Entity

Time filter

Source Type

Breitenfurt bei Wien, Austria

Kong X.,University of Hertfordshire | Forkel R.,Karlsruhe Institute of Technology | Sokhi R.S.,University of Hertfordshire | Suppan P.,Karlsruhe Institute of Technology | And 19 more authors.
Atmospheric Environment | Year: 2015

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. © 2014 Published by Elsevier Ltd. Source


Giordano L.,Empa - Swiss Federal Laboratories for Materials Science and Technology | Brunner D.,Empa - Swiss Federal Laboratories for Materials Science and Technology | Flemming J.,ECMWF | Hogrefe C.,U.S. Environmental Protection Agency | And 34 more authors.
Atmospheric Environment | Year: 2015

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. © 2015 The Authors. Source


Brunner D.,Empa - Swiss Federal Laboratories for Materials Science and Technology | Savage N.,UK Met Office | Jorba O.,Barcelona Supercomputing Center | Eder B.,U.S. Environmental Protection Agency | And 33 more authors.
Atmospheric Environment | Year: 2015

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. © 2014 The Authors. Source


Im U.,European Commission - Joint Research Center Ispra | Bianconi R.,Enviroware srl | Solazzo E.,European Commission - Joint Research Center Ispra | Kioutsioukis I.,European Commission - Joint Research Center Ispra | And 37 more authors.
Atmospheric Environment | Year: 2014

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. SO4 2- 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. © 2014 Elsevier Ltd. Source


Knote C.,U.S. National Center for Atmospheric Research | Tuccella P.,University of LAquila | Curci G.,University of LAquila | Emmons L.,U.S. National Center for Atmospheric Research | And 15 more authors.
Atmospheric Environment | Year: 2015

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 ± 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. © 2014 The Authors. Source

Discover hidden collaborations