Clim Dyn (2011) 36:1067–1081
DOI 10.1007/s00382-010-0757-7
Influence of the orographic roughness of glacier valleys across
the Transantarctic Mountains in an atmospheric regional model
Nicolas C. Jourdain • Hubert Gallée
Received: 27 July 2009 / Accepted: 29 January 2010 / Published online: 16 February 2010
Ó Springer-Verlag 2010
Abstract Glacier valleys across the Transantarctic
Mountains are not properly taken into account in climate
models, because of their coarse resolution. Nonetheless,
glacier valleys control katabatic winds in this region, and
the latter are thought to affect the climate of the Ross Sea
sector, frsater formation to snow mass balance. The purpose
of this paper is to investigate the role of the production of
turbulent kinetic energy by the subgrid-scale orography in
the Transantarctic Mountains using a 20-km atmospheric
regional model. A classical orographic roughness length
parametrization is modified to produce either smooth or
rough valleys. A one-year simulation shows that katabatic
winds in the Transantarctic Mountains are strongly
improved using smooth valleys rather than rough valleys.
Pressure and temperature fields are affected by the representation of the orographic roughness, specifically in the
Transantarctic Mountains and over the Ross Ice Shelf. A
smooth representation of escarpment regions shows better
agreement with automatic weather station observations than
a rough representation. This work stresses the need to
improve the representation of subgrid-scale orography to
simulate realistic katabatic flows. This paper also provides a
way of improving surface winds in an atmospheric model
without increasing its resolution.
N. C. Jourdain (&) H. Gallée
Laboratoire de Glaciologie et Géophysique de l’Environnement,
Saint Martin d’Héres, France
e-mail: [email protected]
1 Introduction
1.1 The Transantarctic Mountains
The intense radiative cooling of air over ice slopes determines the behavior of the Antarctic Surface Boundary
Layer (SBL). Most surface winds over the ice sheet are
from katabatic origin (Parish 1988; Parish and Bromwich
2007). The orography of Antarctica presents several confluence zones all around the continent from where katabatic
outflows extend over seas or ice shelves. Some of these
zones are located in the Transantarctic Mountains, west of
the Ross Ice Shelf (RIS) and the Ross Sea (Fig. 1). Using
infrared imagery, Bromwich (1989) has shown that katabatic drainage flows from David, Reeves, Priestley and
O’Kane glaciers converge in Terra Nova Bay (TNB) and
propagate horizontally for hundreds of kilometers over the
ocean (see locations in Fig. 1). Bromwich (1989) has also
found katabatic signatures emerging from the main glacier
valleys onto the RIS. The most important signature in
terms of horizontal extent over the RIS and in terms of
occurrence frequency is Byrd glacier. Less prominent, but
also significant are outflows from Skelton, Mulock, Nimrod
and Beardmore glaciers (Fig. 1).
The intense and persistent outflows converging in TNB
strongly control the TNB polynya which is responsible for
about 10% of the annual ice production over the Ross Sea
continental shelf (Kurtz and Bromwich 1985; Morales
Maqueda et al. 2004). Moreover sea-ice and polynyas are
embedded in dense water formation: this is important since
Broecker et al. (1998) suggested that there could be a
significant contribution of deep water from the Ross Sea to
thermohaline circulation in the Pacific Sector. In addition
to their impact on polynya and dense water formation,
katabatic outflows from glacier valleys are thought to play
123
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N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys
Ross
Sea
RIS
R00
ns
Transa
rc
tai
tic
n
ou
M
models (e.g. Gallé et al. 2005; Bailey and Lynch 2000; van
Lipzig et al. 2002; Heinemann and Klein 2003). The aim of
this paper is to evaluate the ability of a regional atmospheric model to capture katabatic outflows from glacier
valleys, using a relatively coarse resolution (20 km). Most
of the glacier valleys are represented, even coarsely, in a
20-km orography. Katabatic winds that develop in the
simulations follow the slopes of the model orography, so
that valleys are actually confluence zones in the model.
However, strong katabatic winds do not develop if too
much Turbulent Kinetic Energy (TKE) is produced in the
Transantarctic Mountains. The motivation of this paper is
to analyze the sensitivity of katabatic flows to the spatial
distribution of TKE production by subrid-scale orography.
1.2 Mountain drag
TNB
RIS
Bm
N
R70
P
O
R
D S
M By
Fig. 1 Roughness length for momentum (m) computed using h0 = 0
(R00) and using h0 = 70 m (R70). Orography is represented with
black lines (every 250 m). Upper left box shows the position of the
domain in Antarctica. The position of the glaciers is indicated:
Priestley (P), O’Kane (O), Reeves (R), David (D), Skelton (S),
Mulock (M), Byrd (By), Nimrod (N) and Beardmore (Bm)
a significant role in cyclonic activity over that region.
Mesocyclones are indeed frequent over antarctic coastal
regions, and the greatest cyclonic activity has been
observed over the Ross Sea and the RIS (e.g. Carrasco and
Bromwich 1993; Carrasco et al. 2003; Heinemann and
Klein 2003; Rasmussen and Turner 2003). Katabatic winds
also induce snow erosion, and ablation zones are observed
in the Transantarctic Mountains (referred to as blue-ice
areas, Bintanja 1999).
Although glacier valleys that dissect the Transantarctic
Mountains seem important, they are not properly taken into
account in weather and climate models. The width of those
valleys is indeed similar or smaller than the size of the
horizontal grid mesh. For example Byrd, Reeves and
Priestley glaciers’ width are estimated at 24, 45 and 8 km,
respectively (Turner and Pendlebury 2004). In comparison,
the horizontal resolution in Antarctica is about 100 km in
General Circulation Models (GCMs) and about 20–60 km
in most of the latest climatic studies made with limited area
123
To represent the barrier effect of subgrid-scale orography,
some models use an envelope orography that corrects the
surface height with subgrid-scale orography variance
(Wallace et al. 1983). Another approach is to simulate the
high rate of turbulent kinetic energy produced in areas of
high topography spatial variability by introducing an
effective roughness length (Fiedler and Panofsky 1972). In
this approach, turbulent fluxes of momentum, heat and
moisture are computed using transfer coefficients and
Monin–Obukhov similarity theory; surface properties are
introduced in term of roughness lengths for momentum,
heat or moisture (e.g. Stull 1988; Andreas 2002); roughness
lengths are corrected to describe the effects of subgridscale orography. This correction is often referred to as
‘‘orographic roughness length’’ and has been widely used
(e.g. Miller et al. 1989; Georgelin et al. 2000; Kim et al.
2003). An alternative parametrization has been proposed
by Wood et al. (2001) and applied by Beljaars et al.
(2004): the effects of turbulent drag are specified with an
explicit orographic stress profile. This can be used to predict an anisotropic drag (Brown 2001). Rontu (2006)
compared the two available methods to predict drag, and
did not find significant differences at a synoptic scale,
except in low level wind distribution.
Note that the drag under consideration in this paper is
turbulent drag, which is the form drag exerted by subgrid
scale orography. Many investigations deal with drag related to gravity wave (e.g. Kim et al. 2003) or blocking of
the low-level flow (e.g. Kim and Doyle 2005; Lott and
Miller 1997). There are usually no interactions between
these parametrizations, although there is no physical reason
to separate the different processes, and even though turbulent drag has an impact on gravity waves (Vosper and
Brown 2008). However, turbulent drag is related to horizontal scales smaller than 5 km whereas other drags are
due to larger horizontal scales (Beljaars et al. 2004). For
N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys
this reason, the subgrid-scale orography used to compute
orographic roughness lengths usually has a resolution of
about 1 km or less. Some parametrizations have also been
developed to take into account mountain lift forces perpendicular to the drag. Those forces represent the component of the forces of orographic origin that modify the
direction of the flow, without working against it (Lott 1998;
Catry et al. 2008).
The aim of this paper is neither to compare all the
existing turbulent drag parametrizations nor to provide a
new one. We use the classical orographic roughness length
parametrization. Note that the parametrization is based on
empirical results and that there are several ways to compute
orographic roughness length (e.g. Reijmer et al. 2004;
Unden et al. 2002; Andreas 1987). Constraints on the
parametrizations are weak in Antarctica and in particular in
the Transantarctic Mountains as there is a small amount of
roughness length measurements there. Reijmer et al.
(2004) compared different parametrizations of each
roughness length at the Antarctic continental scale using
Regional Atmospheric Climate MOdel (RACMO) at a
resolution of 55 km. They found that lower roughness
lengths for momentum resulted in an increase of nearsurface wind speed and a decrease of coupling between the
surface and the overlaying air, with a warming of the low
troposphere and a cooling of the ice surface. They showed
that surface heat fluxes are best modeled by using the
method described in Andreas (1987).
Here we modify an existing method based on that of the
European Centre for Medium-Range Weather Forecasts
(ECMWF 2004), to test the influence of orographic
roughness of wide valleys in the Transantarctic Mountains.
The tests are performed using the regional atmospheric
model MAR at an horizontal resolution of 20 km. One-year
experiments are compared to observations from Automatic
Weather Stations (AWS) in the Transantarctic Mountains
to evaluate simulated katabatic winds using either smooth
valleys or rough valleys. We show that the orographic
roughness parametrization does not only influence katabatic flows, but also pressure and air temperature far from
the Transantarctic Mountains. Physical mechanisms are
given in order to explain the simulated sensitivity, and the
experiments are evaluated using AWS temperature, AWS
pressure and soundings at various locations.
1069
are represented in the hydrologic cycle. Nucleation and
sedimentation of crystals are represented. The ice sheet is
assumed to be entirely covered with snow. MAR is coupled
to a snow model (Gallée and Duynkerke 1997), with snow
metamorphism laws of Brun et al. (1992). Snow albedo
depends on snow metamorphism. MAR has a prescribed
fractional sea ice cover, and the vertical structure of sea ice
is simulated in a five-layer ice model. The radiative
scheme is that of Morcrette (2002). It takes into account
clouds via their optical thickness. The turbulent fluxes in
the SBL are calculated from an implicit scheme based on
Monin–Obukhov similarity theory. Blowing snow is also
represented in such a way that it influences the hydrologic
cycle and the atmospheric stability (Gallée et al. 2001,
2005).
The roughness length Z0 depends on the wind speed and
the surface type which may be ocean (Charnock 1955), sea
ice or continental ice (Andreas 1987). The standard roughness length of the ice sheet takes into account sastrugis and
blowing snow (Gallée et al. 2001, 2005). An orographic
roughness length ZOR
0 is added to standard roughness length
where the subgrid-scale orography is highly variable. It is
computed as a function of the isotropic standard deviation l
of the 1-km RAMP orography data (Radarsat Antarctic
Mapping Project Digital Elevation Model, Liu et al. 2001).
For a 20-km mesh ij of MAR, the orographic roughness
length for momentum is thus computed as:
8
2lij
>
OR
>
>
> Z0ij ¼ eAij 1
<
!12
ð1Þ
>
l
N
0:4
ij
ij
> A ¼ 0:4 0:8
>
þ
>
: ij
logð1 þ 2; 000lij Þ
Dx
where Dx is the horizontal resolution. Nij the number of
local maxima of a 1-km orography within a mesh ij, a
maximum being counted only if it is at least higher by a
height h0 from its neighbors. The orographic roughness
scheme has been adapted from the European Centre
for Medium-Range Weather Forecasts scheme (ECMWF
2004), which corresponds here to h0 = 0. The roughness
lengths for heat and moisture are equal and their calculation uses parameters that differ from the calculation of the
roughness length for momentum (Andreas 1987). Furthermore, Z0 is limited to 3.3 m (one-third of the first level).
2.2 Configuration and experimental set-up
2 Materials and methods
2.1 The atmosphere model MAR
MAR is a hydrostatic regional mesoscale model based on
three-dimensional primitive equations (Gallée and Schayes
1994; Gallée 1995; Gallée et al. 2005). Four hydrometeors
The grid is a cartesian grid obtained from an oblique stereographic projection (the center of the projection is the
center of the domain). The horizontal resolution is 20 km
and there are 33 vertical r-levels, the lower is about 10 m
high, and the model top corresponds to 0.1 hPa. The time
step for dynamics is 60 s. We focus on the Ross Sea Sector,
123
1070
N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys
and the boundaries of the domain are chosen as far as
possible from this sector, following Giorgi and Mearns
(1999). The whole domain of integration is shown in
Fig. 1. Its size is 4,360 9 3,000 km.
Sea surface temperature and sea ice fraction are prescribed from ERA-40 reanalysis (the sea ice fraction is
mainly based on SSM/I data). Atmospheric winds, temperature and humidity are prescribed from ERA-40 at the
lateral boundaries and relaxed towards the reanalysis
within a six-point nudging zone. The upper atmospheric
boundary is also relaxed towards ERA-40.
The height h0 is tuned to change the spatial distribution
of the orographic roughness length. The set of experiments
is made of a simulation with h0 = 0 (many local maxima,
large orographic roughness lengths), and a simulation with
h0 = 70 m (fewer local maxima, smaller orographic
roughness lengths). The roughness lengths for each case
are shaded in Fig. 1. The experiments with h0 = 70 m and
h0 = 0 are referred to as R70 (smooth valleys set-up) and
R00 (rough valleys set-up), respectively. The value h0 = 0
gives an orographic roughness similar to that used in the
ECMWF model. The value h0 = 70 m has been chosen
after several tests (not shown) so that the Transantarctic
Mountains are dissected with valleys of zero orographic
roughness length (referred to as smooth valleys). Comparing Fig. 1 to the MODIS Mosaic of Antarctica (MOA,
Haran et al. 2006) Image Map, the change in roughness
Fig. 2 Location of AWSs. The
table shows AWS elevations.
Gray contours represent surface
elevations (every 250 m)
length also affects large regions outside of the valley glaciers and the impact of smoothing the valleys is therefore
likely overestimated.
The two experiments are integrated from 1 January 1992
to 31 December 1992. The initial state is an interpolation of
ERA-40 on 1 January 1992. The AWS data used for
comparison purpose is taken from the Department of
Atmospheric and Oceanic Sciences at the University of
Wisconsin-Madison (UW-AOS, Stearns and Weidner
1992) and from the Italian National Research Program in
Antarctica (PNRA, http://www.climantartide.it). Their
location is given in Fig. 2. Hourly averaged data are used
and compared to hourly outputs from the atmospheric
model. We choose the nearest model grid-point to the
actual AWS location where the model surface elevation is
not 100 m higher or lower than the actual AWS site.
Temperature and wind speed are extrapolated from the first
r-level (about 10 m) to the height of AWSs (3 m) using
Monin–Obukhov similarity theory (see Businger et al.
1971; Dyer 1974).
In the following part, we examine the role of the orographic roughness parametrization on the climate of the
region for one year. The aim is first to verify that surface
katabatic winds are better simulated with smooth valleys
along a year. Then, the impact of the representation of
smooth glaciers valleys on the simulated surface pressure
and temperature is investigated at different locations.
UW-AOS :
Lynn
1772 m
Manuela
78 m
Marble Pt 108 m
Pegasus N
8m
Pegasus S
5m
Marilyn
64 m
Gill
54 m
Lettau
38 m
Byrd
1530 m
PNRA :
Alessandra 160 m
Arelis
150 m
Eneide
92 m
Lola
1621 m
Modesta 1900 m
Silvia
536 m
Byrd
Eneide
Silvia Arelis
Ale
Lola
Gill
Lettau
Pegasus
Marilyn
Modesta
Lynn Manuella
Marble Point
123
N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys
3 Results
3.1 Response of katabatic winds
First, we check the influence of the orographic roughness
length parametrization on 3-m wind speed. We choose
AWSs located in or downstream of the Transantarctic
Mountains (Fig. 3). Note that complex three-dimensional
wind structures may appear in confluence zones (Argentini
et al. 1992). Superimposition of outflows from glacier
valleys cannot be resolved by the model if a less buoyant
flow comes from a small unresolved valley. However, the
use of eight stations over one year should reduce uncertainties in our analysis. Monthly wind speeds are better
captured in R70 (smooth valleys) than in R00 (rough valleys) for most of the stations, and differences between the
two experiments can reach 10 m s-1 at some sites. Note
that R70 tends to over-estimate wind speed at some locations, which could either be attributed to the relatively
coarse grid-scale or to too wide areas of low surface
roughness length in R70.
Standard deviations and correlations are summarized in
Taylor diagrams (Fig. 4), for 1-month sliding means (LF,
low frequency) and for high frequency (HF) wind speeds
(difference between wind speeds and sliding means). The
standard deviation of HF wind speeds is closer to AWS
data in R70 than in R00 (although it is under-estimated in
both R70 and R00), and HF wind speeds have significant
correlations to AWS data in the two experiments (even if
correlations are lower than 0.5). The significant correlations of HF wind speeds to AWS data gives confidence in
the physical representation of katabatic winds in the model,
and R70 (smooth valleys) better captures extreme events.
The standard deviation of R70 LF wind speeds are in
good agreement with AWS data, whereas R00 LF wind
speeds are strongly under-estimated. The correlations of LF
wind speeds to AWS data is greater than 0.5 (significant at
the 95% level) for 6 AWSs on eight in the two experiments. These results show that seasonal and intra-seasonal
variabilities are well reproduced in R70, while R00 underestimates their amplitude.
The intensification of katabatic winds in confluence
zones of R70 is simulated within a thicker layer in austral
winter than in summer. In TNB, for instance, the increase
of wind speed simulated in R70 (smooth valleys) as compared to R00 (rough valleys) is significant within the first
400 m above surface in winter, and within the first 250 m
above surface in summer (Fig. 5). The wind convergence
into most of the glacier valleys is strongly increased near
surface in R70 as compared to R00 (Fig. 6). The tropospheric flow is adjusted between 400 and 2,000 m a.g.l.
(above ground level) so that the mass continuity is ensured
in both experiment. Wind divergence in the lower
1071
troposphere is therefore slightly increased above glacier
valleys in R70 as compared to R00 (Fig. 5).
The increase of katabatic wind speed in the Transantarctic Mountains produces more blowing snow in winter in
R70 than in R00. The JAS mean upward snow flux from
the snow surface to the atmospheric SBL reaches
5.0 9 10-4 kg m-2 s-1 in the Transantarctic Mountains in
R70, while it does not exceed 1.5 9 10-4 kg m-2 s-1 in
R00 (not shown). Consequently, there are almost no zones
of annual ablation in R00 (rough valleys), whereas there
are several ones in R70 (smooth valleys). Blue-ice areas
resulting from snow erosion by the wind have actually been
found in glacier valleys of the Transantarctic Mountains
(Bintanja 1999; Winther et al. 2001; Frezzotti et al. 2004;
van den Broeke et al. 2006). However, there are no quantitative blowing snow measurements available in the
Transantarctic Mountains (to our knowledge). The differences between R00 and R70 therefore only gives a coarse
indication of the role of katabatic winds, and further
evaluation should be addressed in future research.
3.2 Response of surface temperature and pressure
Figure 7 shows that yearly mean surface air temperatures
are affected by the orographic roughness parametrization.
The SBL is warmer over the RIS in R70, by up to 2 K
downstream of Marie Byrd Land (MBL), by up to 3 K
downstream of the Transantarctic Mountains, and by up to
7 K south of McMurdo station. In contrast, the SBL in
TNB and north of Lady Newness Bay (LNB) is colder in
R70, by up to 6 K. The atmospheric surface layer is generally colder in the Transantarctic Mountains in R70, but
the difference between R00 and R70 does not exceed 2 K.
The differences between surface air temperature in R70
and R00 are generally higher from fall to spring, but they
exhibit a high variability regarding the month under consideration: the two experiments are very similar in January,
November and December 1992, whereas the highest differences are found in August and April (Fig. 8). Surface air
temperatures are significantly influenced by the change in
katabatic flow, but the physical processes related to the
SBL temperature variations differ from one location to
another. We therefore chose to investigate three different
regions in the following sections: the Transantarctic
Mountains, the RIS, and the vicinity of McMurdo.
3.2.1 The Transantarctic Mountains
The temperature profiles downstream of the Transantarctic
Mountains differ from those at higher terrain elevation. A
typical temperature profile in the center of the Transantarctic Mountains is shown in Fig. 9a. The direct consequence of a higher roughness length in R00 is the expected
123
1072
N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys
8
12
Arelis
Alessandra
10
6
V (m/s)
V (m/s)
8
4
6
4
2
2
0
JAN
FEB
MAR
APR MAY JUN
JUL
AUG
SEP
OCT NOV
0
DEC
JAN
20
18
FEB
MAR
APR MAY JUN
JUL
AUG
SEP
OCT NOV
DEC
FEB
MAR
APR MAY JUN
JUL
AUG
SEP
OCT NOV
DEC
FEB
MAR
APR MAY JUN
JUL
AUG
SEP
OCT NOV
DEC
FEB
MAR
APR MAY JUN
JUL
AUG
SEP
OCT NOV
DEC
14
Lola
Eneide
12
16
10
12
V (m/s)
V (m/s)
14
10
8
8
6
6
4
4
2
2
0
JAN
FEB
MAR
APR MAY JUN
JUL
AUG
SEP
OCT NOV
0
DEC
16
18
Modesta
16
14
V (m/s)
V (m/s)
12
10
8
6
10
8
6
4
4
2
2
JAN
FEB
MAR
APR MAY JUN
JUL
AUG
SEP
OCT NOV
0
DEC
JAN
16
22
20
Silvia
14
12
0
JAN
Lynn
Manuela
14
16
12
14
10
V (m/s)
V (m/s)
18
12
10
8
8
6
6
4
4
2
2
0
JAN
FEB
MAR
APR MAY JUN
JUL
AUG
SEP
OCT NOV
DEC
0
JAN
Fig. 3 One-month sliding mean 3 m wind speed observed at some AWS sites (black). Simulated values at this location are in red (R00) and blue
(R70)
123
N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys
0
1
0.1
0.3
0.4
Co
0.5
rr
e
0.8
la
0.6
Lo
t
800
io
n
C
o
e
0.7
Z 600
f
f
σ/σAWS
nt
ie
0.8
Ly
ic
Al
Ar
Ly
Si
En
12
10
8
6
4
2
1.5
1.0
0.5
0
-0.5
-1.0
-1.5
-2
-4
-6
-8
-10
-12
1000
0.2
Mo
0.6
1073
400
0.9
Ma
0.4
200
0.95
Si
Mo
0.2
Al
Lo
En
Ma
Ar
(HF)
0
0.99
0.4
0.2
0.8
0.6
AWS
σ/σAWS
Fig. 5 Difference between R70 and R00 wind speed (1-month sliding
mean, m s-1) component perpendicular to a section of 60 km wide
downstream of Reeves glacier (TNB)
5
0
0.1
1.5
z=10m a.g.l.
0.2
0.3
0.4
Co
0.5
rr
e
Mo
0.6
4
3
la
t
1
o
e
0.7
2
Beardmore
n
C
En
io
0
f
f
Ar
σ/σAWS
-1
nt
ie
0.8
ic
1
-2
0.9
-3
Lo
Al
0.5
Ly
Mo
Lo
(LF)
Ar
0
Ma
En
Al
Si
-4
0.95
Ly
Ma
-5
0.99
Si
1.8
z=500m a.g.l.
1.5
1.2
0.5
AWS
1.5
0.9
σ/σAWS
0.6
0.3
Fig. 4 Taylor diagram (Taylor 2001) related to wind speed comparisons at some AWS sites for R00 (red) and R70 (blue). r is the
standard deviation of simulated wind speeds. LF low-frequency
signal, i.e. wind speed filtered using a 1-month running mean; HF
high-frequency wind speeds, i.e. the LF component has been
removed. Correlation coefficients greater than 0.025 in the HF
diagram are significant at the 99% confidence level according to the
Student test, while only correlation coefficients greater than 0.5 in the
LF diagram are significant at 95% confidence level (the running mean
decreases the number of degrees of freedom)
stronger vertical mixing. As the air near the surface is
warmer than the snow surface, the stronger mixing reinforces heat flux from atmosphere to snow in R00. Snow
surface temperature is therefore higher in R00 than in R70.
As the low troposphere in R00 looses more heat than in
R70, R00 is colder than R70 for most of the lowest 800 m.
0
-0.3
-0.6
-0.9
-1.2
-1.5
-1.8
100 km
Fig. 6 Mean difference of horizontal wind divergence between R70
and R00 in July 1992 (10-4 s-1), near the surface (upper) and at
500 m above the surface (lower)
A typical downstream profile is given in Fig. 9b. The
transition between the profiles in the mountains, such as in
Fig. 9a, and downstream, such as in Fig. 9b, is continuous
123
1074
N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys
9
3000
7
McM
LNB
4
2800
3
2700
2
2600
1
>90%
Z (m)
MBL
(a)
2900
5
0
2500
2400
-1
>99%
R00
2300
-2
-3
2200
-4
2100
-5
R70
R70 R00
X
+
240
241
>90%
242
243
-7
244
245
246
247
248
T(K)
-9
Fig. 7 Difference of 1992 mean surface air temperature between R70
and R00 (K) and orography (isolines every 500 m). LNB Lady
Newness Bay, McM McMurdo station, MBL Marie Byrd Land
1200
(b)
1100
R00
1000
(b)
13
11
9
7
5
4
3
2
1
0
-1
-2
-3
-4
-5
-7
-9
2.5
2.0
1.5
1.0
900
Z (m)
(a)
>90%
R70
800
700
600
500
400
R70
300 X
R00
>99%
+
250 251 252 253 254 255 256 257 258 259 260
T(K)
Fig. 9 Monthly air temperature profile (K) in February 1992. a at a
point in the middle of the Transantarctic Mountains (86.7°S,
151.0°W, 2,044 m); b at a point downstream of the Transantarctic
Mountains, at the lower end of the slope (83.5°S, 170.9°E, 293 m).
Solid is R00 and dashed is R70. Vertical axis is elevation (m). Soil
surface temperatures are represented by 9(R70) and ?(R00). Ground
level corresponds to horizontal axis. The significance level of the
difference R70–R00 is indicated on the right side when it is higher
than 90% (student t test)
0.5
0
-0.5
-1.0
-1.5
-2.0
-2.5
-3.0
-3.5
-4.0
-4.5
-5.0
Fig. 8 a Difference of April 1992 mean surface air temperature
between R70 and R00 (K). b Difference of April 1992 mean surface
pressure between R70 and R00 (hPa)
along the slope. The difference between snow temperatures
and SBL temperatures reaches 10 K in R70 (smooth valleys) while it reaches 4 K in R00 (rough valleys). This
means that the discoupling between snow and atmosphere
123
increases for smaller roughness lengths. Consequently, the
SBL downstream of the Transantarctic Mountains is generally warmer in R70 than in R00 (see Fig. 7). Such a
discoupling is found because surface wind speed is low
downstream of the Transantarctic Mountains (it may be
weaker than 1 m s-1). Indeed, katabatic wind speed
reaches a maximum in the middle of the slope, but cold air
accumulation over the RIS prevents the katabatic flows to
propagate over the RIS (not shown).
Note that the bump of R70 profile at 350 m a.g.l.
(Fig. 9b) is a signature of the katabatic airstream: air parcels at the top of the katabatic layer descend following an
adiabatic profile (see Kodama and Wendler 1986; Gallée
and Schayes 1994). This bump is not found in R00. The
slightly colder air in R70 above 100 m is an effect of
temperature advection at this location (not shown). Finally,
simulated annual mean 3-m temperatures are in relative
N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys
Table 1 Difference of annual mean temperature between each
experiment and the AWS data
TR00 - TAWS
Lynn
-1.9
-1.7
Manuela
-1.7
-2.9
Marble Pt
-3.3
-2.0
Pegasus N
-5.9
?0.4
Pegasus S
-5.8
?1.4
Marilyn
-4.2
-3.5
Gill
Lettaua
-9.3
-10.3
-8.6
-9.6
Byrda
-4.9
-4.9
990
980
970
960
Indicates more than 50% missing values in the observations
agreement with observations upstream of TNB for both
R00 and R70 (see Lynn and Manuela stations in Table 1),
R00 being slightly more realistic than R70 at Manuela.
To summarize, rough valleys produce stronger vertical
mixing than smooth valleys in the Transantarctic Mountains. Katabatic outflows towards the RIS reach their
maximum intensity in the middle of the slope. And the
outflows downstream of the Transantarctic Mountains are
so weak that there is a discoupling between snow surface
and atmosphere. This discoupling is stronger in R70 and
leads to a warmer SBL in R70 than in R00.
3.2.2 The RIS
Three major factors control the circulation over the RIS:
katabatic winds from the Transantarctic Mountains and
from Marie Byrd Land, geostrophic airflow associated with
synoptic scale cyclones (Simmonds et al. 2003), and barrier winds blocked by the steep mean orography of the
Transantarctic Mountains (O’Connor et al. 1994). To
investigate the role of the orographic roughness set-up on
this complex circulation over the RIS, the surface pressure
simulated over there is compared with observations. Both
R00 (rough valleys) and R70 (smooth valleys) exhibit
agreement with observed surface pressure in the center of
the RIS (Fig. 10). The high correlation coefficients (0.79
for R00 and 0.81 for R70) show that circulation patterns are
reasonably well captured by MAR.
The greatest difference between R00 and R70 is reached
in March–April with a correlation to AWS observations of
0.61 in R00 and 0.75 in R70. A cyclonic anomaly is found
in R70 from March to May in the lower troposphere, with a
maximum in April (Figs. 11, 8b). The weaker vorticity
simulated in R00 from fall to spring is consistent with the
higher surface pressure found at Gill location (as compared
to R70, Fig. 10). We suggest that larger Ekman pumping
simulated in R00 (rough valleys) might reduce the strength
Fig. 10 Twenty-day sliding mean surface pressure (hPa) at AWS
Gill: simulated in R00 (red), simulated in R70 (blue) and observed
(black). Prior to the sliding mean, missing values in the observations
are filled using linear interpolation for periods shorter than 6 h. The
non filtered signal gives annual mean pressure of 983.7 hPa (R00),
982.8 hPa (R70), and 983.4 hPa (AWS). Correlations to observed
pressure are 0.79 (R00) and 0.81 (R70). Associated RMSE are
7.6 hPa (R00) and 7.3 hPa (R70)
-15
-20
Relative Vorticity (10-6 s-1)
a
1000
TR70 - TAWS
Surface pressure (hPa)
AWS
1075
-25
-30
-35
-40
J
F
M
A
M
J
J
A
S
O
N
D
Fig. 11 Monthly relative vorticity averaged over the RIS at r-level
0.83, which corresponds approximately to 1,500 m a.g.l. (in
10-6 s-1). Solid R00, dashed R70. Here the RIS is defined by
surface elevation between 40 and 60 m
of active cyclonic systems over the RIS. The reason why
relative vorticity exhibits more differences in April than
over the rest of the year remains unclear, because other
months exhibit similar circulation patterns (see for instance
October in R70, Fig. 11). Finally, the warmer air over the
RIS in R70 in April (Fig. 8) might be a consequence of the
cyclonic anomaly that brings more marine air onto the RIS.
Comparisons to AWS Marilyn, Gill and Lettau show
that there is a strong cold bias in both R70 and R00 over
the RIS (Table 1). The cold bias is simulated throughout
the year for most of the stations (not shown), and it is
123
N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys
particularly strong in April (Table 2). A detailed investigation shows that short cold events along winter are often
too cold in the simulations (by up to 30 K), whereas warm
events are often well marked. A possible cause of this bias
is an underestimation of downward longwave energy
fluxes, probably due to an underestimation of the cloud
cover in this area by the model (as noticed by Fogt and
Bromwich 2008 in their forecasts). Bromwich et al. (2005)
have suggested that implementing a variable surface
roughness scheme could improve the temperature biases in
their simulations. Here we show that the modification of
the orographic roughness length implemented in R70 only
reduces a small part (0.7 K) of the annual cold bias (-9 K)
over the RIS (Table 1). Nonetheless, biases over the RIS in
April are strongly reduced in R70 as compared to R00
(Table 2).
To summarize, the circulation is well captured over the
RIS both in R00 and in R70. The sensitivity of the circulation and temperature over the RIS is difficult to predict
since it strongly depends on the complex circulation over
the RIS. Nonetheless, significant change in the circulation
may occur over the RIS, with subsequent modification of
temperature advection. We suggest that Ekman pumping
might be responsible for such a sensitivity. Finally, R70
(smooth valleys) is closer to observations than R00 (rough
valleys).
particularly warmer (colder) there. Warmer air advection
from the RIS in R70 may also increase SBL temperatures
at that location (not shown). Temperature comparisons at
stations Pegasus North and South (vicinity of McMurdo)
show that R70 is more realistic than R00, both in mean and
in variability (Fig. 12).
We attempt an evaluation of vertical profiles of temperature, humidity, wind speed and wind direction using
soundings at McMurdo station (Fig. 13). Simulated tropospheric temperatures below 200 hPa are colder than the
observations, by 0.5–2.5 K. R00 and R70 do not exhibit
any significant difference above 700 hPa. Even below
0
Gill
-10
-20
T (˚C)
1076
-30
-40
-50
-60
3.2.3 Vicinity of McMurdo
Table 2 Difference of April mean temperature between each
experiment and the AWS data
AWS
TR00 - TAWS
-2.5
-2.2
Manuela
-2.7
-3.4
Marble Pt
-5.6
-0.6
Pegasus N
Pegasus S
-9.0
-8.2
?3.4
?4.2
-6.9
-0.2
Gill
-12.1
-9.6
Lettau
-19.5
-13.9
Byrd
-12.3
-11.4
123
Pegasus N
-10
-20
-30
-40
TR70 - TAWS
Lynn
Marilyn
0
T (˚C)
The highest temperature differences for the whole year
between R00 and R70 are found near McMurdo Station
(Fig. 7). This station is located south of Ross Island, at a
place where simulated surface wind speeds are generally
very weak (monthly means are generally weaker than
2 m s-1). These weak winds are responsible for an
amplification of the SBL stability in R70 and thus for an
additional decrease of exchanges between the SBL and the
ground. Consequently, the SBL (the snow surface) is
Fig. 12 Zero-day sliding mean temperature (°C) at the locations of
Gill and Pegasus North: observed (black), simulated in R00 (red) and
simulated in R70 (blue). Prior to the sliding mean, missing values in
the observations are filled using linear interpolation for periods
shorter than 6 h. The non filtered signals at Gill give annual mean
temperature of -36.4°C (R00), -35.7°C (R70), and -26.9°C (AWS);
correlations to observed temperature are 0.84 (R00) and 0.85 (R70);
associated RMSE are 12.8°C (R00) and 12.2°C (R70). The non
filtered signals at Pegasus North give annual mean temperature of
-26.8°C (R00), -20.5 K (R70), and -20.9 K (AWS); correlations to
observed temperature are 0.80 (R00) and 0.81 (R70); associated
RMSE are 9.5°C (R00) and 7.1^C (R70)
N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys
1077
Fig. 13 Average of the differences between MAR and McMurdo
soundings over the year 1992. Y-axis is pressure (hPa). Upper left is
air temperature (K); upper right is air specific humidity (g kg-1);
lower left is wind speed (m s-1); lower right wind direction (°). ERA40 reanalysis are also represented (green)
700 hPa, differences between R00 and R70 are lower than
at the AWS Pegasus North. This might be due to the
position of McMurdo station, adjacent to the sea, whereas
the AWS is further inland. The biases simulated in the
upper atmosphere are very similar to the biases found in
ERA-40 at this location because MAR is relaxed to ERA40 at the upper boundary. Regarding the good skills of
ERA-40 in the lower atmosphere, it is important to note
that assimilation of McMurdo data is performed in the
reanalysis.
As water vapor is responsible for a large part of the
greenhouse effect, the dry bias and the cold bias mentioned
above are probably linked. The water supply mainly comes
from synoptic systems that pass northeast and east of Ross
Island (Monaghan et al. 2005), and the dry bias might
either come from an underestimation of evaporation over
ocean or from a poor representation of humidity transport
through the boundaries of the domain (because clouds from
the host model are not taken into account).
Wind speed and direction in MAR seem quite far from
observed values. Bromwich et al. (2005) obtained good
results at McMurdo using Polar-MM5 at a resolution of
30 km (temperature bias \1.5 K and wind speed bias
\1 m s-1, from ground to 150 hPa), but their model was
re-initialized every 12 h with GFS (National Centers for
Environmental Prediction Global Forecasting System, in
which soundings and AWSs are assimilated). With a twoway nesting allowing a resolution of 3.3 km, very acceptable results have been obtained by Monaghan et al. (2005)
and Powers (2007), but the complexity of the topography
of the McMurdo vicinity was still not completely resolved
at such a resolution. Our topography at 20 km horizontal
resolution could therefore be insufficient for detailed
comparisons at McMurdo station. Moreover, this sector is
influenced by mesocyclones and synoptic lows, and a shift
in the location of these structures with respect to observations could significantly change the wind at a given
point. And since there are major peaks near McMurdo
(Mount Erebus at 3,794 m and Mount Terror at 3,230 m), a
high vertical extent of the simulated troposphere may
suffer from a poor representation of the topography.
To summarize, the parametrization of orographic
roughness plays a significant role at Pegasus AWS. R70
better captures surface air temperature. However, both R00
123
1078
N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys
and R70 do not well represent the tropospheric temperatures, humidity, and winds. The biases in the upper atmosphere are related to ERA-40 which forces MAR in the
upper relaxation zone. The biases in the lower atmosphere
may be related to a poor representation of the topography at
a resolution of 20 km.
4 Discussion and conclusions
In this paper, we have examined the sensitivity of katabatic
flows to the spatial distribution of TKE production by
subrid-scale orography. A classical orographic roughness
parametrization has been modified to perform one-year
simulations with either smooth or rough glacier valleys.
Seasonal and intraseasonal wind speeds in the Transantarctic Mountains are better captured using the smooth
valleys set-up rather than the rough valleys set-up. Monthly
winds are somewhat over-estimated with a smooth orography, while they are strongly under-estimated with a
rough orography. High frequency variability of katabatic
flows is under-estimated in each experiment, but much
more with rough valleys. Decreasing orographic roughness
lengths produces more convergence into glacier valleys,
which is balanced by divergence of the tropospheric flow
between 400 and 2,000 m above the surface.
Significant impacts of the orographic roughness
parametrization are found in SBL temperature. In the
Transantarctic Mountains, the consequence of a higher
roughness length is the expected stronger vertical mixing,
and a subsequent stronger heat flux from atmosphere to the
snow surface. A discoupling may appear at the foot of the
Transantarctic Mountains. The latter results from low wind
speeds due to cold air accumulation, and it is increased for
smaller roughness lengths. Surface wind convergence
using rough orography is stronger over the RIS, because of
Ekman pumping. However, the influence of the orographic
roughness on the atmosphere over the RIS is closely linked
to the complex circulation over this region. Finally, tropospheric temperatures, humidity, and winds at McMurdo
are not well captured by MAR, whatever the orographic
roughness. The biases simulated in the upper atmosphere
are related to the upper relaxation to ERA-40, whereas the
lower biases may be related to a poor representation of the
topography at a resolution of 20 km.
The cold biases in the two experiments are strong over
the RIS. We therefore compare our results to other
experiments from previous studies in Table 3. The simulated surface temperature exhibit significant biases in all
the experiments, even if re-initialization and data assimilation are likely to improve simulations. However, one of
our purpose is to improve the parametrizations in climate
models, and we have chosen to let the model drift to
123
Table 3 Mean annual temperature bias in some long experiments at
different stations (in K)
Type
Dx
MAR-R70
free
20 km
Polar-MM5
Polar-MM5
RACMO
Byrd Gill
Lynn
?0.3
-4.9 -8.6
-1.7
RI-72h 60 km
-5.6
–
-1.6
RI-12h 30 km
?0.6
-1.6 -0.7
–
free
55 km
?2.7
–
–
ERAinterim RA
80 km
?5.7
-1.0 ?1.2
NNR
2.5°
-1.5/?8.0 –
RA
South pole
–
–
-2.6/-
?1.8
–
Temperature in italic are JJA/DJF biases. Stations referred as South
Pole are Clean Air (90°S), Henry (89.0°S;1.0°W), Lindsay
(89.0°S;89.5°W), or an average among them. Comparisons to AWS
have been found in Guo et al. (2003), Bromwich et al. (2005), van
Lipzig et al. (2002), and Connolley and Harangozo 2001; ERAinterim
comparisons have been performed by ourselves
Free simulations that are only forced at surface and lateral boundaries, RA re-analysis, and RI re-initialized simulations (with related
time steps). Dx horizontal resolution
identify the major problems in the model. In view of
Figs. 3 and 4, it appears that the temperature biases in the
model do not prevent from simulating realistic katabatic
flows. This is not surprising because the speed of the katabatic wind is proportional to the cube root of the inversion
strength (Kodama et al. 1985). Therefore our sensitivity
study is thought to be reliable. Nonetheless, the issue of
MAR temperature over the RIS should be addressed in
future model development (work on parametrizations such
as clouds and radiative transfer is needed).
This work gives a way to improve the surface winds,
and, at a slight degree, surface air temperatures in atmospheric models. Further studies could provide a more
accurate representation of the subgrid-scale effects of glacier valleys by using an anisotropic drag (Brown 2001) to
reduce drag in the direction of the valleys only. It may be
useful in distinguishing the contribution of Ekman pumping in lows above the RIS from the contribution of katabatic air supply from glacier valleys. Subgrid-scale
orographic effects could also be improved by using high
resolution simulations in areas of complex topography such
as the Transantarctic Mountains. Which resolution is needed remains an open question since Monaghan et al. (2005)
and Powers (2007) have noted that the complexity of the
topography of the McMurdo vicinity was still not completely resolved even by the 3.3 km grid.
The improvement of surface wind speeds over coastal
polynyas is of great importance if an ocean–sea ice model
is either forced by fields from an atmospheric model (like
in (like in Mathiot et al. 2008, 2009) or coupled to an
atmospheric model (Jourdain et al. 2009). Katabatic winds
influence ice advection and heat flux from ocean to atmosphere (because of ocean surface warming resulting from
vertical mixing) in polynyas (Pease 1987; Prasad et al.
N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys
2005). Consequently, more intense surface katabatic winds
would increase sea ice production and therefore dense
water formation. There is therefore a need to represent the
effects of glacier valleys in the roughness of the Transantarctic Mountains. One cannot expect to obtain reasonable results in the underlying forced or coupled ocean–
sea ice model without taking into account these effects on
surface katabatic winds.
The next point is to know if a smooth representation of
valleys is required in Atmosphere-only Limited Area
Models (ALAMs) and in Atmospheric-only General Circulation Models (AGCMs). Smooth valleys must be taken
into account if these models are used as numerical
weather prediction models because katabatic winds have
a large influence on surface weather and related human
activities. As far as the annual surface temperature are
concerned (Fig. 7), it does not seem crucial to choose one
experiment or the other since the differences are weak.
Moreover, the change in surface katabatic flow does not
greatly impact the general circulation patterns over the
SBL (an exception being over the RIS in fall, Fig. 10).
However, simulating a realistic SBL remains important
since models are often evaluated using surface observations. Additionally, we have noted that snow erosion is
closely linked to the strength of katabatic flows, and a
better representation of surface winds is needed to accurately compute snow mass balance in Antarctica (sublimation and erosion). In that sense, a roughness length
parametrization representing smoothed valleys (R70)
should be preferred to a more classical roughness length
parametrization (R00).
The present modeling study advances our existing
knowledge of the role of subgrid-scale orography of glacier
valleys on the atmosphere dynamics and thermodynamics
in the Ross Sea Sector. It provides a framework for future
modeling effort to better represent coastal surface wind
speeds in Antarctica, which is essential for numerical
weather prediction in coastal polar regions. An accurate
representation of coastal surface wind speeds is also
essential when coupling to or forcing an ocean–sea ice
model, and for an good representation of the snow surface
mass balance. The method developed in this paper and the
new regional atmosphere–sea ice–ocean coupled model
TANGO (Triade Atmosphére-Neige Glace Océan, Jourdain
et al. 2009) will be useful tools to investigate the role of
katabatic flows from glacier valleys on dense water formation in the Ross Sea. Finally, this work stresses the need
to improve the representation of subgrid-scale orography to
simulate realistic katabatic flows.
Acknowledgment The manuscript benefited from helpful comments of several anonymous reviewers. We thank Christophe Eugéne
Menkes for support in the revision process.
1079
References
Andreas EL (1987) A theory for the scalar roughness and the scalar
transfer coefficients over snow and sea ice. Bound Layer
Meteorol 38:159–184
Andreas EL (2002) Parametrizing scalar transfer coefficients over
snow and sea ice: a review. J Hydrometeorol 3:417–432
Argentini S, Mastrantonio G, Fiocco G, Ocone R (1992) Complexity
of the wind field as observed by a sodar system and by automatic
weather stations on the Nansen Ice Sheet, Antarctica, during
summer 1988-89: two case studies. Tellus Ser B Chem Phys
Meteorol B 44:422–429
Bailey DA, Lynch AH (2000) Development of an Antarctic regional
climate system model. Part I: sea ice and large-scale circulation.
J Clim 13:1337–1349
Beljaars ACM, Brown AR, Wood N (2004) A new parametrization of
turbulent orographic form drag. Q J R Meteorol Soc 130:1327–
1347
Bintanja R (1999) On the glaciological, meteorological, and climatological significance of Antarctic Blue Ice Areas. Rev Geophys
37(3):337–359
Broecker WS, Peacock SL, Walker S, Weiss R, Fahrbach E,
Schroeder M, Mikolajevicz U, Heinze C, Key R, Peng T-H,
Rubin S (1998) How much deep water is formed in the Southern
Ocean? J Geophys Res 103(C8):15833–15843
Bromwich D, Monaghan A, Manning K, Powers J (2005) Real-time
forecasting for the Antarctic: an evaluation of the Antarctic
mesoscale prediction system (AMPS). Mon Weather Rev 133(3):
579–603
Bromwich DH (1989) Satellite analysis of Antarctic katabatic wind
behaviour. Bull Am Meteorol Soc 70:738–749
Brown RD (2001) Arctic snow cover conditions during the Summer
of 1998. The state of the Arctic cryosphere during the extreme
warm summer of 1998: documenting cryospheric variability
in the Canadian Arctic, chap 1:4, p 7 p. CCAF Summer 1998
Project Team, CCAF Final Report. Available at http://www.
socc.ca
Brun E, David P, Sudul M, Brunot G (1992) A numerical model to
simulate snow cover stratigraphy for operational avalanche
forecasting. J Glaciol 38(128):13–22
Businger JA, Wyngaard JC, Izumi Y, Bradley EF (1971) Flux–profile
relationships in the atmospheric surface layer. J Atmos Sci
28(2):181–189
Carrasco J-F, Bromwich D, Monaghan A (2003) Distribution and
characteristics of mesoscale cyclones in the Antarctic: Ross Sea
Eastward to the Weddel Sea. Mon Weather Rev 131:289–301
Carrasco JF, Bromwich DH (1993) Mesoscale cyclogenesis dynamics
over the southwestern Ross Sea, Antarctica. J Geophys Res 98
D7:12973–12995
Catry B, Geleyn J-F, Bouyssel F, Cedilnik J, Brozkova R, Derkova M,
Mladek R (2008) A new sub-grid scale lift formulation in a
mountain drag parametrization scheme. Meteorol Zeitsch
17:1–16
Charnock M (1955) Wind stress on a water surface. Q J R Meteorol
Soc 81:639–640
Connolley W, Harangozo S (2001) A comparison of five numerical
weather prediction analysis climatologies in southern high
latitudes. J Clim 14(1):30–44
Dyer AJ (1974) A review of flux–profile relationships. Bound Layer
Meteorol 7(3):363–372
ECMWF (2004) IFS documentation—cycle CY28r1—part IV—
Section 10.5. Technical report, European Center for Meteorological Weather Forecast, Reading, England. Available at
http://www.ecmwf.int/research/ifsdocs/CY28r1/Physics/Physics11-06.htm
123
1080
N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys
Fiedler F, Panofsky HA (1972) The geostrophic drag coefficient and
the effective roughness length. Q J R Meteorol Soc 98:213–220
Fogt RL, Bromwich DH (2008) Atmospheric moisture and cloud
cover characteristics forecast by AMPS. Weather Forecast
23:914–930
Frezzotti M, Pourchet M, Flora O, Gandolfi S, Gay M, Urbini S,
Vincent C, Becagli S, Gragnani R, Proposito M, Severi M,
Traversi R, Udisti R, Fily M (2004) New estimations of
precipitation and surface sublimation in East Antarctica from
snow accumulation measurements. Clim Dyn 23(7):803–813
Gallée H (1995) Simulation of the Mesocyclonic activity in the Ross
Sea, Antarctica. Mon Weather Rev 123:2050–2069
Gallée H, Duynkerke PG (1997) Air–snow interactions and the
surface energy and mass balance over the melting zone of
west Greenland during the Greenland Ice margin experiment.
J Geophys Res 102:13813–13824
Gallée H, Guyonmarc’h G, Brun E (2001) Impact of snow drift on the
Antarctic ice sheet surface mass balance: possible sensitivity to
snow-surface properties. Bound Layer Meteorol 99:1–19
Gallée H, Peyaud V, Goodwin I (2005) Simulation of the net snow
accumulation along the Wilkes Land transect, Antarctica, with a
regional climate model. Ann Glaciol 41:1–6
Gallée H, Schayes G (1994) Development of a three-dimensional
meso-gamma primitive equations model, katabatic winds simulation in the area of Terra Nova Bay, Antarctica. Mon Weather
Rev 22:671–685
Georgelin M, Bougeault P, Black T, Brzovic N, Buzzi A, Calvo J,
Casse V, Desgagne M, El-Khatib R, Geleyn J-F, Holt T, Hong
S-Y, Kato T, Katzfey J, Kurihara K, Lacroix B, Lalaurette F,
Lemaitre Y, Mailhot J, Majewski D, Malguzzi P, Masson V,
McGregor J, Minguzzi E, Paccagnella T, Wilson C (2000) The
second COMPARE exercise: a model intercomparison using a
case of a typical mesoscale orographic flow, the PYREX IOP3.
Q J R Meteorol Soc 126:991–1029
Giorgi F, Mearns O (1999) Introduction to special section: regional
climate modeling revisited. J Geophys Res 104 D6:6335–6352
Guo Z, Bromwich DH, Cassano J (2003) Evaluation of Polar MM5
Simulations of Antarctic atmospheric circulation. Mon Weather
Rev 131:384–411
Haran T, Bohlander J, Scambos T, Painter T, Fahnestock M compilers
(2005, updated 2006). MODIS mosaic of Antarctica (MOA)
image map. National Snow and Ice Data Center. Digital media,
Boulder, Colorado
Heinemann G, Klein T (2003) Simulations of topographically forced
mesocyclones in the Weddel Sea and the Ross Sea region of
Antarctica. Mon Weather Rev 131:302–316
Jourdain NC, Mathiot P, Gallée H, Barnier B (2009) Influence of
coupling on atmosphere, sea ice and ocean regional models in
the Ross Sea sector, Antarctica. Clim Dyn (submitted)
Kim Y-J, Doyle JD (2005) Extension of an orographic-drag
parametrization scheme to incorporate orographic anisotropy
and flow blocking. Q J R Meteorol Soc 131:1893–1921
Kim YJ, Eckermann SD, Chun H-Y (2003) An overview of the past,
present and future of gravity-wave drag parametrization for
numerical climate and weather prediction models: survey article.
Atmosphere-Ocean 41:65–98
Kodama Y, Wendler G (1986) Wind and temperature regime along
the slope of adelie land, Antarctica. J Geophys Res 91:6735–
6741
Kodama Y, Wendler G, Gosink J (1985) The effect of blowing snow
on katabatic winds in Antarctica. Ann Glaciol 6:59–62
Kurtz DD, Bromwich DH (1985) A recurring, atmospherically forced
polynya in Terra Nova Bay. Antarctic Res Ser 43:493–508
Liu H, Jezek KC, Li B, Zhao Z (2001) RADARSDAT Antarctic
Mapping Project digital elevation model. Version 2 Technical
report, Boulder, CO, NSIDC
123
Lott F (1998) Alleviation of stationary biases in a GCM through a
Mountain Drag Parametrization Scheme and a simple representation of Mountain Lift Forces. Mon Weather Rev 127:788–800
Lott F, Miller MJ (1997) A new subgrid-scale orographic parametrization: its formulation and testing. Q J R Meteorol Soc
123:101–127
Mathiot P, Jourdain NC, Barnier B, Gallée H (2008) Sensitivity of a
model of the Ross Ice Shelf Polynya to different atmospheric
forcing sets. Mercator Ocean Q Newslett 28:22–30
Mathiot P, Jourdain NC, Barnier B, Gallée H, Molines JM, Le
Sommer J (2009) Sensitivity of coastal polynyas and high
salinity shelf water production in the Ross Sea, Antarctica, to the
Atmospheric Forcing. Ocean Dyn (submitted)
Miller MJ, Palmer TN, Swinbank R (1989) Parametrization and
influence of subgridscale orography in general circulation and
numerical prediction models. Meteorol Atmos Phys 40:84–109
Monaghan AJ, Bromwich DH, Powers JG, Manning KW (2005) The
climate of the McMurdo, Antarctica, region as represented by
one year of forecasts from the Antarctic Mesoscale Prediction
System. J Clim 18:1174–1189
Morales Maqueda MA, Willmott AJ, Biggs NRT (2004) Polynia
dynamics: a review of observations and modeling. Review of
Geophysics 42:1–37
Morcrette J (2002) Assessment of the ECMWF Model cloudiness and
surface radiation fields at the ARM SGP site. Mon Weather Rev
130:257–277
O’Connor WP, Bromwich DH, Carrasco JF (1994) Cyclonically
forced barrier winds along the Transantarctic Mountains near
Ross Island. Mon Weather Rev 122(1):137–150
Parish T, Bromwich D (2007) Reexamination of the near-surface
airflow over the Antarctic continent and implications on
atmospheric circulations at high Southern latitudes. Mon
Weather Rev 135(5):1961–1973
Parish TR (1988) Surface winds over the Antarctic continent: a
review. Rev Geophys 26:169–180
Pease CH (1987) The size of wind-driven coastal polynyas. J Geophys
Res 92:7049–7059
Powers JG (2007) Numerical prediction of an Antarctic severe wind
event with the Weather Research and Forecasting (WRF) model.
Mon Weather Rev 135:3134–3157
Prasad TG, Mc Clean JL, Hunke EC, Semtner AJ, Ivanova D (2005)
A numerical study of the western Cosmonaut polynya in a
coupled ocean–sea ice model. J Geophys Res 110:C10008.1–
C10008.21
Rasmussen EA, Turner J (2003) Polar Lows: Mesoscale Weather
Systems in the Polar Regions. Cambridge University Press,
Cambridge
Reijmer CH, van Meijgaard E, van der Broeke MR (2004) Numerical
studies with a regional atmospheric climate model based on
changes in the roughness length for momentum and heat over
Antarctica. Bound Layer Meteorol 111:313–334
Rontu L (2006) A study on parametrization of orography-related
momentum fluxes in a synoptic-scale NWP model. Tellus
58A:69–81
Simmonds I, Keay K, Lim EP (2003) Synoptic activity in the Seas
around Antarctica. Mon Weather Rev 131:272–288
Stearns CR, Weidner GA (1992) Antarctic Automatic Weather
Stations: austral summer 1991–1992. Antarct J US 27:280–282
Stull RB (1988) An introduction to boundary layer meteorology.
Kluwer, Dordrecht
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106(D7):7183–7192
Turner J, Pendlebury S (2004) The international Antarctic weather
forecasting handbook. British Antarctic Survey, Cambridge
Unden P, Rontu P, Järvinen H, Lynch H, Calvo J et al. (2002) The
HIRLAM-5 scientific documentation. Technical report
N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys
van den Broeke M, van de Berg WJ, van Meijgaard E. Reijmer C
(2006) Identification of Antarctic ablation areas using a regional
atmospheric climate model. J Geophys Res 111:D18:110
van Lipzig NPM, van Meijgaard E, Oerlemans J (2002) The spatial
and temporal variability of the surface mass balance in
Antarctica: results from a regional atmospheric climate model.
Int J Climatol 22:1197–1217
Vosper SB, Brown AR (2008) The effect of small-scale hills on
orographic drag. Q J R Meteorol Soc 133:1345–1352
1081
Wallace JM, Tibaldi S, Simmons AJ (1983) Reduction of systematic
forecast errors in the ECMWF model through the introduction of
an envelope orography. Q J R Meteorol Soc 109:683–717
Winther JG, Jespersen MN, Liston GE (2001) Blue-ice areas in
Antarctica derived from NOAA AVHRR satellite data. J Glaciol
47(157):325–334
Wood N, Brown AR, Hewer FE (2001) Parametrizing the effects of
orography on the boundary layer: an alternative to effective
roughness lengths. Q J R Meteorol Soc 127:759–777
123