The Mass Budget of Arctic
Glaciers
Extended abstracts
Workshop,Workshop, 13-15
13-15 January
January 2005,
2005, Pontresina
Pontresina (Switzerland)(Switzerland)
IASC
IASC Working
Working group
group onon Arctic
Arctic Glaciology
Glaciology

Institute for Marine and Atmospheric Research Utrecht
Utrecht University, The Netherlands

The mass budget of Arctic
Glaciers
Extended abstracts
Workshop, 13-15 January 2005, Pontresina(Switzerland)
IASC Working group on Arctic Glaciology
Organised by J. Oerlemans

Institute for Marine and Atmospheric Research Utrecht
Utrecht University, The Netherlands
1

2

CONTENTS
PrefaceJohannes Oerlemans

5

Program7

List of participants

9

Mass balance observations on the Aldegonda Glacier in 2003/2004
placement
11
B.R. Mavlyudov and I.Y. Solovyanova
Meteorological measurements and the surface energy balance on
McCall Glacier, Alaska
Lisette Klok and Matt Nolan

14

Deriving glacier mass balance from ELA and AAR on Storglaciären 18
Dirk-Sytze Kootstra, Regine Hock and Peter Jansson
Simple modelling of calving glaciersJohannes Oerlemans

20

On elevation changes of Svalbard glaciers
J.O. hagen, T. Eiken and K. Melvold

25

Automatic Weather Stations in the ablation zone of the west
Greenland ice sheet
Michiel van den Broeke, Paul Smeets, Wim Boot, Johannes
Oerlemans, Wouter Greuell and Roderik van de Wal
Glacier Velocities in the Canadian High Arctic from RADARSAT-1
Speckle Tracking
N.H. Short and A.L. Gray
Use of time-lapse cameras on automatic weather stations
Matt Nolan

28

33
38

Reconstruction of past accumulation rates in an alpine firn region:
Fiescherhorn, Swiss Alps
40
Aurel Schwerzmann, Martin Funk, Heinz Blatter, Martin Lüthi, Margit
Schwikowski, Anne Palmer
Elevation change of the West Greenland ice-sheet margin near
Kangerlussuaq 2000-2003
43
Andreas P. Ahlstrøm, Niels Reeh, Lars Stenseng and Rene Forsberg
Comparative simulations of snow and superimposed ice at theKongsvegen glacier, Svalbard
50
Stefan Erath, Friedrich Obleitner, Jack Kohler, Wouter Greuell andKjetil Melvold

3

4

PREFACE
It has become a tradition that the Working Group on Arctic Glaciology of theInternational Arctic Science Committee (IASC-WAG) organizes an annual
workshop on the mass budget of Arctic glaciers. However, in 2004 this workshop
was embedded in a much larger event, namely the conference on Arctic
Glaciology, organized together with the International Glaciological Society in Geilo,
Norway (August 2004).
In January 2005 the tradition was picked up again, and a number of glaciologists
gathered in Pontresina, Switzerland for a three-day workshop (13-15 January).
Two days were dedicated to the presentation of ongoing research work anddiscussion, and one day to an excursion to the Automatic Weather Station on the
Morteratschgletscher.
A number of extended abstracts from the presentations has been collected in thislittle book, together with the programme and the list of participants. The
contributions were not reviewed, but hopefully provide some useful information andinspiration for further studies and collaboration.
I like to thank the Institute for Marine and Atmospheric Research of Utrecht
University (IMAU) for the generous support that made it possible to organize theworkshop and print this report. I am also grateful to Carleen Tijm-Reijmer for her
help with the editorial work.
Johannes Oerlemans
Chairman, IASC Working Group on Arctic Glaciology

56PROGRAM
Thursday 13 January• 13:00 - 13:15•••

Welcome and practical announcements. J. Oerlemans
chairman IASC-WAG.
13:15 - 13:45 Deriving glacier mass balance from ELA and AAR on
Storglaciären. D.-S. Kootstra, R. Hock, P. Jansson.
13:45 - 14:15 Mass balance observations on the Aldegonda Glacier(Svalbard) in 2003/2004. B.R. Mavlyudov & I.Y. Solovjanova.
14:15 - 14:45 New data of geometry changes on Svalbard glaciers. J.O.
Hagen.
14:45 - 15:15

Coffee break• 15:15 - 15:45••••

The surface albedo of Kongsvegen as derived from MODIS
satellite data. W. Greuell.
15:45 - 16:15 Glacier velocities in the Canadian High Arctic from RADARSAT1 data. N.H. Short & A.L. Gray.
16:15 - 16:45 Mass balance measurements and records in Alaska. M. Nolan.
16:45 - 17:00 Availability of data from Storglaciären. P. Holmlund.
17:00 - 17:15 Introduction to the excursion on saturday (AWS on the
Morteratschgletscher). J. Oerlemans.

Friday 14 January• 09:00 - 09:30••

Comparative simulations of the mass and energy budget at theequilibrium line of Kongsvegen glacier, Svalbard. S. Erath & F. Obleitner.
09:30 - 10:00 Modelling superimposed ice formation at a high Arctic glacier,Svalbard: an intercomparison of approaches. J.L. Wadham.
10:00 - 10:30 Elevation change of the West Greenland ice-sheet margin near
Kangerlussuaq 2000-2003. A. Ahlstrøm.
10:30 - 11:00

Coffee break• 11:00 - 11:30••

Automatic Weather Stations in the ablation zone of theGreenland Ice Sheet. M.R. van den Broeke et al.
11:30 - 12:00 Meteorological measurements and the surface energy balanceon McCall Glacier, Alaska. E.J. Klok and M. Nolan.
12:00 - 12:30 Recent glaciological studies on Hans Glacier, Southern
Svalbard: preliminary results and perspectives. J. Jania.
12:30 - 13:45

Lunch• 13:45 - 14:15

Studies of the Crystal Cave within Hansbreen, Svalbard, using
speleological technique during winter 2003/2004 (MOVIE). S. Kostka, T.
Lekarski, P. Glowacki, J. Jania.
7• 14:15 - 14:45•

Reconstruction of past accumulation rates in high alpine firn
regions. H. Blatter.
14:45 - 15:15 Ice-coring in caves. P. Holmlund.
15:15 - 15:45

Coffee break• 15:45 - 16:15••

Form and flow of large ice caps in the Russian and Canadianhigh Arctic islands. J.A. Dowdeswell.
16:15 - 16:45 Simple modeling of calving glaciers. J. Oerlemans.
17:00 - 18:30 Plenary discussion on a new IASC-WAG programme for the IPY (andbeyond). GLACIODYN.

• 20:30 - 22:00

National representatives meeting

Saterday 15 January
Excursion to the Morteratschgletscher

8LIST OF PARTICIPANTS
1. A. Ahlstrøm (aa@oersted.dtu.dk)
Technical University of Denmark, Copenhagen
2. H. Blatter (heinz.blatter@env.ethz.ch)
ETH-Zürich, Switzerland
3. M.R. van den Broeke (m.r.vandenbroeke@phys.uu.nl)
IMAU, Utrecht University, Netherlands
4. J.A. Dowdeswell (jd16@cam.ac.uk)
Scott Polar Research Institute, Cambridge, U.K.
5. S. Erath
Universität Innsbruck, Austria
6. U. Fischer (ufischer@vaw.baug.ethz.ch)
VAW ETH-Zürich, Switzerland
7. W. Greuell (w.greuell@phys.uu.nl)
IMAU, Utrecht University, Netherlands
8. R. Hock (regine.hock@natgeo.su.se)
University of Stockholm, Sweden
9. J.O. Hagen (j.o.m.hagen@geo.uio.no)
University of Oslo, Norway
10. P. Holmlund (pelle@natgeo.su.se)
University of Stockholm, Sweden
11. J. Jania (jjania@us.edu.pl)
University of Silesia, Sosnowiec, Poland
12. E.J. Klok (klok@knmi.nl)
IMAU, Utrecht University, Netherlands
13. M. Kuhn (michael.kuhn@uibk.ac.at)Universität Innsbruck, Austria
14. A. Mittermeier
University of Alaska, Fairbanks, Alaska
15. M. Nolan (fnman@uaf.edu)
University of Alaska, Fairbanks, Alaska
16. F. Obleitner (friedrich.obleitner@uibk.ac.at)Universität Innsbruck, Austria
17. A. Ohmura (atsumu.ohmura@env.ethz.ch)
ETH-Zürich, Switzerland
18. J. Oerlemans (j.oerlemans@phys.uu.nl)
IMAU, Utrecht University, Netherlands
19. F. Paul (fpaul@geo.unizh.ch)
Universität Zürich, Switzerland
20. T. Schuler (thomas.schuler@geo.uio.no)
University of Oslo, Norway
21. N. Short (naomi.short@ccrs.nrcan.gc.ca)
Canada Centre for Remote Sensing, Ottawa
22. I. Solovjanova (solir22@mail.ru)
Arctic and Antarctic Res. Inst., St-Petersburg, Russia
23. J.L. Wadham (j.l.wadham@bristol.ac.uk)
University of Bristol, U.K.
9

ABSTRACTS

10MASS BALANCE OBSERVATIONS ON THE ALDEGONDAGLACIER IN 2003/2004 PLACEMENT
B. R. MAVLYUDOV1 AND I. Y. SOLOVYANOVA2
1
2

Institute of Geography Russian Academe of Science. Staromonetny, 29. Moscow, Russia
Arctic and Antarctic Research Institute. Bering, 38. St-Petersburg. Russia

Mass balance of the glaciers of Spitsbergen is an important and reliably indicator ofclimate change in subarctic areas. In 2003 mass balance and water balancemeasurements were initiated in the basin of Aldegonda Glacier (a poly-thermal
valley glacier in West Spitsbergen), about 10 km from the Russian settlement
Barentsburg the program of mass balance and water balance measurements ofglacial basin has begun. Snow surveys were carried out in May 2003 and 2004.
Aldegonda Glacier is located on the western coast of Grønfjord (NordenskjoldLand). It flows in a west-east direction and has a length of about 3 km, and a width
up to 2 km (Fig. 1). The maximum ice thickness is 216 m (according to radio-echo
thsounding). In the 20 century the glacier has retreated from the coast over a
distance of 2 km. The low point of the glacier tongue is located at 80 m asl, and thehighest points are about 600 m asl; the average surface elevation of the glacier isabout 275 m asl.

Figure 1. Aldegonda Glacier basin and location on Spitsbergen

Meteorological conditions for 2003 - 2004 and some characteristics of Aldegonda
Glacier are summarized in the Table 1.
The winter balance (Bw) of Aldegonda glacier was determined from snow surveys.
The snow cover distribution on the glacier area was uniform in both years. In 2003
the mean snow thickness was 125 cm (85 - 218 cm); the maximum snow thickness
11was observed in the slope parts of the glacier. The mean snow density was 0.39
33g/cm (0.34 - 0.55 g/ cm , Cv = 0.20). In 2004 the mean snow thickness was 124
3
3
cm (38 - 176 cm), the mean density of snow was 0.36 g/cm (0.33-0.39 g/cm ). The
mean water storage in the snow pack in the spring of 2003 was 56.7 cm w.e., in
the spring of 2004 it was 48.6 cm w.e. In 2003 and 2004 there was no significant
correlation between snow thickness and elevation (in the spring of 1978 thecorrelation of snow depth with height was r = 0,96).
Table 1. The data from Barentsburg meteorological stations and somecharacteristics of Aldegonda Glacier.

June
July
AugustMean summer temperaturesummerwinther

2002/2003
Air temperature, °С
2,5
6,2
5,9
4,9
Precipitation sum, mm
67,4
537

Ablation period, days
Bw (cm w.e.)Bs (cm w.e.)Bn (cm w.e.)
Net balance (106 m3)
ELA (m)90
56,0-226,8-170,812,94
700

2003/2004
2,6
5,2
4,8
4,2
63,4
331
80
48,5
-207,8-159,3
11,99
700

The net balance (Bn) of Aldegonda Glacier in 2002/03 was -170.8 cm w.e. (Table1); in 2003/04 it was 159.3 cm w.e. In 2002/2003 it ranged from -366 cm w.e. at theglacier tongue to -69 cm w.e. at an elevation of 450 m asl. In 2003/2004 thespecific balance ranged from -348 cm w.e. to -109 cm w.e. These number show
make clear that the glacier lost a large amount of mass (Fig. 2).
The summer balance (Bs) was calculated by subtracting the winter balance (+Bw)
from the net balance (±Bn). In 2002/03 the summer balance was equal to -227 cm
w.e.; in 2003/2004 it was - 208 cm w.e.
Accumulation on Aldegonda Glacier correlates well with the sum of winterprecipitation. According to the Barentsburg meteorological station the sum of
precipitation in the cold period of 2002/2003 was 579 mm, and the specificaccumulation on the glacier as obtained from snow surveys was equal to 561 mm.
Based on the assumption that this good agreement holds in general, the netbalance of the glacier was calculated for the entire period for which data from themeteorological station were available (1912-2002). The summer ablation was
calculated with Mihalev's empirical formula for Spitsbergen:
3

A = (ТVI-VIII + 8.7) ,where A is ablation in mm w.e. and ТVI-VIII is the mean summer air temperature in°C.
12Figure 2. Mean values of specific winter, summer and net balance as afunction of altitude on Aldegonda Glacier.

mm w.e.

The average net balance for period from 1911 to 2003 was equal to -82 cm w.e.,
and for period from 1936 to 2003 equal to -77 cm w.e. (Fig. 3). A topographicalmap from 1936 and our GPS survey of the glacier surface in 2002 has allowed us
to estimate the glacier surface change for the period of 67 years: the glaciersurface has lowered by 64 m on average. The mean net balance of the glacier was
equal to -86 cm w.e., which is rather close to the reconstructed value mentioned
above (-77 cm w.e.).
1000

500

0

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

year
-500

-1000

-1500

-2000
winther precipitation, mm

ablation, mm w.e.

Bn mm w.e.

Figure 3. Winter precipitation and reconstructed ablation and net balance.

13

METEOROLOGICALMEASUREMENTS
AND
THE
SURFACE ENERGY BALANCE ON MCCALL GLACIER,
ALASKALISETTE KLOK* AND MATT NOLAN
Water and Environmental Research Institute, Institute of Northern Engineering, University of
Alaska Fairbanks, USA.
*Visiting scientist at IMAU, Utrecht University, the Netherlands

Introduction
We examined meteorological data of a summer period (27 May to 20 August 2004)of two automatic weather stations (AWS) to gain more knowledge about therelationship between climate and ice melt on McCall Glacier in Alaska. This
research is part of the US National Science Foundation’s Freshwater Initiative,
which aims to document changes in the freshwater inputs in the Arctic hydrologicalsystem and how they relate to climate change. Average weather conditions, theenergy balance and total ablation for the summer period are briefly described inthis abstract.

Figure 1. Two automatic weather stations: JJMC on the glacier tongue of
McCall Glacier (left) and AHAB on a mountain ridge above the glacier (right).
(Photos taken in May 2004 by Matt Nolan.)

McCall Glacier is located at 69˚18’N 143˚48’ W, in the Romanzof Mountains of theeastern Brooks Range in northeast Alaska. Since 1890, it has retreated about 800
2m, and currently, its length is 7.5 km, and its area 6.5 km . The glacier elevation
ranges from about 1400 to 2400 m a.s.l.
One AWS (JJMC) is located in the ablation area on the glacier tongue at 1715 ma.s.l. and another (AHAB) at 2415 m a.s.l. on a mountain ridge several hundred
meters above the McCall Glacier (Fig. 1). These AWS were installed in 2003 by theWater and Environmental Research Institute, University of Alaska, Fairbanks.
14

JJMC is a floating station such that the heights of the instruments remain constantthroughout the ablation season. Near JJMC, two ablation stakes measure surface
melt and a sonic height ranger (Campbell SR50) measures ablation and snowfall.
For our analysis, we used 15-minute averages of air temperature (2.06 m, fine wire
couple), relative humidity (2.06 m, HMP45AC Vaisala with a Young radiation
shield), wind speed and direction (3.05 m, Met One 034B) and the four radiation
components (1.44 m, Kipp & Zonen) from this station. Besides, we used data froma thermistor-string that measures ice temperatures every 0.5 m until a depth of
13.5 m. From AHAB, we used hourly averages of air temperature and humidity (3.0m, HMP45AC Vaisala with a Young radiation shield), wind speed (3.0m, Met One
034B), and air pressure (Vaisala CS105).

Analysis of meteorological measurements
Between 27 May and 20 August 2004, daily mean temperature at AHAB and JJMC
ranged between –8 and +12 ˚C. The average air temperature at JJMC was 5.3 ˚Cand at AHAB 4.2 ˚C. The temperature gradient between JJMC and AHAB is thus
very small (–0.2 K per 100 m altitude). 2004 was exceptional warm summer in
Alaska. For Barrow (northern Alaska), it was the second warmest summer on–1–1
record. The average wind speed at AHAB was 3.6 m s and at JJMC 3.1 m s .
The mean relative humidity was 69% and 72%, respectively.

Air temperature (˚C)

8

a

7
6
5
4
3

AHAB
JJMC

2

-1

Wind speed (m s )

1

b
4

3

2

0

6

12
18
Hour (local time)

24

Figure 2. Mean daily cycle in air temperature (A) and wind speed (B) at AHAB
and JJMC over the period 27 May to 20 August, 2004.

15

Fig. 2 shows mean daily variation in air temperature and wind speed for JJMC and
AHAB. The air temperature at AHAB shows a clear daily cycle with a daily range of
5.3 ˚C. Contrarily, JJMC hardly shows a daily variation in temperature. This can be
explained by the cooling effect of the glacier. The temperature at JJMC is
influenced by an almost constant surface temperature at melting point. Anotherinteresting result is the absence of a wind speed maximum at JJMC in theafternoon, which normally is observed on glaciers at lower latitudes. Since McCallis at a high elevation and at a high latitude, there is probably insufficient contrast
between the temperature of the ambient atmosphere and the glacier surface toproduce a maximum in the temperature inversion during day and a katabatic flow
(Streten et al., 1974). It is also possible that a valley wind retards the glacier windduring the afternoon (Streten et al., 1974).

Energy balance and specific mass balance at JJMC
For calculating the surface energy balance at JJMC, the radiation components ofthe energy balance were directly taken from the AWS and the turbulent heat fluxes
were calculated from the meteorological measurements, using a bulk method andprofile functions of Holtslag and De Bruin (1988). We used a surface roughness
length of 2.4 mm estimated by Wendler and Weller (1974) from wind profile
measurements on McCall Glacier.
The largest energy flux averaged over the analyzed period is outgoing longwave–2–2
radiation (–313 W m ), followed by incoming longwave radiation (284 W m ).
Incoming solar radiation shows the largest day-to-day fluctuations, and its average–2–2
is 181 Wm . Average reflected solar radiation is –71 W m , implying that the
average albedo is 0.39. The minimum daily mean albedo of the analyzed period isabout 0.19, which indicates a value for the ice albedo at JJMC. Increased soot anddust concentrations on the glacier surface can explain the low value for the ice
albedo since 2004 was a year with numerous forest fires in Alaska. The turbulent
heat fluxes are small and directed towards the surface: the average sensible and–2latent heat flux is 27 and 5 W m , respectively. The sub-surface heat flux is–2
estimated at –5 W m (directed away from the surface).
We compared the modeled specific mass balance from the energy balance to datafrom the sonic height ranger and ablation stakes (Fig. 3). Since the pole of thesonic height ranger melted out halfway the ablation period, there is a data gap in
the record and we had to correlate the sonic height ranger data to the stake data.
Over the period that ice was melting (15 June to 20 August), the modeled specificmass balance is –2.06 ± 0.18 m w.e (which is the sum of 0.12 m w.e snowfall,
0.003 m w.e. sublimation (deposition), and –2.18 m w.e. melt) while the measured
specific mass balance is –1.94 ± 0.09 m w.e. The radiation balance contributes
74% to the energy available for melting.

16

Specific mass balance (m w.e.)

0

-0.5

-1

-1.5
modeledsonic height ranger
ablation stakes

-2

-2.5
160

180

200
Day of year

220

240

Figure 3. Specific mass balance measured by the sonic height ranger and theablation stakes. The records start at 15 June and the modeled curve ends at
20 August. For this day, error bars indicate the accuracy of the modeled (thin)and measured (thick) specific mass balance.

References:
Holtslag, A.A.M. and H.A.R. de Bruin (1988). Applied modeling of nighttime surface energybalance over land. Journal of Applied Meteorology, 27, 689–704.
Streten, N.A., N. Ishikawa, and G. Wendler (1974). Some observations of the local windregime on an Alaskan Arctic Glacier. Arch. Met. Geoph. Biokl., B(22), 337–350.
Wendler, G. and G. Weller (1974). A heat-balance study of McCall Glacier, Brooks Range,
Alaska: a contribution to the International Hydrological Decade. Journal of Glaciology,
13(67): 13–26.

http://www.uaf.edu/water/faculty/nolan/glaciers/McCall/index.htm

17DERIVING GLACIER MASS BALANCE FROM ELA ANDAAR ON STORGLACIÄRENDIRK-SYTZE KOOTSTRA1,2, REGINE HOCK2 AND PETER JANSSON2
1
2

Department of Physical Geography, Utrecht University
Department of Physical Geography and Quaternary Geology, Stockholm University

Generally good correlations have been found between glacier mass balance andequilibrium line altitude (ELA) or accumulation area ratio (AAR). We investigate thisrelationship on Storglaciären, Sweden, and test whether the long-term massbalance – ELA or AAR relationships can be determined from transient massbalance measurements throughout just one season as suggested by Dyurgerov(1996). Detailed repeated mass balance measurements were conducted during themelt season 2004 along with observations of snow line retreat. Transient massbalances were related to ELA and AAR revealing good linear correlations,however, the slope of the curves differ significantly from the slopes obtained for the
59-year long mass balance record of Storglaciären (Fig. 1). Hence, it is notpossible to substitute the long-term relationship for Storglaciären by the one
obtained from the transient measurements in 2004.
2

Net mass balance (m)

test

1

0

-1

-2
0

20

40
60
test AAR (%)

80

100

Figure 1. Net mass balance versus AAR for the 59-year mass balance seriesof Storglaciären (crosses) and the transient mass balances during the 2004
melt season (dots). The circled dot denotes the net balance value for 2004.

18

Various analyses indicated that this deviation was not caused by errors in thetransient mass balance calculations or that 2004 was an abnormal year. There
does not seem to be an obvious physical reason for the different relationships.
More years should be analyzed since the 2004 mass balance was only slightly
negative. It remains open if the relationships agree more closely for more negative
years.

References:
Dyurgerov, M., 1996. Substitution of long-term mass balance data by measurements of one
summer. Zeitschrift für Gletscherkunde und Glazialgeologie, 32, 177-184.

19

SIMPLE MODELLING OF CALVING GLACIERS
JOHANNES OERLEMANS
Institute for Marine and Atmospheric Research, Utrecht University

Modelling of tidewater glaciers has been very limited (Van der Veen, 1997; Vieli etal., 2001). Here, a simple, highly parameterized model of a tidewater glacier ispresented. The mean ice thickness and the ice thickness at the glacier front are
parameterized in terms of glacier length and, when the glacier is calving, water
depth. A linear relation between calving rate and water depth is used. The change
in glacier length is determined by the total change in the mass budget (surfacebalance and calving flux), but not by the details of the glacier profile and the related
velocity field.
The basic equations are:(mass conservation):

dV
=B+F
dt

(1)

(ice thickness):

Hm = "m L

(2a)

!
!
(calving flux)(specific balance)!
!

(mean surface height)

Hf = max{" fL; # $ ( %w / %i )d}(2b)

F = min{0 ; c d Hf }(3)

B = " (hm # E)L

(4)

hm =

b0 + H m + b f + H f
2

!

(5)

In this set of equations V is ice volume, t is time, B is the total surface massbalance, F is the calving flux, Hm is the mean ice thickness, Hf is the ice thickness!
at the glacier front, αm and αf are the corresponding constants of proportionality, L
is glacier length, d is water depth at the glacier front, ρi and ρw are the densities of
ice and water, ε is a constant ≥1, c is the calving parameter, β the balance
gradient, hm the mean surface elevation and E the equilibrium-line altitude.
The model described by Eqs. (2)-(5) has been investigated by Oerlemans and Nick(2005). They show that the solution diagram of a tidewater glacier with a submarine
shoal (a Gaussian bump on a linearly sloping bed) has two zones of hysteresis:
one related to the height – mass balance feedback and one due to the coupling
between calving rate and water depth (Fig. 1). Depending on the bed geometry,these two zones are embedded or separated. It should be noted that theformulation for the frontal ice thickness, Eq. (2b), allows a smooth transition from aland-based glacier to a calving glacier.
20

Figure 1. Solution diagram for a tidewater glacier on a bed with a submarine
shoal, here located at a distance of about 38 km from the glacier head. Stableequilibrium states and bifurcation points (black dots) are shown as a functionof E. Two zones of hysteresis are apparent, one associated with the calving
process (A) and one due to the height – mass balance feedback (B). Modified
from Oerlemans and Nick (2005).

It is possible to couple the simple model of a calving glacier described above to asedimentation model, implying that the formation of a moraine shoal affects the
dynamics of the glacier and vice versa.
The deposition of sediment carried by the ice is a complicated process (e.g. Hunter
et al., 1996). Surface debris is partly carried by meltwater streams and channels,
findings its way to the bottom of the estuary in one way or another. Another part ofthe surface debris as well as the englacial debris will be deposited when theicebergs melt, either close to the glacier front or far away. Altogether, it seems
reasonable to assume that the deposition rate is largest at the glacier front anddrops of smoothly with the distance to the glacier front.
The deposition function P(x) used in this study is shown in Fig. 2. The length scale
λ determines over what distance along the flowline most of the sediment isdeposited a typical value used is 750 m. The deposition rate varies enormously
2 -1(Hallet et al., 1997). A characteristic value for large tidewater glaciers is 100 m a(this is per unit of glacier width).

21

P (x)!

Height0
sea level
bed
x0

x

Figure 2. Sediment deposition function used in the calving glacier model.

A number of calculations was carried out for a typical geometry of a small-slope
tidewater glacier. The climatic forcing was prescribed as a period movement of theequilibrium line.
An example is shown in Fig. 3. The initial condition is L=0 and then a glacier starts
to form because the equilibrium line is lowering (the highest point of the bed is 200
m above sea level and it drops of linearly with a slope of 0.01). After 2000 years ofintegration, when the equilibrium line attains its minimum value, glacier lengthchanges very little for a long time. The total surface balance decreases because
the equilibrium line goes up, and the calving rate becomes smaller because thewater depth decreases. During the period 2000–4000 years these fluxes are
reduced by 50%, while the glacier length shows little change. Finally, at t=4070 thesystem becomes unstable and the glacier withdraws from the morainic shoal.
Because the water depth at the glacier front then increases rapidly, the calving flux
doubles within 100 years and glacier retreat accelerates.
The corresponding evolution of the bed is shown in Fig. 4. Because the glacier
grows rapidly into the estuary, the sediment is spread over a large area and theresult is a small increase in the bed elevation over a large distance. When theadvance of the glacier front decelerates the building of a moraine shoal starts.
Shortly before the retreat of the glacier starts the moraine shoal is about 100 mhigh and a few km wide, and changes little afterwards.
It can be concluded that the change of the glacier bed due to sediment deposition
has a profound influence on the behavior of the tidewater glacier. The formation ofa moraine shoal tends to lock the position of the glacier front. Because the bed
slope in front of the shoal is steep, and gets steeper all the time, only a very small
adjustment of the glacier length is needed to change the calving flux in such a waythat it balances the surface mass balance. However, in the case of a risingequilibrium line, ultimately the surface mass balance becomes so small that the22total mass budget becomes negative. At that point the decay has to set in andrunaway retreat is inevitable.
The combined glacier-sediment model presented in this paper is about the simplest
that can be constructed. Yet, it exhibits interesting dynamic behavior. The model isable to simulate a typical advance-retreat cycle for a tidewater glacier without
subtle tuning or numerical tricks.
500
400

E (m)

300
200
100
0

L (km)

-100
40
35
30
25
20
15
10
5
0

0

1000

2000

3000

4000

5000

2000

3000

4000

5000

2000

3000

4000

5000

coastline

0

1000

4

6 10

4

-1

4

4 10

2

Flux (m a )

5 10
3 10

4

B

4

2 10

-F

4

1 10

0
4

-1 10

0

1000

Time (a)Figure 3. The response of the coupled glacier-sediment model to periodic
forcing of the equilibrium line (upper graph). The glacier length L is shown inthe middle. the lower panel shows the components of the mass budget: thetotal surface mass balance B and the calving flux F.

23

50
sea level

0

b (x)

-50

5000

-100
t=0 2500

-150
-200
-250
20

24

28

32

36

40

44

x (km)
Figure 4. Evolution of the bed profile for the reference calculation withperiodic forcing. Labels refer to time in a.

The next step obviously is to try to apply the model to real glaciers. This will require
some modifications. Rather than assuming a constant width, the model should thendescribe laterally-averaged conditions in which convergence or divergence of theflow is taken into account. It will be interesting to see if it is possible to simulate thelate-holocene history of some well documented tidewater glaciers.

References
Hallet, B., Hunter, L., and Bogen, J., 1996. Rates of erosion and sediment evacuation byglaciers: A review of field data and their implications. Glob. Planet. Change 12, 213-235.
Hunter, L. E., Powell, R. D., and Lawson, D. E. 1996. Morainal-bank sediment budgets andtheir influence on the stability of tidewater termini of valley glaciers entering Glacier Bay,
Alaska, U.S.A., Ann. Glaciol. 22, 211-216.
Hunter, L. E., Powell, R. D., and Lawson, D. E. 1996b. Flux of debris transported by ice atthree Alaskan tidewater glaciers. J. Glaciol 42(140), 123-135.
Oerlemans, J., and Nick. F. M., 2005. A minimal model of a calving glacier. Ann. Glaciol. 42,
in press.
Van der Veen, C. J. (ed.), 1997. Calving Glaciers: Report of a Workshop, February 28 March 2, 1997, Byrd Polar Research Center Report no. 5, Ohio State University(Columbus, Ohio, U.S.A.), 194 pp.
Vieli, A., Funk, M., and Blatter, H., 2001. Flow dynamics of tidewater glaciers: a numericalmodelling approach. J. Glaciol. 47(159), 595-606.

24

ON ELEVATION CHANGES OF SVALBARD GLACIERS
J.O. HAGEN, T. EIKEN AND K. MELVOLD
Department of Geosciences, Faculty of Mathematics and Natural Sciences, University ofOslo, P.O. Box 1047 Blindern, 0316 Oslo, Norway. E-mail: j.o.m.hagen@geo.uio.no

Recently, ground based GPS measurements, airborne and satellite borne laser
and radar altimeters have been used for mapping geometry changes on glaciers,
ice caps and ice sheets. Geometry changes given as longitudinal elevation profiles
have then been used in mass balance estimates by input to digital terrain models
to calculate the volume changes (mass balance). However, the geometry of
glaciers is affected by both the mass balance and the dynamics and it is therefore
not straightforward to interpret elevation changes.
In Svalbard several glaciers have been measured by repeated elevation profiles
both by ground-based GPS measurements [1] and by airborne laser altimetry [2],[3].
We have shown by three examples from Spitsbergen glaciers that different
dynamics of the glaciers can give quite different elevation profile changes
independent of the mass balance [1]. Surging glaciers are frequent in Svalbard
and during the quiescent period the glacier will shrink in the lower part and be
thickening in the upper part independent of climate or mass balance. This must be
taken into consideration when elevation profiles are used in mass balance
discussions.
In addition to our ground-based GPS measurements repeated laser profiles werecarried out by NASA in 1996 and 2002 [2], see Fig 1. The NASA results showed ingeneral a thinning of the glaciers, mostly in southern Spitsbergen, and less innorthern and eastern ice masses. On a few spots the GPS and laser profiles areoverlapping and could be compared. On the glacier Kongsvegen in north-west
Spitsbergen our GPS data and the laser profiles showed the same trend withthickening in upper part and lowering of the surface in the ablation area. Thisshould indicate that both data sets give reliable data, at least in the general pattern.
However, the numbers are not exactly the same.
On Austfonna ice cap the NASA data indicate a clear thickening of the upper
central part of the ice cap and a peripheral thinning [3]. The net balance derived
form our shallow cores indicate, however, a balance close to zero [4], and the GPS
profiles indicate less pronounced thickening and less thinning. The GPS-profiles
also show that different parts of the ice cap can develop differently. In two profiles
taken from the summit, one to the North-West and the other to the South-West(Etonbreen) showed a different trend with thickening towards the lower part in
North-West and thinning in South-West, Fig. 2. The ground based differential GPS
profiles across the ice cap have been done in 1999 and 2004. They are notoverlapping the NASA profiles, but in parts the profiles are crossing. The next stepin these analyses will be to compare the data sets for crossing points in order tosee if we can calibrate the two data sets together.
It is, however, clear from all the different data sets that we need extensive data to
be able to make reliable assessments of mass balance, covering different types
and sizes of glaciers in which the dynamic effect must be considered.
25

Figure 1. Location of repeated elevation profiles on Svalbard glaciers.
K – Kongsvegen, A - Austfonna

References[1] Hagen, J. O., T. Eiken, J. Kohler, K. Melvold, in print: Geometry changes on Svalbard
glaciers – mass balance or dynamic response? Annals of Glaciology 42[2] Bamber, J. L., W. Krabill, V. Raper and J. A. Dowdeswell, in print: Interpretation ofElevation Changes on Svalbard Glaciers and Ice Caps from Airborne Lidar Data. Annals
of Glaciology 42[3] Bamber, J. L., W. Krabill, V. Raper and J. A. Dowdeswell (2004). Anomalous recent
growth of part of a large Arctic ice cap: Austfonna, Svalbard. Geophys. Res. Lett.
31(12): doi:10.1029/2004GL019667.
[4] Hagen, J.O., K. Melvold, F. Pinglot and J. A. Dowdeswell. 2003a. On the net massbalance of the glaciers and ice caps in Svalbard, Norwegian Arctic. Arct. Antarct. Alp.
Res., 35(2), 264-270.

26

Profile - Austfonna - Etonbreen
Elevation 2004

2004-1999
3

800

2

700

1

600

0

500

-1

400

-2

300

Total elevation difference (m)Elevation m a.s.l.

Elevation 1999
900

-3
0

5

10

15
20
Distance (km)2530Austfonna North-West 1999-2004

Elevation (m a. ell.)Profile-04

Diff 04-99

5

800

4

750

3

700

2

650

1

600

0

550

-1

500

-2

450

-3

400

Total difference (m)Profile-99

850

-4
0

2.0

3.9

5.8

7.8

9.7

11.6 13.5 15.5 17.4 19.3 21.3

Distance (km)Figure 2. Two profiles on Austfonna showing a different trend towards theedge of the ice cap.

27

AUTOMATIC WEATHER STATIONS IN THE ABLATIONZONE OF THE WEST GREENLAND ICE SHEETMICHIEL VAN DEN BROEKE, PAUL SMEETS, WIM BOOT,
OERLEMANS, WOUTER GREUELL AND RODERIK VAN DE WAL

JOHANNES

Utrecht University, Institute for Marine and Atmospheric Research (UU/IMAU)

BackgroundIn 1990 and 1991, the Ice and Climate research group of UU/IMAU started its
experimental Greenland research by performing two detailed meteorologicalexperiments in the ablation zone of the west Greenland ice sheet near
Kangerlussuaq. The experiments consisted of several multi-level automatic
meteorological stations on the tundra and on the ice (Fig. 1). A base camp wasestablished at the ice sheet edge, where a cabled balloon was operated to gauge
the lowest 1000 m of the atmosphere. In 1991, the Free University of Amsterdam
performed turbulence measurements and a 30 m profile tower at 1500 m asl(Oerlemans and Vugts, 1993). During these summer campaigns, weekly massbalance measurements were performed to track seasonal melting. The massbalance measurements range in elevation from the ice edge (360 m asl) to theequilibrium line at approximately 1500 m asl, and are indicated by flags in Fig. 1.

Figure 1. Experimental set-up of GIMEX-90/91 campaigns and locations ofmass balance and AWS sites that have remained active (situation of January2005).

28

Figure 2. Overview of UU/IMAU activities in the area of Kangerlussuaq, 19902005.

These mass balance measurements have been continued up to the present day(Fig. 2), although at lower temporal resolution, namely once per year (at the end of
August). In August 1994, the mass balance transect was extended to 1850 m asl toinclude a site in the accumulation zone.
In August 1995, sonic height rangers were deployed at sites 5 and 6 to measure
ablation and accumulation continuously. The Kangerlussuaq mass balance series
now covers 13 seasons and is the longest of its kind in Greenland (Van de Wal etal., 1996; Greuell et al., 2001).
In the early 1990s, IMAU technicians started developing automatic weather
stations (AWS) for use in the ablation zone of ice sheets and glaciers. A stiff
construction was envisaged that would be able to stand freely on the ablating ice
surface, without tipping over under strong wind conditions and without tilting too
much. The final result consisted of an aluminum mast on four legs, interconnectedwith steel guy wires, with pins at the end for better grip. This design proved to bevery reliable with only limited tilt even at places with several meters of annual
ablation; it has been applied to glaciers in Iceland, Norway and the Alps and on
blue ice areas in Antarctica. In Greenland, the first year-round AWS data werecollected onwards from August 1996 (Fig. 2). Several years of successful AWS
operation followed at sites 5 and 6. Unfortunately, because the operation andmaintenance of the AWS was not funded from a dedicated project, maintenance of
the AWS became problematic which resulted in data quality loss from 2001
onwards (Fig. 2).
29

The new AWS network, August 2003
In 2003 it was decided that reliable, continuous meteorological data were requiredfor better interpretation of spatial and temporal mass balance variability. Moreover,regional climate models also urgently require need validation data from the ice
sheet and improved parameterizations of surface processes.
With funds from COACH (Cooperation in Oceanographic and AtmosphericResearch) and NAP (Netherlands Arctic Program), new stations were built andinstalled in 2003 at sites 5, 6 and 9 by IMAU technicians. Moreover, a prototype
eddy correlation station with dedicated power supply was installed at site 6. Fig. 3
shows the experimental setup at site 6. The first year of data collection from the
new network (August 2003-August 2004) revealed some imperfections in sensor
performance, but overall results are very exciting: for the first time, year-round eddy
correlation observations had been collected in the Greenland ablation zone forboth heat and moisture, while the vulnerable thermocouple had survived a full yearof observations unharmed.

Figure 3. Site 6, August 2003. From right to left: wind generator andsolar panel for eddy correlation station, the eddy correlation station,
the AWS and the sonic height ranger.
The data presently being analyzed are providing us with insight about e.g. radiation
errors of unventilated temperature huts, the validity of flux-profile relations,
temporal variability of surface roughness for momentum (smoother in winter than insummer), validity of the expressions relating surface roughness for heat andmoisture to that of momentum etc. The eddy correlation station was removed in
30August 2004 to assess its performance and to further improve its design. Based on
the present achievements, it is expected that similar eddy correlation stations will
be installed at other Greenland as well as Antarctic sites, pending funding
decisions. To conclude, the revitalized AWS network is likely to produce data
useful for the interpretation of mass balance observations, the validation regional
climate models and development of improved parameterizations for surface
exchange of momentum, heat and moisture.

Useful references
Abdalati, W., and K. Steffen, 2001: Greenland ice sheet melt extent: 1979-1999, Journal ofGeophysical Research 106, 33,983-33,988.
Ambach, W., 1977: Untersuchungen zum energieumsatz in der ablationszone desGrönländische inlandeises, Expedition Glaciologique Internationale au Groenland 4, No.
5, Bianco Lunos Bogtrykkeri A/S, København, pp 63.
Box, J. E. and A. Rinke, 2003: Evaluation of Greenland ice sheet surface climate in the
HIRHAM regional climate model, Journal of Climate 16, 1,302-1,319.
Cassano, J. J., J. E. Box, D. H. Bromwich, L. Li, S. Konrad, 2001: Evaluation of Polar MM5
simulations of Greenland's atmospheric circulation, Journal of Geophysical Research
106, 33,867-33,890.
Church, J.A., J.M. Gregory, P. Huybrechts, 2001: Changes in sea-level, in IPCC Third
Scientific Assessment of Climate Change, edited by J.T. Houghton, and D. Yihui, pp.
640-693, CUP, Cambridge.
Comiso, J.C., 2003: Warming trends in the Arctic from clear sky satellite observations, J.
Climate 16, 3498-3510.
Fettweis, X., H. Gallée, J.-P. van Ypersele, 2004: Surface mass balance of the Greenland
ice sheet simulated with a coupled atmosphere-snow regional model, Climate Dynamics,
in press.
Fichefet, T., C. Poncin, H. Goosse, P. Huybrechts, I. Janssens and H. Le Treut, 2003:
Implications of changes in freshwater flux from the Greenland ice sheet for the climate of
the 21st century, Geophysical Research Letters 30, doi: 10.129/2003GL017826.
Gallée, H., O. F. de Ghélin and M. R. van den Broeke, 1995: Simulation of atmosphericcirculation during the GIMEX-91 experiment using a meso-g primitive equations model,
J. Climate 8, 2843-2859.
Greuell, W. and W. H. Knap, 2000: Remote sensing of the albedo and detection of the slush
line on the Greenland ice sheet, J. Geophys. Res., 105 15, 567-15,576.
Greuell, W., B. Denby, R. S. W. van de Wal and J. Oerlemans, 2001: 10 years of massbalance measurements along a transect near Kangerlussuaq, central West Greenland,
J. Glaciol. 156, 157-158.
Heinemann, G., 1999: The KABEG ’97 field experiment: an aircraft-based study of katabatic
wind dynamics over the Greenland ice sheet, Bound. Layer Meteorol. 93, 75-116.
Huybrechts, P., I. Janssens, C. Poncin and T. Fichefet, 2002: The response of theGreenland ice sheet to climate changes in the 21st century by interactive coupling of an
AOGCM with a thermomechanical ice sheet model, Annals of Glaciology 35, 409-415.
Oerlemans, J. and H. F. Vugts, 1993: A meteorological experiment in the melting zone of theGreenland ice sheet, Bulletin of the American Meteorological Society 74, 355-365.
Rahmstorf, S., 1995: Bifurcations of the Atlantic Thermohaline Circulation in Response to
Changes in the Hydrological Cycle, Nature 378, 145-149.
Steffen, K. and J. Box, 2001: Surface climatology of the Greenland ice sheet: Greenland
Climate Network 1995-1999, Journal of Geophysical Research 106(D24), 33,95133,964.
Van den Broeke, M. R., P. G. Duynkerke and J. Oerlemans, 1994: The observed katabatic
flow at the edge of the Greenland ice sheet during GIMEX-91, Global Planet. Change 9,
3-15.

31

Van den Broeke, M. R., P. G. Duynkerke and E. A. C. Henneken, 1994: Heat, momentum
and moisture budgets of the katabatic layer over the melting zone of the west Greenland
ice sheet, Boundary-Layer Meteorol. 71, 393-413.
Van den Broeke, M. R., and H. Gallée, 1996: Observation and simulation of barrier winds at
the western margin of the Greenland ice sheet, Quart. J. R. Met. Soc. 122, 1365-1383.
Van de Wal, R. S. W., M. Wild and J. De Wolde, 2001: Short-term volume changes of theGreenland ice sheet in response to doubled CO2 conditions, Tellus 53(B), 94-102.
Wild, M., P. Calanca, S. C. Scherrer and A. Ohmura, 2003: Effects of polar ice sheets on
global sea level in high-resolution greenhouse scenarios, Journal of GeophysicalResearch 108(D5) 4165, doi:10.10129/2002JD002451.
Zwally, H.J., W. Abdalati, T. Herring, K. Larson, J. Saba, and K. Steffen, 2002: Surface meltinduced acceleration of Greenland ice-sheet flow, Science 297, 218-220.

32

GLACIER VELOCITIES IN THE CANADIAN HIGH ARCTICFROM RADARSAT-1 SPECKLE TRACKING
N. H. SHORT1 AND A. L. GRAY2
1

Noetix Research Inc., 265 Carling Ave. suite 403, Ottawa, ON, K1S 2E1, Canada;
naomi.short@ccrs.nrcan.gc.ca (on contract to the Canada Centre for Remote Sensing)2
Canada Centre for Remote Sensing, 588 Booth St., Ottawa, ON, K1A 0E4, Canada;e-mail: laurence.gray@ccrs.nrcan.gc.ca

The surface velocities of 11 glaciers from the Queen Elizabeth Islands of theCanadian high Arctic have been monitored between 2000 and 2004 using
RADARSAT-1 data and the speckle tracking technique, creating a broad overview
of glacier dynamics in this region. Where ice thickness measurements are available
they have been combined with the velocity results to estimate calving rates. In a
few cases they have also been used to derive theoretical balance velocities tocompare with the velocity measurements.
RADARSAT-1 Fine Mode data are optimal for the high relief and relatively narrowglaciers of the Canadian Arctic and most data sets were obtained at this ~9 m
resolution. Only large glaciers were selected for study since the speckle tracking
technique dictates that sites should be > 1.5 km in width. Fig. 1 shows the locationsof the studied glaciers. The speckle tracking velocity measurements are thought to-1
be accurate to ±14 m a , with additional errors over floating ice tongues
depending on the tides.
The results show that the flow of large outlet glaciers in the Canadian Arctic can befast and variable. Speeds of several hundreds of metres a year are common and-1
flow rates of ~ 1 km a are attainable under surge conditions. At any one timeseveral glaciers appear to be actively surging and active phases of 5-9 years
minimum have been observed. Other glaciers demonstrate significant shortmedium term variations in flow rates even though they are not considered to besurge type, perhaps suggesting that pulses and cycles other than full surges are an
important part of Arctic ice dynamics. Some example results are shown in Figures
2-6.
These observations of local, short-medium term flow instabilities have implications
for the interpretation of measured ice thickness and surface height changes,
particularly in the field of detecting cryospheric climate induced change. The
identification of ‘control’ regions, those unaffected by short-medium term flow
instabilities might be a strategy for finding reliable evidence of long-term climate
forcing in the Arctic.
It appears that speckle tracking results can make useful contributions to thecalculation of calving rates (see Table 1), more ice thickness measurements areneeded to expand this application. To perform reliable comparisons with balancevelocities, finer resolution SAR data and more measurements of ice thickness andablation rates are needed.

33

Figure 1. Axel Heiberg and Ellesmere Islands with the locations of the studied
glaciers. Unofficial names are denoted with quotes. The background is amosaic of RADARSAT-1 ScanSAR imagery collected in January andFebruary 1998.

Table 1. Calving rates for select glaciers
Glacierand year

Terminus
width (km)Centre line
thickness*
(m)
120

Mean
velocity-1(m a )
650
875
800
370 †

Retreat oradvance
3 -1(km a )
0.15 advanceno change
0.27 retreat
0.03 advance

Calving rate
3 -1
‡ (km a )Otto 2002
3.5
0.12 ± 0.03
2003
0.37 ± 0.09
2004
0.61 ± 0.15
Wykeham
3.0
225
0.22 ± 0.05
2004
Ekblaw
2.1
400
350 †
no change
0.29 ± 0.07
2003
* From Gogenini (1995).
† Velocity estimates have been adjusted for tidal effects‡ Overall errors of 25% are quoted to accommodate the spatial limitations of the ice
thickness measurements and high velocity errors due to degradation of speckle tracking
to feature tracking.

34

References
Gogenini, P., 1995. ftp://tornado.rsl.ukans.edu/pub/greenland/1995/pdf/may26_95.pdf(Accessed May 2004)
Hattersley-Smith, G., Fuzesy, A. and Evans, S. 1969. Glacier depths in northern Ellesmere
Island: airborne radio echo sounding in 1966. Defence Research Board, Ottawa. Report
Geophysics Hazen 36, DREO Technical Note No. 69-6.

Some example results

Figure 2. 2004 surface velocities of Middle and Iceberg glaciers. Profiles aremarked at 5 km intervals.

35

Figure 3. Surface velocity profiles of Middle Glacier in 2000 and 2004. ELA
indicates approximate equilibrium line altitude.

Figure 4. Velocity profiles of Iceberg Glacier in 2000 and 2004.

36

Figure 5. Surface velocities of Otto Glacier in 2003 with profile location
marked at 5 km intervals. SR indicates location of subglacial ridge and GL thegrounding line, both after Hattersley-Smith et al., (1969).

Figure 6. Surface velocity profiles of Otto Glacier in 2000, 2002, 2003 and2004. Grounding line (GL) and subglacial ridge (SR) locations taken fromHattersley-Smith et al., (1969).

37

USE OF TIME-LAPSE CAMERASON

AUTOMATICWEATHER STATIONS
MATT NOLAN
UAF, Fairbanks, Alaska

Time-lapse cameras have the ability to substantially improve our understanding ofglacier-climate interactions, particularly in association with automatic weather
stations. A fundamental problem with automatic weather stations (AWSs) isinherent to their nature -- they are located in remote locations with little oversight by
their creators. Often problems associated with riming, blowing snow, and sensor
tilting go undetected for long-periods of time and may never be detected as they
are sometimes self-correcting. In such cases, the digital data recorded for aparticular sensor may be well outside of the normal accuracy bounds yet the
instrument may still be functioning perfectly. Here we present several examples of
how we are starting to use time-lapse cameras to improve our understanding ofglacier-climate interactions on McCall Glacier, Alaska.
We installed a time-lapse camera near an AWS was placed in the firn. This station
began tilting immediately after it was left for the summer. The tilt was slow, but
detectable using the camera. The tilt most strongly affected the sonic ranger
attached to the station, which was intended to measure changes in snow depth.
Comparison of the sonic ranger data with changes in snow depth measured fromthe camera's images of a segmented snow-stake (a "snow ruler") showed that thesonic ranger was in error by more than 0.50 m by the end of the summer.
We installed a time-lapse camera on an AWS on a mountain top to watch snow
melt progress through summer in the ablation zone, where another AWS was
located on the ice. This camera showed not only snow line progression, but alsolate-summer snow falls and the patterns of subsequent wind scour that moved thissnow in a discontinuous pattern off the surface. These images are complementary
to the AWS data measured on the ice surface, which only represent point values ofsnow depths, albedo, and wind speeds.
We installed a camera a few meters from this same AWS in the ablation are towatch how it functioned throughout the summer. This station was a 'floating'station, which was not directly attached to the ice surface. Rather, the station isinternally stable and rigid, and allowed to ride the melting ice surface downward.
The time-lapse camera showed that this new station design was reliably stable towithin the resolution of the camera, with no major tilting events observed. The
images also showed the progression of snow melt past the station (complementary
to the one installed higher up on the mountain) to show the local conditions which
the four component radiometer was measuring. Here a small stream formed
temporarily beneath the radiometer and underneath the station, likely as a result of
surface disturbance during station installation in spring. This stream did notcontinue running once snow melt was complete and it appeared that the surface
measured there was representative of the surrounding surface.
Finally, we present a time-series of images acquired every minute over a single
sunny day. These images show the temporal patterns of shadows caused by the38steep valley walls and the Arctic sun's travel around the sky. We used this timeseries to validate a shading model used in spatially-distributed mass balance
modeling. This model uses a digital elevation model of the area and the sun'sgeometry throughout the day to predict which parts of the glacier will be shaded.
Movies were made of both the time-lapse camera output and the model, at thesame time-step and approximately the same vantage point. These movies, runside-by-side, show that the model is representing the actual shading quite well,though artifacts in the DEM are clearly present and introduce noise.
In summary, we believe that time-lapse cameras make valuable additions tostandard AWS instrumentation. Further technological developments are required,however, as currently choices are limited for off-the-shelf packages for time-lapse
cameras that can work reliably in cold, harsh weather on low power in bright sun.
The off-the-shelf camera systems we used met these requirements fine, butsuffered from having sub-megapixel resolution which limits their applicability andvisual appeal.

39

RECONSTRUCTION OF PAST ACCUMULATION RATES IN
AN ALPINE FIRN REGION: FIESCHERHORN, SWISS ALPSAUREL SCHWERZMANN1,2, MARTIN FUNK1, HEINZ BLATTER2, MARTIN LÜTHI1,3,
MARGIT SCHWIKOWSKI4, ANNE PALMER4,5
1

Laboratory for Hydraulics, Hydrology und Glaciology, ETH-Zentrum, CH-8092 Zürich,
Switzerland
2
Institute for Atmospheric and Climate Science, ETH-Zentrum, CH-8092 Zürich, Switzerland
3
present adress: University of Alaska, Fairbanks, Alaska
4Paul Scherrer Institute, CH-5232 Villigen, Switzerland (also at University of Berne)5present adress: University of Tasmania, Hobart, Tasmania

Firn and ice layers in polar and high mountain ice masses constitute valuable
archives of past climates and the history of chemical composition of theatmosphere. A new ice core was drilled on Fiescherhorn Glacier in December 2002
in the context of the project VITA (Varves, Ice cores and Tree rings: Archives withannual resolution) of the Swiss National Center of Competence in Research(NCCR Climate). The project VITA explores a combination of different types ofnatural archives that are able to record environmental and climatic changes on thehighest possible time scale. One of the purposes of the ice core drilling was thereconstruction of past annual acumulation. In this work we present a method todetermine the thinning of annual firn layers with depth.
The study site is located in the accumulation area of the Fiescherhorn glacier in the2
Swiss Alps on a plateau of about 0.5 km between the mountains
Grossfiescherhorn (4049 m asl) and Hinterfiescherhorn (4025 m asl). The plateau
has a mean elevation of about 3900 m asl and slopes 12% towards the northeast.
The ice core drilled in December 2002 (Hole B02) reached the bed of the ice at a
depth of 150 m. It is situated at an elevation of 3900 m asl in the middle of theaccumulation plateau from Fiescherhorn glacier. At this altitude, melt may occur inwarm summers and can also be caused by melt due to albedo decrease by dust
layers (Palmer and others, 2004). Melt water percolation may not only diffuse
chemical tracer species but the formation of ice lenses through refreezing of melt
water also results in a complex density profile.
In one year, a snow layer of thickness hA and density ρA is accumulating at surface
point A. Covered by new layers, this selected layer thins to hB and density ρB as it
moves to greater depth at point B. From ice core analysis, only today's observed
annual layer thicknesses hB are known. To reconstruct the original layer thickness
hA of the same layer at the time of its accumulation at the surface, today's
observed thickness hB must be corrected by a factor k, which can be interpreted asa “geometrical thinning factor”:

k=

hA
hB

(1)

Small variations in accumulation rate at the surface, such as variations in surface
snow density or submergence velocity, do not change the mean vertical velocity at!

40

depth. Such irregularities can be expected to diminish with depth (Nye 1975) whichleads to a steady state long-term averaged vertical velocity.
Relaxation of the restrictive conditions of steady state to the assumption that only
the density field is in steady state and only a function of depth below ice surface(Sorge's Law: see e.g. Bader, 1954) requires either additional measurements or an
additional assumption of ice mass continuity. This case may be generalized to a 3dimensional field such as a borehole in a sloping region, because the integrated
thinning does not depend on the particle path, but only on the starting point at thesurface and the point of the ice sample in the borehole. On the other hand, thisrequires some information on the position where the corresponding ice samples inthe borehole were accumulated on the surface. Generally, this limits the applicationof the method to the upper half of the borehole.
For the integration, we follow Raymond and others (1996), but along a trajectoryalong the velocity field, starting at point A at the surface and ending at point B
somewhere in the borehole,

hAw.e. = k w.e. hB =

!

"AwA
h
"w wB B

(2)

By multiplying measured annual layer thicknesses with the “water equivalent”
w.e.
thinning factor kin Eq. (2), the modeled annual layer thicknesses are obtained
as a reconstruction of the accumulation rate history. It is interesting to note that
under the given assumptions, the determination of the density profile in theborehole is not required. The Figure compares uncorrected and the corrected layer
thicknesses in water equivalent.

Figure. Thick solid line: measured annual layer thicknesses as a function of
depth in water equivalent. Thin solid line: reconstruction of the accumulationw.e.
rate history by multiplying the measured values with thinning factor k(witherror bars). Dashed line: reconstruction of the accumulation rate history with
the Nye (1963) correction.

41

References
C. F. Raymond and B. Weertmann and L. Thompson and E. Mosley-Thompson and D. Peeland R. Mulvaney, 1996: Geometry, motion and mass balance of Dyer Plateau,
Antarctica, J. Glaciol., 42(142), 510-518
H. Bader, 1954: Sorge's law of densification of snow on high polar glaciers, J. Glaciol., 2,319-323
A. S. Palmer and M. Schwikowski and T. Jenk and H. W. Gäggeler and A. Schwerzmann,
2004: Preliminary results from the 2002 Fiescherhorn ice core, Swiss Alps, Paul
Scherrer Institut, Villingen, Switzerland, Annual report 2003. pp. 29
J. F. Nye, 1963: Correction factor for accumulation measured by the thickness of the annual
layers in an ice sheet, J. Glaciol., 4(36), 785--788
J. F. Nye, 1975: Deducing thickness changes of an ice sheet from radio-echo and othermeasurements, J. Glaciol., 14(70), 49-56

42

ELEVATION CHANGE OF THE WEST GREENLAND ICESHEET MARGIN NEAR KANGERLUSSUAQ 2000-2003
ANDREAS P. AHLSTRØM1, NIELS REEH1, LARS STENSENG2 AND RENEFORSBERG2
1Ørsted-DTU, Technical University of Denmark, Ørsteds Plads, Building 348, DK-2800 Kgs.
Lyngby, Denmark2
Department of Geodynamics, Danish Space Center, Juliane Maries Vej 30, DK-2100
Copenhagen, Denmark

Abstract
Surface topography data acquired by laser scanning altimetry during the airborne
campaign over the western Greenland ice-sheet margin in August 2003 wascompared to a laser altimetry survey carried out over the same region in August
2000. For each point in the 2000 data set, a search was performed in the 2003
data set to find points within a horizontal search radius of 5 meters. The points
found were then divided in four quadrants and used to make a weighted meansinterpolation to obtain an elevation estimate from 2003 at the point of the 2000
observation. The distributed data set resulting from this comparison provides a
relatively complete picture of the ice sheet surface elevation change over a 50x50
km area of the ice margin and also serves as input to mass balance modeling. The
elevation change data set arising from this comparison shows a mean thinning ofroughly 0.5 meters from 2000 to 2003. The southernmost area shows a clear
pattern of overall thinning, whereas the northernmost area shows a more variable
pattern of moderate thinning and less pronounced thickening. An outline of theintended use of this data in a dynamic approach to mass-balance modeling will bepresented as work in progress.

Introduction
Measuring the mass balance of the Greenland Ice-Sheet presents a formidable
logistical challenge due to its vast size and the limited representability of individual
stake measurements. It is therefore desirable to develop methods making it
possible to estimate the mass balance from remote sensing. One way to do this isbased on the application of the principle of mass conservation (Reeh and others,2003). If the ice thickness h and horizontal surface velocity u = (ux, uy) is measured
once and the ice surface elevation S is measured and subsequently remeasured
after some time Δt, then it is possible to derive the mass balance bs of theintervening period by assuming a relationship F=u/u between the surface velocity
and the mean velocity u of the underlying ice column, as expressed by the massconservation equation

43

This method has earlier been applied on the centre flowline of Storstrømmen
Glacier in northeast Greenland (Reeh and others, 1999; Reeh and others, 2002),
but has not yet been applied to the ice-sheet on a regional scale. The resultspresented here documents our progress towards obtaining this goal by presenting
the current state of the necessary input data and showing new results obtained
regarding the surface elevation change of the ice sheet.

Figure 1. Overview of the region showing the flight tracks of the 2003 survey
over the Greenland ice-sheet margin. The black flight lines show which partwas used to produce the bed elevation model seen in Figure 5. The blacklines also shows the area covered by the 2000 survey for which elevation
change (see Figure 4) has been calculated. The figure is adapted from
Ahlstrøm and others (in press).

Data collection and results
To facilitate a regional application of the mass-conservation principle, airborne
campaigns have been launched in 2000 and 2003 (and will be repeated in 2005) tomeasure the ice-sheet surface elevation and thickness along a stretch of icemargin 50 km wide and nearly 150 km long near Kangerlussuaq, West Greenland(see Figure 1). The ice-sheet surface elevation was determined by combining datafrom a laser altimeter (Optech ADM GPA100), two onboard differential GPS
instruments and an inertial navigation system to keep track of the aircraft attitude.
The laser altimeter used in 2000 was nadir-looking and acquired data at a 50 Hz
rate which was subsequently averaged to 1 Hz yielding points with approx. 70 m
along-track distance at the normal operation aircraft speed of 250 km/h. The laser
altimeter used in 2003 was a scanning instrument, acquiring a dense swath of datawith a width depending on the altitude of the aircraft above the surface, but
44

typically of a few hundred metres (see Figure 2 to compare the density of data
points from the 2000 and 2003 surveys). The density of the scanner data is on theorder of a metre between points, implying that a large amount of data is created.

Figure 2 (left). Comparison of the averaged data density of the laser altimetry
from the 2000 survey (fat red dots) and the 2003 survey (the light grey mesh).
The real density from the 2000 survey is approximately one point per 1.4 m at
normal flight speed, whereas the averaged data points from 2000 shown in
the figure are roughly 70 m apart.
Figure 3 (right). The red dot signifies the averaged nadir data point from2000, and the grid represents the scanner data from 2003. To interpolate apoint from the 2003 data over the location of the 2000 data point, all scannerdata points within a search radius of 5 m were selected, averaged within four
quadrants which were finally linearly interpolated.

The data was checked by comparing data from several crossing flight lines andthose with obvious errors identified were discarded. Repeated laser altimetry
currently provides the most accurate feasible method to map ice-sheet surface
elevation change over a larger area. In this case we compared the two laser
altimetry data sets, by choosing available scanner data points from 2003 in aradius of 5 m around each averaged nadir data point from 2000. The selected 2003
data were then grouped into four quadrants and finally linearly interpolated to thelocation of the single 2000 data point (see Figure 3). Elevation changes over 5
metres obtained with this method were ignored to discard areas with crevasses
and highly irregular topography. For the area covered by both the 2000 and the2003 survey, a total of 7303 usable points were identified. With the survey in 2005
we will be able to compare to laser scanner data sets, producing a very dense data
set of elevation change which might be of interest in studying details in the45

changes of the ice-sheet surface topography. The calculated along-track elevation
change was gridded to a resolution of 250 m using the method ‘inverse distance toa power two’ with subsequent smoothing, producing the map shown in Figure 4,
which also shows the actual points used. The accuracy of the two data sets has not
yet been adequately analyzed, but is expected to be within 0.25 m for the 2003
data set (Kristian Keller, personal communication). The accuracy of the nadir laser
altimetry data from 2000 has previously been assessed in a cross-track analysis byHowe Ditlevsen (2001).

Figure 4. Contoured map of the observed elevation change of the ice-sheet
margin from 2000 to 2003. The thick grey points (seen as lines) show thelocation of the data points used in the grid interpolation. The light grey
contours show the elevation above sea level. See the text for a discussion of
the resulting pattern of elevation change.

Even though the laser altimetry provides the basis for a digital elevation model(DEM) of the ice-sheet surface, the satellite-derived alternative provided by repeattrack interferometric synthetic aperture radar (InSAR) analysis is preferable for use
in a mass-conservation calculation because of its distributed high resolution. The
laser altimetry can then be used to independently check the accuracy of of the
InSAR DEM. Such a DEM was derived from two image pairs from the satellites
ERS-1 and ERS-2 acquired from October 1995 to January 1996 during the twin
mission and is shown in Figure 5. A comparison with a DEM based on the nadirlaser altimeter data from 2000 gave a mean difference of 5 metres with an r.m.s. ofthe difference of 10 metres (Ahlstrøm and others, 2002). The DEM shown in Figure
5 has a reduced resolution of 250 metres to conform to the bed elevation model
described below. The repeat-track InSAR analysis also yields a line-of-sight46

surface velocity component which can be combined with glaciologically based
assumptions to provide an estimate of the surface velocity field (ux, uy) needed in
the mass-conservation equation (Mohr and others, 2003). An example of thederived surface velocity field from an outlet glacier in the region studied is shown in
Figure 6.
Ice-sheet thickness was obtained by manual interpretation of radargrams collectedduring the airborne survey in 2003 with the 60 MHz ice-penetrating radar of theTechnical University of Denmark (Christensen and others, 2000). The ice-sheet
thickness data was reduced to an along-track resolution of 200 metres andconverted to a gridded digital elevation model using block kriging with eight
directional semi-variograms to take into account the influence of the dominantvalley systems beneath the ice sheet on the gridding result (Ahlstrøm and others,
in press). The grid resolution was chosen to be 250 metres as a compromise
between the high along-track resolution, typically on the order of a metre and the
between-track distance of roughly 2500 metres. The resulting grid is displayed as acontour map in Figure 5.

Figure 5. The left side shows a digital elevation model based on satellitederived repeat-track interferometric synthetic aperture radar analysis. The
right side shows a digital elevation model of the bed underneath the ice sheet,based on an airborne ice-penetrating radar survey in 2003. The figure isadapted from Ahlstrøm and others (in press). The contours are in metresabove sea level.

Conclusion and Outlook
The results are presented here as work in progress towards the application of the
mass-conservation principle to model the mass balance of the Greenland ice-sheet
margin on a regional scale. However, an interim product such as the observed
elevation change deserves attention in its own respect. The map shown in Figure 4
gives a detailed picture of the observed elevation change of a 50x50 km part of the47

Greenland ice-sheet margin over three years in a region which has been reported
to be sensitive to climate change (Huybrechts and others, 1991). Even though
work still has to be done to assess the accuracy of the laser altimetry data, it still
represents the best estimate currently available.
The mean observed elevation change from August 2000 to August 2003 over thewhole region shown in Figure 4 is -0.5 metres. However, the change is not
uniformly distributed: the northern and central parts seem to be in balance,
whereas the southern part is thinning. The thinning of the ice sheet in the southernpart of the region studied is a robust result even when considering the uncertainty
in the accuracy of the laser altimetry data. The survey was carried out by the end of
the ablation season in late August in both 2000 and 2003, minimizing the influenceof snow on the result. The region is situated in a precipitation shadow from coastal
ice caps and mountains and receives relatively little snow. The snow line is likely to
be as high as 1400 m a.s.l. at the time of the survey assuming that it is comparable
to the equilibrium line elevation. The elevation change pattern seen in Figure 4
does not show any elevation dependency and remains to be explained. Possible
candidates are increased surface ablation, a change in the precipitation or icedynamical changes. The latter could be influenced by a portion of the margin
draining into an ice-dammed lake (provisionally named ‘Lake 860’ from its
elevation signature on maps) known to drain catastrophically with intervals of
several years.
Further work includes InSAR processing of large image strips from both ascending
and descending satellite tracks, providing two surface velocity vectors instead ofjust one as is currently available. The InSAR-derived surface velocity which
represents a winter snapshot should be compared to stake velocities obtained
within the region, along the K-transect near Kangerlussuaq and near the lake
Tasersiaq. A more detailed error analysis of the laser altimetry data is also
required, both for the nadir data from 2000 and the scanner data from 2003.
Finally, mass-balance modeling using the outlined mass-conservation method will
be applied and compared to results from distributed surface ablation modeling andablation measurements from stakes.

Acknowledgements
This research was funded by the EU under EC-5FP contract EVK2-2001-00262(Space borne measurements of Arctic Glaciers and implications for Sea Level SPICE). The European Space Agency owns the copyright to the ERS-1 and ERS-2
images.

References
Ahlstrøm, A. P., C. E. Bøggild, J. J. Mohr, N. Reeh, E. L. Christensen, O. B. Olesen, K.
Keller. 2002. Mapping of a hydrological ice-sheet drainage basin on the West Greenland
ice-sheet margin from ERS-1/-2 SAR interferometry, ice-radar measurement andmodelling, Ann. Glaciol., Vol. 34, 309-314.
Ahlstrøm, A. P., J. J. Mohr, N. Reeh, E. L. Christensen and R. LeB. Hooke. In press.
Controls on the basal water pressure in subglacial channels near the margin of theGreenland ice sheet, J. Glaciol. (accepted).
Christensen, E. L., N. Reeh, R. Forsberg, J. H. Jørgensen, N. Skou, K. Woelders. 2000. A
low-cost glacier-mapping system, J. Glaciol., 46(154), 531-537.

48

Howe Ditlevsen, E. 2001. Højdebestemmelse af isranden syd for Kangerlussuaq med
kinematisk GPS og flybåren laseraltimetri samt sammenligning, Speciale (MSc thesis),
University of Copenhagen.
Huybrechts, P., A. Letréguilly, N. Reeh. 1991. The Greenland Ice Sheet and greenhousewarming, Palaeogeog. Palaeoclimatol. Palaeoecol., Vol. 89, 399-412.
Mohr, J. J., N. Reeh, S. N. Madsen. 2003. Accuracy of three-dimensional glacier surface
velocities derived from radar interferometry and ice-sounding radar measurements, J.
Glaciol., 49(165), 210-222.
Reeh, N., S. N. Madsen, J. J. Mohr. 1999. Combining SAR interferometry and the equationof continuity to estimate the three-dimensional glacier surface-velocity vector, J. Glaciol.,45(151), 533-538.
Reeh, N., J. J. Mohr, W. B. Krabill, R. Thomas, H. Oerter, N. Gundestrup and C. E. Bøggild.
2002. Glacier specific ablation rate derived by remote sensing measurements, Geophys.
Res. Lett., 29(16), 10-1 – 10-4.
Reeh, N., J. J. Mohr, S. N. Madsen, H. Oerter and N. Gundestrup. 2003. Three-dimensional
glacier surface velocites of Storstrømmen glacier derived from radar interferometry andice-sounding radar measurements, J. Glaciol., 49(165), 201-209.

49

COMPARATIVESIMULATIONS
OF
SNOW
AND
SUPERIMPOSED ICE AT THE KONGSVEGEN GLACIER,SVALBARD
STEFAN ERATH1, FRIEDRICH OBLEITNER1, JACK KOHLER2, WOUTER GREUELL3
4
AND KJETIL MELVOLD
1

Institute for Meteorology and Geophysics, Innsbruck University, Austria
Norwegian Polar Institute, Tromsoe, Norway
3
Institute for Marine and Atmospheric Research Utrecht, Utrecht University
4
The Norwegian Water Resources and Energy Directorate, Oslo
2

Abstract
We consider glaciological and meteorological data that were collected at about theequilibrium line of a high Arctic glacier (Kongsvegen, Svalbard). The study is basedon detailed investigations of the seasonal evolution of the local mass and energy
budget of the glacier, where snow modeling turned out to be a valuable tool.
Amongst the particular aspects the formation of multi-year superimposed ice has
been focused, which has important implications on the mass balance of thisglacier.
This study is of a more methodical nature addressing the potential of three different
snow models (SNOWPACK, SNTHERM and SOMARS) to reproduce the local
development of snow and ice at this site. Their skill is mainly measured in terms of
profile characteristics of snow temperature and density and their temporal
evolution. These verifying data confirm that principally each model is well able tosimulate the basic features observed. Focusing on the performance of SOMARS
we demonstrate the importance of properly derived precipitation input andcorresponding impact on the simulation results. A corresponding procedure toensure a basic model comparability is introduced. The largest differences between
the model results and measurements occur in the near surface layers (±1.5°C and-3±70 kg m ), which is acceptable in view of the limited tuning effort and the inherent
measurement uncertainties. Amongst the common notable lacks in skill, thestandard versions of these models systematically underestimate the developmentof superimposed ice. Moreover, none of these models does reproduce a detailed
structure of the snow layering. We note however, that depending on fine-tuning
effort each model has a similarly high potential to overcome the demonstrated
lacks in skill.

Introduction

2

Kongsvegen is a high arctic polythermal glacier with an area of 102 km and 25 km
length. A surface cold layer reaches the bedrock in the ablation area where theglacier is partly frozen to the ground. The ice is mostly temperate in the higher
basins where the formation of superimposed ice is an important factor regarding
the energy and mass budget of the glacier (Lefauconnier et al., 1999).
Glaciological and meteorological data have been collected at about the equilibriumline of Kongsvegen glacier (79°N, 13°E, 543 m asl). The basic meteorologicalparameters are measured as well as the short- and longwave radiation
50

components, snow height and snow temperatures at different depths. During thetwice-yearly maintenance of the station, detailed glaciological investigations of thesnow and ice conditions were performed as well. Covering several annual cycles
meanwhile (2000-2004), these data allowed for process oriented investigations of
the multi-year evolution of the local mass and energy budget of the glacier(Obleitner and Lehning, 2004). The current study is of a more methodical natureand focuses on the comparative skill of different snow models to simulate thedevelopment of snow and ice at the site of investigation. A special emphasis is puton the performance of SOMARS (Greuell, 1994).

The environmental regime
Radiation exerts the dominant forcing on the energy budget at this site, with polarnight conditions from late October through early February. The air temperatures
can be as low as -40°C, but the winter regime is coreless due to the vital synoptic
activity enforcing episodic break ups of the otherwise persistent radiation
inversions. The summer temperatures settle around 0°C, which can be attributed tothe stabilizing influence of melting snow and ice. The wind regime is characterizedby moderately developed glacier winds, which are influenced by topography andsynoptic disturbances. Moreover, the site is characterized by a cold, 1.5 to 2 m
deep snow pack at the end of winter. Intensive melt usually starts in May, when theformation of superimposed ice is initiated too. At this elevation and with the current
climate conditions, there is multi-year formation of superimposed ice, but the final
amount strongly depends on the individual summer conditions and the microtopographical conditions at the snow-ice interface (Obleitner and Lehning, 2004).
During certain summers, superimposed ice was observed to build up in abnormallylarge extent (up to 0.5 m), which has important implications on the mass balance of
the whole glacier.

Model setups
We investigate the skill of three well known snow models SNOWPACK, SNTHERM
and SOMARS (Bartelt and Lehning, 2002; Jordan, 1991; Greuell, 1994) toreproduce the seasonal evolution of the physical properties of snow and ice at themeasurement site. The respective skill of a specific version of SNOWPACK (6.1)
has already been documented (Obleitner and Lehning, 2004). Here, special
emphasis is given to SOMARS, which is intended to be used for distributed massbalance modeling of Spitzbergen glaciers. It is advantageous in the context of acomparative study that each model can be fed with the same basic input. Theirnumerical setup considers 25m of ice upon which the evolution of seasonal snow is
initialized with measured profiles of snow temperature and density. Thus starting in
April 2000 the governing boundary layer processes are driven by the measured
meteorological data. We use hourly values of air temperature and humidity, windspeed, short-wave incoming and reflected radiation and long-wave atmosphericradiation. In a first approach, precipitation input was derived from filtered ultrasonicrecords, which was converted into water equivalent using a constant new snow
-3
density of 150 kg m . Due to differences in the treatment of metamorphosis andsettling processes, however, model specific adjustments turned out to benecessary order to yield the correct snow height at the end of winter. Shortly
recalling the relevant model characteristics, SNOWPACK’s metamorphosis routine
is based on microstructure parameters and accumulation is added if measured
51

snow height is less than the simulated one. Naturally, such forces the model toreproduce the correct amount of snow height. SNTHERM uses a densification
parameterization, which is based on grain size and temperature. The original
version of SOMARS on the other hand uses a less sophisticated settling routine,
which might be sufficient for certain applications. In the context of our local studies,
however, we experienced serious deficiencies to reproduce the observed evolutionof snow and SOMARS was modified by partly adopting SNTHERM’s
parameterization. To summarize, the accumulation input procedures are slightly
different for each model, however, force them to reproduce the measured snowheights within ±15 cm. This is essential for proper modeling of snow processes andfor model comparison, too. Profiles of density (water equivalent) and temperaturefrom annual snow pit data and records of snow temperature at certain depths weretaken as measures of verification.

Results of SOMARS simulations
We firstly document the importance of properly derived accumulation input on thesimulation results, which is a general issue for snow modeling. In this context
filtering of the original ultrasonic records is a crucial issue, which in our case wasdone by the application of reasonable thresholds ruling out obvious disturbances of-3
the signal. Moreover, the assumption of a new snow density (150 kg m ) is
necessary in order to deduce water equivalents as input into the model. There isno general rule for this as there is a high natural variability (e.g. snow drift effects)
and as e.g. the model specific parameterization of the initial settling processes
plays a role there, too. In a first attempt an "effective" new snow density (350 kg m
3
) was introduced to SOMARS. Figure 1 demonstrates, that such works reasonably

Figure 1. Measured and modeled snow density at the end of theaccumulation period in successive years 2000 – 2004, straightforwardly
feeding SOMARS with filtered measured ultrasonic data.

52

Figure 2. Measured and modeled snow density from 2000 – 2004, feeding
SOMARS with filtered ultrasonic data, which are scaled by end winteraccumulation observed in snow pits.

during certain accumulation periods (winter 2003), whereas unacceptable lacks inskill are left with respect to winters 2001 and 2002, respectively. Of course there isan inherent natural variability to be considered as well, but the main deficiencies
appeared due to improper derivation of accumulation input. It is obvious at thispoint already, that additional glaciological data (like snow pits) are essential inorder to rule out the inherent problems.
We have tested different methods to improve these first results. Different filtering
methods and new snow density algorithms yielded partly reasonable results, whichmostly suffered from a wide lack in objective criteria on the other hand. In this view
we finally preferred to simply scale the pre-filtered ultrasonic records by the end
winter accumulation observed in snow pits. Figure 2 documents the corresponding
improvement in reproducing the observed annual accumulation.
Snow height, as well as overall density is well simulated now, however, there is still
an obvious lack in reproducing the layered structure. Gustafsson et al. (2004) havedemonstrated that such is a common deficiency of other snow models as well. In
the case of SOMARS, this is definitely due to a rather insufficient treatment of snow
metamorphosis processes. It is to be noted in this context moreover, that for the
purpose of this study a new densification algorithm according to Jordan (1991) has
been implemented into SOMARS.
A thus optimized accumulation input yields otherwise reasonable results a well.
Figure 3 shows comparisons of simulated snow temperature profiles withmeasurements in annual snow pits. There is a basic agreement matching theexperiences with other models (Obleitner and Lehning, 2004). The larger
deviations in the uppermost layers may also be attributed to measurement
53Figure 3. Measured and modeled snow temperature from 2000 – 2004,
feeding SOMARS with ultrasonic data scaled by end winter accumulation
observed in snow pits

problems (solar heating of sensors) or insufficient temporal coincidence (meanhourly values vs. instant values).
A thus verified model run shows some generally interesting features of theseasonal evolution of snow and ice at the site of investigation. Snow height in theend of winter varied between 1.3 and 2.1 m during the 4 years of investigation
corresponding to 53 to 75 cm w.e., respectively. Ablation usually takes place
during the period mid June until mid September. The calculated annual specificmass balances (1 Oct. – 30 Sep.) range from +0.08 m w.e (2002) to -0.32 m w.e(2003). The annually different winter and summer conditions are largely reflected in
the temperature and precipitation of a nearby climate station in Ny Alesund(78°53'N, 11°80'E) and there are obvious consequences on the penetration of thewinter and summer heat waves into the ice below. Figure 4 indicates thatthroughout the years the winter coldness systematically penetrates deeper into theunderlying ice layers. Partly, this can be related to subtle interaction between
atmospheric development and the depth of snow at the relevant period of time. As
an example, the most intensive cooling occurs relatively early during autumn 2004,
when little snow covers the glacier on the other hand. Another factor in this context
is that during the period of concern the ice surface continuously lowers due toprevailing net ablation. Therefore, an ice layer at a certain reference depth isincreasingly exposed to the approaching cold wave.
By contrast, the depth of summer heat wave penetration remains relatively
constant. This can be related to phase changing processes modifying the
conductive effects. The governing factors are production and refreeze of melt
water, which basically depend on length and climatic conditions of the melt period,54

as well as on the cold content of the underlying ice on the other hand. The polar
day conditions imply effective production of melt water. The latter penetrates thesnow pack within short periods of time and refreezes at the cold snow ice interfaceof the glacier.

Figure 4. Contour plot of simulated snow and ice temperatures (°C) during
April 2000 –April 2004. The thick (blue) line indicates the snow ice interface.

It has been demonstrated that considerable amounts of superimposed ice are thus
developing at the Kongsvegen measurement site (Obleitner and Lehning, 2004).
This study has shown moreover, that the local slope and the micro-topographicalconditions at the snow/ice interface of the glacier may play an additional role indetermining the final amount of superimposed ice. In extreme cases, the melt water
can intermittently form ponds, resulting in abnormally high stocks of superimposed
ice. Depending on these factors there is an inherent local and temporal variabilityof such effects, which are important for the thermal regime and the mass balanceof the glacier.
Figure 4 proves that SOMARS is reasonably reproducing the changes in surface
height at the measurement location. Net ablation as well as the annuallydeveloping amount of superimposed is confirmed by different measurements at thesite. A major deviation regards the amount of superimposed ice formed during
summer 2000, when abnormally high amounts of superimposed developed due toponding water phenomena (Obleitner and Lehning, 2004). SOMARS includes aparameterization of slope and time dependent runoff at the snow/ice interface,which in principle could be used to reduce runoff at the ice/snow interface.
However, we did not correspondingly optimize the SOMARS run within this study.

55

Snow model intercomparisons
The available data provide a most suitable basis for a verified comparison of theresults from different snow models. We used SOMARS, SNTHERM and
SNOWPACK, which are well documented and state of the art representatives. For
the purpose of this study we used the currently available versions of each modeland there was no model specific fine-tuning. Note however, that SOMARS has
been modified by the mentioned densification routine. The models were fed by thesame meteorological and glaciological input data and their basic skill is measured
focusing on snow density and temperature profiles. The conditions at the snow/ice
interface are essentially interesting, as being considered to critically react onproper treatment of the relevant processes transferring mass and energy between
the atmosphere, snow (above) and ice (below).
There are a number of factors that are hampering a straightforward comparison of
the model results. This was an experience of the SNOWMIP intercomparison effort
as well (Essery, 1998). Thus, we have already noted the importance of model
specific treatment of precipitation input (effective new snow density?), which has
important impacts on proper reproduction of snow height affecting the temperatureand density distribution as well. Moreover, the models use different methods todistinguish the precipitation phases (snow or rain?). As to the verifying data, wenote the inherent measurement uncertainties, which may be estimated to ±50 kg m
3
and ±0.5 °C at least. Further uncertainty exists regarding geometrical references
between measurements and model results (e.g. 5 cm volume increments of density
measurements vs. nodal values of changing thickness, depth of pit vs. depth ofmodel snow pack). Some of these problems can certainly be overcome by finetuning individual model runs or developing specific post-processing tools allowing
for better comparison of measurements and simulation results. As a minimum
requirement however, we took care that each model reproduces the end wintersnow height to within ±15 cm. The SOMARS runs were most optimized with that
respect while corresponding effort regarding SNTHERM and SNOWPACK was
beyond the scope of this study.

Figure 5. Comparison of density profiles as measured in a snow pit andsimulated by SOMARS, SNTHERM and SNOWPACK, respectively (26 April
2001, 15UTC).

56

Figure 5 compares the measured and simulated snow density profiles. Considering
the inherent measurement uncertainty, the overall level as well as densification
with depth is reasonably captured by each model. In the uppermost layers,
however, SNTHERM and SNOWPACK perform better than SOMARS, which is dueto their more sophisticated treatment of snow metamorphism processes and thelower values of effective new snow density. In the central layers the models tend tounderestimate the measured snow density. As a common feature as well, none of
the considered models reproduces a detailed layered structure of the snow pack.
This indicates a general lack of these models and a corresponding need for
improvement (Gustafsson et al., 2004; Obleitner and Lehning, 2004).
Figure 6 compares the simulated snow temperature profiles with themeasurements in the snow pit. The inherent measurement uncertainty is difficult toestimate e.g. due to solar heating of the sensor in the uppermost layers for
instance. A value of ±0.5°C might be considered as a minimum estimate in thiscontext. In view of this, the models do reasonably reproduce the overall shape ofthe measured temperature profiles. However, the simulated average temperatureof the snow pack differs within a range of 3°C. SNTHERM and SOMARS tend to
be too cold and SNOWPACK is calculating too warm on the other hand. Moreover,
SOMARS simulates the temperature minimum at greater depth as observed. This-3
might be related to the relatively large new snow density (350 kg m as compared-3
to 150 kg m ) and correspondingly enhanced penetration of the winter cold wave
into the deeper snow layers.

Figure 6. Comparison of snow temperature profiles as measured in a snow pit
and simulated by SOMARS, SNTHERM and SNOWPACK, respectively (26April 2001, 15UTC).

It has been mentioned that the thermal conditions at the snow/ice interface areparticularly interesting, as critically responding on proper treatment of the relevant
processes transferring mass and energy between the atmosphere, snow and ice.
The formation of superimposed ice plays an important role in this context too,
which has a great potential to effectively modify the thermal regime in these layers.
Figure 7 documents the model's ability to simulate the time evolution of thetemperature at the snow-ice interface. Generally, the model results agree to within
about ±1°C to the verifying data and the major deviations can mostly be related to57

documented measurement problems. The arrival of spring melt water (0°C) at thesnow-ice interface may be considered as another interesting measure of skill. In
spring 2000 for example, this was recorded on 27 June. SNTHERM and SOMARS
do well reproduce this date (26 June). This is surprising in view of the manifold
dependences including history of the development of winter snow and different
parameterization of water transport within the snow pack. SNOWPACK's water
arrives 7 days later, which is mainly due to having simulated a too cold winter snow
pack. On the other hand the performance of this simulation has already been
improved by modifying the water transport routine to treat ponding of melt water.
Abnormally large amount of superimposed ice were thus formed during thesubsequent summer 2000, which could successfully be simulated (Obleitner andLehning, 2004). SOMARS and SNTHERM have not been optimized with that
respect, thus tend to underestimate the amount of superimposed ice during thisparticular summer (2000). This may be considered as a reason for simulatingcomparatively low temperatures during the following spring 2001 (Figure 6).

Figure 7. Comparison of measured and simulated temperature at the snowice interface. The SNOWPACK and SNTHERM simulations cover a shorter
period only (April 2000 until April 2002).

Summary
Three state of the art snow models (SNOWPACK, SNTHERM and SOMARS) havebeen fed with the same set of meteorological and glaciological input data. The
measurements cover a four year period and have been collected at about thecurrent equilibrium line of Kongsvegen, which is a high Arctic polythermal glacier inwestern Spitzbergen. The formation of superimposed ice is a particularlyinteresting aspect there.
Focusing on the performance of SOMARS mainly, the basic importance of proper
derivation of precipitation input is demonstrated firstly. This is based on ultrasonicrecords, which were pre-processed to match the snow depth of pits dug at the endof each winter. Conversion into water equivalents was achieved by the applicationof an "effective" new snow density. Different values (ranging from 150 to 350 kg m58

3

) were necessary in order to force each model to reproduce the observed wintermass balance, which is related to the model specific parameterizations of initialsnow compaction processes. This procedure is effective and facilitates comparisonof the model outputs. SOMARS was further optimized by adopting parts of thesettling routine of SNTHERM and scaling precipitation input to the observations insnow pits at the end of winters. The original versions of the models were usedotherwise, with exception of SNOWPACK, whose water transport routine wasmodified to treat ponding water effects. There was no specific fine tuning of thesimulation result towards the verifying observations.
The model’s skill is mainly measured by comparison with data from annuallyavailable snow pit data and continuous records of snow temperatures at the snowice interface. The verifying data from a test pit at the end of winter 2000
demonstrate that each model is well able to reproduce the overall evolution of thesnow pack. The inter-model differences in simulated snow temperature are in therange of ±1.5°C, which are mostly systematic throughout the depth of the snow
pack and must be seen in the light of an estimated measurement uncertainty in theorder of ±0.5°C. We note that the models winter cold wave systematicallypenetrate too deep into the snow pack. This may indicate improper treatment ofeffective heat conduction, especially in the near surface layers.
The accuracy of the verifying density measurements is probably not better than
-3
±50 kg m . In view of this, the considered snow models capture the overall leveland distribution with depth equally good. However, there are systematic deviations
regarding the slope of the simulated density profiles. As compared to SOMARS,SNTHERM and SNOWPACK show stronger densification with depth (and time).
This must be related to different parameterization of snow metamorphism, where
the treatment of the processes immediately after snow fall may be of major
importance. The largest inter-model differences occur in the near surface layers,
where SNTHERM and SNOWPACK perform better than SOMARS reflecting amore sophisticated parameterization of the relevant processes. Reasonably, someof these features trace on the simulated temperature profiles as well. For instance,
SOMARS simulates the densest and therefore the coldest snow pack. Suchevidences call for further investigation of the inherent feedback processes, which
was beyond the scope of this study. We finally note in this context, that none of thethree models reproduces any detailed layered structure within the snow pack. Thisis another topic to be focused in further studies.
The multi-year evolution of the temperature at the snow–ice interface is considered
as an important measure of the model’s skill as critically responding to properly
treating the relevant processes transferring mass an energy between air, snow andunderlying ice. A comparison of model results with direct measurements
demonstrates some systematic differences between the model results, which
widely conform to the evidences from the profile investigations. Thus, SNTHERM
and SOMARS are systematically to cold, whereas SNOWPACK performs
optimally. The onset of spring melt is correspondingly affected. It is to mentioned inthis context, however, that SNOWPACK has been optimized regarding the
production of superimposed ice. The latter is a prominent feature at themeasurement site with important consequences on the mass and energy budget of
the glacier. The standard versions of the three models tend to produce too small
amounts of superimposed ice (0.15 cm typically). On the other hand,
SNOWPACK’s water transport routine has been modified to reproduce the59generally larger amounts of superimposed ice, particularly during certain years (0.5m during summer 2000, for instance). The accordingly enhanced release of latent
heat might be a reason for SNOWPACK’s higher temperatures in the layers ofconcern.
Amongst the factors hampering a straightforward comparison of the model results
we note the model dependent treatment of precipitation input (effective new snow
density?) or different methods to subsequently distinguish the precipitation phases(snow or rain?). As to the verifying data, we note the inherent measurement
uncertainties and further uncertainties regarding geometrical references between
measurements and model results (volume increments vs. nodal values of changing
thickness, depth of pits vs. depth of model snow pack). Some of these problems
can certainly be overcome by fine tuning individual model runs or developing
specific post-processing tools allowing for better comparison of measurements andsimulation results. Such was beyond the scope of this study and therefore
corresponding outcome may not be misinterpreted in terms of attempting toprovide absolute measures of the skill of individual models. No doubt, each of theconsidered models has a similarly high potential to be optimized, corresponding
effort and support from the model developers provided. Nevertheless, some of thedemonstrated features might be valuable for less experienced users or stimulate
further investigations fostering progress in snow modeling.

Acknowledgements and remarks
Particular thanks are due to J. Oerlemans for organizing another "Pontresinaworkshop" and for patiently waiting for input to these proceedings. The financial
support by M. Kuhn is gratefully acknowledged as well.
The SOMARS calculations were performed by S. Erath as part of his diploma work
and F. Obleitner contributed the SNTHERM and SNOWPACK simulations. The
latter results were taken from a former study developed in cooperation with M.
Lehning who provided the model and assistance in modifying its water transportmodule. Field work was performed in close cooperation with J. Kohler and K.
Melvold, W. Greuell provided his model and valuable comments on its proper use..

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