New Paper: Glacier algae accelerate melting of the Greenland Ice Sheet

This week our new paper on the melt-accelerating effects of glacier algae on the Greenland Ice Sheet was finally accepted for publication. This paper was an absolute epic to get through peer-review – I might post about that later – but for now I’ll focus on the paper content. I’m really pleased to see this out in the literature as it represents the culmination of a huge interdisciplinary effort over a period of three years, and we have learned a great deal in the development process. Most importantly, the work opens many doors for important follow-on work.

Fig1
Figure showing the area of interst on the Greenland Ice Sheet at three manifications – A) the whole of Greenland woith our study area in the red square (and upscaling limits indicated by red line), B) 100 km x 100 km magnification of the area in the red box in A, C) a high resolution view of the field site marked with a yellow dot in B.

Context

The Greenland Ice Sheet is the largest continuous mass of ice in the northern hemisphere and is the largest single cryospheric contributor to sea level rise. The rate of melting is increasing over time, but there is still significant uncertainty around predictions of the ice sheet’s melting rate and contribution to sea level rise in the future. One of the major factors controlling the rate of melting is the darkness – or albedo – of the ice, because darker (lower albedo) surfaces warm up more in the sun. One of several factors darkening the ice is the growth of algae on the ice surface. This has been known since the late 1800’s but has only just started to be quantified. Uetake et al. (2010), Yallop et al (2012), Stibal et al (2017) in particular confirmed that algae exist on the ice surface and that they can have a local darkening effect, while Cook et al. (2017) began developing numerical methods to describe their effects. However, until now the darkening effect of the algae has not been quantified nor has it been mapped at scale. Our new paper achieves this and also provides new methods for numerical modelling of biological albedo reduction and remotely sensing algae from drones and satellites. Of course all the code and data is fully documented, 100% open and available via doi’s and active repositories on my github account.

New Findings:

The paper uses a ranges of techniques to quantify algal effects on ice albedo and melt rate. We found that in localized patches of heavy biomass loading, algae accelerated melting by up to 26%. Distributed over the south-western sector of the ice sheet, up to 13% of total runoff can be attributed to algae. The local mineral dusts were found to be mostly colourless, weakly absorbing and very small particles that have a tiny effect on the surface albedo – negligible compared to the algae. This means that we are likely systematically underestimating future sea level rise by neglecting to account for these algae in predictive models.

Fig2
A) the mass absorptio coefficient of the various pigments found int he ice algae, B) the surface albedo measured around our field site divided into surface classes (Hbio = heavy biomass, Lbio = light biomass, CI = clean ice, SN = snow), C) the relationship between algal cell abundance and surface albedo, D) a microscope image of the glacier algae plus some of the local minerals.

New Methods:

There are two main methodological contributions made by this paper. The first is a new numerical model that takes a geometrical optics approach to modelling the optical properties of the ice and glacier algae (both too large for Mie scattering to be practical) and uses empirical mass-absorption coefficients for the algae and the local mineral dusts. This enables both a separation of biological and mineralogical effects on the ice albedo and predictions about albedo-reducing effects of algal and mineral accumulation. I will explain this in more detail in a follow-up post. I am very pleased to have made the codes openly available at github.com/jmcook1186 and to have provided both Matlab and Python implementations. This was quite an effort as it required a lot of translation of legacy radiative transfer code from the original FORTRAN/Matlab as well as ongoing development in two languages. The reason to do this was two-fold: 1) to migrate the BioSNICAR codebase to an open source language so that I and others can use it without relying on an expensive software license; b) to migrate the code into my preferred language to accelerate further development. Development of BioSNICAR_GO and related codes from here will focus on the Python implementation.

The second methodological contribution is a supervised classification scheme for identifying various types of ice including algal ice of varying biomass concentrations from remote sensing data (e.g. from drones and satellites). Supervised classification was achieved using scikit-learn, but the interesting thing was that the training data came from field spectroscopic measurements rather than direct labelling of images. The advantages of this are numerous and include high confidence in our data labels and minimal effects from mixed-surface pixels. These will be discussed in detail in a follow-up post.

Fig7
Satellite (A,B,C,D,E) images processed to show surface types (coloured) and albedo (greyscale). The same was done for drone imagery at the field site (F,G).

Final Thoughts:

There are implications for climate modelling and sea level projections, but for me what is most compelling is that this is a demonstration of emergent macroscale effects of invisibly small organisms acting collectively to influence the fate of a continent-sized ice sheet. This continues to astonish me and I see it as a beautiful demonstration of emergent complexity, causality across scales and non-linearity and also an analogy to human societies where the size of individuals or groups are not linearly related to their impact. For both humanity and the glacier-algal system existential risks emerge from growth, production and ecological engineering of habitats in the pursuit of comfort and proliferation.

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