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Image of the Week – Climbing Everest and highlighting science in the mountains

Image of the Week – Climbing Everest and highlighting science in the mountains

Dr Melanie Windridge, a physicist and mountaineer, successfully summited Mount Everest earlier this year and has been working on an outreach programme to encourage young people’s interest in science and technology. Read about her summit climb, extreme temperatures, and the science supporting high-altitude mountaineering in our Image of the Week.


It’s bigger than it looks! Experiencing the majesty of Everest

In April/May this year I climbed Mount Everest. To the top. It was two months of patient toil but in surroundings so majestic, impressive and inspiring. The Western Cwm (an amphitheatre-like valley shaped by glacial erosion) is vast, the summit ridge is steep and Khumbu Glacier was fascinating in itself. Our base camp was on the glacier and it changed daily in subtle ways – the ice melted, the rocks moved, the paths morphed. And the icefall was slightly different each time I passed through – the route changing through a collapsed area, a crevasse widening, or the rope buried by ice-block debris fallen from above. It’s a wonderful, interesting place and I am grateful to have experienced it. You can read more about the climb on my personal blog.

Fig.2: The view up the Western Cwm from Camp 1. Lhotse can be seen in the distance and the summit of Everest mid-left. [Credit: Melanie Windridge].

Everest, of course, is extreme. It is steep almost everywhere, so you barely get a let-up anywhere beyond the Western Cwm. The temperature differences are extreme too – it is extremely hot or extremely cold. I took a couple of temperature loggers with me to the summit (one in a base-layer pocket under my down suit and one in an outer pocket of my rucksack). You can see from the graph of summit night (the climb from Camp 4 to the summit of Everest) (Fig. 3) how the temperature varied by tens of degrees.  Since climbers dress for the coldest temperatures, this can be quite uncomfortable when the sun comes out.  The temperature on summit night got down to about -25°C, but during the day it rose to 10 degrees or more so that we were sweating into our down suits.

 

Fig.3: Graph showing the readings from two separate temperature loggers on summit night – one in a base-layer pocket under the down suit (Down suit temperature) and one in an outer pocket of the rucksack (Air temperature). The temperature rises quickly after sunrise, which was experienced on the summit [Credit: Melanie Windridge and Scott Watson].

Sharing the Science of the Summit

It was science that really got me interested in Everest, when I realised that the main reason the British had succeeded in 1953 but hadn’t in the 1920s and 30s was because of scientific understanding and the state of technology. But so often we don’t talk about the science that supports us in these great endeavours; instead we put it all down to the strength of the human spirit. I think we need to talk about both.

As part of my climb, I have been working on an outreach project to highlight how science and technology have improved safety and performance on Everest. I have made Science of Everest videos for the Institute of Physics YouTube channel and will be giving public talks. I wanted to show how science supports us and what has improved in recent decades to contribute to the falling death rate on Everest.

In the video series I look at changes in weather forecasting, communications, oxygen, medicine and clothing. We also consider risk and preparation – videos that went out before I left for Everest – because, as a scientist, I looked into past data to see how I could give myself the best chance of reaching the summit and returning safely.

 

 

Communication has improved not only because we have a greater variety than was available to the first ascentionists or the early commercial climbers (we have satellite phones, mobile/cell-phones and WiFi now), but also because everything is a lot smaller. Electronic components have greatly reduced in size so that radios used on the mountain now are small and handheld in comparison to the bulky sets of the 1950s (see video above).

 

 

Of course, the implication of the project is wider than just Everest. I am interested in the importance of science and exploration in general. For me, Everest is an icon of exploration – the way that human curiosity, ingenuity, determination and endurance come together to drive us forward. Reaching into the unknown is good for us, on a societal level and on a personal level. I hope to give an appreciation of the value of science in our lives, give students an insight into interesting careers that use science, and show the value of doing things that scare us!

 

Further reading

Edited by Scott Watson and Clara Burgard


Dr Melanie Windridge is a physicist, speaker, writer… with a taste for adventure. She is Communications Consultant for fusion start-up Tokamak Energy, author of “Aurora: In Search of the Northern Lights” and is currently working on a book about Mount Everest.
Website: www.melaniewindridge.co.uk (see the Science & Exploration blog to read about the Everest climb)
Twitter @m_windridge, Facebook /DrMelanieWindridge, Instagram @m_windridge
Science of Everest videos on the Institute of Physics YouTube channel http://bit.ly/EverestVids

Image of the Week – The shape of (frozen sea) water

 

Figure 1: Annual evolution of the sea ice area with two different floe shape parameters of 0.44 (red) and 0.88 (blue). The model is spun-up between 2000 – 2006 and then evaluated for a further ten years between 2007 – 2016 and the mean values over this period displayed by the thick lines. Thin lines show the results for individual years. [Credit: Adam Bateson]

Polar sea ice exists as isolated units of ice that we describe as floes. These floes do not have a constant shape (see here for instance); they can vary from almost circular to being jagged and rectangular. However, sea ice models currently assume that all floes have the same shape. Much focus has been paid to the size of floes recently, but do we also need to reconsider how floe shape is treated in models?


Why might floe shape matter?

In recent years, sea ice models have started to examine more and more how individual floes influence the overall evolution of sea ice.

A particular focus has been the size of floes (see here and here) and the parameterisation of processes which influence floe size (see here for example). However less attention has been given to the shape of the floe. The shape of the floe is important for several reasons:

  • Lateral melt rate: the lateral melt rate describes how quickly a floe melts from its sides. Two floes with the same area but different shape can have a different perimeter; the lateral melt rate  is proportional to the floe perimeter.
  • Wave propagation: a straight floe edge will impact propagating waves differently to a curved or jagged floe edge. The distance waves travel under the sea ice and hence the extent of sea ice that waves can fragment will be dependent on these wave-floe edge interactions.
  • Floe mechanics: an elongated floe (i.e. much longer in one direction than another) will be more likely to break from incoming waves if its longer edge is aligned with the direction the waves are travelling.

How do models currently treat floe shape?

One approach used within sea ice models to define floe shape is the use is the use of a parameter, α. The smaller the floe shape parameter, the longer the floe perimeter (and hence, the higher the lateral melt rate). A standard value used for the parameter is 0.66 (Steele, 1992). Figure 2 shows how this floe shape parameter varies for some common shapes.

Figure 2: The floe shape parameters for some common shapes are given for comparison to the standard value of 0.66. [Credit: Adam Bateson]

The standard value of the floe shape parameter, 0.66, was obtained from taking the mean floe shape parameter measured over all floes greater than 1 km from a singular study area of 110 km x 95 km at one snapshot in time. Despite the limited data set used to estimate this shape parameter, it is being used for all sea ice throughout the year for all floe sizes. However, this would only be a concern to the accuracy of modelling if it turns out that sea ice evolution in models is sensitive to the floe shape parameter.

 

Model sensitivity to floe shape

To investigate the model sensitivity to the floe shape parameter two simulations have been run: one uses a floe shape parameter of 0.88 and the other uses 0.44, chosen to represent likely extremes. The two simulations are run from 2000 – 2016, with 2000 – 2006 used as a spin-up period. Figure 1 displays the mean total ice area throughout the year and results of individual years for each simulation. Figure 3 is an equivalent plot to show the annual evolution of total ice volume for each simulation.

The results show that the perturbation from reducing the floe shape parameter is smaller than the variation between years within the same simulation.  However, the model does show a permanent reduction in volume throughout the year and a 10 – 20 % reduction in the September sea ice minimum. The impact of the floe shape is hence small but significant, particularly for predicting the annual minimum sea ice extent and volume.

Figure 3: Annual evolution of the sea ice volume with two different floe shape parameters of 0.44 (red) and 0.88 (blue). The model is spun-up between 2000 – 2006 and then evaluated for a further ten years between 2007 – 2016 and the mean values over this period displayed by the thick lines. Thin lines show the results for individual years.

More recent studies on floe shape

In 2015, Gherardi and Lagomarsino analysed the floe shape behaviour from four separate samples of satellite imagery from both the Arctic and Antarctic. The study found different distributions of floe shapes in different locations, however there was no correlation between floe shape and size. This property would allow models to treat floe shape and size as independent properties. More recently, in 2018, Herman et al. analysed the results of laboratory experiments of ice breaking by waves. It was found that wave break-up influenced the shape of the floes, tending to produce straight edges and sharp angles.  These features are associated with a smaller floe parameter i.e. would produce an increased lateral melt rate.

What next?

More observations are needed to identify whether the use of a constant floe shape parameter is justified. The following questions are important:

  • Do further observations support the finding that floe size and shape are uncorrelated?
  • What range of values for the floe shape parameter can be observed in reality?
  • Do we see significant variations in the floe shape parameter between locations?
  • Do these variations occur over a large enough scale that they can be represented within existing model resolutions?

Further reading

Edited by Violaine Coulon and 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 – Stuck in the ice: could it have been predicted?

Image of the Week –  Stuck in the ice: could it have been predicted?

Expeditions in the Southern Ocean are invaluable opportunities to learn more about this fascinating but remote region of the world. However, sending vessels to navigate the hostile Antarctic waters is an expensive endeavor, not only financially but also from a human perspective. When vessels are forced to turn back due to hazardous conditions or, even worse, become stuck in the ice (as shown in our Image of the Week), a mission full of expectations can quickly turn into a nightmare. Hence there is an increasing demand for reliable information on the navigability of the Southern Ocean a few weeks to a few months in advance. This information could support the final decision whether to start the journey or not, and would allow minimizing the associated risks.


What’s the problem?

In late February 2018, the British vessel RRS James Clark Ross was heading to the Eastern Antarctic Peninsula to investigate the consequences of the calving of a massive iceberg from the Larsen C ice shelf. Unfortunately the vessel had to turn back before reaching its goal due to the unexpected presence of thick sea ice in the region. This story is not unusual. During Christmas 2013, a Russian ship named the Akademik Shokalskiy also got stuck in several meters of Antarctic sea ice. Ironically, one of the rescuing vessels itself (the Chinese Xuě Lóng) got trapped in the ice as well. To prevent such events from happening again, we need to be able to predict the upcoming sea-ice conditions. Can sea-ice conditions be forecast at seasonal time scales? If so, how?

 

Antarctic sea ice, the Year of Polar Prediction and SIPN South

To prevent accidents and unforeseen problems, one goal of the Year Of Polar Prediction is to enhance environmental forecasting capabilities from operational (hours to days) to tactical (weeks to months) time scales in high latitude regions. Several studies support the notion that Antarctic sea ice may be predictable a few months ahead, at least in certain regions (Holland et al. 2017, Chen and Yuan 2004, Holland et al. 2013, Marchi et al. 2018).

To investigate further the predictability of Antarctic sea ice, the Sea Ice Prediction Network South (SIPN South) was launched in 2017. It is a two-year international project endorsed by the YOPP. SIPN South pursues three strategic objectives:

  • Hosting seasonal outlooks of Antarctic sea ice to better understand the sources of sea-ice predictability and the origins of systematic forecast errors in different types of models.
  • Providing news and information on the current state of Antarctic sea ice, disseminating research to a wider audience and reporting ongoing field campaigns.
  • Coordinating realistic seasonal prediction exercises to investigate the potential use of this information for users and customers, primarily ships navigating in the region.

 

February 2018 seasonal sea-ice forecasts

As a first major milestone, SIPN South provided coordinated forecasts of sea ice for February 2018. February is the month with the smallest sea-ice area in the Antarctic, and therefore most of the shipping traffic in the region happens around that time. Participants were asked to provide an estimation of sea-ice coverage (area, concentration) for each day of February 2018, and were asked to issue their predictions by mid-December 2017. 13 research groups participated in this first forecasting experiment, following different approaches: several groups used fully coupled climate dynamical models, while others applied statistical regression methods to predict future ice conditions.

As we all know, the weather is unpredictable beyond a few days. However, previous research has suggested that the statistics of weather (its mean, its variability) can potentially be predicted from months to decades, due to the coupling of the atmosphere with “slower” components of the climate system like the ocean. To reflect this and to accurately estimate the statistics of weather, groups tend to provide not just one forecast, but several of them. These “ensembles” of forecasts provided by each group therefore represent all possible states of the atmosphere, ocean and ice that may prevail in February 2018 – given the known initial conditions of December.

The results of the coordinated experiment are shown in Figure 2. The February mean sea-ice area is shown for each group (colors), along with two actual observational references (black). Bear in mind that the forecast data were issued two months before the actual target date! Here, the forecasts are expressed as anomalies with respect to a reference climatology. All forecasts tend to overestimate the February sea ice area in the Ross Sea. A reason for this wrong estimation might be a very unusual cyclone, which passed over the Ross Sea around the 20th of January 2018 (i.e., between the time the forecasts were issued and the period for verification). This cyclone brought relatively warm air into the region. Furthermore it fractured the ice, opening more areas of open water and possibly increasing the effect of the ice-albedo feedback. Events like this one are not individually predictable several weeks in advance, but a well-designed forecasting system should at least account for this possibility. Despite running ensembles of forecasts, the sea-ice reduction in the Ross Sea was not captured by most forecasts. This may point towards a common and systematic deficiency in these prediction systems.

Figure 2: February 2018 mean regional sea-ice area anomaly (compared to 1979-2014 observed climatology) by longitude, for the 13 submissions, with observed estimates given in black. Solid lines show the ensemble mean for each contribution, with transparent shading indicating the ensemble range (min-max) [Credit: F. Massonnet].

Communicating climate information

Sea-ice area, as shown in Fig. 2, is a primary parameter used by scientists to quantify ice presence in a given region. It is also a useful number to diagnose model-data mismatch. However, sea-ice area is of little use for those who actually need climate information. For someone operating a vessel, the important information is how likely that vessel is to encounter sea ice in a given region for a given day in February. Information from Fig. 2, while certainly useful to scientists, is meaningless to those willing to extract practical information for navigation.

Alongside the work to understand fundamental drivers of sea-ice predictability in order to eventually improve the predictions, it is necessary to consider how potential users will interact with the forecasts. As explained above, climate forecasts are probabilistic in nature. Communicating probabilistic information to a non-trained audience is always a challenging task: for example, how would you interpret a forecast saying that there is a 50% chance of rain for tomorrow?

To reflect the irreducible uncertainty of climate forecasts (see previous section), sea-ice forecasts are generally expressed in terms of sea-ice probability, i.e. the probability that a given region of the Southern Ocean has sea-ice concentration larger than 15%. This probability is derived for each day and each grid cell from the ensemble forecasts contributed by each group (Fig. 3). If well calibrated, this type of information can be useful to those planning operations weeks in advance. For example, all but one model had forecast a high (>80%) probability of ice presence in the Larsen C area (eastern tip of the Antarctic Peninsula) where the RRS James Clark Ross got stuck five months ago. That is, there was a high risk, according to those forecasts, that ice would be present in that area in February. Of course, this does not mean that navigation would have been impossible (ice breakers can still operate in icy waters, provided the ice is thin), but these forecasts provided a first-order warning that there was a significant risk of encountering hazardous ice conditions there.

Figure 3: Probability of sea-ice presence for 15th February 2018, as forecasted by the five groups that submitted daily sea-ice concentration information. The sea-ice edge as observed by two products is shown in white. The probability of presence for a given day corresponds to the fraction of ensemble members that simulate sea-ice concentration larger than 15% in a given grid cell for that day. A dynamic animation of the figure showing all 28 days of February is available on the SIPN South website. [Credit: F. Massonnet]

Forecasting February 2019

The core phase of the Year of Polar Prediction entails “Special Observing Periods”, that is, intensive efforts to monitor the Arctic and Antarctic regions but also to enhance modeling activities (see this previous post). The (unique) Special Observing Period in the Southern Ocean will take place between mid-November 2018 and mid-February 2019. A new call for contributions will be launched by SIPN South to collect sea-ice forecasts for austral summer 2019, hoping that the first exercise in 2018 will raise the interest of even more research groups. A key question will be to assess whether the systems will be able to forecast better the sea-ice conditions in the challenging Ross Sea area, where most forecasts failed. Better insights will hopefully be gained in tracing the origin of systematic model error and lead to an improvement of Antarctic sea ice predictions within the next decade. As reliable climate information is crucially needed in this remote but important region of the world, future efforts to predict Antarctic sea ice will be very welcome!

 

Further reading

Edited by Adam Bateson and Clara Burgard

 


François Massonnet is a F.R.S.-FNRS Post-Doctoral Researcher at the Université catholique de Louvain and scientific collaborator at the Barcelona Supercomputing Center (Spain). He is assessing climate models as tools to understand (retrospectively and prospectively) polar climate variability and beyond. He tweets as @FMassonnet. Contact Email: francois.massonnet@uclouvain.be

 

 

Image of the Week – The future of Antarctic ice shelves

Percent change in ice shelf melting, caused by the ocean, during the four future projections. The values are shown for all of Antarctica (written on the centre of the continent) as well as split up into eight sectors (colour-coded, written inside the circles). Figure 3 of Naughten et al., 2018 ). ©American Meteorological Society. Used with permission.

Climate change will increase ice shelf melting around Antarctica. That’s the not-very-surprising conclusion of a recent modelling study, resulting from a collaboration between Australian and German researchers. Here’s the less intuitive result: much of the projected melting is actually linked to a decrease in sea ice formation. Learn why in our Image of the Week…


Different types of Antarctic ice

Sea ice is just frozen seawater. But ice shelves (as well as ice sheets and icebergs) are originally formed of snow. Snow falls on the Antarctic continent, and over many years compacts into a system of interconnected glaciers that we call an ice sheet. These glaciers flow downhill towards the coast. If they hit the coast and keep going, floating on the ocean surface, the floating bits are called ice shelves. Sometimes the edges of ice shelves will break off and form icebergs, but they don’t really come into this story (have a look at this and this previous post if you want to read about icebergs nevertheless!).

Climate models don’t typically include ice sheets, or ice shelves, or icebergs. This is due to a combination of insufficient resolution and software engineering challenges, and is one reason why future projections of sea level rise are so uncertain. However, some standalone ocean models, i.e. ocean models without a coupled atmosphere, do include ice shelves. At least, they include the little pockets of ocean beneath the ice shelves – we call them ice shelf cavities – and can simulate the melting and refreezing that happens on the undersides of ice shelves.

Modelling future ice shelf melting

We took one of these ocean/ice-shelf models and forced it with the atmospheric output of regular climate models, which periodically make projections of climate change from now until the end of this century. As forcing, we used the atmospheric output of the Australian model ACCESS 1.0 in two experiments, and the mean of the atmospheric output from 19 other climate models taking part in the Coupled Model Intercomparison Project Phase 5  (Multi-Model Mean or “MMM”) in another two experiments. Each set of experiments considered two different scenarios for future greenhouse gas emissions (“Representative Concentration Pathways” or RCPs), for a total of four simulations. Each simulation required 896 processors on the supercomputer in Canberra. By comparison, your laptop or desktop computer probably has about 4 processors. These are pretty sizable models!

In every simulation, and in every region of Antarctica, ice shelf melting increases over the 21st century. The total increase ranges from 41% to 129% depending on the emissions scenario and choice of climate model. The largest increases occur in the Amundsen Sea region, marked with red circles in our Image of the Week, which also happens to be the region exhibiting the most severe melting in recent observations. In the most extreme scenario, i.e. with the highest future greenhouse gas emissions and the more sensitive climate model, ice shelf melting in this region nearly quadruples.

Understanding the drivers of melting

So what processes are causing this melting? This is where the sea ice comes in. When sea ice forms, it spits out most of the salt from the seawater (brine rejection), leaving the remaining water saltier than before. Salty water is denser than fresh water, so it sinks. This drives a lot of vertical mixing, and the heat from warmer, deeper water is lost to the atmosphere. The ocean surrounding Antarctica is unusual in that the deep water is generally warmer than the surface water. We call this warm, deep water Circumpolar Deep Water, and it’s currently the biggest threat to the Antarctic Ice Sheet. (I say “warm” – it’s only about 1°C, so you wouldn’t want to go swimming in it, but it’s plenty warm enough to melt ice.)

In our simulations, warming winters cause a decrease in sea ice formation. This leads to less brine rejection, causing fresher surface waters, causing less vertical mixing, and the warmth of Circumpolar Deep Water is no longer lost to the atmosphere. As a result of reduced vertical mixing, ocean temperatures near the bottom of the Amundsen Sea increase and this better-preserved Circumpolar Deep Water
finds its way into ice shelf cavities, causing large increases in melting.

 

Slices through the Amundsen Sea – you’re looking at the ocean sideways, like a slice of birthday cake, so you can see the vertical structure. Temperature is shown on the top row (blue is cold, red is warm); salinity is shown on the bottom row (blue is fresh, red is salty). Conditions at the beginning of the simulation are shown in the left 2 panels, and conditions at the end of the simulation are shown in the right 2 panels. At the beginning of the simulation, notice how the warm, salty Circumpolar Deep Water rises onto the continental shelf from the north (right side of each panel), but it gets cooler and fresher as it travels south (towards the left) due to vertical mixing. At the end of the simulation, the surface water has freshened and the vertical mixing has weakened, so the warmth of the Circumpolar Deep Water is preserved. Figure 8 of Naughten et al., 2018, ©American Meteorological Society. Used with permission.

 

Going to the next level

This link between weakened sea ice formation and increased ice shelf melting has troubling implications for sea level rise. The next step is to simulate the sea level rise itself, which requires some model development. Ocean models like the one we used for this study have to assume that ice shelf geometry stays constant, so no matter how much ice shelf melting the model simulates, the ice shelves aren’t allowed to thin or collapse. Basically, this design assumes that any ocean-driven melting is exactly compensated by the flow of the upstream glacier such that ice shelf geometry remains constant.

Of course this is not a good assumption, because we’re observing ice shelves thinning all over the place, and a few have even collapsed. But removing this assumption would necessitate coupling with an ice sheet model, which presents major engineering challenges. We’re working on it – at least ten different research groups around the world – and over the next few years, fully coupled ice-sheet/ocean models should be ready to use for the most reliable sea level rise projections yet.

Further reading

Edited by Clara Burgard


Kaitlin Naughten is a postdoc at the British Antarctic Survey in Cambridge, UK. She is an ocean modeller focusing on interactions between Antarctic ice shelves, sea ice, and the Southern Ocean. Tweets as @kaitlinnaughten Website: climatesight.org

Image of the Week – Making waves: assessing supraglacial water storage for debris-covered glaciers

Fig. 1: Deriving the bathymetry and temperature of a large supraglacial pond on Khumbu Glacier, Everest region of Nepal. The sonar-equipped unmanned surface vessel nicknamed ‘BathyBot’ (left), and kayak retrieval of temperature loggers (right) [Credit: Scott Watson].

A creeping flux of ice descends Everest, creating the dynamic environment of Khumbu Glacier. Ice and snow tumble, debris slumps, ice cliffs melt, englacial cavities collapse, ponds form and drain, all responding to a variable energy balance. Indeed, Khumbu Glacier is a debris-covered glacier, meaning it features a layer of sediment, rocks and house-sized boulders that covers the ice beneath. Recent advances in understanding debris-covered glacier hydrology come from combining in situ surveys with remotely sensed satellite data.


Khumbu Glacier

The dramatic beauty of Nepal’s Everest region attracts a mix of trekkers, climbers, and scientists. Flowing down from the slopes of Mount Everest, the debris-covered Khumbu Glacier has drawn scientists from the mid-1900s, and offers temporary residence for research teams and a myriad of climbers. In some locations, Khumbu Glacier has thinned by up to 80 m in the last three decades, leading to moraines overlooking the glacier with impressive topographic relief and providing an instant visualisation of glacier mass loss for trekkers heading to Everest Base Camp.

Melt at the surface of this glacier is moderated by an undulating debris layer, which insulates the ice beneath,   and enhanced locally by dynamic surface features such as supraglacial ponds and ice cliffs thinly veiled by debris. These features contribute disproportionately to melt and lead to the development of hummocky, pitted surface topography. The resulting variable surface topography and melt rates complicate meltwater runoff and flow routing across the glacier. To better understand them, in situ surveying (Fig. 1) is increasingly combined with fine spatial-temporal resolution satellite imagery to reveal the hydrological evolution of debris-covered glaciers, which is closely linked to their mass loss.

Hydrology of Khumbu Glacier

As with debris-free glaciers, water may be routed through supraglacial, englacial, and subglacial pathways, which are conceptually distinct but physically link to one another.

At Khumbu Glacier, surface channels collect and rapidly convey meltwater generated in the upper ablation area (Fig. 2), just below the treacherous Khumbu Icefall, incising at a faster rate than the surface melt. In the middle of the debris-covered area, such streams disappear into the glacier’s interior through cut-and-closure and/or hydrofracture.

Fig.2: The upper ablation area of Khumbu is drained by supraglacial channels which enter the glacier’s interior through hydrofracture and cut-and-closure, while the lower portion is characterised by pitted surface depressions and an increasing density of ponds. Right panel looking east to west shows the hummocky topography and ponding on Khumbu Glacier. [Credit: Evan Miles (left), Ann Rowan (right)].

In areas of low surface gradient , and particularly throughout the hummocky lower reaches of the glacier, supraglacial ponds collect water in surface depressions. These features haveregulate the runoff of debris-covered glaciers by seasonally storing meltwater. The annual melt cycle thus leads to pond expansion and contraction, or their disappearance when the protecting debris layer thaws and relict meltwater conduits become avenues for drainage (Fig 3). The areal fluctuation of ponds can be quantified using  satellite images at different times, but cloud cover during the summer monsoon season limits useable imagery at a time when the ponds are most dynamic. Therefore, field-instrumented ponds provide valuable insights into their active melt season behaviour.

Fig. 3: A small 4.5 m deep pond that drained over the course of a year [Credit: Watson et al., 2017a].

Turbid ponds associated with debris influx from ice cliffs are often ephemeral but some can grow to hold vast quantities of water (Fig. 1). Stored water absorbs and transmits solar energy to melt adjacent ice, which generates additional meltwater and leads to pond expansion. The ponds also thermally undercut ice cliffs, leading to both subaqueous and subaerial  retreat (Fig. 4). Khumbu Glacier has been developing a growing network of ponds in recent years, which means meltwater is increasingly stored on the surface of the glacier before contributing to downstream river discharge. Ponds that coalesce into larger and more persistent lakes behind unstable deposits of sediment can in some cases pose a hazard  to downstream communities. Field and satellite-based techniques are therefore used simultaneously to monitor lake development.

 

Fig. 4. Supraglacial ponds often exist alongside ice cliffs. These ‘hot spots’ of melt can be observed with repeat point cloud differencing [Credit: Watson et al., 2017b]. An interactive view of the drained pond basin (right) is available here.

What lies beneath?

Ephemeral ponds drain into the ‘black box’ glacier interior, where relatively little is known about the internal structure and hydrology. Scientists have occasionally ventured into the subsurfac e realm through networks of englacial conduits that become exposed as the glacier thins (Fig. 5); such conduits often re-emerge at the glacier surface but may also lead to the bed. The conduits carry meltwater through the glacier but can become dormant if blocked by falling debris or creeping ice, or when the meltwater that sustains them finds a route of lesser resistance. Whilst satellite data can be used to infer the presence of conduits, field-based methods are required for hydrological budgeting and quantifying meltwater transit times. For example, dye tracing can detect the subsurface passage of meltwater where strategically placed fluorometers measure the receipt and dilution of the dye upon re-emergence. Such methods are crucial for developing an improved understanding of the links between, for example, flow in the supraglacial channels up-glacier and discharge at the outlet.

Fig. 5: An exposed conduit on Lirung Glacier (left) [Credit: Miles et al., 2017] and researchers inside a conduit on Ngozumpa Glacier (right) [Credit: Benn et al., 2017].

 

Outlook

Multiple teams working across the Himalaya are advancing our understanding of debris-covered glacier hydrology, which is essential to forecast their future and quantify their downstream impact. With the ready availability of increasingly high temporal resolution satellite imagery (e.g. Sentinel-2, Planet Labs), the link between field and spacebourne observations will become increasingly complementary. Developing these links is crucial to upscale observations from specific sites more broadly across the Himalaya.

Further reading

Edited by Violaine Coulon and Sophie Berger


Scott Watson is a Postdoc at the University of Arizona, USA. He studies glaciers in the Everest region and the surface interactions of supraglacial ponds and ice cliffs. He also investigates natural hazards and the implications of glacial lake outburst floods.
Tweets @CScottWatson. Website: www.rockyglaciers.co.uk

 

 

Evan Miles is a Research Fellow at the University of Leeds, UK, where he is a part of the EverDrill project’s hot-water drilling at Khumbu Glacier. His recent work has examined the seasonal hydrology and dynamics of debris-covered glaciers, with a focus on the melt associated with dynamic surface features such as supraglacial ice cliffs and ponds.
Tweets @Miles_of_Ice

EverDrill website: www.EverDrill.org

Image of the Week – Climate feedbacks demystified in polar regions

Figure 1: Major climate feedbacks operating in polar regions. Plus / minus signs mean that the feedbacks are positive / negative. Yellow and red arrows show solar shortwave and infrared radiation fluxes, respectively. Orange arrows show the flux exchanges between the different components of the climate system (ocean, atmosphere, ice) for several feedbacks. TOA refers to ‘top of the atmosphere’ [Credit: Fig 1 from Goosse et al. (2018)].

Over the recent decades, the Arctic has warmed twice as fast as the whole globe. This stronger warming, called “Arctic Amplification“, especially occurs in the Arctic because ice, ocean and atmosphere interact strongly, sometimes amplifying the warming, sometimes reducing it. These interactions are called “feedbacks” and are illustrated in our Image of the Week. Let’s see why these feedbacks are important, how we can measure them and what their implications are.


Climate feedbacks in polar regions

When it comes to climate science, feedback loops are very common. A climate feedback is a process that will either reinforce or diminish the effect of an initial perturbation in the climate system.

If the initial perturbation, for instance the warming of a region, is amplified by this process, we talk about a “positive feedback”. A positive feedback can be seen as a “vicious circle” as it will lead to an ever-ongoing amplification of the perturbation. The most prominent positive feedback in the Arctic is the “ice-albedo feedback“: as the surface warms, ice melts away, exposing darker surfaces to sunlight, which absorb more heat, leading to even more melting of the ice around.

On the contrary, if the initial perturbation is dampened by the process, we talk about a “negative feedback”. An example for a negative feedback is the “ice production-entrainment feedback”. In winter, when sea ice forms, it rejects salt into the ocean. As a result, the top ocean layer becomes denser and starts to sink. As the surface water sinks, it leaves room for warmer water below to rise to the surface. This warmer ocean surface then inhibits the formation of new sea ice.

The main climate feedbacks at play in polar regions involve the atmosphere, ocean and sea ice. They are represented in our Image of the Week. Plus and minus signs in this figure mean that the feedbacks are positive and negative, respectively.

 

How can we measure these feedbacks?

All the climate feedbacks depicted in our Image of the Week are far from being totally understood and are usually measured using different methods. That is why a new study (from which our Image of the Week is taken) proposes a common framework to quantify them.

In this framework, the feedback factor is the ratio between the changes due to the feedback only and the response of the full system including all feedbacks. It is positive for a positive feedback and negative for a negative feedback. In order to compute this feedback factor, we need to identify:

  1. the perturbation
  2. the reference variable involved in the feedback loop
  3. the full system, which includes all feedbacks
  4. the reference system in which the feedback under consideration does not operate.

 

If we take the example of the “ice production-entrainment feedback” (explained above):

  1. the perturbation is a given amount of sea-ice production
  2. the reference variable is sea-ice thickness
  3. the full system is sea ice and the ocean column with the entrainment process
  4. the reference system is sea ice and the ocean column without entrainment.

 

The feedback factor related to the “ice production-entrainment feedback” is then the ratio between the changes in ice thickness due to the feedback only and the total changes in ice thickness following a given amount of ice production. As it is a negative feedback, the related feedback factor is negative. As illustrated in Fig. 2, this feedback factor becomes even more negative, i.e. the strength of the feedback increases, with higher ice production. Therefore, this feedback is highly nonlinear, which is typical of feedbacks in polar regions.

Figure 2: Feedback factor related to the ice production-entrainment feedback as a function of ice production. It is computed from mean temperature and salinity profiles in the Weddel Sea for January-February 1990-2005 [Credit: Fig. 5 from Goosse et al. (2018)].

The advantage of this framework is that you can apply it to all feedbacks present in our Image of the Week. Therefore, it is possible to compute their effects in a similar way, making the comparison easier.

 

Reducing uncertainties in model projections

Accounting for all those climate feedbacks is difficult, as they involve several components of the climate system and interactions between them. Therefore, their misrepresentation (or lack of representation) is one of the sources of error in model projections, i.e. climate model runs going up to 2100 and beyond. Climate feedbacks are therefore one explanation why models largely disagree when it comes to projecting global temperature and sea-ice evolution.

This means that, if we want to better predict what is going to happen in the polar regions, we must better measure what the feedbacks do in reality and better represent them in climate models.

On the modelling side, the main problem is that feedbacks are often described qualitatively to understand climate processes, and many models cannot evaluate these feedbacks quantitatively. There is therefore a clear motivation to use the common framework presented in this study to compute climate feedbacks in models.

However, additionally to improving model projections, identifying the critical climate feedbacks at play in polar regions is also a way to better target observational campaigns, such as the Year of Polar Prediction (YOPP) and the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC).

 

References

Edited by Sophie Berger and Clara Burgard


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 — 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 – Polar Prediction School 2018

Image of the Week – Polar Prediction School 2018

Early career scientists studying polar climate are one lucky group! The 29 young scientists who took part in the 10 day Polar Prediction School this year were no exception. They travelled to Arctic Sweden to learn and discuss the challenges of polar prediction and to gain a better understanding of the physical aspects of polar research.


The Year of Polar Prediction

The Year of Polar Prediction (YOPP) was launched on May 15th 2017; a large 2 year project that ‘aims to close gaps in polar forecasting capacity’ and ’lead to better forecasts of weather and sea-ice conditions to improve future environmental safety at both poles’. With these aims in mind, and with the support of the related APPLICATE project and the Association for Polar Early Career Scientists (APECS), a ten day Polar Prediction School took place in Abisko, Sweden in mid-April.

Abisko is a little town of 85 inhabitants, located north of the Arctic Circle (68°N) next to a National Park and a large lake. Due to the interesting habitats found in the region it is an excellent place to undertake polar research. Consequently, a scientific research station is located in the town, where research mainly focuses on biology, ecology, and meteorology.

Heading back to the research station (seen at the back of the picture) after a long hike [Credit: C. Burgard].

The 29 school participants were made up of Master students, PhD students, and PostDocs, with some studying the Arctic and some the Antarctic. The participants had diverse research backgrounds, with research that focused on atmospheric sciences, oceanic sciences, glaciers, sea ice and hydrology of polar regions, and used a range of techniques, from weather or climate models to in-situ or satellite observations. However, in the end, we were all linked together by our interest in the polar regions. Both this diversity and this link in our research helped us to exchange ideas about the common issues and the differences in all our disciplines.

The school programme

The course aimed to broaden students’ knowledge around their very specific PhD area. Therefore, the school covered a huge range of topics including polar lows, polar ocean-sea ice forecasting, remote sensing of the cryosphere, boundary layers, clouds and much more! Each day was made up of a mixture of lectures and practical sessions, which included:

  • Computer modelling exercises, for example using a simple 1D sea ice model
  • Observations, which included measuring temperature and wind from a weather station on the frozen lake next to the station, and daily radiosonde launches at lunchtime, in sync with radiosonde launches worldwide. These results were compared to model predictions each day.
  • Data assimilation, which focused on understanding the shortcomings in reanalysis products that we all use, including sources of uncertainty and error in the products and how they may impact our own work.

After dinner each evening a different group gave an informal weather briefing for the next day, which was often condensed down to how cloudy it would be, the amount of snow predicted (very little), and temperature (which averaged 2-3°C). Not quite the harsh, sub-zero temperatures that most of us had packed for! Each day was broken up by two coffee breaks (always accompanied by an obligatory cinnamon roll!) and meals which were taken all together in the main research building. This dragged everyone out of the lecture room to chat and refresh before the next session.

As is usual for any worthwhile meteorological fieldwork, we installed a small weather mast on the lake [Credit: C. Burgard].

Living Arctic weather for real

The usual weather in Abisko during April is fairly dry with temperatures ranging from 2°C to -6°C. In preparation for the cold, most of us had brought an abundance of wooly jumpers, thick thermal layers and numerous pairs of socks. However, on arrival in Abisko, the sun was shining and it was a balmy 7°C for the first two days. Whilst erecting the meteorology mast many of us were wearing T-shirts and sunglasses, after abandoning our warmer gear. The warm weather was not to last! Cloudy, relatively mild (2°C to -2°C) conditions persisted throughout most of the week, and it remained dry, which made it easier to forecast the weather but we were all hoping for a little snow! Finally, on the final day of the summer school, large snowflakes fell, although sadly it all melted quite quickly.

When we arrived, the whole area was coated in a thick layer of white snow and the frozen lake was covered. However, by the end of our visit, the bare earth was visible, and the top of the lake was slushy puddles of water. The changes in weather throughout the summer school made for interesting observation records. The albedo (reflectivity) of the lake surface went from approximately 0.8 for the fresh, white snow, but was reduced to 0.4 for the darker, water covered lake surface. It was great to see some theory in action!

Exploring the region

Luckily, we were also given a free day , in which we could explore the region, go skiing or just relax. One large group went off hiking, whilst a smaller group went cross country skiing and a few had a walk to the nearby frozen waterfall. But don’t worry, the science still continued! A group of 3 people stayed close by to release the lunchtime radiosonde.

Abisko children launching a radiosonde! [Credit: J. Turton]

Our visit to the area coincided with the exciting annual ice fishing contest! Whilst cars and small DIY tools are common place in many cities, in Abisko it is a snow mobile (or skidoo) and an ice drill, so they were well versed in the art of ice fishing! The majority of the town’s occupants arrived at the lake and started drilling small holes to catch some fish. After two hours, a number of prizes were awarded (e.g for the longest fish caught). Unfortunately, some of the holes were a little too close to our meteorology mast, and some cables were pulled out, but thankfully we still collected some good data!

An important aspect of any research is engaging with the local communities and communicating effectively with them. So all of the summer school attendees gathered by the lake to watch the ice fishing contest, and a large number of the children from Abisko gathered to watch us release the radiosonde, even helping launch one. They found our activities just as exciting as we found theirs!

And we did some science communication as well!

A crucial aspect of science is how you communicate it to a variety of audiences. The way you might discuss your thesis to your viva panel should be completely different to the way you describe your science to your Great Aunt Linda or to a group of 10-year olds who are attending your outreach event. As part of the summer school, we learnt a range of tips and tricks for communicating science, thanks to Jessica Rohde. Jess is the communications officer for IARPC (Interagency Arctic Research Policy Committee) Collaborations and has years of science communication experience under her belt. Each evening we had a short lecture by Jess, which focused on a specific area of communication including presentation slide design, knowing your audience, listening to the audience and finding the story behind your science. Once we had learnt the theory we then put what we had learnt into practice. We did a bit of  improv’, which included 1-minute elevator pitches and tailoring your science to taxi drivers, the Queen of England and models (no not computer models, the Kate Moss variety). An important take-home message was that there is no such thing as the ‘general public’. When designing your outreach event, the ‘general public’ could involve children of all ages (and therefore all learning levels), parents, teachers, professors and pensioners. Therefore, you should listen to the needs of your audience and understand what their motivation is.

You can check out the final results of these sessions here!

In summary…

In the end, although the school was quite intense, everyone was sad to part. We are sure we will all remember this exciting time, where we learnt about the many aspects of polar prediction, and what to consider when tackling science communication. We hope that this school will be organized again in the next years to provide this amazing and unforgettable experience to all those who could not join this year’s Polar Prediction School!

Further reading

Edited by Morgan Jones


Rebecca Frew is a PhD student at the University of Reading (UK). She investigates the importance of feedbacks between the sea ice, atmosphere and ocean for the Antarctic sea ice cover using a hierarchy of climate models. In particular, she is looking at the how the importance of different feedbacks may vary between different regions of the Southern Ocean.
Contact: r.frew@pgr.reading.ac.uk

 

 

Jenny Turton is a post doc working at the institute for Geography at the University of Erlangen-Nuernberg, in the climate system research group. Her current research focuses on the interactions between the atmosphere and surface ice of the 79N glacier in northeast Greenland, as part of the GROCE project. 

 

 

 

Clara Burgard is a PhD student at the Max Planck Institute for Meteorology in Hamburg. She investigates the evolution of sea ice in general circulation models (GCMs). There are still biases in the sea-ice representation in GCMs as they tend to underestimate the observed sea-ice retreat. She tries to understand the reasons for these biases.

Image of the Week – Antarctica: A decade of dynamic change

Fig. 1 – Annual rate of change in ice sheet height attributable to ice dynamics. Zoomed regions show (a) the Amundsen Sea Embayment and West Marie Byrd Land sectors of West Antarctica, (b) the Bellingshausen Sea Sector including the Fox and Ferrigno Ice Streams and glaciers draining into the George VI ice shelf and (c) the Totten Ice Shelf. The results are overlaid on a hill shade map of ice sheet elevation from Bedmap2 (Fretwell et al. 2013) and the grounding line and ice shelves are shown in grey (Depoorter et al. 2013). [Credit: Stephen Chuter]

  

Whilst we tend to think of the ice flow in Antarctica as a very slow and steady process, the wonders of satellites have shown over the last two decades it is one of the most dynamic places on Earth! This image of the week maps this dynamical change using all the satellite tools at a scientist’s disposal with novel statistical methods to work out why the change has recently been so rapid.


Why do we care about dynamic changes in Antarctica ?!

The West Antarctic Ice Sheet has the potential to contribute an approximate 3.3 m to global sea level rise (Bamber et al. 2009). Therefore, being able to accurately quantify observed ice sheet mass losses and gains is imperative for assessing not only their current contribution to the sea level budget, but also to inform ice sheet models to help better predict future ice sheet behaviour.

An ice sheet can gain or lose mass primarily through two different processes:

  • changes in surface mass balance (variations in snowfall and surface melt driven by atmospheric processes) or
  • ice dynamics, which is where variations in the flow of the ice sheet (such as an increase in its velocity) leads to changes in the amount of solid ice discharged from the continent into the ocean. In Antarctica ice flow dynamics are typically regulated by the ice shelves that surround the ice sheet; which provide a buttressing stress to help hold back the rate of flow.

Understanding the magnitude of each of these two components is key to understanding the external forcing driving the observed ice sheet changes.

This Image of the Week shows the annual rates of ice sheet elevation change which are attributed to changes in ice dynamics between 2003 and 2013 (Fig. 1) (Martín-Español et al. 2016). This is calculated by combining observations from multiple satellites (GRACE, ENVISAT, ICESat and CryoSat-2) with in-situ GPS measurements in  a Bayesian Hierarchical Model. The challenge we face is that the observations we have of ice sheet change (whether that being total height change from altimetry or mass changes from GRACE) vary on their spatial and temporal scales and can only tell us the total mass change signal, not the magnitudes or proportions of the underlying processes driving it. The Bayesian statistical approach used here takes these observations and separates them proportionally into their most likely processes, aided by prior knowledge of the spatial and temporal characteristics for each process we want to resolve. This allows us reducing the reliance on using forward model outputs to resolve for processes we cannot observe. As a result, it is unique from other methods of determining ice sheet mass change, which rely on model outputs which in some cases have hard to quantify uncertainties.  This methodology has been applied to Antarctica and is currently being used to resolve the sea level budget and its constituent components through the ERC GlobalMass project.

What can we learn from Bayesian statistical approach?

This approach firstly allows us to quantitively assess the annual contribution that the Antarctic ice sheet is making to the global sea level budget, which is vital to better understanding the magnitude each Earth system process is playing in sea level change. Additionally, by being able to break down the total change into its component processes, we can better understand what external factors are driving this change. Ice dynamics has been the dominant component of mass loss in recent years over the West Antarctic Ice Sheet and is therefore the process being focussed on in this image.

Amundsen Sea Embayment : a rapidly thinning area

Since 2003 there have been major changes in the dynamic behaviour over the Amundsen Sea Embayment and West Marie Byrd Land region (Fig 1, inset a). This region is undergoing some of the most rapid dynamical changes across Antarctica, with a 5 m/yr ice dynamical thinning near the outlet of the Pope and Smith Glacier. Additionally the Bayesian hierarchical model results show that dynamic thinning has spread inland from the margins of Pine Island Glacier, agreeing with elevation trends measured by satellite altimetry over the last two decades (Konrad et al. 2016).

These changes are driven primarily by the rapid thinning of the floating ice shelves at the ice sheet margin in this region

The importance of ice dynamics  is also illustrated in Fig 2, which shows  surface processes and ice dynamics components of mass changes over the Amundsen Sea Embayment from the bayesian hierarchical model. Fig 2 demonstrates that ice dynamics is the primary driver of mass losses in the region. Ice dynamic mass loss increased dramatically from 2003-2011, potentially stabilising to a new steady state since 2011.

Fig. 2 – Annual mass changes due to ice dynamics (pink line) and SMB (blue line) for the period 2003-2013 from the Bayesian hierarchical model approach. Red dots represent mass change anomaly (changes from the long term mean) due to surface mass balance calculated by the RACMO2.3 model and allow for comparison with our Bayesian framework results. (calculated from observations of ice velocity and ice thickness at the grounding line and allow for comparison with our Bayesian framework results (Mouginot et al, 2014). [Credit: Fig. 9b from Martín-Español et al., 2016].

 

The onset of  dynamic thinning can also be seen in glaciers draining into the Getz Ice Shelf, which is experiencing high localised rates of ice shelf thinning up to 66.5 m per decade (Paolo et al. 2015) . This corroborates with ice speed-up recently seen in the region (Chuter et al. 2017; Gardner et al. 2018). We have limited field observations of ice characteristics in this region and therefore more extensive surveys are required to fully understand causes of this dynamic response.

Bellingshausen Sea Sector :  Not as stable as previously thought…

 The Bellingshausen Sea Sector (Fig 1, inset b) was previously considered relatively a dynamically stable section of the Antarctic coastline, however recent analysis from a forty year record of satellite imagery has shown that the majority of the grounding line in this region has retreated  (Christie et al. 2016). This is reflected in the presence of a dynamic thinning signal in the bayesian hierarchical model results near the Fox and Ferrigno Ice streams and over some glaciers draining into the George VI ice shelf, which have been observed from CryoSat-2 radar altimetry (Wouters et al. 2015). The dynamic changes in this region over the last decade highlight the importance of continually monitoring all regions of the ice sheet with satellite remote sensing in order to understand the what the long term response over multiple decades is to changes in the Earth’s climate and ocean forcing.

Outlook

Multiple  satellite missions have allowed us to measure changes occurring across the ice sheet in unprecedented detail over the last decade. The launch of the GRACE-Follow On mission earlier this week and the expected launch of ICESat-2 in September will ensure this capability continues well into the future. This will provide much needed further observations to allow us to understand ice sheet dynamics over time scales of multiple decades. The bayesian hierarchical approach being demonstrated will be developed further to encompass these new data sets and extend the results into the next decade. In addition to satellite measurements, the launch of the International Thwaites Glacier Collaboration  between NERC and NSF will provide much needed field observations for the Thwaites Glacier region of the Amundsen Sea Embayment, to better understand whether it has entered a state of irreversible instability .

Data
The  Bayesian hierarchical model mass trends shown here (Martín-Español et al. 2016) are available from the UK Polar Data Centre. In addition, the time series has been extended until 2015 and is available on request from Stephen Chuter (s.chuter@bristol.ac.uk). This work is part of the ongoing ERC GlobalMass project, which aims to attribute global sea level rise into its constituent components using a Bayesian Hierarchical Model approach. The GlobalMass project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 69418.

References

Christie, Frazer D. W. et al. 2016. “Four-Decade Record of Pervasive Grounding Line Retreat along the Bellingshausen Margin of West Antarctica.” Geophysical Research Letters 43(11): 5741–49. http://doi.wiley.com/10.1002/2016GL068972.

Chuter, S.J., A. Martín-Español, B. Wouters, and J.L. Bamber. 2017. “Mass Balance Reassessment of Glaciers Draining into the Abbot and Getz Ice Shelves of West Antarctica.” Geophysical Research Letters 44(14).

Gardner, Alex S. et al. 2018. “Increased West Antarctic and Unchanged East Antarctic Ice Discharge over the Last 7 Years.” Cryosphere 12(2): 521–47.

Martín-Español, Alba et al. 2016. “Spatial and Temporal Antarctic Ice Sheet Mass Trends, Glacio-Isostatic Adjustment, and Surface Processes from a Joint Inversion of Satellite Altimeter, Gravity, and GPS Data.” Journal of Geophysical Research: Earth Surface 121(2): 182–200. http://dx.doi.org/10.1002/2015JF003550.

Mouginot, J, E Rignot, and B Scheuchl. 2014. “Sustained Increase in Ice Discharge from the Amundsen Sea Embayment, West Antarctica, from 1973 to 2013.” Geophysical Research Letters 41(5): 1576–84.

Paolo, Fernando S, Helen A Fricker, and Laurie Padman. 2015. “Volume Loss from Antarctic Ice Shelves Is Accelerating.” Science 348(6232): 327–31. http://www.sciencemag.org/content/early/2015/03/31/science.aaa0940.abstract.

Edited by Violaine Coulon and Sophie Berger


Stephen Chuter is a post-doctoral research associate in Polar Remote Sensing and Sea Level at the University of Bristol. He combines multiple satellite and ground observations of ice sheet and glacier change with novel statistical modelling techniques to better determine their contribution to the global sea level budget. He tweets as @StephenChuter and can be found at www.stephenchuter.wordpress.com. Contact email: s.chuter@bristol.ac.uk

Image of the Week — Biscuits in the Permafrost

Fig. 1: A network of low-centred ice-wedge polygons (5 to 20 m in diameter) in Adventdalen, Svalbard [Credit: Ben Giles/Matobo Ltd]

In Svalbard, the snow melts to reveal a mysterious honeycomb network of irregular shapes (fig. 1). These shapes may look as though they have been created by a rogue baker with an unusual set of biscuit cutters, but they are in fact distinctive permafrost landforms known as ice-wedge polygons, and they play an important role in the global climate.


Ice-wedge polygons: Nature’s biscuit-cutter

In winter, cracks form when plummeting air temperatures cause the ground to cool and contract. O’Neill and Christiansen (2018) used miniature accelerometers to detect this cracking, and found that it causes tiny earthquakes, with large magnitude accelerations (from 5 g to at least 100 g (where g = normal gravity)!). Water fills the cracks when snow melts. When the temperature drops, the water refreezes and expands, widening the cracks. Over successive winters, the low tensile strength of the ice compared to the surrounding sediment means that cracking tends to reoccur in the ice. As the cycle of cracking, infilling, and refreezing continues over centuries to millennia, ice wedges develop.

Subsurface ice wedge growth causes small changes in the ground surface microtopography. There are linear depressions, known as troughs, above the ice wedges (fig. 2). Adjacent to the troughs, the soil is pushed up into raised rims. From these raised rims, the elevation drops off into the polygon centre, forming low-centred polygons (fig. 2a).

Shaping Arctic landscapes

Permafrost in the Northern hemisphere is warming due to increasing air temperatures (Romanovsky et al. (2010). As air temperatures rise, the active layer (the ground that thaws each summer and refreezes in winter) deepens.

As permafrost with a high ice content thaws out, the ice melts and the ground subsides. On the other hand, permafrost containing no ice does not experience subsidence. Consequently, permafrost thaw can cause differential subsidence in ice-wedge polygon networks. This re-arranges the surface microtopography: ice wedges melt, the rims collapse into the troughs, and the polygons become flat-centred and then eventually high-centred (fig. 2b and c; Lara et al. (2015)). Wedge ice is ~20 % of the uppermost permafrost volume, and so this degradation could have a big impact on the shape of Arctic landscapes.

Are ice wedge polygons climate amplifiers?

Fig. 2: Schematic diagrams of polygon types and features [Credit: Wainwright et al. (2015)].

The transition from low-centred to high-centred ice-wedge polygons affects water distribution across the polygonal ground. The rims of low-centred polygons tend to block water drainage, whereas the troughs facilitate relatively fast and effective drainage of water from the polygonal networks (Liljedahl et al., 2012). So, during summer, the centres of low-centred polygons are frequently flooded with stagnant water, whereas the central mounds of high centred polygons are well drained (and good to sit on at lunchtime!). The contrast in hydrology influences vegetation, surface energy transfer, and biogeochemistry, in turn influencing carbon cycling and the release of greenhouse gases into the atmosphere.

High-centred polygons can have increased carbon dioxide emissions compared to low-centred polygons, on account of their lower soil moisture, reduced cover of green vascular vegetation and the well-drained soil (Wainwright et al., 2015). On the other hand, once plant growth during peak growing season is accounted for, this can actually cause a net drawdown of carbon dioxide in high-centred polygons (Lara et al., 2015). In contrast, there is general agreement that low-centred polygons are associated with high summer methane flux (Lara et al., 2015; Sachs et al., 2010; Wainwright et al., 2015). This is due to multiple interacting environmental factors. Firstly, low centred polygons have a higher temperature, which increases methane production rates. Secondly, they also have moister soil, which decreases the consumption of methane, owing to the lower oxygen availability. Thirdly, the low-centred polygons often have more vascular plants that help transport the methane away from its production site and up into the atmosphere. Lastly, the low-centred polygons had higher concentrations of aqueous total organic carbon, which provides a good food source for methanogens.

Outlook

As the climate warms, ice wedge polygons will increasingly degrade. The challenge now is to figure out whether the transition from low-centred to high-centred polygons will enhance or mitigate climate warming. This depends on the balance between the uptake and release of methane and carbon dioxide, as well as the rate of transition from high- to low-centred polygons.

Further Reading

Lara, M.J., et al. (2015), Polygonal tundra geomorphological change in response to warming alters future CO2 and CH4 flux on the Barrow Peninsula. Global Change Biology, 21(4), 1634-1651

Liljedahl, A.K., et al. (2016), Pan-Arctic ice-wedge degradation in warming permafrost and its influence on tundra hydrology. Nature Geoscience, 9, 312-316.

Wainwright, H.M., et al. (2015), Identifying multiscale zonation and assessing the relative importance of polygons geomorphology on carbon fluxes in an Arctic tundra ecosystem. Journal of Geophysical Research: Biogeosciences, 707-723.

On permafrost instability: Image of the Week – When the dirty cryosphere destabilizes! | EGU Cryosphere Blog

On polygons in wetlands: Polygon ponds at sunset | Geolog

Edited by Joe Cook and Sophie Berger


Eleanor Jones is a NERC PhD student on the EU-JPI LowPerm project based at the University of Sheffield and the University Centre in Svalbard. She is investigating the biogeochemistry of ice-wedge polygon wetlands in Svalbard. She tweets as @ElouJones. Contact Email: eljones3@sheffield.ac.uk