During night-time, the heterogeneous hydrolysis of N2O5 on the surface of deliquescent aerosol particles represents a major source for the formation of HNO3 and leads to an important reduction of NOx in the atmosphere. In Chen et al., Atmos. Chem. Phys. 18:673–689, 2018 [5], we investigate an improved parameterization of the heterogeneous N2O5 hydrolysis. This approach is based on laboratory experiments and takes into account the temperature, relative humidity, aerosol particle composition as well as the surface area concentration. The parametrization was implemented in the online coupled model system COSMO-MUSCAT (Consortium for Small-scale Modelling and Multi-Scale Chemistry Aerosol Transport, https://cosmo-muscat.tropos.de). In Chen et al., Atmos. Chem. Phys. 18:673–689, 2018 [5], the modified model was applied for the simulation of the HOPE-Melpitz campaign (10–25 September 2013) where especially the nitrate prediction over western and central Europe was analysed. The modelled particulate nitrate concentrations were compared with filter measurements over Germany. In this first study, the particulate nitrate results are significantly improved by using the developed N2O5 parametrization, particularly if the particulate nitrate was dominated by the local chemical formation (September 12, 17–18 and 25). The aim of the current study consists in an evaluation over a longer time period for different meteorological conditions and emission situations. For this reason, we have simulated the period from March to November 2010. The results were compared with other approaches and evaluated by filter measurements. The improvement was confirmed for the results in spring and autumn, but nitrate is strongly over-predicted also for the new parametrization during the summer time.

Atmospheric aerosols modify the radiative budget of the Earth due to their optical, microphysical and chemical properties, and are considered one of the most uncertain climate forcing agents. In order to characterise the uncertainties associated with satellite and modelling approaches to represent aerosol optical properties, mainly aerosol optical depth (AOD) and Ångström exponent (AE), their representation by different remote-sensing sensors and regional online coupled chemistry–climate models over Europe are evaluated. This work also characterises whether the inclusion of aerosol–radiation (ARI) or/and aerosol–cloud interactions (ACI) help improve the skills of modelling outputs.Two case studies were selected within the EuMetChem COST Action ES1004 framework when important aerosol episodes in 2010 all over Europe took place: a Russian wildfire episode and a Saharan desert dust outbreak that covered most of the Mediterranean Sea. The model data came from different regional air-quality–climate simulations performed by working group 2 of EuMetChem, which differed according to whether ARI or ACI was included or not. The remote-sensing data came from three different sensors: MODIS, OMI and SeaWIFS. The evaluation used classical statistical metrics to first compare satellite data versus the ground-based instrument network (AERONET) and then to evaluate model versus the observational data (both satellite and ground-based data).Regarding the uncertainty in the satellite representation of AOD, MODIS presented the best agreement with the AERONET observations compared to other satellite AOD observations. The differences found between remote-sensing sensors highlighted the uncertainty in the observations, which have to be taken into account when evaluating models. When modelling results were considered, a common trend for underestimating high AOD levels was observed. For the AE, models tended to underestimate its variability, except when considering a sectional approach in the aerosol representation. The modelling results showed better skills when ARI+ACI interactions were included; hence this improvement in the representation of AOD (above 30 % in the model error) and AE (between 20 and 75 %) is important to provide a better description of aerosol–radiation–cloud interactions in regional climate models.

The heterogeneous hydrolysis of N2O5 on the surface of deliquescent aerosol leads to HNO3 formation and acts as a major sink of NOx in the atmosphere during night-time. The reaction constant of this heterogeneous hydrolysis is determined by temperature (T), relative humidity (RH), aerosol particle composition, and the surface area concentration (S). However, these parameters were not comprehensively considered in the parameterization of the heterogeneous hydrolysis of N2O5 in previous mass-based 3-D aerosol modelling studies. In this investigation, we propose a sophisticated parameterization (NewN2O5) of N2O5 heterogeneous hydrolysis with respect to T, RH, aerosol particle compositions, and S based on laboratory experiments. We evaluated closure between NewN2O5 and a state-of-the-art parameterization based on a sectional aerosol treatment. The comparison showed a good linear relationship (R = 0.91) between these two parameterizations. NewN2O5 was incorporated into a 3-D fully online coupled model, COSMO–MUSCAT, with the mass-based aerosol treatment. As a case study, we used the data from the HOPE Melpitz campaign (10–25 September 2013) to validate model performance. Here, we investigated the improvement of nitrate prediction over western and central Europe. The modelled particulate nitrate mass concentrations ([NO3−]) were validated by filter measurements over Germany (Neuglobsow, Schmücke, Zingst, and Melpitz). The modelled [NO3−] was significantly overestimated for this period by a factor of 5–19, with the corrected NH3 emissions (reduced by 50 %) and the original parameterization of N2O5 heterogeneous hydrolysis. The NewN2O5 significantly reduces the overestimation of [NO3−] by ∼ 35 %. Particularly, the overestimation factor was reduced to approximately 1.4 in our case study (12, 17–18 and 25 September 2013) when [NO3−] was dominated by local chemical formations. In our case, the suppression of organic coating was negligible over western and central Europe, with an influence on [NO3−] of less than 2 % on average and 20 % at the most significant moment. To obtain a significant impact of the organic coating effect, N2O5, SOA, and NH3 need to be present when RH is high and T is low. However, those conditions were rarely fulfilled simultaneously over western and central Europe. Hence, the organic coating effect on the reaction probability of N2O5 may not be as significant as expected over western and central Europe.

Heterogeneous hydrolysis of N 2 O 5 on the surface of deliquescent aerosol particles leads to HNO 3 formation and acts as a major sink of NOx in the atmosphere during nighttime. The reaction constant of this heterogeneous hydrolysis is determined by temperature (T), relative humidity (RH), aerosol particle composition as well as the surface area concentration (S). However, its parameterization in previous 3-D modelling studies did not comprehensively consider these parameters. In this investigation, we propose a sophisticated parameterization of the heterogeneous hydrolysis of N 2 O 5 with respect to T, RH, aerosol particle compositions and S, based on laboratory experiments. This new parameterization was incorporated into a 3-D fully online coupled model: COSMO-MUSCAT. As case study, we used the data from the HOPE-Melpitz campaign (10–25 September 2013). Here, we investigated the improvement of nitrate prediction over the western and central Europe. The modelled particulate nitrate mass concentrations ([NO 3 − ]) were validated by filter measurements over Germany (Neuglobsow, Schmucke, Zingst, and Melpitz). The modelled [NO 3 − ] were significantly overestimated for this period by a factor of 5–19, with the corrected NH3 emissions (reduced by 50 %) and the original parameterization of N 2 O 5 heterogeneous hydrolysis. The proposed new parameterization significantly reduces the overestimation of [NO 3 − ] by ~ 35 %. Particularly, the overestimation factor was reduced to approximately 1.4 within our case study period (September 12, 17–18 and 25, 2013), when [NO 3 − ] was dominated by local chemical formations. Furthermore, the organic coating effect on a suppression of the N 2 O 5 reaction probability may have been also significantly overestimated in previous modelling studies, due to a strong overestimation of the N 2 O 5 reaction probability on coatings. Based on the original parameterization, previous studies reported a decrease of modelled [NO 3 − ] up to 90 %, where both secondary organic aerosol (SOA) and N 2 O 5 were built-up over western and central Europe. For this case study, the suppression of organic coating was negligible over western and central Europe, with influence on [NO 3 − ] less than 2 % on average and 20 % at the most significant moment. As for a significant impact of the organic coating effect, N 2 O 5 , SOA and NH 3 need to be present when RH is high and T is low. However, those conditions were rarely fulfilled simultaneously over western and central Europe. Hence, the organic coating effect on reaction probability of N 2 O 5 over Europe may not be as important as expected in previous studies.

The climate effect of atmospheric aerosols is associated with their influence on the radiative budget of the Earth due to the direct aerosol–radiation interactions (ARIs) and indirect effects, resulting from aerosol–cloud–radiation interactions (ACIs). Online coupled meteorology–chemistry models permit the description of these effects on the basis of simulated atmospheric aerosol concentrations, although there is still some uncertainty associated with the use of these models. Thus, the objective of this work is to assess whether the inclusion of atmospheric aerosol radiative feedbacks of an ensemble of online coupled models improves the simulation results for maximum, mean and minimum temperature at 2 m over Europe. The evaluated models outputs originate from EuMetChem COST Action ES1004 simulations for Europe, differing in the inclusion (or omission) of ARI and ACI in the various models. The cases studies cover two important atmospheric aerosol episodes over Europe in the year 2010: (i) a heat wave event and a forest fire episode (July–August 2010) and (ii) a more humid episode including a Saharan desert dust outbreak in October 2010. The simulation results are evaluated against observational data from the E-OBS gridded database. The results indicate that, although there is only a slight improvement in the bias of the simulation results when including the radiative feedbacks, the spatiotemporal variability and correlation coefficients are improved for the cases under study when atmospheric aerosol radiative effects are included.

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.

Abstract. Forecasts from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as the model itself (e.g. physical parameterization, chemical mechanism). Multi-model ensemble forecasts can improve the forecast skill provided that certain mathematical conditions are fulfilled. We demonstrate through an intercomparison of two dissimilar air quality ensembles that unconditional raw forecast averaging, although generally successful, is far from optimum. One way to achieve an optimum ensemble is also presented. The basic idea is to either add optimum weights to members or constrain the ensemble to those members that meet certain conditions in time or frequency domain. The methods are evaluated against ground level observations collected from the EMEP and Airbase databases. The two ensembles were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). Verification statistics shows that the deterministic models simulate better O3 than NO2 and PM10, linked to different levels of complexity in the represented processes. The ensemble mean achieves higher skill compared to each station's best deterministic model at 39 %–63 % of the sites. The skill gained from the favourable ensemble averaging has at least double the forecast skill compared to using the full ensemble. The method proved robust for the 3-monthly examined time-series if the training phase comprises 60 days. Further development of the method is discussed in the conclusion.

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 similar to 90% due mainly to the under-predictions 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. (C) 2014 Elsevier Ltd. All rights reserved.

Model evaluation studies are essential for determining model performance as well as assessing model deficiencies, and are the focus of the Air Quality Model Evaluation International Initiative (AQMEII). The chemistry-transport model system COSMO-MUSCAT participates in this initiative. In this paper the robustness and variability of the model results against changes in the model setup are analyzed. Special focus is given to the formation of secondary particulate matter and the ability to reproduce unusually high levels of PM10 in Central Europe caused by long-range transported smoke of widespread agricultural burning and forest fires in western Russia. Seven different model configurations are investigated in this study. The COSMO-MUSCAT results are evaluated in comparison with ground-base measurements in Central Europe. The analysis is performed for two selected periods in April/ May and October 2006 which are characterized by elevated concentrations of PM. The model sensitivity is studied against changes in the used grid resolution, the meteorological forcing and the applied aerosol module. Possible reasons for differences in model results will be discussed.

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. (C) 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Model evaluation studies are essential for determining model performance as well as assessing model deficiencies, and are the focus of the Air Quality Model Evaluation International Initiative (AQMEII). The chemistry-transport model system COSMO–MUSCAT participates in this initiative. In this paper the robustness and variability of the model results against changes in the model setup are analyzed. Special focus is given to the formation of secondary particulate matter and the ability to reproduce unusually high levels of PM10 in Central Europe caused by long-range transported smoke of fires in western Russia. Seven different model configurations are investigated in this study. The COSMO–MUSCAT results are evaluated in comparison with ground-based measurements in Central Europe. The analysis is performed for two selected periods in April/May 2006 and October 2006 which are characterized by elevated concentrations of PM. Furthermore, the sensitivity of the results is studied against the used grid resolution and the meteorological forcing. Here, COSMO–MUSCAT is applied with different horizontal grid sizes and, alternatively, forced by reanalysis data with finer resolution. The use of finer grid resolutions in COSMO–MUSCAT has direct consequences on the meteorological forcing as well as on the calculated emission and deposition rates. The presented results suggest a large impact of the meteorological effects on the PM concentrations. The more accurate spatial appointment of the emissions and deposition fluxes seems to be of little consequence compared to the meteorological forcing.

The physical and chemical processes that determine the distribution of air pollutants occur on a wide range of temporal and spatial scales. Multiscale models can provide finer resolution in certain key regions, e.g. around large sources. The paper focuses on some numerical aspects of modelling urban and regional scale interactions as well as on requirements on the used parameterisations in this context. Multiblock grid techniques ("two-way nesting") and implicit-explicit time integration schemes are suitable for an efficient numerical treatment of such scale interactions. In the online coupled model system LM-MUSCAT, both approaches are implemented for the chemistry-transport code. Gas phase processes, especially the formation of photooxidants, as well as the transport and the transformation of particulate matter, can be investigated. The advantages of the multiblock technique to establish the interactions between different scales in a natural way are demonstrated for one selected scenario in the Saxony area. The influence of grid resolutions on the simulation results is discussed.

The interaction of gases and aerosol particles with clouds entails a number of key environmental processes. On the one hand, they directly influence the life cycles of trace constituents and facilitate conversions of these trace constituents. On the other hand, multiphase transformations strongly influence cloud formation. Over a long time, the complexities of the cloud processes involved have discouraged investigators from simultaneously treating all aspects of multiphase chemistry and microphysics with equal rigor. Many recently available models focus either on complex multiphase chemistry only in a few aggregated drop classes (Ervens et al., 2003; Herrmann et al., 2000), or detailed microphysics for strongly simplified chemical mechanisms (Bott, 1999).

Regional modelling of atmospheric trace gases and particulate matter is of major importance for air pollution studies as well as climate considerations. In this context, the chemistry transport model system LM-MUSCAT is applied for several air quality studies and the investigation of local climate effects. Two selected applications from diff erent objects of research are presented in the paper. The first study enables a detailed quantificatio n of the contributions of cooling tower emissions to particle concentration levels in specifi c Saxonian urban areas. In the sec- ond project, the influence of Saharan dust emissions on radia tive forcing and, hence, on the meteorology is quantified. These regional effects are also r elevant for climate change studies. Regional scale models are well suited for simulation of individual dust storm events, or for comparisons with in-situ observations made during field experiments. Improvements i n the pa- rameterisation of dust processes which are obtained by such regional model investigations can help to improve parameterisations in global scale models. Both applications require a detailed process description as well as a high spatial resolution. Su ch simulations are very expensive in terms of computing time and demand the use of powerful parallel computers.

Air quality models base on mass balances described by systems of time-dependent, three-dimensional advection-diffusion-reaction equations. The solution of such systems is numerically expensive in terms of computing time. This requires the use of fast parallel computers. Multiblock grid techniques and implicit-explicit (IMEX) time integration schemes are suited to take benefit from the parallel architecture. A parallel version of the multiscale chemistry-transport code MUSCAT (MUiltiScale Chemistry Aerosol Transport) is presented which is based on these techniques (Wolke and Knoth, 2000).

The physical and chemical processes in the atmosphere are very complex. They occur simultaneously, Coupled and in a wide range of scales. These facts have to be taken into account in the numerical methods for the solution of the model equations. The numerical techniques should allow the use of different resolutions in space and also In time. Air quality models base on mass balances described by systems of time-dependent, three-dimensional advection-diffusion-reaction equations. A parallel version of the multiscale chemistry-transport code MUSCAT is presented which is based on multiblock grid techniques and implicit-explicit (IMEX) time integration schemes. The meteorological fields are Generated simultaneously by the non-hydrostatic meteorological model LM. Both codes run in parallel mode on a predefined number of processors and-exchange informations by an implemented coupler interface. The ability and performance of the model system are discussed for a "Berlioz" ozone episode.

The physical and chemical processes in the atmosphere are very complex. They occur simultaneously, coupled and in a wide range of scales. These facts have to be taken into account in the numerical methods for the solution of the model equations. The numerical techniques should allow the use of different resolutions in space and also in time. Air quality models base on mass balances described by systems of time-dependent, three-dimensional advection-diffusion-reaction equations. To date, one limitation of schemes for the numerical solution of such systems has been their inability to solve equations both quickly and with a high accuracy in multiple grid cell models. This requires the use of fast parallel computers. Multiblock grid techniques and implicit-explicit (IMEX) time integration schemes are suited to take benefit from the parallel architecture. A parallel version of the multiscale chemistry- transport code MUSCAT is presented which is based on these techniques. The meteorological fields are generated simultaneously by the non-hydrostatic meteorological model LM. Both codes run in parallel mode on a predefined number of processors and exchange information by an implemented coupler interface. The ability and performance of the model system are discussed for a ''Berlioz'' ozone episode.