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Methane is the second-most important greenhouse gas after carbon dioxide and accounts for around 10 % of total European Union greenhouse gas emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modelling techniques appear as powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modelling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modelling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modelling component will be based on the WRF model coupled to a Lagrangian particle dispersion module. A description of the modelling tools, input data sets, and 1-year forward modelling evaluation from 1 April 2018 to 31 March 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modelling for Denmark, will be provided in a second paper. A good general agreement is found between the modelling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model–observation discrepancies for the summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from 1 % to 2 %. Domain-wide correlation coefficients and root-mean-square errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. On the other hand, model–observation discrepancies for winter months show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 % to 3 %. Domain-wide correlation coefficients and root-mean-square errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modelling results, the methane enhancement above the background concentrations came almost entirely from anthropogenic sources; however, these sources contributed with only up to 2 % to the methane total-column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe and demonstrate a huge potential for methane inverse modelling using improved TROPOMI products in large-scale applications. [ABSTRACT FROM AUTHOR]

In this study we present a series of sensitivity experiments conducted for the Last Glacial Maximum (LGM, ∼21 ka) over Europe using the regional climate Weather Research and Forecasting model (WRF). Using a four-step two-way nesting approach, we are able to reach a convection-permitting horizontal resolution over the inner part of the study area, covering central Europe and the Alpine region. The main objective of the paper is to evaluate a model version including a series of new developments better suitable for the simulation of paleo-glacial time slices with respect to the ones employed in former studies. The evaluation of the model is conducted against newly available pollen-based reconstructions of the LGM European climate and takes into account the effect of two main sources of model uncertainty: a different height of continental glaciers at higher latitudes of the Northern Hemisphere and different land cover. Model results are in good agreement with evidence from the proxies, in particular for temperatures. Importantly, the consideration of different ensemble members for characterizing model uncertainty allows for increasing the agreement of the model against the proxy reconstructions that would be obtained when considering a single model realization. The spread of the produced ensemble is relatively small for temperature, besides areas surrounding glaciers in summer. On the other hand, differences between the different ensemble members are very pronounced for precipitation, in particular in winter over areas highly affected by moisture advection from the Atlantic. This highlights the importance of the considered sources of uncertainty for the study of European LGM climate and allows for determining where the results of a regional climate model (RCM) are more likely to be uncertain for the considered case study. Finally, the results are also used to assess the effect of convection-permitting resolutions, at both local and regional scales, under glacial conditions. [ABSTRACT FROM AUTHOR]

In this study we present a series of sensitivity experiments conducted for the Last Glacial Maximum (LGM, ~21000 years ago) over Europe using the regional climate Weather Research and Forecasting model (WRF). Using a 4 step 2-way nesting approach, we are able to reach a convection-permitting horizontal resolution over the inner part of the study area, covering Central Europe and the Alpine region. The main objective of the paper is to evaluate a model version including a series of new developments better suitable for the simulation of paleo-glacial time slices with respect to the ones employed in former studies. The evaluation of the model is conducted against newly available pollen-based reconstructions of the LGM European climate and takes into account the effect of two main sources of model uncertainty: a different height of continental glaciers at higher latitudes of the northern hemisphere and different land cover. Model results are in good agreement with evidence from the proxies, in particular for temperatures. Importantly, the consideration of different ensemble members for characterizing model uncertainty allows to increase the agreement of the model against the proxy reconstructions that would be obtained when considering a single model realization. The spread of the produced ensemble is relatively small for temperature, beside areas surrounding glaciers in summer. On the other hand, differences between the different ensemble members are very pronounced for precipitation, in particular in winter over areas highly affected by moisture advection from the Atlantic. This highlights the importance of the considered sources of uncertainty for the study of European LGM climate and allows to determine where the results of an RCM are more likely to be uncertain for the considered case study. Finally, the results are also used to demonstrate the added value of convection-permitting resolutions, at both local and regional scales, under glacial conditions. [ABSTRACT FROM AUTHOR]

Methane is the second most important greenhouse gas after carbon dioxide, and accounts for around 10 % of total European Union greenhouse gases emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modeling techniques appear as a powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modeling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modeling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modeling component will be based on the WRF model coupled to a lagrangian particle dispersion module. A description of the modeling tools, input data sets and one-year forward modeling evaluation from April 01, 2018 to March 31, 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modeling for Denmark, will be provided in a second paper. A good general agreement is found between the modeling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model-observation discrepancies for summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from 1 to 2%. Domain-wide correlation coefficients and root-mean-square-errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. For winter months, otherwise, model-observation discrepancies show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 to 3%. Domain-wide correlation coefficients and root-mean-square-errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modeling results, the methane enhancement above the background concentrations came almost entirely from anthropogenicsources; however, these sources contributed with only up to 2 % to the methane total column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe, and demonstrate a huge and under explored potential for methane inverse modeling using improved TROPOMI products in large-scale applications. [ABSTRACT FROM AUTHOR]

In this study, we assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities in western Europe. The SWG is based on a random sampling of analogs of the geopotential height at 500 hPa (Z500). The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill score (continuous rank probability skill score and correlation) compared with persistence and climatology forecasts for lead times of 5 and 10 d for different areas in Europe. We find that the low predictability episodes of our model are related to specific weather regimes, depending on the European region. Comparing the SWG forecasts to ECMWF forecasts, we find that the SWG shows a good performance for 5 d. This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g., region for analogs) must be tuned for each region in order to optimize its performance. [ABSTRACT FROM AUTHOR]

Wind-blown dust (WBD) emitted by the Earth's surface due to sandblasting can potentially have important effects on both climate and human health via interaction with solar and thermal radiation, reducing air quality. Apart from the main dust "centres" around the world, like deserts, dust can be emitted from partly vegetated mid- and high-latitude areas like Europe if certain conditions are suitable (strong winds, bare soil, reduced soil moisture, etc.). Using a wind-blown dust model (WBDUST) along with a chemical transport model (Comprehensive Air-quality model with Extensions, CAMx) coupled to a regional climate model (Weather Research and Forecasting, WRF), this study is one of the first to provide a model-based estimate of such emissions over Europe as well as the long-term impact of WBD emissions on the total particulate matter (PM) concentrations for the 2007–2016 period. We estimated average WBD emissions of about 0.5 and 1.5 Mgyr-1km-2 in fine and coarse modes. Maximum emissions occur over Germany, where the average seasonal fine- and coarse-mode emission flux can reach 0.5 and 1 gs-1km-2 , respectively. Large variability is seen in the averaged daily emissions with values of up to 2 gs-1km-2 for the coarse-mode aerosol on selected days. The WBD emissions increased the modelled winter PM 2.5 and PM 10 concentrations by up to 10 and 20 µgm-3 , respectively, especially over Germany, where the highest emissions occur. The impact on other seasons is lower. Much higher impacts are modelled, however, on selected days when occasionally the urban PM 2.5 and PM 10 concentrations are increased by more than 50 and 100 µgm-3. The comparison with measurements revealed that if WBD is considered, the summer biases are reduced; however, the winter PM is overestimated even more greatly (so the bias increases). We identified a strong overestimation of the modelled wind speed (the maximum daily wind is almost 2 times higher in WRF than the measured ones) suggesting that WBD emissions are also overestimated – hence the enhanced winter PM biases. Moreover, we investigated the secondary impacts of the crustal composition of fine WBD particles on secondary inorganic aerosol (SIA): sulfates (PSO 4), nitrates (PNO 3) and ammonium (PNH 4). Because the water pH value, and thus the uptake of the gaseous precursors of SIA, is perturbed and because the increased aerosol surface serves as an oxidation site, we modelled seasonal PSO 4 and PNO 3 concentrations increased by up to 0.1 µgm-3 and PNH 4 ones decreased by up to -0.05 µgm-3 , especially during winter. In terms of average daily impact, these numbers can, however, reach much larger values of up to 1–2 µgm-3 for sulfates and nitrates, while the decrease in ammonium due to WBD can reach -1 µgm-3 on selected days. The sensitivity test on the choice of the inorganic equilibrium model (ISORROPIA vs. EQuilibrium Simplified Aerosol Model V4, EQSAM) showed that if EQSAM is used, the impact on SIA is slightly stronger (by a few 10 %) due to larger number of cations considered for water pH in EQSAM. Our results have to be regarded as a first estimate of the long-term WBD emissions and the related effects on PM over Europe. Due to the strong positive wind bias and hence strong WBD emissions, we should consider these results as an upper bound. More sensitivity studies involving the impact of the driving meteorological fields, WBD model choice and the input data used to describe the land surface need to be carried out in future to better constrain these emissions. [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compounds (BVOCs) emitted from the natural ecosystem are highly reactive and can thus impact air quality and aerosol radiative forcing. BVOC emission models (e.g., Model of Emissions of Gases and Aerosols from Nature – MEGAN) in global and regional chemical transport models still have large uncertainties in estimating biogenic trace gases because of uncertainties in emission activity factors, specification of vegetation type, and plant emission factors. This study evaluates a set of updates made to MEGAN v2.04 in the Weather Research and Forecasting model coupled with chemistry (WRF-Chem version 3.9). Our study considers four simulations for each update made to MEGAN v2.04: (i) a control run with no changes to MEGAN, (ii) a simulation with the emission activity factors modified following MEGAN v2.10, (iii) a simulation considering the changes to the plant functional type (PFT) emission factor, and (iv) a simulation with the isoprene emission factor calculated within the MEGAN module instead of being prescribed by the input database. We evaluate two regions, Europe and the southeastern United States, by comparing WRF-Chem results to ground-based monitoring observations in Europe (i.e., AirBase database) and aircraft observations obtained during the NOMADSS field campaign. We find that the updates to MEGAN v2.04 in WRF-Chem caused overpredictions in ground-based ozone concentrations in Europe and in isoprene mixing ratios compared to aircraft observations in the southeastern US. The update in emission activity factors caused the largest biases. These results suggest that further experimental and modeling studies should be conducted to address potential shortcomings in BVOC emission models. [ABSTRACT FROM AUTHOR]

Urban areas are hot spots of intense emissions, and they influence air quality not only locally but on a regional or even global scale. The impact of urban emissions over different scales depends on the dilution and chemical transformation of the urban plumes which are governed by the local- and regional-scale meteorological conditions. These are influenced by the presence of urbanized land surface via the so-called urban canopy meteorological forcing (UCMF). In this study, we investigate for selected central European cities (Berlin, Budapest, Munich, Prague, Vienna and Warsaw) how the urban emission impact (UEI) is modulated by the UCMF for present-day climate conditions (2015–2016) using two regional climate models, the regional climate models RegCM and Weather Research and Forecasting model coupled with Chemistry (WRF-Chem; its meteorological part), and two chemistry transport models, Comprehensive Air Quality Model with Extensions (CAMx) coupled to either RegCM and WRF and the "chemical" component of WRF-Chem. The UCMF was calculated by replacing the urbanized surface by a rural one, while the UEI was estimated by removing all anthropogenic emissions from the selected cities. We analyzed the urban-emission-induced changes in near-surface concentrations of NO2 , O3 and PM 2.5. We found increases in NO2 and PM 2.5 concentrations over cities by 4–6 ppbv and 4–6 µgm-3 , respectively, meaning that about 40 %–60 % and 20 %–40 % of urban concentrations of NO2 and PM 2.5 are caused by local emissions, and the rest is the result of emissions from the surrounding rural areas. We showed that if UCMF is included, the UEI of these pollutants is about 40 %–60 % smaller, or in other words, the urban emission impact is overestimated if urban canopy effects are not taken into account. In case of ozone, models due to UEI usually predict decreases of around -2 to -4 ppbv (about 10 %–20 %), which is again smaller if UCMF is considered (by about 60 %). We further showed that the impact on extreme (95th percentile) air pollution is much stronger, and the modulation of UEI is also larger for such situations. Finally, we evaluated the contribution of the urbanization-induced modifications of vertical eddy diffusion to the modulation of UEI and found that it alone is able to explain the modeled decrease in the urban emission impact if the effects of UCMF are considered. In summary, our results showed that the meteorological changes resulting from urbanization have to be included in regional model studies if they intend to quantify the regional footprint of urban emissions. Ignoring these meteorological changes can lead to the strong overestimation of UEI. [ABSTRACT FROM AUTHOR]