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The Lagrangian particle dispersion model FLEXPART in its original version in the mid-1990s was designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as those released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modeling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g., greenhouse gases, short-lived climate forcers like black carbon and volcanic ash, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to global. In particular, inverse modeling based on source–receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.4, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physicochemical parameterizations, input/output formats, and available preprocessing and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes in runs with very large numbers of particles. The deviation from 100 % efficiency is almost entirely due to the remaining nonparallelized parts of the code, suggesting large potential for further speedup. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g., to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option of running backward in time from atmospheric concentrations at receptor locations for many years, but this has now been extended to also work for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, the temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing for more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART's customary binary format. In this paper, we describe these new developments. Moreover, we present some tools for the preparation of the meteorological input data and for processing FLEXPART output data, and we briefly report on alternative FLEXPART versions.

A correction for the vertical gradient of air density has been incorporated into a skewed probability density function formulation for turbulence in the convective boundary layer. The related drift term for Lagrangian stochastic dispersion modelling has been derived based on the well-mixed condition. Furthermore, the formulation has been extended to include unsteady turbulence statistics and the related additional component of the drift term obtained. These formulations are an extension of the drift formulation reported by Luhar et al. (Atmos Environ 30:1407–1418, 1996 ) following the well-mixed condition proposed by Thomson (J Fluid Mech 180:529–556, 1987 ). Comprehensive tests were carried out to validate the formulations including consistency between forward and backward simulations and preservation of a well-mixed state with unsteady conditions. The stationary state CBL drift term with density correction was incorporated into the FLEXPART and FLEXPART-WRF Lagrangian models, and included the use of an ad hoc transition function that modulates the third moment of the vertical velocity based on stability parameters. Due to the current implementation of the FLEXPART models, only a steady-state horizontally homogeneous drift term could be included. To avoid numerical instability, in the presence of non-stationary and horizontally inhomogeneous conditions, a re-initialization procedure for particle velocity was used. The criteria for re-initialization and resulting errors were assessed for the case of non-stationary conditions by comparing a reference numerical solution in simplified unsteady conditions, obtained using the non-stationary drift term, and a solution based on the steady drift with re-initialization. Two examples of “real-world” numerical simulations were performed under different convective conditions to demonstrate the effect of the vertical gradient in density on the particle dispersion in the CBL.

A two-dimensional Ising model is presented in order to reproduce cloud field organization at the mesoscale ( i.e., from 10 up to 10 km ). This model takes into account different interactions between an air mass and its environment. It is shown that this novel and original approach allows to reproduce quite well satellite observations. Although created initially for pristine ( i.e., natural) oceanic conditions, this model can be adapted to any other situations, mutatis mutandis .

New particle formation (NPF) in the free troposphere (FT) is thought to be a significant source of particles over the oceans. The entrainment of particles initially formed in the marine FT is further suspected to be a major contributor to cloud condensation nuclei (CCN) number concentrations in the marine boundary layer (BL). Yet, little is known about the process and, more broadly, about the composition of the marine FT, which remains poorly explored due to access difficulties. Here we report measurements performed in April 2018 at the Maïdo Observatory with a nitrate-based chemical ionization atmospheric pressure interface time-of-flight mass spectrometer, which have allowed the first molecular-level characterization of the remote marine FT composition. A number of molecules and clusters were identified and classified into nine groups according to their chemical composition; among the identified species, the groups containing methanesulfonic acid (MSA) and C2 amines show signals that are on average significantly higher when the site is under conditions representative of the marine FT (compared to the BL). The correlation analysis revealed apparent connections between the signals of the identified compounds and several variables concurrently measured at the site (under FT conditions) or related to air mass history, suggesting that oxalic acid, malonic acid, and observed C2 amines could be of terrestrial origin, with, in addition, a possible marine source for oxalic acid and amines, while iodic acid, sulfur species, and maleic acid have a dominant marine origin. Identification of FT conditions at the site was based on the analysis of the standard deviation of the wind direction; this parameter, which can easily be derived from continuous measurements at the site, is shown in the first part of the study to be a relevant tracer when compared to predictions from the Meso-NH atmospheric model. Similar to other high-altitude sites, FT conditions are mainly encountered at night at Maïdo; therefore, the link to NPF could not be established, and further research is needed to assess the composition of precursors to nanoparticle formation in the marine FT.

Volatile organic compounds (VOCs) act as precursors to ozone and secondary organic aerosols, which have significant health and environmental impacts. They can also reduce the atmospheric oxidative capacity. However, their budget remains poorly quantified, especially over remote areas such as the tropical oceans. Here, we present high-resolution simulations of atmospheric composition over Réunion Island, located in the Indian Ocean, using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The coexistence and spatial heterogeneity of anthropogenic and biogenic emission sources in this region present a valuable but challenging test of the model performance. The WRF-Chem model is evaluated against several observational datasets, including proton transfer reaction mass spectrometry (PTR-MS) measurements of VOCs and oxygenated VOCs (OVOCs) at the Maïdo Observatory, Réunion Island (2160 m above sea level), in January and July 2019, representing austral summer and winter, respectively, and capturing the seasonal extremes for the region. While the primary goal of our study is to gain a better understanding of the (O)VOC budget at remote tropical latitudes, important model refinements have been made to improve the model performance, including the implementation of high-resolution anthropogenic and biogenic isoprene emissions, updates to the chemical mechanism, and adjustments to the boundary conditions. These refinements are supported by comparisons with PTR-MS data as well as with meteorological measurements at Maïdo; in situ NOx and O3 measurements from the air quality Atmo-Réunion network; Fourier transform infrared spectroscopy (FTIR) measurements of O3, CO, ethane, and several OVOCs, also at Maïdo; and satellite retrievals from the TROPOspheric Monitoring Instrument (TROPOMI).TROPOMI NO2 data suggest that anthropogenic emissions, particularly from power plants near Le Port, dominate NOx levels over the island. Both TROPOMI and in situ surface NO2 comparisons are used to adjust the power plant emissions at Le Port. Surface ozone concentrations are overestimated by ∼6 ppbv on average, likely due to the neglect of halogen chemistry in the model, though other factors may also contribute. While modelled NO2 over oceans is too low in summer when the lightning source is excluded, including this source results in model overestimations, as corroborated by comparisons with upper tropospheric NO2 mixing ratios derived from TROPOMI using the cloud-slicing technique (Marais et al., 2021). The model generally succeeds in reproducing the PTR-MS isoprene and its oxidation products (Iox), except for a moderate underestimation (∼30 %) of noontime isoprene concentrations, and modelled concentration peaks near dawn and dusk, which are not seen in the observations. The ratio of Iox to isoprene (0.8 at noon in January) is fairly well reproduced by the model. The methanol and monoterpenes observations both suggest overestimations of their biogenic emissions, by factors of about 2 and 5, respectively. Acetaldehyde anthropogenic emissions are likely strongly overestimated, due to the lumping of higher aldehydes into this compound. Without this lumping, the modelled acetaldehyde would be underestimated by almost one order of magnitude, suggesting the existence of a large missing source, likely photochemical. The comparisons suggest the existence of a biogenic source of methyl ethyl ketone (MEK), equivalent to about 3 % of isoprene emissions, likely associated with the dry deposition and conversion of key isoprene oxidation products to MEK. A strong model underestimation of the PTR-MS signal at mass 61 is also found, by a factor of 3–5 during daytime, consistent with previously reported missing sources of acetic and peracetic acid.

This paper demonstrates the environmental impacts of the wildfires occurring at the beginning of April 2020 in and around the highly contaminated Chernobyl Exclusion Zone (CEZ). Due to the critical fire location, concerns arose about secondary radioactive contamination potentially spreading over Europe. The impact of the fire was assessed through the evaluation of fire plume dispersion and re-suspension of the radionuclide Cs-137, whereas, to assess the smoke plume effect, a WRF-Chem simulation was performed and compared to Tropospheric Monitoring Instrument (TROPOMI) satellite columns. The results show agreement of the simulated black carbon and carbon monoxide plumes with the plumes as observed by TROPOMI, where pollutants were also transported to Belarus. From an air quality and health perspective, the wildfires caused extremely bad air quality over Kiev, where the WRF-Chem model simulated mean values of PM2.5 up to 300 µg/m3 (during the first fire outbreak) over CEZ. The re-suspension of Cs-137 was assessed by a Bayesian inverse modelling approach using FLEXPART as the atmospheric transport model and Ukraine observations, yielding a total release of 600 ± 200 GBq. The increase in both smoke and Cs-137 emissions was only well correlated on the 9 April, likely related to a shift of the focus area of the fires. From a radiological point of view even the highest Cs-137 values (average measured or modelled air concentrations and modelled deposition) at the measurement site closest to the Chernobyl Nuclear Power Plant, i.e., Kiev, posed no health risk.

Réunion Island is situated in the Indian Ocean and holds one of the very few atmospheric observatories in the tropical Southern Hemisphere. Moreover, it hosts experiments providing both ground-based surface and column observations of CO2, CH4, and CO atmospheric concentrations. This work presents a comprehensive study of these observations made in the capital Saint-Denis and at the high-altitude Maïdo Observatory. We used simulations of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), in its passive tracer option (WRF-GHG), to gain more insight to the factors that determine the observed concentrations. Additionally, this study provides an evaluation of the WRF-GHG performance in a region of the globe where it has not yet been applied.A comparison of the basic meteorological fields near the surface and along atmospheric profiles showed that WRF-GHG has decent skill in reproducing these meteorological measurements, especially temperature. Furthermore, a distinct diurnal CO2 cycle with values up to 450 ppm was found near the surface in Saint-Denis, driven by local anthropogenic emissions, boundary layer dynamics, and accumulation due to low wind speed at night. Due to an overestimation of local wind speed, WRF-GHG underestimates this nocturnal buildup. At Maïdo, a similar diurnal cycle is found but with much smaller amplitude. There, surface CO2 is essentially driven by the surrounding vegetation. The hourly column-averaged mole fractions of CO2 (XCO2) of WRF-GHG and the corresponding TCCON observations were highly correlated with a Pearson correlation coefficient of 0.90. These observations represent different air masses to those near the surface; they are influenced by processes from Madagascar, Africa, and further away. The model shows contributions from fires during the Southern Hemisphere biomass burning season but also biogenic enhancements associated with the dry season. Due to a seasonal bias in the boundary conditions, WRF-GHG fails to accurately reproduce the CH4 observations at Réunion Island. Furthermore, local anthropogenic fluxes are the largest source influencing the surface CH4 observations. However, these are likely overestimated. Furthermore, WRF-GHG is capable of simulating CO levels on Réunion Island with a high precision. As to the observed CO column (XCO), we confirmed that biomass burning plumes from Africa and elsewhere are important for explaining the observed variability. The in situ observations at the Maïdo Observatory can characterize both anthropogenic signals from the coastal regions and biomass burning enhancements from afar. Finally, we found that a high model resolution of 2 km is needed to accurately represent the surface observations. At Maïdo an even higher resolution might be needed because of the complex topography and local wind patterns. To simulate the column Fourier transform infrared (FTIR) observations on the other hand, a model resolution of 50 km might already be sufficient.

The tropical and subtropical Indian Ocean (IO) is expected to be a significant source of water-soluble organic aerosols (WSOAs), which are important factors relevant to cloud formation of aerosol particles. Current atmospheric numerical models significantly underestimate the budget of organic aerosols and their precursors, especially over tropical oceans. This is primarily due to poor knowledge of sources and the paucity of observations of these parameters considering spatial and temporal variation over the tropical open ocean. To evaluate the contribution of sources to WSOA as well as their formation processes, submicrometer aerosol sampling was conducted at the high-altitude Maïdo observatory (21.1∘ S, 55.4∘ E; 2160 m a.s.l.), located on the remote island of La Réunion in the southwest IO. The aerosol samples were continuously collected during local daytime and nighttime, which corresponded to the ambient conditions of the marine boundary layer (MBL) and free troposphere (FT), respectively, from 15 March to 24 May 2018. Chemical analysis showed that organic matter was the dominant component of submicrometer water-soluble aerosol (∼ 45 ± 17 %) during the wet season (15 March–23 April). On the other hand, sulfate dominated (∼ 77 ± 17 %) during the dry season (24 April–24 May), most of which was attributable to the effect of volcanic eruption. Measurements of the stable carbon isotope ratio of water-soluble organic carbon (WSOC) suggested that marine sources contributed significantly to the observed WSOC mass in both the MBL and the FT in the wet season, whereas a mixture of marine and terrestrial sources contributed to WSOC in the dry season. The distinct seasonal changes in the dominant source of WSOC were also supported by Lagrangian trajectory analysis. Positive matrix factorization analysis suggested that marine secondary organic aerosol (OA) dominantly contributed to the observed WSOC mass (∼ 70 %) during the wet season, whereas mixtures of marine and terrestrial sources contributed during the dry season in both MBL and FT. Overall, this study demonstrates that the effect of marine secondary sources is likely important up to the FT in the wet season, which may affect cloud formation as well as direct radiative forcing over oceanic regions.

Approximately 150 Tg of water vapor and 0.42 Tg of sulfur dioxide were injected directly into the stratosphere by the January 2022 Hunga volcanic eruption, which represents the largest water vapor injection in the satellite era. A comparison of numerical simulations to balloon‐borne and satellite observations of the water‐rich plume suggests that particle coagulation contributed to the Hunga aerosol's effective dry radius increase from 0.2 μm in February to around 0.4 μm in March. Our model suggests that the stratospheric aerosol effective radius is persistently perturbed for years by moderate and large‐magnitude volcanic events, whereas extreme wildfire events show limited impact on the stratospheric background particle size. Our analysis further suggests that both the particle optical efficiency and the aerosols' stratospheric lifetime explain Hunga's unusually large aerosol optical depth per unit of the SO2 injection, as compared with the Pinatubo eruption. Plain Language Summary The Hunga Tonga‐Hunga Ha'apai (HTHH) submarine volcano erupted in January 2022, injecting a modest amount of SO2 but a record amount of water vapor relative to other eruptions into the mid‐stratosphere observed in the satellite era. Our climate model simulations of the stratospheric aerosol suggest that the large water vapor injection caused the new particle formation of many new sulfuric acid solution particles with small radius, enhancing the collision efficiency. Rapid collision led to a peak radius of 0.2 μm by February and 0.4 μm by March of 2022. Our analysis finds that both particle optical properties and aerosol persistence collectively exert an influence on normalized anomaly aerosol optical depth, explaining the high aerosol optical depth per unit emission of SO2 by HTHH, relative to the 1991 Pinatubo eruption. Key Points Co‐injected water vapor triggered a sudden and large production of aerosols, followed by a fast particle growth via coagulation Simulations and observations from La Réunion and Lauder suggest that the stratospheric aerosol size was elevated for 2 years Both the high optical efficiency and long stratospheric lifetime of HTHH aerosol contribute to a higher AOD per unit emission than Pinatubo

Sulfuric acid (H2SO4) is commonly accepted as a key precursor for atmospheric new particle formation (NPF). However, direct measurements of [H2SO4] remain challenging, thereby preventing the determination of this important quantity, and, consequently, a complete understanding of its contribution to the NPF process. Several proxies have been developed to bridge the gaps, but their ability to predict [H2SO4] under very specific conditions, such as those encountered in volcanic plumes (including, in particular, high sulfur dioxide mixing ratios), has not been evaluated so far. In this context, the main objective of the present study was to develop new proxies for daytime [H2SO4] under volcanic plume conditions and compare their performance to that of the proxies available in the literature. Specifically, the data collected at Maïdo during the OCTAVE (Oxygenated organic Compounds in the Tropical Atmosphere: variability and atmosphere–biosphere Exchanges) 2018 campaign, in the volcanic eruption plume of the Piton de la Fournaise, were first used to derive seven proxies based on knowledge of the sulfur dioxide (SO2) mixing ratio, global radiation, condensation sink (CS) and relative humidity (RH). A specific combination of some or all of these variables was tested in each of the seven proxies. In three of them (F1–F3), all considered variables were given equal weight in the prediction of [H2SO4], whereas adjusted powers were allowed (and determined during the fitting procedure) for the different variables in the other four proxies (A1–A4). Overall, proxies A1–A4 were found to perform better than proxies F1–F3, with, in particular, improved predictive ability for [H2SO4] > 2 × 108 cm−3. The CS was observed to play an important role in regulating [H2SO4], whereas the inclusion of RH did not improve the predictions. A last expression accounting for an additional sink term related to cluster formation, S1, was also tested and showed a very good predictive ability over the whole range of measured [H2SO4]. In a second step, the newly developed proxies were further evaluated using airborne measurements performed in the passive degassing plume of Etna during the STRAP (Synergie Transdisciplinaire pour Répondre aux Aléas liés aux Panaches volcaniques) 2016 campaign. Increased correlations between observed and predicted [H2SO4] were obtained when the dependence of predicted [H2SO4] on the CS was the lowest and when the dependence on [SO2] was concurrently the highest. The best predictions were finally retrieved by the simple formulation of F2 (in which [SO2] and radiation alone were assumed to explain the variations in [H2SO4] with equal contributions), with a pre-factor adapted to the STRAP data. All in all, our results illustrate the fairly good capacity of the proxies available in the literature to describe [H2SO4] under volcanic plume conditions, but they concurrently highlight the benefit of the newly developed proxies for the prediction of the highest concentrations ([H2SO4] > 2–3 × 108 cm−3). Moreover, the contrasting behaviours of the new proxies in the two investigated datasets indicate that in volcanic plumes, like in other environments, the relevance of a proxy can be affected by changes in environmental conditions and that location-specific coefficients do logically improve the predictions.