Abstract. The Green Ocean Amazon experiment – GoAmazon2014/5 explored the interactions between natural biogenic forest emissions from Central Amazonia and urban air pollution from Manaus. Previous GoAmazon2014/5 studies showed that nitrogen oxides (NOx = NO + NO2) and sulfur oxides (SOx) emissions from Manaus strongly interact with biogenic volatile organic compounds (BVOCs), affecting secondary organic aerosol (SOA) formation. In previous studies, ground based and aircraft measurements provided evidence of SOA formation and strong changes in aerosol composition and properties. Aerosol optical properties also evolve, and their impacts on the Amazonian ecosystem can be significant. As particles age, some processes such as SOA production, black carbon (BC) deposition, particle growth, and the BC lensing effect change the aerosol optical properties, affecting the solar radiation flux at the surface. This study analyzes data and models SOA formation using the Weather Research and Forecasting with Chemistry (WRF-Chem) model to assess the spatial variability of aerosol optical properties as the Manaus plumes interact with the natural atmosphere. The following aerosol optical properties are investigated: single scattering albedo (SSA), asymmetry parameter (gaer), absorption Ångström exponent (AAE), and scattering Ångström exponent (SAE). These simulations were validated using ground based measurements at three experimental sites: Amazon Tall Tower Observatory – ATTO (T0a), downtown Manaus (T1), Tiwa Hotel (T2) and Manacapuru (T3), as well as the G1 aircraft flights. WRF-Chem simulations were performed over seven days during March 2014. Results show a mean biogenic SOA (BSOA) mass enrichment of 512 % at the T1 site, 450 % in regions downwind of Manaus such as the T3 site and 850 % in areas north of the T3 site in simulations with anthropogenic emissions. The SOA formation is rather fast, with about 80 % of the SOA mass produced in 3–4 hours. Comparing the plume from simulations with and without anthropogenic emissions, SSA shows a downwind reduction of approximately 10 %, 11 % and 6 % at the T1, T2 and T3 sites, respectively. Other regions, such as those further downwind of the T3 site, are also affected. Gaer values increased from 0.62 to 0.74 at the T1 site and from 0.67 to 0.72 at the T3 site when anthropogenic emissions are active. During the Manaus plume aging process, a plume tracking analysis shows an increase in SSA from 0.91 close to Manaus to 0.98 160 km downwind of Manaus as a result of SOA production and BC deposition.

Abstract. 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 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 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 and under explored potential for methane inverse modeling using improved TROPOMI products in large-scale applications.

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