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.
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.
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.
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.
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.
The study presents a precipitation intercomparison based on two satellite-derived datasets (TRMM 3B42, CMORPH), four raingauge-based datasets (GPCC, E-OBS, Willmott & Matsuura, CRU), ERA Interim ...reanalysis (ERAInt), and a single climate simulation using the WRF model. The comparison was performed for a domain encompassing parts of Europe and the North Atlantic over the 11-year period of 2000–2010. The four raingauge-based datasets are similar to the TRMM dataset with biases over Europe ranging from –7 % to +4 %. The spread among the raingauge-based datasets is relatively small over most of Europe, although areas with greater uncertainty (more than 30 %) exist, especially near the Alps and other mountainous regions. There are distinct differences between the datasets over the European land area and the Atlantic Ocean in comparison to the TRMM dataset. ERAInt has a small dry bias over the land; the WRF simulation has a large wet bias (+30 %), whereas CMORPH is characterized by a large and spatially consistent dry bias (–21 %). Over the ocean, both ERAInt and CMORPH have a small wet bias (+8 %) while the wet bias in WRF is significantly larger (+47 %). ERAInt has the highest frequency of low-intensity precipitation while the frequency of high-intensity precipitation is the lowest due to its lower native resolution. Both satellite-derived datasets have more low-intensity precipitation over the ocean than over the land, while the frequency of higher-intensity precipitation is similar or larger over the land. This result is likely related to orography, which triggers more intense convective precipitation, while the Atlantic Ocean is characterized by more homogenous large-scale precipitation systems which are associated with larger areas of lower intensity precipitation. However, this is not observed in ERAInt and WRF, indicating the insufficient representation of convective processes in the models. Finally, the Fraction Skill Score confirmed that both models perform better over the Atlantic Ocean with ERAInt outperforming the WRF at low thresholds and WRF outperforming ERAInt at higher thresholds. The diurnal cycle is simulated better in the WRF simulation than in ERAInt, although WRF could not reproduce well the amplitude of the diurnal cycle. While the evaluation of the WRF model confirms earlier findings related to the model’s wet bias over European land, the applied satellite-derived precipitation datasets revealed differences between the land and ocean areas along with uncertainties in the observation datasets.
A high ozone (O3) concentrations episode during a heat wave event in the Northeastern Mediterranean was investigated using the WRF/Chem model. To understand the major model uncertainties and errors ...as well as the impacts of model inputs on the model accuracy, an ensemble modelling experiment was conducted. The 51-member ensemble was designed by varying model physics parameterization options (PBL schemes with different surface layer and land-surface modules, and radiation schemes); chemical initial and boundary conditions; anthropogenic and biogenic emission inputs; and model domain setup and resolution. The main impacts of the geographical and emission characteristics of three distinct regions (suburban Mediterranean, continental urban, and continental rural) on the model accuracy and O3 predictions were investigated. In spite of the large ensemble set size, the model generally failed to simulate the extremes; however, as expected from probabilistic forecasting the ensemble spread improved results with respect to extremes compared to the reference run. Noticeable model nighttime overestimations at the Mediterranean and some urban and rural sites can be explained by too strong simulated winds, which reduce the impact of dry deposition and O3 titration in the near surface layers during the nighttime. Another possible explanation could be inaccuracies in the chemical mechanisms, which are suggested also by model insensitivity to variations in the nitrogen oxides (NOx) and volatile organic compounds (VOC) emissions. Major impact factors for underestimations of the daytime O3 maxima at the Mediterranean and some rural sites include overestimation of the PBL depths, a lack of information on forest fires, too strong surface winds, and also possible inaccuracies in biogenic emissions. This numerical experiment with the ensemble runs also provided guidance on an optimum model setup and input data.
•Comprehensive WRF/Chem modeling with 51 ensemble members was conducted.•High O3 concentration episode in the Northern Adriatic area was studied.•Revealed the impacts of the physical schemes and model setup on the simulated O3.•The highest uncertainty found for the selection of PBL, LSM and SL schemes.•Varying emissions and radiation schemes did not significantly affect simulated O3.
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.
•Two WRF-Chem simulations contributed to AQMEII-Ph2 differing in the microphysics.•Sensitivity of aerosol-radiation feedbacks to the microphysics scheme is analysed.•Smaller and more numerous cloud droplets are simulated with Morrison scheme.•Therefore, Morrison scheme is more effective in scattering shortwave radiation.•Higher liquid water droplet and convective precipitation found in Lin scheme.
•We measure cooling at an orchard inside a very shallow basin only a few metres deep.•Two locations inside the basin at different elevations are analyzed.•On calm clear sky nights temperature ...difference between locations can be up to 5°C.•A simple 1D radiative model is presented to simulate the cooling.•Model analysis shows a strong dependency of the temperature difference on humidity.
Cold air pools (CAPs) may develop during nights in very shallow depressions. The depth of the stagnant air within a CAP influences the process of the cooling of nocturnal air and the resulting minimum temperature. A seven-month long field experiment was performed during winter 2013/2014 in an orchard near Krško, Slovenia, located inside a very shallow basin only a few metres deep and approximately 500m wide. Two locations at different elevations inside the basin were selected for measurement. The results showed that the nights (in terms of cooling) can be classified into three main categories; nights with overcast skies and weak cooling, windy nights with clear sky and strong cooling but with no difference in temperatures between locations inside the basin, and calm nights with even stronger cooling and significant temperature differences between locations inside the basin. On calm nights with clear skies, the difference at two measuring sites inside the basin can be up to 5°C but the presence of even weak winds can cause sufficient turbulent mixing to negate any difference in temperature. To better understand the cooling process on calm, clear nights, we developed a simple 1-D thermodynamic conceptual model focusing on a very shallow CAP. The model has 5-layers (including two air layers representing air inside the CAP), and an analytical solution was obtained for the equilibrium temperatures. Sensitivity analysis of the model was performed. As expected, a larger soil heat conductivity or higher temperature in the ground increases the morning minimum temperatures. An increase in temperature of the atmosphere also increases the simulated minimum temperatures, while the temperature difference between the higher and lower locations remains almost the same. An increase in atmosphere humidity also increases the modelled equilibrium temperatures, while an increase of the humidity of the air inside the CAP results in lower equilibrium temperatures. The humidity of the air within the CAP and that of the free atmosphere strongly influence the differences in equilibrium temperatures at higher and lower locations. The more humid the air, the stronger the cooling at the lower location compared to the higher location.
ABSTRACT
This study aims to assess the capability of regional climate models (RCMs) to simulate precipitation climatology over the southeastern Alpine flanks, where complex orography prevails. ...Precipitation simulations from 14 different 40‐year downscaling experiments (1961–2000) driven by the ERA‐40 reanalysis from the ENSEMBLES project are analysed in terms of realistic reproduction of spatial and temporal seasonal precipitation patterns over Slovenia. The best RCM performance was found for winter when precipitation is mainly caused by large‐scale processes. A significantly lower model performance was observed for summer when precipitation is mainly caused by convective processes. Indeed, the largest spread of simulated precipitation between ensemble members was observed in summer, which is likely due to differences in the simulation of convective processes by the models. In addition, the simulations were analysed using partial least‐square regression to quantify dependence of the simulated precipitation on physiographic factors. The applied regression model showed that orography and longitude are the most important variables associated with the spatial variability of precipitation in Slovenia. The empirical orthogonal functions were used to analyse the spatial patterns of inter‐annual precipitation variability. The analysis showed that all models reproduced the most dominant mode of variability, but significant differences occurred between the models, especially in summer. In general, a thorough analysis has shown that the quality of simulations is highly variable among different regions as well as for different seasons.
PDF
