Cryospheric Sciences

snow cover

Image of the Week — Orange is the new white

Figure 1. True color composite of a Sentinel-2 image showing the dust plume off the coast of Libya on 22-Mar-2018 (see also on the ESA website) [Credit: processed by S. Gascoin]

On 22 March 2018, large amounts of Saharan dust were blown off the Libyan coast to be further deposited in the Mediterranean, turning the usually white snow-capped Mountains of Turkey, Romania and even Caucasus into Martian landscapes.  As many people were struck by this peculiar color of the snow, they started documenting this event on social media using the “#orangesnow hashtag”. Instagram and twitter are fun, but satellite remote sensing is more convenient to use to track the orange snow across mountain ranges. In this new image of the week, we explore dusty snow with the Sentinel-2 satellites…

Марс атакует ? #smurygins_family_trip

A post shared by Alina Smurygina (@sinyaya_ptiza) on

Sentinel-2: a great tool for observing dust deposition

Sentinel-2 is a satellite mission of the Copernicus programme and consists of two twin satellites (Sentinel-2A and 2B). Although the main application of Sentinel-2 is crop monitoring, it is also particularly well suited for characterizing the effect of dust deposition on the snowy mountains because:

  1. Sentinel-2A and 2B satellites provide high-resolution images with a pixel size of 10 m to 20 m (depending on the spectral band), which enables to detect dust on snow at the scale of hillslopes.
  2. Sentinel-2 has a high revisit capacity of 5 days which increases the probability to capture cloud-free  images shortly after the dust deposition.
  3. Sentinel-2 has many spectral bands in the visible and near infrared region of the light spectrum, making easy to separate the effect of dust on snow reflectance — i.e. the proportion of light reflected by snow — from other effects due to snow evolution. The dust particles mostly reduce snow reflectance in the visible, while coarsening of the snow by metamorphism (i.e. the change of microstructure due to transport of vapor at the micrometer scale) tends to reduce snow reflectance in the near infrared (Fig. 2).
  4. Sentinel-2 radiometric observations have high dynamic range and are accurate and well calibrated (in contrast to some trendy miniature satellites), hence they can be used to retrieve accurate surface dust concentration, provided that the influence of the atmosphere and the topography on surface reflectance are removed.

Figure 2: Diffuse reflectance for different types of snowpack. These spectra were computed with 10 nm resolution using the TARTES model (Libois et al, 2013) using the following parameters: snowpack density: 300 kg/m3, thickness: 2 m, fine snow specific surface area (SSA): 40 m2/kg, coarse snow SSA: 20 m2/kg, dust content: 100 μg/g. The optical properties of the dust are those of a sample of fine dust particles from Libya with a diameter of 2.5 μm or less (PM2.5) (Caponi et al, 2017). The Sentinel-2 spectral bands are indicated in grey. [Credit: S. Gascoin]

Dust on snow from Turkey to Spain

The region of Mount Artos in the Armenian Highlands (Turkey) was one of the first mountains to be imaged by Sentinel-2 after the dust event. Actually Sentinel-2 even captured the dust aloft on March 23, before its deposition (Fig. 3)

Figure 3: Time series of three Sentinel-2 images near Mount Artos in Turkey (true color composites of level 1C images, i.e. orthorectified products, without atmospheric correction). [Credit: Contains modified Copernicus Sentinel data, processed by S. Gascoin]

Later in April another storm from the Sahara brought large amounts of dust in southwestern Europe.

Figure 4: Sentinel-2 images of the Sierra Nevada in Spain (true color composites of level 1C images). [Credit: Contains modified Copernicus Sentinel data, processed by S. Gascoin]

This example in the spanish Sierra Nevada nicely illustrates the value of the Sentinel-2 mission since both images were captured only 5 days apart. The high resolution of Sentinel-2 is also important given the topographic variability of this mountain range. This is how it looks in MODIS images, having a 250 m resolution.

Figure 5: MODIS Terra (19) and Aqua (24) images of the Sierra Nevada in Spain. True Color composites of MODIS corrected reflectance. [Credit: NASA, processed by S. Gascoin]

Sentinel-2 satellites enable to track the small-scale variability of the dust concentration in surface snow, even at the scale of the ski runs as shown in Fig. 6.

Figure 6: Comparison of a true color Sentinel-2 image and a photograph of the Pradollano ski resort, Sierra Nevada. [Credit: photograph taken by J. Herrero / Contains modified Copernicus Sentinel data, processed by S. Gascoin]

A current limitation of Sentinel-2, however, is the relative shortness of the observation time series. Sentinel-2A was only launched in 2015 and Sentinel-2B in 2017. With three entire snow seasons, we can just start looking at interannual variability. An example in the Prokletije mountains in Albania is shown in Fig. 7.

Figure 7. Sentinel-2 images of the Prokletije mountains in Albania (true color composites of level 1C images) [Credit: Contains modified Copernicus Sentinel data, processed by S. Gascoin]

These images suggest that the dust event of March 2018 was not exceptional in this region, as 2016 also highlights a similar event. The Sentinel-2 archive will keep growing for many years since the EU Commission seems determined to support the continuity and development of Copernicus programme in the next decades. In the meantime to study the interannual variability the best option is to exploit the long-term records from other satellites like MODIS or Landsat.

Beyond the color of snow, the water resource

Dust on snow is important for water resource management since dust increases the amount of solar energy absorbed by the snowpack, thereby accelerating the melt. A recent study showed that dust controls springtime river flow in the Western USA (Painter et al, 2018).

“It almost doesn’t matter how warm the spring is, it really just matters how dark the snow is.”

said snow hydrologist Jeff Deems in an interview about this study in Science Magazine. Little is known about how this applies to Europe…

Further reading

 Edited by Sophie Berger

Simon Gascoin is a CNRS researcher at Centre d’Etudes Spatiales de la Biosphère (CESBIO), in Toulouse. He obtained a PhD in hydrology from Sorbonne University in Paris and did a postdoc on snow and glacier hydrology at the Centro de Estudios Avanzados en Zonas Áridas (CEAZA) in Chile. His research is now focusing on the application of satellite remote sensing to snow hydrology. He tweets here and blog here.



Marie Dumont is a researcher, leading the snow processes, observations and modelling research team at the snow study centre (CNRM/CEN, Grenoble, France). Her research focuses on snow evolution mostly in alpine region using numerical modelling and optical remote sensing.




Ghislain Picard is a lecturer working at the Institute of Geosciences and Environment at the University Grenoble Alpes, in the climate and ice-sheets research group. His research focuses on snow evolution in polar regions in the context of climate change. Optical and microwave remote sensing is one of its main tools.

Image of the week – Skiing, a myth for our grandchildren?

Image of the week – Skiing, a myth for our grandchildren?

Ski or water ski? Carnival season is typically when many drive straight to the mountains to indulge in their favorite winter sport. However, by the end of the century, models seem to predict a very different future for Carnival, with a drastic reduction in the number of snow days we get per year. This could render winter skiing something of the past, a bedtime story we tell our grandchildren at night…

Christoph Marty and colleagues investigated two Swiss regions reputed for their great skiing resorts and show that the number of snow days (defined as a day with at least 5 cm of snow on the ground) could go down to zero by 2100, if fuel emissions and economic growth continue at present-day levels, and this scenario is less dramatic than the IPCC’s most pessimistic climate change scenario (Marty et al., 2017). They show that temperature change will have the strongest influence on snow cover. Using snow depth as representative for snow volume, they predict that snow depth maxima will all be lower than today’s except for snow at elevations of 3000 m and higher. This implies that even industrially-sized stations like Avoriaz in the French Alps, with a top elevation of 2466 m, will soon suffer from very short ski seasons.

Marty et al. (2017) predict a 70% reduction in total snow volume by 2100 for the two Swiss regions, with no snow left for elevations below 500 m and only 50% snow volume left above 3000 m. Only in an intervention-type scenario where global temperatures are restricted to a warming of 2ºC since the pre-industrial period, can we expect snow reduction to be limited to 30% after the middle of the century.

Recent work by Raftery et al (2017) shows that a 2ºC warming threshold is likely beyond our reach, however limiting global temperature rise, even above the 2ºC target, could help stabilize snow volume loss over the next century. We hold our future in our hands!

Further reading/references

  • Marty, C., Schlögl, S., Bavay, M. and Lehning, M., 2017. How much can we save? Impact of different emission scenarios on future snow cover in the Alps. The Cryosphere, 11(1), p.517.
  • Raftery, A.E., Zimmer, A., Frierson, D.M., Startz, R. and Liu, P., 2017. Less than 2 C warming by 2100 unlikely. Nature Climate Change, 7(9), p.637.
  • Less snow and a shorter ski season in the Alps | EGU Press release

Edited by Sophie Berger

Marie Cavitte just finished her PhD at the University of Texas at Austin, Institute for Geophysics (USA) where she studied the paleo history of East Antarctica’s interior using airborne radar isochrone data. She is involved in the Beyond EPICA Oldest Ice European project to recover 1.5 million-year-old ice. She tweets as @mariecavitte.

Image of the Week – Climate Change and the Cryosphere

Image of the Week – Climate Change and the Cryosphere

While the first week of COP22 – the climate talks in Marrakech – is coming to an end, the recent election of Donald Trump as the next President of the United States casts doubt over the fate of the Paris Agreement and more generally the global fight against climate change.

In this new political context, we must not forget about the scientific evidence of climate change! Our figure of the week, today summarises how climate change affects the cryosphere, as exposed in the latest assessment report of the Intergovernmental Panel on Climate Change (IPCC, 2013, chapter 4)

Observed changes in the cryosphere

Glaciers (excluding Greenland and Antarctica)

  • Glaciers are the component of the cryosphere that currently contributes the most to sea-level rise.
  • Their sea-level contribution has increased since the 1960s. Glaciers around the world contributed to the sea-level rise from 0.76 mm/yr (during the 1993-2009 period) to 0.83 mm/yr (over the 2005-2009 period)

Sea Ice in the Arctic

  • sea-ice extent is declining, with a rate of 3.8% /decade (over the 1979-2012 period)
  • The extent of thick multiyear ice is shrinking faster, with a rate of 13.5%/decade (over the 1979-2012 period)
  • Sea-ice decline sea ice is stronger in summer and autumn
  • On average, sea ice thinned by 1.3 – 2.3 m between 1980 and 2008.

Ice Shelves and ice tongues

  • Ice shelves of the Antarctic Peninsula have continuously retreated and collapsed
  • Some ice tongue and ice shelves are progressively thinning in Antarctica and Greenland.

Ice Sheets

  • The Greenland and Antarctic ice sheets have lost mass and contributed to sea-level rise over the last 20 years
  • Ice loss of major outlet glaciers in Antarctica and Greenland has accelerated, since the 1990s

Permafrost/Frozen Ground

  • Since the early 1980s, permafrost has warmed by up to 2ºC and the active layer – the top layer that thaw in summer and freezes in winter – has thickened by up to 90 cm.
  • Since mid 1970s, the southern limit of permafrost (in the Northern Hemisphere) has been moving north.
  • Since 1930s, the thickness of the seasonal frozen ground has decreased by 32 cm.

Snow cover

  • Snow cover declined between 1967 and 2012 (according to satellite data)
  • Largest decreases in June (53%).

Lake and river ice

  • The freezing duration has shorten : lake and river freeze up later in autumn and ice breaks up sooner in spring
  • delays in autumn freeze-up occur more slowly than advances in spring break-up, though both phenomenons have accelerated in the Northern Hemisphere

Further reading

How much can President Trump impact climate change?

What Trump can—and can’t—do all by himself on climate | Science

US election: Climate scientists react to Donald Trump’s victory  | Carbon Brief

Which Trump will govern, the showman or the negotiator? | Climate Home

GeoPolicy: What will a Trump presidency mean for climate change? | Geolog

Previous posts about IPCC reports

Image of the Week — Ice Sheets and Sea Level Rise

Image of the Week —  Changes in Snow Cover

Image of the Week — Atmospheric CO2 from ice cores

Image of the Week — Ice Sheets in the Climate

Edited by Emma Smith