The formulations of tropospheric gas-phase chemistry ("mechanisms") used in the regional-scale chemistry-transport models participating in the Air Quality Modelling Evaluation International ...Initiative (AQMEII) Phase 2 are intercompared by the means of box model studies. Simulations were conducted under idealized meteorological conditions, and the results are representative of mean boundary layer concentrations. Three sets of meteorological conditions - winter, spring/autumn and summer - were used to capture the annual variability, similar to the 3-D model simulations in AQMEII Phase 2. We also employed the same emissions input data used in the 3-D model intercomparison, and sample from these datasets employing different strategies to evaluate mechanism performance under a realistic range of pollution conditions. Box model simulations using the different mechanisms are conducted with tight constraints on all relevant processes and boundary conditions (photolysis, temperature, entrainment, etc.) to ensure that differences in predicted concentrations of pollutants can be attributed to differences in the formulation of gas-phase chemistry. The results are then compared with each other (but not to measurements), leading to an understanding of mechanism-specific biases compared to the multi-model mean. Our results allow us to quantify the uncertainty in predictions of a given compound in the 3-D simulations introduced by the choice of gas-phase mechanisms, to determine mechanism-specific biases under certain pollution conditions, and to identify (or rule out) the gas-phase mechanism as the cause of an observed discrepancy in 3-D model predictions. We find that the predictions of the median diurnal cycle of O3 over a set of emission conditions representing a network of station observations is within 4 ppbv (5%) across the different mechanisms. This variability is found to be very similar on both continents. There are considerably larger differences in predicted concentrations of NOx (up to plus or minus 25%), key radicals like OH (40%), HO2 (25%) and especially NO3 (>100%). Secondary substances like H2O2 (25%) or HNO3 (10%), as well as key volatile organic compounds like isoprene (>100%) or CH2O (20%) differ substantially as well. Calculation of an indicator of the chemical regime leads to up to 20% of simulations being classified differently by different mechanism, which would lead to different predictions of the most efficient emission reduction strategies. All these differences are despite identical meteorological boundary conditions, photolysis rates, as well as identical biogenic and inorganic anthropogenic emissions. Anthropogenic VOC emissions only vary in the way they are translated in mechanism-specific compounds, but are identical in the total emitted carbon mass and its spatial distribution. Our findings highlight that the choice of gas-phase mechanism is crucial in simulations for regulatory purposes, emission scenarios, as well as process studies that investigate other components like secondary formed aerosol components. We find that biogenic VOCs create considerable variability in mechanism predictions and suggest that these, together with nighttime chemistry should be areas of further mechanism improvement.
Numerical simulations were performed in order to investigate the impact of the direct effect of aerosol particles on radiation and the indirect aerosol effect on meteorological variables and ...subsequent distributions of near surface ozone and PM10 over Europe. The fully coupled meteorology-chemistry community model WRF/Chem has been applied for June and July 2006 for a baseline case without any aerosol feedback on meteorology, a simulation with the direct effect included, and a simulation including the direct as well as the indirect aerosol effect. The impact of the subsequent changes in temperature, boundary layer height, and clouds that were triggered by the direct effect of aerosol on radiation (“semi-direct effect”) was found to dominate the direct effect of aerosol particles on solar radiation. Over Central Europe the mean reduction of global radiation alone was mostly 3–7 W m−2, but changes in cloud cover due to semi-direct effects resulted in monthly mean changes between ±50 W m−2. The inclusion of the indirect aerosol effect resulted in a pronounced decrease of cloud water content by up to 70% and a significantly higher mean rain water content over the North Atlantic. Although generally plausible, the effect appears to be too strong due to too low simulated aerosol particle numbers in this area. Regional changes in precipitation between −100% and 100% were simulated over the European continent. For the simulation including only the direct aerosol effect these changes are almost entirely due to semi-direct effects. Mean ozone mixing ratios over Europe in July were modified by up to 4 ppb or 10% over continental Europe, mostly related to changes in cloud cover. For PM10 the inclusion of the direct effect resulted for the considered episode in a mean decrease by 20–50% due to an increased atmospheric boundary layer height except for the regions with high PM10 concentrations. When the indirect aerosol effect was additionally taken into account an increase of the monthly PM10 concentration by 1–3 μg m−3 was found for July 2006 over large parts of continental Europe.
The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together seventeen modeling groups from Europe and North America, running eight operational ...online-coupled air quality models over Europe and North America using common emissions and boundary conditions. The simulated annual, seasonal, continental and sub-regional particulate matter (PM) surface concentrations for the year 2010 have been evaluated against a large observational database from different measurement networks operating in Europe and North America. The results show a systematic underestimation for all models in almost all seasons and sub-regions, with the largest underestimations for the Mediterranean region. The rural PM10 concentrations over Europe are underestimated by all models by up to 66% while the underestimations are much larger for the urban PM10 concentrations (up to 75%). On the other hand, there are overestimations in PM2.5 levels suggesting that the large underestimations in the PM10 levels can be attributed to the natural dust emissions. Over North America, there is a general underestimation in PM10 in all seasons and sub-regions by up to ∼90% due mainly to the underpredictions in soil dust. SO42− levels over EU are underestimated by majority of the models while NO3− levels are largely overestimated, particularly in east and south Europe. NH4+ levels are also underestimated largely in south Europe. SO4 levels over North America are particularly overestimated over the western US that is characterized by large anthropogenic emissions while the eastern USA is characterized by underestimated SO4 levels by the majority of the models. Daytime AOD levels at 555 nm is simulated within the 50% error range over both continents with differences attributed to differences in concentrations of the relevant species as well as in approaches in estimating the AOD. Results show that the simulated dry deposition can lead to substantial differences among the models. Overall, the results show that representation of dust and sea-salt emissions can largely impact the simulated PM concentrations and that there are still major challenges and uncertainties in simulating the PM levels.
•Seventeen modeling groups from EU and NA simulated PM for 2010 under AQMEII phase 2.•A general model underestimation of surface PM over both continents up to 80%.•Natural PM emissions may lead to large underestimations in simulated PM10.•Dry deposition can introduce large differences among models.
The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together sixteen modeling groups from Europe and North America, running eight operational online-coupled ...air quality models over Europe and North America on common emissions and boundary conditions. With the advent of online-coupled models providing new capability to quantify the effects of feedback processes, the main aim of this study is to compare the response of coupled air quality models to simulate levels of O3 over the two continental regions. The simulated annual, seasonal, continental and sub-regional ozone surface concentrations and vertical profiles for the year 2010 have been evaluated against a large observational database from different measurement networks operating in Europe and North America. Results show a general model underestimation of the annual surface ozone levels over both continents reaching up to 18% over Europe and 22% over North America. The observed temporal variations are successfully reproduced with correlation coefficients larger than 0.8. Results clearly show that the simulated levels highly depend on the meteorological and chemical configurations used in the models, even within the same modeling system. The seasonal and sub-regional analyses show the models' tendency to overestimate surface ozone in all regions during autumn and underestimate in winter. Boundary conditions strongly influence ozone predictions especially during winter and autumn, whereas during summer local production dominates over regional transport. Daily maximum 8-h averaged surface ozone levels below 50–60 μg m−3 are overestimated by all models over both continents while levels over 120–140 μg m−3 are underestimated, suggesting that models have a tendency to severely under-predict high O3 values that are of concern for air quality forecast and control policy applications.
•Sixteen modeling groups from EU and NA simulated O3 for 2010 under AQMEII phase 2.•A general model underestimation of surface O3 over both continents up to 22%.•Models tend to over/under estimate surface O3 in all regions during autumn/winter.•Boundary conditions influence O3 predictions especially during winter and autumn.•Models tend to under-predict high O3 values that are of concern for policy.
This study describes the implementation of a one way coupled high resolution numerical weather and river runoff forecasting system within the Perl Object Environment (POE) framework and presents its ...application and performance analysis for the Alpine catchment of the Ammer River located in southern Germany. The simulation system employs the hydrological water balance model WaSiM-ETH run at 100 m × 100 m grid resolution one way coupled with the numerical weather prediction model (NWP) MM5 driven at 3.5 km grid cell resolution. The state and event driven forecasting system implements the input data download, input data provision via SOAP based WEB service and the run of the hydrology model with observed and with predicted NWP meteorology fields. It applies a lagged ensemble prediction system (EPS) taking into account combination of recent and previous NWP forecasts. The simulation system has been setup and designed for flood forecasting in the alpine environment. It is run operationally as well as in extended time slice experiments for all episodes with highest observed runoff in the period 01.10.2005–30.09.2010. The system application demonstrates the great potential of the POE based system in networking, distributed computing as well in the setup of various experiments. The river runoff simulation results show high correlation with observed runoff when driven with precipitation interpolated from station observations. The performance of the forecast shows limitations resulting from deficient timing and amount of the predicted rainfall in the complex mountainous area. Forecast skills were improved after application of a lagged ensemble prediction system.
As a contribution to phase2 of the Air Quality Model Evaluation International Initiative (AQMEII), eight different simulations for the year 2010 were performed with WRF-Chem for the European domain. ...The four simulations using RADM2 gas-phase chemistry and the MADE/SORGAM aerosol module are analyzed in this paper. The simulations included different degrees of aerosol–meteorology feedback, ranging from no aerosol effects at all to the inclusion of the aerosol direct radiative effect as well as aerosol cloud interactions and the aerosol indirect effect. In addition, a modification of the RADM2 gas phase chemistry solver was tested. The yearly simulations allow characterizing the average impact of the consideration of feedback effects on meteorology and pollutant concentrations and an analysis of the seasonality. Pronounced feedback effects were found for the summer 2010 Russian wildfire episode, where the direct aerosol effect lowered the seasonal mean solar radiation by 20 W m−3 and seasonal mean temperature by 0.25°. This might be considered as a lower limit as it must be taken into account that aerosol concentrations were generally underestimated by up to 50%. The high aerosol concentrations from the wildfires resulted in a 10%–30% decreased precipitation over Russia when aerosol cloud interactions were taken into account. The most pronounced and persistent feedback due to the indirect aerosol effect was found for regions with very low aerosol concentrations like the Atlantic and Northern Europe. The low aerosol concentrations in this area result in very low cloud droplet numbers between 5 and 100 droplets cm−1 and a 50–70% lower cloud liquid water path. This leads to an increase in the downward solar radiation by almost 50%. Over Northern Scandinavia, this results in almost one degree higher mean temperatures during summer. In winter, the decreased liquid water path resulted in increased long-wave cooling and a decrease of the mean temperature by almost the same amount. Precipitation over the Atlantic Ocean was found to be enhanced by up to 30% when aerosol cloud interactions were taken into account. The inclusion of aerosol cloud interactions can reduce the bias or improve correlations of simulated precipitation for some episodes and regions. However, the domain and time averaged performance statistics do not indicate a general improvement when aerosol feedbacks are taken into account. Except for conditions with either very low or very high aerosol concentrations, the impact of aerosol feedbacks on pollutant distributions was found to be smaller than the effect of the choice of the chemistry module or wet deposition implementation.
•We compare four WRF-Chem simulations which contributed to AQMEII phase2.•Simulations include different degrees of aerosol–radiation feedback and aerosol cloud interactions.•Lower solar radiation, temperature, PBL height, and ozone with direct aerosol effect.•With aerosol cloud interactions higher solar radiation for clean conditions.•Neutral on average performance except for very low aerosol concentrations.
Air pollution simulations critically depend on the quality of the underlying meteorology. In phase 2 of the Air Quality Model Evaluation International Initiative (AQMEII-2), thirteen modeling groups ...from Europe and four groups from North America operating eight different regional coupled chemistry and meteorology models participated in a coordinated model evaluation exercise. Each group simulated the year 2010 for a domain covering either Europe or North America or both. Here were present an operational analysis of model performance with respect to key meteorological variables relevant for atmospheric chemistry processes and air quality. These parameters include temperature and wind speed at the surface and in the vertical profile, incoming solar radiation at the ground, precipitation, and planetary boundary layer heights. A similar analysis was performed during AQMEII phase 1 (Vautard et al., 2012) for offline air quality models not directly coupled to the meteorological model core as the model systems investigated here. Similar to phase 1, we found significant overpredictions of 10-m wind speeds by most models, more pronounced during night than during daytime. The seasonal evolution of temperature was well captured with monthly mean biases below 2 K over all domains. Solar incoming radiation, precipitation and PBL heights, on the other hand, showed significant spread between models and observations suggesting that major challenges still remain in the simulation of meteorological parameters relevant for air quality and for chemistry–climate interactions at the regional scale.
•We evaluate the meteorological performance of coupled chemistry-meteorology models.•13 modeling groups from Europe and 4 groups from North America participated.•Temperature, precipitation and radiation are mostly well simulated.•Significant biases exist in surface wind speeds and nighttime boundary layer heights.•Differences between model systems are usually larger than aerosol feedback effects.
Ten state-of-the-science regional air quality (AQ) modeling systems have been applied to continental-scale domains in North America and Europe for full-year simulations of 2006 in the context of Air ...Quality Model Evaluation International Initiative (AQMEII), whose main goals are model inter-comparison and evaluation. Standardised modeling outputs from each group have been shared on the web-distributed ENSEMBLE system, which allows statistical and ensemble analyses to be performed. In this study, the one-year model simulations are inter-compared and evaluated with a large set of observations for ground-level particulate matter (PM10 and PM2.5) and its chemical components. Modeled concentrations of gaseous PM precursors, SO2 and NO2, have also been evaluated against observational data for both continents. Furthermore, modeled deposition (dry and wet) and emissions of several species relevant to PM are also inter-compared. The unprecedented scale of the exercise (two continents, one full year, fifteen modeling groups) allows for a detailed description of AQ model skill and uncertainty with respect to PM.
Analyses of PM10 yearly time series and mean diurnal cycle show a large underestimation throughout the year for the AQ models included in AQMEII. The possible causes of PM bias, including errors in the emissions and meteorological inputs (e.g., wind speed and precipitation), and the calculated deposition are investigated. Further analysis of the coarse PM components, PM2.5 and its major components (SO4, NH4, NO3, elemental carbon), have also been performed, and the model performance for each component evaluated against measurements. Finally, the ability of the models to capture high PM concentrations has been evaluated by examining two separate PM2.5 episodes in Europe and North America. A large variability among models in predicting emissions, deposition, and concentration of PM and its precursors during the episodes has been found. Major challenges still remain with regards to identifying and eliminating the sources of PM bias in the models. Although PM2.5 was found to be much better estimated by the models than PM10, no model was found to consistently match the observations for all locations throughout the entire year.
This study reviews the top ranked meteorology and chemistry interactions in online coupled models recommended by an experts' survey conducted in COST Action EuMetChem and examines the sensitivity of ...those interactions during two pollution episodes: the Russian forest fires 25 Jul–15 Aug 2010 and a Saharan dust transport event from 1 Oct to 31 Oct 2010 as a part of the AQMEII phase-2 exercise. Three WRF-Chem model simulations were performed for the forest fire case for a baseline without any aerosol feedback on meteorology, a simulation with aerosol direct effects only and a simulation including both direct and indirect effects. For the dust case study, eight WRF-Chem and one WRF-CMAQ simulations were selected from the set of simulations conducted in the framework of AQMEII. Of these two simulations considered no feedbacks, two included direct effects only and five simulations included both direct and indirect effects. The results from both episodes demonstrate that it is important to include the meteorology and chemistry interactions in online-coupled models. Model evaluations using routine observations collected in AQMEII phase-2 and observations from a station in Moscow show that for the fire case the simulation including only aerosol direct effects has better performance than the simulations with no aerosol feedbacks or including both direct and indirect effects. The normalized mean biases are significantly reduced by 10–20% for PM10 when including aerosol direct effects. The analysis for the dust case confirms that models perform better when including aerosol direct effects, but worse when including both aerosol direct and indirect effects, which suggests that the representation of aerosol indirect effects needs to be improved in the model.
•Aerosol feedbacks during two pollution episodes were examined.•Eight WRF-Chem and one WRF-CMAQ simulations performed in AQMEII phase-2.•The simulations including aerosol direct effects only performed better.•The representation of aerosol indirect effects in the model needs to be improved.
More than ten state-of-the-art regional air quality models have been applied as part of the Air Quality Model Evaluation International Initiative (AQMEII). These models were run by twenty independent ...groups in Europe and North America. Standardised modelling outputs over a full year (2006) from each group have been shared on the web-distributed ENSEMBLE system, which allows for statistical and ensemble analyses to be performed by each group. The estimated ground-level ozone mixing ratios from the models are collectively examined in an ensemble fashion and evaluated against a large set of observations from both continents. The scale of the exercise is unprecedented and offers a unique opportunity to investigate methodologies for generating skilful ensembles of regional air quality models outputs. Despite the remarkable progress of ensemble air quality modelling over the past decade, there are still outstanding questions regarding this technique. Among them, what is the best and most beneficial way to build an ensemble of members? And how should the optimum size of the ensemble be determined in order to capture data variability as well as keeping the error low? These questions are addressed here by looking at optimal ensemble size and quality of the members. The analysis carried out is based on systematic minimization of the model error and is important for performing diagnostic/probabilistic model evaluation. It is shown that the most commonly used multi-model approach, namely the average over all available members, can be outperformed by subsets of members optimally selected in terms of bias, error, and correlation. More importantly, this result does not strictly depend on the skill of the individual members, but may require the inclusion of low-ranking skill-score members. A clustering methodology is applied to discern among members and to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill. Results show that, while the methodology needs further refinement, by optimally selecting the cluster distance and association criteria, this approach can be useful for model applications beyond those strictly related to model evaluation, such as air quality forecasting.
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