# Saharan dust events in the European Alps: role in snowmelt and geochemical characterization

Biagio Di Mauro, Roberto Garzonio, Micol Rossini, Gianluca Filippa, Paolo Pogliotti, Marta Galvagno, Umberto Morra Di Cella, Mirco Migliavacca, Giovanni Baccolo, Massimiliano Clemenza, et al.

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Biagio Di Mauro, Roberto Garzonio, Micol Rossini, Gianluca Filippa, Paolo Pogliotti, et al., Saharan dust events in the European Alps: role in snowmelt and geochemical characterization. The Cryosphere, 2019, 13 (4), pp.1147-1165. 10.5194/tc-13-1147-2019. meteo-03657896

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# Saharan dust events in the European Alps: role in snowmelt and geochemical characterization

Biagio Di Mauro<fnref n="1" />, Roberto Garzonio<fnref n="1" />, Micol Rossini<fnref n="1" />, Gianluca Filippa<fnref n="2" />, Paolo Pogliotti<fnref n="2" />, Marta Galvagno<fnref n="2" />, Umberto Morra di Cella<fnref n="2" />, Mirco Migliavacca<fnref n="3" />, Giovanni Baccolo<fnref n="1" />,<fnref n="4" />, Massimiliano Clemenza<fnref n="4" />,<fnref n="5" />, Barbara Delmonte<fnref n="1" />, Valter Maggi<fnref n="1" />, Marie Dumont<fnref n="6" />, François Tuzet<fnref n="6" />,<fnref n="7" />, Matthieu Lafaysse<fnref n="6" />, Samuel Morin<fnref n="6" />, Edoardo Cremonese<fnref n="2" />, and Roberto Colombo<fnref n="1" />

**Correspondence:** Biagio Di Mauro (biagio.dimauro@unimib.it)

Received: 7 November 2018 – Discussion started: 15 November 2018

Revised: 5 March 2019 – Accepted: 19 March 2019 – Published: 8 April 2019

**Abstract.** The input of mineral dust from arid regions impacts snow optical properties. The induced albedo reduction generally alters the melting dynamics of the snowpack, resulting in earlier snowmelt. In this paper, we evaluate the impact of dust depositions on the melting dynamics of snowpack at a high-elevation site (2160 m) in the European Alps (Torgnon, Aosta Valley, Italy) during three hydrological years (2013–2016). These years were characterized by several Saharan dust events that deposited significant amounts of mineral dust in the European Alps. We quantify the shortening of the snow season due to dust deposition by comparing observed snow depths and those simulated with the Crocus model accounting, or not, for the impact of impurities. The model was run and tested using meteorological data from an automated weather station. We propose the use of repeated digital images for tracking dust deposition and resurfacing in the snowpack. The good agreement between model prediction and digital images allowed us to propose the use of an RGB index (i.e. snow darkening index – SDI) for monitoring dust on snow using images from a digital camera. We also present a geochemical characterization of dust reaching the Alpine chain during spring in 2014. Elements found in dust were classified as a function of their

origin and compared with Saharan sources. A strong enrichment in Fe was observed in snow containing Saharan dust. In our case study, the comparison between modelling results and observations showed that impurities deposited in snow anticipated the disappearance of snow up to 38 d a out of a total 7 months of typical snow duration. This happened for the season 2015–2016 that was characterized by a strong dust deposition event. During the other seasons considered here (2013–2014 and 2014–2015), the snow melt-out date was 18 and 11 d earlier, respectively. We conclude that the effect of the Saharan dust is expected to reduce snow cover duration through the snow-albedo feedback. This process is known to have a series of further hydrological and phenological feedback effects that should be characterized in future research.

## 1 Introduction

Mineral dust (hereafter referred as dust) plays an important role in Earth's climate and in biogeochemical cycles (Mahowald et al., 2010, 2013; Thornton et al., 2009). It provides nutrients such as iron, nitrogen, and phosphorous to marine and terrestrial ecosystems (Aciego et al., 2017; Jickells,

<footnote n="1">¹Earth and Environmental Sciences Department, University of Milano-Bicocca, 20126 Milan, Italy</footnote>

<footnote n="2">²Environmental Protection Agency of Aosta Valley, Aosta, Italy</footnote>

<footnote n="3">³Max Planck Institute for Biogeochemistry, Jena, Germany</footnote>

<footnote n="4">⁴National Institute of Nuclear Physics (INFN), University of Milano-Bicocca, 20126 Milan, Italy</footnote>

<footnote n="5">⁵Department of Physics “Giuseppe Occhialini”, University of Milano-Bicocca, 20126 Milan, Italy</footnote>

<footnote n="6">⁶Université Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Etudes de la Neige, Grenoble, France</footnote>

<footnote n="7">⁷UGA/CNRS, Institut des Géosciences de l'Environnement (IGE), Saint Martin d'Hères, France</footnote>
2005; Yu et al., 2015), and it influences the shortwave radia-
tion balance of the atmosphere (Ginoux, 2017; Mahowald et
al., 2013). Because of its peculiar optical properties, dust ef-
ficiently scatters incoming solar radiation and exerts a direct
climate forcing in the atmosphere (Tegen and Lacis, 1996).
As a function of key variables (e.g. imaginary part of the
refractive index, height of the dust layer, dust particle size,
and dust optical depth), the net radiative forcing of dust can
be either negative or positive at the top of the atmosphere
(Liao and Seinfeld, 1998; Tegen et al., 1996), representing
a significant uncertainty in current climate models (Potenza
et al., 2016). The main sources of dust are arid and hyper-
arid regions of the planet. Under specific atmospheric condi-
tions, fine and coarse particles of dust can be suspended in
the troposphere, generating characteristic dust storms (Fran-
cis et al., 2018; Goudie and Middleton, 2001). Finer dust
(<5 µm) has a prolonged atmospheric lifetime, of the order
of days, allowing for its long-range transport (Mahowald et
al., 2013; Tegen and Lacis, 1996). When dust is deposited
on snow- and ice-covered regions, its radiative impact at the
surface results in a positive radiative forcing (Painter et al.,
2012; Skiles et al., 2018). Snow optical properties largely de-
pend on its microstructure and on the presence of impurities
(also referred as light-absorbing particles – LAPs), such as
carbonaceous or mineral particles (Warren and Wiscombe,
1980). Indeed, dust lowers the snow albedo in the visible
wavelengths, enhancing the absorption of solar radiation (Di
Mauro et al., 2015; Painter et al., 2007) and thus triggering
the snow-albedo feedback (Hansen and Nazarenko, 2004).
The alterations of the optical properties of snow are known
to accelerate the melting processes (Drake, 1981; Painter et
al., 2012). First estimations of the impact of dust on snow
date back to the beginning of the last century; Jones (1913)
estimated 1 month of earlier snowmelt due to dust deposition
in the US. Drake (1981) estimated 4 d of advancement in the
snowmelt.

The impact of dust on snow melting has been largely in-
vestigated in the western US, where both radiative and hy-
drological effects have been assessed using aerial, satellite,
and automatic weather station (AWS) data (Painter et al.,
2012a, b, 2013a, 2018; Reynolds et al., 2013; Skiles et al.,
2012). In this area, the proximity of arid regions to the moun-
tain ranges determines massive dust depositions on snow-
covered mountain ranges. Dust depositions caused an earlier
snowmelt that ranged from 35 d (Painter et al., 2007) to a
maximum of 51 d (Skiles et al., 2012), strongly impacting
water supplies around the area (Painter et al., 2012, 2018).
Increases in dust deposition have been recently observed in
this area, and they were linked to human activity and climate
change (Neff et al., 2008). Other studies, conducted in Ice-
land (Dagsson-Waldhauserova et al., 2015; Wittmann et al.,
2017), in the Himalayas (Gautam et al., 2013), in Norway
(Matt et al., 2018), and in the European Alps (Dumont et al.,
2017; Di Mauro et al., 2015; Tuzet et al., 2017; Greilinger et
al., 2018) reported significant impacts of dust on snow opti-

cal properties and snowpack dynamics. Impacts on glaciers
optical properties and mass balance were also reported in the
literature (Gabbi et al., 2015; Di Mauro et al., 2017; Oerle-
mans et al., 2009).

The composition of dust varies as a function of its ori-
gin (Krueger et al., 2004) and timing (Kumar et al., 2018),
with an effect on its optical properties (Caponi et al., 2017).
Lawrence et al. (2010) presented a comprehensive character-
ization of the mineralogical and geochemical properties of
dust deposited from the atmosphere in the San Juan Moun-
tains (Colorado, US). In this area, dust is dominated by silt
and clay particles, indicating a regional source area. In the
European Alps, a large fraction of dust reaching high moun-
tains and glaciers originate from the Sahara (Haeberli, 1977;
Kandler et al., 2007; Krueger et al., 2004; Schwikowski et al.,
1995; Thevenon et al., 2009), but inputs from local sources
cannot be excluded. Even though the Alps are located at
a distance of about 3000 km from the largest desert of the
planet, they are frequently affected by dust depositions. Due
to their considerable elevation, the Alps act as an orographic
barrier, enhancing cloud formation, precipitation, and hence
dust scavenging from the atmosphere to the ground (De An-
gelis and Gaudichet, 1991; Prodi and Fea, 1979). Dust de-
position in the Alps is a well-known process, and its fre-
quency is studied using ice cores from mountain glaciers
(De Angelis and Gaudichet, 1991; Thevenon et al., 2009).
Each year, the Sahara provides up to 760 million tons of dust
to the atmosphere (Callot et al., 2000). Dust reaching Eu-
rope is dominated by silicates and aluminium oxide (Goudie
and Middleton, 2001); other contributions come from quartz,
calcium-rich particles, sulfates, hematite, and soot (Kandler
et al., 2007). The optical properties of particles are directly
related to dust composition (Linke et al., 2006), and hence
the latter is expected to modify the dust radiative effect on
snow (Reynolds et al., 2013).

Saharan dust can provide nutrients to many alpine ecosystems (Field et al., 2010; Okin et al., 2004). Aciego et al. (2017) recently showed that dust transported from Asia to the western US provides nutrients to montane forest ecosystems. This aspect has never been evaluated for mountain ecosystems in the European Alps, where dust may compete with fine debris from local rocks in providing nutrients to soils. Conversely, the direct deposition of dust on plants can limit the photosynthetic capacity (Neves et al., 2009). Steltzer et al. (2009) reported results from a manipulation experiment conducted in the western US to study the dependence of vegetation phenology on snowmelt. They measured an advancement of 7 d in snowmelt when dust was manually added to the snowpack. This process can simulate a dry deposition from the atmosphere. In the Alps, most dust depositions occur by wet deposition (mainly snowfalls; Sodermann et al., 2006), so dust is expected to be included within ice grains. Flanner et al. (2012) showed that when black carbon is internally mixed in ice grains, its radiative effect is stronger. If this also holds true for dust, wet deposition of
dust may exert a stronger effect with respect to dry depo-
sitions. Shifts in vegetation phenology also affect the timing
of migration, breeding, and asynchronies between interacting
animal species (Cohen et al., 2018; Thackeray et al., 2016).
Dust-induced snowmelt can anticipate the beginning of the
growing season, and this can result in an earlier start of the
seasonal cycle of both animals and plants. Changes in snow-
falls and dust depositions are likely to occur more frequently
in a warming climate. At the moment, the impact of Saharan
dust events on the biogeochemistry of ecosystems in the Eu-
ropean Alps has been poorly analysed (Avila and Peñuelas,
1999).

Seasonal snow represents an important reservoir of fresh water in mountain ranges and polar regions. Recent climate changes showed that this exerts a strong impact on the duration of snow cover (Vaughan et al., 2013), in particular in the European Alps (Beniston, 2005; Beniston et al., 2018). It has been observed that, especially in spring, snow cover extent has decreased in the Northern Hemisphere (Brown and Robinson, 2011; Brown et al., 2009). Earlier snowmelt can have an impact on vegetation phenology (Steltzer et al., 2009) and water availability (Beniston et al., 2003), and it is expected to alter hydrologic regimes in the future. Accelerated snowmelt due to dust can alter also surface hydrology in large mountain chains like the European Alps. In the Po Plain, for example, the most important renewable energy source is represented by hydropower. Meltwater from seasonal snow is a fundamental resource for agriculture during spring and summer (Huss et al., 2017).

In this paper, we quantitatively estimate the impact of dust
from the Sahara on snow dynamics. As a test area, we use
the experimental site in Torgnon (Aosta Valley, western Ital-
ian Alps) equipped with several sensors for measuring snow
properties. Snow dynamics were simulated with a multilayer,
physically based energy balance model (Crocus, Vionnet et
al., 2012), which can incorporate the effect of LAPs (min-
eral dust and black carbon) in snow and estimate their im-
pact on snowmelt (Tuzet et al., 2017). The timing and inten-
sity of Saharan dust depositions were simulated using two
independent models (ALADIN-Climate and NMMB/BSC-
Dust). Observed and simulated snow variables are compared,
and the role of impurities on snowmelt is discussed. We
also made use of repeated images from a digital camera to
track the deposition and resurfacing of impurities. Finally,
we present a geochemical characterization of dust reaching
the Alps, and thus we discuss the possible biogeochemical
and hydrological role of dust in the Alps.

## 2 Data and methods

2.1 Torgnon experimental site

The study area is located in the north-western Italian
Alps (Aosta Valley, Italy) at an elevation of 2160 m a.s.l.

(45°50′40″ N, 7°34′41″ E). The experimental site belongs
to the Phenocam (Torgnon-nd, https://phenocam.sr.unh.
edu/webcam/, last access: 2 April 2019), ICOS (IT-Tor;
https://www.icos-ri.eu/, last access: 2 April 2019), and
LTER (lter_eu_it_077; https://deims.org/site, last access:
2 April 2019) networks. The area is a subalpine unmanaged
pasture classified as intra-alpine with a semi-continental cli-
mate. The site is generally covered by snow from the end of
October to late May. Further information regarding the site
can be found in Galvagno et al. (2013). An AWS was in-
stalled in 2009 at the experimental site of Torgnon. Air tem-
perature is measured by a HMP45 (Vaisala Inc.); snow depth
is measured with a SR50A sonic sensor (Campbell Scientific,
Inc.). Albedo is measured with a Kipp & Zonen CNR4 net
radiometer. The snow water equivalent (SWE) is measured
with a gamma monitor (GMON; Campbell Scientific, Inc.)
sensor. Solid and liquid precipitations were measured with
a Pluvio2 OTT instrument. Wind speed and direction were
measured with a CSAT3 three-dimensional sonic anemome-
ter (Campbell Scientific, Inc.). Data are available at hourly
time resolution.

## 2.2 Digital image analysis

In recent years, digital images analysis was applied to mon-
itor vegetation phenology (Julitta et al., 2014; Migliavacca
et al., 2011; Richardson et al., 2007), landslides, glaciers
(Jung et al., 2010), and snow (Corripio, 2004; Dumont et al.,
2011; Hinkler et al., 2002; Parajka et al., 2012). Regarding
the latter two, snow albedo and snow cover were success-
fully estimated using digital cameras in alpine areas. For this
study, digital RGB images were collected using a Nikon dig-
ital camera (model d5000, also referred as “Phenocam”) in-
stalled at the experimental site in 2013 in the vicinity of the
AWS. Following Richardson et al. (2007), the camera was
pointed north and set at an angle of about 20° below horizon-
tal. The camera focal length is 33 mm, and the field of view
is 79.8°. The camera was fixed at 2.5 m above the ground,
and the same scene was repeatedly photographed. Digital im-
ages were collected in the Joint Photographic Experts Group
(JPEG) format with a resolution of 12 megapixels and three-
colour channels (namely red, green, and blue) featuring eight
bits of radiometric resolution. The images were collected
from 10:00 to 17:00 LT (local time: UTC+1), with an hourly
temporal resolution. Exposure mode and white balance were
set to automatic.

A region of interest (ROI) was firstly identified in an ap-
proximately flat area to analyse snow evolution. Images were
acquired during the hydrological years 2013–2016. Red,
green, and blue chromatic coordinates were extracted from
the selected ROI using the Phenopix R package (Filippa et
al., 2016). Then, the snow darkening index (SDI; Di Mauro
et al., 2015) was calculated from a red and green digital num-
bers (DNs) as follows:
<figure><img src="image_4.png" /><figcaption>Figure 1. (a) Location of Torgnon (Aosta) and Artavaggio plains (Lecco) in the European Alps. (b) A picture of the experimental site of Torgnon (2160 m a.s.l.). (c) Aerial view of the site in Torgnon with the location of different instruments installed. The field of view of the Phenocam is represented with a blue shaded area.</figcaption></figure>

$$SDI = \frac{DN_{Red} - DN_{Green}}{DN_{Red} + DN_{Green}}. \qquad (1)$$

The SDI was correlated with the concentration of dust in snow (Di Mauro et al., 2015) and was used to represent the spatial distribution of impurities from space (Ganey et al., 2017; Di Mauro et al., 2017) and from hyperspectral images of ice cores (Garzonio et al., 2018). The SDI calculated from RGB data collected from an unmanned aerial vehicle (UAV) was found to be correlated with the SDI calculated from field spectroscopy data (Di Mauro et al., 2015). This motivated the idea to monitor dust deposition and resurfacing dynamics using repeated digital images from the camera installed in Torgnon. In this work, the SDI was calculated for each available image, then a daily average was calculated. Days with an SDI > 0 were considered to be markers of the presence of dust on snow.

## 2.3 Snowpack modelling

Snow dynamics in Torgnon were simulated using the SURFEX and ISBA-Crocus model, hereinafter referred as Crocus. Crocus is a snow model initially developed for avalanche forecasting and used for hydrological estimation as well as numerical prediction (Brun et al., 1989). Crocus is a one-dimensional multilayer model that simulates the evolution of the snowpack based on input meteorological driving conditions (Brun et al., 1989, 1992). Snow dynamics are represented as a function of energy and mass transfer within the snowpack, between both the snowpack and the atmosphere, and the snowpack and the ground below (Vionnet et al., 2012). In this study, we used a specific Crocus version using the Two-stream Analytical Radiative TransfEr in Snow (TARTES) radiative transfer model (Libois et al., 2013) to

obtain simulations of snow spectral albedo as a function of snow properties, LAP concentrations, and LAP optical properties. The TARTES model is based on the asymptotic approximation of the radiative transfer theory (AART; Kokhanovsky and Zege, 2004) and accounts for the effect of snow microstructure and impurities, such as dust and black carbon. The snow spectral albedo simulated with TARTES was used to calculate the SDI (using the formulation proposed in Di Mauro et al., 2015), and it was compared with the SDI calculated from the digital camera. A complete description of this specific Crocus model version can be found in Tuzet et al. (2017). Crocus is embedded in the SURFEX surface scheme and is permanently coupled with the ISBA-DIF soil model.

Variables needed for running Crocus simulations are the following: air temperature, direct and diffuse shortwave incoming radiation, longwave radiation, wind speed and direction, specific humidity, surface pressure, and solid and liquid precipitation. The model was forced using meteorological data from the station in Torgnon for the seasons 2013 and 2016 at an hourly time step. All variables were measured at the station of Torgnon, except for diffuse shortwave incoming radiation, which was measured (with a BF3 sensor; Delta-T Devices Ltd., Cambridge, UK) in another station located 2 km from Torgnon. The instrument used for precipitation measurements (Pluvio2 OTT) does not feature a windshield. This can be problematic, since underestimations of snowfall can occur during intense wind events. For this reason, we corrected the data following the prescriptions proposed in Kochendorfer et al. (2017). Some manual adjustments to solid precipitations were needed in the case of strong wind events. In addition to the above-mentioned meteorological data, the Crocus version of Tuzet et al. (2017) needs dust and black carbon deposition fluxes.
In this study, these fluxes were taken from the atmospheric model ALADIN-Climate (Nabat et al., 2015). For evaluating the impact of dust depositions on the snowpack dynamics, key variables (e.g. albedo, snow depth, and snow water equivalent) measured from the AWS were compared with Crocus simulations with and without impurities (dust and black carbon) in snow. In addition, soil temperature was initialized using a spin-up simulation of 4 years.

## 2.4 Dust concentration, size distribution, and geochemistry

On 6 April 2016, a field campaign was organized to collect snow samples at the experimental site of Torgnon. Six snow pits were dug in different locations placed a few metres away from the AWS. For each snow pit, we collected a surface sample at 0 cm and three samples at depths equal to 20, 40, and 60 cm from the surface. Samples were collected using sterilized Corning tubes (50 mL) and kept frozen until measurements. Dust concentration and size distribution were measured using a Coulter counter technique. Samples were melted in a clean room (class 1000 clean room at EuroCold Laboratory Facilities, University of Milano-Bicocca) and analysed with a Multisizer™ 4e COULTER COUNTER®. The instrument was set with a 100 µm orifice, allowing for the detection of particles with a diameter (equivalent spherical) between 2 and 60 µm, divided into 400 size channels. To obtain dust mass from particle volume, a crustal density of 2.5 g cm⁻³ was adopted. Total dust concentration was calculated considering the integral of the concentration between 2 and 60 µm. Details about the technique can be found in Ruth et al. (2008).

In addition, dust samples collected in the Alps at 150 km from Torgnon (Artavaggio, Lecco, Italy, 1650 m a.s.l.) in March 2014 are used here to characterize the bulk composition of dust events and the elemental input to alpine ecosystems. Snow samples were transported before melting in a cold facility, where they were stored until the preparation for the successive analyses. At first, they were melted, and an aliquot (5–10 mL) was measured through Coulter counter technique (CC) for the determination of dust size distribution. These data were already published (Di Mauro et al., 2015). A second aliquot consisting in few millilitres of melted snow was dedicated to instrumental neutron activation analysis (INAA) for the analysis of elemental composition (Greenberg et al., 2011). To this aim, dust was extracted and separated using a filtration system equipped with polycarbonate membranes (pore size 0.4 µm, well below the typical volume mode grain size of Saharan dust deposited on the Alps). Two distinct samples were prepared. One sample (SH1) was extracted from the reddish snow corresponding to the snow deposited during the Saharan event; it consisted of 7.2 ± 0.2 mg of dust. A second sample (SH2) was prepared for comparison, filtering clean white snow. In this case, given the low concentration of impurities, it was possible to

retrieve 202 ± 11 µg of particulate matter. For both samples, in addition to absolute concentration (mass fraction), normalized ones were also calculated. The average upper continental crust composition (UCC; Rudnick and Gao, 2003) was selected as a normalizing reference to highlight the influence played by crust-derived material and the possible role of non-crustal sources for specific elements. Neutron irradiation was performed at the LENA laboratories at the University of Pavia (Borio di Tigliolet al., 2010), where a TRIGA Mark II research nuclear reactor is installed (250 kW). Activated samples were successively analysed using high-purity germanium detector available at the Radioactivity laboratory of the Milano-Bicocca University. Two irradiations and several acquisitions of the γ spectra were necessary to detect the largest number of radionuclides, ranging from the short-lived species to the long-lived ones. For a complete description of the method, including the estimation of errors, see Baccolo et al. (2015, 2016).

## 2.5 Dust transport and deposition modelling

In addition to the ALADIN-Climate model, dust transport and deposition were monitored using the NMMB/BSC-Dust model. This is an online multi-scale atmospheric dust model (Pérez et al., 2011); it was used here to provide dust forecasts from the Sahara to the European Alps. NMMB/BSC-Dust provides both atmospheric concentration and deposition fluxes of dust with a 0.3° × 0.3° horizontal resolution. During the three seasons considered here, we classified dust events as “strong” events with dust deposition fluxes larger than 800 mg m⁻² and “weak” events with lower concentrations. The timings of the events simulated with the NMMB/BSC-Dust model were qualitatively compared to those simulated with the ALADIN-Climate model during the period analysed here (2013–2016).

# 3 Results and discussion

## 3.1 Modelled dust deposition events

The period between 2013 and 2016 was characterized by two strong events (dust fluxes > 800 mg m⁻²) and several weak events (dust fluxes < 800 mg m⁻²) distributed during the seasons. The strong events that occurred on February 2014 and on April 2016. The event of February 2014 was already analysed in the scientific literature (Di Mauro et al., 2015; Tuzet et al., 2017; Dumont et al., 2017). The event of April 2016 lasted several days and transported a considerable dust amount to the western sector of the European Alps (Fig. 2; Greilinger et al., 2018). According to the NMMB/BSC-Dust model, during these two strong events, dust was primarily deposited in the Alpine chain by wet deposition. In Fig. 2, we show an example (5 April 2016) of the concentration of dust deposited according to NMMB/BSC-Dust and a longitudinal and latitudinal transect. NMMB/BSC-Dust predicted
<figure><img src="image_5.png" /></figure>

**Figure 2.** NMMB/BSC-Dust forecast for the event of April 2016. In the top panel is the estimated surface concentration by wet deposition. The lower panels represent, respectively, a latitudinal and longitudinal transect centred on the city of Aosta (45.74° N; 7.36° E). Images are from the NMMB/BSC-Dust model, operated by the Barcelona Supercomputing Center (http://www.bsc.es/ess/bsc-dust-daily-forecast/, last access: 2 April 2019).

up to 1600 $mg m^{-2}$ of dust deposition in the western Alps. In the latitudinal and longitudinal profiles, it is clearly visible that the plume reached almost 6 km in altitude. The highest concentrations in the atmosphere were reached in southern France and in the north-west of Italy. The experimental site of Torgnon is located in the Italian western Alps, and it represents a good candidate for analysing the effect of this strong dust event on snow dynamics.

## 3.2 Observed and simulated snow dynamics

In Fig. 3, a comparison of variables observed at the Torgnon station and simulated with the Crocus model using impurity fluxes is presented. We show time series of the snow albedo, SWE, and snow depth (SD). In general, the Crocus model represented snow dynamics well during the hydrological seasons 2013–2016. In Fig. 4, we present a quantitative comparison (coefficient of determination – $R^2$ – and root-mean-square error – RMSE) between snow variables observed and simulated, including and excluding the effect of LAPs. Crocus simulations accounting for the impact of LAPs showed a better agreement with observations than Crocus simulations that not account for the effect of LAPs. The snow albedo was underestimated from Crocus during the accumulation period (see Fig. 3a). Instead, during the melting period, the decreasing trend observed in snow albedo was reproduced well by the Crocus model accounting for the role of impurities. During the accumulation period, the albedo modelled by both Crocus simulations was always lower than the observed albedo. During the melting season, a clear divergence is observed between the Crocus simulation with LAPs and that without LAPs. Instead, the Crocus simulation with LAPs is more correlated with the observed snow depth. The observed and simulated SWE and snow depth show a large interannual variability. The SWE is strongly overestimated in the season 2013–2014; while during the accumulation period snow depth is represented well in the model, the melting rate is higher in the observed snow depth. This results in a delay of snow melt-out dates in both Crocus simulations (with and without impurities). A similar pattern in snow depth is also observed in the season 2014–2015. Unfortunately, the measured SWE was not available for this season. During the season 2015–2016, the correlation between the observed and simulated snow depth accounting for the impact of impurities was very high, both for snow depth ($R^2$ = 0.96; RMSE = 0.05 m) and the SWE ($R^2$ = 0.97; RMSE = 13 mm). The difference in snow melt-out dates between observed and simulated data accounting for LAPs was 12, 10, and 11 d, respectively, for the seasons 2013–2014, 2014–2015, and 2015–2016. Instead, the comparison between snow melt-out dates simulated with and without impurities was 18, 11, and 38 d for the seasons 2013–2014, 2014–2015, and 2015–2016, respectively. For snow depth (Fig. 4a) and the SWE (Fig. 4b), Crocus simulations with LAPs generally resulted in a lower RMSE and higher $R^2$ with respect to Crocus simulations without LAPs. Instead, for snow albedo (Fig. 4c), the resulting RMSE was smaller for Crocus simulations without LAPs. We underline that these RMSE values are associated with very low explained variance ($R^2$ ~ 0.2 for the seasons 2014–2015, 2015–2016, and all years, and $R^2$ = 0.43 for the season 2013–2014). Thus, Crocus simulations with LAPs perform better than Crocus simulations without LAPs in modelling snow dynamics at Torgnon.
In Fig. 3d, we show also a qualitative comparison between the dust fluxes simulated with ALADIN-Climate and with the NMMB/BSC-Dust model. In general, a good agreement between the two models was observed. The two most intense events (February 2014 and April 2016) are identified by both models. Smaller events are also reproduced, whereas sometimes small events are seen only by ALADIN-Climate.

Once dust fluxes are deposited on the snowpack, they are buried by subsequent snowfalls. In Fig. 3e, we show the multilayer concentration of dust in snow simulated with Crocus. It is clear that dust is resurfacing towards the end of the season, when the snow albedo feedback intensifies and promotes the melting. The surface concentration of dust (average of the first 10 cm of snow) in the three seasons considered in this study show an important interannual variability (Fig. 3f). In fact, whereas the first two seasons show surface concentrations of dust lower than 150 µg g⁻¹, the last season (2015–2016) shows concentrations up to 350 µg g⁻¹ at the end of the season. This can be due to the longer duration of the dust event in April 2016 and may also explain the large change (38 d) in the snow melt-out dates observed in the data.

Results from samples collected in Torgnon showed that significant concentrations of dust were present in the snowpack in April 2016 (Fig. 5). It is interesting to note that the mode of the dust size distribution is 7.9 µm for surface snow and 8.5 µm for snow samples collected at 20 and 40 cm depth. Instead, dust particles found in snow at the bottom of the snowpack (60 cm depth) feature a mode of 3.2 µm. This deeper layer can be probably due to the scavenging of small dust particles by meltwater or to other undetected processes. The first three distributions can be due to the weak depositions that happened in February and March and were then buried by new snow. Dust size distributions are compatible with other measurements of dust enclosed in snow and ice in the Alps (3–5 µm; Maggi et al., 2006) and in the Caucasus (1.98–4.16 µm; Kutuzov et al., 2013). Differences between our samples and these studies may be ascribed to the different elevation of the samplings. Samples shown in Fig. 5 feature a significant noise in the tail of the distribution. This can be ascribed to the aggregation of fine particles or to an input of local particles with larger diameters. A contribution of large particles of local origin cannot be excluded, and it may have a strong influence on snowmelt. At the moment, we do not have enough data to decouple the effect of large and small particles on the snow albedo. Total concentration of dust in Torgnon was estimated by adding up different channels from the size distributions. Among the six different snow profiles measured, surface concentrations reached a maximum of 65 µg$_{dust}$ g⁻¹, with a mean of 45.6 µg$_{dust}$ g⁻¹ and a standard deviation of 15.8 µg$_{dust}$ g⁻¹.

Measurements of dust concentrations are available only for 6 April 2016. On this day, the dust concentration profile simulated by Crocus spans from 11 (bottom) to 108.7 (top) µg$_{dust}$ g⁻¹. Modelled and measured surface concentrations of dust showed some difference: 43.7 µg$_{dust}$ g⁻¹ for

the most concentrated surface sample and 65.1 µg$_{dust}$ g⁻¹ for the mean of the six snow pits. This variability can be explained by the strong spatial mismatch between the spatial resolution of ALADIN-Climate model (50 km) and the point measurement of dust concentration. Differences can also depend on snow sampling, vertical resolution, and Crocus layer thickness. Model improvements are needed to downscale the spatial resolution of LAPs fluxes. The installation of a wet and dry sampler (e.g. deposimeter) at experimental sites may help to drive the Crocus model with measured deposition fluxes. It is important to notice that ALADIN-Climate predicted also depositions of black carbon. At the moment, we do not have measurements to validate this estimation, but the presence of black carbon in snow may have amplified the snow-albedo feedback in the snowpack. The role of black carbon in Alpine snow still represents a great uncertainty in snow modelling and climate prediction in the Alps. While the role of industrial black carbon on post-industrial glacier retreat has been debated (Painter et al., 2013b; Sigl et al., 2018), its role in seasonal snow melting has not been studied in the European Alps.

Hereafter we focus on the season 2015–2016, since Crocus simulations with impurities resulted in a 38 d advancement of the snow melt-out date compared to the corresponding simulations without impurities. This season was characterized by dust surface concentration in snow being almost double with respect to the other two seasons considered in this study (see Fig. 3f). In Fig. 6, we show the comparison of the snow depth simulated with Crocus, including and excluding the impact of impurities. During the season 2015–2016, about 1 m of snow was on the ground in Torgnon. The model without impurities predicted a longer duration of snow on the ground than the model with impurities. Two late snowfalls occurred in May, and this probably increased the difference between the simulations. Since air temperatures were still close to 0° (data not shown), snow was preserved at the ground in the simulations without impurities, and this further prolonged the snow season duration. Considering that the first significant snowfalls occurred in January, the snow season was shortened by about 20% of the total because of impurities.

In Fig. 6, we plot the SDI calculated from the radiative transfer model (TARTES) included in Crocus (SDI-Crocus hereafter) and from the RGB camera (SDI-Phenocam hereafter). Regarding the digital camera data, days with SDI > 0 are represented as shaded green bands. We observed an agreement between the two datasets. SDI-Crocus increased during April. In particular, at the beginning of April two peaks in SDI-Crocus are also seen by SDI-Phenocam. A peak then is not clearly seen by the digital camera. This could be due to the occurrence of two small snowfalls during the resurfacing of dust layers. At the end of April, the concentration of dust on the surface of snow is represented well, both by Crocus and digital images. During this last period, a marked change in snowmelt rate is observed from the snow depth
<figure><img src="image_6.png" /></figure>

**Figure 3.** (a, b, c) Time series of albedo, snow water equivalent (SWE), and snow depth (SD) measured with the AWS and simulated with Crocus model including and excluding the impact of LAPs. SWE data are missing in December 2013 because of problems with the power supply. (d) Dust fluxes simulated with ALADIN (maroon bars, note that the scale is inverted), and strong (large stars) and weak (small stars) dust events simulated with NMMB/BSC-Dust. (e) Dust concentration (µg g⁻¹) in the snowpack (yellow to black palette) simulated with Crocus and superimposed on the snow depth profile (grey shaded area). (f) Surface concentration (averaged over the first 10 cm) of dust simulated with Crocus.

<figure><img src="image_7.png" /></figure>

**Figure 4.** Comparison between (a) snow depth, (b) snow water equivalent (SWE), and (c) albedo observed from the AWS in Torgnon and simulated with Crocus accounting (red dots) and not accounting (cyan dots) for the impact of LAPs on snow.

series around the 20 April (Fig. 6). The agreement between SDI-Crocus and SDI-Phenocam suggests that low-cost digital RGB data can be used for monitoring the resurfacing of dust in snowfields, useful for satellite and model validation. In order to use these RGB data quantitatively, further comparisons with field spectroscopy and ground data are needed.

In Fig. 7a and b, we show two examples of digital images collected from the digital camera. The spatial variability of SDI can be explained by local topography. The experimental site is located in a plain area, with a gentle slope (~5°). Microtopography created by snow melting and refreezing cycles can locally concentrate and dilute impurities in the snowfield, also in relation to the differential sun ex-

posure. Surface runoff may also represent an important process in shaping snow surface and in distributing dust in snowfields. This may explain the variability observed in SDI and also the differences between the measured dust concentration in snow samples and Crocus modelled concentrations. Dust redistribution on snowfields might strongly affect its radiative impact. In Fig. 7c and d, we present two SDI maps acquired before (10 April) and after (20 April) the resurfacing of dust layers. The transition from cold to warm colours reflects the increase in the values of the index. Positive values of the SDI are associated here with the presence of dust on snow (Di Mauro et al., 2015). On the right of both images, a snow pit is visible. It is interesting to note that in the SDI map
<figure><img src="image_8.png" /></figure>

**Figure 5.** Dust particle distribution (expressed in µg*dust* kg<sub>snow</sub><sup>-1</sup>) for a snow profile sampled at Torgnon on 6 April at different depths (0, 20, 40, and 60 cm). Blue lines are experimental data, and black lines are moving averages (kernel: 25 points). Numbers in the plots represent the peak of the size distributions. Please note that the scale changes within different plots.

from 20 April, a red layer is visible in the snow pit. This can
be possibly associated with the precedent weak depositions
from February and March, which were concentrated in a thin
snow layer by melting during early spring. At the end of the
season, weak and strong depositions are concentrated by sur-
face melting. This process amplifies the feedback mechanism
of dust on snow. In fact, while the melting of snow concen-
trates the dust on the surface, higher concentrations of dust
intensify the melting. This feedback is expected to act for
each day with sufficient solar radiation during the melting
period. The feedback is also expected to be enhanced until
the total disappearing of the snow cover.

The SDI is also sensitive to other impurities, such as
black carbon (Di Mauro et al., 2017) and organic material
(Ganey et al., 2017). We cannot exclude that other impu-
rities were present on snow surface, but at present we do
not have enough data to evaluate these aspects. We interpret
SDI variability only in relation to dust deposition and resur-
facing. In the selected ROI (Fig. 7a), frequency distribution
of the SDI shows a peak at 0.005. Using this information,

<figure><img src="image_9.png" /></figure>

**Figure 6.** Comparison between snow depth simulated using Cro-
cus with impurities (grey area) and without impurities (purple line).
Observed data are also shown (black line). Dust concentration in
snow is represented. Shaded green bands represent days with SDI-
Phenocam > 0. SDI-Crocus is represented as a continuous green
line.

we inverted the non-linear (rational) model developed in Di
Mauro et al. (2015) that links mineral dust concentration and
SDI values, and we obtained an estimated dust concentra-
tion equal to 56 µg_dust_ g<sub>snow</sub><sup>-1</sup>. This value is very close to the
concentrations measured with the Coulter counter integrating
particles smaller than 60 µm, which reached a maximum of
65 µg_dust_ g<sub>snow</sub><sup>-1</sup>.

Our estimations of shifts in the melt-out day are compa-
rable to previous findings in the western US estimating a
reduction of snow cover up to 51 days due to the presence
of mineral dust in snow (Painter et al., 2007; Skiles et al.,
2012). Despite the different deposition rates in the Alps, the
advancement of the snowmelt due to dust is comparable to
published results regarding the western US. This is true at
least for one season (2015–2016) characterized by a major
Saharan dust deposition. Tuzet et al. (2017) estimated up
to 9 d of advanced snowmelt during 2014 in a lower eleva-
tion site located in the European Alps as well. In this pa-
per, we estimate an advance in snow melt-out days of 18,
11, and 38 d for the three seasons considered. The estima-
tion for the season 2015–2016 is very high, also considering
that snow cover normally lasts about 7 months at this ele-
vation (2160 m a.s.l.). In the future, impurity concentration
estimated with the atmospheric model should be evaluated
using ground observations. In this sense, data from in situ
spectrometers (e.g. Dumont et al., 2017; Picard et al., 2016)
and repeated digital images can be very helpful. In fact, the
concentration of different impurities may be retrieved from
spectral reflectance using both inversion of radiative transfer
models and spectral indices.

Dust transport is a natural phenomenon, but it can be inten-
sified by anthropogenic activities (Neff et al., 2008). Further
research is needed to assess possible inputs of local dust to
mountain environments. Recently, dust was found to be more
important than temperature in determining snowmelt in the
western US (Painter et al., 2018).
<figure><img src="image_10.png" /><figcaption>**Figure 7.** (a, b) Examples of digital images acquired from the Phe-nocam installed at Torgnon before and after the resurfacing of dust layers. (c, d) Snow darkening index (SDI) calculated using the red and green channels of the images. A region of interest (ROI, a) was used to create SDI time series. The asterisk in (a) indicates the po-sition of the snow pit.</figcaption></figure>

Although no trends were found in the annual number of Saharan dust days since 1997 (Flentje et al., 2015), further re-search is needed to assess the role of impurities on snow dy-namics in the Alps. Measurements of surface concentrations of dust and black carbon in snow are very scarce in the whole Alpine chain. At the Jungfraujoch station (3454 m a.s.l.), dust concentration in the atmosphere is measured continuously (Collaud Coen et al., 2004). A comparison with these data will be fundamental in validating Saharan dust fluxes in the Alps and quantifying their effect on snow dynamics.

Snow duration was very short during the 2015–2016 hy-drologic year. Usually, grassland in Torgnon is covered by a thick snow cover from the end of October to late May (aver-age 1928–2010). During 2015–2016 snow arrived in January and disappeared at the beginning of May. It is known that ear-lier snowmelt impacts the carbon uptake period (Galvagno et al., 2013), altering carbon exchange with the atmosphere during spring. Shifts in phenological dates, such as the be-ginning and end of season, may impact ecosystem function-ing related to net and gross ecosystem productivity in alpine grasslands and might lead to early depletion of soil mois-ture and early senescence related to summer water stress. Extreme events like heatwaves have impacts on phenology of mountain grasslands (Cremonese et al., 2017). With future climate change, these extreme events are likely to increase. With the intensification of climate change, snow is expected to occur later in autumn and to be depleted earlier in spring (Frei et al., 2018; Verfaillie et al., 2018), with significant con-sequences for the hydrological cycle. The effect of Saharan dust in the European Alps is to accelerate the melt via the di-rect and indirect effect on snow albedo, thus enhancing snow season shortening.

### 3.3 Geochemical characterization of dust in snow

Dust composition is strictly tied to its optical characteris-tics and hence to its radiative effect on snow (Caponi et al., 2017; Reynolds et al., 2013). Iron oxides contained in dust are particularly absorptive in the visible wavelengths (Alfaro et al., 2004; Linke et al., 2006), and this further enhances the albedo feedback when dust is deposited on snow. The com-position of dust is also important for the correct representa-tion of dust in radiative transfer models and global climate models (Albani et al., 2014). Saharan dust events provide an input of nutrients to alpine ecosystems (Goudie and Middle-ton, 2001), and this has been poorly studied in the scientific literature (Arvin et al., 2017).

The main source area of Saharan dust events reaching the Alps is represented by northern Algeria (potential source area in Northern Africa 1 – PSANAF-1 – in Formenti et al., 2011). For this reason, the dataset presented in this study can be considered representative for the main composition of long-range dust deposition on snow in the Alps. Saharan dust events are regional episodes that move large quantities of mineral dust from arid regions to different latitudes and longitudes. There are two main pathways for the transport of dust: it can reach Europe overpassing the Mediterranean and also by looping back over the Atlantic (Israelevich et al., 2012; Sodemann et al., 2006). For this reason, we can assume that the bulk geochemical composition of dust events that oc-curred in different locations in the Alps and at different times is comparable.

Between 18 and 20 February 2014 a relevant event was observed, involving not only southern Europe and the Alps but also a large fraction of Europe. It was described as one of the most intense events of this kind that was observed in previous years. The event was associated to a particularly favourable atmospheric setting which could uplift a massive amount of Saharan dust from northern Africa and transport it toward Europe in association to south-westerly winds driven by an anticyclonic structure located on the central Mediter-ranean. Given the magnitude of the event, many studies re-ported it, spanning from microbiology (Meola et al., 2015; Weil et al., 2017) to remote and proximal sensing (Dumont et al., 2017; Di Mauro et al., 2015; Tuzet et al., 2017) and at-mospheric chemistry and physics (Belosi et al., 2017; Telloli et al., 2018).

Hereafter, we provide results from a geochemical charac-terization of dust sampled in snow in the Alps (Artavaggio, Lecco, Italy). The analysis of the elemental composition al-lowed detecting 36 elements, spanning from the so-called major elements (the ones whose oxides constitute more than 1 % of the average composition of Earth’s crust) to many mi-nor and trace ones. Data of interest are shown in Fig. 8, the full list of elemental concentrations is reported in Table 1. We acknowledge that only one snow sample containing dust is not enough to provide a complete overview on the compo-sition of Saharan dust in snow in the Alps, but our analysis
may pave the way for a more exhaustive characterization of dust composition in the future.

The concentrations of major elements normalized to the upper continental crust composition are shown in Fig. 8a. It can be easily appreciated that SH1 and SH2 display a very different composition. SH1, corresponding to the dusty snow deposited during the Saharan advection episode of February 2014, presents a typical crustal signature, with UCC normalized values close to 1. On the contrary, SH2 shows very low normalized concentrations, suggesting that in this case, the crustal fraction is not the dominant one. Since all the considered major elements are strongly depleted (normalized concentrations span from 0.17 in the case of Na to 0.38 for Fe), it can be inferred that its composition is probably dominated by the only major element which is not considered here: carbon. Unfortunately INAA is not suited for its detection, but it is known that the carbonaceous fraction is an important component of snow impurities (Li et al., 2016; Wang et al., 2015). Comparing SH1 to Sahel and Saharan dust source composition, a substantial correspondence can be appreciated, as illustrated in Fig. 8. This is not unexpected, but direct observations linking the geochemical properties of Saharan dust to the dust deposited in the Alps are quite scarce.

One of the main differences between SH1 and SH2 is regarding iron (Fe). With respect to this element, SH1 presents absolute concentrations that are more than 2 orders of magnitude higher than in SH2. This suggests that Saharan dust could be important for supplying this essential element to high-elevation alpine ecosystems where other nutrient sources could be limited, as already pointed out to in relation to other species and to other environments (Avila et al., 1998; Greilinger et al., 2018; Rizzolo et al., 2017). Another issue related to Fe concentration in atmospheric dust is related to its optical properties, since iron oxide concentration and mineralogy strongly influence them (Alfaro et al., 2004; Caponi et al., 2017; Formenti et al., 2014; Linke et al., 2006). The large abundance of Fe is thus expected to affect the radiative effects of dust on snow (Reynolds et al., 2013).

Looking at Ca and Ti, further information can be inferred about the most likely provenance of SH1. Northern African sources (grey lines in Fig. 8a) can be clearly distinguished in relation to the content of these two elements, and indeed two groups are recognized. A first one is characterized by high Ca concentration and low Ti content, and the second group shows an opposite composition, with a larger amount of Ti and a lower amount of Ca (see Fig. 8a). Carbonates are rich in Ca (a constituent of these rocks) and poor in Ti, in relation to their limited content in accessory and heavy minerals. The first group (high Ca and low Ti) corresponds to the samples collected in Western Sahara, where carbonate rocks are common. On the contrary, samples from northern Africa display an opposed composition. Comparing SH1 to these groups, it is clear that its composition is in accordance with the second group. Its provenance is more probably related to the

<figure><img src="image_11.png" /></figure>

**Figure 8.** The elemental composition of the Saharan dust extracted from the snow precipitated on the Alps in February 2014 (red line, SH1), and the composition of the particulate matter retrieved from the clean snow deposited few days later (blue line, SH2). Grey lines refer to samples collected from the Sahara–Sahel dust corridor (Moreno et al., 2006). Data are expressed in terms of concentration normalized to the average upper continental crust (UCC; Rudnick and Gao, 2003). (a) Major elements, (b) anthropogenic elements, and (c) incompatible elements (with respect to Fe), listed following the order proposed by Sun and McDonough (1989).

mobilization of dust from the central sector of the Sahara–Sahel dust corridor, i.e. the Hoggar, Chad, and Niger basins (Moreno et al., 2006).

The elemental composition of dust might also have an important effect on the biogeochemical cycles of the alpine grasslands. Among the elements listed in Table 1 there are elements such as K and Ca that are known to be relevant to ecosystem functioning (Sardans and Peñuelas, 2015; Schaffner et al., 2012). For both elements, SH1 shows notably higher concentrations (see Table 1). This requires more attention and further studies to understand the feedback of Saharan dust deposition on the biogeochemistry of high-elevation ecosystems.

Anthropogenic elements are presented in Fig. 8b. They are W, Hg, Sb, Se, and Zn. This group of elements concerns those elements presenting important positive deviations with respect to UCC composition. They are defined “anthropogenic” to highlight that their biogeochemical cycles have been strongly impacted by human activities in the last decades and that their mobilization in the environment related to anthropogenic activities exceeds the natural one (Sen and Peucker-Ehrenbrink, 2012). Again, the signature of SH1 is completely different from that of SH2. Unlike the case of major and incompatible elements, for anthropogenic elements the sample presenting the higher relative concentra-
<table><caption>**Table 1.** The elemental composition of SH1 (snow sample containing mineral dust) and SH2 (clean snow sample). Data are expressed (in terms of µg g⁻¹) and are referred to the mass of the extracted material, not to the considered snow volume. Values in brackets are measurement uncertainties. Normalized concentrations were calculated considering the upper continental crust as a reference (Rudnick and Gao, 2003).</caption><thead><tr><th rowspan="2">Element</th><th colspan="2">SH1</th><th colspan="2">SH2</th></tr><tr><th>Conc. (µg g<sup>-1</sup>)</th><th>Conc. (UCC norm.)</th><th>Conc. (µg g<sup>-1</sup>)</th><th>Conc. (UCC norm.)</th></tr></thead><tbody><tr><td>Na</td><td>5600(500)</td><td>0.47(0.04)</td><td>2000(300)</td><td>0.17(0.03)</td></tr><tr><td>Mg</td><td>12 000(2000)</td><td>0.8(0.1)</td><td>3600(900)</td><td>0.24(0.06)</td></tr><tr><td>Al</td><td>63 000(14 000)</td><td>0.8(0.2)</td><td>13 500(3000)</td><td>0.17(0.04)</td></tr><tr><td>Si</td><td>200 000(35 000)</td><td>0.6(0.1)</td><td>48 000(25 000)</td><td>0.15(0.08)</td></tr><tr><td>K</td><td>17 000(2000)</td><td>0.75(0.09)</td><td>4500(400)</td><td>0.19(0.02)</td></tr><tr><td>Ca</td><td>16 000(5000)</td><td>0.6(0.2)</td><td>6000(2000)</td><td>0.25(0.09)</td></tr><tr><td>Ti</td><td>6000(500)</td><td>1.6(0.1)</td><td>900(200)</td><td>0.25(0.06)</td></tr><tr><td>Mn</td><td>470(50)</td><td>0.61(0.07)</td><td>180(33)</td><td>0.23(0.04)</td></tr><tr><td>Fe</td><td>40 000(4000)</td><td>1.0(0.1)</td><td>180(30)</td><td>0.38(0.05)</td></tr><tr><td>Sc</td><td>13(1)</td><td>0.91(0.08)</td><td>3.1(0.4)</td><td>0.22(0.03)</td></tr><tr><td>V</td><td>100(10)</td><td>1.0(0.1)</td><td>26(5)</td><td>0.27(0.05)</td></tr><tr><td>Cr</td><td>123(27)</td><td>1.3(0.03)</td><td>84(22)</td><td>0.9(0.2)</td></tr><tr><td>Co</td><td>14(1)</td><td>0.82(0.06)</td><td>6.6(0.7)</td><td>0.38(0.05)</td></tr><tr><td>Ni</td><td>35(8)</td><td>0.7(0.2)</td><td>38(11)</td><td>0.8(0.2)</td></tr><tr><td>Zn</td><td>132(13)</td><td>2.0(0.2)</td><td>233(30)</td><td>3.5(0.4)</td></tr><tr><td>As</td><td>6(1)</td><td>1.3(0.2)</td><td>4(1)</td><td>0.9(0.2)</td></tr><tr><td>Se</td><td>&lt;0.002</td><td>-</td><td>0.5(0.1)</td><td>5(1)</td></tr><tr><td>Rb</td><td>82(8)</td><td>0.98(0.09)</td><td>28(5)</td><td>0.34(0.06)</td></tr><tr><td>Sr</td><td>118(19)</td><td>0.37(0.06)</td><td>86(23)</td><td>0.27(0.07)</td></tr><tr><td>Sb</td><td>1.3(0.2)</td><td>3.1(0.4)</td><td>12(2)</td><td>30(5)</td></tr><tr><td>Cs</td><td>3.7(0.4)</td><td>0.75(0.08)</td><td>1.5(0.2)</td><td>0.30(0.05)</td></tr><tr><td>Ba</td><td>500(100)</td><td>0.8(0.2)</td><td>300(200)</td><td>0.5(0.4)</td></tr><tr><td>La</td><td>43(5)</td><td>1.4(0.1)</td><td>9(2)</td><td>0.29(0.05)</td></tr><tr><td>Ce</td><td>87(4)</td><td>1.39(0.06)</td><td>29(2)</td><td>0.46(0.04)</td></tr><tr><td>Nd</td><td>38(8)</td><td>1.4(0.3)</td><td>10(6)</td><td>0.4(0.2)</td></tr><tr><td>Sm</td><td>7.2(0.9)</td><td>1.5(0.2)</td><td>1.5(0.2)</td><td>0.32(0.05)</td></tr><tr><td>Eu</td><td>1.5(0.2)</td><td>1.5(0.2)</td><td>0.24(0.07)</td><td>0.24(0.07)</td></tr><tr><td>Tb</td><td>1.05(0.08)</td><td>1.5(0.1)</td><td>0.22(0.05)</td><td>0.31(0.06)</td></tr><tr><td>Ho</td><td>1.05(0.08)</td><td>1.3(0.1)</td><td>0.24(0.05)</td><td>0.29(0.07)</td></tr><tr><td>Yb</td><td>3.9(0.6)</td><td>1.9(0.3)</td><td>0.7(0.2)</td><td>0.3(0.1)</td></tr><tr><td>Hf</td><td>7.6(0.7)</td><td>1.4(0.1)</td><td>1.5(0.2)</td><td>0.28(0.04)</td></tr><tr><td>Ta</td><td>1.8(0.5)</td><td>2.0(0.6)</td><td>0.4(0.1)</td><td>0.4(0.1)</td></tr><tr><td>W</td><td>3.1(0.7)</td><td>16(3)</td><td>19(3)</td><td>100(15)</td></tr><tr><td>Hg</td><td>0.4(0.1)</td><td>8(2)</td><td>2.9(0.9)</td><td>60(20)</td></tr><tr><td>Th</td><td>12(1)</td><td>1.2(0.1)</td><td>2.6(0.6)</td><td>0.24(0.05)</td></tr><tr><td>U</td><td>2.5(0.6)</td><td>0.9(0.2)</td><td>2.2(0.9)</td><td>0.8(0.3)</td></tr></tbody></table>

tion is SH2. SH1 shows values near 1, suggesting that its composition is also mainly crustal for these elements. On the contrary, sample SH2 presents extremely high enrichments, near 100 in the case of W. Such values are not compatible with a crustal origin. Contributions from other sources must be involved. Atmospheric emissions related to human activities are the best candidate for explaining the enrichment of almost all of these elements. Hg, Sb, Se, and Zn are all quite volatile elements, are easily mobilized in the atmosphere, and are related to industrial processes. Indeed, the sampling site is located less than 100 km far from the Po Valley, one of the most industrialized and densely inhabited regions of Eu-

rope. The same interpretation is not sufficient for explaining the considerably high amount of W in SH2. There is no previous available information about its occurrence in snow, and in general its behaviour in the environment is quite obscure (Koutsospyros et al., 2006). It is traditionally considered a non-volatile element, given its refractory properties. The high concentration found in SH2 could be related to the anthropogenic activities, since W is used in many industrial and manufacturing activities (Koutsospyros et al., 2006). Thus, a different transport mechanism is probably involved. Volatile elements can easily be scavenged from the atmosphere after being adsorbed on particulate matter (Marx et
al., 2008), refractory and non-volatile elements are instead
more easily transported directly as airborne particles gener-
ated by industrial processes (Sheppard et al., 2007).

The composition of a suite of elements found in trace is
presented in Fig. 8c. The elements displayed there were or-
dered following their incompatibility degree with respect to
Fe (Sun and McDonough, 1989). This is a useful geochem-
ical feature for understanding the provenance and the geo-
chemical signature of rock-related samples. Focussing on
SH1, it can be appreciated that there is a slight enrichment of
poorly incompatible elements (the ones on the right side of
Fig. 8c). The same feature is also recognized in the African
dust sources, as it was extensively discussed by Moreno et
al. (2006), which related the point to the geochemical and
mineralogical properties of the sources. Sr and Ta are the two
elements presenting the most evident anomalies: a depletion
in the first case and an enrichment in the second case. The
concentration of Sr is generally related to the presence or ab-
sence of carbonates, since Sr is a well-known substituent for
Ca in carbonate lattice. In Fig. 8c, it is possible to appreciate
that SH1 and most of the African sources are significantly de-
pleted in Sr, confirming what was already suggested by ma-
jor elements. Indeed, the samples with low Sr content are the
same samples presenting low Ca concentrations, pointing to
a limited presence of carbonates and confirming that sources
from Western Sahara were not involved in this episode.

The case of Ta is completely different, given the analyt-
ical difficulties related to its detection; its behaviour in the
environment is not yet well constrained, but it seems quite
common to deal with samples that present an enrichment, in
particular when atmosphere-related samples are considered
(Filella, 2017). Looking at Fig. 8c, it can be seen that both
the African sources, and to a lesser extent SH1, present a
positive anomaly for Ta. Recent studies suggested that the Ta
enrichment in rocks, sediments, and atmospheric particulate
matter could be attributed to the effect of chemical weather-
ing. Being extremely stable from a chemical and geochem-
ical perspective, the loss of mobile fractions during weath-
ering, enhanced by atmospheric transport, could explain the
enrichment of Ta (Baccolo et al., 2016; Vlastelic et al., 2015).

4 Conclusions

In this paper, we investigated the role of impurity depositions
in snow dynamics. In particular, we analysed the role of Sa-
haran dust events on snowmelt in a high-elevation site of the
European Alps. We estimated that impurities induced an ad-
vance in snow melt-out dates of 38 d for the season 2015–
2016. During the other seasons considered here (2013–2014
and 2014–2015), the advancement in snow melt-out dates
was 18 and 11 d, respectively. The season 2015–2016 was
characterized by dust depositions that were almost double
with respect to the other years considered in this study. Snow
key variables (snow water equivalent, snow albedo, and snow

depth) simulated with Crocus model were compared with ob-
served variables from an AWS in the Aosta Valley (western
Alps). Good agreement between observations and simula-
tions accounting for the role of impurities was observed. The
size distribution of dust found in snow confirms the Saha-
ran origin of the event during April 2016 (Baumann-Stanzer
et al., 2018). The geochemical characterization of dust and
particulate matter samples distinguished the snow associated
with Saharan dust from clean snow. Dusty snow showed a
composition compatible with the geochemistry of the dust
sources located in the central sector of the Sahara–Sahel dust
corridor, i.e. the Hoggar, Chad, and Niger basin northern
African sources. On the contrary, clean snow was character-
ized by strong contaminations related to anthropogenic ele-
ments. These results demonstrate that an accurate geochemi-
cal characterization of dust deposited on the Alps allows the
identification of the different Saharan sources involved in the
single transport events, but the fingerprint of the local sources
may also play an important role.

In the paper, we also made use of repeated digital images
for monitoring dust deposition and resurfacing in the snow-
pack of Torgnon. Dust deposition and resurfacing agreed
well with modelling predictions. This allowed us to propose
the use of an RGB index (i.e. snow darkening index – SDI)
for tracking dust on snow using repeated digital images from
digital cameras. The good agreement between dust deposi-
tion and the SDI suggests that data from this experimen-
tal site can be used as a possible calibration and validation
for satellite imagery (e.g. MODIS, Landsat, and Sentinel)
and for regional and global climate model (WFR-Chem and
CLM) validation.

Several questions are still open regarding the role of dust in
the Alps. For example, the spatial distribution of dust concen-
tration on snow at alpine scale has never been quantitatively
estimated. Possible differences between eastern and western
Alps may arise as a function of distance from the sources.
Another unresolved issue is the input from local sources:
coarser dust particles can be suspended from snow-free ar-
eas and deposited on snow. Regarding the geochemical and
mineralogical characteristics of dust, future research should
explore, in detail, the relation between dust characteristics
and their radiative effect on snow. In addition to the well-
known snow-albedo feedback, other complex mechanisms
can influence the impact of dust on snow. For example, the
presence of dissolved carbonates may accelerate the melt of
snow lowering the melting point of snow and ice crystals.
The role of carbonaceous particles on snow optical properties
in the Alps is also an open question. Measurements of black
carbon, brown carbon, organic carbon, and elemental carbon
concentration in snow are virtually absent in surface snow in
the Alps. The Po Plain is one of the most polluted areas of
the planet. At lower elevations, black carbon emissions from
fossil fuel combustion and biomass burning may reach snow-
covered areas and exert an impact on snow optical proper-
ties. Future research efforts should aim at providing spatially
distributed measurements of carbonaceous particles, and this
will be a fundamental contribution in the determination of
the role of natural and anthropogenic activity on snowmelt at
the regional scale.

***Data availability.*** Data used in this paper will be made available upon request to the first author.

***Author contributions.*** BDM conceived the idea of the research, analysed the data, and wrote the paper with contributions from all other authors. RG analysed data from the AWS and Crocus. MG, GF, PP, UMdC, and EC established and maintained the experimental site in Torgnon, provided the data from AWS, and analysed RGB data from the Phenocam. MD, FT, and ML created Crocus simulations and helped in their interpretation. GB, MC, BD, and VM measured dust concentration and geochemical composition and helped in their interpretation. MM helped in the interpretation of the geochemical data. MR, SM, EC, and RC supervised the research.

***Competing interests.*** The authors declare that they have no conflict of interest.

***Acknowledgements.*** We acknowledge the ARPA (Environmental Protection Agency) of the Aosta Valley region for maintaining the AWS in Torgnon and for providing the dataset. RGB images were analysed using the Phenopix R package (https://r-forge.r-project.org/projects/phenopix/, last access: 2 April 2019). The complete set of Phenocam images is available at the following website: https://phenocam.sr.unh.edu/webcam/sites/torgnon-nd/ (last access: 2 April 2019). Edoardo Cremonese acknowledges the support of the NextData Data-LTER-Mountain project CNRM. CEN and IGE are part of Labex OSUG@2020. The modelling work was funded by the ANRJCJC EBONI grant no. 16-CE01-0006. We thank the editor and the two reviewers for the constructive comments on a previous version of the paper.

***Review statement.*** This paper was edited by Jürg Schweizer and reviewed by Stanislav Kutuzov and Marion Greilinger.

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