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Retrieving important mass-balance model parameters from AWS measurements and high-resolution mesocale atmospheric modeling

Article (PDF Available) inJournal of Glaciology 58(209):625-628 · June 2012with112 Reads
DOI: 10.3189/2012JoG11J258
Thomas Mölg
Thomas Mölg
Dieter Scherer at Technische Universität Berlin
  • 34.43
  • Technische Universität Berlin
Retrieving important mass-balance model parameters
from AWS measurements and high-resolution
mesoscale atmospheric modeling
Physically based distributed mass-balance models (e.g.
Kayastha and others, 1999; Klok and Oerlemans, 2002;
¨lg and others, 2009) are our most important tools for
understanding observed glacier mass change and attributing
this change to particular causes. Like all process-oriented
models, they require many parameters that control the
specific processes simulated. Among these, two critical but
poorly constrained parameters are the density,
, and the
temperature threshold, TT
, of solid precipitation on
glaciers. The latter specifies at what ambient air tempera-
tures precipitation turns from solid into liquid water. In the
literature, both parameters are usually associated with fresh
snowfall, although, precisely speaking, they also need to
incorporate other solid forms of precipitation, such as
graupel, which occurs frequently during convective precipi-
tation events on glaciers (e.g. Mo
¨lg and Kaser, 2011). These
two parameters exert a major control on the accumulation
term in a mass-balance model, and on ablation as well
through the snowfall–albedo link. Typically
can only be
measured on special occasions, i.e. when solid precipitation
occurs during fieldwork. Measurements of TT
from remote
sites are for the most part unavailable. Unfortunately,
uncertainty in these two parameters may significantly affect
model results (e.g. Reijmer and Hock, 2008; Mo
¨lg and
others, 2009).
In a recent paper, Mo
¨lg and Kaser (2011) showed for the
first time that the atmospheric surface layer over a small
mountain glacier can be explicitly resolved in a numerical
limited-area atmospheric model (LAM), which captures
synoptic-scale atmosphere–ocean dynamics in its compu-
tational domain. They also showed that the simulated surface
layer conditions can be applied successfully as input to a
physically based distributed mass-balance model, eliminat-
ing the need for statistical downscaling. This approach opens
many prospects for questions on multi-scale linkages in the
climate system and on climate–glacier relations. Apart from
these big topics, however, we argue that the output from such
high-resolution LAMs also serves as a valuable addition to
glaciological field programs, in order to improve local
cryospheric modeling. In this brief note we demonstrate the
determination of the parameters
and TT
from a fully
physical framework as represented bya LAM, in combination
with routine field measurements. Thus, we aim to introduce a
new approach for constraining uncertain parameters more
objectively in future mass-balance modeling studies, which
is of potential interest whenever automatic weather station
(AWS) and LAM data are available.
In contrast to rare measurements of
, glacier surface
height change is nowadays recorded continuously on most
AWSs at remote glacier sites, using an ultrasonic ranging
sensor (e.g. Hardy and others 2003; Van den Broeke and
others, 2004). Interpreting its data – the actual height change
– is not trivial, but usually reliable on a daily or greater
timescale (Hardy and others, 2003). Here we use the
quality-controlled daily sonic ranger data from the top of
Kersten Glacier, Kilimanjaro, East Africa, where processes
other than precipitation (e.g. snowdrift) have little and
infrequent impact on the sensor’s accumulation signal (Mo
and others, 2009). Using the short interval of 1 day helps to
ensure that measured surface-height increases primarily
reflect precipitation, rather than net accumulation over time
(cf. fig. 6 in Hardy and others, 2003).
We also consider the output from LAM runs for the same
mountain from Mo
¨lg and Kaser (2011) who achieved a
spatial resolution of 812 m by multiple grid nesting. This set-
up adequately resolves the glacierized altitudes on Kiliman-
jaro, and successfully reproduces the high-altitude meteoro-
logical conditions recorded by an AWS on Kersten Glacier.
¨lg and Kaser (2011) discuss the quality of the LAM output
in detail. Their assessment includes simulated precipitation
at the AWS, which was found to perform well in mass-
balance calculations. LAM data are available for a month of
the region’s rainy season, April 2006, which is the only
simulation period with frequent precipitation events on the
glacier. To account for LAM errors and uncertainties, three
further runs with slightly changed settings (two concerning
initial soil moisture, one a different land surface model) are
considered in the present study and were conducted by
¨lg and others (2012). Therefore, we analyze in total four
April 2006 simulations, henceforth LAM-1 to LAM-4.
In mass-balance models,
is required to convert sonic
ranger-derived input data to mass or, vice versa, to convert
water equivalent input to actual height changes. Assuming
that cumulative precipitation is reliably simulated by the
LAM, which is justified here (see ‘Data sources’ above), and
therefore that the total accumulation height recorded by the
sonic ranger reflects total LAM precipitation, the mean
can be determined (Fig. 1a). Accounting for four
LAM runs and assuming 10% uncertainty in the sonic
ranging sensor, which is more pessimistic than its nominal
accuracy of 0.01m, yields 269 23 kg m
(1). This
estimate is in good agreement with the 250 kg m
obtained by 2 year long measurements on tropical Glaciar
Zongo, Bolivia (Sicart and others, 2002). These measure-
ments represent one of the few datasets where a sonic ranger
and a precipitation gauge were operated simultaneously, in
order to determine the density of the actual precipitation
height directly from field data. Precipitation at relatively
high air temperatures (Sicart and others, 2002) and signifi-
cant amounts of graupel in addition to snow (Mo
¨lg and
Kaser, 2011) may cause the high mean
at low latitudes.
At mid- and high latitudes, mean
can be <100 kg m
(e.g. Judson and Doesken, 2000).
As the typical rainy-season pattern appears in both the
sonic ranger record and LAM output, i.e. a succession of wet
and dry spells (slopes and plateaus in Fig. 1a, respectively),
we can also explore the possibility of inferring
on an
event basis. For this exercise, we only consider days where
the non-dimensional difference between the two data
sources (Fig. 1b) is <20%. LAM-1 contributes 3 days
(indicated in Fig. 1b), and eight more qualify from the other
three LAM runs. The resultant daily
ranges between 230
Journal of Glaciology, Vol. 58, No. 209, 2012 doi: 10.3189/2012JoG11J258 625
and 329 kg m
, and obvious correlations exist with simu-
lated mean daily wind speed at 10 m (V
) and air
temperature at screen level (T
) on Kersten Glacier, –0.44
and 0.47 respectively. In a multiple linear regression, these
two variables explain 36% of the variance in daily
. Due
to the small data sample (n= 11 days) the correlations are
not significant, but they indicate the potential to develop
parameterizations of
from variables readily measured by
an AWS (near-surface wind speed and air temperature) in the
future, when longer-term high-resolution LAM simulations
will be available. Performing the same exercise with clusters
of wet days as events (horizontal bars below x-axis in
Fig. 1b), or with anomalies of
and T
, did not lead
to other insights.
For distributed mass-balance modeling, the input record of
total precipitation is usually distributed across the glacier by
an air-temperature threshold that separates solid from liquid
precipitation (e.g. Klok and Oerlemans, 2002) or by two such
thresholds between which the solid vs liquid fraction is
linearly interpolated (e.g. Reijmer and Hock, 2008). The
input should therefore always be all-phase precipitation.
Some authors have also incorporated relative humidity in the
distribution algorithm (e.g. Kayastha and others, 1999). In
general, the methodology and choice of thresholds vary
widely between studies. LAM output provides a physical
constraint for the temporal resolutions at which process-
based mass-balance models are run (1 hour). Although the
simulated hydrometeor species in LAMs are a three-dimen-
sional field, we only look at the lowest layer directly above
the terrain (here 58 m) since the altitude separating rain and
snow can be hundreds of meters higher in the free air upwind
than on the mountainside (Minder and others, 2011).
In Figure 2 we consider the mountain section from
4000 m a.s.l. to the peak of Kilimanjaro (5573 m a.s.l. in the
LAM), to ensure the temperature range for the analysis is
large enough. The hourly relation between T
and the
fraction of solid precipitation shows expected scatter
(Fig. 2a), but also reveals that values are concentrated near
100% or at 0% (81% of all points lie above the 90% or
below the 10% fraction). The analysis by temperature bins
(Fig. 2b) suggests that linear interpolation is reasonable if
mean values are considered, here in a T
range from –48C
to +48C for the full transition from solid to liquid precipi-
tation. This range is larger than what is considered in most
mass-balance model studies. It should not be regarded as
site-specific, though, but rather representative of convective
precipitation events. For example, the transition zone at
mid-latitude shows a similarly broad range during con-
vective events (e.g. Liu and Moncrieff, 2007).
However, Figure 2b also demonstrates that for tempera-
ture bins from –28C to 1.58C the mean is not a good
Fig. 1. Actual accumulation recorded by the sonic ranging sensor
(black), and water equivalent precipitation simulated by the
atmospheric model (gray, here LAM-1), in April 2006 near the
summit of Kilimanjaro. (a) Accumulated daily data. (b) Daily
precipitation non-dimensionalized by the respective mean value;
numbers in the plot indicate selected daily events, and horizontal
bars below the x-axis localize wet spells.
Fig. 2. (a) Hourly 2 m air temperature versus the fraction of solid precipitation (snow + graupel + hail) on the lowermost atmospheric model
layer in the April 2006 simulation (LAM-1). (b) Different statistics of the data in (a) for air-temperature bins of 0.58C width. Analysis for
altitudes above 4000 m a.s.l. in the innermost LAM domain.
¨lg and Scherer: Correspondence626
representation of the data sample, since the distributions are
strongly U-shaped (the percentiles 25 and 75 closely
approximate the y-axis minimum and maximum, respect-
ively). A step-like transition from solid to liquid precipi-
tation, here at –18C where the mode switches (Fig. 2b),
could therefore also be argued for. For this approach the T
interval with the U-distribution is problematic, but we find
that relative humidity at screen level is an effective predictor
for the actual tail of the distribution at the resolved timescale
(Fig. 3). Thus the parameterization of precipitation phase
from T
alone is not straightforward, which is consistent
with the complexity of mesoscale dynamical processes that
control the snow–rain transition in mountains (Minder and
others, 2011). Hence, different approaches and their
sensitivity characteristics should be considered in mass-
balance model uncertainty determinations. Figures 2 and 3
are from LAM-1 output, but LAM-2 to LAM-4 outputs
provide a consistent result.
Running mesoscale atmospheric models at high spatial
resolutions (order 10
m) is common in weather
research and dynamic meteorology, but not established in
glaciological research. One exception is the use of high-
resolution LAMs in ‘local’ set-up (i.e. one domain and
forcing by local atmospheric profiles from the upstream
zone) for studying local winds and snow distribution
(Lehning and others, 2008). Resolving mountain glaciers
and their atmospheric surface layer explicitly in LAMs that
downscale large-scale climate dynamics to local meteoro-
logical conditions, on the other hand, is a recent develop-
ment and can drastically improve our understanding of
multi-scale linkages in the climate system (Mo
¨lg and Kaser,
2011). Besides this, the output from such simulations
provides a great opportunity to constrain glacier model
parameters in a way that is consistent with the region’s
synoptic-scale dynamics and governing physical processes
across the scales.
Here we have demonstrated the potential of this
approach for the density and temperature threshold of solid
precipitation on glaciers, which are often ‘best-guess’ values
in mass-balance studies. Other candidate parameters
include altitudinal gradients of meteorological variables
¨lg and Kaser, 2011). For our chosen parameters, the
sonic ranger measurements must primarily reflect
precipitation and not other processes like wind drift or
avalanche activity. Therefore, field experience is indispen-
sable to evaluate what the sonic ranger signal is recording
(Hardy and others, 2003). If this experience exists, however,
routine measurements together with high-resolution atmos-
pheric modeling are a powerful combination for improving
cryospheric studies in all climate zones of the Earth. We
should therefore increasingly run atmospheric models in
addition to on-site measurements, or seek expert collabora-
tion, as one way of reducing parameter uncertainty in glacier
mass-balance models.
This research was supported by the Alexander von Hum-
boldt Foundation. The comments of Regine Hock and an
anonymous reviewer helped to clarify the paper.
Institute of Ecology, Thomas MO
Technische Universita
¨t Berlin, Dieter SCHERER
Berlin, Germany
4 March 2012
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¨lg and Scherer: Correspondence 627
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