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 ...

In the Arctic, impurities in the atmosphere and cryosphere can strongly affect the atmospheric radiation and surface energy balance. While black carbon has hence received much attention, mineral dust has been in the background. Mineral dust is not only transported into the Arctic from remote regions but also, possibly increasingly, generated in the region itself. Here we study mineral dust in the Arctic based on global transport model simulations. For this, we have developed a dust mobilization scheme in combination with the Lagrangian particle dispersion model FLEXPART. A model evaluation, based on measurements of surface concentrations and annual deposition at a number of stations and aircraft vertical profiles, shows the suitability of this model to study global dust transport. Simulations indicate that about 3% of global dust emission originates from high‐latitude dust sources in the Arctic. Due to limited convection and enhanced efficiency of removal, dust emitted in these source regions is mostly deposited closer to the source than dust from for instance Asia or Africa. This leads to dominant contributions of local dust sources to total surface dust concentrations (~85%) and dust deposition (~90%) in the Arctic region. Dust deposition from local sources peaks in autumn, while dust deposition from remote sources occurs mainly in spring in the Arctic. With increasing altitude, remote sources become more important for dust concentrations as well as deposition. Therefore, total atmospheric dust loads in the Arctic are strongly influenced by Asian (~38%) and African (~32%) dust, whereas local dust contributes only 27%. Dust loads are thus largest in spring when remote dust is efficiently transported into the Arctic. Overall, our study shows that contributions of local dust sources are more important in the Arctic than previously thought, particularly with respect to surface concentrations and dust deposition.

Abstract. A new, more physically based wet removal scheme for aerosols has been implemented in the Lagrangian particle dispersion model FLEXPART. It uses three-dimensional cloud water fields from the European Centre for MediumRange Weather Forecasts (ECMWF) to determine cloud extent and distinguishes between in-cloud and below-cloud scavenging. The new in-cloud nucleation scavenging depends on cloud water phase (liquid, ice or mixed-phase), based on the aerosol's prescribed efficiency to serve as ice crystal nuclei and liquid water nuclei, respectively. The impaction scavenging scheme now parameterizes below-cloud removal as a function of aerosol particle size and precipitation type (snow or rain) and intensity.Sensitivity tests with the new scavenging scheme and comparisons with observational data were conducted for three distinct types of primary aerosols, which pose different challenges for modeling wet scavenging due to their differences in solubility, volatility and size distribution: (1) 137 Cs released during the Fukushima nuclear accident attached mainly to highly soluble sulphate aerosol particles, (2) black carbon (BC) aerosol particles, and (3) mineral dust. Calculated e-folding lifetimes of accumulation mode aerosols for these three aerosol types were 11.7, 16.0, and 31.6 days respectively, when well mixed in the atmosphere. These are longer lifetimes than those obtained by the previous removal schem, and, for mineral dust in particular, primarily result from very slow in-cloud removal, which globally is the primary removal mechanism for these accumulation mode particles.Calculated e-folding lifetimes in FLEXPART also have a strong size dependence, with the longest lifetimes found for the accumulation-mode aerosols. For example, for dust particles emitted at the surface the lifetimes were 13.8 days for particles with 1 µm diameter and a few hours for 10 µm particles. A strong size dependence in below-cloud scavenging, combined with increased dry removal, is the primary reason for the shorter lifetimes of the larger particles. The most frequent removal is in-cloud scavenging (85 % of all scavenging events) but it occurs primarily in the free troposphere, while below-cloud removal is more frequent below 1000 m (52 % of all events) and can be important for the initial fate of species emitted at the surface, such as those examined here.For assumed realistic in-cloud removal efficiencies, both BC and sulphate have a slight overestimation of observed atmospheric concentrations (a factor of 1.6 and 1.2 respectively). However, this overestimation is largest close to the sources and thus appears more related to overestimated emissions rather than underestimated removal. The new aerosol wet removal scheme of FLEXPART incorporates more realistic information about clouds and aerosol properties and it compares better with both observed lifetimes and concentration than the old scheme.

Abstract. Antarctic surface snow has been studied by means of continuous measurements and observations over a period of 3 yr at Dome C. Snow observations include solid deposits in form of precipitation, diamond dust, or hoar, snow temperatures at several depths, records of deposition and erosion on the surface, and snow profiles. Together with meteorological data from automatic weather stations, this forms a unique dataset of snow conditions on the Antarctic Plateau. Large differences in snow amounts and density exist between solid deposits measured 1 m above the surface and deposition at the surface. We used the snow-cover model SNOWPACK to simulate the snow-cover evolution for different deposition parameterizations. The main adaptation of the model described here is a new event-driven deposition scheme. The scheme assumes that snow is added to the snow cover permanently only during periods of strong winds. This assumption followed from the comparison between observations of solid deposits and daily records of changes in snow height: solid deposits could be observed on tables 1 m above the surface on 94 out of 235 days (40 %) while deposition at the surface occurred on 59 days (25 %) during the same period, but both happened concurrently on 33 days (14 %) only. This confirms that precipitation is not necessarily the driving force behind non-temporary snow height changes. A comparison of simulated snow height to stake farm measurements over 3 yr showed that we underestimate the total accumulation by at least 33 %, when the total snow deposition is constrained by the measurements of solid deposits on tables 1 m above the surface. During shorter time periods, however, we may miss over 50 % of the deposited mass. This suggests that the solid deposits measured above the surface and used to drive the model, even though comparable to ECMWF forecasts in its total magnitude, should be seen as a lower boundary. As a result of the new deposition mechanism, we found a good agreement between model results and measurements of snow temperatures and recorded snow profiles. In spite of the underestimated deposition, the results thus suggest that we can obtain quite realistic simulations of the Antarctic snow cover by the introduction of event-driven snow deposition.

Abstract. Deposition of small amounts of airborne dust on glaciers causes positive radiative forcing and enhanced melting due to the reduction of surface albedo. To study the effects of dust deposition on the mass balance of Brúarjökull, an outlet glacier of the largest ice cap in Iceland, Vatnajökull, a study of dust deposition events in the year 2012 was carried out. The dust-mobilisation module FLEXDUST was used to calculate spatio-temporally resolved dust emissions from Iceland and the dispersion model FLEXPART was used to simulate atmospheric dust dispersion and deposition. We used albedo measurements at two automatic weather stations on Brúarjökull to evaluate the dust impacts. Both stations are situated in the accumulation area of the glacier, but the lower station is close to the equilibrium line. For this site ( ∼  1210 m a.s.l.), the dispersion model produced 10 major dust deposition events and a total annual deposition of 20.5 g m−2. At the station located higher on the glacier ( ∼  1525 m a.s.l.), the model produced nine dust events, with one single event causing  ∼  5 g m−2 of dust deposition and a total deposition of  ∼  10 g m−2 yr−1. The main dust source was found to be the Dyngjusandur floodplain north of Vatnajökull; northerly winds prevailed 80 % of the time at the lower station when dust events occurred. In all of the simulated dust events, a corresponding albedo drop was observed at the weather stations. The influence of the dust on the albedo was estimated using the regional climate model HIRHAM5 to simulate the albedo of a clean glacier surface without dust. By comparing the measured albedo to the modelled albedo, we determine the influence of dust events on the snow albedo and the surface energy balance. We estimate that the dust deposition caused an additional 1.1 m w.e. (water equivalent) of snowmelt (or 42 % of the 2.8 m w.e. total melt) compared to a hypothetical clean glacier surface at the lower station, and 0.6 m w.e. more melt (or 38 % of the 1.6 m w.e. melt in total) at the station located further upglacier. Our findings show that dust has a strong influence on the mass balance of glaciers in Iceland.