As a bit of an aside from the usual cold stuff, I’ve also gotten very interested recently in applying some scientific skills to streamlining processes in business settings. This led to a collaboration with the Sheffield-based business Gradconsult. They are contracted by Sheffield City Council to deliver a regional recruitment scheme called RISE SCR. RISE aims to grow the economy of the Sheffield city region by retaining graduate talent, while helping local companies find the right people to join their teams. This scheme has now gone through 12 cycles, growing each time.
The monthly reporting process had become long and arduous, with loads of data needing to be recorded for each individual business, application and applicant. This was taking the team several days every month and in the private sector, time = money. Automating the monthly reporting process frees up their time and enables them to deliver a better service to their RISE clients.
My first thought was to set to work in Python, naively thinking that an open source, free pseudocoding language was ideal for this purpose – I could almost see the pandas dataframes assembling in my mind. But it quickly became apparent that this was entirely the wrong road to go down. For a product to be useful to the business it has to a) be familiar, b) work on the company’s existing software, c) require no new specialist knowledge, d) require minimal interaction or modification, e) be transferable and immediately accessible to different team members and partners, f) be aesthetic and clear.
So, it was back to Excel. I admit I have completely binned Excel in favour of Matlab or Python over the past few years. But, it is industry standard for a reason – mostly self-perpetuating ubiquity, but also because it is a very simple interface. However, some things that would have been easy to code were a bit cumbersome in Excel, like finding unique entries in a dataset that satisfy several criteria.
A new paper, led by Johnny Ryan, shows that a consumer grade digital camera mounted to a drone can be used to estimate the albedo of ice surfaces with an accuracy of +/- 5%. This is important because albedo measurements are fundamental to predicting melt, but satellite albedo data is limited in its spatial and temporal resolution and ground measurements can only be for small areas. Methods employing UAV technology can therefore bridge the gap between these two scales of measurement. The work demonstrates that this is achievable using a relatively simple workflow and low cost equipment.
The full workflow is detailed in the paper, involving processing, correcting and calibrating raw digital images using a white reference target, and upward and downward shortwave radiation measurements from broadband silicon pyranometers. The method was applied on the SW Greenland Ice Sheet, providing albedo maps over 280 km2 at a ground resolution of 20 cm.
This study shows that albedo mapping from UAVs can provide useful data and as drone technology advances it will likely provide a low cost, convenient method for distinguishing surface contaminants and informing energy balance models.
After testing the UAV performance in Svalbard in March, I realised the original ‘tripod’ landing assembly was not going to cut it for work in the Arctic. To prevent damage from landing in cryoconite holes and to spread the drone’s weight when landing on snow, I have added some ski’s modified from off-the-shelf landing gear for RC helicopters. This also has the added advantage that if one attachment point fails, the UAV is still landable, which is not the case for the tripod design.
As well as the ski’s, I have now added the Red-Edge camera’s down-welling light sensor to the top of the casing. This will automatically correct the images for changes in the ambient light field in each wavelength.
In recent decades there has been a significant increase in snow melt on the Antarctic Peninsula and therefore more ‘wet snow’ containing liquid water. This wet snow is a microbial habitat In our new paper, we show that distance from the sea controls microbial abundance and diversity. Near the coast, rock debris and marine fauna fertilize the snow with nutrients allowing striking algal blooms of red and green to develop, which alter the absorption of visible light in the snowpack. This happens to a lesser extent further inland where there is less fertilization.
A particularly interesting finding is that the absorption of visible light by carotenoid pigments has greatest influence at the surface of the snow pack whereas chlorophyll is most influential beneath the surface. Higher concentrations of dissolved inorganic carbon and carbon dioxde were measured in interstitial air near the coast compared to inland and a close association was found between chlorophyll and dissolved organic carbon. These observations suggest in situ production of carbon that can support more diverse microbial life, including species originating in nearby terrestrial and marine habitats.
These observations will help to predict microbial processes including carbon exchange between snow, atmosphere, ocean and soils occurring in the fastest-warming part of the Antarctic, where snowmelt has already doubled since the mid-twentieth century and is expected to double again by 2050.
Our new discussion paper, led by Black and Bloom PDRA Andrew Tedstone, examines in detail why there is a stripe of dark, fast-melting ice on the Greenland Ice Sheet, particularly in the south-west. This ‘dark zone’ is clearly visible in satellite imagery of the Greenland Ice Sheet and is important because darker ice melts faster. It is crucial to understand what causes the ice to be dark there because if it grows or darkens in a warming climate then we can expect the deglaciation of Greenland to accelerate more than is currently predicted. There are two main competing hypotheses that could explain the presence of the dark zone: 1) dust melting out from ancient ice is darkening the ice; 2) algae are growing on the ice sheet and changing its colour.
The paper shows that the dark zone changes its shape, size and duration each year. This appears to be most strongly controlled by the sensible heat flux (air temperature) between June and August, number of days with air temperatures above zero, and timing of the snow-line retreat.
These findings provide some insights into which surface processes are most likely to explain the dynamics of the dark zone. The spatial distribution of the dark ice is best explained by the melting out of dust particles from ancient ice, although these particles are not dark enough to explain the colour change of the dark zone. However, these dusts may be crucial nutrients and substrates for ice algae, suggesting that the dusts control where the dark zone is, and the algae determine how dark it gets. Our other recent TCD paper showed how algae can darken ice and snow; however, there are also meteorological conditions required for algal growth including sufficient sunlight and liquid water. We suggest in the paper that the most likely hypothesis is that dust melts out from ancient ice and stimulates the growth of algae when meteorology allows it. Algae need the dust to grow, and the dust is not dark without the algae.
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.
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.
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.
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.
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.