Influence of coupling on atmosphere, sea ice and ocean regional

Clim Dyn (2011) 36:1523–1543 DOI 10.1007/s00382-010-0889-9

Influence of coupling on atmosphere, sea ice and ocean regional models in the Ross Sea sector, Antarctica Nicolas C. Jourdain • Pierre Mathiot Hubert Galleé • Bernard Barnier

•

Received: 11 September 2009 / Accepted: 23 July 2010 / Published online: 24 August 2010 Springer-Verlag 2010

Abstract Air–sea ice–ocean interactions in the Ross Sea sector form dense waters that feed the global thermohaline circulation. In this paper, we develop the new limited-area ocean–sea ice–atmosphere coupled model TANGO to simulate the Ross Sea sector. TANGO is built up by coupling the atmospheric limited-area model MAR to a regional configuration of the ocean–sea ice model NEMO. A method is then developed to identify the mechanisms by which local coupling affects the simulations. TANGO is shown to simulate realistic sea ice properties and atmospheric surface temperatures. These skills are mostly related to the skills of the stand alone atmospheric and oceanic models used to build TANGO. Nonetheless, air temperatures over ocean and winter sea ice thickness are found to be slightly improved in coupled simulations as compared to standard stand alone ones. Local atmosphere ocean feedbacks over the open ocean are found to significantly influence ocean temperature and salinity. In a stand alone ocean configuration, the dry and cold air produces an ocean cooling through sensible and latent heat loss. In a coupled configuration, the atmosphere is in turn moistened and warmed by the ocean; sensible and latent heat loss is therefore reduced as compared to the stand alone simulations. The atmosphere is found to be less sensitive to local feedbacks than the ocean. Effects of local feedbacks are increased in the coastal area because of the presence of sea ice. It is suggested that slow heat conduction within sea ice could amplify the feedbacks. These local feedbacks result

N. C. Jourdain (&) H. Galleé LGGE, UMR 5183, CNRS-UJF, Grenoble, France e-mail: [email protected] N. C. Jourdain P. Mathiot B. Barnier LEGI, UMR 5519, CNRS-UJF-INPG, Grenoble, France

in less sea ice production in polynyas in coupled mode, with a subsequent reduction in deep water formation. Keywords Antarctica Ross Sea Coupling Coupled model Sea ice Ocean Atmosphere Limited area model Regional model Polynya Dense water Katabatic Heat fluxes Polar Feedbacks MAR NEMO LIM OPA TANGO

1 Introduction Antarctica and surrounding seas have an important role in the global climate system. First, air–sea heat and water exchanges around Antarctica control the dense water formation (e.g. Broecker et al. 1998; Budillon et al. 2003), the denser waters (AntArctic Bottom Waters, AABW) being part of the global thermohaline circulation (Carmack 1977; Gordon and Comiso 1988). On the other hand, the Antarctic ice sheet mass balance has a significant impact on the sea level variability (Bindoff et al. 2007, and references therein); it is affected by air–sea exchange that influence both ocean properties near the ice sheet grounding line (Rignot and Jacobs 2002) and snow fall at the periphery of the ice sheet (Noone and Simmonds 2004). Moreover, the Southern sea ice cover affects the tropical climate variability (Chiang and Bitz 2005; Bromwich et al. 1998; Yuan and Martinson 2000). This paper deals with ocean–sea ice– atmosphere interactions around Antarctica because the latter have a key role in the global climate system. Most of the General Circulation Models (GCMs) used by the last IPCC (Intergovernmental Panel on Climate Change) present significant biases around Antarctica. In particular, SST (Sea Surface Temperature) is often too warm with regard to observations, the Antarctic

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N. C. Jourdain et al.: Influence of coupling on atmosphere, sea ice and ocean regional models

Circumpolar Current (ACC) is often too much north, and the seasonal and interannual variability of sea ice extent have biases (Randall et al. 2007; Holland and Raphael 2006; Lefebvre and Goosse 2008). A few atmospheric Limited-Area Models (LAMs) have been developed in the last decades to better represent the physical processes specific to polar regions. They have often shown good skills in the representation of mesoscale dynamics (e.g. Parish and Bromwich 1991; Galleé and Schayes 1994; Heinemann and Klein 2003). A few of these models are also able to simulate a quite realistic mass balance over the ice sheet (van Lipzig et al. 2002; Galleé et al. 2005). Mathiot et al. (2010a, b) have emphasized the beneficial effect of forcing an ocean–sea ice LAM with atmospheric surface variables produced by an atmospheric LAM simulating realistic katabatic winds. However, very few atmospheric LAMs have been coupled to sea ice and/or ocean models. Galleé (1998) managed to obtain a realistic simulation of a wind-driven polynya by coupling an atmospheric LAM to a sea ice model. Bailey and Lynch (2000) and Bailey et al. (2004) also simulated a realistic sea ice cover by coupling an atmospheric LAM, a sea ice model, and a 1-layer ocean model. To our knowledge, there is no study using an atmospheric LAM coupled to a sea ice model and a 3-dimensional ocean model in Antarctica. Lefebvre and Goosse (2008) have shown that the climate variability and related processes are different in the various sectors of the Southern Ocean. This paper deals with the Ross Sea sector, because deep waters from the Ross Sea contribute to the thermohaline circulation in the Pacific sector (Broecker et al. 1998). In this sector, a large amount of dense water formation takes place in the Ross polynya and in the Terra Nova Bay polynya. The Ross polynya is the largest polynya that regularly forms around Antarctica. The katabatic winds associated with the particularly high cyclonic activity in this sector (Carrasco et al. 2003), and the inflow of relatively warm Circumpolar Deep Water onto the continental shelf (Fichefet and Goosse 1999) have been found to control the ice production of this polynya (Morales Maqueda et al. 2004, and references therein). The much smaller Terra Nova Bay polynya is responsible for about 10% of the annual ice production over the Ross continental shelf (Kurtz and Bromwich 1985). Its creation and maintenance result from katabatic outflows from the Transantarctic Mountains. The aim of this paper is to build and to evaluate a climate model of the Ross Sea sector able to simulate interactions between different scales of the atmosphere (from synoptic lows to katabatic winds and mesocyclones) and the different parts of the ocean (from surface to bottom), including sea ice. Jourdain and Galleé (2010) have used the atmospheric LAM MAR (Mode`le Atmosphe´rique Re´gional, Galleé 1995) to simulate realistic katabatic flows in this

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region. Mathiot et al. (2010b) have used a regional configuration of the 3-dimensional sea ice–ocean model NEMO (Nucleus for European Modeling of the Ocean, Madec 2008) in the Ross Sea sector. They have forced NEMO with atmospheric fields from MAR, and have thus managed to simulate an export of dense waters from the polynyas to the Ross Sea Bottom Waters (contributing to the AABW). In this paper, we make a step forward in the representation of the climate of the Ross Sea by coupling MAR to NEMO. The atmosphere–sea ice–ocean coupled model is called TANGO, for Triade Atmosphe`re-Neige, Glace, Oceán. Previous modeling studies have shown that the ocean– atmosphere coupling allows to simulate various modes of variability in GCMs (Randall et al. 2007, and references therein). The ocean–atmosphere coupling in LAMs used in the Tropics also improves the representation of processes such as these involved in tropical cyclones (e.g. Chan et al. 2001). However, the question remains whether a regional ocean–atmosphere coupling in cold regions produces simulations significantly different from the stand alone counterparts. Moreover, the use of lateral boundary conditions in coupled LAMs strongly constrains the simulations. In this paper, we therefore develop an experimental framework to estimate the influence of ocean–sea ice–atmosphere local coupling in a LAM, in the Ross Sea sector. The strategy of the study and the models are described in Sect. 2. The strategy used to run the experiments is described in Sect. 3. The skills of the stand alone models are evaluated and compared to those of TANGO using observations (Sect. 4). Finally, the impact of local feedbacks is compared to the impact of the mutual forcing MAR-NEMO in Sect. 5.

2 Model design 2.1 Strategy of the study TANGO is built up by coupling MAR to NEMO. Our first aim is to evaluate the skills of TANGO as compared to the skills of MAR or NEMO in stand alone configurations. We therefore perform standard stand alone experiments in which MAR and NEMO are forced by reanalyses. These experiments are referred to as Forced Atmosphere-1 (FA1) and Forced Ocean-1 (FO1), respectively. Then we coupled the two models following Dufresne and Grandpeix (1996): NEMO provides the surface ocean characteristics to MAR, and MAR computes energy fluxes and provides them to NEMO. Such a coupling usually gives satisfactory results among the GCMs (e.g. Randall et al. 2007), and allows to keep the energy balance. This coupled model is called TANGO-A, with related experiment referred to as COA.


Comparing COA and (FA1,FO1) to observations enables to evaluate each model. Our second aim is to isolate the behavior of the coupled model that comes from a change in forcings (fields from MAR or from NEMO replace fields from reanalysis) from the behavior that comes from physical feedbacks allowed by coupling. We therefore compare TANGO simulations to stand alone experiments of MAR and NEMO forced by each other (mutually forced experiments, referred to as FA2 and FO2, respectively). However, atmosphere–ocean turbulent fluxes into the ocean of TANGO-A are computed as in MAR, whereas turbulent fluxes into the ocean of the stand alone configurations of NEMO are computed using bulk formula from Large and Yeager (2004). A comparison between COA and (FA2,FO2) thus emphasizes differences of turbulence parametrizations rather than differences due to physical feedbacks. Therefore, we build another coupled configuration called TANGO-B: MAR provides surface air characteristics (temperature, humidity, etc) to NEMO, which, in turns, computes energy fluxes using its own bulk formula, and then provides the surface ocean characteristics to MAR. The related experiment is referred to as COB. The models are described in this section, and the set-up of the experiments is given in the following section. 2.2 The atmosphere model MAR Mode`le Atmosphe´rique Re´gional is a hydrostatic mesoscale LAM based on 3-dimensional primitive equations (Galleé 1995; Galleé and Schayes 1994; Galleé et al. 2005). The configuration used in this study if fully described in Jourdain and Galleé (2010). In particular, surface energy fluxes depend on snow metamorphism (Galleé et al. 2001), and surface turbulent fluxes are computed using the Monin– Obukhov similarity theory. Blowing snow is also represented in the turbulent scheme over sea ice and over the ice sheet (Galleé et al. 2005). Roughness length over the ice sheet takes sastrugis into account. Orographic roughness lengths have been tuned so that surface katabatic winds emerging from glacier valleys of the Transantarctic Mountains are well represented (Jourdain and Galleé 2010). Roughness length for momentum over ocean is adapted from the scheme of Wang (2000), and it is given by: Z0OC ¼ 0:0185

u2H m þ 0:135 ðmÞ uH g

ð1Þ

where m is the dynamical viscosity of air, g the acceleration of gravity, and uH the friction velocity. For low values of uH ; Z0OC is fixed as a constant. Roughness length for momentum over sea ice is computed as over the ice sheet, and is given by:

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Z0SI ¼ 5 105 þ 0:01hS þ 0:536u2H 6:18 105 ðmÞ ð2Þ where the first term is the roughness length for smooth ice, the second represents the effects of sastrugis of height hS, and the two other terms represent the effects of blowing snow on turbulence. The configurations used in this paper have a resolution of 40 km, and a time step of 120 s for dynamics. There are 33 vertical r-levels, the lowest is about 10 m high, and the highest corresponds to 0.1 hPa. MAR has a prescribed time dependent sea ice fraction in stand alone mode, and the vertical structure of sea ice is simulated within a 5-layer model. 2.3 The ocean–sea ice model NEMO The ocean–sea ice model NEMO (version 2.0) is used. The ocean component is OPA-9 (Oceán PAralle´lise´, Madec 2008). It uses a partial step representation of the bottom topography and an improved advection scheme for momentum. It includes a laplacian parametrization for mixing temperature and salinity along isopycnals, and a turbulent kinetic energy (TKE) 1.5 order closure scheme for vertical mixing (e.g. Xue et al. 2000). More details regarding the parametrizations used in this paper can be found in Barnier et al. (2006). The sea ice component is LIM-2 (Louvain Ice Model, Fichefet and Morales Maqueda 1997). The advection scheme conserves second order moments of transported variables, and a viscous-plastic rheology is used to compute internal stress. Thermodynamics is computed within three vertical layers. Our configuration uses the same regional configuration of NEMO as in Mathiot et al. (2010b), at an horizontal resolution of 20 km. Open boundary conditions are used in OPA, as described in Cailleau (2004), while sea ice is relaxed at boundaries in LIM. The time step is 36 min for ocean dynamics, and LIM is called every 3 h. The first ocean level is 3 m deep. Turbulent fluxes between air and the underlying surface are computed as in Large and Yeager (2004), with bulk formula for drag computation at ocean surface, and fixed drag coefficients at sea ice surface. Our configuration does not simulate ocean beneath the Ross Ice Shelf. The Ross Ice Shelf front is considered as a wall from ocean surface to bottom, with no heat or fresh water flux across it. Finally, no tides are represented in the model. 2.4 TANGO-A The software OASIS-3 (Valcke et al. 2004) is chosen to manage exchanges and interpolations between the two models. MAR and NEMO exchange the last 6 h-mean

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surface fields every 6 h. NEMO provides SST, sea ice surface temperature, sea ice fraction, surface oceanic currents, and sea ice albedo to MAR. Surface stress is computed by taking surface currents into account. The atmospheric part of TANGO-A computes the ocean albedo as a function of cloud fraction and solar zenith angle (Briegleb and Ramanathan 1982). This allows MAR to compute the solar flux that is sent to OPA. To compute the solar flux that is sent to the sea ice model, we use the albedo that is provided by LIM because it is a function of sea ice temperature and snow properties, with respect to the cloud fraction provided by MAR (Ebert and Curry 1993; Shine and Henderson-Sellers 1985; Grenfell and Perovich 1984). Infrared fluxes received by the underlying model are computed by MAR using the radiative scheme of Morcrette (2002) and ocean or ice surface temperatures from NEMO. Latent and sensible heat fluxes are computed in MAR and sent to NEMO. Dufresne and Grandpeix (1996) have shown that an iterative relaxation on fluxes and temperatures at sea ice surface leads to large oscillations of sea ice temperature in coupled models. They suggested to link fluxes and temperatures so that: ( /o ¼ wo ð3Þ /i ¼ wi þ ow T i hi oh i

where w and / are the fluxes sent by MAR and received by NEMO, respectively, with indices o and i referring to ocean and ice. Ti is the sea ice surface temperature in LIM, and hi is the sea ice surface temperature seen by MAR. Overlines represent 6 h-means. The Clausius-Clapeyron formula (Stull 1988) is used to compute the latent heat flux derivative. Snow and rain are separately sent to NEMO, and snow erosion by the wind over sea ice is included in the ice mass balance: the snow raised by the wind from the sea ice fraction of a given mesh is homogeneously distributed over the whole mesh and then included in the snow fall term. Thus, a fraction of the snow cover over sea ice is transported by the wind into the open ocean. 2.5 TANGO-B As in TANGO-A, OASIS-3 is used, with a coupling time window of 6 h. The atmospheric component of TANGO-B gives surface wind speed, surface air temperature and humidity to NEMO. The latter uses bulk formulae from Large and Yeager (2004) to calculate turbulent fluxes from those fields. OPA computes its own ocean albedo, using the parametrization of Briegleb and Ramanathan (1982). Net infrared fluxes are computed by NEMO using the

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atmospheric downward long wave Aradiation. Latent heat fluxes derivative used in Eq. 3 are computed as in Large and Yeager (2004), differently from TANGO-A in which Clausius–Clapeyron formula is used (Stull 1988). The cloud fraction used to compute the sea ice albedo is assumed to be 50% everywhere, as in the stand alone configurations of NEMO. Except this, exchanges are the same as in TANGO-A. TANGO-B does not keep the energy balance at the interface MAR/NEMO. This is unlikely to be a significant issue in limited-area coupling because energy imbalance is removed at the lateral boundaries. A coupling such as in TANGO-B is however not suitable for global coupling. (unless heat fluxes computed by the ocean model are sent to the atmospheric model, as is done in Collins et al. 2006). TANGO-B has another issue related to the non-linearity of the fluxes. For instance: 6h 6h 6h uðTair SSTÞ 6¼ u6h T air SST ð4Þ where u is the surface wind speed, and Tair is the surface air temperature. The left-hand side represents TANGO-A, while the right-hand side represents TANGO-B. The differences between TANGO-A and TANGO-B are further discussed in Sect. 6

3 Experimental set-up 3.1 Standard stand alone experiments: FA1 and FO1 Two experiments are performed using the stand alone atmospheric and ocean–sea ice models (see Table 1). The standard atmospheric experiment FA1 uses the same forcings of MAR as in Jourdain and Galleé (2010): the sea ice fraction, SST and lateral boundaries are prescribed from the 6-hourly ERA40 reanalysis (Uppala et al. 2005). The ocean–sea ice experiment uses the same forcings of NEMO as in Mathiot et al. (2010b): the surface conditions come from DFS3 (Drakkar Forcing Set 3, Brodeau et al. 2009) which combines precipitation and radiative fluxes from the CORE data set (Large and Yeager 2004) and wind, humidity and temperatures from ERA40. This experiment is referred to as FO1. The frequency of DFS3 is monthly for precipitation, daily for radiation, and 6-hourly for turbulent variables. A wind correction is performed in FO1 to obtain katabatic outflows in the coastal region stronger than in ERA40 (Mathiot et al. 2010a). Lateral open boundary conditions come from the global ORCA025-G70 experiment (Drakkar Group 2007), which is a NEMO run at a resolution of 0.25, forced by DFS3 from 1958 to 2004, and using the same parametrizations as in FO1. The ORCA025G70 experiment has a sea surface salinity relaxation to a

N. C. Jourdain et al.: Influence of coupling on atmosphere, sea ice and ocean regional models Table 1 Performed experiments

Experiment

Model

FA1

MAR

ERA40

ERA40

–

FO1

NEMO

DFS3

–

ORCA025-G70

FA2

MAR

FO1

ERA40

–

FO2

NEMO

FA2

–

ORCA025-G70

COA

TANGO-A

fluxes by MAR

ERA40

ORCA025-G70

COB

TANGO-B

fluxes by NEMO

ERA40

ORCA025-G70

climatology, to avoid a drift usually found in the ocean stand alone models (see Griffies et al. 2009). We do not use any sea surface salinity relaxation in the limited-area experiments of this paper, because the effect of coupling on the salinity drift is an issue of this paper. The evaluation of FA1 and FO1 is given in Sect. 4. 3.2 Mutually forced stand alone experiments: FA2 and FO2 Two other experiments are performed in a stand alone configuration, but forced by each other instead of by reanalysis (see Table 1). The atmospheric experiment FA2 is forced by SST and sea ice fraction from FO1. The lateral boundary conditions are ERA40, as in FA1. These atmospheric lateral boundary conditions are consistent with the surface ocean conditions that force FA2 since FO1 is driven by ERA40. The ocean–sea ice experiment FO2 is forced by the atmospheric fields from FA2. The open boundary conditions used in FO2 are the same as in FO1 (global ORCA025-G70 ocean experiment). A comparison between FA2/FO2 and the coupled experiments is used to highlight physical feedbacks (Sect. 5). 3.3 Coupled experiments: COA and COB Two coupled experiments are performed: COA and COB, from TANGO-A and TANGO-B, respectively, (see Table 1). Both are laterally forced by ERA40 for the atmosphere (as FA1 and FA2) and by the global ORCA025G70 experiment for the ocean (as FO1 and FO2). The major difference between COA and COB is that turbulent heat fluxes between the models are computed by MAR in COA, whereas they are computed by NEMO in COB (using different bulk formulae). The coupled experiments are evaluated in Sect. 4 and coupled mechanisms are analyzed in Sect. 5. 3.4 Domain and period of integration All the experiments analyzed in this paper use the domain of integration represented in Fig. 1. The boundaries of the

Surface boundary

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Atmospheric lateral boundary

Ocean lateral boundary

0

West Ice Sheet

Campbell Plateau Ross Sea

Ross Ice Shelf

Transa

ns

-1000

-2000

-3000

i nta

u

Mo

-4000

ntarctic

East Ice Sheet

-5000

-6000

Fig. 1 Bathymetry (in m, filled) and topography (contours every 500 m) in the domain of integration

domain are chosen as far as possible from the Ross Sea sector, following Giorgi and Mearns (1999). It is also oriented in such a way as to capture a part of the ACC. The domain also includes the Ross Ice Shelf, the Transantarctic Mountains, and the surrounding ice sheets because coastal winds are strongly affected by atmosphere dynamics over the continental ice. The period of integration is 1992–1993. We are aware that a 2-year experiment does not allow the spin-up of the deep ocean. However, we expect some relatively fast adjustments in surface ocean and in polynyas. The time necessary to differentiate the effects of coupling from the effects of surface and lateral boundary conditions is an important issue discussed at the end of this paper. A 2-year experiment allows to analyze the seasonal cycle of sea ice, and related formation of dense water near the surface, but it does not allow to analyze export of dense water formation from the Ross continental shelf. To limit the drift during the spin-up, a method is developed to build the initial state of the coupled experiments COA,B (Fig. 2). FA1 and FA2 are started on December 1, 1991 before running through 1992 and 1993 (the duration of the spin-up is much shorter in atmosphere models than in ocean models). To build the initial state of FO1, we use an output of the global ORCA025-G70 experiment on January 1, 1991. This global experiment has

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Fig. 2 Strategy developed for coupled simulations

previously been run from 1958, so that most of the adjustments related to the spin-up have already been achieved. FO1 is then run from January 1, 1991. The initial state of FO2 is an output of FO1 on January 1, 1992. The initial state of COA,B is the state of FA2 on January 1, 1992 for the atmosphere, and the state of FO1 on January 1, 1992 for the ocean. To summarize, we let 33 years of adjustment for the ocean in global configuration, and then one year of adjustment in limited-area configuration. The computational cost of the atmospheric model prevents us from running a long ocean simulation forced by the atmospheric model. The question of spin-up is further investigated throughout this paper.

in the configuration ORCA025 captures remarkably well the main currents, the fronts, sea surface height, and surface eddy kinetic energy. ORCA025 also struggles to correctly capture the sea ice cover and the surface layers in the Weddel Sea (Renner et al. 2009). Finally, Treguier et al. (2007) have shown that the structure of the ACC (Antarctic Circumpolar Current) and of the southern latitude fronts are well represented in the global experiment ORCA025-G70 (lateral boundaries of our experiments). As the ocean dynamics and structure in the limited-area simulations is very close to the associated global simulations (not shown), we did not make further comparisons to observations.

4 Evaluation of the models

4.2 Sea ice

In this section, the realism of our experiments is discussed using either previous work or comparisons to observations. The aim of this section is to answer the following questions:

The simulated total sea ice area in the whole domain is compared to that of Special Sensor Microwave Imagers data (SSMI, see Cavalieri and Parkinson 2008, and references therein) in Fig. 3. The initial state of the simulations (provided by the global simulation ORCA025-G70) is far from SSMI data. The models however manage to simulate a realistic sea ice area from April 1992 to October 1992, with a relative difference below 10% in FO1,COA and COB. But melting of sea ice is too fast in the simulations, and the sea ice area almost reaches its minimum at the end of January while it is reached in early March in SSMI data. As in the global simulation ORCA025-G70, the total sea ice area in FO1 is overestimated by 5–20% in austral winter, and underestimated in summer (almost no sea ice left in FO1 against 6% of winter sea ice surface in observations).

• • •

How realistic are the standard stand alone experiments (FA1 and FO1)? What are the skills of the coupled experiments (COA and COB)? Does coupling make the LAM closer to observations?

4.1 Ocean With the same configuration as in our experiments, but at a global scale, Barnier et al. (2006) have shown that NEMO

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N. C. Jourdain et al.: Influence of coupling on atmosphere, sea ice and ocean regional models 4.0

3.0

2.0

1.0

0.0

J F M A M J J A S O N D J F M A M J J A S O N D

1992

1993

Fig. 3 Daily sea ice total area (106 km2) in the domain of integration, from SSMI satellite data (green), FO1 (black), COA (red), and COB (blue)

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accommodated by multiscale fracturing/faulting processes (Rampal et al. 2008). The rheology has an impact on sea ice convergence, and the sea ice thickness could therefore be affected. Vancoppenolle et al. (2009) argue that the absence of tides in the model leads to underestimation of deformed ice production, and therefore to a negative bias in ice thickness. Simulated sea ice surface temperature in winter is shown in Fig. 5. A comparison between sea ice surface temperature from TANGO and those from infrared imagery (Comiso 2000, his Fig. 7) shows that the sea ice in COA is colder than in satellite data by 2–3 K between 70S and 75S, whereas sea ice in COB and FO1 is warmer than in satellite data by 2–4 K at those latitudes. As the uncertainty given by Comiso (2000) is 4 K, our simulations are thought to give reasonably realistic sea ice surface temperatures, and sea ice surface temperature is generally colder in COA than in COB and FO1. 4.3 Atmosphere

This bias is also found in the global simulation ORCA025G70 and using other configurations of NEMO (e.g. Mathiot 2009), but its origin remains an open question. The minimum sea ice cover in the domain of integration is 2,000 km2 in FO1, while it is 370,000 km2 in SSMI data. This bias is slightly reduced in the coupled experiments, where the minimum sea ice area is 40,000 km2 and 51,000 km2 in COA and COB, respectively. We use sea ice thickness data from the Antarctic Sea ice Processes and Climate program (ASPeCt, Worby et al. 2008) to evaluate sea ice thickness in the simulations. This data set is a compilation of individual ship-based observations over the period 1981–2005. There are no observations in the Ross Sea in 1992 and 1993, so we use the whole data set for comparison. Figure 4 shows the joint probability of simulated sea ice thickness and observed thickness at different seasons. The simulated thickness has been extracted at the nearest grid point corresponding to each measurement, and at the day corresponding to the observation (but for different years). Figure 4 shows that the simulated sea ice thickness in JFM is not well captured in our experiments, as is sea ice area. FO1 is noticeably worse than COA and COB in JFM. In contrast, the sea ice thickness is in relatively good agreement with observations in both COA and COB in AMJ and JAS, while it is significantly underestimated in FO1 in AMJ. As sea ice area, OND sea ice thickness is underestimated in the coupled experiments, and slightly better captured in FO1. Finally, almost no sea ice thicker than 1 m is simulated, whereas it reaches up to 2 m in ASPeCt data. This may be related to missing summer (multiyear) sea ice in the simulations, or to the sea ice model rheology. Indeed, the viscous-plastic equations do not represent the actual rheology: deformation

Using the same configuration and the same forcing set as in FA1, Jourdain and Galleé (2010) have shown that MAR alone has skills in representing katabatic winds and synoptic variability in the Ross Sea sector. MAR is however too cold and too dry over the ice sheet, especially near surface. Here we use AWS (Automatic Weather Stations) from the Department of Atmospheric and Oceanic Sciences at the University of Wisconsin-Madison (Stearns and Weidner 1992). We consider 3 h time series of air temperatures at 3 m above ground layer, and model temperatures are colocated with observations as in Jourdain and Galleé (2010). FA1 better captures surface air temperatures at the locations of ocean islands AWS (locations given in Fig. 5) than over the ice sheet (Table 2 and Jourdain and Galleé 2010 for ice sheet AWS). All the simulated temperature time series are strongly correlated to AWS temperature although the sites are located relatively far from the lateral boundaries. This shows that the models are able to transport the synoptic features through the domain. The temperatures further off-shore (Scott Island, Young Island) are less correlated to AWS data than in the coastal sites (Whitlock, Possession Island). The off-shore mesoscale meteorology has indeed more degrees of freedom, whereas the coastal mesoscale meteorology is significantly controlled by katabatic outflows from the Ross Ice Shelf and the Transantarctic Mountains. The annual mean temperature offshore is also better captured far from the coast (bias ^-1.1 K in FA1) than near the coast (bias ^-4.5 K in FA1). This suggests a seaward transport of the ice sheet cold bias by katabatic outflows. Off-shore temperatures in COA are nevertheless in good agreement with observations

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Simulated thickness (m)

2.0

JFM

JFM

JFM

AMJ

AMJ

AMJ

JAS

JAS

JAS

OND

OND

OND

1.6 1.2 0.8 0.4


0

1.6 1.2 0.8 0.4


0

1.6 1.2 0.8 0.4


0

1.6 1.2 0.8 0.4 0 0

0.4

0.8

1.2

1.6

0

0.4

Observed thickness (m) 0

40

80

120

COA

160

0.8

1.2

1.6

0

200

-30

-20

-10

0

10

COB - COA

20

0.4

0.8

1.2

1.6

2.0

Observed thickness (m)

Observed thickness (m) 30

-90

-60

-30

0

30

60

90

FO1 - COA

Fig. 4 Joint probability distribution of simulated and observed (from ASPeCt data) sea ice thickness (m) in the Ross Sea. The distributions are built as follows: a gaussian function of standard deviation r = 0.1 m is attributed to each point in the scatter plot simulated

versus observed; the gaussian functions are then summed. All years from 1981 to 2005 are used in the observations. Left COA, middle COB - COA, right FO1 - COA

even if the variability is slightly too strong. COB is too warm by about 3 K at the off-shore sites. Marine temperature time series of TANGO are closer to the observations than the temperature time series from

MAR alone, both in average and in correlation. This shows that the atmosphere over ocean is better simulated in TANGO than in MAR alone although there are more degrees of freedom in TANGO. The SST that is used in

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Table 2 1993 means and standard deviation of 3 h-temperatures (C) at 3 m above ground layer, at 4 AWS (locations on Fig. 5)

COA

AWS

Data

Mean

SD

Corr (AWS)

Young Island

AWS

-6.9

7.0

1

Scott Island

Possession Island

COB

Scott Island

Whitlock

+

Young Island

+ +

+

Poss. Island

Whitlock

FA1

-8.0

7.9

0.80

COA

-6.9

7.2

0.84

COB

-4.0

4.4

0.84

AWS

-7.8

7.1

1

FA1

-9.0

8.6

0.78

COA COB

-8.3 -5.4

8.4 5.3

0.83 0.83

AWS

-13.0

8.8

1

FA1

-17.4

7.7

0.89

COA

-17.9

8.3

0.90

COB

-15.2

7.0

0.90

AWS

19:8H

8:8H

1H

FA1

-24.3

9.8

0:87H

COA

-22.9

9.6

0:89H

COB

-22.6

9.3

0:88H

Related correlation coefficients are significant at the 99% level according to the Student test. Values marked by H indicate about 50% of missing values all along the year

FO1

-42

-38

-34

-30

-26

-22

-18

-14

-10

-6

-2

Fig. 5 Mean sea ice surface temperature (C) in August 1992. The location of AWSs used in Table 2 are represented by ?

FA1 is that used in ERA40 reanalysis, that is to say coarse fields. The ocean mesoscale features provided by the coupling has probably made the atmosphere more realistic over ocean. Simulated total precipitation over ocean is compared to those from GPCP (Global Precipitation Climatology Project, 2.5 9 2.5, Huffman et al. 1997). Precipitation over the ocean is about one half of GPCP precipitation in FA1 and in the coupled experiments (Fig. 6). Beranger et al. (2006) have shown that there is a high uncertainty regarding precipitation at these latitudes, but GPCP is in the lower range of the available data south of 50S. Note

that simulated precipitation over the ice sheet is in good agreement with GPCP (Fig. 6). It is difficult to accurately evaluate continental snow fall because blowing snow makes it hard to obtain reliable measurements (Eisen et al. 2008). However, Genthon et al. (2003) have shown that GPCP precipitation is reasonably realistic over the ice sheet, which is in agreement with Galleé et al. (2005). To summarize, MAR and TANGO have skills to produce realistic precipitation over the ice sheet and in the coastal region, but precipitation is likely to be underestimated over the deep sea. To summarize, the LAMs used in this paper generally give realistic simulations. We first conclude that both TANGO-A and TANGO-B are suitable for future modeling studies in Antarctica. Moreover, we have shown that the coupled simulations are slightly improved as compared to the standard stand alone configurations (winter sea ice, temperature over ocean). In the following section, we raise the question as to whether the behavior of TANGO comes from mutual forcing of MAR and NEMO or from the representation of physical feedbacks. 5 Assessment of the role of mutual forcing and feedbacks The experiments are compared at different locations. The role of coupling is first investigated in the region of the ACC (deep sea) because the absence of sea ice in this

123

1532


Fig. 6 Mean 1992–1993 total precipitation (thick contours every 0.5 mm/day). Left is MAR-FA1, and right is GPCP. The gray line represents the coast line. The thick dashed line represents the limit

separating the zone where mean snow falls are greater than mean rain falls in MAR-FA1. Area where mean precipitation is lower than 0.2 mm/day is shaded in gray

region make it easier to understand mechanisms. Then, the coastal ocean (Ross Sea) is investigated to analyze the role of sea ice in coupled processes. Finally, the impact of coupling on dense water formation in coastal polynyas is assessed. 5.1 Assessment of the role of coupling in the ACC zone (deep sea) Our first analysis focuses on the region of the ACC, that is to say in the deep sea, so that questions related to sea ice can be avoided at first order. Monthly fields are averaged in a box of 800 9 800 km and from the surface to 700 m depth (Fig. 7). The box is located to receive waters transported by the ACC at the end of their crossing through the domain of integration (from West to East). The crossing pathway is shown with currents integrated from surface to 700 m depth in Fig. 7. This choice allows to integrate at best the effects of coupling because a domain crossover from West to East in our simulations takes about 5 to 6 months near surface at 60S, and about 13 months near 500 m depth at 60S. Results are shown in Fig. 8 and Table 3. 5.1.1 Effects of mutual forcings First, the four stand alone experiments are compared to each others. The aim is to understand the part of the coupled experiments that is explained by the mutual surface forcing between MAR and NEMO (instead of usual forcing from reanalysis). The simulated atmospheric fields (Fig. 8a, b, d) are very similar in the four experiments, which shows that the atmospheric state in that area is mainly controlled by the lateral boundary conditions. Both FA1 and FA2 are colder and dryer than DFS3 (temperature and humidity from ERA40). This gives stronger loss of sensible and latent heat fluxes in FO2 (forced by FA2) than

123

V

0.2 m/s

Fig. 7 Mean 1992 currents integrated between surface and 700 m depth (filtered using a 10-point boxcar smoother). The ice sheet is shaded in black. The black box is that used for the averages shown in Fig. 8 and the gray zone is the Ross continental shelf zone used in Fig. 10. The green line represents the maximum sea ice extent in FO2

in FO1 (forced by DFS3) at the ocean surface. The first hundreds meters of the ocean thus become colder in FO2 than in FO1 (Fig. 8c). The freshwater flux brought by DFS3 precipitation into FO1 is close to GPCP data and almost 2 times higher than the freshwater flux brought by the atmospheric experiment FA2, which forces FO2 (Fig. 8d). Low precipitation from MAR explains the positive drift of FO2 salinity with regards to FO1 salinity (Fig. 8e). The mean precipitation in 1992 north of 60S (the waters that may go through the box) is actually 2.36 mm day-1 in DFS3 against 0.74 mm day-1 in FA2, which explains a mean salinity drift of 0.0024 psu month-1 in FO2 with respect to FO1. This is approximatively the value of the drift in Fig. 8e from June 1992 to February 1993; after February, the drift rate slightly decreases, which is an effect of the open


B

7.0 6. 5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0

5.2 5.0

surface air temperature

4.6 4.4 4.2 4.0 3.8 3.6 3.4 3.2

J F M AM J J A S O N D J F M AM J J A S O N D 3.9

C

T(°C)

3.5

precip (mm/day)

integrated ocean temperature

3.6 3. 4 3.3 3.2 3.1 3.0 2.9 2.8 2.7

J F M AM J J A S O N D J F M AM J J A S O N D

3.3 3.1 2.9 2.7 2.5 2.3 2.1 1.9 1.7 1.5 1.3 1.1 0.9 0.7

precipitation (rain+snow)


34.29

FO1

34.28

S(psu)


D

3.8 3.7

E

surface humidity

4.8

q (g/kg)

A

T(°C)

Fig. 8 Monthly fields averaged in the box represented in Fig. 7. a Atmospheric surface temperature, b atmospheric surface specific humidity, c oceanic mean temperature (integrated from surface to 700 m depth), d total precipitation, e mean salinity (integrated from surface to 700 m depth)

1533

34.27

integrated ocean salinity

FO2

FA1

FA2

COA

COB

DFS3

34.26 34.25 34.24 34.23 J F M AM J J A S O N D J F M AM J J A S O N D

Table 3 Mean 1992–1993 values of fields inside the box represented in Fig. 7 FA1 FA2 FO1

FO2

COA

COB

Tsurf air (C)

3.3

3.2

–

qsurf air (g/kg)

DFS3

–

3.3

3.3

3.7

3.9

3.9

–

–

4.0

4.0

4.2

precip (mm/day) 1.3

1.3

–

–

1.3

1.4

2.4

T700 ocean (C) – S700 ocean (psu) –

– –

3.39 3.21 3.27 3.32 – 34.244 34.268 34.267 34.265 –

boundaries that prevent the ocean model from further drifting. Note that the negative drift of FO1 salinity results from a cut-off of the salinity relaxation used in ORCA025G70 to build the initial state in this paper. 5.1.2 Effects of coupled processes At first order, COB follows FO2/FA2 in that area. TANGO in that box is therefore mainly constrained by the lateral

boundary conditions and by the parametrization of physical processes in the models. However, COB is not identical to FO2, which indicates that some local coupled processes are simulated. The upper ocean of COB is indeed warmer than that of FO2 from July 1992 to the end of the experiment. This can be linked to the slightly warmer and less dry air simulated in the coupled experiment from the second part of 1992 to the end of the experiment (Fig. 8a, b). A local coupled mechanism is proposed in Fig. 9 to explain the warmer and less dry air simulated in COB as compared to FA2. The cold and dry air in FA2 cools the upper ocean in FO2, but there is no feedback to the atmosphere. In contrast, the cooling of the upper ocean in COB reduces the temperature and humidity gradient between atmosphere and ocean. As a consequence, heat fluxes are reduced, and the cooling of the upper ocean is reduced in COB as compared to FO2. The impact of this local feedback on evaporation enables a stabilization of the ocean salinity (Fig. 8e), which prevents from using a sea surface salinity relaxation.

123

1534


Fig. 9 Heat exchange between atmosphere and ocean in the stand alone ocean model(black) and in the coupled model (gray, italic). The difference between black and gray schemes explains the warmer upper ocean in COB as compared to FO2

This local negative feedback provides to TANGO a better energy and humidity balance between atmosphere and ocean than in forced models. In fact, even using a perfectly realistic heat flux parametrization would induce biases in stand alone models because feedbacks are not simulated. The difference in salinity between FO2 and COB after February 1993 cannot be fully explained by the precipitation and evaporation budgets north of 60S (not shown). This difference is related to transport of waters influenced by sea ice formation at the south of the box (see following subsection). 5.2 Assessment of the role of coupling in the coastal ocean The second step of our analysis focuses on the Ross Sea sector, which corresponds to the Ross continental shelf shown by the shaded area in Fig. 7. This zone differs from the ACC zone (the box of the previous section) because it is far from the boundaries. It is also different because it is a coastal area, with a more marked seasonal cycle and an important sea ice cover. The same analysis as in previous section is performed, except that oceanic fields are integrated from surface to bottom (the latter varying between 700 and 1,000 m depth). Results are shown in Fig. 10 and Table 4. 5.2.1 Effects of mutual forcings Again in this area, the simulated atmospheric fields (Fig. 10a, b, d) are similar in the four experiments at first

123

order, even if differences in air temperature are higher than in the ACC box (Table 4). The ocean mean temperature drifts are almost the same as in the ACC box (Fig. 10c), for the same reasons as in previous section: a relatively cold and dry atmospheric component as compared to ERA40. There is consequently less sea ice formed in FO1 (forced by DFS3) than in FO2 (forced by FA2) in fall. However, the higher snow fall in FO1 leads to a significantly higher sea ice volume in winter. As opposed to the ocean temperature, the ocean salinity exhibits a behavior specific to coastal ocean. A large part of the excess of precipitation in FO1 (with respect to FO2) falls on the ice pack, and the winter ocean salinity therefore exhibits lower differences between FO1 and FO2 than in the ACC zone. But during the melting season, the excess of fresh water goes into the ocean in FO1, which explains the strong decrease of salinity in this experiment. 5.2.2 Effects of coupled processes The differences between FO2 and COB are stronger in this coastal area than in the ACC area. For instance, the summer air surface temperature is 2.5 K warmer in COB than in FA2 (Fig. 10A). The local feedback shown in Fig. 9 partly explains the differences between COB and FO2: warmer and more humid air, and more precipitations in COB. However, the differences in the coastal area are much stronger than the differences shown for the ACC zone, suggesting that other mechanisms are involved. First, we suggest that the mechanism of Fig. 9 could be extended to the air–sea ice exchange. Such a mechanism could explain colder air and sea ice surface in FO2 as compared to COA (Fig. 10a, g). The colder sea ice surface in FO2 therefore leads to more sea ice production than in COB (Fig. 10h). The effect of the local feedback is probably amplified over sea ice as compared to the effect over open ocean because of heat conduction within sea ice. Any SST anomaly is indeed quickly mixed in the ocean surface layer, whereas heat conduction within sea ice is relatively slow (the characteristic time of conduction through ice of thickness 0.5 m is estimated to 2.5 days). As a consequence, the feedback of surface temperature to heat fluxes could be stronger over sea ice than over open ocean in limited-area simulations. The sea ice melting is faster in COB than in FO2 (Fig. 10f,h). We suggest that open water fraction within the ice pack could also be involved in local feedbacks. First, an increase of open water area leads to a relatively warmer and moister air in COB but not in FO2 where feedbacks to the atmosphere are missing (Fig. 10a, b). Melting is therefore increased in COB, which constitutes a local positive feedback. The lower sea ice volume in COB as compared to FO2 explains the lower mean salinity


-7 -11

T(°C)

B

-3

2.6 2.4

surface air temperature

surface humidity

2.2 2.0 1.8

q (g/kg)

A

-15 -19

1.6 1.4 1.2 1.0

-23

0.8 -27

0.6 0.4

-31 J F M AM J J A S O N D J F M AM J J A S O N D

C

-0.40 -0.45

integrated ocean temperature

precipitation (rain+snow)

1.45

precip (mm/day)

-0.35


D 1.65

-0.25 -0.30

T(°C)

Fig. 10 Monthly fields averaged over the Ross continental shelf represented in Fig. 7. a Atmospheric surface temperature, b atmospheric surface specific humidity, c oceanic mean temperature (integrated from surface to 700 m depth), d total precipitation, e mean salinity (integrated from surface to 700 m depth), f sea ice mean fraction (% of the total area), g mean sea ice surface temperature, h sea ice volume (expressed as a sea ice thickness homogeneously distributed over the whole area)

1535

-0.50 -0.55 -0.60 -0.65 -0.70

1.25 1.05 0.85 0.65

-0.75 -0.80

0.45 J F M AM J J A S O N D J F M AM J J A S O N D

F

1.0

34.30

0.9

34.29

0.8

34.28

0. 7

fraction (%)

S(psu)

E

J F M AM J J A S O N D J F M AM J J A S O N D 34.31

34.27 34.26 34.25 34.24 34.23

0.6 0.5 0. 4 0.3

integrated ocean salinity

0.2 0.1

34.22

0

34.21 J F M AM J J A S O N D J F M AM J J A S O N D -4 -8

T(°C)

H sea ice surface temperature

0.8 sea ice volume

0.6

-12 -16 -20 -24

0.5 0.4 0.3 0.2

-28 -32


0.7

eq. thick. (m)

G

sea ice area

0.1 0


FO1

FO2

simulated in the coupled experiments as compared to FO2 (Fig. 10e). This difference in salinity is probably exported into the ACC, leading to the difference between COB and FO2 in 1993 (Fig. 8e). Time series of oceanic temperature and salinity profiles averaged over the Ross continental shelf are shown in Figs. 11 and 12 for COB and FO2. The effect of coupling on the vertical thermal and haline structure of the Ross Sea

FA1

FA2


COA

COB

DFS3

is strong within the ocean mixed layer depth (MLD, between 20 and 200 m depth). However, the change within the MLD is not sufficient to explain the differences between COB and FO2 seen in Fig. 10. Indeed, the bottom water properties are also strongly affected by coupling (Fig. 14). Waters are much denser over the western part of the Ross continental Shelf than over the eastern part, which is due to the presence of the Terra Nova Bay (TNB)

123

1536


Table 4 Mean 1992–1993 values of fields above the Ross continental shelf represented in Fig. 7

Tsurf air (C)

FA1

FA2

-19.9

-18.8

FO1

FO2

COA

COB

DFS3

-18.4

-17.3

-16.2

–

–

qsurf air (g/kg)

0.95

1.01

–

–

1.04

1.08

1.13

precip (mm/day)

0.70

0.72

–

–

0.79

0.78

1.24

T700 ocean (C)

–

–

-0.49

-0.66

-0.58

-0.61

–

S700 ocean (psu)

–

–

34.253

34.277

34.273

34.270

–

Sea ice fraction

–

–

0.65

–

Sea ice srf temp (C)

–

–

Sea ice vol. (m eq.)

–

–

0.63 -17.0 0.48

polynya and the Ross polynya. The coupled experiment COB produces less dense water than FO2 in the western region. This is in agreement with more sea ice production and therefore more salt rejection in FO2. This difference of bottom density remains small in 1993, but the latter could increase with time since waters reside a long time over the Ross continental shelf. This question is further developed in Sect. 6. The prominence of dense waters in the western part of the Ross continental shelf suggests that the differences between COB and FO2 is marked in Ross polynya and Terra Nova Bay polynya. This question is investigated in the following section. 5.3 Assessment of the activity of coastal polynyas in TANGO There are two major coastal polynyas in the Ross Sea: the Ross polynya and the Terra Nova Bay polynya (see ‘‘Introduction’’). We are aware that Ice Shelf Waters (ISW) are involved in the dynamics of the Ross polynya, a process that is not represented in our model. Results concerning the Ross polynya must therefore be regarded as a sensitivity test. As in the paper of Mathiot et al. (2010b), the simulated coastal polynyas are defined as: • •

the ice production is greater than 0.7 m per month the ocean depth if shallower than 1200 m

The Terra Nova Bay polynya is defined as the polynya located between 74.6S and 75.4S, and west of 167W, and the Ross polynya is defined as the polynya along the Ross Ice Shelf front, west of 180. These polynyas are created by the wind stress that export sea ice out of polynyas as soon as it is formed (e.g. Morales Maqueda et al. 2004). Thus, winds maintain an open area, where much sea ice is formed because of heat loss through longwave, latent and sensible heat fluxes. The Ross polynya is also partly maintained by inflow of relatively warm Circumpolar Deep Water onto the continental shelf (Fichefet and Morales Maqueda 1999). The ice production in the Ross polynya is plotted in Fig. 13. The ice

123

0.64 -18.7 0.43

0.68 -20.2 0.43

-17.1 0.38

– –

production of the two polynyas are significantly correlated between all the simulations (Table 5). This is not surprising because the cold air outflows from the ice shelf or from the Transantarctic Mountains strongly control Ross and TNB polynyas (Carrasco et al. 2003; Kurtz and Bromwich 1985). The wind stress on sea ice in polynyas is indeed very similar between all our experiments (not shown) because air outflows from the ice sheet are not significantly influenced by coupling. Some differences are nonetheless found between the amount of ice production in the polynyas of the three simulations. The sea ice production in polynyas is lower in COB than in FO2 (by 15% for Ross polynya), as over the whole Ross Sea (see previous section). The air over polynyas is slightly warmer in COB than in FO2 (by 0.6C in average from April to October over Ross polynya). Such differences could be explained by the feedback of Fig. 9. This feedback is amplified by the longwave upward flux (from open water) that warms the air in COB but not in FO1. The lower sea ice production in COB suggests that the production of dense water could be influenced by the coupling. This is an interesting concern because the dense bottom waters in the Ross Sea (the HSSW, High Salinity Shelf Waters) contribute to the formation of the Ross Shelf Bottom Waters (RSBW), the latter being involved in the global thermohaline circulation (Carmack 1977; Gordon and Comiso 1988; Orsi et al. 1999). The dense waters formed in Terra Nova Bay polynya and Ross polynya indeed follow the major topographic channels towards the deap seas (Fig. 14 and Budillon and Rintoul 2003; Mathiot et al. 2010b). The potential density of dense waters formed in polynyas is higher in FO2 than in COB, by up to 0.05 kg m-3 in the annual mean of 1993. Time series of the bottom potential density averaged over the Ross continental shelf (shaded area in Fig. 7) is represented in Fig. 15. The density of bottom waters is reduced from January to September 1992 because of local mixing and export towards the deep sea. Then, from September to January, the density of bottom waters increases

N. C. Jourdain et al.: Influence of coupling on atmosphere, sea ice and ocean regional models 0

0.5

40 0

DEPTH (m)

Fig. 11 Oceanic temperature averaged over the Ross continental shelf as a function of time and depth (C). Upper FO2, lower difference between COB and FO2. Lines represent the mixed layer depth (defined as the layer in which Dr\0:01 kg m3 ), for FO2 (dashed), and COB (blue)

1537

80 -0.5 120 -1 160

-1.5

200

240

FO2 -2

DEPTH (m)

J

F

M

A M

J

J

A S

O

N

D

J

F

M

A

M

J

J

A

S

O

N D

0

1.2

40

0.8

80

0.4

120

0

160

-0.4

200

-0.8

COB-FO2

240

-1.2 J

F

M

A M

J

J

A S

O

N

D

1992

in FO2, because dense waters are formed in polynyas during winter. The seasonal cycle of bottom potential density is delayed by about 6 months with the polynyas activity because of bottom advection towards the continental slope (the mean annual speed is a few cm s-1, whereas the characteristic time for convection of dense waters is 14 h for 700 m depth in the model). There is again a reduction of the density of bottom waters from January to May 1993 in FO2, when local mixing and export towards the deep sea become stronger the advection of dense water from polynyas. And then the density increases again in FO2 because of the advection of winter-time dense waters from polynyas. The only representation of coupled processes (see FO2 versus COB in Fig. 15) enables a difference of 0.47 kg m-3 in the bottom waters potential density at the end of the experiment. This difference is important for the future of water masses in the deep ocean. For instance, the bottom waters formed in FO2 can contribute to the formation of the RSBW (potential density r2 [ 37.18 kg m-3, following the definition of Orsi et al. 1999). In contrast, the bottom density in COB remains lower than 37.18 kg m-3 along the experiment, which would prevent these waters from reaching the bottom of the deep ocean.

J

F

M

A

M

J

J

A

S

O

N D

1993

Figure 14 shows that dense waters follow the topographic channels towards the deep ocean. But none of our experiments is long enough to simulate an export of dense waters into the deep ocean. This points out the need of a longer spin-up, which is further discussed in the following section.

6 Discussion 6.1 Differences between TANGO-A and TANGO-B Both TANGO-A and TANGO-B give realistic simulations although their coupling and heat flux parametrization differ, and we have concluded that both of them are suitable for future modeling studies in Antarctica. However, there is a need to understand the differences between COA and COB because they are of the same magnitude as the differences between COB and FO2 in region covered with ice. There is no strong difference between the two versions of TANGO over and in the ACC area (Fig. 8). In contrast, COA and COB exhibit large differences over the Ross continental shelf, COA being dryer, colder, and thus producing more

123


Fig. 12 Salinity averaged over the Ross continental shelf as a function of time and depth (C). Upper FO2, lower difference between COB and FO2. Lines represent the mixed layer depth (defined as the layer in which Dr\0:01 kg m3 ), for FO2 (dashed), and COB (blue)

DEPTH (m)

1538 0

34.35

40

34.20

80

34.05

120

33.90

160

33.75

200

33.60

FO2

240

33.45

DEPTH (m)

J

F

M

A M

J

J

A S

O

N

D

J

F

M

A M

J

J

A

S

O

N

D

0

0.3

40

0.2

80

0.1

120

0

160

-0.1

200

-0.2

COB-FO2

240

-0.3 J

F

M

A M

J

J

A S

O

N

D

J

F

M

A M

1992

Sea ice production (109 m3/month)

16

J

J

A

S

O

N

D

1993

Table 5 Mean, standard deviation, and correlation with FO2 of the sea ice production in polynyas (m month-1)

14

Polynya

Field

Exp.

Mean

SD r

Corr (FO2)

ROSS

Production (109 m3/month)

FO2

5.85

4.10

1

COA

4.53

3.82

0.82

COB

4.97

4.29

0.95

FO2

1.94

1.71

1

COA

0.61

0.98

0.63

COB

1.80

1.70

0.94

12 10 8

TNB

6 4 2

Production (109 m3/month)

Correlations are significant at the 99% level according to the Student test

0 J F M A M J J A S O N D J F M A M J J A S O N D

1992

1993 9

3

Fig. 13 Sea ice production rate in the Ross Polynya (10 m /month), smoothed using a 31-day filter. Black dashed represents FO2, red is COA, and blue is COB. Associated statistics are given in Table 5

sea ice (Fig. 10). The difference between COA and COB is also important in polynyas (Fig. 13), with impacts on dense water formation (Fig. 15).

123

As previously mentioned in Sect. 2, a first difference is that the ocean of TANGO-A receives 6 h-average heat fluxes, whereas TANGO-B compute heat fluxes using 6 haverage atmospheric fields. Using a stand alone MAR simulation with outputs every 30 min, we have computed latent and sensible heat fluxes at different locations and dates using bulk formula from Large and Yeager (2004). Computing a linear fit over more than 160,000 points, we find that:

N. C. Jourdain et al.: Influence of coupling on atmosphere, sea ice and ocean regional models 37.25 37.20 37.15 37.10 37.05

8 6h 6h 6h > > q0 cp Ch u6h SST Tair > > > < 6h ¼ 1:0007q0 cp Ch uðSST Tair Þ 0:0140 6h > > q0 Lv Ce u6h qsat 6h qair 6h > > > 6h : ¼ 0:9992q0 Lv Ce uðqsat qair Þ 0:0291

1539

ð5Þ

37.00

Ross polynya

36.95 36.90 36.85 36.80

TNB polynya

36.75 36.70

0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08

Fig. 14 1993 mean bottom potential density r2 in the Ross Sea, for FO2 (upper), and for the difference COB - FO2 (in kg m-3, reference at 2,000 m depth). Black contours are bathymetry, from surface to 800 m depth every 200 m (thin lines), and from 1,000 m depth to bottom every 1000 m (thick lines). Red contours are the ice sheet topography every 250 m

37.20 37.18

σ2 (kg.m-3)

37.16 37.14 37.12 37.10 37.08 37.06 J F M A MJ J A S O N D J F M A M J J A S O N D

1992

1993

Fig. 15 Time serie of the bottom potential density r2 averaged over the Ross continental shelf where the ocean is deeper than 500 m (because the denser waters follow the topographic channels). The black dotted line represents FO2, the red one represents COA and the blue one COB

where Ch and Ce are the transfer coefficients for sensible heat and latent heat, respectively, u the 10 m wind speed, SST the sea surface temperature, Tair the air temperature at 10 m, qsat the saturated specific humidity, qair the air specific humidity at 10 m, and q0, cp, Lv the usual physical constants to express fluxes in W m-2. Note that Ch and Ce depend on u and air stability following Large and Yeager (2004). For a typical flux of 100 W m-2, the coupling method in TANGOB would lead to a bias of about 0.1 W m-2. The exchange method within the coupling is thus unlikely to explain the differences between COA and COB. The difference cannot come from the sea ice albedo differently parametrized in COA and in COB because solar fluxes are close to zero in winter. The bulk formula used to compute turbulent heat fluxes over open ocean both in COA (as in MAR) and in COB (as in NEMO) has been compared: it appears that both parametrizations give similar heat fluxes in similar conditions (not shown). This indicates that the origin of the differences between COA and COB does not lay in the formulation of the heat flux parametrizations at the atmosphere–ocean surface. The influence of the parametrization of heat fluxes over sea ice is analyzed in Fig. 16. The latent heat fluxes between air and sea ice are weaker than sensible heat fluxes for each parametrization. The values of sensible heat flux are lower using MAR parametrization (COA) than using NEMO parametrization (COB) in the range Usrf (Tair - Tice) [ [-80; 80] m K s-1, but higher out of this range. The sensible heat flux in coupled configuration act as a relaxation between ocean and atmosphere temperatures, the relaxation being stronger in COB than in COA. Figure 16 suggests that the relatively stronger relaxation in COB can make the difference (Tair - Tice) smaller than in COA. As a consequence, larger values of (Tair - Tice) can be reached in COA. Note that the surface boundary layer can become very stable, and a discoupling between sea ice and air may appear (such an effect has been shown over the ice sheet in Jourdain and Galleé 2010). Another possible origin of this difference is the formula used to compute the latent heat flux derivative (used in Eq. 3) that is different in COB (Large and Yeager 2004) and COA (Clausius–Clapeyron, e.g. Stull 1988). Finally, we suggest that the computation of heat fluxes between atmosphere and sea ice has a significant impact on the sea ice production in winter, with a strong influence on dense water formation in the Ross Sea. The impact of heat flux parametrizations on the bottom waters potential density is indeed about twice the impact of coupling at the end

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Fig. 16 Upper contribution of atmosphere–sea ice sensible heat flux to the total sea ice energy (1019 J) as a function of Usrf (Tair - Tice) (m K s-1), for fluxes computed by MAR in COA (red) and for fluxes computed by NEMO in COB (blue). Lower contribution of atmosphere–sea ice latent heat flux to the total sea ice energy (1019 J) as a function of Usrf (qair - qsat(Tice)) (m s-1), for fluxes computed by MAR in COA (red) and for fluxes computed by NEMO in COB (blue). Every 6-hourly fields above sea ice in July 1992 have been considered for this plot

experiments simulating the Ross Sea ocean variability are compared in this section. All the experiments use the NEMO code. The first one is a global coupled experiment in which NEMO is coupled to the atmospheric European Centre Hamburg Model ECHAM-5 (Luo et al. 2003). The initial state of the ocean is taken from Levitus, the configuration is ORCA025 (as for the global experiment used in this paper as lateral boundaries and initial state), and the experiment is 60 years long. This experiment is referred to as D025. The second experiment has been performed by Mathiot et al. (2010b). It uses the same Ross Sea regional configuration and forcing as FO1, but the lateral boundary conditions and surface forcings of the year 1993 are repeated 12 years. This experiment is referred to as FOlong. The bottom potential density r2 on the Ross continental shelf of each experiments are compared to that of COB in Fig. 17. First, the method used to extract an initial state and boundary conditions in our experiments and in FOlong prevents a too large drift of the bottom density along the spin-up (Dr2 ¼ 0:80 kg m3 in D025 against 0.05 kg m-3 in FOlong). The use of a LAM also constrains the drift of the model, and makes the spin-up shorter (7 years for FOlong against 25–30 years for D025). As our coupled configurations (domain and parametrizations) are close to FOlong, an estimation of the spin-up in TANGO for this particular domain is 7 years. It is possible that coupling processes reduce the time of spin-up by better adjusting atmosphere and ocean surfaces as shown in Fig. 9. Further investigations using longer experiments will be needed to know the exact duration of the spin-up in TANGO.

37.1

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of our experiment (see COB–COA versus FO2–COB in Fig. 15). 36.7

6.2 Spin-up Although the initial state of the experiments has been chosen to avoid a too large drift along the spin-up, there is a spin-up due to coupling and to the absence of salinity relaxation in the LAM (relaxation which exists in the simulation producing the initial state). The question of spin-up remains a major concern for future work with limited-area atmosphere–sea ice–ocean coupling in Antarctica. To bring some elements of answer, different

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Fig. 17 Bottom water potential density r2 (kg m-3) over the Ross continental shelf as a function of time (years) for D025 (black, monthly means), FOlong (green, annual means), ans COB (blue, monthly means). The configuration does not simulate the ocean south of 78S in D025


6.3 Limitations of TANGO Some physical features are not represented in both forced and coupled models. First, ocean beneath the Ross Ice Shelf is not simulated; this could be important for water masses properties and the Ross polynya (Beckmann and Goosse 2003; Olbers and Hellmer 2009). Secondly, icebergs melting induced freshwater and latent heat flux are not represented in our experiments (see Jongma et al. 2009). Some missing processes in the ocean model might also affect the properties of the coastal ocean surrounding Antarctica. First, water exchange between deep ocean and coastal ocean is not well captured, both because of inaccuracy of deep overflows representation, and because of missing tidal currents and internal tides in our configuration. Finally, there is no biology in the modeled ocean although phytoplankton blooms play an important role in solar absorption and therefore on the convection and on the circulation around Antarctica (e.g. Lengaigne et al. 2009). We are aware that all these missing processes and forcings in the models might affect the realism of the experiments, but we think that our conclusions remain valuable. Moreover, this paper is a first step in the development of TANGO, which will probably be a suitable model for developing and testing new parametrizations. Note that the coupling method can also be improved by iterating ocean–atmosphere exchange (Lemarie´ et al. 2010).

7 Conclusion In this paper, we have built the model TANGO by coupling MAR to NEMO. The coupled model captures the main features of the Ross Sea sector: the sea-ice seasonal cycle is well represented even if the melting season is too fast, dense water is formed in polynyas, and atmospheric temperature variability is in good agreement with observations. The realism of air surface temperature and sea ice has even been slightly improved in the coupled simulations as compared to stand alone simulations. The cold and dry bias of MAR, that is found in our stand alone configurations, is reduced by the local feedback shown in Fig. 9. Local atmosphere–ocean feedbacks in the ACC zone are found to significantly influence ocean temperature and salinity. In stand alone ocean configuration, the dry and cold air produces a cooling of the ocean through sensible and latent heat loss. In coupled mode, the atmosphere is in turn moistened and warmed by the ocean; sensible and latent heat loss is therefore reduced as compared to the stand alone simulations. The ocean heat and salinity content are also significantly affected by the coupling, both in

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the first hundreds meters of the ACC and in the coastal ocean. We remind that a sea surface salinity relaxation is used in most of the global experiments in order to avoid salinity drifts (Griffies et al. 2009). Here we show that without any salinity relaxation in TANGO, there is a stabilization of the ocean salinity drift, which is due to the representation of feedbacks between atmosphere and ocean. The effects of local feedbacks are increased in presence of sea ice. It is suggested that heat conduction within sea ice could amplify the feedbacks. We find that local feedbacks result in less sea ice production in coupled mode, in particular in polynyas. Consequently, less deep water is formed in coupled mode. The atmosphere is found to be less sensitive to local feedbacks than the ocean. Comparisons between forced experiments with the same lateral boundary conditions but with different surface forcings show that most of the atmospheric variability is related to synoptic scale forcing from the lateral boundaries, even if the coupling significantly improves the atmospheric temperatures. Sea ice and ocean are more influenced by local surface forcings than the atmosphere (Figs. 8, 10). The lateral boundary conditions however prevent drifts from being too long and too large along the ocean spin-up. The choice of the initial state also helps to reduce its amplitude. The spin-up time in our configuration of TANGO has been roughly estimated to about 7 years. The local feedbacks captured by TANGO in the polynyas tend to decrease the density of bottom waters on the Ross continental shelf. This is significant even after 2 years of integration, although longer simulations will be needed to accurately quantify the effect of coupling. This is nevertheless a key feature because bottom waters on continental shelves around Antarctica contribute to the thermohaline circulation. However, the effect of turbulent heat flux parametrization over sea ice is larger than the effect of coupling in the dense water formation process. This points out the need of further investigations concerning turbulence and heat fluxes parametrizations. In this paper, we have analyzed the effects of local coupling. Ocean–atmosphere feedbacks also play a role in the atmospheric and oceanic general circulation. For instance, the strength of the Atlantic meridional overturning circulation is affected by ocean–atmosphere global feedbacks Griffies et al. 2009). Such impacts are missing in our limited-area study. However, we have shown that the effects of limited-area coupling are significant, even if lateral conditions strongly constrain the solution of the model. Finally, the use of limited-area coupling allows to keep the model in phase with observations, while keeping local atmosphere–sea ice–ocean feedbacks. TANGO is thus an interesting tool to develop or to tune

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parametrization of physical processes (such as turbulent heat fluxes) when comparisons to observations are needed. Acknowledgments We would like to thank Arnaud Caubel, Wonsun Park and Jean-Marc Molines for their technical help. The coupled global simulation D025 was kindly provided by Se´bastien Masson. Numerical experiments were performed at IDRIS and MIRAGE computational centers. This work has benefited from useful comments of 2 anonymous reviewers.

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