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 (
The cryosphere is an important part of the global climate system. Small changes in reflected and absorbed radiation at snow or ice surfaces can have large impacts on the state of the cryosphere, and on the Earth's climate and its hydrological cycle (e.g. Budyko, 1969; Flanner et al., 2007; Painter et al., 2013). Albedo, the reflectivity of a surface, is a dominant component of the surface energy balance. The albedo of snow depends, for example, on the snow grain size, wetness and impurities in the near-surface snow layer (e.g. Wiscombe and Warren, 1980; Meinander et al., 2014). Estimation of snow albedo is important for predicting seasonal snowmelt and run-off rates and for calculating the regional and global energy budgets. Due to impurities in snow, the albedo of the snow can be reduced. This involves direct albedo reduction by the impurities but also changes in the snow grain size triggered by the impurities, especially at temperatures close to the melting point, which can strongly enhance the albedo reduction (Hansen and Nazarenko, 2004; Myhre et al., 2013). Melting of the snow can further reduce the albedo if underlying ground with much lower albedo is exposed. This initiates a positive feedback loop; i.e. more snowmelt results in more absorbed radiation which in turn amplifies the melting. Even though direct global radiative forcing of mineral dust in the atmosphere is calculated as negative in the IPCC report (IPCC, 2013), regionally this depends on both the optical properties of the dust, deposited amounts and the albedo of the underlying surface. Icelandic volcanic dust (mostly from basaltic material) is darker and more absorbing than mineral dust from most other regions. It is expected to cause positive radiative forcing, due to its dark colour, the high albedo of snow and ice, and a “clumping mechanism”, where fine dust impurities in snow form larger particles (Dagsson-Waldhauserova et al., 2015) and accelerate snowmelt. In this study, “the term radiative forcing” means the instantaneous surface-enhanced absorption due to deposited dust (Painter et al., 2007). In its effect on snow albedo, dust is somewhat similar to black carbon (Yoshida et al., 2016; Goelles et al., 2015) which has received much interest recently as a short-lived climate forcer, especially in the Arctic (e.g. Quinn et al., 2008; AMAP, 2015; Meinander et al., 2016). Other studies (e.g. Di Mauro et al., 2015; Zhao et al., 2014; He et al., 2014) have shown the impact of dust and black carbon and their effect on radiative forcing and energy balance.
Painter et al. (2007) have shown that snow cover duration in a mountain range
in the United States was shortened through surface shortwave radiative
forcing by deposition of desert dust. Similarly, Flanner et al. (2014) have
shown that the snow albedo effect of deposited volcanic ash from an eruption
in Iceland could counteract the otherwise negative radiative forcing of
volcanic eruptions caused by sulfur emissions. Increased snow impurity
content has an important effect on the albedo of the Greenland ice sheet.
Dumont et al. (2014) estimated that the contribution of impurities to surface
mass balance was at least
Recent modelling studies have shown that the transport of dust from Iceland is a substantial dust source for Greenland (Groot Zwaaftink et al., 2016; Baddock et al., 2017). Furthermore, volcanic eruptions such as those in 2010 and 2011 can have a large impact (Petit et al., 2013; Davies et al., 2010).
Iceland with glacier outlines and soil map adapted from Arnalds (2015). The two AWSs at B13 and B16 as well as the firn core drill site on Brúarjökull are highlighted.
Sources of dust in Iceland are the proglacial areas and sandy deserts which
cover more than 22 % of Iceland (Arnalds et al., 2001). Even though
Iceland is not situated in an arid climate, it's aeolian activity is very
high (Arnalds et al., 2016), due to the large area of sandur plains and
frequent strong winds resulting in numerous dust events. On average, 135 dust
days per year occurred in Iceland, with 101 dust days in southern Iceland and
34 dust days in north-eastern Iceland including dust haze and resuspension of
volcanic material. A dust day is defined as a day when at least one weather
station recorded at least one dust observation (Dagsson-Waldhauserova et al.,
2013, 2014). Airborne redistribution of dust has a strong influence on
climate, snowmelt and Icelandic soils. Satellite images have shown that dust
particles can be transported over the Atlantic and Arctic Ocean, sometimes
for more than 1000 km (Arnalds, 2010; Baddock et al., 2017). Therefore,
Icelandic dust is likely to contribute to Arctic and European air pollution
and can affect the climate via dust deposition on Arctic glaciers or sea ice
(Arnalds et al., 2016). Icelandic glaciers cover about 11 % of the
country and the focus area of this study is Vatnajökull, Iceland's
largest glacier with an area of more than 8000 km
In this study we explore what impact dust events in Iceland have on the glacier surface albedo, how often they occur and what their impact on the energy balance of glaciers in Iceland is. Therefore, dust deposition rates were calculated with a dispersion model and compared with albedo measurements on an Icelandic glacier.
A recently developed scheme for dust mobilisation called FLEXDUST (Groot
Zwaaftink et al., 2016) is used to estimate dust emission. The model can be
applied globally, but in this study we only included dust emission from
Icelandic sources. FLEXDUST produces dust emission estimates that can be
imported directly into the Lagrangian particle dispersion model FLEXPART
(Stohl et al., 1998, 2005) to estimate mineral dust transport, concentrations
in the atmosphere and deposition on global and regional scales. FLEXDUST is
based on meteorological data from the European Centre for Medium-Range
Weather Forecasts (ECMWF), land cover data by the Global Land Cover by
National Mapping Organizations (GLCNMO) and additionally, for Iceland, a
high-resolution (
Using the dust emission rates provided by FLEXDUST, dispersion of the dust
in the atmosphere was simulated with FLEXPART version 10. Our simulations
were driven with ECMWF operational analysis data with a resolution of
1
The year 2012 was extremely warm in the northern hemisphere. For example, in Greenland new records were set for the total glacier mass loss, surface melt extent and duration (Tedesco et al., 2013; Nghiem et al., 2012; Dumont et al., 2014). Also in Iceland, 2012 was characterised by warm temperatures, exceptionally low glacier albedo on Brúarjökull and negative glacier mass balances. Additionally, a significant number of northerly winds, likely transporting dust from Dyngjusandur towards Brúarjökull, was observed. Furthermore, no volcanic eruptions that could complicate dust deposition and albedo analysis occurred in 2012. We therefore chose this year to analyse dust events, albedo changes, glacier energy and mass balance. Besides this time series analysis in 2012, we also modelled spatial distribution of dust deposition in 2013 and will compare model results to dust amounts in snow samples from 7 October 2013. Unfortunately, such samples were not taken in 2012, but the comparison gives valuable insight into the spatial distribution that cannot be retrieved from the time series analysis. Time series of observed albedo and modelled dust deposition agreed better in 2012 than 2013.
Since 1996, AWSs B13 and B16 at Brúarjökull have been used to measure
the incoming (
The AWS data, specifically albedo, temperature and wind, were compared with dust concentration and deposition values from FLEXPART for the measurement period in the year 2012 between days of the year (DOY) 130 and 283.
Surface snow samples, from the previous year's melted out firn layer, were
collected on 7 October 2013 at 16 sites on Vatnajökull (Dragosics et al.,
2016). The samples contain dust deposited at these sites during the summer of
2013. The top
Total dust deposition [g m
The total energy balance (
Albedo is a key variable in the surface energy balance and it is used to calculate ice melting. If the energy balance is positive, this indicates an energy gain to the surface; if it is negative, it means an energy loss. The accuracy of the instruments (Kipp & Zonen CNR1, 2000) measuring longwave and shortwave radiation fluxes at AWSs was 3 % (Guðmundsson et al., 2009).
To quantify the enhanced melt rates due to dust on the surface, the development of surface albedo for a dust-free surface must be estimated at specific locations and meteorological conditions. This albedo estimate and in situ AWS data are used to calculate the energy balance at the AWS sites. The results can be compared to energy balance calculated from only the AWS data including the observed albedo. The development of surface albedo of snow depends on meteorological processes in the surface boundary layer, the energy budget of the surface, snowfall events, etc. A regional climate model, which is forced with reanalysis data from a general circulation model at the lateral boundary and simulates the boundary layer meteorology and surface energy balance, can be used to simulate the clean surface albedo. Here we use the HIRHAM5 climate model. The HIRHAM5 model combines the dynamical core of the HIRLAM7 numerical forecasting model (Eerola, 2006) with the physical schemes from the ECHAM5 general circulation model (Roeckner et al., 2003). Model simulations have been validated over Greenland using AWS and ice core data (e.g. Lucas-Picher et al., 2012; Langen et al., 2015). Using the same method described in Langen et al. (2015), we run the surface scheme in HIRHAM5 by forcing it with atmospheric parameters from a previous model run. This method allows us to implement an improved albedo scheme (Nielsen-Englyst, 2015) without running the full model. This is described in more detail in the appendix and Schmidt et al. (2017).
As there was no ice at the surface at either of the two AWSs, we allowed the modelled clean surface albedo to drop to the value of clean firn, which we assumed to be 0.55. This value is based on the recommended value by Cuffey and Paterson (2010), but also represented in observed albedo in the years 2002, 2009 and 2014 (Fig. S1 in the Supplement). For those years, measured albedo remained mostly above 0.55 for the whole measuring period. Under dry conditions, the modelled albedo can only drop to 0.77. The albedo of fresh snow was assumed to be 0.9. Based on albedo measurements this value is assumed to be realistic after new snow events as seen in Fig. S1. Sometimes measured albedo values can reach high values, even above 1, especially in autumn. This can be explained due to the high solar zenith angle, multiple reflections and instrumental error (Kipp & Zonen CNR1, 2000).
The timescale
The AWS B16 is situated in the accumulation area, but B13 is close to the equilibrium line of the glacier. This means that only in some years, e.g. in 1997, 2004, 2005 and 2012 (Fig. S3), the mass balance was negative and the previous years' surface melted out at B13 and exposed firn with dust. Since 2012 was a year of very warm temperatures and negative mass balance, not only deposition during dust events influenced the albedo and energy balance. Warm and dry periods with northerly winds also increased the frequency of dust events. Due to the negative mass balance the exposed darker firn layer lowered the albedo in addition to surface dust. At station B13, between days 206 and 225, simulation values have been manually set to the minimum value of 0.55 because HIRHAM5 simulated a snowfall event, which was not observed.
The annual dust deposition distribution for the surface of Vatnajökull
for 2013 showed a similar pattern in the model simulation and in the
observations (Fig. 2). The model simulated the
highest concentrations in the south-western part of Vatnajökull
(Tungnaárjökull, Skaftárjökull, Síðujökull),
followed by the north-western and northern parts (Brúarjökull). This
distribution is due to the major dust mobilisation areas around
Vatnajökull, such as Dyngjusandur, Tungná- and
Skaftáör
FLEXPART model simulation of the spatial dust distribution on Vatnajökull from 1 January to 7 October 2013, the day when the surface snow samples have been taken. The circles show the location of snow sample sites with dust deposition for the same year.
Table 1 gives the measured and modelled dust deposition during the years 2012 and 2013 for stations on Brúarjökull, our main area of investigation. Again, the model tended to overestimate dust deposition.
Upper graph: albedo measurement from the AWS at B13 in red and B16 in blue for the measurement period in 2012. Lower graph: daily dust deposition showing dust events modelled by FLEXPART. Dust events are highlighted in grey and named E1–E10.
Dust events at station B13. The modelled maximum and minimum dust concentration, the maximum simulated daily deposition, the total deposition during the event, the measured albedo change, maximum and minimum temperature and wind direction from the AWSs and the precipitation sum from the ECMWF model are reported.
FLEXPART results for both dust concentrations in the air and dust deposition
on the glacier surface were reported for the dust events for the year 2012
at station B13 (Table 2) and B16
(Table 3). Albedo, temperature and wind at 2 m
elevation were measured at the AWSs, while precipitation data were taken
from the ECMWF model. At station B13 there were 10 modelled dust events
during the measuring period (9 May to 14 October 2012), and all of them were
associated with an observed albedo drop during the event at the AWSs. Four
events had high dust concentrations and depositions (bold in
Table 2), and six smaller events occurred as well
(Fig. 3). The highest deposition values were
simulated during event 6 with 6.6 g m
Same as Table 2 but for station B16.
The albedo was almost always lower at site B13 than at site B16, due to the
lower elevation and thus higher temperatures and increased melting at this
site, probably also because of its proximity to a major dust source area
(Dyngjusandur). The biggest dust events happened in spring (mid-May) and
autumn (end of August and October), especially at station B16. Dust event 5
coincided with warm summer temperatures and exposure of the ablation area,
where albedo at B13 reached its lowest value, 0.08, on day 223. At the lower
elevation site B13 (
Observed albedo, simulated dust deposition, observed temperature and simulated precipitation dust event no. 1 at stations B16 (blue) and B13 (red). Modelled deposition is shown for 3 h and daily averages.
Observed albedo, simulated dust deposition, observed temperature and simulated precipitation dust event no. 2 at stations B16 (blue) and B13 (red). Modelled deposition is shown for 3 h and daily averages.
Two dust events have been chosen for a detailed description. Event no. 1 (Fig. 4) was by far the biggest event at B16 and temperatures were below freezing all the time, and event no. 2 (Fig. 5) happened, as was often the case, during melting temperatures. The analysis of event no. 2 was supported by the availability of a clear-sky MODIS image showing the dust cloud and deposition (Fig. 6).
MODIS images of Iceland on
Dust event 1 is one of four major modelled dust storms on
Brúarjökull in 2012 (Fig. 4) and the only
event for which total simulated dust deposition was higher at station B16
(3.7 g m
Near-surface dust concentration reached values of 193
Dust event 2 is the second largest modelled dust event in terms of dust
concentration and fourth biggest in terms of total deposition at station B13
(2.5 g m
Notice also that the albedo reduction was stronger at B13 than at B16, in
agreement with the higher dust deposition at B13. Precipitation occurred
until day 146, so mainly before dust deposition, suggesting that dust
deposition was the main factor in this albedo drop. Wind was strongest on
day 146 (13.6 m s
Figure 6 shows a comparison between two different MODIS images (before and after the dust event). It indicates the presence of the dust plume very clearly during event 2 over the glacier, as the brownish hues are normally not present there.
Using the values reported in Table 2, we calculated averages to characterise
an average dust event at the B13 site. On average a dust event at station B13 in
2012 lasted for 6 days, had a maximum dust concentration of 122
Measured albedo (red line) and albedo simulated with HIRHAM5 (black line) for a clean glacier surface without dust at the stations B13 (upper graph) and B16 (lower graph). Highlighted in grey are modelled dust event periods by FLEXPART.
Deposition of dark dust particles on a glacier surface lowers the surface albedo, thus also the surface energy balance, and in general increases the energy available for melt. In order to estimate the contribution of this effect, the surface energy balance (and the surface melt from energy balance) at the two AWS sites B13 and B16 was estimated from the AWS data in 2012. To estimate the effect, the regional climate model HIRHAM5 was used to simulate a clean glacier surface for the weather conditions occurring at the AWSs B13 and B16 in 2012. The simulated clean surface albedo (black line in Fig. 7) is compared to the observed albedo including impurities (red line in Fig. 7). Generally, the model captures the measured albedo variability; however, the observed albedo is more variable and reaches lower values between events of snowfall. Since this is a simple model, we are not expecting the model to capture all details. The statistical fit for HIRHAM5 compared to the AWS data showed a better fit for years with higher albedos where the previous summer surface did not melt out. The average bias, taken as the difference between HIRHAM5 and AWS data, is 0.08 for the years 1997–2014, whereas for the year 2012 it is 0.18 which means an overestimate by the model. The correlation coefficient for measured and simulated albedo data for the year 2012 is 0.77, which is higher than the average value of 0.68 for other years.
Measured energy balance (red line) and energy balance with simulated albedo with HIRHAM5 (black line) for a clean glacier surface without dust at the stations B13 (upper graph) and B16 (lower graph). Cumulative snowmelt is shown in dotted lines for AWSs in red and HIRHAM5 in black. Highlighted in grey are modelled dust event periods by FLEXPART.
The difference between the modelled clean surface and the real surface is
greater at B13 than B16. This was expected since dust concentration is much
higher at the lower site B13 and snowfall is more common at the upper site B16.
We also know from mass balance measurements that at B13 all the winter snow
melted, exposing firn and surface dust from previous years (this happened at
day
High temperatures at B13 up to
The total summer melt at B13 in 2012 estimated from the energy balance
calculated for a dust-free surface was 1.7 m w.e., whereas for the measured
albedo the melt was estimated at 2.8 m w.e. From this we conclude that the
melt increased by 1.1 m w.e., or by
In this paper, we have shown that dust events modelled by FLEXDUST
correspond to reductions in the observed albedo at two AWS sites on
Vatnajökull. This indicates that the model is able to capture the
occurrence of individual dust events. Furthermore, we showed that the model
captures both the observed spatial distribution of dust on the glacier as
well as the order of magnitude of the total annual deposition amounts. This
suggests that the model can be used for longer-term studies, to quantify the
dust deposition on Vatnajökull, including its interannual variability.
Table 2 shows the dust events of the year 2012 at
station B13 on Brúarjökull, where in total 10 dust events occurred;
four main events and six smaller events. The AWS measurements show a drop in
albedo in connection to all dust events predicted by FLEXPART within the
AWS survey period. The prevailing wind direction during dust events at
site B13 is from a northerly direction, while for the whole period downslope
(SW) winds dominate. The wind direction during dust events corresponds to
the main dust source Dyngjusandur, north of Vatnajökull. At site B16,
situated further upglacier, nine dust events occurred
(Table 3) where the first dust event with
In Arnalds et al. (2014), the average deposition of dust on Icelandic glaciers is
estimated as
Firn core B drilled on Brúarjökull showed a dust layer of
To estimate the impact of dust on the surface energy balance and melt rates, the regional climate model HIRHAM5 was used to simulate the surface albedo for a dust-free (i.e. clean) snow surface during the summer 2012. The surface energy balance (and melt rate) was calculated using the simulated albedo and the albedo observed from the AWS data. At the lower site, B13, the difference between dust-free and real surface is 1.1 m w.e. more snowmelt (1.7 m w.e. snowmelt for the clean surface and 2.8 m w.e. for the real surface). This does not only include dust events lowering surface albedo, but also dust and tephra that were deposited during previous years, melting out from below. At the upper site B16 the difference results in 0.6 m more snowmelt (1.0 m w.e. for the clean surface and 1.6 m w.e. for the AWSs). Since B16 is situated in the accumulation area, no dust is expected to melt out from below. It cannot be excluded that small amounts of organic material or black carbon are deposited on the snow surface and influence albedo, but from in situ investigations this has not been observed in this area.
The year 2012 was a year of intensive summer melt. At site B13 on Vatnajökull the measured summer mass balance was 2.3 m w.e. mass loss, which means 0.5 m more mass loss than the average since 1993 (1.7 m w.e.). Summer mass balance measurements on Vatnajökull show 2.3 m w.e. of total mass loss at B13 which is 0.5 m less melt compared to calculated energy balance converted into snowmelt (2.8 m w.e.). Most of these differences are assigned to summer snowfall that melts, and was not captured with the mass balance measurements.
Oerlemans et al. (2009) reported that decreased albedo at Vadret da Morteratsch glacier caused an additional removal of about 3.5 m of ice for the 4-year period 2003–06. This means 0.9 m more melt on average per year. Gabbi et al. (2015) compared a glacier surface with deposits of black carbon and Saharan dust to pure snow conditions for a 100-year period (1914–2014). They found that the mean annual albedo decreased by 0.04–0.06; therefore the mean annual mass balance was reduced by about 28–49 cm. These alpine melt rates due to impurities are in the same order of magnitude as our results.
Albedo comparisons for other years (Fig. S3) have shown very low albedo values for the years 1997, 2004, 2005 and 2012. The surface dirt causing the low albedo in 1997 is related to the Gjálp eruption in 1996, and the following huge jökulhlaup with deposition of fine-grained particles on the Skeiðarársandur sandur plain. This was a vast source of dust in the dry and warm 1997 summer. The low albedo in 2005 and 2012 are most likely also related to the 2004 and 2011 Grímsvötn eruptions (e.g. Guðmundsson et al., 2006; Möller et al., 2013.) In 2004 increased melt rates due to high wind-driven turbulent heat fluxes at the end of July, followed by exceptionally warm and sunny weather in August, sped up the melting of old firn (Guðmundsson et al., 2006).
The results in this paper show the impact of positive radiative forcing on snowmelt of Icelandic glaciers caused by deposition of dust that strongly enhances absorption of light. The duration of dust radiative effects on glacier surfaces is extended compared to purely atmospheric effects because of the short lifetime of dust in the atmosphere.
The data are not publicly available.
The authors declare that they have no conflict of interest.
The study described in this manuscript was supported by NordForsk as part of the two Nordic Centres of Excellence Cryosphere-Atmosphere Interactions in a Changing Arctic climate (CRAICC), and eScience Tools for Investigating Climate Change (eSTICC). Part of this work was supported by the Centre of Excellence in Atmospheric Science funded by the Finnish Academy of Sciences Excellence (project no. 272041), by the Finnish Academy of Sciences project A4 (contract 254195). Data from in situ mass balance surveys and on glacier automatic weather stations are from joint projects of the National Power Company and the Glaciology group of the Institute of Earth Science, University of Iceland. C. Groot Zwaaftink was also funded by the Swiss National Science Foundation SNF (155294), and Louise Steffensen-Schmidt, Finnur Pálsson and Sverrir Guðmundsson by the Icelandic Research Fund (project SAMAR) and the National Power Company of Iceland. Ólafur Arnalds was in part funded by Icelandic Research Fund (grant no. 152248-051) Edited by: S. M. Noe Reviewed by: two anonymous referees