Pascal and several team members have just had published their paper, A physically based 3-D model of ice cliff evolution over debris-covered glaciers, in the Journal of Geophysical Research: Earth Surface. They used a new data set of high-resolution observations of cliff evolution over one ablation season to identify patterns of changes over four cliffs on the debris-covered tongue of Lirung Glacier (Nepalese Himalaya). The four cliffs have different shape, dominant orientation and slopes, and different degree and history of coupling to a supraglacial pond. The observations show that cliffs on the same glacier and at short distance from each other can both flatten and recline, remain self-similar, or expand radially. Based on the observations a model accounting for the three main processes controlling cliff evolution was developed: atmospheric melt, pond contact ablation enhancement for the cliff base, and reburial by surrounding debris. This modeling approach is able to simulate the cliff evolution over one melt season in a satisfying way in terms of horizontal and vertical extent as well as mean slope and aspect. Modelled volume losses could be validated and were in agreement with TIN-based observations (Brun et al. 2016, Journal of Glaciology).
Simulated cliff evolution based on the monthly updated outlines.
The model is able to capture the main cliff dynamics and geometric transformation. Importantly, the model application has clearly shown that for very high resolution studies, neglecting a dynamic update of the cliff geometry would lead to erroneous results in terms of backwasting patterns and volumes.
Research team member Evan Miles, based at the University of Cambridge, has recently passed his PhD viva and will now be Dr Miles! His thesis, titled ‘Spatio-temporal variability and energy-balance implications of surface ponds on Himalayan debris-covered glaciers’ has combined remote sensing, field surveys, and numerical modelling to understand the role of supraglacial ponds.
Figure 1: Evan Miles and Anna Chesnokova installing thermistor strings and a pressure transducer in a supraglacial pond on Lirung Glacier, May 2014.
New OA paper studying spatio-temporal variability of supraglacial ponds
Evan and several other team members have just had an Open Access paper published in the Journal of Glaciology based on one aspect of Evan’s doctoral work, Spatial, seasonal and interannual variability of supraglacial ponds in the Langtang Valley of Nepal, 1999–2013. The study uses 15 years of Landsat data (172 scenes) to analyse the variability of supraglacial ponds on a set of five debris-covered glaciers. This is a major advance in several ways, as prior studies have used only a few scenes to assess ponded area for debris-covered glaciers. The use of such an extensive dataset allowed the authors to also assess the spatial patterns of ponding and interannual changes in pond cover in a more robust way than has been done previously.
Figure 2: Spatial distribution of supraglacial ponds as percent of May–October observations (n =68), also showing results for other lakes outside the debris-covered tongues (orange ellipses), 1999–2013.
One of the most important results is the seasonal variability of ponds, which controls the role they can play in a glacier’s mass balance. Expect more news on that topic soon!
Figure 3: Seasonal pattern of thawed pond cover as percent of observable debris-covered glacier area, with individual scenes coloured by year of observation (n =172) and dot tails highlighting the effect of a 34% overestimation of pond area. The solid black line is the monthly mean, with dashed lines showing the ± 1σ spread.
Evan Miles to start Post-Doc in Leeds
Finally, Evan has accepted a two-year research position at the University of Leeds to work with Dr Duncan Quincey, Dr Bryn Hubbard (Aberystwyth University), and Dr Ann Rowan (Sheffield University) as part of the NERC-funded EverDrill project. This is an exciting and ambitious effort to drill to the bed of Khumbu Glacier and install a sensor suite to understand englacial and subglacial processes, a first for the Himalaya, and a critical need identified by other MountainHydrology team members. More project info can be found here. Evan will continue to be peripherally involved in many MountainHydrology projects.
[Joe is a Senior Glacier Hydrologist at the International Centre for Integrated Mountain Development in Kathmandu, Nepal]
True fact: there have been not one but two workshops dedicated specifically to the installation of automatic weather stations (AWS) on glaciers.
The newly-installed AWS at Yala Glacier. We didn’t get these views when we did the work. (Photo credit: Jitendra Bajracharya)
One of the biggest unknowns in how glaciers will respond to climate change are the meteorological conditions and melt rates at the glacier surface, and how these conditions relate to data from standard observation networks and/or climate reanalysis products. But setting up precise sensors on a surface that can move, melt, and be buried by snow – sometimes all of these in the same day – is a big challenge. Unfortunately, for all challenges (including drinking milk upside down through a straw) you either learn by experience (AKA “mistakes”), or you learn from the experiences of others. For some reason I’ve tended to go with the former.
Our recent AWS installation at Yala Glacier is another attempt to obtain a year-long record of meteorological conditions at 5350 m in the Himalayas. At this altitude, temperatures are rarely above zero and the melt of snow or ice is basically controlled by the radiation balance at the surface (see below for a more technical discussion). So our station will record radiation received and emitted or reflected by the surface, air temperature and relative humidity, wind speed and direction, and surface height changes from melt and snowfall.
Experience tells us that ‘floating’ weather stations, such as tripods that simply sit on the top of the surface, don’t work so well on glaciers. The surface melts down unevenly, the station can be buried and damaged by heavy snowfall, and there is no way to get a record of surface lowering: the surface height sensor needs to be mounted at a fixed height in order to get information that makes any sense.
Our first attempt to measure conditions on Yala Glacier, found after typhoon HudHud in October 2014.
For the new station, we used a slick tower design that can be built up in the field (full credit to Alex Jarosch and Faron Anslow; tower recipe below or see P. 52-55 here). Essentially, we connect three 2.0 m aluminum pipes vertically to make a 6.0 m tall triangular structure. Horizontal supports brace the top 2.0 m of the tower, and the bottom 4.0 m of each leg is drilled in to the ice. If you’re going to try this at home, don’t forget to stick small plastic caps on the bottom of the pipes that go in the ice. Without these, the weight of the tower would be supported on a very small surface area and it would melt into the ice – probably due to heat conduction through the aluminum. If the tower sinks into the ice during the experiment, the surface height measurements are meaningless. (Thanks, experience!)
Once the base and the tower are installed and leveled, the waterproof enclosure (which contains the battery, solar charge controller, and the datalogger) and all sensors were mounted to the tower. In the time-lapse animation shown below, you can see the clouds rolling up and down over us as we mount the sensors. In response, we shed layers and then put them back on, because the thickness of the cloud layer really affects the ‘felt’ temperature at the surface (you should really read the technical explanation below). Air temperatures during the setup hovered around 0C.
The tower from above: logger box and temperature/humidity sensor is on the left, wind sensor is top right, and it looks like three people are required to mount the net radiometer (which measures shortwave and longwave radiation – really, there is no longer an excuse to not read just a little bit more in depth below).
The full installation took only half a day, and we were back drinking tea in camp by mid-afternoon (though thankfully not upside down and with straws). But getting the equipment and the tower components up there literally took a small army. We have nothing but huge gratitude and respect for Dawa Sherpa and Ngawang Sherpa who helped haul everything up the glacier, and to all the trekking agency staff who carried everything up from the trailhead at 1600 m to the basecamp at 5000 m.
[Thanks to Maxime Litt and Desiree Treichler for their help in the field, but also for the pre-field testing and programming. This is a critical step in the recipe.]
Glacier Station Recipe
9 x 2.0 m aluminum pipe (48.25 mm OD)
9 x 0.50 m aluminum pipe (48.25 mm OD)
3 internal pipe connectors
3 external pipe connectors
3 plastic cap ends (large corks also approved)
18 x 90 degree joints (48.25 mm OD)
Ice auger (4-5 m)
Plumber’s level
Tools
AWS components and all mounting hardware (!)
Patience
Reasonably good weather
Preparation, preparation, preparation
Radiation Balance Details
The net radiation at the surface (Q*) can be calculated from incoming and outgoing shortwave and longwave radiation:
Q* = Sin – Sout + Lin – Lout
Shortwave radiation comes from the sun: its highest at solar noon, and zero at night. But the amount of radiation reaching the surface depends on clouds and the atmospheric conditions, and the amount of shortwave radiation absorbed at the surface depends on the reflectivity (or albedo) of the surface. Brighter surfaces reflect more radiation, and have a higher albedo, which means less energy available for melt.
Longwave radiation is a mainly function of temperature: incoming longwave radiation is emitted by the atmosphere, and the earth’s surface emits longwave radiation upwards. Temperatures near the surface will be warmer on cloudy nights because the clouds both (a) emit greater longwave radiation towards the surface than a clear sky and (b) trap some of the longwave radiation emitted by the ground. Incoming longwave radiation is also a function of water vapour in the atmosphere, which affects the temperature profile.
