JOURNAL OF GEOPHYSICAL RESEARCH: OCEANS, VOL. 118, 2640–2652, doi:10.1002/jgrc.20133, 2013
Simulated heat flux and sea ice production at coastal polynyas in the
southwestern Weddell Sea
V. Haid1 and R. Timmermann1
Received 5 October 2012; revised 29 January 2013; accepted 15 February 2013; published 30 May 2013.
[1] Coastal polynyas are areas in an ice-covered ocean where the ice cover is exported,
mostly by off-shore winds. The resulting reduction of sea ice enables an enhanced
ocean-atmosphere heat transfer. Once the water temperatures are at the freezing point,
further heat loss induces sea ice production. The heat exchange and ice production in
coastal polynyas in the southwestern Weddell Sea is addressed using the Finite-Element
Sea-ice Ocean Model, a primitive-equation, hydrostatic ocean circulation model coupled
with a dynamic-thermodynamic sea-ice model, which allows to quantify the amount of
heat associated with cooling of the water column. Three important polynya regions are
identified: at Brunt Ice Shelf, at Ronne Ice Shelf and along the southern part of the
Antarctic Peninsula. Multiyear winter means (May–September 1990–2009) give an
upward heat flux to the atmosphere of 311 W/m2 in the Brunt polynyas, 511 W/m2 in
Ronne Polynya and 364 W/m2 in the Antarctic Peninsula polynyas, whereof 57 W/m2 ,
49 W/m2 and 48 W/m2 , respectively, are supplied as oceanic heat flux from deeper layers.
The mean winter sea ice production is 7.2 cm/d in the Brunt polynyas corresponding to an
ice volume of 1.31010 m3 /winter, 13.2 cm/d at Ronne polynya (4.41010 m3 /winter),
and 9.2 cm/d in the Antarctic Peninsula polynyas (2.11010 m3 /winter). The heat flux to
the atmosphere inside polynyas is 7 to 9 times higher than the heat flux in the adjacent
area; polynya ice production per unit area exceeds adjacent values by a factor of 9 to 14.
Citation: Haid, V., and R. Timmermann (2013), Simulated heat flux and sea ice production at coastal polynyas in the southwestern
Weddell Sea, J. Geophys. Res. Oceans, 118, 2640–2652, doi:10.1002/jgrc.20133.
1. Introduction
[2] For the deep circulation of the world ocean, the production of dense water masses in the polar regions is a driving force of great importance. In the Southern Ocean, these
dense water masses are formed on the continental shelves
by cooling and salinification of the water. The salt enrichment is due to the salt rejection of freezing sea water. The
highest freezing rates are encountered at coastal polynyas,
areas where the sea ice is removed mechanically (usually by
winds) while freezing conditions prevail. Here, the oceanatmosphere interaction is hardly obstructed, and the heat flux
is strongly enhanced compared to the ambient ice-covered
ocean. Once the water is at freezing point, ice production is
very high. As a consequence, the high brine rejection leads
to the formation of a very dense water mass.
[3] The southwestern Weddell Sea is considered a major
contributor to Antarctic Bottom Water (AABW) production [Orsi and Bullister, 1999]. On the wide continental
shelves, water velocities are relatively small at 5–10 cm/s
[Fahrbach et al., 1992; Foldvik et al., 2001; Kottmeier and
1
Climate Dynamics, Alfred Wegener Institute, Bremerhaven, Germany.
Corresponding author: V. Haid, Climate Dynamics, Alfred Wegener
Institute, Bussestr. 24, 27570 Bremerhaven, Germany. ([email protected])
©2013. American Geophysical Union. All Rights Reserved.
2169-9275/13/10.1002/jgrc.20133
Sellmann, 1996], and the salt rejected by ice formation accumulates in the water column. Coastal polynyas are the areas
of the highest ice production during the winter months and
thus contribute most to the dense water formation [Smith
et al., 1990; Morales Maqueda et al., 2004]. It is therefore
of great interest to understand the processes and quantify the
fluxes at coastal polynyas in the southwestern Weddell Sea.
[4] In recent studies on polynyas in the Weddell Sea,
Comiso and Gordon [1998] found that strong meridional
winds are related to large polynya areas and that years featuring large polynyas coincide with years of large sea ice
extent in the Atlantic sector. They considered this a confirmation of the importance of polynyas in ice formation,
although the large ice extent may simply be caused by
the strong meridional winds leading to a faster northward
transport, without directly affecting the ice volume. Markus
et al. [1998] identified polynya areas from SSM/I data
and estimated heat flux and ice production using European
Centre for Medium-Range Weather Forecasts (ECMWF)
air temperatures and wind data while assuming constant
relative humidity and an ocean temperature at the freezing point. Focusing on the Ronne Polynya, Renfrew et al.
[2002] established a more complete surface energy budget
and derived ice production based on SSM/I data, automated
weather station observations, and NCEP/NCAR Reanalysis
data. Tamura et al. [2008] estimated thin ice thickness from
SSM/I data and calculated ice production using a heat flux
2640
HAID AND TIMMERMANN: HEAT FLUX AND SEA ICE PRODUCTION
model and ECMWF data for all major Antarctic polynyas
including Ronne Polynya. For polynyas in the Weddell and
Ross Seas, Drucker et al. [2011] computed sea ice production with a heat balance algorithm from ECMWF air
temperature and wind data, and ice motion and thin ice
thickness derived from AMSR-E data.
[5] While all of the previous studies neglected the heat
content of the ocean, the work presented in this paper uses
a full three-dimensional ocean model and thus includes the
oceanic heat fluxes. It aims to quantify polynya heat flux
to the atmosphere, the heat flux due to ocean cooling and
the resulting sea ice production during the winter months in
the southwestern Weddell Sea, addressing multiyear mean as
well as interannual variability. Fluxes within polynyas will
be set into relation with fluxes in the ambient pack ice. In the
following section, we will introduce the model and the data
sets used, in sections 3–6, we present and discuss our results
and conclude with a summary in section 7.
2. Model and Data
[6] The Weddell Sea coastal regions are difficult to access,
especially in winter, when sea ice production is at its highest.
Direct measurements are scarce and hard to obtain. Models
provide a possibility to gain knowledge on a wide range of
spatial and temporal scales. Their drawbacks and limits have
to be kept in mind, however.
2.1. The Sea Ice-Ocean Model
[7] We used the coupled Finite Element Sea-ice Ocean
Model (FESOM) [Danilov et al., 2004; Timmermann
et al., 2009] to study the processes of polynya development, heat flux and sea ice formation at the coastal
polynyas of the southwestern Weddell Sea. FESOM combines a hydrostatic, primitive-equation ocean model with a
dynamic-thermodynamic sea-ice model.
[8] The ocean component of the model solves the
horizontal momentum equation using the hydrostatic and
Boussinesq approximations. The vertical mixing scheme
follows Pacanowski and Philander [1981] in combination
with additional vertical mixing near the surface as proposed by Timmermann and Beckmann [2004]. Temperature
and salinity are determined by the traditional tracer
evolution equations.
[9] The sea ice component applies thermodynamics following Parkinson and Washington [1979] and the elasticviscous-plastic rheology suggested by Hunke and Dukowicz
[1997]. A snow layer evolution depending on precipitation,
air temperature, and melting processes is included in the
model [Owens and Lemke, 1997]. Heat storage within the ice
or snow layer is not considered. The ice drift is determined
by wind stress, ocean surface velocity, sea surface slope and
the internal forces of the ice, which are dependent on ice
thickness and concentration following Hibler III [1979]. For
the polynya in front of Ronne Ice Shelf, modelled ice drift
has been shown to be in good agreement with remote sensing
data [Hollands et al., 2013].
[10] The two model components communicate after each
time step and exchange heat, salt, and momentum fluxes.
Both share the same global unstructured horizontal grid with
3–5 km resolution close to the southwestern Weddell Sea
coastline (Figure 1). The ocean model features 37 z-layers
with increased resolution toward the surface (6 layers within
the top 100 m).
[11] The atmospheric forcing in our study is derived from
the NCEP/NCAR Reanalysis [Kalnay et al., 1996]. Daily
data sets of 10 m wind velocity, 2 m temperature, 2 m
specific humidity, precipitation rate, relative cloud cover
and latent heat flux were used, from these evaporation and
incoming longwave and shortwave radiation were calculated. The latent heat flux of the NCEP/NCAR data set was
only used to calculate mass exchange by evaporation; the
Figure 1. Model grid (black) and topography (color scale) in the southwestern Weddell Sea (sector of
the global model domain).
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HAID AND TIMMERMANN: HEAT FLUX AND SEA ICE PRODUCTION
latent heat exchange between ocean and atmosphere was calculated independently as part of the sea ice/ocean surface
energy balance (see below).
[12] As most data sets, also the NCEP/NCAR Reanalysis comes with uncertainties, especially in the Antarctic
region where only sparse measurements could be used for
data assimilation. The coarse resolution of their model grid
also complicates the representation of small scale features
like the high but narrow mountain range of the Antarctic
Peninsula. The even smaller coastal polynyas and consequently the local effect of polynyas on the atmosphere are
not represented in the data set. However, we assume that the
steady offshore winds and the small width of the polynyas
keep the magnitude of the effect small so that we consider
the NCEP/NCAR Reanalysis to be a valid first approach to
force the sea ice-ocean model. The forcing data were spatially interpolated from the 1.875ı NCEP grid to the model
grid points; also, the data were interpolated between the
subsequent daily fields to each model time step.
[13] The model was initialized on 1 January 1980 with
data from the Polar Science Center Hydrographic Climatology [Steele et al., 2001]. The time step was 3 min, and
the results were recorded as daily mean values. For analysis, only data from the 20 year time period from 1990 to
2009 was used. The focus was put on the winter months
May–September.
2.2. Heat Flux Components
[14] Following Parkinson and Washington [1979], we
split the heat flux to the atmosphere Qa into several
components: The radiative heat flux is a combination of the
shortwave radiative heat flux Qsw and the longwave radiative heat flux Qlw ; the turbulent heat flux can be split into
sensible heat flux Qs and latent heat flux Ql .
Qa = Qsw + Qlw + Qs + Ql .
[15] Here, the radiative fluxes are calculated as net upward
heat fluxes (i.e., upward flux minus downward flux), so that
heat flux to the atmosphere is positive. The downward shortwave radiation is dependent on angular zenith distance of
the sun and inhibition by relative cloud cover C; part
of it is reflected at the surface depending on its albedo
˛ , so that
Qsw = (˛ – 1) S0 cos2 C
(cos + 2.7) ev,a 10–5 + 1.085 cos + 0.1
with the solar constant S0 , the cloud factor C = 1 – 0.6 C3
[Laevastu, 1960] and the vapor pressure in the air ev,a in Pa.
In the winter months at high latitudes, the shortwave radiation, if at all, is a very small contribution to the atmospheric
heat flux.
[16] The longwave radiative heat flux is a function of the 2
m air temperature Ta , the surface temperature Ts and relative
cloud cover:
[17] The sensible heat flux is determined by the oceanatmosphere temperature difference and the 10 m wind speed
u10 following
Qs = cp a Cs u10 (Ts – Ta )
with the specific heat of air cp , the density of air a , the heat
transfer coefficient for sensible heat Cs = 1.75 10–3 over
ice, snow and water [Maykut, 1977; Parkinson and Washington, 1979], and the surface temperature Ts . For open water,
Ts is the temperature of the ocean model surface layer, for
ice it is obtained as part of the diagnostic computation of the
sea ice surface energy budget.
[18] The latent heat flux is the heat flux linked with evaporation, sublimation and their reversed processes. However,
while the mass flux associated with evaporation is determined by the forcing data, the latent heat flux is re-calculated
using wind speed and the difference between specific humidity at the surface qs (where saturation is assumed) and at 2 m
height qa
Ql = Le a Cl u10 (qs – qa )
with the heat of evaporation Le , the density of air a and the
heat transfer coefficient for latent heat Cl = 1.75 10–3 over
ice, snow and water [Maykut, 1977; Parkinson and Washington, 1979]. Thus, we obtain a self-consistent energy budget
while at the same time precipitation P and evaporation E are
based on one data set, providing a good estimate for net precipitation P – E, which is important for the ocean surface
freshwater budget in this global model.
[19] The heat flux to the atmosphere is partly compensated
by eroding the heat content of the ocean and partly by the
latent heat released in the process of ice formation. The former contribution results in an upward oceanic heat flux and
causes a decrease in temperature of the water column. It is
usually considered negligible, and the water is assumed to
be at freezing point. However, intrusions of warmer water
onto the continental shelf occur perennially [Foster et al.,
1987] and are evident in the wintertime measurements on the
southern continental shelf obtained from a CTD-equipped
Weddell Seal in 2007 [Nicholls et al., 2008]. In contrast to
satellite-data-based energy budget models, in this study, the
ocean model provides us with data about the heat gained by
ocean cooling.
2.3. Sea Ice Concentration Data
[20] To validate simulated sea ice concentration, we used
the AMSR-E 89 GHz sea ice concentration data set [Spreen
et al., 2008] without land mask, which we obtained from
the Center for Marine and Atmospheric Sciences (ZMAW)
in Hamburg, Germany. Since in the Weddell Sea, much of
the coastline is determined by the ice shelf fronts and is
thus highly variable, any prescribed land mask would very
soon be outdated. In our comparisons, we therefore only
mask the satellite data in areas of solid land (using RTopo-1
[Timmermann et al., 2010] as a reference) and mark the
model’s coastline in the figures for orientation.
3. Polynya Activity
Qlw = s T 4s – a T 4a
with the emissivities of the ice/ocean surface s = 0.97 and
of the atmosphere a = 0.765 + 0.22 C3 [König-Langlo and
Augstein, 1994] and the Stefan-Boltzmann constant .
3.1. Long-Term Mean
[21] A map of simulated mean sea ice concentrations
in the southwestern Weddell Sea for the winter months
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HAID AND TIMMERMANN: HEAT FLUX AND SEA ICE PRODUCTION
Figure 2. Twenty year mean winter (May–September) sea
ice concentration in the southwestern Weddell Sea. Black
lines define the regions on which this study focuses: Brunt
region, Ronne region and Antarctic Peninsula (Ant. Pen.)
region as well as the Southwestern Weddell Sea, which
includes the other three regions.
May–September 1990–2009 (Figure 2) reveals that most of
the southwestern Weddell Sea coastline features small but
recurring polynyas. Three regions were defined: The Brunt
region off Brunt Ice Shelf in the east, the Ronne region in
front of Ronne Ice Shelf in the south and the Antarctic Peninsula region along the southern part of the Antarctic Peninsula
in the west. These regions were chosen due to their enhanced
polynya activity. The polynyas east of our Brunt region are
located close to the continental shelf break or over the deep
ocean. Thus, they are of little significance to the production of dense water masses [Fahrbach et al., 1994]. The area
in front of Filchner Ice Shelf is not specifically considered,
since the grounded iceberg A-23A (since 1986) and various
smaller icebergs at Berkner Bank often lead to the formation of a fast ice bridge that prevents polynya formation
in front of Filchner Ice Shelf [Grosfeld and Gerdes, 1998].
Markus [1996] found that polynyas in this situation tend to
open in the lee of the fast ice bridge. Since the iceberg is not
included in the model, significant differences between model
and reality can be expected in this area.
[22] The northern part of the Antarctic Peninsula was
not included, since increasing air temperatures toward the
north reduce the importance of polynyas there. In our simulation, their extent is likely to be overestimated, since
the NCEP/NCAR Reanalysis model strongly smooths the
topography of the Antarctic Peninsula. Thus, the westerly
winds are too strong in the forcing [Windmüller, 1997;
Stössel et al., 2011], causing excess off-shore sea ice drift
in the simulation.
[23] The simulated sea ice thickness (Figure 3) features
thin ice at the main polynya sites. Compared to the ice concentration, polynya signatures are visible over larger areas
since the newly formed ice, while drifting away from the
Figure 3. Twenty year mean winter (May–September) sea
ice thickness in the southwestern Weddell Sea.
polynya, only slowly grows thicker over time. While at the
northward and eastward facing fronts of Brunt Ice Shelf,
the ice banks up and ice thickness has a local maximum, at
the westward borders, we find very thin ice with thickness
increasing in southwesterly direction. Along the Coats Land
coast and especially in front of Filchner Ice Shelf, Berkner
Island and reaching as far west as 54ı W, the ice accumulates
against the coastline; here, we find the maximum thickness.
Farther west in front of Ronne Ice Shelf, thin ice is found
which leaves a visible track on its northward drift. Another
thickness minimum is found at the coastline of the Antarctic
Peninsula between 72.5ı S and 69ı S, which again represents
a polynya formation area.
3.2. A Case Study
[24] A polynya event (29 May–03 Jun 2008) is presented
in Figure 4 to illustrate the performance of the model in
producing coastal polynyas. In general, the model’s ability
to reproduce polynyas is very good in time and in space.
Polynyas open at the same locations, during similar time
intervals and to a similar width as observed.
[25] For day 150, the two polynyas at Brunt Ice Shelf
exhibit similar size and ice concentration values in the simulation and in satellite observations. In front of Ronne Ice
Shelf, the satellite data shows an opening of the Ronne
polynya, which in the simulation is only visible as a trace of
reduced ice concentration along the coastline. The polynyas
along the Antarctic Peninsula agree well with observations,
showing strong signatures in the northern part and very weak
in the south. The satellite data additionally shows the signature of the fast ice bridge north of Filchner Ice Shelf and a
flaw polynya on its western side.
[26] On day 152, the polynyas at the peninsula have
closed. Ronne polynya and the Brunt polynyas have opened
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HAID AND TIMMERMANN: HEAT FLUX AND SEA ICE PRODUCTION
Figure 4. (Left) Sea ice concentration maps from model and (right) SSM/I observations. Note that the
location of the ice shelf front represents the model geometry, not the actual ice shelf front in winter 2008.
The model does not include the grounded iceberg north of Filchner Ice Shelf and thus produces no fast
ice bridge where a flaw polynya could develop.
wider and simulation and satellite data agree well. Still, the
satellite observations show a flaw polynya at the fast ice
bridge, although smaller than on day 150.
[27] Three days later, on day 155, the satellite data shows
Ronne polynya, the Brunt polynyas and the flaw polynya
in the closing stage with rising ice concentrations. The simulation also features a weakened signature at the Brunt
polynyas, while Ronne polynya is reduced in size, but still
shows very low sea ice concentrations.
3.3. Integrated Polynya Area
[28] In this study, we define polynyas as the area where
sea ice concentration A < 70% or ice thickness hi < 20 cm,
thus polynya area is the corresponding area for all model
nodes, which meet the criterion. We find a polynya size of
1013 km2 in the Brunt region in the 20 year mean over
all winter (May–September) days. In the Ronne region,
coastal polynyas on average cover an area of 1998 km2 ,
which is twice the size of the Brunt polynyas; in the
Antarctic Peninsula region, we find a mean winter polynya
size of 1712 km2 . Over the years, 2003–2009 (chosen due
to the availability of the data), a comparison of the simulated polynya area to the polynya area derived with the
criterion of 70% ice concentration from SSM/I data shows
that the simulated daily mean polynya size is smaller in
Brunt region by 40% and in Ronne region by 30%, while
in the Antarctic Peninsula region, the simulated polynyas
exceed the satellite-derived area by 68%. On a basin-wide
scale, the various regional differences between polynyas
from the SSM/I data and the simulation compensate each
other very well, and the simulation underestimates the
observation-derived value by only 10%. The overestimation
of polynya size at the peninsula can largely be attributed
to the fact that the westerly winds in the forcing data are
hardly affected by the Antarctic Peninsula due to its coarse
representation. Thus, the NCEP forcing has winds with
an overestimated westerly component [Windmüller, 1997;
Stössel et al., 2011], while observations indicate that the
wind field east of the peninsula is often dominated by
barrier winds [Schwerdtfeger, 1975; Parish, 1983].
[29] Interannual variability of polynya area is very pronounced, as indicated by a compilation of winter mean
polynya areas (Figure 5). Seasonal means of half or twice
the long-term mean are not uncommon. The range of
polynya area in Brunt region spans from 440 to 2000 km2 ,
which is rather moderate compared to the range in Ronne
region (600–5200 km2 ) and at the Antarctic Peninsula (390–
4100 km2 ).
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HAID AND TIMMERMANN: HEAT FLUX AND SEA ICE PRODUCTION
Figure 5. Simulated winter (May–September) mean polynya area in the three regions.
4. Atmospheric Heat Flux
4.1. Multiyear Mean Within Polynyas
[30] Mean polynya values, here and in the following
sections, are calculated from area-weighted daily averages
over model nodes classified as polynya, i.e., all model nodes
with an ice concentration < 70% or ice thickness < 20 cm.
Days when no polynya is present are not considered (contrary to the calculation of the mean polynya area in the
section above). During winter (May–September), we find a
20 year mean heat flux to the atmosphere of 311 W/m2 in
the polynyas of the Brunt region. The Ronne polynyas feature a mean of 511 W/m2 ; in the Antarctic Peninsula region
the mean winter heat flux is 364 W/m2 . The annual mean
values and the standard deviations from the multiyear mean
are found in Table 1. If all polynyas in the Southwestern
Weddell Sea (Figure 2) are considered, the mean winter heat
flux to the atmosphere is 368 W/m2 . These mean values
are calculated considering only the days when a polynya
is present, thus they represent the mean flux that can be
expected over a polynya in the corresponding region.
[31] While at the Brunt polynyas the highest heat flux
occurs during the months July and August ( 345 W/m2 ),
at Ronne polynya ( 550 W/m2 ) and at the peninsula coastline ( 420 W/m2 ), June must also be listed among the
months with a particularly high polynya heat flux. These
values are monthly means averaged over 20 years and thus
are well below peak values that are possible under favorable
circumstances.
[32] The annual mean heat fluxes to the atmosphere and
their components at polynyas during the winter months
May–September for the three most active polynya regions
are presented in Figure 6. As might be expected, in winter,
the shortwave radiation is the smallest contributor. Given
that we define upward fluxes to have a positive sign and
the reflected upward shortwave radiation is a fraction of the
downward shortwave radiation, the net shortwave radiation
must always be a negative number.
Table 1. Annual and 20 Year Winter Mean of Atmospheric Heat Flux and Ice Production of the
Polynyas in Brunt, Ronne and Antarctic Peninsula (Ant. P.) Regiona
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Mean
Std dev
a
Heat Flux [W/m2 ]
Sea Ice Prod. [cm/d]
Accum. Sea Ice Prod. [km3 ]
Brunt
Ronne
Ant. P.
Brunt
Ronne
Ant. P.
Brunt
Ronne
Ant. P.
324
331
348
295
314
306
295
324
328
294
316
349
308
309
385
307
264
298
297
237
311
˙31
467
535
590
463
454
508
443
528
550
547
511
521
575
467
500
507
501
550
437
566
511
˙45
323
358
509
329
329
392
336
410
389
347
419
390
442
376
364
292
365
304
278
320
364
˙55
7.01
6.63
7.70
5.74
6.93
7.81
7.20
7.04
8.46
7.41
7.86
8.35
7.71
7.57
9.61
6.19
6.11
7.35
6.83
4.90
7.22
˙1.04
11.80
13.60
15.58
11.50
10.94
13.08
11.58
13.98
14.55
14.43
13.48
13.73
15.06
10.94
12.99
12.74
12.84
13.81
11.43
14.46
13.13
˙1.39
7.84
8.98
12.69
7.37
7.59
9.83
8.76
10.53
9.15
8.72
10.89
9.88
11.14
9.59
7.66
7.59
9.56
7.28
6.38
7.92
8.97
˙1.57
17.5
16.5
21.9
9.9
12.1
13.8
14.2
8.9
18.0
8.6
8.2
17.5
9.2
11.2
22.8
18.0
10.9
5.2
10.0
4.5
12.9
˙5.2
35.6
17.7
81.6
37.4
21.8
52.3
12.6
84.4
88.8
18.6
55.4
38.0
80.1
86.0
28.3
24.4
46.8
20.3
18.5
27.8
43.8
˙26.6
13.1
17.1
40.6
13.9
14.9
14.6
7.3
40.5
47.1
15.5
28.6
14.2
38.6
24.0
32.5
10.6
24.4
3.1
14.1
5.4
21.0
˙12.9
Minimum and maximum values for each column is printed in italic font.
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HAID AND TIMMERMANN: HEAT FLUX AND SEA ICE PRODUCTION
Figure 6. Simulated winter (May–September) mean of the atmospheric heat flux over polynyas in the
three regions. The dark grey color represents the net shortwave radiation (negative values). Since the
components are summed up and the entire column length gives the heat flux to the atmosphere, the length
of the grey and yellow column parts together represents the net longwave radiation, the red part is the
latent and the blue part the sensible heat flux. The small black lines mark the oceanic heat flux, i.e., the
heat flux not compensated by sea ice formation but by cooling the water column.
Table 2. Twenty Year Winter Mean (Bold Font) of the Atmospheric Heat Flux Components and the Oceanic Heat Flux
in the Polynyas of Brunt, Ronne and Antarctic Peninsula Region With the Annual Mean Minimum and Maximum Value
as Well as the Standard Deviationa
Region
Shortwave
Longwave
Latent
Sensible
Oceanic
a
Brunt
Ronne
Antarctic Peninsula
Min
Max
Mean
Std dev
Min
Max
Mean
Std dev
Min
Max
Mean
Std dev
–8.9
83.4
38.9
127.6
30.2
–2.8
64.4
74.6
236.5
97.3
–4.7
75.8
56.3
183.7
57.4
˙1.9
˙5.2
˙7.3
˙21.9
˙21.8
–7.1
105.6
56.4
264.0
34.9
–0.5
120.7
82.9
371.6
82.2
–4.5
115.5
71.2
328.4
49.2
˙2.3
˙4.0
˙7.2
˙34.4
˙12.9
–12.7
83.1
43.5
149.5
25.3
–1.3
101.4
86.6
339.5
94.9
–7.7
92.3
59.0
220.3
48.2
˙3.2
˙5.2
˙11.2
˙42.5
˙16.6
All values are given in W/m2 .
[33] The shortwave heat flux has an average of
–4.7 W/m2 at the Brunt polynyas, –4.5 W/m2 at Ronne
polynya and –7.7 W/m2 at the polynyas along the Antarctic
Peninsula (which are located a bit further north on average).
The longwave radiation features a winter mean of 76 W/m2
over the Brunt polynyas, 116 W/m2 over Ronne polynya
and 92 W/m2 over the Antarctic Peninsula polynyas. The
latent heat flux provides a slightly smaller contribution:
56 W/m2 in Brunt region, 71 W/m2 in Ronne region and
59 W/m2 in Antarctic Peninsula region. In all polynyas,
winter heat flux and its variability are dominated by the sensible heat transfer, which contributes 59–64% of the total
heat flux. This fraction, as well as the relative contributions of latent and radiative heat fluxes, is in very good
agreement with the findings of Renfrew et al. [2002] during
high-winter (June–July 1998). The simulated 20 year winter
mean of the sensible heat flux over polynyas is 184 W/m2
in Brunt region, 328 W/m2 in Ronne region and 220 W/m2
in the Antarctic Peninsula region. An overview over the
atmospheric heat flux components within polynyas including annual minimum and maximum values and standard
deviations is presented in Table 2.
[34] The 20 year mean of heat gained by the atmosphere
over the polynyas during the months May–September is
4.7 ˙ 2.0 1018 J in the Brunt region, 14.7 ˙ 9.0 1018 J in
Ronne region and 7.7 ˙ 4.8 1018 J in Antarctic Peninsula
region. Renfrew et al. [2002] estimated the mean heat gain
by the atmosphere at Ronne polynya during the freezing season for the period 1992–1998 to be 3.48 ˙ 0.98 1019 J.
In their study, Renfrew et al. [2002] defined the duration of
the freezing period individually for every year. For the same
time frame, our simulation yields an atmospheric heat gain
of 3.76 ˙ 1.14 1019 J, which implies (1) a good agreement between the two estimates and (2) the importance of a
2646
HAID AND TIMMERMANN: HEAT FLUX AND SEA ICE PRODUCTION
careful consideration of the time frame any estimate represents. Further comparisons of our results with independent
studies are found in section 6 about sea ice production.
4.2. Multiyear Mean Outside Polynyas
[35] To assess the importance of polynyas in terms of heat
transferred to the atmosphere compared to the ambient pack
ice, we calculated the heat flux outside the polynyas in the
three regions (also depicted in Figure 6). For the area with
ice concentrations higher than 70%, the mean winter heat
flux is 30 W/m2 in Brunt region, 70 W/m2 in Ronne region
and 46 W/m2 in the Antarctic Peninsula region, which is
only 10–14% of the heat flux within polynyas, but due to
the much larger area amounts to an atmospheric heat gain of
8.7 ˙ 1.2 1020 J/season in the Southwestern Weddell Sea.
[36] In the Brunt region, most heat flux components are
negligible outside polynyas and the longwave radiation is
responsible for almost all the heat transferred to the atmosphere with an average of 31 W/m2 . The latent heat flux over
ice gives a small negative contribution (mean: –4.0 W/m2 ),
which corresponds to resublimation of atmospheric humidity. In the Ronne region, due to the very low air temperatures,
the sensible heat flux (mean: 26 W/m2 ) is of similar magnitude as the net longwave radiation (mean: 39 W/m2 ). Both
other heat flux components are negligible; latent heat flux
is positive (but very small) here. At the Antarctic Peninsula the heat flux over high ice concentration areas is mainly
due to the longwave radiation (mean: 37 W/m2 ) although in
years with low temperatures and strong winds the sensible
heat flux can substantially add to this (mean: 9.1 W/m2 ). The
modelled latent heat flux transfers heat out of the atmosphere
at a rate of –1.8 W/m2 .
4.3. Interannual Variability Within Polynyas
[37] Compared to the strong interannual variability found
for polynya area (Figure 5), interannual variability of atmospheric heat flux (averaged over polynya days) (Figure 6)
is much smaller and so is the variability of the key parameters air temperature, wind speed and specific humidity
(Figure 7). A compilation of annual maximum and minimum
values, multiyear mean and standard deviation of these three
forcing parameters is found in Table 3.
[38] For the Brunt polynyas, we find the highest winter heat flux to the atmosphere in 2004 (385 W/m2 ) and
the lowest atmospheric heat flux in 2009 (237 W/m2 ). Air
temperature, wind speed and specific humidity feature
20 year winter means of –20.7ı C, 6.5 m/s and 0.83 g/kg,
respectively. For 2004, neither of these exhibit extraordinary values of the winter mean (–20.0ı C, 6.2 m/s and
0.87 g/kg), but the maximum in the turbulent heat flux
components is easily explained when the mean of the daily
product of air temperature and wind speed (Figure 7, bottom row) is considered, which represents the main driving
parameter of the sensible heat flux. Short periods of strong
winds and low temperatures leave only little trace in the
means of the individual parameters, but when strong anomalies coincide, as, for example, in the case of cold air outbreaks, they produce a heat flux maximum that persists in
the seasonal mean. Similarly, the heat flux minimum in 2009
is hard to explain by the seasonal average of air temperature
(–19.5ı C) and wind speed (6.4 m/s), but shows clearly
in the mean product of air temperature and wind speed.
Again, short-term events dominate the seasonal mean of the
atmospheric heat flux.
[39] Of the three different regions, Ronne polynya features the coldest air temperatures in winter with a 20
year mean of –33.3ı C, the highest wind speeds with a
mean of 7.4 m/s and, mainly due to the cold temperatures, the lowest specific humidity of 0.29 g/kg. At Ronne
polynya the minimum heat flux in 2008 is accompanied by
the second-warmest air temperatures (–30.1ı C), a close-toaverage wind speed (7.6 m/s) and the second-highest specific
humidity (0.41 g/kg). Therefore, sensible and latent heat flux
are low and together cause the atmospheric heat flux minimum. The maximum in 1992 (590 W/m2 ), again, is not
explained by looking at the seasonal means of the individual
forcing parameters. It coincides with below-average air temperatures (–34.4ı C), below-average wind speed (6.8 m/s)
and below-average specific humidity (0.28 g/kg). However,
a look at the mean product of air temperature and wind speed
reveals a peak in 1992 that triggers the heat flux maximum.
[40] At the polynyas in the Antarctic Peninsula region, we
find the 20 year winter mean of the air temperature to be
–23.3ı C, the mean wind speed is 7.0 m/s and the mean specific humidity is 0.73 g/kg. The by far largest atmospheric
heat flux (509 W/m2 ) is in 1992. It coincides with the maximum in Ronne region, but is more easily explained by the
coincidence of minimum air temperature (–26.1ı C), highest wind speed (8.7 m/s) and the minimum specific humidity
(0.51 g/kg). The minimum winter heat flux of 278 W/m2 in
2008 is just as obviously caused by the warmest air temperature (–18ı C), the third-lowest wind speed (5.9 m/s) and the
highest specific humidity (1.07 g/kg).
[41] Since the sensible heat flux is the main contributor
to the atmospheric heat flux and the surface temperature
in polynyas can be assumed to vary little, the mean of the
daily product of air temperature and wind speed features a
strong relation with the mean total atmospheric heat flux. Of
course, not all details correlate, but it gives a very good first
approach to the variability and individual seasonal means of
the atmospheric heat flux.
5. Oceanic Heat Flux
[42] Sea ice production per unit area is strongly dependent on the heat flux to the atmosphere. If no further energy
is supplied, a direct proportionality is expected. However,
if the ocean is not at the freezing point, part of the heat
loss to the atmosphere is compensated by the ocean’s heat
content, thereby cooling the water column. This part is
expected to be highest in Brunt region, since in the east, the
warm water of the Weddell Gyre enters upon the continental
shelf as a coastal current, but also farther west intrusions of
Modified Warm Deep Water occur [Foster and Carmack,
1976, Nicholls et al., 2008, 2009]. Originating from Warm
Deep Water, its temperatures of up to 0.5ı C are higher
than the freezing point, but it experiences fast cooling on
the shelf.
[43] In our simulation, the average oceanic heat flux due
to the erosion of oceanic heat content is 57 W/m2 in the
Brunt polynyas, 49 W/m2 in Ronne polynya and 48 W/m2
in the Antarctic Peninsula region (all averaged over polynya
days only) (Table 2). As expected, the oceanic heat flux is
2647
HAID AND TIMMERMANN: HEAT FLUX AND SEA ICE PRODUCTION
Figure 7. Simulated winter (May–September) mean of the main forcing components over polynyas in
the three regions. Note that the y-axis of the air temperature graphs is flipped upside down.
Table 3. Twenty Year Winter Mean (Bold Font) of Wind Speed, Air Temperature and Specific Humidity in Brunt, Ronne and Antarctic Peninsula (Ant. P.) Region With the Annual Mean
Minimum and Maximum Value as Well as the Standard Deviation
Wind Speed [m/s]
Min.
Max.
Mean
Std dev
Air Temperature [ı C]
Specific Humidity [g/kg]
Brunt
Ronne
Ant. P.
Brunt
Ronne
Ant. P.
Brunt
Ronne
Ant. P.
3.64
6.24
4.72
˙0.66
3.68
5.09
4.31
˙0.44
4.24
7.98
5.72
˙0.94
–26.9
–21.8
–23.9
˙1.3
–37.1
–30.5
–33.8
˙1.6
–26.1
–21.1
–24.1
˙1.2
0.57
0.78
0.68
˙0.07
0.23
0.44
0.32
˙0.05
0.53
0.85
0.67
˙0.07
2648
HAID AND TIMMERMANN: HEAT FLUX AND SEA ICE PRODUCTION
highest in the east, but does only slightly decrease and is still
substantial in the western polynyas. The small difference in
oceanic heat flux between the regions may partly be due to
the warm water experiencing thorough cooling even before
reaching the Brunt polynyas, but also attests the intrusion
of above-freezing point waters to the farthest corners of the
continental shelf. The contribution of the oceanic heat flux
to the atmospheric heat flux is 19% in the Brunt polynyas,
10% in Ronne polynya and 13% in the Antarctic Peninsula polynyas in the long-term winter mean. So, although
the absolute values of oceanic heat flux are similar for the
three regions, in the Brunt region the relative contribution to
atmospheric heat flux is higher by a factor of two.
[44] Outside polynyas, only 16 W/m2 in Brunt region,
21 W/m2 in Ronne region and 20 W/m2 in Antarctic
Peninsula region are due to oceanic cooling, which is
30–57% of the total heat flux to the atmosphere outside
polynyas. The fact that the oceanic heat flux is higher within
polynyas than outside is easily explained by the increased
convection under polynyas due to the higher salt enrichment.
The seasonal means of the oceanic heat flux of the individual
years are marked with a black line in Figure 6. As already
explained, the difference between atmospheric and oceanic
heat flux is compensated by latent heat gained from sea ice
production.
6. Sea Ice Production
[45] The ice production per unit area in our simulation
has a 20 year mean of 7.24 cm/d (b
= 11.1 m/winter) in
the Brunt polynyas. In the Ronne polynya, the mean ice
= 20.2 m/winter) and in the
production is 13.23 cm/d (b
polynyas of the Antarctic Peninsula region, we found 9.21
cm/d (b
= 14.1 m/winter) (all averages over polynya days
only). Since the oceanic heat supply within polynyas is a
non-negligible, but small contribution to the atmospheric
heat flux, minima and maxima of ice production per unit
area (Figure 8) coincide with the minima and maxima of
winter-mean atmospheric heat flux in most cases. At the
Brunt polynyas, the highest mean winter ice production per
unit area is in 2004 (9.6 cm/d b
= 14.7 m/winter), while 1992
features the highest ice production per unit area in Ronne
= 23.9 m/winter) and the Antarctic Peninregion (15.6 cm/d b
sula region (12.7 cm/d b
= 19.43 m/winter). The lowest ice
production in the Brunt polynyas is found in 2009 with
4.9 cm/d b
= 7.5 m/winter. At the Ronne polynya, ice production minima are found for 1994, 2003, and 2008. The
absolute minimum (2003) does not coincide with the smallest atmospheric heat flux: Atmospheric heat flux for 2003 is
slightly higher than for 2008. Instead, 2003 stands out as the
year with the highest oceanic heat flux (82 W/m2 , Figure 5),
which, combined with a small heat loss to the atmosphere,
limits ice production to 10.94 cm/d (b
= 16.7 m/winter).
The Antarctic Peninsula polynyas have their lowest ice
= 9.8 m/winter), which
production in 2008 (6.4 cm/d b
again coincides with the absolute minimum of atmospheric
heat flux.
[46] Outside the polynyas the regions feature ice production rates per unit area of 0.53 cm/d (b
= 0.8 m/winter) for
Brunt region, 1.39 cm/d (b
= 2.1 m/winter) for Ronne region
and 0.86 cm/d (b
= 1.3 m/winter) for the Antarctic Peninsula
region. The ice production per unit area outside the polynyas
is only 7% of the ice production within polynyas in the
Brunt region, 11% in Ronne region and 9% in the Antarctic
Peninsula region.
[47] If accumulated, the multiyear mean of ice production per winter season amounts to 12.9 km3 in the Brunt
polynyas, 43.8 km3 in the Ronne polynyas and 21.0 km3
in the polynyas along the southern Antarctic Peninsula. For
the entire Southwestern Weddell Sea (Figure 2), polynya ice
Figure 8. Simulated winter (May–September) mean of ice production per unit area in polynyas and
outside polynyas in the three regions.
2649
HAID AND TIMMERMANN: HEAT FLUX AND SEA ICE PRODUCTION
Figure 9. Simulated winter (May–September) mean of ice volume produced in polynyas in the three
regions.
production in winter features a mean of 105 km3 , which is
11% of the total ice production of 993 km3 , but originates
from only 0.6% of the area.
[48] The highest and lowest production of ice volume
(Figure 9) depends very much on polynya area so that only
in the Brunt region does the lowest (highest) annual polynya
ice volume production coincide with the lowest (highest)
annual ice production per unit area. In 2009, the Brunt
polynyas form only 4.5 km3 of ice on an area of 479 km2 ,
while their highest production is 22.8 km3 in 2004 on an area
of 1403 km2 . Both times, the coincidence with the minimum
(maximum) ice production per unit area is amplified by a
relatively small (large) polynya area.
[49] For Ronne polynya, the highest accumulated ice production over the winter months occurs in 1998 (88.8 km3 ),
when the third highest ice production per unit area coincides with the second largest polynya area (4052 km2 ). The
minimum total ice production is found in 1996 (12.6 km3 ),
induced by a relatively low production per area and the
smallest polynya area (603 km2 ).
[50] In the polynyas along the Antarctic Peninsula, the
smallest amount of sea ice was produced in 2007 (3.1 km3 ),
by the second lowest sea ice production per unit area and the
smallest mean polynya area of only 390 km2 . The maximum
ice production occurred in 1998. Although the ice production per area was below average, the largest annual polynya
extent (4079 km2 ), by far, more than compensated for it.
[51] Previous studies on the polynya sea ice formation in
the Weddell Sea usually based their calculations on satellite observations of sea ice concentration (mostly SSM/I)
and coarse global atmospheric data sets (often ECMWF
or NCEP/NCAR). The heat flux to the atmosphere that
resulted from the energy budget was converted into sea ice
production using the assumption that the ocean surface is
permanently at freezing temperature and that the oceanic
heat flux can be neglected. Our study, although still dependent on a coarse-scale atmospheric data set, is independent
of the satellite observations and includes the heat flux provided by ocean cooling, which turned out to be 10–20% of
the heat flux to the atmosphere. We thus expect less ice production in our simulation than in previous studies that did
not consider the ocean’s heat content. Ice production rates
based only on the heat flux to the atmosphere are prone
to overestimate the true rates and must be regarded as an
upper limit.
[52] Markus et al. [1998] determined the seasonal mean
ice production in their southern region including Ronne
polynya in the years 1992–1994 to be 87 km3 . For the same
time period, our simulation yields 77 km3 in the Ronne
polynya. The numbers agree well, although study areas do
not match exactly, and the region of Markus et al. [1998]
includes the coastline of Filchner Ice Shelf.
[53] For the period 1992–1998, Renfrew et al. [2002] calculated a mean ice production in Ronne Polynya of 24 ˙
5.1 m per unit area and a total of 111 ˙ 31km3 during the
full freezing season, which they individually identified for
every year. Using the same intervals, our simulation gives
19.3˙6.2 m per unit area and a total of 104˙36km3 . Tamura
et al. [2008] found the ice production from March to October in Ronne polynya to accumulate up to 85 km3 as a mean
over the years 1992–2001. For the same period, our model
gives 89 km3 . The good agreement in both cases is facilitated by Renfrew et al. [2002] and Tamura et al. [2008] using
a study area containing a very similar part of the coastline
as our Ronne region; it also indicates the robustness of our
(and their) results.
[54] For the period of April–October 2003–2008, Drucker
et al. [2011] calculated a mean accumulated ice production
of 112 km3 /season for the Brunt polynyas and 99 km3 /season
for Ronne polynya. The corresponding values from our simulation are 89 km3 /season in the Brunt region and only
50 km3 in Ronne region. The differences can be explained
by the negligence of the oceanic heat flux and the different
extent of the study areas. Drucker et al. [2011] include no
locations with water depth over 1000 m and their eastern
region stretches farther east and includes part of the polynya
forming off Riiser-Larsen Ice Shelf. Also, their southern
region extends farther west including a part of the polynyas,
which we include in the Antarctic Peninsula region.
[55] In general, the comparisons show good agreement,
and as expected, our values are slightly lower than the ice
production calculated from satellite data. Only the study by
Tamura et al. [2008] gives a lower sea ice production than
our simulation. However, the values are very close and the
uncertainties in the data sets used and parameterization on
both sides can easily explain this outcome.
7. Summary
[56] Using a coupled sea-ice ocean model forced with
data from the NCEP/NCAR Reanalysis, we investigated the
importance of coastal polynyas in the southwestern Weddell Sea in terms of heat transfer to the atmosphere, the heat
flux supplied by ocean cooling and resulting sea ice produc-
2650
HAID AND TIMMERMANN: HEAT FLUX AND SEA ICE PRODUCTION
tion. We found the Ronne Ice Shelf front to be the region
where the largest polynyas form and the highest atmospheric
heat flux and sea ice production occur. Two other important
regions were identified located at the Brunt Ice Shelf and
along the southern part of the Antarctic Peninsula.
[57] The atmospheric heat flux over coastal polynyas during the months May–September has a 20 year mean of
313 W/m2 in Brunt region, 515 W/m2 in Ronne region and
374 W/m2 in Antarctic Peninsula region. The interannual
variability of the atmospheric heat flux is high and usually
dominated by the variability of the sensible heat flux, which
is the dominant contributor also in the long-term mean.
Outside polynyas, the atmospheric heat flux is mostly determined by the longwave radiation budget, but still variability
is ruled by the sensible heat flux component.
[58] The oceanic heat flux was found to be a nonnegligible component of the ocean surface heat budget even
at these high latitudes. Maximum values were found in the
easternmost region next to Brunt Ice Shelf with 57 W/m2 in
the 20 year mean due to the warm water entering upon the
shelf, but is almost as high in both regions farther west with a
little less than 50 W/m2 , indicating that surface cooling does
not prevent above-freezing point water from reaching far
onto the continental shelf, which is consistent with observations presented by Nicholls et al. [2008]. Therefore, 10–20%
of the atmospheric heat flux during winter at the polynyas
in the southwestern Weddell Sea does not result in sea ice
production, but is compensated by an erosion of the ocean’s
heat content.
[59] In the 20 year mean, we find a sea ice production of
7.2 cm/d at the Brunt polynyas leading to 12.9 km3 per winter, 13.2 cm/d at Ronne polynya creating 43.8 km3 per winter
and 9.21 cm/d in the polynyas along the Antarctic Peninsula
giving 21.0 km3 per winter. Keeping in mind the reduction of
ice formation by the oceanic heat flux, our results compare
very well with previous studies about atmospheric heat flux
and sea ice production in Weddell Sea polynyas by Markus
et al. [1998], Renfrew et al. [2002], Tamura et al. [2008] and
Drucker et al. [2011].
[60] Within the investigated regions, areas with a sea ice
concentration higher than 70% feature an atmospheric heat
flux of only 11–14% and a sea ice production per unit
area of 7–11% of the corresponding value within polynyas.
Due to the small area of coastal polynyas in the Weddell Sea, the contribution of the much larger ice-covered
ocean to heat exchange and sea ice production is prevailing on a large scale, primarily because the leads within
the pack ice add up to a considerable area. Locally, however, the coastal polynyas are of paramount importance
since here the ocean-atmosphere exchange is enhanced by
an order of magnitude. Only the exceptionally high freezing rates and the local stability of coastal polynyas enable
the salinity enrichment necessary for the production of
dense shelf water, an indispensable ingredient for bottom
water formation.
[61] The high interannual variability of the atmospheric
heat flux and the sea ice formation in polynyas is dominated
by the interannual variability of the atmosphere. Neither air
temperature (T) nor wind speed (u) anomalies alone are sufficient to explain the interannual variability of the sensible
heat flux; only seasonal averages of the product u’T’ yield
time series that correspond to those of heat flux and ice
production anomalies. While this is a finding well familiar from turbulence theory, it still reminds us that looking
at mean fields of atmospheric variables alone may be misleading when trying to assess potential impacts of future
climate change.
[62] As a follow-up, two subsequent studies are underway: a detailed assessment of the consequences of the brine
rejection entailed by sea ice production on the on-shelf
water masses, and an investigation on how atmospheric forcing data sets with different resolutions influence polynya
formation, heat flux and sea ice formation.
[63] Acknowledgments. The work for this study was funded by
Deutsche Forschungsgemeinschaft in SPP 1158 under grant number
TI 296/5. Special thanks go to our project partners G. Heinemann
and L. Ebner from the Department of Environmental Meteorology,
University of Trier, Germany, for inspiration and collaboration. The
NCEP/NCAR Reanalysis atmospheric forcing data was obtained from
NOAA Climate Diagnostics Center, Boulder, USA via the website http://
www.cdc.noaa.gov.
References
Comiso, J. C., and A. L. Gordon (1998), Interannual variability in
summer sea ice minimum, coastal polynyas and bottom water formation in the Weddell Sea, in Antarctic Sea Ice: Physical Processes, Interactions and Variability, edited by Jeffries, O., Antarct.
Res. Ser., vol. 74, AGU, Washington, D. C, 293–315, doi:10.1029/
AR074p0273.
Danilov, S., G. Kivman, and J. Schröter (2004), A finite element ocean
model: Principles and evaluation , Ocean Model., 6, 125–150, doi:
10.1016/S1463-5003(02)00063-X.
Drucker, R., S. Martin, and R. Kwok (2011), Sea ice production and
export from coastal polynyas in the Weddell Sea, Geophys. Res. Lett., 38
(L17502), 1–4, doi:10.1029/2011GL048668.
Fahrbach, E., G. Rohardt, and G. Krause (1992), The Antarctic coastal
current in the southeastern Weddell Sea, Polar Biol., 12, 171–182.
Fahrbach, E., G. Rohardt, M. Schröder, and V. Strass (1994), Transport and
structure of the Weddell Gyre, Ann. Geophysicae, 12, 840–855.
Foldvik, A., T. Gammelsrød, E. Nygaard, and S. Østerhus (2001), Current measurements near Ronne Ice Shelf: Implications for circulation and
melting, J. Geophys. Res., 106, 4463–4477.
Foster, T. D., and E. C. Carmack (1976), Frontal zone mixing and Antarctic
Bottom water formation in the southern Weddell Sea, Deep Sea Res., 23
(4), 301–317.
Foster, T. D., A. Foldvik, and J. H. Middleton (1987), Mixing and bottom
water formation in the shelf break region of the southern Weddell Sea,
Deep Sea Res., 34(11), 1771–1794.
Grosfeld, K., and R. Gerdes (1998), Circulation beneath the Filchner Ice
Shelf, Antarctica, and its sensitivity to changes in the oceanic environment: A case-study, Ann. Glaciol., 27, 99–104.
Hibler III, W. D. (1979), A dynamic-thermodynamic sea ice model, J. Phys.
Ocean., 9(4), 815–846.
Hollands, T., V. Haid, W. Dierking, R. Timmermann, and L. Ebner (2013),
Sea ice motion at the Ronne Polynia, Antarctica: SAR observations vs.
model results, submitted.
Hunke, E. C., and J. K. Dukowicz (1997), An elastic-viscous-plastic model
for sea ice dynamics, J. Phys. Ocean., 27, 1849–1868.
Kalnay, E., et al. (1996), The NCEP/NCAR 40-year reanalysis project, B.
Am. Meteorol. Soc., 77, 437–471.
König-Langlo, G., and E. Augstein (1994), Parameterization of the downward long-wave radiation at the earth’s surface in polar regions,
eteorol. Zeitschrift, 3(6), 343–347.
Kottmeier, C., and L. Sellmann (1996), Atmospheric and oceanic
forcing of Weddell Sea ice motion, J. Geophys. Res., 101 (C9),
20809–20824.
Laevastu, T. (1960), Factors affecting the temperature of the surface layer
of the sea , Commentat. Phys.-Math., 25, pp. 136.
Markus, T. (1996), The effect of the grounded tabular icebergs in
front of the Berkner Island on the Weddell Sea ice drift as seen
from satellite passive microwave sensors, in IGARSS ’96: Remote
Sensing for a Sustainable Future, IEEE Press, Piscataway, N.J,
1791–1793.
Markus, T., C. Kottmeier, and E. Fahrbach (1998), Ice formation in coastal
polynyas in the Weddell Sea and their impact on oceanic salinity,
2651
HAID AND TIMMERMANN: HEAT FLUX AND SEA ICE PRODUCTION
in Antarctic Sea Ice: Physical Processes, Interactions and Variability,
edited by Jeffries, O., Antarct. Res. Ser., vol. 74, AGU, Washington, D.
C., 273–292, doi:10.1029/AR074p0273.
Maykut, G. A. (1977), Estimates of the regional heat and mass balance of
the ice cover, in A Symposium on Sea Ice Processes and Models, vol. I,
University of Washington, Seattle, 65–74.
Morales Maqueda, M. A., A. J. Willmott, and N. R. T. Biggs (2004),
Polynya dynamics: A review of observations and modelling, Rev. Geophys., 42(RG1004), 1–37, doi:10.1029/2002RG000116.
Nicholls, K. W., L. Boehme, M. Biuw, and M. A. Fedak (2008),
Wintertime ocean conditions over the southern Weddell Sea continental shelf, Antarctica, Geophys. Res. Lett., 35, L21605, doi:
10.1029/2008GL035742.
Nicholls, K. W., S. Østerhus, K. Makinson, T. Gammelsrød, and E.
Fahrbach (2009), Ice-ocean processes over the continental shelf of the
southern Weddell Sea, Antarctica: A review, Rev. Geophys., 47, RG3003,
doi:10.1029/2007RG000250.
Orsi, A. G. J., and J. Bullister (1999), Circulation, mixing and production
of Antarctic Bottom Water, Prog. Oceanogr., 43, 55–109.
Owens, W. B., and P. Lemke (1997), Sensitivity studies with a sea ice-mixed
layer-pycnocline model in the Weddell Sea, J. Geophys. Res., 95 (C6),
9527–9538.
Pacanowski, R. C., and S. G. H. Philander (1981), Parameterization of
vertical mixing in numerical models of the tropical oceans, J. Phys.
Oceanogr., 11, 1443–1451.
Parish, T. R. (1983), The influence of the Antarctic Peninsula on the wind
field over the western Weddell Sea, J. Geophys. Res., 88(C4), 2684–2692,
doi:10.1029/JC088iC04p02684.
Parkinson, C. L., and W. M. Washington (1979), A large-scale numerical
model of sea ice, J. Geophys. Res, 84(C1), 311–337.
Renfrew, I. A., J. C. King, and T. Markus (2002), Coastal polynyas in
the southern Weddell Sea: Variability of the surface energy budget, J.
Geophys. Res., 107(C6), 3063, doi:10.1029/2000JC.000720.
Schwerdtfeger, W. (1975), The effect of the Antarctic Peninsula on the
temperature regime of the Weddell Sea, Mon. Weather Rev., 1031,
45–51.
Smith, S. D., R. D. Muench, and C. H. Pease (1990), Polynyas and leads:
An overview of physical processes and environment, J. Geophys. Res.,
95(C6), 9461–9479.
Spreen, G., L. Kaleschke, and G. Heygster (2008), Sea ice remote sensing
using AMSR-E 89 GHz channels, J. Geophys. Res., 113(C02S03), 1–14,
doi:10.1029/2005JC003384.
Steele, M., R. Morley, and W. Ermold (2001), PHC: A global ocean
hydrography with a high quality Arctic Ocean, J. Climate, 14,
2079–2087.
Stössel, A., Z. Zhang, and T. Vihma (2011), The effect of alternative realtime wind forcing on Southern Ocean sea ice simulations, J. Geophys.
Res., 116(C11021), 1–19, doi:10.1029/2011JC007328.
Tamura, T., K. I. Ohshima, and S. Nihashi (2008), Mapping of sea ice production for Antarctic coastal polynyas, Geophys. Res. Lett., 35(L07606),
1–5, doi:10.1029/2007GL032903.
Timmermann, R., and A. Beckmann (2004), Parameterization of vertical mixing in the Weddell Sea, Ocean Model., 6, 83–100, doi:
10.1016/S1463-5003(02)0061-6.
Timmermann, R., S. Danilov, J. Schröter, C. Böning, D. Sidorenko, and K.
Rollenhagen (2009), Ocean circulation and sea ice distribution in a finite
element global sea ice-ocean model, Ocean Model., 27, 114–129, doi:
10.1016/j.ocemod.2008.10.009.
Timmermann, R., et al. (2010), A consistent dataset of Antarctic ice sheet
topography, cavity geometry, and global bathymetry, Earth Syst. Sci.
Data, 3(2), 261–273, doi:10.5194/essd-2-261-2010.
Windmüller, M. (1997), Untersuchung von atmosphärischen
Reanalysedaten im Weddellmeer und Anwendung auf ein dynamisch
thermodynamisches Meereismodell, Diplomarbeit, pp. 1–65, Institut für
Meereskunde, Christian-Albrechts-Universität, Kiel, Germany.
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