CR
Cryospheric Sciences

Snow science

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 — Listening to the Snow

Image of the Week — Listening to the Snow

When working in the middle of an ice sheet, you rarely get to experience the amazing wildlife of the polar regions. So what are we doing hundreds of kilometres from the coast with an animal tracker device? We are listening to the snow of course! It is not crazy; It is what Image of the Week today is all about!


Going Wireless

E. Bagshaw testing the range of an ETracer in a 12m borehole at the bottom of a 2m deep snow pit. [Credit: N. B. Karlsson].

E. Bagshaw testing the range of an ETracer in a 12m borehole at the bottom of a 2m deep snow pit. [Credit: N. B. Karlsson].

In June 2016, Liz Bagshaw and I travelled to the EGRIP (East Greenland Ice Core Project) camp to test a handful of wireless sensors named “ETracers” in a new setting. The “wireless” part is very important, because it means that we can make measurements without having to connect our instrument to a cable, which may fail or snap. Instead, the sensors transmit all their data as radio waves. We use the same frequency that biologists use for tracking animals – although there weren’t many to see in the middle of the Greenland Ice Sheet!

The ETracer sensors were originally developed for measuring the meltwater under the ice at the margin of the Greenland ice sheet. We wanted to test if they could also tell us something about what is going on in the snow.  For example, how does the snow temperature change and how is the snow compacting in different parts of the ice sheet? These questions might seem theoretical but their answers are important when working with data from satellites, since the satellite measurements may be affected by different snow conditions.

Pink Baubles

The ETracers stacked on small magnets. This temporarily stops their bleeps [Credit: E. Bagshaw].

The ETracers stacked on small magnets. This temporarily stops their bleeps bleeps and is an efficient way of silencing them while we are listening for other ETracers [Credit: E. Bagshaw].

Armed with an antenna (see image of the week), radar receivers and what looked like small pink plastic baubles we set to work. The pink baubles are in fact the ETracers – small devices that contain temperature, pressure and conductivity sensors.  First, we used a 60m deep borehole that was drilled earlier in the season. In order to test the range of the Etracer we lowered one to the bottom of the hole. We set up the antenna and receiver at the surface, and started listening for the ETracer signal.  We were very pleased when the Etracer sensor happily chirped back informing us that it was below -30 degrees C at the bottom of the hole.

Our colleagues had also drilled several 12m boreholes for us, and we now installed ETracers at the bottom of the holes as well as on the surface. For over a month, the ETracers sent back information to our receivers on the ground about temperature, pressure and conductivity of the snow.

We are still analysing our data but the most important part of our work is done: we have shown that the ETracers can accurately measure the properties of the snow. Next year, we will return to the camp and set up more experiments. Stay tuned – or rather keep listening!

You can read more about setting up the EGRIP camp in a previous Image of the Week post “Ballooning on the Ice“.

Edited by Emma Smith and Sophie Berger

Image of the Week – The Journey of a Snowflake

Image of the Week – The Journey of a Snowflake

REMARK: If you’ve enjoyed reading this post, please make sure you’ve voted for it in EGU blog competition (2nd-to last)!

You remember last winter, when everything was white and you had so much fun building a snowman with your friends? What you see on this image above, is what you would see, if you took a tiny tiny piece of your snowman and put it under a low-temperature scanning electron microscope (SEM). The colours are called “pseudo colours”, they are computer generated based on the number of electrons reflected from a particular part of the image when scanned with a focussed beam of electrons. This is a standard technique used with SEM images to help identify patterns and structure in the image.

This week, let us take you on a journey…from the water vapour in a cloud to the snowman in your garden, to find out what leads to the complex structure you can see on our Image of the Week!


To this purpose we need to start at the very beginning, with a seemingly obvious question:

What is snow?

Snow is, very simply, precipitation in the form of ice crystals that originate in clouds. These ice crystals form when water vapour condensates directly to ice, without becoming liquid water.

From water vapour to ice crystals

For its formation, an ice crystal needs the atmosphere to be colder than the freezing point (0°C) and at least slightly humid. Additionally, like water droplets, ice crystals need a condensation nucleus, for example a dust or pollen particle, to start the growing process around – see the clip from Frozen Planet (below). During the transformation from water vapour to ice, an ice crystal always takes the form of a hexagon due to the way hydrogen and oxygen atoms bond to form water. Afterwards, the temperature and humidity of the atmosphere will shape the crystals and determine how fast they grow (see Fig. 2).

 

Between 0 and -60°C, the basic form (also called “habit”) of an ice crystal changes three times (near -3, -8 and -40°C). Between -3 and -8°C as well as below -40°C, the crystals take the form of six-sided plates, while between 0 an -3°C as well as between -8 and -40°C, the crystals form solid columns that are hexagonal in cross section.

Fig2: Ice Crystal Morphology diagram, indicating the basic form of ice crystals as a function of temperature and supersaturation (humidity). For more information see Libbrecht (2005) [Credit: K. Libbrecht]

In drier air, the growth occurs preferentially across flat surfaces, while in moister air, growth occurs preferentially at the tips, edges and corners. Also, the higher the moisture, the more water droplets can be absorbed, enhancing the growth rate and leading to more complex crystals. On the way between cloud and ground, an ice crystal will pass through layers of different temperature and moisture, leading sometimes to melting and refreezing on the way, changing parts of the crystal as well. All these factors together lead to the fact that finding two identical snowflakes is NEXT TO IMPOSSIBLE

 

From ice crystals to snowflakes

When an ice crystal becomes heavier than the surrounding air, it falls down. If it meets other ice crystals on its way to the ground, they will aggregate, forming new structures and the snowflake will grow. When reaching the ground, a snowflake can therefore be an aggregate of hundreds or sometimes even thousands of ice crystals.

From snowflakes to snowmen

Even when the snowflake has reached the ground, the journey is not finished. If the snow does not melt away rapidly, ice crystals will still modify their texture, size, and shape due to changing temperature and moisture conditions, melting and refreezing processes and/or compressing due to subsequent snowfall.

When the snow cover grows due to many subsequent snowfalls (over the winter or in cold areas over several years), a complex layered structure forms, made up of a variety of ice crystals, that reflect both the weather conditions at the time of deposition and the changes within the snow cover over time.

OR… you build a snowman out of it 🙂

Olaf the enchanted snowman from Disney's 2013 animated feature film, Frozen. [Credit : Disney Wikia]

Olaf the enchanted snowman from Disney’s 2013 animated feature film, Frozen. [Credit : Disney Wikia]

Further Reading

Edited by Emma Smith and Sophie Berger

Image of The Week – A Game of Drones (Part 1: A Debris-Covered Glacier)

Image of The Week – A Game of Drones (Part 1: A Debris-Covered Glacier)

What are debris-covered glaciers?

Many alpine glaciers are covered with a layer of surface debris (rock and sediment), which is sourced primarily from glacier headwalls and valley flanks. So-called ‘debris-covered glaciers’ are found in most glacierized regions, with concentrations in the European Alps, the Caucasus, Hindu-Kush-Himalaya, Karakoram and Tien Shan, the Andes, and Alaska and the western Cordillera of North America. Debris cover is important for ice dynamics for several reasons:

  • A layer of surface debris thicker than a few centimetres suppresses ice ablation (Brock et al., 2010), as it insulates the underlying ice from atmospheric heat and insolation.
  • In contrast, a thin layer of debris serves to enhance melt rates through reduced albedo (reflectance) and enhanced heat transfer to underlying ice.
  • A continuous or near-continuous layer of debris can result in debris-covered glaciers persisting at lower elevations than, and attaining lengths which exceed those of their ‘clean ice’ counterparts (Anderson and Anderson, 2016).

Miage Glacier – the largest debris-covered glacier in the European Alps

The Ghiacciaio del Miage, or Miage Glacier, is Italy’s longest glacier and is the largest debris-covered glacier in the European Alps. It is situated in the Aosta Valley, on the southwest flank of the Mont Blanc/Monte Bianco massif. The glacier descends from ~3800 m to ~1700 m above sea level (a.s.l.) across a distance of around 10 km, and is fed by four tributary glaciers. The glacier surface is extensively debris-covered below ~2400 m a.s.l., and the average surface debris thickness is 0.25 m across the lower 5 km of the glacier (Foster et al., 2012).

 

Figure 2: Up-glacier view of Miage Glacier, in which three of the glacier’s four tributaries are visible – from upper centre-left: Tête Carée Glacier, Bionnassay Glacier, Dome Glacier.

Figure 2: Up-glacier view of Miage Glacier, in which three of the glacier’s four tributaries are visible – from upper centre-left: Tête Carée Glacier, Bionnassay Glacier, Dome Glacier.

Glacier surveying using Unmanned Aerial Vehicles

Researchers from Northumbria University, UK, acquired these images of the glacier using a lightweight unmanned aerial vehicle (UAV) during a recent field visit to Miage Glacier. During the visit the team carried out a range of activities including the installation and maintenance of a network of weather stations and temperature loggers across the glacier and geomorphological surveying of the glacier and its catchment, whilst undergraduate students collected data for their final-year research projects. The UAV imagery reveals the emergence of surface debris cover from beneath winter snow cover and the persistence of a channelized hydrological network in the snowpack, characterised as a cascade of streams and storage ponds. A recent study by Fyffe et al. (2015) found that high early-season melt rates and runoff concentration in intermoraine troughs promotes the development of a channelized subglacial hydrological system in mid-glacier areas, whilst the drainage system beneath continuously debris-covered areas down-glacier is largely inefficient due to lower melt inputs and hummocky topography.

(Edited by Emma Smith and Sophie Berger)


Matt Westoby is a postdoctoral researcher at Northumbria University, UK. He is a quantitative geomorphologist, and uses novel high-resolution surveying technologies including repeat UAV-based Structure-from-Motion to quantify surface processes and landscape evolution in glacial and ice-marginal environments. Fieldwork on the Miage Glacier in June 2016 was supported in part by an Early Career Researcher Grant from the British Society for Geomorphology. He tweets as @MattWestoby Contact e-mail: mjwestoby@gmail.com

Image of the Week: Changes in Snow Cover

Image of the Week: Changes in Snow Cover

Who is dreaming of a white spring?

In daily life we might be more interested in the chances of a white Christmas, but the amount of snow-covered ground in the spring is a very good indicator of climate change. The figure above shows the projected change in snow cover extent in the Northern hemisphere in March-April according to different future scenarios (i.e. Representative Concentration Pathways or RCPs of the IPCC). All the scenarios predict a decrease in spring snow, and the reduction goes up to 30% by 2100, for the most pessimistic scenario.

Below is shown the changes in snow cover in historical times for the Northern hemisphere, the grey line is the change in snow cover in the spring. The red crosses are based on satellite data and show the snow cover in June. Undoubtedly, we are heading for a warmer climate but it would also seem that springtime skiing holidays could become a thing of the past.

The COP21 meeting will determine what steps will be taken in the future and which scenario path we will follow. Regardless of whether you worry about the future of our planet or the future of your skiing holiday – you should take an interest.

March–April NH snow cover extent (SCE, circles) over the period of available data, filtered with a 13-term smoother and with shading indicating the 95% confidence interval; and June SCE (red crosses, from satellite data alone), also filtered with a 13-term smoother. The width of the smoothed 95% confidence interval is influ- enced by the interannual variability in SCE. Updated from Brown and Robinson (2011). For both time series the anomalies are calculated relative to the 1971–2000 mean.

March–April NH snow cover extent (circles) over the period of available data, filtered with a 13-term smoother and with shading indicating the 95% confidence interval; and June (red crosses, from satellite data alone), also filtered with a 13-term smoother. The width of the smoothed 95% confidence interval is influenced by the interannual variability in SCE. For both time series the anomalies are calculated relative to the 1971–2000 mean.

 

The figures in this blog post are taken from the IPCC report (Fig. TS-18 and Fig. 4.19 respectively). You can read more here:

Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen and T. Zhang, 2013: Observations: Cryosphere. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.