I recently published an article in Open Access Government about the potential for machine learning technologies to revolutionise Polar science, with focus on optical remote sensing data from drones and satellites. You can read it online or download it from OAGov_Oct18
2018 saw the Black & Bloom postdocs exploring a new field site in the north western sector of the Greenland Ice Sheet. After two seasons working in the south west near Kangerlussuaq, the team migrated north to investigate dark ice where the melt seasons are shorter and the temperatures lower.
We soon learned that there were additional challenges to working up here beyond the colder weather. Upernavik itself is on a small island in an archipelago near where the ice sheet flows and calves into the sea. While this produces spectacular icebergs, it also means access to the ice sheet is possible only by helicopter. The same helicopter serves local communities elsewhere in the archipelago with food, transport and other essential services. While we were in Upernavik, a huge iceberg floated into the harbour in nearby Inarsuit, threatening the town with the potential for a huge iceberg-induced tsunami. The maritime Arctic weather also played havoc with the flight schedules, and resupplying local communities (rightly) took priority over science charters.
These factors combined to prevent us from leaving Upernavik for 3.5 weeks. It seemed like we would never make it onto the ice. However, we finally got a weather window that coindided with heli and pilot availablity. With the difficulty of getting on to the ice weighing on our minds, we had to consider the risk of similar difficulties getting back out. We repacked to ensure we had several weeks of emergency supplies to make sure we would not be flying in to a potential search and rescue disaster.
Once on the ice, we quickly built a camp and started recording measurements quickly. The albedo measurements and paired drone flights went very smoothly, with refined methods developed over the past two seasons. However, we only saw exposed glacier ice for 1.5 days, and continuous snowfall kept it buried for the rest of the season.
Overall it was an interesting site, and the important thing is that we can confirm that the algal bloom we studied in the south west is also present in the northern part of the ice sheet, is composed of the same species and also makes the ice dark. We have sampled the mineral dusts too, to see how they compare with the more southern site.
The EGU annual meeting in Vienna is one of the major events in the earth science calendar, where the latest ideas are aired and discussed and new collaborations forged. My talk this year was in the “Remote Sensing of the Cryosphere” session. Here’s an overview:
Albedo is a primary driver of snow melt. For clean snow and snow with black carbon, radiative transfer models to an excellent job of simulating albedo, yet there remain aspects of snow albedo that are poorly understood. In particular current models do not take into account algal cells that grow and dramatically discolour ice in some places (except our 1-D BioSNICAR model) and few take into account changes in albedo over space and time.
This led me to wonder about using cellular automata as a mechanism for distributing albedo modelling using radiative transfer over three spatial dimensions and time, and also enabling a degree of stochasticity to be introduced to the modelling (which is certainly present in natural systems).
Cellular automata are models built on a grid composed of individual cells. These individual cells update as the model progresses through time according to some function – usually a function of the values of the neighbouring cells. Cellular automata have been used extensively to study biological and physical systems in the past – for examples Conway’s Game of Life, Lovelock’s DaisyWorld and Bak’s Sandpile Model not only gave insight into particular processes, but arguably changed the way we think about nature at the most fundamental level. Those three models were epoch-changing for the concepts of complexity and chaos theory.
For the snowpack, I developed a model I am calling CASPA -an acronym for Cellular Automaton for SnowPack Albedo. CASPA draws on a cellular automaton approach with a degree of stochasticity to predict changes in snowpack biophysical properties over time
At each timestep the model updates the biomass of each cell. This happens according to a growth model (an initial inoculum doubles in biomass). This biomass has a user-defined probability of growing in situ (darkening that cell) or spreading to a randomly selected adjacent cell. Once this has occurred, the radiative transfer model BioSNICAR is called and used to predict the albedo, and the energy absorbed per vertical layer. The subsurface light field is visualised as the planar intensity per vertical layer, per cell. The energy absorbed per layer is also used to define a temperature gradient which is used to drive a grain evolution model. In the grain evolution model, wet and dry grain growth ca occur, along with melting, percolation and refreezing of interstitial water. This is consistent with the grain evolution model in the Community Land Model. The new grain sizes are fed back into SNICAR ready for the albedo calculation at the next timestep.
At the same time, inorganic impurities can be incorporated into the model. These include dust and soot. These can be constant throughout the model run, or can vary according to a user-defined scavenging or deposition rate. They can also melt-out from beneath, by having the inorganic impurities rising up through successive vertical layers per timestep.
In this way, the albedo of a snowpack can be predicted in three spatial dimensions plus time. Taking the incoming irradiance into account, the radiative forcing can be calculated at each vertical depth at each cell per timestep. Furthermore, the energy available as photosynthetically active radiation in each layer can be quantified. Ultimately. these values can feed back into the growth model. Coupling the CASPA scheme wit a sophisticated ecological model could therefore be quite powerful.
By default the model outputs a 2D albedo map and a plot of biomass against albedo. It is interesting to realise that the subtle probabilistic elements of the cellular model can lead to drastically different outcomes for the biomass and albedo of the snowpack even with identical initial conditions. This is also true of natural systems and the idea that an evolving snowpack can be predicted using a purely deterministic model seems, to me, erroneous. There are interesting observations to make about the spatial ecology of the system. Even this simplified system can runaway into dramatic albedo decline or almost none. It makes me wonder about natural snowpacks and the Greenland dark zone – how much of the interannual variation emerges from internal stochasticity rather than being a deterministic function of meteorology or glaciology?
In terms of quantifying biological effects on snow albedo, CASPA can be run with the grain evolution and inorganic impurity scavenging models turned ON or OFF. Comparing the albedo reduction taking into account the physical evolution of the snow with that when the snow physics remain constant provides an estimate of the indirect albedo feedbacks and the direct albedo reduction due to the algal cells.
This modelling approach opens up an interesting opportunity space for remote sensing in the cryosphere. In parallel to this modelling I have been working hard on a supervised classification scheme for identifying various biological and non-biological ice surface types using UAV and satellite remote sensing products. Coupling this scheme with CASPA offers an opportunity to upsample remote sensing imagery in space and time, or to set the initial conditions for CASPA using real aerial data and then experimenting with various future scenarios. At the moment, I lack any UAV data for snow with algal patches to actually implement the workflow, but it is proven using multispectral UAV data from bare ice on the Greenland ice sheet. When I obtain multispectral data for snow with algal blooms, it is possible to automate the entire pipeline from loading the image, classifying it using a supervised classifier, converting it into an n-dimensional array that can be used as an initial state for the CASPA cellular automaton, whose conditions can be tweaked to experiment with various environmental scenarios.
Therefore, the limiting factor for CASPA at the moment is availability of multispectral aerial data and field spectroscopy for training data for algal blooms on snow. In the spirit of open science and to try to stimulate a development community, I have made this code 100% open and annotated despite being currently unpublished, and I’d be delighted to receive some pull requests!
In summary, I suggest coupling radiate transfer with cellular automata and potentially remote sensing imagery is a promising way to push albedo modelling forwards into spatial and temporal variations and an interesting way to build a degree of stochasticity into our albedo forecasting and ecological modelling.
In collaboration with Rolex Awards for Enterprise, Proudfoot Media and I have produced a documentary film explaining the latest research into the surprising hidden biology shaping Earth’s ice. The story is told by young UK Arctic scientists with contributions from guests including astronaut Chris Hadfield and biologist Jim Al-Khalili. We went to great lengths to make this a visually striking film that we hope is a pleasure to watch and communicates the otherwordly beauty and incredible complexity of the Arctic glacial landscape. We aim to educate, entertain and inspire others into exploring and protecting this most sensitive part of our planet in their own ways.
We think the film is equally suited to the general public as school and university students, and we are delighted to make this a free-to-all teaching resource. Please watch, share and use!
Alongside this film, I also collaborated with musician Hannah Peel on an audiovisual piece designed to communicate the complexity of process occurring on the Greenland Ice Sheet through sound. View the piece (good headphones recommended!) and write up here
I recently published an article in French pop-sci magazine La Recherche about the wondrous microbial ecosystems on glaciers and ice sheets (here for French speakers). For those English speakers who do not subscribe to la Recherche, here is a translation.
Also, I strongly recommend the excellent translator who worked on this article with me – contact me if you need translation services and I can link you up.
The microbes accelerating glacier melting
Our planet is getting warmer and losing its ice. Mountain glaciers are disappearing and the great Greenland and Antarctic ice sheets are shrinking. These masses of ice are giant coolers for the planet and they reflect energy from the Sun back out into space, meaning the smaller they become, the more the planet warms. Surprisingly, the process of melting the vast glaciers and ice sheets is accelerated by microscopic life.
Glacier and ice sheet melting depends upon more than just temperature. Most of the energy driving melt comes from sunlight that hits the ice surface. Dirtier, darker ice absorbs more solar energy than clean, bright ice meaning more energy is available to drive melting. On the Greenland Ice Sheet in particular, the ice becomes very dark in the summer, with large areas reflecting just 20-30% of the sunlight hitting them. This is not a new phenomenon – in fact it was noticed by explorers during the great polar expeditions of the late 1800s. Intrigued, they examined samples of ice under their microscopes. The dark colour of the ice was not simply due to dust as they expected – astonishingly, the ice was stained by life (Nordenskjold, 1875). The ice surface is a patchwork of greys, reds and purples coloured by the collective effect of countless microscopic organisms, with potential knock-on effects for Earth’s climate (Uetake et al., 2010; Takeuchi et al., 2006; Yallop et al., 2012; Cook et al., 2017).
Microbes on Ice
When explorer Adolf E Nordenskjold arrived on the Greenland Ice Sheet in 1870 he immediately noticed the dark grey-purple colour of the ice. His colleague, a biologist called Berggren, examined the ice under the microscope and discovered a rich variety of microbial life. The importance of their discovery was clear to them – this life darkens the ice and increases its melt rate. Nordenskjold even suggested that the microbial life was the “greatest enemy of the mass of ice” and an accelerator of deglaciation at the global scale (Nordenskjold, 1875)!
Until recently, Nordenskjold’s observations of life on ice have remained obscure footnotes in the history of Polar exploration; however, as climate science has become increasingly urgent in the twenty-first century, Nordenskjold’s work has gained new significance. Contemporary scientists have confirmed the presence of a microbial ecosystem growing on the surface of the Greenland Ice Sheet and elsewhere and are now attempting to quantify their ice-darkening effect. Although it is an extreme environment where temperatures are low and nutrients scarce, there is abundant sunlight and liquid water to support photosynthesis, meaning microalgae can grow on the ice surface (Uetake et al., 2010; Yallop et al., 2012). The days are long in the Arctic in summer, with the sun staying above the horizon for twenty-four hours per day for part of the season, exposing the algae to intense and prolonged solar energy. This powers photosynthesis but over time the exposure stresses the ice algae, causing them to produce biological sunscreen molecules to protect their delicate photosynthetic machinery. These ‘carotenoids’ colour their cells very dark purple and enhance the biological darkening of the ice surface.
At the same time, the ice surface is peppered with holes that are often cylindrical but can have complex and irregular shapes (Cook et al., 2015). These holes range from centimeters to meters in diameter and depth and contain mixtures of biological and nonbiological material bundled up into small balls that sit on the hole floors. Nordenskjold first noticed these holes on the Greenland Ice Sheet and named them ‘cryoconite holes’, from the Greek for ‘holes with frozen dust’. These holes are the most biodiverse microbial habitat on Earth’s ice. They form when dust and debris becomes tangled up by long, thread-like cyanobacteria. The cyanobacteria are photosynthetic and as they grow they exude polymers that act as biological glues, binding the bundles of material together into stable granules. This biological bundling and binding of material creates a microhabitat for other microbes, especially those that can feed on molecules produced by the photosynthesizing cyanobacteria. As the granules grow they become heavier, meaning they settle on the ice surface. The biological material makes them especially dark, so the ice underneath melts quickly, causing holes to form in the ice surface with the granules sitting on the hole floor. The holes provide protection from the weather and intense sunlight and also prevent the microbes from being washed away. The cyanobacteria therefore sculpt the ice surface and engineer a comfortable, stable habitat where diverse microbial life can thrive in this extreme environment.
Cryoconite holes are more than icy buckets that hold microbial life. They are more like microbial mini-cities on ice, with each connected to many others by meltwater flowing between ice crystals just under the ice surface. Cryoconite microbes engage in engineering and construction, production, consumption, competition, predation, growth, reproduction, death, decay, immigration and emigration. There is both import and export of nutrients, waste and other biological material. At the same time, the hole itself changes its shape and size in response to changing environmental conditions with the emergent effect of maintaining the light intensity at the hole floor, promoting photosynthesis (Cook et al., 2010). Algal blooms and cryoconite are crucial components of the wider Arctic ecosystem, acting as stores of carbon (which they draw down from the atmosphere and fix into organic molecules), nutrients and biomass which can all be delivered to soils, rivers and oceans as glaciers melt (Stibal et al., 2012). Truly, these are widely interconnected complex adaptive systems created biologically on Earth’s ice.
The Cutting Edge of Life on Ice
While life on ice has been known for many years, most of the literature on the subject has been produced during the twenty-first century. Modern molecular biological techniques have enabled scientists to catalogue the species present in cryoconite and algal blooms, and modern instruments can measure their darkening effect. However, there are several major gaps in our understanding of life on ice. To quantify their effect on ice darkening worldwide, we need a reliable method to map icy microbes at the scale of entire ice sheets. From a biological perspective, we know which organisms live in algal blooms and cryoconite so we must now concentrate on determining how they function and what ecosystem services they might provide that could impact human society.
To estimate the total coverage of life on ice, we must detect it without actually being present to take samples. It is relatively easy to take samples and analyse them in a laboratory to tell if life is present, but doing the same from the air is a different problem. In addition to biological darkening, soots and mineral dusts colour the ice. Also, as the ice melts the crystals change shape and melt water can fill the spaces between them, which in itself changes the way the ice absorbs and reflects solar energy. Disentangling the biological signal from these other darkening processes has proven to be challenging.
However, because the darkening of ice by living cells is due to biological molecules that absorb light at specific wavelengths, we may be able to use the spectrum of reflected light to identify them. Chlorophyll, for example, absorbs red and blue light much more effectively than it absorbs green light (which is why we see leaves as green). For other biological molecules, the peak absorption will be at slightly different wavelengths, and non-biological materials will have their own absorption patterns too. However, while identifying ‘signature spectra’ is simple when only one material is present, it is much more difficult when several species with different light absorbing properties are mixed with non-biological materials. All of the light absorbers can be scattered unevenly and mixed vertically within the volume of ice which can itself be a complex aggregate of variously sized ice crystals and liquid water. The reflected light is a tangle of signals that can be hard to unpick.
At our laboratory at the University of Sheffield, we are working on a purpose-built drone which will fly back and forth over a patch of the Greenland Ice Sheet taking images at specific wavelengths of light. By analysing these images we hope to be able to produce a map of life on ice. Using the drone means we can follow the flight on foot and take ground samples to examine in the laboratory, enabling us to link the drone images to actual concentrations of different light absorbers on the ground. The wavelengths imaged by the drone match up with those measured by several Earth observation satellites, meaning that achieving life-detection using a drone should then enable the same from space.
As well as knowing where the life is, we also need a deeper understanding of how it functions. Recognition of ice surfaces as microbial habitats came at the same time as an explosion in accessible and affordable techniques in field molecular microbial ecology, meaning several groups have used high-throughput sequencing of marker-genes to identify the particular microbes present within cryoconite communities (e.g. Cameron et al., 2012; Edwards et al., 2014; Stibal et al., 2014, 2015). Environmental genomic techniques have also been used to investigate the total genetic composition of cryoconite communities (Edwards et al., 2013). To date, these have been snapshot studies, but in the very near future great insights into the functioning of cryoconite microbes will come from rapid metagenomic, metabolomic and metatranscriptomic studies. It has been suggested that ice surface microbes might be good targets for bioprospecting. Since they are able to thrive in conditions of low temperature, high light and low nutrients, they may well utilize survival strategies that we can exploit, either by extracting novel genes and biomolecules, or by observing and gaining ecological knowledge. Cryoconite has been suggested to be a potential source of antifreeze proteins, novel antibiotics and cold-active enzymes. The shape, illumination conditions and flushing with flowing meltwater make cryoconite holes natural analogs to industrial bioreactors which are commonly used to synthesise valuable biomolecules (Cook et al., 2015).
Deep insights will come from combining the expertise of microbial ecologists with glaciologists and physicists who, together, will link processes operating at the molecular level with changes in ice surface colour and patterns of melt, which suggests insights into the ecology of ice surfaces might one day be obtainable from the sky or from space. While this is some way off, great insights could be gained from a shift towards a holistic understanding of the ice surface as a ‘living landscape’.
We are working hard to achieve remote detection of life on ice for the purposes of mapping biological ice darkening from satellites and improving our ability to predict future ice melt. However, there is another potential outcome from this work… what if instead of looking down from space at our own planet, we turn the sensors around and start looking out?
The Greenland Ice Sheet is, in many ways, a good place for developing life detection technologies that can be applied to the search for life on other icy planets and moons. Take, for example, Europa. A recently funded NASA project will examine this icy moon of Jupiter for signs of life because of its potentially habitable icy shell and subsurface ocean. On Europa, the icy surface is sunlit and seeded with possibly mineral-rich snow that forms when liquid water in its subsurface oceans escapes via huge geysers (Hand et al., 2017). There is therefore a potentially dusty ice surface illuminated by sunlight that could support photosynthesis, just like the Greenland Ice Sheet (although the solar energy flux and temperature is lower on Europa and photosynthesis is highly unlikely). Any life detection technology that works on the Greenland Ice Sheet will have to overcome the challenges of ice optics, interference by mineral dusts and uncertain biological pigment composition, which would also be the main challenges for remote detection of life on the surface of other icy planets and moons. The frontiers of glacier biology on Earth may therefore intersect with the cutting edge search for extraterrestrial life.
While many people think of Arctic and Antarctic ice as lifeless places, there is in fact abundant microbial activity on Earth’s glaciers and ice sheets. But more surprising is the huge impacts of these tiny organisms. By changing the colour of the ice surface, microbes are potentially enhancing the rate at which glaciers and ice sheets are shrinking, but we cannot yet build them into our climate models. The research priority now is mapping these ecosystems from space because this will enable us to estimate their impact on ice melt worldwide and improve our melt forecasts. The same technologies that will enable us to detect life on Earth may eventually be useful tools for searching for icy life elsewhere in the universe. There is also much to be learned about way these microbes function that can educate us about the limits of life in extreme environments. The true sharp edge of glacier biology research involves understanding how these microbes are able to sense, survive and drive environmental change. The study of life on Earth’s ice is deeply interdisciplinary and ultimately it requires us to recognize – as Nordenskjold did – the intricate bridges joining the very big and the very small.
Cameron K, Hodson A J, Osborn M (2012) Carbon and nitrogen biogeochemical cycling potentials of supraglacial cryoconite communities. Polar Biology, 35: 1375-1393
Cook J, Hodson A, Telling J, Anesio A, Irvine-Fynn T, Bellas C (2010) The mass-area relationship within cryoconite holes and its implications for primary production. Annals of Glaciology, 51 (56): 106-110
Cook, J.M., Edwards, A., Irvine-Fynn, T.D.I., Takeuchi, N. 2015. Cryoconite: Dark biological secret of the Cryosphere. Progress in Physical Geography, 40 (1): 66 -111, doi: 10.1177/0309133315616574Cook et al., 2017
Edwards A, Pachebat J A, Swain M, Hegarty M, Hodson A, Irvine-Fynn T D L, Rassner S M, Sattler B (2013) A metagenomic snapshot of taxonomic and functional diversity in an alpine glacier cryoconite ecosystem. Environmental Research Letters, 8 (035003): 11pp
Edwards A, Mur L, Girdwood S, Anesio A, Stibal M, Rassner S, Hell K, Pachebat J, Post B, Bussell J, Cameron S, Griffith G, Hodson A (2014) Coupled cryoconite ecosystem structure-function relationships are revealed by comparing bacterial communities in Alpine and Arctic glaciers. FEMS Microbial Ecology, 89 (2): 222-237
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In 2017 I slept in various ice-camps in Greenland in spring, summer and autumn. Living on ice requires some specialist techniques different to camping on dry land, and they vary depending on the season. In summer, the main problem is the melting surface. A tent pitched directly on the ice surface will descend into a wet ditch because of the heat generated by a person inside. To counter this, tents are pitched on sheets of ply with reflective insulating sheets underneath. These slow the ablation under the tents and provide a flat surface to sleep on; however, they often work too well and leave the tents wobbling on raised platforms after a few days.
The most important thing is securing the tent to the ice surface, because it can get very windy on the ice sheet. The ice surface can descend several centimetres per day, meaning short stakes or pins will melt out very fast. For that reason long bamboo or plastic poles are drilled up to 1 metre into the ice at an oblique angle under the tent, providing points to secure the tents to. This is especially important for geodesic dome tents, where tension is required roughly evenly across the poles for the tent to keep its shape. In 2017 the combination of strong winds and very fast surface lowering meant the large mess tent quickly became raised above the surrounding ice and, despite our best repitching efforts, the poles floated freely above the ice. With no ground to push against, the poles became structureless and weak and eventually collapsed.
In winter, a stronger and more permanent solution can be achieved using Abalykov threads. These are loops of tape or rope frozen into the ice itself. There is no surface melting in autumn/winter and the surface does not have a weak weathered layer. The strong, cold surface ice is perfect for drilling obliquely with short ice screws so that two drill-holes meet 10-15cm below the ice surface an create a tunnel from the surface and back. A pipe-cleaner can then be used to drag rope or tape through the hole and tie the tent down. the hole can then be packed with snow or water which will quickly refreeze around the rope and form a super strong tie-point to secure the tent. Using a snow shovel to cover the tent’s snow-skirt with snow helps prevent wind and snow from getting between the flysheet and the inner, keeping it cosy inside and helping to keep the tent a bit better streamlined against the wind.
This Youtube clip posted by Glenmore Lodge (Scotland) explains how to make an Abalykov thread for ice-climbing – it’s the same for securing tents in an ice camp.
The Abalykov threads stood up to extremely strong winds in Greenland in September/October. The poles and fabric seemed more at risk of failure than the threads!