Influence of the orographic roughness of glacier valleys across the Transantarctic Mountains in an atmospheric regional model

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1 Clim Dyn (11) 3:17 11 DOI 1.17/s Influence of the orographic roughness of glacier valleys across the Transantarctic Mountains in an atmospheric regional model Nicolas C. Jourdain Hubert Gallée Received: 7 July 9 / Accepted: 9 January 1 / Published online: 1 February 1 Ó Springer-Verlag 1 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 -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 nicolas_jourdain@yahoo.fr 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 19; Parish and Bromwich 7). 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 (199) 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 (199) 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 1% of the annual ice production over the Ross Sea continental shelf (Kurtz and Bromwich 195; Morales Maqueda et al. ). Moreover sea-ice and polynyas are embedded in dense water formation: this is important since Broecker et al. (199) 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

2 1 N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys R Ross Sea Transarctic RIS Mountains models (e.g. Gallé et al. 5; Bailey and Lynch ; van Lipzig et al. ; Heinemann and Klein 3). 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 ( km). Most of the glacier valleys are represented, even coarsely, in a -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. Mountain drag R7 TNB P O R RIS D S M By Bm N Fig. 1 Roughness length for momentum (m) computed using h = (R) and using h = 7 m (R7). Orography is represented with black lines (every 5 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. 3; Heinemann and Klein 3; Rasmussen and Turner 3). 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, 5 and km, respectively (Turner and Pendlebury ). In comparison, the horizontal resolution in Antarctica is about 1 km in General Circulation Models (GCMs) and about km in most of the latest climatic studies made with limited area 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. 193). 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 197). 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 19; Andreas ); 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. 199; Georgelin et al. ; Kim et al. 3). An alternative parametrization has been proposed by Wood et al. (1) and applied by Beljaars et al. (): the effects of turbulent drag are specified with an explicit orographic stress profile. This can be used to predict an anisotropic drag (Brown 1). Rontu () 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. 3) or blocking of the low-level flow (e.g. Kim and Doyle 5; 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 ). However, turbulent drag is related to horizontal scales smaller than 5 km whereas other drags are due to larger horizontal scales (Beljaars et al. ). For

3 N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys 19 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 199; Catry et al. ). 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. ; Unden et al. ; Andreas 197). 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. () 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 (197). Here we modify an existing method based on that of the European Centre for Medium-Range Weather Forecasts (ECMWF ), 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 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. Materials and methods.1 The atmosphere model MAR MAR is a hydrostatic regional mesoscale model based on three-dimensional primitive equations (Gallée and Schayes 199; Gallée 1995; Gallée et al. 5). Four hydrometeors 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. (199). 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 (). 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 etal.1, 5). The roughness length Z depends on the wind speed and the surface type which may be ocean (Charnock 1955), sea ice or continental ice (Andreas 197). The standard roughness length of the ice sheet takes into account sastrugis and blowing snow (Gallée et al. 1, 5). An orographic roughness length Z OR 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. 1). For a -km mesh ij of MAR, the orographic roughness length for momentum is thus computed as: >< >: Z OR ij ¼ l ij e A ij 1 A ij ¼ : : l ijn ij Dx þ : logð1 þ ; l ij Þ! 1 ð1þ where Dx is the horizontal resolution. N ij 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 h from its neighbors. The orographic roughness scheme has been adapted from the European Centre for Medium-Range Weather Forecasts scheme (ECMWF ), which corresponds here to h =. 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 197). Furthermore, Z is limited to 3.3 m (one-third of the first level).. Configuration and experimental set-up 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 km and there are 33 vertical r-levels, the lower is about 1 m high, and the model top corresponds to.1 hpa. The time step for dynamics is s. We focus on the Ross Sea Sector,

4 17 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,3 9 3, km. Sea surface temperature and sea ice fraction are prescribed from ERA- reanalysis (the sea ice fraction is mainly based on SSM/I data). Atmospheric winds, temperature and humidity are prescribed from ERA- at the lateral boundaries and relaxed towards the reanalysis within a six-point nudging zone. The upper atmospheric boundary is also relaxed towards ERA-. The height h is tuned to change the spatial distribution of the orographic roughness length. The set of experiments is made of a simulation with h = (many local maxima, large orographic roughness lengths), and a simulation with h = 7 m (fewer local maxima, smaller orographic roughness lengths). The roughness lengths for each case are shaded in Fig. 1. The experiments with h = 7 m and h = are referred to as R7 (smooth valleys set-up) and R (rough valleys set-up), respectively. The value h = gives an orographic roughness similar to that used in the ECMWF model. The value h = 7 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. ) Image Map, the change in roughness 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 199 to 31 December 199. The initial state is an interpolation of ERA- on 1 January 199. 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 199) and from the Italian National Research Program in Antarctica (PNRA, Their location is given in Fig.. 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 1 m higher or lower than the actual AWS site. Temperature and wind speed are extrapolated from the first r-level (about 1 m) to the height of AWSs (3 m) using Monin Obukhov similarity theory (see Businger et al. 1971; Dyer 197). 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. Fig. Location of AWSs. The table shows AWS elevations. Gray contours represent surface elevations (every 5 m) UW-AOS : Lynn Manuela Marble Pt Pegasus N Pegasus S Marilyn Gill Lettau Byrd 177 m 7 m 1 m m 5 m m 5 m 3 m 153 m Byrd PNRA : Alessandra Arelis Eneide Lola Modesta Silvia 1 m 15 m 9 m 11 m 19 m 53 m Ale Lola Silvia Eneide Arelis Gill Pegasus Marilyn Lettau Modesta Lynn Manuella Marble Point

5 N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys 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. 199). 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 R7 (smooth valleys) than in R (rough valleys) for most of the stations, and differences between the two experiments can reach 1 m s -1 at some sites. Note that R7 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 R7. Standard deviations and correlations are summarized in Taylor diagrams (Fig. ), 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 R7 than in R (although it is under-estimated in both R7 and R), and HF wind speeds have significant correlations to AWS data in the two experiments (even if correlations are lower than.5). The significant correlations of HF wind speeds to AWS data gives confidence in the physical representation of katabatic winds in the model, and R7 (smooth valleys) better captures extreme events. The standard deviation of R7 LF wind speeds are in good agreement with AWS data, whereas R LF wind speeds are strongly under-estimated. The correlations of LF wind speeds to AWS data is greater than.5 (significant at the 95% level) for AWSs on eight in the two experiments. These results show that seasonal and intra-seasonal variabilities are well reproduced in R7, while R underestimates their amplitude. The intensification of katabatic winds in confluence zones of R7 is simulated within a thicker layer in austral winter than in summer. In TNB, for instance, the increase of wind speed simulated in R7 (smooth valleys) as compared to R (rough valleys) is significant within the first m above surface in winter, and within the first 5 m above surface in summer (Fig. 5). The wind convergence into most of the glacier valleys is strongly increased near surface in R7 as compared to R (Fig. ). The tropospheric flow is adjusted between and, m a.g.l. (above ground level) so that the mass continuity is ensured in both experiment. Wind divergence in the lower troposphere is therefore slightly increased above glacier valleys in R7 as compared to R (Fig. 5). The increase of katabatic wind speed in the Transantarctic Mountains produces more blowing snow in winter in R7 than in R. The JAS mean upward snow flux from the snow surface to the atmospheric SBL reaches kg m - s -1 in the Transantarctic Mountains in R7, while it does not exceed kg m - s -1 in R (not shown). Consequently, there are almost no zones of annual ablation in R (rough valleys), whereas there are several ones in R7 (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. 1; Frezzotti et al. ; van den Broeke et al. ). However, there are no quantitative blowing snow measurements available in the Transantarctic Mountains (to our knowledge). The differences between R and R7 therefore only gives a coarse indication of the role of katabatic winds, and further evaluation should be addressed in future research. 3. 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 R7, by up to 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 R7, by up to K. The atmospheric surface layer is generally colder in the Transantarctic Mountains in R7, but the difference between R and R7 does not exceed K. The differences between surface air temperature in R7 and R 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 199, whereas the highest differences are found in August and April (Fig. ). 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 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 R is the expected

6 17 N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys Alessandra 1 Arelis 1 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1 1 Eneide 1 1 Lola JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1 Modesta Silvia JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Manuela Lynn JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Fig. 3 One-month sliding mean 3 m wind speed observed at some AWS sites (black). Simulated values at this location are in red (R) and blue (R7) 1 1 1

7 N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys 173 σ/σaws (HF) σ/σaws (LF) 1.. Ar. Mo. Ar Mo En.1 Al Lo En Ma Mo Si.1 Lo En Al Ar Si Ma Si.. En. Si Al Ly Ma.3 Lo Ly.3... Lo Al Ly Ly Ma.5 Mo Ar 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 R. Snow surface temperature is therefore higher in R than in R7. As the low troposphere in R looses more heat than in R7, R is colder than R7 for most of the lowest m..5 C o r r σ/σaws.5. C o r r σ/σaws.. e l a t e l a t AWS i o n.7 i o n.7 C o e f f.. C o e f f. i c i e n t.9 i c i e n t AWS Fig. Taylor diagram (Taylor 1) related to wind speed comparisons at some AWS sites for R (red) and R7 (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.5 in the HF diagram are significant at the 99% confidence level according to the Student test, while only correlation coefficients greater than.5 in the LF diagram are significant at 95% confidence level (the running mean decreases the number of degrees of freedom) Z Fig. 5 Difference between R7 and R wind speed (1-month sliding mean, m s -1 ) component perpendicular to a section of km wide downstream of Reeves glacier (TNB) z=1m a.g.l. Beardmore z=5m a.g.l. 1 km 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 Fig. Mean difference of horizontal wind divergence between R7 and R in July 199 (1 - s -1 ), near the surface (upper) and at 5 m above the surface (lower)

8 17 N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys LNB McM MBL Z (m) (a) R R7 R7 X T(K) >9% >99% >9% Fig. 7 Difference of 199 mean surface air temperature between R7 and R (K) and orography (isolines every 5 m). LNB Lady Newness Bay, McM McMurdo station, MBL Marie Byrd Land (a) (b) along the slope. The difference between snow temperatures and SBL temperatures reaches 1 K in R7 (smooth valleys) while it reaches K in R (rough valleys). This means that the discoupling between snow and atmosphere Fig. a Difference of April 199 mean surface air temperature between R7 and R (K). b Difference of April 199 mean surface pressure between R7 and R (hpa) Z (m) (b) R7 R R7 3 X R T(K) >9% >99% Fig. 9 Monthly air temperature profile (K) in February 199. a at a point in the middle of the Transantarctic Mountains (.7 S, 151. W,, m); b at a point downstream of the Transantarctic Mountains, at the lower end of the slope (3.5 S, 17.9 E, 93 m). Solid is R and dashed is R7. Vertical axis is elevation (m). Soil surface temperatures are represented by 9(R7) and?(r). Ground level corresponds to horizontal axis. The significance level of the difference R7 R is indicated on the right side when it is higher than 9% (student t test) increases for smaller roughness lengths. Consequently, the SBL downstream of the Transantarctic Mountains is generally warmer in R7 than in R (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 R7 profile at 35 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 19; Gallée and Schayes 199). This bump is not found in R. The slightly colder air in R7 above 1 m is an effect of temperature advection at this location (not shown). Finally, simulated annual mean 3-m temperatures are in relative

9 N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys 175 Table 1 Difference of annual mean temperature between each experiment and the AWS data 1 AWS T R - T AWS T R7 - T AWS Lynn Manuela Marble Pt Pegasus N -5.9?. Pegasus S -5.?1. Marilyn Gill Lettau a Byrd a a Indicates more than 5% missing values in the observations Surface pressure (hpa) agreement with observations upstream of TNB for both R and R7 (see Lynn and Manuela stations in Table 1), R being slightly more realistic than R7 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 R7 and leads to a warmer SBL in R7 than in R. 3.. 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. 3), and barrier winds blocked by the steep mean orography of the Transantarctic Mountains (O Connor et al. 199). 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 R (rough valleys) and R7 (smooth valleys) exhibit agreement with observed surface pressure in the center of the RIS (Fig. 1). The high correlation coefficients (.79 for R and.1 for R7) show that circulation patterns are reasonably well captured by MAR. The greatest difference between R and R7 is reached in March April with a correlation to AWS observations of.1 in R and.75 in R7. A cyclonic anomaly is found in R7 from March to May in the lower troposphere, with a maximum in April (Figs. 11, b). The weaker vorticity simulated in R from fall to spring is consistent with the higher surface pressure found at Gill location (as compared to R7, Fig. 1). We suggest that larger Ekman pumping simulated in R (rough valleys) might reduce the strength Fig. 1 Twenty-day sliding mean surface pressure (hpa) at AWS Gill: simulated in R (red), simulated in R7 (blue) and observed (black). Prior to the sliding mean, missing values in the observations are filled using linear interpolation for periods shorter than h. The non filtered signal gives annual mean pressure of 93.7 hpa (R), 9. hpa (R7), and 93. hpa (AWS). Correlations to observed pressure are.79 (R) and.1 (R7). Associated RMSE are 7. hpa (R) and 7.3 hpa (R7) Relative Vorticity (1 - s -1 ) J F M A M J J A S O N D Fig. 11 Monthly relative vorticity averaged over the RIS at r-level.3, which corresponds approximately to 1,5 m a.g.l. (in 1 - s -1 ). Solid R, dashed R7. Here the RIS is defined by surface elevation between and 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 R7, Fig. 11). Finally, the warmer air over the RIS in R7 in April (Fig. ) 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 R7 and R over the RIS (Table 1). The cold bias is simulated throughout the year for most of the stations (not shown), and it is

10 17 N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys particularly strong in April (Table ). A detailed investigation shows that short cold events along winter are often too cold in the simulations (by up to 3 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 in their forecasts). Bromwich et al. (5) 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 R7 only reduces a small part (.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 R7 as compared to R (Table ). To summarize, the circulation is well captured over the RIS both in R and in R7. 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, R7 (smooth valleys) is closer to observations than R (rough valleys) Vicinity of McMurdo particularly warmer (colder) there. Warmer air advection from the RIS in R7 may also increase SBL temperatures at that location (not shown). Temperature comparisons at stations Pegasus North and South (vicinity of McMurdo) show that R7 is more realistic than R, both in mean and in variability (Fig. 1). 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 hpa are colder than the observations, by.5.5 K. R and R7 do not exhibit any significant difference above 7 hpa. Even below T ( C) Gill The highest temperature differences for the whole year between R and R7 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 ms -1 ). These weak winds are responsible for an amplification of the SBL stability in R7 and thus for an additional decrease of exchanges between the SBL and the ground. Consequently, the SBL (the snow surface) is T ( C) Pegasus N Table Difference of April mean temperature between each experiment and the AWS data - AWS T R - T AWS T R7 - T AWS Lynn Manuela Marble Pt Pegasus N -9.?3. Pegasus S -.?. Marilyn Gill Lettau Byrd Fig. 1 Zero-day sliding mean temperature ( C) at the locations of Gill and Pegasus North: observed (black), simulated in R (red) and simulated in R7 (blue). Prior to the sliding mean, missing values in the observations are filled using linear interpolation for periods shorter than h. The non filtered signals at Gill give annual mean temperature of -3. C (R), C (R7), and -.9 C (AWS); correlations to observed temperature are. (R) and.5 (R7); associated RMSE are 1. C (R) and 1. C (R7). The non filtered signals at Pegasus North give annual mean temperature of -. C (R), -.5 K (R7), and -.9 K (AWS); correlations to observed temperature are. (R) and.1 (R7); associated RMSE are 9.5 C (R) and 7.1^C (R7)

11 N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys 177 Fig. 13 Average of the differences between MAR and McMurdo soundings over the year 199. 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 ( ). ERA- reanalysis are also represented (green) 7 hpa, differences between R and R7 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- at this location because MAR is relaxed to ERA- at the upper boundary. Regarding the good skills of ERA- 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. 5), 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. (5) obtained good results at McMurdo using Polar-MM5 at a resolution of 3 km (temperature bias \1.5 K and wind speed bias \1 ms -1, from ground to 15 hpa), but their model was re-initialized every 1 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. (5) and Powers (7), but the complexity of the topography of the McMurdo vicinity was still not completely resolved at such a resolution. Our topography at 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 3,79 m and Mount Terror at 3,3 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. R7 better captures surface air temperature. However, both R

12 17 N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys and R7 do not well represent the tropospheric temperatures, humidity, and winds. The biases in the upper atmosphere are related to ERA- 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 km. 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 and, 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-, whereas the lower biases may be related to a poor representation of the topography at a resolution of 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 Table 3 Mean annual temperature bias in some long experiments at different stations (in K) Type Dx South pole Byrd Gill Lynn MAR-R7 free km? Polar-MM5 RI-7h km Polar-MM5 RI-1h 3 km? RACMO free 55 km?.7 ERAinterim RA km?5.7-1.?1.?1. NNR RA.5-1.5/?. -./- Temperature in italic are JJA/DJF biases. Stations referred as South Pole are Clean Air (9 S), Henry (9. S;1. W), Lindsay (9. S;9.5 W), or an average among them. Comparisons to AWS have been found in Guo et al. (3), Bromwich et al. (5), van Lipzig et al. (), and Connolley and Harangozo 1; 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, 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. 195). 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 1) 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. (5) and Powers (7) 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., 9) or coupled to an atmospheric model (Jourdain et al. 9). Katabatic winds influence ice advection and heat flux from ocean to atmosphere (because of ocean surface warming resulting from vertical mixing) in polynyas (Pease 197; Prasad et al.

13 N. C. Jourdain, H. Gallée: Influence of the orographic roughness of glacier valleys 179 5). 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. 1). However, simulating a realistic SBL remains important since models are often evaluated using surface observations. Additionally, we have noted that snow erosion is closelylinkedtothestrengthofkatabaticflows,anda 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 (R7) should be preferred to a more classical roughness length parametrization (R). 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. 9) 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. 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