Bioalbedo: new model and TCD paper

I’m very pleased to report our new paper is now in open discussion in The Cryosphere. The paper presents a new model for predicting the spectral bioalbedo of snow and ice, which confirms that ice algae on ice surfaces can change its colour and by doing so enhance its melt rate (“bioalbedo”). We also used the model to critique the techniques used to measure bioalbedo in the field. The model is based on the SNow ICe and Atmosphere Radiative model (SNICAR), but adapted to interface with a mixing model for pigments in algal cells. We refer to the coupled models as BioSNICAR.

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The darkening effect of algal growth (bioalbedo) in the visible wavelengths can be seen in this UAV image of our 2016 field camp at S6 on the Greenland Ice Sheet

The model uses Mie theory to work out the optical properties of individual algal cells with refractive indices calculated using a pigment mixing model. The user can decide how much of each pigment the cell contains, the cell size, the biomass concentration in each of n vertical layers, the snow/ice optical properties, angle and spectral distribution of incoming sunlight and the mass concentration, optical properties and distribution of inorganic impurities including mineral dusts and black carbon (soot). From this information, the model predicts the albedo of the surface for each wavelength in the solar spectrum. This can then be used to inform an energy balance model to see how much melt results from changes to any of the input values, including growth or pigmentation of algae.

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This figure shows the effect of algal cells with different pigmentation on spectral albedo of snow/ice. In A) the cells have 1.5% chlorophyll a and 10% photoprotective carotenoids, B) 1.5% chlorophyll a and 5% photoprotective carotenoids, C) 1.5% chlorophyll a and 1% photoprotective carotenoids and D) 1.5% chlorophyll a only. These percentages are % total cell dry mass. The biomass is shown in the legend, which applies to all four subplots.

 

The model shows that smaller cells with photoprotective pigments have the greatest albedo-reducing effect. The model experiments suggest that in most cases algal cells have a greater albedo-reducing effect than mineral dusts (depending upon optical properties) but less than soot.

 

Fig4
A) An equal biomass concentration with varying vertical distribution in the snow/ice; B) mineral dusts in varying mass concentrations doing a good job of recreating the ‘red-edge’ (see previous post); ) mineral dusts obscuring the spectral signature of algal cells; D) the effect of water in interstitial pore spaces. Ice grains are 1000 microns in diameter and the legend refers to the thickness of liquid water coating around the grains (microns). Note the shift of the absorption feature centred at 1030 nm towards shorter wavelengths when more water is present.

 

As well as making predictions about albedo change, the modelling is useful for designing field experiments, as it can quantify the error resulting from certain practises, such as using devices with limited wavelength ranges, or neglecting to characterise the vertical distribution of cells. I’ll cover this in some further posts. The most important thing is metadata collection, since standardising this enables the measurement conditions to be as transparent as possible and encourages complementarity between different projects. Importantly, following a protocol for albedo measurements and collecting sufficient metadata will make it easier to couple ground measurements to satellite data. We outline two key procedures: hemispheric albedo measurement, and hemispherical-conical reflectance factor measurement. To accompany the discussion in our paper, we’ve produced some metadata collection sheets that might be useful to other researchers making albedo measurements in the field (download here: metadata sheets) and made our code and data available in an open repository.

Weston Park Museum: Everyday Wonders

The Western Park Museum recently got in touch to talk about their excellent Arctic World exhibition. I know the museum well as it is a two-minute stroll across the park from my office in Sheffield, so I was really pleased to offer some thoughts. The idea was to produce a new book (‘Everyday Wonders: 50 objects from Weston Park Museum’) that gives a whistle-stop journey through the museum, stopping by at 50 of the most iconic and interesting artifacts on display. I was asked to comment on Snowy, the polar bear – the centre-piece of the Arctic World exhibition. A photo of my contribution to the book is below, but I encourage anyone who is interested to visit the museum and perhaps purchase a copy for themselves.

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The Musueum can be found at:

@MuseumsSheffield

http://www.museums-sheffield.org.uk/

Video: Alive and Well: Microbes Add to Melting of Greenland Ice Sheet

Peter Sinclair and Yale Climate Connections have released an excellent video detailing the role of microbial life in driving Greenland Ice Sheet melt, featuring several researchers from the Dark Snow Project and Black and Bloom. We were lucky enough to have Peter with us for a couple of days at the beginning of our trip in 2016.

Svalbard UAV: Lessons learned

 

Here are a few things I learned after ten days of field testing the UAV multispectral data acquisition in Svalbard…

Video showing take off in stabilize mode, switch to loiter mode at about 5 m, quick control test then into automatic mission. 

1. The UAV is surprisingly robust.

The aircraft was transported to the sites on a sled on the back of a snowmobile, hitting sastrugi and general lumps and bumps in the landscape, was hold baggage on three flights, launched and landed in snow, flown in winds and operated at temperatures down to -25 degrees. It is still is perfect working order and flew every mission without issue. I’m now pretty confident in the flight case and arrangement of kit inside, and trust that there won’t be significant flight issues in Greenland in summer.

2. Very low temperatures rapidly deplete the LiPo batteries

One noticeable, and unsurprising, effect of flying in these conditions was that the battry ran down very quickly. The toughest day was about -19 but with strong winds, and we only managed a 2.5 minute automated flight before the battery voltage dropped low enough for me to get twitchy and land the UAV manually.

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Taking off on a particularly cold flight, near Telbreen, Svalbard

3. A dGPS and ground control points will be essential for ground truthing the UAV imagery 

It was impossible to accurately pinpoint ground sampling locations using handheld GPS and ground feature ID. This was especially true in Svalbard because of the homogenous snow cover, but will also be an issue in Greenland in the summer. This is not really surprising, but the need for an accurate dGPS location lock has been reinforced by the test flights.

4. The workflow for geotagging multispectral images using Photoscan is not as straightforward as I originally thought. 

The red-edge camera does not output GeoTIFFs – they have to be postprocessed with ground control point data before they can be stacked and aligned. This is not a big problem, but good to know in advance of summer data collection.

5. New landing gear needed

The three-leg solution currently used on the UAV is better than I expected, but there is still the real problem that the legs sink into soft ground (e.g. snow) which risks pushing the camera lenses and the internal electronics into the snow. This could scratch the lenses or soak the electronics when the snow melts. Also, with the current model, a problem with any one of the three legs compromises the whole UAV because it becomes impossible to land flat. I’m going to develop something new before Greenland.

Video showing manual landing on a patch of compacted snow after an auto mission. It would be more stable on soft ground with a skid-type landing gear rather than the three legs. Includes heckling by Tedstone…!

6. Some a priori knowledge of image area can be useful

High resolution imagery is not available for all locations – for some of our field sites there was not sufficient google earth coverage to orient ourselves or draw a polygon by eye in Mission Planner.  It is possible to estimate using the compass and the map scale, but with some existing knowledge of the GPS coordinates of the grid perimeter would help to plan an accurate mission.

7. It’s amazing how small the UAV looks, even when flying quite close by

Even flying the UAV at 30 or 40 m elevation, it quickly becomes difficult to keep track of its orientation when it flies a hundred metres or so on a mission. This does have implications for the length of mission I’d be comfortable flying, since I want to be able to rescue it manually if there are any GPS or autopilot issues – maybe I’m soft but my comfortable range is less than the CAA ‘dronecode’ distance limits- even though these do not currently apply in Svalbard/Greenland. Of course, the conditions (esp. visibility) affect what feels comfortable.

8. The controller is awkward in gloves

At -25 C gloves were pretty essential, but it is also difficult to have fine control over the switches and sticks on the controller. I was flying in a very thin pair of gloves or gloveless, which meant my fingers quickly went numb, especially when there was any wind. This will be less of a problem in Greenland in summer, but I will still get some warmer, thinner gloves with rubber finger pads to help with the UAV control in the cold.

9. Pre-flight checklists are invaluable

It’s so easy to overlook or forget something in harsh conditions or when rushed or excited. The written checklists developed before we went out to Svalbard were extremely useful for making sure everything went smoothly. These are a condition of CAA compliant flights in the UK and our experience in Svalbard demonstrates why! I will probably add a few additional checks or reorder a few things before our Greenland deployment – site specific things like take off and landing zone preparation (in Svalbard it was often a compacted snow platform, in Greenland I intend to use plyboard to avoid melt ponds and cryoconite holes).

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One of the field sites at Reiperbreen, Svalbard

Challenges in quantifying ‘bioalbedo’

On Wednesday last week I traveled to the University of Bristol to give a seminar at the Centre for Glaciology. I presented a new physical model for the spectral albedo of ice with algal growth, along with some field data from 2016. Preparing for the talk, discussions with fellow researchers and insightful questions in the Q&A all reinforced some key issues that remain unresolved in bioalbedo studies – fundamental questions that have proven difficult to answer. First, do algae darken ice? Second, are they widespread enough to have ice sheet scale impact?

The answer to the first question is a clear yes. That dark materials contaminating an ice surface lower its albedo is not surprising. However, the crucial follow-up question is “by how much?” and this is much more challenging to answer; however, physical modelling provides a clear framework for determining the impact of an algal bloom on ice albedo. With sufficient information from empirical lab and field studies, we can quantify the bioalbedo effect and characterize its variability over space and time.

Standing in the so called ‘dark zone’ on the Greenland ice sheet, the answer to the second question also seems to be a clear ‘yes’. The ice surface is dark for as far as the eye can see in all directions, and wherever ice is sampled and examined under the microscope, it is found to be teeming with algal cells. However, what is visible from standing in the dark zone and what is important at the ice-sheet scale are two different things. To quantify algal coverage over the ice sheet we need to be able to detect blooms remotely, ideally from space using spectral data from satellites. This method of mapping is routine for terrestrial vegetation and algal blooms in the ocean; however, there are specific challenges to doing the same for algal blooms on ice.

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A field camp in the ‘dark zone’ on the Greenland ice sheet, where the surface is darkened by expansive, dense algal blooms along with other impurities.

 

The most common way to identify photosynthetic life in satellite reflectance data is to apply the ‘red-edge’ biomarker. This refers to a sharp rise in the reflectance spectrum of a surface due to vegetation because of efficient absorption by chlorophyll and very little absorption at near-infrared wavelengths (which has been suggested to be the result of evolutionary pressure to avoid overheating, or alternatively a side-effect of the evolution of cell-spacing in early aqueous plants). This has also been proposed as a spectral feature that could be used to map photosynthetic life on other planets. Amazingly, the red-edge has been detected in Earth-shine (light that has reflected multiple times between the Earth and moon and faintly illuminates the dark part of crescent moons), which provides a hemisphere-integrated reflectance signal for our planet. Since ice algae is photosynthetic, it follows that it could be mapped using the red-edge biomarker.

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The ‘red-edge’ in the reflectance spectrum for green vegetation. This diagram is from Seager and Ford (2002)

However, there are several issues that may complicate matters and increase the risk of a ‘false-positive’ result from applying the red-edge biomarker to Earth’s ice. These are

1. Carotenoids obscuring chlorophyll

Ice algae produce photoprotective carotenoid pigments that absorb over a wide range of visible wavelengths. They have a strong but broad absorption spectrum (which is why they protect the algae from ‘sunburn’). This could obscure the chlorophyll ‘bump’ near 500 nm and make interpretation of the red-edge more difficult. While the carotenoids themselves might provide a diagnostic reflectance spectrum, they too are hard to distinguish from other reflectance-reducers on ice.

2. Dust

Dust also absorbs strongly in visible wavelengths and also reflects effectively at red wavelengths, leading to a pseudo-red-edge feature in the reflectance spectrum. The precise shape of the reflectance spectrum varies for each mineral, and actually no mineral exactly replicates the vegetation red-edge signal. However, dust on ice is not composed of a single mineral, and both the dust and any biological impurities are mixed together and set in a complex ice matrix with its own reflectance spectra. It is feasible that the slope of the red-edge might be diagnostic of biological impurities, but this requires truly hyperspectral (i.e. spectral resolution of 1-2 nm) and will not be achievable using current satellite data. These issues combined lead to a high chance of a false positive result from the application of the red-edge biomarker to ice surfaces. This is especially important for explaining the ‘dark ice’ on the Greenland ice sheet since the two leading hypotheses are biological growth and outcropping dust.

3. Spatial integration reducing signal

An additional important issue is that any biomarker signal will be diluted by spatial integration over the viewing footprint of a satellite sensor. The presence of clean ice, ponded water, cryoconite, abiotic impurities or roughness elements will decrease the signal to noise ratio, probably further obscuring the red-edge signal.

These issues do not necessarily prohibit the use of the red-edge biomarker, but they do necessitate robust correction for abiotic impurities (particularly dusts) and rigorous ground truthing to validate the application of the biomarker to satellite data. There was a fascinating discussion in the planetary sciences in the early-mid twentieth century surrounding a reflectance signal detected on Mars which spread to cover wider areas each spring. This was proposed to be evidence of Martian plant life (e.g. Lowell, 1911); however, this hypothesis was discredited by further spectral analysis (Millman, 1939) and was then shown to be due to blowing dusts (Sagan and Pollack, 1969).

While physical modelling paired with ground reflectance measurements and sample analysis can answer the first fundamental question (do algae darken ice?), the second question (are they widespread enough to have an albedo-lowering effect at the ice sheet scale?) may prove challenging to answer robustly.

Refs:

Arnold, (2008) https://link.springer.com/article/10.1007%2Fs11214-007-9281-4

 

Lowell, P. (1911) The cartouches of the canals of Mars. Lowell Obs. Bull. 1(12), 59–86.

Millman, P.M. (1939) Is there vegetation on Mars? Sky 3, 10–11.

Sagan, C. and Pollack, J.B. (1969) Windblown dust on Mars. Nature 223, 791–794.

Seager and Ford (2002): https://arxiv.org/abs/astro-ph/0212550

Seager et al (2005) https://www.cfa.harvard.edu/~kchance/EPS238-2012/refdata/Seager-red-edge-2005.pdf

Diverse microbial habitats on the GRIS

We are now well into planning 2017 field work so I revisited some archive footage from previous trips. The short clip below provides a good summary of the great diversity of microbial habitats that exist, even within a very small area of ice. These include cryoconite holes, a cryo-pond (the big cryoconite and water filled pool), algal blooms on the ice surface, dispersed cryoconite, streams, cryoconite ‘alluvium’ stranded on the stream banks, weathered ice  and the snowpack. The clip also shows how hummocky and non-uniform the ice surface is near the margin of the ice sheet.

 

To get a better idea of how these habitats are arranged spatially we also flew a small UAV (unmanned aerial vehicle) with a downwards-looking HD camera. The clip below shows some of the footage. The winds were pretty strong and you can actually see the landing gear bow into shot every so often. We’ll have a more sophisticated UAV system in Greenland in 2017 that will collect images at specific wavelengths of light.

Finally, here is a short clip of the 2016 team at the S6 camp enjoying a beautiful full moon over the ice sheet. This site is well into the ‘dark zone’ where impurity loading is very high. We’ll be back there this summer to measure the effect of this on the reflectivity and therefore melt rate of the ice sheet.