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Cryospheric Sciences

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This guest post was contributed by a scientist, student or a professional in the Earth, planetary or space sciences. The EGU blogs welcome guest contributions, so if you’ve got a great idea for a post or fancy trying your hand at science communication, please contact the blog editor or the EGU Communications Officer Laura Roberts Artal to pitch your idea.

Image of the Week – Does size really matter? A story of ice floes and power laws

The retreating Arctic sea ice is one of the most well-known facets of Climate Change. Images of polar bears desperately swimming through polar seas searching for somewhere to rest and feed resonate strongly with the public. Beyond these headlines however, the Arctic Ocean is displaying a rapid transition from having mostly permanent ice cover to a more seasonal cover.


Figure 1: Sea ice extent in 2014 during the melting season. The pink lines mark the inner and outer extent of the marginal ice zone. The data comes from the CPOM setup of the CICE sea-ice model run with 9 years spin up from 2005. [Credit: Adam Bateson]

The Marginal Ice Zone

As both atmospheric and ocean average temperatures increase over the 21st century, the region of the Arctic considered either marginal or seasonal i.e. regions where sea ice is present for at least some of the year but with periods of either no or incomplete sea ice cover, is projected to increase significantly. Our image of the Week (Fig. 1) shows how the Marginal Ice Zone (defined here as regions with 15 % – 80 % ice coverage) evolves through the melting season. This means that the thermodynamic (i.e. melting, freezing) and dynamic (i.e. mechanical) processes which dominate the marginal ice zone are likely to become more important in influencing how the sea ice evolves in future.

Floe size matters

A key parameter to describe the behaviour of this region is the size of the individual ice floes – sheets of floating sea ice – which form the sea ice cover (see also a previous blog on this topic). Floe size impacts melt rate, floe mechanical response, atmosphere-ocean momentum exchange and wave-ice interactions (Fig. 2). The sea ice component of climate models usually assumes all floes have the same, constant size; this assumption removes the ability of sea ice models to represent the complexity of the marginal ice zone. As a result processes which influence floe size such as wave induced break up of floes and lateral melting can’t be represented adequately in current climate models.

Figure 2: Video shows significant wave height in 2014 (darker blue colours indicate bigger waves; note also that a white colour indicates no waves, not necessarily sea ice cover). The purple/pink lines mark the inner and outer extent of the Marginal Ice Zone respectively. The data come from the CPOM setup of the CICE sea-ice model run with 9 years spin up from 2005. [Credit: Adam Bateson]

How can we represent different floe sizes in models?

Given the changing Arctic environment, representing floe size as a variable quantity is likely to be important for future accuracy of sea ice modelling. Currently sea ice models tend to divide the Polar Regions into grid cells, with properties defined as an average across the grid cell. However floe sizes can vary significantly over sub kilometre scales. There are four alternative approaches to representing such a non-uniform distribution of floe sizes within a grid cell:

  1. Define floes individually within the model and allow each floe to evolve independently.
  2. Use a categorical floe size distribution i.e. assign floes to size categories of 1 – 10 m, 10 – 20 m etc. (e.g. Horvat et. al, 2015).
  3. Impose a floe size distribution on each grid cell which evolves over time driven by relevant processes such as lateral melting or floe break-up (e.g. Williams et. al, 2013 a & b).
  4. A single floe size for each grid cell is diagnosed from the fractional ice coverage.

 

Option 1 would be the ideal approach from a Physics perspective. It assumes nothing about what form the floe size distribution may take and allows us to properly assess the impact of different processes for floes of different sizes. This approach is computationally expensive however, which means it will take longer for models to run. Option 4 would the simplest option and wouldn’t have negligible impacts on model run times, however it wouldn’t be possible to include in the model any processes which influence floe size. Option 2 and option 3 represent intermediates between these two extremes. In particular option 3 would be a preferred compromise if floes can be represented as a coherent distribution which evolves over time at the grid length scale.

We should now look at whether observations support the use of such a distribution.

The power law distribution

Figure 3: The cumulative number density for floe size can be represented by a power law. Note that both scales are log scales, and that C in the equation is a constant. The blue and green lines show the distribution for smaller and larger exponents respectively. Note the cumulative number density for a given floe diameter, x, is the fraction of floes size x and larger. [Credit: Adam Bateson]

The floe size distribution is most commonly fitted to a power law (Fig. 3). Power law systems have the property of self-similarity, a term attributed to a system which looks the same over different scales (e.g. the metre or kilometre scale). Power law distributions are relatively easily to investigate mathematically, and can easily be incorporated into a model without significant computational expense.

Do observations support the use of a power law?

Many individual experiments to assess the floe size distribution have shown a good fit to the power law. However, a large range of values for the power law exponent have been reported with observations ranging from 0.9 to 4. Other papers have proposed two power laws over different size ranges, with smaller exponents used for the smaller floe range. Herman et. al (2010) proposed that a distribution with a variable exponent would produce a better fit than a power law. There are further questions we need to consider as well. Over what scale are power laws a valid approximation? What determines the exponent of the power law and can it be assumed that this is constant? Is the power law only valid over a certain range of floe sizes and if so what determines this range?

These questions are not trivial, and the available observations are not sufficient to answer them. However, there is still value in testing different distributions and approaches within models. This can provide information about how sensitive the sea ice cover is to different distributions and which processes in particular are important to accurately model winter ice growth and summer ice loss in the marginal ice zone.

References/Further Reading

Herman, Agnieszka. “Sea-ice floe-size distribution in the context of spontaneous scaling emergence in stochastic systems.” Physical Review E 81, no. 6 (2010): 066123.

Horvat, Christopher, and Eli Tziperman. “A prognostic model of the sea-ice floe size and thickness distribution.” The Cryosphere 9, no. 6 (2015): 2119-2134.

Williams, Timothy D., Luke G. Bennetts, Vernon A. Squire, Dany Dumont, and Laurent Bertino. “Wave–ice interactions in the marginal ice zone. Part 1: Theoretical foundations.” Ocean Modelling 71 (2013): 81-91.

Williams, Timothy D., Luke G. Bennetts, Vernon A. Squire, Dany Dumont, and Laurent Bertino. “Wave–ice interactions in the marginal ice zone. Part 2: Numerical implementation and sensitivity studies along 1D transects of the ocean surface.” Ocean Modelling 71 (2013): 92-101.

Edited by Sophie Berger


Adam Bateson is a PhD student at the University of Reading (United Kingdom), working with Danny Feltham. His project involves investigating the fragmentation and melting of the Arctic seasonal sea-ice cover, specifically improving the representation of relevant processes within sea-ice models. In particular he is looking at lateral melting and wave induced fragmentation of sea-ice as drivers of break up, as well as the role of the ocean mixed layer as either an amplifier or dampener to the impacts of particular processes. Contact: a.w.bateson@pgr.reading.ac.uk or @a_w_bateson on twitter.

Image of the Week – Ice Stupas: a solution for Himalayan water shortage?

Image of the Week – Ice Stupas: a solution for Himalayan water shortage?

As the world searches for practical innovations that can mitigate the impact of climate change, traditional methods of environmental management can offer inspiration. In Hindu Kush and Karakoram region, local people have been growing, or grafting, glaciers for at least 100 years. Legend has it that artificial glaciers were grown in mountain passes as early as the twelfth century to block the advance of Genghis Khan and the Mongols!


 

What exactly are we talking about?

People in the Himalayas need water for the irrigation of their crops. Naturally, they get this water during the melting period of local glaciers. Glacial melt, however, is insufficient to satisfy the demands in early spring (April-June). Artificial ice structures can increase the availability of water for crop irrigation during this period. They are grown during the winter season preventing the water to waste away into the ocean. The Ice Stupa project is bringing these practices back from the realm of folklore for the everyday use of mountain farmers again.

 

How it works

An artificial glacier is built following a simple technique. Water is piped away from high altitude reservoirs (glacial lakes or streams) in winter. Further downstream, the water is allowed to “leave” the pipe. Due to gravity, the pressure that has built up on the way forces the water to leave the pipe as a water fountain. In contact with subzero temperatures, the water fountain freezes, building a huge cone of ice. In its final form, this artificial glacier looks like a traditional buddhist building, hence the name “stupa“. In the following video, you can get a better visual idea of the process!

 

 

An ice stupa is needed for crop irrigation. The water contained in the stupa should therefore also be released during the right time of the year. To this purpose, it is also designed to conserve water in ice form as long into the summer as possible. It can then, as it melts, provide irrigation to the fields until the real glacial melt waters are sufficient in June. Since these ice cones extend vertically upwards towards the sun, they receive less of the sun’s energy per unit of volume of water stored. Hence, they will take much longer to melt compared to an artificial glacier of the same volume formed horizontally on a flat surface.

 

Further reading

Edited by Clara Burgard


Suryanarayanan Balasubramanian is a mathematician who has been managing the Ice Stupa Project for the past three years. He studies the life cycle of Ice Stupas through field measurements and dynamic models. He is currently developing the project in Peru, Switzerland, Nepal and India. Contact Email: gayashiva91@gmail.com

Back to the Front – Larsen C Ice Shelf in the Aftermath of Iceberg A68!

Back to the Front – Larsen C Ice Shelf in the Aftermath of Iceberg A68!

Much of the Antarctic continent is fringed by ice shelves. An ice shelf is the floating extension of a terrestrial ice mass and, as such, is an important ‘middleman’ that regulates the delivery of ice from land into the ocean: for much of Antarctica, ice that passes from land into the sea does so via ice shelves. I’ve been conducting geophysical experiments on ice for over a decade, using mostly seismic and radar methods to determine the physical condition of ice and its wider system, but it’s only in the last couple of years that I’ve been using these methods on ice shelves. The importance of ice shelf processes is becoming more widely recognised in glaciological circles: after hearing one of my seminars last year, a glaciology professor told me that he was revising his previous opinion that ice shelves were largely ‘passengers’ in the grand scheme of things and this recognition is becoming more common. Slowly, we are coming to appreciate that ice shelves have their own specific dynamics and, moreover, that they are the drivers of change on other ice masses.


The MIDAS Project

In 2015, I joined the MIDAS project – led by Swansea and Aberystwyth Universities and funded by the Natural Environment Research Council – dedicated to investigating the effects of a warming climate on the Larsen C ice shelf in West Antarctica (Fig. 1). My role was to to assist with geophysical surveys (Fig. 2) on the ice shelf – but more about that later!

Figure 2: Adam Booth overseeing seismic surveys on the Larsen C ice
shelf in 2015 [Credit: Suzanne Bevan].

Larsen C is located towards the northern tip of the Antarctic Peninsula, and is one of a number of “Larsen neighbours” that fringe its eastern cost. MIDAS turns out to have been an extremely timely study, culminating in 2017 just as Larsen C hit the headlines by calving one of the largest icebergs – termed A68 – ever recorded. On 12th July 2017, 12% of the Larsen C area was sliced away by a sporadically-propagating rift through the eastern edge of the shelf, resulting in an iceberg with 5800 km2 area (two Luxembourgs, one Delaware, one-quarter Wales…). As of 14th October 2017 (Fig. 1), A68 is drifting into the Weddell Sea, with open ocean between it and Larsen C. See our previous post “Ice ice bergy” to find out more about how and why ice berg movement is monitored.

The aftermath of A68

As colossal as A68 (Fig, 1) is, its record-breaking statistics are only (hnnngh…) the tip of the iceberg, and of greater significance is the potential response of what remains of Larsen C. This potential is best appreciated by considering what happened to Larsen B, a northern neighbour of Larsen C. In early 2002, over 3000 km2 of Larsen B Ice Shelf underwent a catastrophic collapse, disintegrating into thousands of smaller icebergs (and immortalised in the music of the band British Sea Power). Rewind seven years further back, to 1995: Larsen B calved an enormous iceberg, exceeding 1700 m2 in area. An ominous extrapolation from this is that large iceberg calving somehow preconditions ice shelves to instability, and several models of Larsen C evolution suggest that it could follow Larsen B’s lead and become more vulnerable to collapse over the coming years.

The enormous mass of the intact ice shelf acts like a dam that blocks the delivery of terrestrial ice into the ocean, and the disappearance of the ice shelf removes so-called ‘backstress’ – essentially ‘breaking the dam’.

Then what? Well, ice shelves are in stress communication with their terrestrial tributaries, therefore processes affecting the shelf can propagate back to the supply glaciers. The enormous mass of the intact ice shelf acts like a dam that blocks the delivery of terrestrial ice into the ocean, and the disappearance of the ice shelf removes so-called ‘backstress’ – essentially ‘breaking the dam’. In the aftermath of Larsen B’s collapse, its tributary glaciers were seen to accelerate, thereby delivering more of their ice into the Weddell Sea. It is this aftermath that we are particularly concerned about, since it’s the accelerated tributaries that promote accelerated sea-level rise. Ice shelf collapse has little immediate impact on sea-level: since it is already floating, the shelf displaces all the water that it ever will. But, in moving more ice from the land to the sea, we risk increased sea levels and, with them, the associated socio-economic consequences.

How can we improve our predictions?

Figure 3: Computational model of the changed stress state, Δτuu, of Larsen C following the calving of A68 (output from BISICLES model, from Stephen Cornford, Swansea University). The stress change is keenly felt at the calving front, but also propagates further upstream [Credit: Stephen Cornford]

A key limitation in our ability to predict the evolution of Larsen C is a lack of observational evidence of how ice shelf stresses evolve in the short-term aftermath of a major calving event. These calving events are rare: we simply haven’t had much opportunity to investigate them, so while our computer predictions are based on valid physics (e.g., Fig. 3) it would be valuable to have actual observations to constrain them. Powerful satellite methods are available for tracking the behaviour of the shelf but these provide only the surface response; Larsen C is around 200 m thick at its calving front so there is plenty of ice that is hidden away from the satellite ‘eye in the sky’, but that is still adapting to the new stress regime. So how can we “see” into the ice?

To address this, we’ve recently been awarded an “Urgency Grant” – Response to the A68 Calving Event (RA68CE) – from NERC to send a fieldcrew to the Larsen C ice shelf, involving researchers from Leeds, Swansea and Aberystwyth, together with the British Geological and British Antarctic Surveys.

Figure 4: Emma Pearce and Dr Jim White preparing seismic equipment – intrepid geophysicists ready to wrap-up warm for field deployment on Larsen C! [Credit: Adam Booth]

The field team – Jim White and Emma Pearce (Fig. 4) – will undertake seismic and radar surveys at two main sites (Fig. 3) to assess the new stress regime around the Larsen C calving front. One of these sites is being reoccupied after seismic surveying in 2008-9, during the Swansea-led SOLIS project, allowing us to make a long-term comparison. These, and two other sites, will also be instrumented with EMLID REACH GPS sensors, to track small-scale ice movements than can’t be captured in the satellite data. The field observations will be supplied to a team of glacial modellers at Swansea University, to allow them to improve future predictions (e.g. Fig. 3), while their remote sensing team continues to monitor the evolving stress state at surface.

It’s truly exciting to be coordinating the first deployment, post A68, on Larsen C. Our data should provide a unique missing piece from the predictive jigsaw of Larsen C’s evolution, ultimately improving our understanding of the causes and effects of large-scale iceberg calving – both for Larsen C and beyond!

 

For ice-hot news from the field, follow Emma Pearce on twitter: @emm_pearce

 

Edited by Emma Smith


Further Reading

  • More information on Larsen C at the project MIDAS website
  • Learn more about ice shelf evolution with the Ice Flows game – eduction by stealth! Also check out the EGU Cryoblog post about it!
  • Borstad et al., 2017; Fracture propagation and stability of ice shelves governed by ice shelf heterogeneity; Geophysical Research Letters, 44, 4186-4194.
  • Wuite et al., 2015; Evolution of surface velocities and ice discharge of Larsen B outlet glaciers from 1995 to 2013. The Cryosphere, 9, 957-969.
  • Cornford et al., 2013; Adaptive mesh, finite volume modelling of marine ice sheets; Journal of Computational Physics, 232, 1, 529-549.

Adam Booth is a lecturer in Exploration Geophysics at the University of Leeds, UK. He is the PI on the NERC-funded project “Ice shelf response to large iceberg calving” (NE/R012334/1). After obtaining his PhD from the University of Leeds in 2008, he held postdoctoral positions at Swansea University and Imperial College London, in which he worked with diverse research applications of near-surface geophysics. He tweets as: @Geophysics_Adam

Image of the Week – Sea-ice dynamics for beginners

Image of the Week – Sea-ice dynamics for beginners

When I ask school children or people who only know about sea ice from remote references in the newspapers: ‘How thick do you think is the Arctic sea ice?’, I often get surprising answers: ’10 meters? No, it must be thicker – 100 meters!’. It seems like sea ice, often depicted as a uniform white cover around the North Pole and as a key element in accelerated warming of the Polar Regions, imposes a majestic image. Unfortunately, sea ice is much more fragile.


Growing in the current

Actually, sea ice is on average just about 2 m thick. It used to be thicker, up to 3 m, but such ice needs several winters to grow and is quite rare in the modern-day Arctic as winters are warmer than they used to be (see this previous post). Currently, more than half of the Arctic sea-ice area melts away completely during summer and grows back during the next winter. Such a thin layer of frozen water floating on the ocean is not strong enough to resist the forces of the wind, which pushes it around in ocean surface currents. In order for the ice to move, it has to deform and breaks into ice floes (read more in this previous post). Some ice floes move apart (divergence) in leads and polynyas (see this previous post), while others are pressed together (convergence) in pressure ridges, where blocks of ice pile up against and on top of each other (see our Image of the Week).

Ice grows from the ocean surface layer by water freezing. This is called thermodynamical growth. Thermodynamical growth produces most of the ice forming in the time from freeze-up in fall until the ice becomes about 1 to 2 m thick in mid-winter. At that point, sea ice approaches equilibrium thickness, i.e. the sea ice is thick enough to insulate the cold atmosphere from the relatively warm ocean. But because sea ice deforms, it can continue growing during the rest of the winter too. Pressure ridges sails can stick several meters out of the icy landscape, while their much larger and bulkier keels are hidden below the surface. Ridges can store large volume of sea ice – about a third (Hansen et al, 2013)! At the same time, new ice can grow in leads where open water is exposed to the atmosphere.

The following video is a collection of movies showing consequences and acts of sea-ice deformation. The first part is taken from R/V Lance – the ice-strengthened research ship of Norwegian Polar Institute, while she is navigating along a lead in late winter. Observe how much space is taken by pressure ridges! The second part of the movie shows a pressure ridge growing. Listen to the sound of deforming ice!

Another positive feedback

In winter, temperatures are so low that all the fractures, leads and pressure ridges freeze back – they heal. In summer, however, these damages are the first to appear again. Dark water with low albedo (read more about the albedo feedback in this post) is exposed and the ice melts faster in such regions. Because the Arctic sea ice became relatively thin over the recent decades, it also became less resistant to the forces of the wind. Such thin ice breaks more easily (e.g. Itkin et al, 2017). This means that, as more of such damaged ice is present in summer, the ice cover melts faster. So, here is an additional positive feedback for the Arctic ice under climate change: thinner ice melts faster also because it has become weaker and therefore breaks up easier.

Further reading

Edited by Clara Burgard


Polona Itkin is a Post-doctoral Researcher at the Norwegian Polar Institute, Tromsø. She investigates the sea ice dynamics of the Arctic Ocean and its connection to the sea ice thickness. In her work she combines the information from in-site observations, remote sensing and numerical modeling. Polona is part of the social media project ‘oceanseaiceNPI’ – a group of scientists that communicates their knowledge through social media channels:

Instagram.com/OceanSeaIceNPI, Twitter.com/OceanSeaIceNPI, Facebook.com/OceanSeaIceNPI, contact Email: polona.itkin@npolar.no

Image of the Week – Of comparing oranges and apples in the sea-ice context

Image of the Week – Of comparing oranges and apples in the sea-ice context

In the last fifty years, models and observations have enabled us to better understand sea-ice processes. On the one hand, global climate models have been developed, accounting for the sea-ice component in the climate system. On the other hand, satellite instruments have been developed to monitor the “real” sea-ice evolution. These satellite observations are often used to evaluate climate models. However, lately, doubts have arisen to whether comparing model output to observations is the most reasonable method. Are we sometimes comparing oranges to apples? To discuss this matter, sea-ice modelers and sea-ice observers met over three days in Hamburg earlier this month at the Workshop on improved satellite retrievals of sea-ice concentration and sea-ice thickness for climate applications.


How do we measure past and current seaice changes?

The great advantage of satellites over in-situ measurements is that they measure changes in sea-ice concentration (fraction of ocean covered by sea ice in a given area) and sea-ice thickness in a continuous way with an almost complete spatial coverage of the polar regions (see this previous post) and with a high temporal resolution.
Of course, satellites do not directly measure sea-ice concentration. Rather, they measure the brightness temperature (left part of Fig. 2), which is a measure of the radiation emitted by the Earth’s surface and atmosphere, with passive microwave sensors (such as SSM/I). From this brightness temperature, it is possible to compute sea-ice concentration (see this article for further information). Microwave sensors are used because they can “see” the surface through clouds and during polar night, which is not possible with visible sensors (such as MODIS).

In a similar way, satellites do not directly measure sea-ice thickness. Rather, they measure sea-ice freeboard (right part of Fig. 2), i.e. the height of the ice above the sea surface, with different kinds of sensors (laser altimetry [e.g. ICESat], radar altimetry [CryoSat]). Sea-ice thickness is then retrieved through appropriate algorithms (see e.g. this article).

 

Fig. 2: Satellite measurement techniques that lead to the observed brightness temperatures and sea-ice freeboard [Credit: C. Burgard].

 

Are the satellite retrievals accurate?

Each method of deriving sea-ice concentration or thickness from satellite measurements has its own uncertainties. For example, algorithms to retrieve sea-ice concentration use several assumptions about the state of the atmosphere and surface emissivity. Also, melt ponds and thin ice show up as lower concentration regions. Similarly, different assumptions about snow depth on ice and about sea-ice density impact the retrieved sea-ice thickness. Therefore, the sea-ice variables retrieved from satellite observations may deviate from their actual “real” state.

 

How do we project future sea-ice changes?

In order to project the future sea-ice evolution, different climate models are used (see this article for example). These climate models are usually evaluated against satellite observations in order to assess their performance. While all models present biases compared to observations, it cannot always be concluded that this is necessarily a problem of the models as observations also have uncertainties as previously said. Therefore, the main discussions at the workshop in Hamburg (Fig. 3) were about reducing uncertainties in the comparison between observations and models.

 

How can we better compare satellite observations and models?

The discussion in Hamburg was very lively as modelers and observers exchanged about how they actually reach the results they provide, explaining in detail their models and algorithms. It became rapidly clear that comparing observed sea-ice concentration to modeled sea-ice concentration might be like comparing apples to oranges under certain circumstances.

Thomas Lavergne, a researcher at the Norwegian Meteorological Institute, gave a presentation related to this discussion by presenting the picture that is our Image of the Week. The classical method up to now has always been to transform the measured satellite signal into a “satellite product”, a quantity that is directly computed by models (direction from right to left in the Image of the Week), so that we can compare this quantity to the corresponding model variable. As already mentioned, this can lead to assumptions and introduce errors into the observations, while one would expect observations to be the best representation of the “real” world.

Another possible approach, already well accepted in the community of weather forecasting, is to transform the model variables all the way into simulated brightness temperatures and compare these to satellite data (direction from left to right in the Image of the Week). The algorithms that transform model variables into simulated satellite quantities are observation operators. Although an active field of research, the observation operators for sea ice are not ready, and the comparison of sea-ice simulations to satellite observations will for the foreseeable future rely on satellite “products”.

At the workshop, Lavergne advocated for a middle-ground solution, where satellite products “take a step back” and climate models “take a step forward” using tailored observation operators. This would reduce the need for assumptions in the satellite products but still be manageable for modelers, and would most likely offer the best consensus for the two communities. This way, observed and modeled quantities can still be compared with each other and uncertainties introduced into the comparison can be reduced.

Fig. 3: Happy sea-ice observers and sea-ice modelers in Hamburg [Credit: Julika Doerffer, CEN, Universität Hamburg]

 

Perspectives

The workshop aim was to bring together sea-ice observers and modelers. While no real consensus on the proposed approach was found, the reflection has been launched and probably deserves some more attention in the future in order to better compare sea-ice models and satellite observations. This might move the debate from an apple-to-oranges comparison to a pear-to-pear one. This will hopefully improve sea-ice models and satellite observations and improve future projections of sea-ice evolution.

 

Further reading

With contributions by Thomas Lavergne and Clara Burgard

Edited by Clara Burgard and Sophie Berger


David Docquier is a post-doctoral researcher at the Earth and Life Institute of Université catholique de Louvain (UCL) in Belgium. He works on the development of processed-based sea-ice metrics in order to improve the evaluation of global climate models (GCMs). His study is embedded within the EU Horizon 2020 PRIMAVERA project, which aims at developing a new generation of high-resolution GCMs to better represent the climate.

Image of the Week – Karthaus Summer School 2017

Gloriously cloudless day for the fieldtrip to the Ötztal Alps [Credit: C. Reijmer].

Glaciologists often undertake fieldwork in remote and difficult to access locations, which perhaps explains why they happily travel to similar locations to attend meetings and workshops. The Karthaus Summer School, which focuses on Ice Sheets and Glaciers in the Climate System, is no exception. The idyllic village of Karthaus, located in the narrow Schnalstal valley in Südtirol (Italy), has been hosting this 10-day glaciology course nearly every year since 1995. In September, an international crowd of some 30+ PhD students and postdocs, and 11 lecturers assembled in Karthaus for the 2017 edition of this famous course, for an intensive program of lectures, food, some science, more food (with wine!), and lots of socialising.


The lecture theatre with a backdrop of green hills, on the day the cows came down from the hills [Credit: D. Medrzycka].

The morning sessions

A typical morning of the course involved four hours of lectures, which covered a wide range of topics including continuum mechanics, thermodynamics, ice-ocean interactions, ice cores, geophysics, and geodynamics, with a special focus on numerical modelling and its applications for investigating ice-climate interactions. The lectures covered fundamentals processes, their applications and limitations, and current knowledge gaps for a wide range of complex concepts related to ice dynamics. All our lecturers happily answered our (many) additional questions during the coffee and cake breaks, enjoyed in the fresh mountain air outside the lecture theatre.

 
 
 
 
 
 
 

The biggest challenge was not the group work itself, but trying to not get distracted by the sun and the hills surrounding us [Credit: V. Zorzut].

The afternoon sessions

After a three-course lunch, we spent the afternoon sessions applying the theory learned in the morning lectures. The group projects were designed to get us to go into more detail on certain topics, and work on real-world applications for specific research problems. We presented the results of our work at the end of the course during a 15 minute group presentation. For those who could afford a bit of free time after these sessions, the rest of the afternoon could be spent either hiking or trail running in the steep hills overlooking the village (trying to beat I. Hewitt’s time up Kruezspitze), playing football, chilling in the sauna, or catching up on some sleep before dinner.

 

The evenings

Everyone who has ever attended the Karthaus course mentions the food, complementing both the quality and (legendary) quantity of it. Every evening, we were served a memorable five course meal accompanied by generous amounts of local wine. Dessert was followed by musical entertainment, with inspired performances by Frank Pattyn on the piano. On the last evening, Frank was accompanied by Johannes Oerlemans who treated us to two of his original tango arrangements on the guitar, followed by a passionate rendition of Jacques Brel’s Le port d’Amsterdam by our own Kevin Bulthuis (vocals). We wrapped up each day of the course in the local bar, socialising, playing card games, sampling the local beers, and making our way through the many different flavours of schnaps and grappa. Big thanks to the owners, Paul and Stefania Grüner, and staff (with a special shout-out to Hannes) of the Goldene Rose Hotel, and the village of Karthaus, for taking great care of us!

Frank Pattyn (piano) and Johannes Oerlemans (guitar) performing an original tango arrangement [Credit: D. Medrzycka].


 

Out and about

On the penultimate day of the course the group headed to a number of glaciers in the Ötztal Alps. The excursion, which happened to take place on a perfectly cloudless day, gave us the opportunity to observe first hand the changes affecting glaciers in the region, and the impact of these retreating ice masses on the landscape and humans inhabiting it. It also provided a much needed break from the intense week! After walking down the ski slopes of the Hochjochferner, a small valley glacier accessible by cable car from Kurzras, we stopped to enjoy the sun and have lunch at the Schöne Aussicht (Bellavista) hut (2845 m a.s.l.). Those with more energy scrambled up to the ridge running along the Italian/Austrian border (3270 m a.s.l.), through at times knee-deep snow, to take a peak at the Hintereisferner, a valley glacier on the Austrian side of the border. Four of us continued on along the ridge, and by chance visited the laser scanner (LiDAR) system operated by researchers from the University of Innsbruck, used to monitor changes in surface elevation on the glacier.

Standing on the ridge running along the Italian/Austrian border. View onto the Hintereisferner [Credit: D. Medrzycka].


 

Final thoughts

The 10 day course was certainly an intensive (and intense) experience, and I would recommend it to all glaciology students without reservations, whether they are looking for a basic introduction to ice dynamics, or aiming to fill a few knowledge gaps. Whilst some of the topics covered in the course were only remotely related to my own PhD research (and far out of my comfort zone!), the lectures and project work forced me to think in alternate ways. Although I may have finished the course with more questions than I had at the start, I now know where to go look for the answers!

A big part of the experience was without a doubt the social aspect of the course. Between the never ending and excellent food (as a result of which some of us developed “food babies”), and the long evenings at the local bar (resulting in increasing amounts of sleep deprivation), there were plenty of opportunities to talk science, gain new insights into our ongoing research, and discuss ideas for future projects. As with all great Summer Schools, one of the major perks was the opportunity to hang out with fellow students, expand our network of fellow researchers, and establish the groundwork for continued professional collaborations. Huge thanks to the convenor, Johannes Oerlemans, the village of Karthaus, and all the lecturers and fellow students for a memorable 10 days! I am looking forward to working with all of you in the future.

The crowd of the Karthaus summerschool: 2017 edition [Credit: C. Reijmer].

Edited by Morgan Gibson and Clara Burgard


Dorota Medrzycka is a PhD candidate at the University of Ottawa (Canada), working with Luke Copland. Her research focuses on the dynamics of glaciers and ice caps in the Canadian High Arctic, with a focus on ice flow instabilities (including glacier surging). Her project combines field studies and remote sensing techniques to monitor ice motion, and gain insight into the factors controlling the variability in ice dynamics in the Canadian Arctic. Contact: dorota.medrzycka@uottawa.ca.

Mapping the bottom of the world — an Interview with Brad Herried, Antarctic Cartographer

Mapping the bottom of the world — an Interview with Brad Herried, Antarctic Cartographer

Mapping Earth’s most remote continent presents a number of unique challenges. Antarctic cartographers and scientists are using some of the most advanced mapping technologies available to get a clearer picture of the continent. We asked Brad Herried, a Cartographer and Web Developer at the Polar Geospatial Center at the University of Minnesota, a few questions about what it’s like to do this unique job both on and off the ice.


Before we go too much further… what is the Polar Geospatial Center, and what does it do for polar science and scientists?

The Polar Geospatial Center (PGC), founded in 2007 by Director Paul Morin, is a research group of about 20 staff and students at the University of Minnesota with a simple mission: solve geospatial problems at the poles (Antarctica and the Arctic). Because we are funded (primarily) through the U.S. National Science Foundation (NSF) and NASA Cryospheric Sciences, that is the community we support – other U.S.-funded polar researchers. We provide custom maps, high-resolution commercial satellite imagery, and Geographic Information System (GIS) support for researchers who would like to use the data for their research but may not have the expertise to do so.

Our primary service is providing high-resolution satellite imagery (i.e. from the DigitalGlobe, Inc. constellation) to U.S.-funded polar researchers – at no additional cost to their grants – through licensing agreements with the U.S. Government. It has proven beneficial to researchers to have a service so that we do the hard parts of data management, remote sensing, and automation of satellite imagery processing so that they don’t have to. So, a glaciologist or geomorphologist or wildlife ecologist studying at the poles may come to us and say: I would like to use satellite imagery to study phenomenon x or y. Some groups use it just for logistics (these are some of the least mapped places on Earth after all) to get to their site. Some groups’ entire research is done using remote sensing.

What kinds of data and resources do you use?

The PGC’s polar archive of high-resolution commercial imagery is absolutely astounding (like, in the thousands of terabytes). The imagery, although licensed to us by U.S. Government contracts, is collected by the DigitalGlobe, Inc. constellation of satellites (e.g. WorldView-2), much like the imagery where you can see your house/car in Google Earth. The benefit is that we can provide it at no cost to our users (researchers). That resource, along with the expertise of the staff at PGC, can provide solutions to users, whether it’s making a simple map of a remote research site or providing a time-series of satellite imagery for a researcher studying change detection (like, say for a glacier front in Greenland).

This also presents a challenge. How do we manage and effectively deliver that much data? We have relied on skilled staff, ingenuity, cheap storage, high-performance computing, and automation to become successful.

As the saying goes, automate or die.

What’s your role at the PGC? How did you find your way into a job like this?

I started at the PGC as a graduate student in 2008. I knew nothing about Antarctica or the Arctic, but my background and studies in GIS & cartography offered a wide range of jobs. After I graduated, I became a full-time employee as the lead cartographer of the (at the time, very small) group. Currently, I do a lot more GIS web application development and geospatial data management. We have recognized the need for more automated, “self-service” systems for our users to get the data they need in a timely manner, and less of asking a PGC employee for a custom product. As the saying goes, automate or die. But, of course, I still spend a fair bit of my times creating maps to keep my cartographic juices going.

Antarctica and the South Polar Regions. Map from the American explorer Richard Byrd’s second expedition in 1933. [Credit: Byrd Antarctic Expeditions]

What kind of work do PGC employees do in Antarctica?

The PGC staffs an office at the United States’ McMurdo Station annually from October to February, with 3-5 staff rotating throughout the field season. It is really an extension of our responsibilities, with a couple interesting twists, both good and bad. First, a majority of our users (NSF-funded researchers) come through McMurdo Station in preparation for their fieldwork. It’s a beneficial and unique experience to meet with them one-on-one and solve problems, ironically, faster than email exchanges back in the States. Second – and this is true of all of Antarctica – the internet bandwidth is very limited. So, we have to a) prepare more regarding what data/imagery we have on site and b) do more with less. That always proves to be a fun challenge because it is impossible to access our entire archive of imagery from down there.

How could I forget collecting Google Street View in Antarctica.

There have been several years, however, when we do get to go out into the field! In past years, we have conducted various field campaigns in the nearby McMurdo Dry Valleys to collect survey ground control to make our satellite imagery more accurate. And, how could I forget collecting Google Street View (with some custom builds of the typical car-camera system for snowmobiles, heavy-duty trucks, and backpacks). The Google Street View provides a window into the world of Antarctica – history, facilities, science, and of course its beautiful landscapes – to a wide audience who only dream of visiting Antarctica.

Brad on a snowmobile collecting Google Street View imagery [Credit: Brad Herried]

What are some of the interesting projects PGC has worked on? What’s exciting at PGC right now?

The PGC does a lot to contribute to polar mapping. There’s not exactly a ton of geospatial data or maps for the polar regions, especially Antarctica. What data or maps there are, it is not often of very high quality. For example, there are regions of Antarctica (especially in inland East Antarctica) which have not been properly mapped or surveyed since the 1960s. Those maps offer little help if you’re trying to land an aircraft in the area. So, PGC has done a lot to improve that geospatial data including creating more accurate coastlines, improving geographic coordinates of named features (sometimes the location can be off by 10s of kilometers!), organizing historic aerial photography, and digitizing map collections. These are important to have, but it all changes when you can collect data 100 times more accurate with satellites…

There’s not exactly a ton of geospatial data or maps for the polar regions, especially Antarctica.

Where it gets really interesting is how we can apply our archive of satellite imagery to help researchers solve problems or come up with cutting-edge solutions with the data. One example is the ArcticDEM project. In a private-public collaboration, PGC is using high performance computing (HPC) to develop a pan-Arctic Digital Elevation Model (DEM) at a resolution 10 times better than what exists now. This project requires hundreds of thousands of stereoscopic satellite imagery pairs to be processed using photogrammetry techniques to build a three-dimensional model of the surface for the entire Arctic. There are countless more applications for the imagery and we’ll continue to push the limits of the technology to produce innovative products to help measure the Earth and solve really important research questions.

ArcticDEM hillshade in East Greenland. DEM(s) created by the Polar Geospatial Center from DigitalGlobe, Inc. imagery. [Credit: Brad Herried/ Polar Geospatial Center].

 

What resources can cryosphere researchers and other polar scientists without US funding get from PGC to enhance their research?

Our website provides a wealth of non-licensed data, freely available to download. That includes our polar map catalog (with over 2,000 historic maps of the polar regions), aerial photography, and elevation data. The ArcticDEM project I mentioned before is freely available (see https://www.pgc.umn.edu/data/arcticdem/), as are all DEMs created (derived) from the optical imagery. Moreover, we work with the international community on a regular basis to continue mapping efforts across both poles.

 

What advice do you have for students interested in a career in science or geospatial science?

This might be a little bit of a tangent, but learn to code. I was trained in cartography ten years ago and we hardly touched the command line. Now? You certainly don’t have to be an expert, say, Python programmer, but you’re behind if you don’t know how to automate some of your tasks, data processing, analysis, or other routine workflows. It allows you to focus on the things you’re actually an expert in (and, employers are most certainly looking for these skills).

ArcticDEM hillshade of Columbia Glacier, Alaska. DEM(s) created by the Polar Geospatial Center from DigitalGlobe, Inc. imagery. [Credit: Brad Herried/ Polar Geospatial Center].

Personally, what has been the highlight of your time at PGC so far?

I will never forget the first time I stepped off the plane landing in Antarctica as a graduate student. A surreal, breathtaking (literally), and completely foreign feeling. To be able to experience the most remote places on Earth first-hand naturally leads to a better understanding of them. So, the highlight for me is this: I find myself asking more questions, talking to the preeminent researchers and students about their work, and discovering the purpose of it all. I may be a small piece in the puzzle of understanding our Earth’s poles, but I’m humbled to be a part.

Interview and Editing by George Roth, Additional Editing by Sophie Berger

Image of the Week: Petermann Glacier

Figure 1: Satellite images showing the front of Petermann glacier from spring to autumn 2016 [Credit: LandSAT 8 (NASA) and L. Dyke]

Our image of the week shows the area around the calving front of Petermann Glacier through the spring, summer, and autumn of 2016. Petermann Glacier, in northern Greenland, is one of the largest glaciers of the Greenland Ice Sheet. It terminates in the huge Petermann Fjord, more than 10 km wide, surrounded by 1000 m cliffs and plunging to more than 1100 m below sea level at its deepest point. In 2010 and 2012, the glacier caught the world’s attention with two large events, which caused the glacier to retreat to a historically unprecedented position.


In Fig. 1 we see the changes happening through the season on Petermann glacier – and they are huge. The animated map highlights many different processes. As areas emerge from the Polar night at the start of spring, the shadows quickly shorten and the light levels become noticeably higher.  This is followed by the melting of the snow, first on south-facing slopes, and eventually to on the high-elevation areas in the mountains. As the sun returns, meltwater starts forming on the surface of the glacier, this is visible as vast turquoise lakes. Finally, the sea ice in the fjord succumbs to the seasonal warming of the ocean and atmosphere, it thins, and then completely disintegrates at around day 205.

The change in the glacier is perhaps the most interesting phenomenon. It is possible to observe the glacier flowing and advancing into the fjord. In addition, several large rifts near the front open through the course of the year. These will eventually spread across the front of the glacier and a new, huge iceberg will be born. These rifts are being closely monitored, and it is likely that when the iceberg calves it will bring cause Petermann Glacier to retreat to a new historical minimum.

The image above is an example of a new type of map, it takes cartography into the 4th dimension—time. Technological advances have only recently made it possible to create a map like this; with the launch of Landsat 8 and Sentinel-2 it is now possible to receive regular, high-resolution, and free satellite images of high latitude areas. These data have been projected onto a new, high quality digital elevation model (Howat et al., 2013) to create this map.

 

Further Reading

Howat, I. M., Negrete, A., and Smith, B. E. (2014). The Greenland Ice Mapping Project (GIMP) land classification and surface elevation datasetsThe Cryosphere8 (1): 1509–1518

Edited by Nanna B. Karlsson


Laurence Dyke is a postdoctoral researcher at The Geological Survey of Denmark and Greenland (GEUS) in Copenhagen (DK). His work is primarily focussed on understanding the history of the Greenland Ice Sheet, from both marine and terrestrial perspectives. He works with marine sediment cores and surface exposure dating to investigate what triggered changes in glacier behaviour over the last 12 thousand years. Understanding the past is key to predicting the future! He tweets as @LaurenceDyke

Image of the Week – Powering up the ground in the search for ice

Electric Resistivity Tomography profile of the north-facing slope of the Rohrbachstein in canton Bern, Switzerland [Credit: University of Fribourg, Switzerland].

In an earlier post, we talked briefly about below-ground ice and the consequences of its disappearing. However, to estimate the consequences of disappearing ground ice, one has to know that there actually is ice in the area of study. How much ice is there – and where is it? As the name suggests, below-ground ice is not so easy to spot with the naked eye. Using geophysical methods, however, it is possible to obtain a good idea of the presence and whereabouts of ground ice, and of frozen ground, in an area of interest.


Looking for ice

Before starting a geophysical survey, which requires instrumentation and time, you might want to take a look at your area of interest and estimate, whether ice presence is even an option. The first indicator is temperature, which has to be in the favor of permafrost presence. Other indicators for presence are surface features such as mounds that could be caused by considerable frost heave, lobes perpendicular to the slope and front angles exceeding the critical angle of repose. They can indicate that ice has had an influence on the geomorphology in the area.

If you suspect ground ice in your area of interest, and you want to confirm or rule out your suspicion as well as investigate the extent of the ice, you might consider doing a geophysical survey. There are a few useful inherent properties of ice that make it possible to distinguish it from rock, air or water. These properties will determine the choice of geophysical methods to use. This week, we will illustrate two methods which, when combined, can be useful tools for determining ground ice presence or absence. The test subject is an area of suspected frozen ground just below 3000 m altitude – the Rohrbachstein in canton Bern, Switzerland.

Electrical resistivity tomography

In an electrical resistivity tomography (ERT) survey, we measure the potential difference (ΔU) of a material, over a given distance, when applied with a certain current strength (I). From the fact that resistance is computed by dividing U by I, the electrical resistivity of the material can be estimated. The resistivity can be seen as the reciprocal of the material’s electrical conductivity and is measured in mΩ. Practically, an array of electrodes are placed in the ground with a certain spacing and a certain length of the profile. The spacing and length of the profile determine the resolution and penetration depth. All electrodes are then connected with a cable to each other and to the instrument, which works as both a voltmeter and a source of current. Then, systematic measurements of potential difference can be conducted throughout the whole profile.

Water has an electrical resistivity of 10-100 mΩ, whereas ground ice has a resistivity of 103 to 106 mΩ. This makes this method practical for distinguishing liquid from frozen water in permafrost areas. The resistivity of rock is between 102 to 105 mΩ, and the resistivity of sediment depends on the mixture of rock, water, ice and air. Air has an extremely high resistivity, which should be easy to point out, but since below-ground material is mostly a mixture of all the mentioned components, things are very often more blurry. What one actually looks for in the measurements is areas of higher, lower and in-between electrical resistivity values. An example of such a case is displayed in our Image of the Week.

Our Image of the Week shows the resistivity profile of a slope at just below 3000 m altitude in the Bernese Alps, Switzerland. For comparison, the same slope is shown in a normal photo in Fig. 2 (not to scale). Blue colours mark high resistivities, red mark low, and green mark somewhere in between. From this profile, we might conclude that the upper layers of the lower slope are moist and underlain by bedrock (red and green, respectively, whereas the upper slope seems to be moist below an area of high resistivity (red below green-blue). Additionally, there is a significant feature of high resistivity in the middle of the slope. This slope could contain ice in those blue areas. However, the high resistivities could also be caused by air volume in this blocky site. To be certain, we can use an additional method.

Fig. 2: Photo of the north-facing slope of the Rohrbachstein in canton Bern, Switzerland. The photo was taken facing east and shows the upper part of the slope analyzed with ERT and seismic refraction, but is not to scale compared with the Image of the Week and Fig.4 [Credit: Laura Helene Rasmussen].

Seismic refraction analysis

To distinguish air from ice, we can do a survey of the subsurface using seismic refraction analysis. Seismic refraction surveys use the fact that the speed (in ms-1) of sound wave propagation is different through different materials. The speed is estimated by placing geophones in a profile line and creating a sound wave by hitting the ground with a sledgehammer in between them (Fig. 3). The geophones detect the sound wave from this hammer blow one by one as it travels through the subsurface, and the time it takes for each geophone to receive the signal is noted. This allows us to calculate the seismic (sound) velocity from the distance and travel time. Different layers in the subsurface with different properties, and thus different seismic velocities, will cause the sound wave arriving at their surface to be refracted with different delay compared to the direct wave (which travels straight from the hammer to each geophone), and that fact can reveal properties of below-ground material.

Fig. 3: Hammer-swinging doing a seismic refraction profile [Credit: Hanne Hendricks].

The advantage of this method for ground ice studies is that ice has a seismic velocity of about 3000 ms-1, whereas sound waves move through air with only 330 ms-1. Thus, a rough profile of that same slope from our Image of the Week and Fig. 2 using seismic refraction geophysics looks like Fig. 4.

In this profile, red colours denote high seismic velocities and blue colours are very low seismic velocities. The high-resistivity feature in the middle of the ERT profile at about 3-4 m depth, which could contain air or ice, would cause red-purple colours (high velocities) if the feature contained ice, and blue colours (low velocities), if it was air volume. As seen from Fig. 4, colours at depths are reddish and certainly not blue, which makes it likely that the ERT feature at 3-4 m depth is actually an ice body. The high-resistivity area in the surface layers of the upper profile, however, corresponds to the blue colours in this seismic refraction profile, and with high resistivity, but low seismic velocity, this area is most likely air volume and not ground ice.

Fig. 4: Seismic refraction profile of the north-facing slope of the Rohrbachstein in canton Bern, Switzerland [Credit: University of Fribourg, Switzerland].

The method depends on the setting

Ground ice does, obviously, come in different forms in different environments, and so the methodological considerations when using geophysical techniques vary in different settings. In this case, we look for ice in a blocky slope. That type of setting presents challenges such as contact problems between sensors and the ground, which can impede the measurements. That issue would not worry a scientist mapping ground ice in a moist Arctic lowland site. The lowland scientist might, however, have to consider resolution issues or salt content in her soil solution when evaluating the results. Perhaps she wants to combine with yet other methods such as drilling permafrost cores for detailed information on ice- and sediment type. As non-destructive methods, covering relatively large spatial areas without having to get a drill rig to the high mountains or a remote Arctic area, however, geophysics can be a good option for ground ice detection.

Further reading

Edited by Clara Burgard and Emma Smith


Laura Helene Rasmussen is a Danish permafrost scientist working at the Center for Permafrost, University of Copenhagen. She has spent many seasons in Greenland, working with the Greenland Ecosystem Monitoring Programme and is interested in Arctic soils as an ecosystem component, their climate sensitivity, functioning and simply understanding what goes on below.

Image of the Week – Bioalbedo: algae darken the Greenland Ice Sheet

Image of the Week – Bioalbedo: algae darken the Greenland Ice Sheet

Most of the energy that drives glacier melting comes directly from sunlight, with the amount of melting critically dependent on the amount of solar energy absorbed compared to that reflected back into the atmosphere. The amount of solar energy that is reflected by a surface without being absorbed is called the albedo. A low albedo surface absorbs more of the energy that hits it compared to a high albedo surface. Our Image of the Week shows patches of dark grey-brown algal blooms on the Greenland Ice Sheet, giving the surface a surprisingly low albedo.


The colour of ice

Clean ice and snow are among the most reflective natural materials on Earth’s surface making them important ‘coolers’ in Earth’s climate system. The term ‘albedo’ describes how effectively a material absorbs or reflects incoming solar energy – it is the ratio of downwelling light arriving at a surface to the amount of upwelling light leaving it. The albedo of fresh, clean snow can be as high as 90%, meaning that out of all the solar energy reaching the surface only 10% is absorbed. However, the albedo of ice and snow can vary widely. This is important because the albedo determines how much of the incoming solar energy is retained within the snow or ice and used to raise the temperature or drive melting. It therefore controls snow and ice energy balance to a large extent.

There are several reasons why the albedo of snow and ice can vary. First, once ice crystals begin to melt they lose their delicate structures that efficiently scatter light and develop rounded granular shapes. Meltwater generated by snow or ice melt fills the gaps between the grains, promoting forward scattering of light deeper into the ice, rather than scattering back towards the surface. This increases the distance travelled through media where absorption can occur, and therefore lowers the albedo as the light is less likely to escape the material after it enters. The more melt, the greater this effect. Second, other materials such as dust or rock debris can enter the snow or ice. These ‘impurities’ generally absorb light more effectively than the ice crystals themselves and therefore reduce the albedo. However, this depends upon their concentration, optical properties and proximity to the surface. Additionally, whether the impurities are inside or outside the ice crystals, where on the planet the material is and the time of day are also important.

Any impurity that darkens a mass of ice or snow increases the amount of solar energy absorbed compared to when the material is impurity-free. This means that impurities promote melting, which is in itself an albedo reducing process. Therefore, the impact of impurities on albedo is non-linear and greater than the direct effect of their absorption alone. There are many different impurities that commonly lower the albedo of ice and snow, including mineral dusts and black carbon (e.g. from fossil fuel combustion). However, there is also a growing literature on another form of impurity that darkens ice and snow on glaciers and ice sheets on both hemispheres: biological growth (also see this previous post). Algae are the primary biological albedo-reducers on ice and snow. Photosynthetic microalgae bloom on the surface where light is abundant, which provides them with energy that they use to turn carbon dioxide and water into sugars. This in turn provides food for other microorganisms. In doing so, they darken the ice surface simply because the algal cells are more effective absorbers than the ice crystals. However, as the algae become exposed to increasing light intensities, they produce pigments that act as sun shields, protecting their cellular machinery from the damaging effects of too much light. This effect enhances the biological darkening and increases the energy absorbed within the snow or ice.

Biological darkening

There are several distinct microbial habitats on glaciers and ice sheets. Snow algae are a feature of melting snowpacks that colour snow surfaces green early in the year and red later because prolonged exposure to sunlight causes them to produce red ‘sunscreen’ pigments (see this previous post). Their influence on snow albedo has yet to be determined, although they have been shown to change the amount of visible light reflected from the surface (Lutz et al., 2014) and in Antarctica they have been shown to influence light absorption at depth within the snowpack (Hodson et al., 2017). Some bacteria have been identified feeding upon the algae, and the algal blooms also provide food for red coloured ice worms. This is probably why, in ‘The History of Animals’, Aristotle wrongly attributed the red discoloration of patches of snow to red worms rather than pigmented algae!

Fig. 2: (a) Albedo for clean snow, bare ice and ice with an algal bloom measured on the Greenland Ice Sheet in July 2017. (b) Microscope image of melted surface ice from the Greenland ice sheet. The red oval shaped particles are ice algae and the angular, clear particles are mineral dust fragments. [Credit: A: J. Cook, B: C. Williamson]

On ice, a different species of algae exists in a thin liquid water film on the upper surface of melting ice crystals. These algae are also photosynthetic but are not bright green or red, but rather grey, brown or purple. They produce a purple pigment that acts as a UV shield that protects their delicate intracellular machinery from excessive light energy. The side effect of this is that the algae become very dark and have an albedo-lowering effect on the ice surface (see our Image of the Week). Ice with algae has a lower albedo than clean ice (Fig 2a) but, up to now, the magnitude of the biological darkening effect has not been quantified because of difficulties isolating algal darkening from that of mineral dusts, soot and the changing optical properties of the ice itself. This also limits our capability to map these algae using remote sensing. Samples of dark coloured ice examined under the microscope clearly show the presence of an algal community darkening the ice (Fig 2b).

In addition to surface-dwelling ice algae, microbial life exists in small pits known as cryoconite holes (see also this previous post). At the bottom of these holes exists a thin layer of granules comprising living microbial cells, dead cells, biogenic molecules, mineral fragments and soot. The organic matter in these granules is very dark, so they warm up when illuminated by the sun and melt into the ice. The relationship between cryoconite and ice surface albedo is complex because, although the cryoconite is dark, the hole geometry hides the granules beneath the ice surface.

Implications for the future of glaciers and ice sheets

The challenge facing scientists now is to quantify the bioalbedo effect by determining the optical properties of individual algal cells and remotely assessing their spatial coverage at the scale of entire glaciers and ice sheets. This will require new methods to be developed for detecting living cells from the air or space. Then, we must understand the factors controlling their growth, so we can predict biological darkening of ice in future climate scenarios. It is possible that algal coverage will increase as glaciers and ice sheets waste away because algae bloom where there is liquid melt water. Because of the darkening effect, an increasingly widespread algal ecosystem in a warming climate will accelerate the demise of its own habitat by enhancing glacier and ice sheet retreat.

Further reading

Edited by Scott Watson and Clara Burgard


Joseph Cook is a Postdoctoral Research Associate on NERC’s Black and Bloom project based at the University of Sheffield, UK where his remit is the measurement and modelling of surface albedo on the Greenland Ice Sheet. His background is in biotic-abiotic interactions on ice. He tweets as @tothepoles and blogs at http://tothepoles.wordpress.com. Contact Email: joe.cook@sheffield.ac.uk