations. The results for summertime average daily maximum 8-h ozone showed that the model tends to underestimate the observed decrease in concentrations. The results for total and speciated PM2.5 vary between seasons, networks and species, but the WRF–CMAQ simulations do capture the substantial decreases in observed PM2.5 concentrations in summer and fall. These 2010–2006 PM2.5 decreases result in simulated increases of summer mean clear-sky shortwave radiation between 5 and 10 W/m2. The WRF–CMAQ configuration without direct feedback effects simulates smaller changes in summertime PM2.5 concentrations, indicating that the direct feedback effect enhances the air quality benefits arising from emission controls and that coupled modeling systems are necessary to quantify such feedback effects.

A multi-model assessment for the 2006 and 2010 simulations under the Air Quality Model Evaluation International Initiative (AQMEII) phase 2 over North America: Part I. Indicators of the sensitivity of O3 and PM2.5 formation regimes

Under the Air Quality Model Evaluation International Initiative, Phase 2 (AQMEII-2), three online-coupled air quality model simulations, with six different configurations, are analyzed for their performance, inter-model agreement, and responses to emission and meteorological changes between 2006 and 2010. In this Part I paper, we focus on evaluating O3 and PM2.5 indicator-based analyses, which are important in the development of applicable control strategies of O3 and PM2.5 pollution in different regions worldwide. The O3 indicators agree on widespread NOx-limited and localized VOC-limited conditions in the U.S. The NOy and O3/NOy indicators overpredict the extent of the VOC-limited chemistry in southeast U.S., but are more robust than the H2O2/HNO3, HCHO/NOy, and HCHO/NO2 indicators at the surface, which exhibit relatively more inter-model variability. The column HCHO/NO2 indicator is underpredicted in the O3 and non-O3 seasons, but there is regional variability. For surface PM2.5 indicators, there is good inter-model agreement for the degree of sulfate neutralization; however there are systematic underpredictions in the southeast U.S. There is relatively poor inter-model agreement for the less robust adjusted gas ratio indicator, which is largely overpredicted in the summer and both under- and overpredicted in winter in the southeast U.S. There is good inter-model agreement for the O3 indicator sensitivities, indicating a predominant shift to more NOx-limited conditions in 2010 relative to 2006. There is less agreement for PM2.5 indicator sensitivities, which are less robust, while indicating shifts to either regime due to different responses of aerosol treatments to changes in emissions and meteorology.

Implementation and initial application of new chemistry-aerosol options in WRF/Chem for simulating secondary organic aerosols and aerosol indirect effects for regional air quality

Atmospheric aerosols play important roles in affecting regional meteorology and air quality through aerosol direct and indirect effects. Two new chemistry-aerosol options have been developed in WRF/Chem v3.4.1 by incorporating the 2005 Carbon Bond (CB05) mechanism and coupling it with the existing aerosol module MADE with SORGAM and VBS modules for simulating secondary organic aerosol (SOA), aqueous-phase chemistry in both large scale and convective clouds, and aerosol feedback processes (hereafter CB05-MADE/SORGAM and CB05-MADE/VBS). As part of the Air Quality Model Evaluation International Initiative (AQMEII) Phase II model intercomparison that focuses on online-coupled meteorology and chemistry models, WRF/Chem with the two new options is applied to an area over North America for July 2006 episode. The simulations with both options can reproduce reasonably well most of the observed meteorological variables, chemical concentrations, and aerosol/cloud properties. Compared to CB05-MADE/SORGAM, CB05-MADE/VBS greatly improves the model performance for organic carbon (OC) and PM2.5, reducing NMBs from −81.2% to −13.1% and from −26.1% to −15.6%, respectively. Sensitivity simulations show that the aerosol indirect effects (including aqueous-phase chemistry) can reduce the net surface solar radiation by up to 53 W m−2 with a domainwide mean of 12 W m−2 through affecting cloud formation and radiation scattering and reflection by increasing cloud cover, which in turn reduce the surface temperature, NO2 photolytic rate, and planetary boundary layer height by up to 0.3 °C, 3.7 min−1, and 64 m, respectively. The changes of those meteorological variables further impact the air quality through the complex chemistry-aerosol-cloud-radiation interactions by reducing O3 mixing ratios by up to 5.0 ppb. The results of this work demonstrate the importance of aerosol indirect effects on the regional climate and air quality. For comparison, the impacts of aerosol direct effects on both regional meteorology and air quality are much lower with the reduction on net surface solar radiation only by up to 17 W m−2 and O3 only by up to 1.4 ppb, which indicates the importance and necessity to accurately represent the aerosol indirect effects in the online-couple regional models.

Analysis of the emission inventories and model-ready emission datasets of Europe and North America for phase 2 of the AQMEII project

This paper highlights the development of the emission inventories and emission processing for Europe (EU) and North America (NA) in the second phase of the Air Quality Model Evaluation International Initiative (AQMEII) project. The main purpose of the second phase of the AQMEII project is to understand the importance of coupled meteorological-chemical models in our understanding of the feedback of chemistry on the meteorology. A second purpose of the second phase of the AQMEII project is to explore the differences between EU and NA in a dynamic evaluation of two modeling years (2006 and 2010). The first phase of AQMEII also considered the modeling year 2006. Comparing the two AQMEII phases, for the EU domain, there were substantial decreases in CO (−19%), NH3 (−11%), and SO2 (−12%) emissions between the phase 2 and phase 1 emissions used for 2006. For the NA domain, there were decreases in CO (−10%), non-methane hydrocarbons (−5%), PM2.5 (−8%), PM10 (−18%), SO2 (−12%), with an increase of 4% in NOx. For the 2010 modeling year, 2009 emissions were used as a proxy for 2010 emissions in the EU domain. Between 2006 and 2009, considerable emission reductions were achieved for 17 EU countries, Norway and Switzerland as well as EU-Non-Member States, for all emitted species aside from NH3, which remained almost stable. Non-EU countries showed little change in emissions levels, though this may be a result of poor data quality. Shipping emissions decreased for PM and SO2 due to Sulfur Emission Control Areas on the North Sea and the Baltic Sea, while increasing for other species. Overall for the EU domain between 2006 and 2009, estimated NOx emissions decreased by 10%, SO2 by 18%, CO by 12%, PM2.5 by 5%, PM10 by 6%, NMVOC by 11%, and NH3 by 1%. Between the 2006 and 2010 modeling years, estimated US NOx emissions decreased by 17%, SO2 by 29%, CO by 21%, PM2.5 by 12%, PM10 by 7%, NMHC by 4% and NH3 by 2% while Canadian and Mexican emissions were assumed to remain constant. These changes in US emissions, however, only reflect changes in sectors for which we had year-specific information. In terms of natural emissions, climatic differences between 2006 and 2010 caused the European emissions of isoprene to peak earlier in the year 2006 than in 2010, and overall achieved higher levels in 2010. Biogenic emissions in North America increased in the east and decreased in the west between 2006 and 2010, due to regional temperature differences between the years from natural variation in solar radiation reaching the ground.