The Lagrangian particle dispersion model FLEX-PART 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 Cen-ters of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEX-PART 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 deposi-Published by Copernicus Publications on behalf of the European Geosciences Union. 4956 I. Pisso et al.: FLEXPART version 10.4tion 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 FLEX-PART is the only model with that capability. Furthermore, the temporal variation and temperature dependence of chemical reactions with ...

Lattice Boltzmann simulations of water-in-oil (W/O) type emulsions of moderate viscosity ratio (≃1/3) and with oil soluble amphiphilic surfactant were used to study the droplet size distribution in forced, steady, homogeneous turbulence, at a water volume fraction of 20%. The viscous stresses internal to the droplets were comparable to the interfacial stress (interfacial tension), and the droplet size distribution (DSD) equilibrated near the Kolmogorov scale with droplet populations in both the viscous and inertial subranges. These results were consistent with known breakup criteria for W/O and oil-in-water emulsions, showing that the maximum stable droplet diameter is proportional to the Kolmogorov scale when viscous stresses are important (in contrast to the inviscid Hinze-limit where energy loss by viscous deformation in the droplet is negligible). The droplet size distribution in the inertial subrange scaled with the known power law ~d(-10/3), as a consequence of breakup by turbulent stress fluctuations external to the droplets. When the turbulent kinetic energy was sufficiently large (with interfacial Péclet numbers above unity), we found that turbulence driven redistribution of surfactant on the interface inhibited the Marangoni effect that is otherwise induced by film draining during coalescence in more quiescent flow. The coalescence rates were therefore not sensitive to varying surfactant activity in the range we considered, and for the given turbulent kinetic energies. Furthermore, internal viscous stresses strongly influenced the breakup rates. These two effects resulted in a DSD that was insensitive to varying surfactant activity.

Abstract. The Lagrangian particle dispersion model FLEXPART was in its original version in the mid-1990s designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as 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 modelling 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, or volcanic emissions, 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 the global scale. In particular, inverse modelling based on source-receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.3, 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, physico-chemical parametrizations, input/output formats and available pre- 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. The deviation from 100 % efficiency is almost entirely due to remaining non-parallelized parts of the code, suggesting large potential for further speed-up. 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 for running backward in time from atmospheric concentrations at receptor locations since many years, but this has now been extended to work also 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, temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing 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 of FLEXPART output data and briefly report on alternative FLEXPART versions.

Abstract. Existing Lagrangian particle dispersion models are capable of establishing source-receptor relationships by running either forward or backward in time. For receptororiented studies such as interpretation of "point" measurement data, backward simulations can be computationally more efficient by several orders of magnitude. However, to date, the backward modelling capabilities have been limited to atmospheric concentrations or mixing ratios. In this paper, we extend the backward modelling technique to substances deposited at the Earth's surface by wet scavenging and dry deposition. This facilitates efficient calculation of emission sensitivities for deposition quantities at individual sites, which opens new application fields such as the comprehensive analysis of measured deposition quantities, or of deposition recorded in snow samples or ice cores. This could also include inverse modelling of emission sources based on such measurements. We have tested the new scheme as implemented in the Lagrangian particle dispersion model FLEXPART v10.2 by comparing results from forward and backward calculations. We also present an example application for black carbon concentrations recorded in Arctic snow.

Abstract. Short-lived climate forcers have been proven important both for the climate and human health. In particular, black carbon (BC) is an important climate forcer both as an aerosol and when deposited on snow and ice surface because of its strong light absorption. This paper presents measurements of elemental carbon (EC; a measurement-based definition of BC) in snow collected from western Siberia and northwestern European Russia during 2014, 2015 and 2016. The Russian Arctic is of great interest to the scientific community due to the large uncertainty of emission sources there. We have determined the major contributing sources of BC in snow in western Siberia and northwestern European Russia using a Lagrangian atmospheric transport model. For the first time, we use a recently developed feature that calculates deposition in backward (so-called retroplume) simulations allowing estimation of the specific locations of sources that contribute to the deposited mass. EC concentrations in snow from western Siberia and northwestern European Russia were highly variable depending on the sampling location. Modelled BC and measured EC were moderately correlated (R=0.53–0.83) and a systematic region-specific model underestimation was found. The model underestimated observations by 42 % (RMSE = 49 ng g−1) in 2014, 48 % (RMSE = 37 ng g−1) in 2015 and 27 % (RMSE = 43 ng g−1) in 2016. For EC sampled in northwestern European Russia the underestimation by the model was smaller (fractional bias, FB > −100 %). In this region, the major sources were transportation activities and domestic combustion in Finland. When sampling shifted to western Siberia, the model underestimation was more significant (FB < −100 %). There, the sources included emissions from gas flaring as a major contributor to snow BC. The accuracy of the model calculations was also evaluated using two independent datasets of BC measurements in snow covering the entire Arctic. The model underestimated BC concentrations in snow especially for samples collected in springtime.

Abstract. Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry–transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG and reactive species fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source Python-based tool to estimate the fluxes of various GHGs and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.

Lattice Boltzmann simulations were used to study the coalescence kinetics in emulsions with amphiphilic surfactant, under neutrally buoyant conditions, and with a significant kinematic viscosity contrast between the phases (emulating water in oil emulsions). The 3D simulation domain was large enough (256(3) ~ 10(7) grid points) to obtain good statistics with droplet numbers ranging from a few thousand at early times to a few hundred near equilibrium. Increased surfactant contents slowed down the coalescence rate between droplets due to the Gibbs-Marangoni effect, and the coalescence was driven by a quasi-turbulent velocity field. The kinetic energy decayed at a relatively slow rate at early times, due to conversion of interfacial energy to kinetic energy in the flow during coalescence. Phenomenological, coupled differential equations for the mean droplet diameter D(t) and the number density n(d)(t) were obtained from the simulation data and from film draining theories. Local (in time) power law exponents for the growth of the mean diameter (and for the concomitant decrease of n(d)) were established in terms of the instantaneous values of the kinetic energy, coalescence probability, Gibbs elasticity, and interfacial area. The model studies indicated that true power laws for the growth of the droplet size and decrease of the number of droplets with time may not be justified, since the exponents derived using the phenomenological model were time dependent. In contrast to earlier simulation results for symmetric blends with surfactant, we found no evidence for stretched logarithmic scaling of the form D ~ [ln (ct)](α) for the morphology length, or exponential scalings associated with arrested growth, on the basis of the phenomenological model.

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