Snow and ice penitentes

Penitentes were first described in the literature by Darwin (1839). On March 22, 1835, he had to squeeze his way through snowfields covered in penitentes near the Piuquenes Pass, on the way from Santiago de Chile to the Argentinian city of Mendoza, and reported the local belief (that is still held) that they were formed by the strong winds of the Andes. These pinnacles of snow or ice (Figure 3.3) grow over all glaciated and snow-covered areas in the Dry Andes above 4000m (Lliboutry 1954a, Lliboutry 1954b, Lliboutry 1965). They range in size from a few cm to over five metres. (Lliboutry 1965, Naruse and Leiva 1997).

Lliboutry (1954a, 1954b, 1965) noted that the key climatic condition for the differential ablation that leads to the formation of penitentes is that dew point is always below zero. Thus, snow will sublimate, which requires higher energy input than melting. Once the process of differential ablation starts, the surface geometry of the evolving penitente produces a positive feedback mechanism, and radiation is trapped by multiple reflections between the walls. The hollows become almost a black body for radiation (Lliboutry 1954a), while decreased wind leads to air saturation, increasing dew point temperature and the onset of melting. In this way, peaks, where mass loss is only due to sublimation, will remain, as well as the steep walls, which intercept only a minimum of solar radiation. In the troughs ablation is enhanced, leading to a downward growth of penitentes. A mathematical model of the process has been developed by Betterton (2001), although the physical processes at the initial stage of penitente growth, from granular snow to micropenitentes, still remain unclear.

3.3 METHODOLOGY

Meteorological data collected at two sites in the Andes by an automatic weather station was used to model the energy balance and the relative importance of its components. A summary of the instrumentation is given in Table 3.2. The model is a distributed model of solar radiation that takes into account the spatial variation both

Table 3.2 Instruments used for measuring air temperature, relative humidity, snow temperature, incoming and outgoing short-wave radiation, wind speed and wind direction

Sensor

Tair.s/ RH

T

sw; swt

U uxy

Vaisala 50Y

107 Thermistor

Kipp & Zonen CM3

RM Young 05103

Range Accuracy

-40 to 60°C 0 to 100% ±0.5°C 2%

-40 to 60°C ±0.5° C

305-2800 nm 10%

0-60 m s-1 360° ±0.3 m s-1 3%

Figure 3.4 (Plate 1) Example of the technique used to estimate the ratio of snow cover and the spatial distribution of albedo, in this case applied to an Alpine glacier, Haut Glacier d'Arolla. On the left photograph, the perspective projection of the DEM appears as grey dots, and from these, the georeferenced map of reflectance values on the right image is produced

Figure 3.4 (Plate 1) Example of the technique used to estimate the ratio of snow cover and the spatial distribution of albedo, in this case applied to an Alpine glacier, Haut Glacier d'Arolla. On the left photograph, the perspective projection of the DEM appears as grey dots, and from these, the georeferenced map of reflectance values on the right image is produced in atmospheric transmittance and in diffuse reflected radiation due to surrounding topography. In this case, we focus on the microscale, to assess the effect of ablation morphology on the whole energy balance.

For a correct estimation of the influence of surrounding land cover on reflected diffuse radiation, whether snow free or snow covered, a novel technique using terrestrial photography was developed (Corripio 2003a, Corripio 2004). This consists of georeferencing oblique photographs to a digital elevation modelDEM) and defining a mapping function between the information contained in a given pixel of the image and the corresponding cell of the DEM. This allows a simple estimation of the spatial variation in albedo and thus the influence of the surrounding land cover to be taken into account. This technique depends on the availability of digital elevation models and relies on the identification of accurate ground control points (GCPs). The procedure was not fully developed until after the field campaign, but in order to illustrate its application to mountain terrain, an example for an Alpine glacier is given in Figure 3.4.

3.4 ENERGY BALANCE MODEL

The energy fluxes at the surface of the glacier can be expressed as

where SW i is incoming short-wave radiation, a is snow albedo, L is long-wave radiation, arrows indicating incoming or outgoing, QH and ELe are sensible and latent turbulent fluxes with the atmosphere. Note that neither convective nor advective heat transfer within the snow pack was considered. However, the temperature at 1 m below the snow surface was measured on the lower AWS with a thermistor and found very stable, with a mean value of — 0.13°C and a standard deviation of 0.0023, suggesting that most variation in temperature within the snowpack is the result of diffusion from the surface, with little or no heat fluxes from internal layers in accordance with other studies of temperate glaciers during the ablation season (Arnold etal. 1996, Obleitner 2000).

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