Albedo
Glacier energy balance is particularly sensitive to albedo, a concern for this study. Lewis
(2006) and Oerlemans (2009) both discuss the importance of albedo in energy balance studies.
In relation to glacial melt, a 10% change in albedo may have a large effect (Cottom, 2009).
Changes in melt itself will lower (increase melt) or raise (decrease melt) albedo (Greuell, 2007).
Albedo is largely a function of snow and ice grain size, but impurities and liquid water alter the
correlation (Brock, 2000). Albedo is difficult to resolve without direct measurement due to a list
of determinants including snow cover, cloud cover, elevation and debris.
Snow, often found on glacier surfaces prior to ice melt seasons, may increase albedo
greatly should it fall on glacier ice during the melt season (Hock, 2005). A positive correlation
exists between winter snow accumulation and summer melt; less snow accumulation leads to
lower summer albedo, itself leading to more melt (Greuell, 2007). An ice surface albedo of
about 0.3 will increase to about 0.8 with new fallen snow. However, albedo drops by as much as
0.3 within a few days of snow fall (Hock, 2005). A thin snow cover, less than a half-centimeter
water equivalent, has an albedo correlated to the underlying surface (Brock, 2000). Further,
Brock Willis (2000) observed snow albedo to almost equal ice albedo by the end of the melt
season due to the build up of impurities.
The cloudy sky producing snow or other precipitation itself affects albedo. Cloudy sky
can increase snow albedo 3-15% (Hock, 2005). A positive feedback loop results when a
decrease in precipitation lowers albedo; the lowering is enhanced by the reduction of cloud cover
(Francou, 2003). For satellite albedo data, care must be taken to account for clouds and shadows
created by complex terrain (Corripio, 2004).
Albedo has been shown to increase with elevation, a concern for glaciers in complex,
high terrain (BrockWillis, 2000 and Paul Earsel, 2005). Albedo patterns sometimes changed
with season and elevation in a study by Brock (2000). Brock (2000) parameterizes ice albedo
with elevation, an important consideration for all mountain glaciers.
Debris on ice and snow offers the greatest effect on glacier albedo. Sediment and rock
debris effects on glacier albedo are outlined by Hock (2005). Typically debris lowers albedo,
however ablation decreases after debris cover reaches a threshold of two centimeters, when
debris begins to act as insulation (Mattson, 2000 BOOK). The greatest ablation rate was
observed with a debris cover thickness of about one centimeter (Mattson, 2000 BOOK). This
thickness of debris cover rather than acting as insulation transmits more shortwave radiation to
the glacier surface (Mattson, 2000 BOOK). In contrast to Mattson’s (2000, BOOK) thin cover,
Pelto (2000, BOOK) observed debris thicknesses greater than 20 centimeters acting as insulation,
also noting finer grained debris as a better insulator. The resulting effect was an estimated
reduction of melt on Columbia Glacier, North Cascades, Washington, USA of 25-30% annually
(Pelto, 2000 BOOK).
The type of debris present will have different affects on albedo. Mineral dust on an ice
surface may stimulate the growth of algae, reducing albedo further (Oerlemans, 2009). Bahadur
(p.43) notes significant biomass and bacterial spread on Himalayan glacier surfaces. Studying
black carbon, Ming (2009) found greater concentrations at lower elevations (possibly transported
by melt water) reducing albedo by about five percent. A similar spatial variability of albedo is
seen by Oerlemans (2009) with glacier tongues having lower albedo, usually from mineral and
humic dust accumulation.
The accuracy of albedo measurements, even with the best methods, is rather low. Due to
instrument and model sensitivity the stated accuracy of effective albedo determination was 0.15
in a study by Weihs (2001). Remotely sensed albedo values have been used in other studies.
Greuell (2007) used MODIS for albedo, finding it to be mostly accurate when compared to in
situ measurements. The disadvantages to the MODIS method are cloud cover disruptions of
measurement, no measurements above 80 degrees latitude, and a resolution that misses small
glaciers (Greuell, 2007). Similarly, Paul Earsel (2005) obtained albedo data from satellite and
corrected for topography and atmosphere. Visible imagery, i.e. photographs, has a flaw in the
inability to detect the transition from snowfall to firn (Greuell, 2007). Other automated methods
may have similar difficulties in determining proper boundaries between snow, firn, debris, and
ice.
As a means to fill in missing or difficult to obtain data, albedo modeling is widely used.
Most work is focused on modeling snow albedo. Many models have been proposed to relate
albedo to grain size and atmospheric properties, but data can be cumbersome (Hock, 2005). As
an example, Brock (2000) modeled snow albedo using daily maximum temperatures since
snowfall. Deep snow followed a logarithmic function and shallow snow an exponential function
to decay albedo to that of the underlying ice (Brock, 2000). Modeling ice albedo with elevation
showed only a minor improvement over a constant ice albedo (Brock, 2000). Less work has
been conducted on ice albedo because it is often taken as a constant; models switch to a fixed ice
albedo after snow has melted (Hock, 2005).
The variability of albedo for each glacier and on each glacier hampers the estimation of
albedo. Albedo has high variability over small areas and from year to year (Corripio, 2004 and
Paul Earsel, 2005 and list of references for short term albedo variability (last paragraph, page 3)
(Oerlemans, 2009)). Due to high variability and an inability to measure all glacier albedo, an
average albedo value must be determined. Greuell (2007) calculated an average albedo value
over a glacier surface for computation. For global calculations and for determining a maximum
threshold of melt, a universal value is used.
0.9
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Albedo
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Figure 1: Published Albedo Values
All of the found, published glacier ice albedo values create a range from 0.03 to 0.85 (see
Figure 1) (e.g. Brock, et al., 2000, Cottom, 2009, Paul and Haeberli, 2008). High values are
likely statements of glacier ice partially or fully covered in snow. Low values originate from
studies concerned with debris, black carbon, and biomass growth on glaciers. Obtained values
fall into three categories: those stated in the text or in a table, those derived from a figure, and
those averaged from a stated minimum and maximum. In the case of continuous time series of
albedo values, the average value between minimum and maximum is taken. Using the compiled
list of albedo values, basic statistics are: mean = 0.386, standard deviation = 0.196, and median =
0.35. For the determination of the maximum glacier melt water contribution to streamflow, a
maximum reasonable amount of melt must be calculated. As albedo is a main driver of glacier
ice melt, a suitably low value must be used. The lower 25th percentile of the compiled albedo
values is used to determine a reasonable upper bound of glacier melt. The lower 25th percentile
value is 0.238.