Surface melting of mountain glaciers: the effect of ice surface properties on melt rates
Dr Mark Smith (SoG), Duncan Quincey (SoG), Mike James (Lancaster University)Contact email: email@example.com
Recent climate change has impacted glacier volumes worldwide through alteration of glacier mass balance (among other things). Many adjustments are well understood; yet our knowledge of changing surface energy balances remains limited. Glacier surface roughness is an important control on turbulent heat exchange at the ice-atmosphere interface, affecting the surface energy balance and melt rates. The relative contribution of turbulent fluxes is predicted to become more significant in a warming climate. Through shading, ice roughness also determines surface shortwave radiation receipt. Yet, ice roughness is afforded little attention in surface energy balance models and is represented as spatially uniform and static. This project utilizes recent developments in high-resolution surveying to obtain spatially-distributed and dynamic ice surface roughness maps and relate these to field measurements of aerodynamic roughness heights. Spatial and temporal roughness variations will be incorporated into existing surface melt models and their performance will be validated against observed melt data. In addition, this project exploits the radar-backscattering response of ice surface roughness as a method of scaling-up this approach through acquisition of TerraSAR-X data over the chosen field site(s).
Fieldwork for this project will be undertaken on glaciers already being studied by the supervisors, which includes undertaking field work in the Himalayas, Austrian Alps, Greenland, Iceland or Arctic Sweden, or potentially a combination of sites.
Background and rationale
What determines the rate of surface ice melt? An ability to model the surface energy balance of glaciers is essential if we are to accurately predict glacier response to projected climate change. Glacier recession has been linked to increased water shortages and outburst flood hazards in many parts of the world. Ice surface roughness plays a key role in determining the surface energy balance and mass balance of glaciers, but owing to data collection demands, has yet to be incorporated fully into melt models.
Recent advances in geomatics have presented new opportunities by offering unprecedented resolution topographic data. Consequently, there is increasing attention on characterizing and parameterizing ice surface roughness. The River Basin Processes and Management cluster in the School of Geography has extensive experience in high-resolution surveying techniques (Smith et al., 2015; Carrivick et al., 2016) including their successful application to the study of ice surfaces. Previous work by the supervisors has assembled a dataset of the spatial variability of ice roughness on a single glacier in northern Sweden and developed new methods of extracting this information from point clouds (published recently in Smith et al., 2016). Current research efforts aim to validate these new methods of estimating aerodynamic roughness from topographic roughness through setting up meteorological masts on the debris-covered Khumbu glacier in the Himalayas (work currently in preparation). This Ph.D. project will build on this active research area.
For global impact, we also seek methods of upscaling our ability to map ice surface roughness using more readily available data. Upscaling requires interrogation of space-borne (or air-borne) remote sensing data products and the development of surrogate measurements of surface roughness, using radar backscattering, for example. Previous attempts to make such links were hindered by inadequate field and remote sensing data (e.g. Rees and Arnold, 2006). However, the launch of TerraSAR-X coupled with advances in ground-based survey techniques means that we are now well-placed to develop surface melt models incorporating spatially-variable roughness from remote sensing data.
Key research questions
In this project you will incorporate spatially variable and dynamic roughness into existing surface melt models and establish the subsequent effect on predicted glacier melting rates. The project can evolve according to your research preferences but key research questions could include:
To what extent does surface roughness vary across and between mountain glaciers? How does this impact glacier melting both in the present day and in a warming climate?
Is surface roughness dynamic? Can the temporal change of roughness through the melt season be predicted? Are their feedbacks between ice surface roughness, albedo and melting?
Does incorporating spatially and temporally variable ice roughness in surface energy balance models improve predictions of melt?
Can relationships between radar backscatter and surface roughness be used to upscale this approach to a regional or global scale?
Several field seasons to mountain glaciers in the Arctic, Alps, Greenland, Iceland and/or Himalayas will be required for this project. Field work will include multi-temporal glacier-wide surveys using the state-of-the-art UoL Terrestrial Laser Scanner (TLS, Figures 1 & 2). Embedded within these large-scale surveys will be ultra-high resolution patch scale photogrammetric surveys (using Structure-from-Motion techniques). Mike James (Lancaster University, external supervisor) has been at the forefront of this technology (James and Robson, 2012) and part of this project will include innovative new SfM processing methods to integrate uncertainty in the survey products. Roughness analysis will be conducted on these topographic datasets and more sophisticated parameterisations of aerodynamic roughness from ice surface topography can be realised. Field measurements of wind surface profiles will be made for comparison. A weather station will be installed at the field site(s) and a vented pressure transducer will record melt water in a proglacial stream. TerraSAR-X data will be obtained and processed to examine backscatter over rough ice surfaces. Existing surface energy balance models will be adapted to incorporate spatially and temporally variable ice-surface roughness.
A geomorphological background and knowledge of remote sensing and GIS-based analysis is essential. Knowledge of glaciology and melt processes is desirable, as is some experience of (or at least willingness to learn) numerical modelling.
The successful candidate will benefit from inter-disciplinary training in project specific research methods including GIS-based analysis and topographic surveying using cutting edge technologies (e.g. TLS, SfM), and numerical modelling, both internally and at external workshops. An additional important part of the training will be to attend national and international conferences to present results and gain feedback. The student will be encouraged to submit high quality papers for publication during the project.
The successful candidate will benefit from being a part of the River Basin Processes and Management research cluster in the School of Geography, and part of the wider water@leeds network and Leeds NERC DTP. The successful candidate will be supported by a wider research network interested in this issue (with collaborators in Texas, York, Aberystwyth and in the Institute for Climate and Atmospheric Science in Leeds).
Brock, B.W., Willis, I.C., Sharp, M.J., 2006. Measurement and parameterization of aerodynamic roughness length variations at Haut Glacier d’Arolla, Switzerland. Journal of Glaciology 52, 281‒297.
Carrivick, J.L., Smith, M.W. and Quincey, D.J. 2016. Structure from Motion in the Geosciences. New Analytical Methods in Earth and Environmental Science. Wiley Blackwell.
Fassnacht, S.R., Stednick, J.D., Deems, J.S., Carrao, M.V., 2009. Metrics for assessing snow surface roughness from digital imagery. Water Resources Research 45, doi:10.1029/2008WR006986.
Herzfeld, U.C., Mayer, H., Feller, W., Mimler, M., 2000. Geostatistical analysis of glacier-roughness data. Ann. Glac. 30, 235–242.
James, M. R., and S. Robson (2012), Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application, J. Geophys. Res., 117, F03017, doi:10.1029/2011JF002289.
Munro, D.S., 1989. Surface roughness and bulk heat transfer on a glacier: comparison with eddy correlation. J. Glac. 35, 343−348.
Nield, J.M., Chiverrell, R.C., Darby, S.E., Leyland, J., Vircavs, L.H., Jacobs, B., 2012. Complex spatial feedbacks of tephra redistribution, ice melt and surface roughness modulate ablation on tephra covered glaciers. ESP&L 38, 95−102.
Rees, W.G., Arnold, N.S., 2006. Scale-dependent roughness of a glacier surface: implications for radar backscatter and aerodynamic roughness modelling. Journal of Glaciology 52, 214−222.
Sicart, J. E.,M. Litt, W. Helgason, V. B. Tahar, and T. Chaperon (2014), A study of the atmospheric surface layer and roughness lengths on the high-altitude tropical Zongo glacier, Bolivia, J. Geophys. Res. Atmos., 119, 3793–3808, doi:10.1002/2013JD020615.
Smith, M.W. 2014. Roughness in the Earth Sciences. Earth Science Reviews 136, 202‒225.
Smith, M.W., Carrivick, J.L. and Quincey, D.J. 2015. Structure from Motion Photogrammetry in Physical Geography. Progress in Physical Geography 40(2) 247-275.
Smith, M.W., Quincey, D.J., Dixon, T., Bingham, R.G., Carrivick, J.L., Irvine‐Fynn, T.D. and Rippin, D.M., 2016. Aerodynamic roughness of glacial ice surfaces derived from high resolution topographic data. Journal of Geophysical Research: Earth Surface 121, 748-766.
Figure 1. Terrestrial Laser Scanner at the margins of Kårsaglaciären, Arctic Sweden (upper) and example glacier-wide Terrestrial Laser Scanner survey of Kårsaglaciären undertaken by supervision team in July 2013 as part of a proof-of-concept project.
Figure 2. Example wind tower set up to obtain measurements of aerodynamic roughness on the debris-covered Khumbu glacier (Himalayas).
Related undergraduate subjects:
- Environmental science