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Validation of three global ocean models in the Weddell Sea

Angelika H. H. Renner a,b,∗, Karen J. Heywood b Sally E. Thorpe a a British

Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, Cambridge, CB3 0ET, UK

b School

of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK

Abstract We present a validation of three global z-level eddy permitting/resolving ocean general circulation models against hydrographic observations and remote sensing sea ice data from the Weddell Sea. The Weddell Sea is a region in which complex processes such as water mass formation, sea ice formation and melt, and circulation under ice shelves take place. The representation of these processes is challenging even for the current generation of eddy-permitting ocean models. Simulating the hydrographic structure of this basin is a stringent test for models, notably so when considering the global influence of the regional processes. The performance of OCCAM (at two resolutions), ORCA025 and TPAC are tested regarding water mass properties, sea ice seasonality (OCCAM and ORCA025 only) and volume transport. OCCAM simulates the deep water masses reasonably well in both resolutions. The eddy resolving run is not significantly better than the eddy permitting simulation. ORCA025 and TPAC represent the surface layers and the Weddell Gyre circulation

better but are generally too warm throughout the water column. All models underestimate Weddell Sea Bottom Water formation. Both OCCAM and ORCA025 struggle to correctly model the sea ice cover: OCCAM overestimates the summer ice extent while little multi-year sea ice remains in ORCA025. TPAC exhibits considerable drift in potential temperature during the model run. The choice of a model for a study has to be made carefully taking into account the model’s performance in the specific area, application and variable of interest. We identify several starting points for improving the models, namely model numerics and parameterisations of subgridscale processes, ensuring more accurate forcing datasets, correct initialisation, and ice-ocean interactions, all of which are likely to have larger benefits than simply increasing the horizontal resolution beyond eddy permitting. Key words: global ocean model, Weddell Sea, hydrography, sea ice, deep water masses, Southern Ocean

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1

Introduction

2

Various complex processes such as deep and bottom water formation, sea ice formation

3

and melt, circulation under ice shelves and mixing make the simulation of the Weddell Sea

4

a challenge for ocean models. A correct representation of the Weddell Sea is, however, of im-

5

portance for the simulation of the global oceans (e.g. Hellmer et al., 2005). The dense water

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masses exported from the Weddell Sea are important drivers of the thermohaline circulation

7

and contribute significantly to the ventilation of the deep oceans (Hellmer and Beckmann,

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2001; Jacobs, 2004). They are formed from saline Warm Deep Water (WDW), the Weddell

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Sea variation of Circumpolar Deep Water, and fresh and very cold shelf waters (Foster and

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Carmack, 1976; Foldvik et al., 1985; Weppernig et al., 1996). The shelf waters interact with

11

the atmosphere in coastal polynyas, where heat loss to the atmosphere and brine rejection

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during sea ice formation lead to the formation of High Salinity Shelf Water (HSSW). A second ∗ British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, Cambridge, CB3 0ET, UK Email address: [email protected] (Angelika H. H. Renner).

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form of shelf water modification occurs underneath ice shelves, where supercooling and melt of

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the ice shelf base produces the extremely cold and rather fresh Ice Shelf Water (ISW). At the

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continental slope, the shelf waters mix with WDW and form Weddell Sea Deep and Bottom

16

Water (WSDW and WSBW, respectively). The downslope flow of these cold and dense water

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masses has been observed along the southern and western Weddell Sea (e.g. Fahrbach et al.,

18

2004). The sea ice cover in the Weddell Sea is highly seasonal. While the release of freshwater

19

during the ice melt in summer is important for the modification of Antarctic Surface Waters

20

(AASW) in the central and northern Weddell Sea, brine rejection during ice formation aids the

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formation of dense water on the shelves (Gill, 1973). Ice formation rates are particularly high

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in coastal polynyas in front of the ice shelves in the southern and southwestern Weddell Sea

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(Renfrew et al., 2002).

24

Teleconnections between the Southern Ocean and the North Atlantic were previously re-

25

ported by Hellmer et al. (2005). They used the adjoint method to include high resolution model

26

results for the Weddell Sea in a global ocean circulation model. They found that improving the

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simulation of processes in the Weddell Sea has a major impact on the export of WSDW and

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the location and transport of the Antarctic Circumpolar Current (ACC). Colder and relatively

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fresh water spreads into the South Atlantic and changes in the North Atlantic are reported.

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This reinforces the importance of correctly simulating the Weddell Sea in global ocean and

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climate models.

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Stand-alone ocean general circulation models (OGCM) are valuable tools to investigate

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smaller scales and improve parameterisations, numerics and the overall representation of oceanic

34

processes. Climate models also benefit from better, higher resolution ocean models (Roberts

35

et al., 2008). With better codes, parameterisations, topography and forcing datasets, and higher

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resolution, OGCMs have greatly improved. However, detailed and correct representation espe-

37

cially of polar processes which are crucial on a global scale remains difficult. To improve the

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simulation of water mass formation and modification in polar regions, sea ice models are cou-

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pled to OGCMs. Sea ice models still struggle to realistically represent the ice cover, both in

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the Arctic and the Southern Ocean and especially with respect to ice thickness (e.g. Timmer-

41

mann et al., 2004; Holland and Raphael, 2006; Martin and Gerdes, 2007; Renner and Lytle,

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2007). These areas are also important for the heat flux between atmosphere and ocean and the

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overturning circulation (Marsland et al., 2007) as the ocean loses heat to the atmosphere and

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shelf waters become cooler. St¨ossel et al. (1998, 2002) demonstrated effects of Antarctic sea

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ice on the simulation of a global OGCM. Amongst other aspects, they show how tuning of a

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parameter such as sea ice salinity changes Antarctic Bottom Water formation. In addition to

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modelling specific difficulties, validation of the models in polar regions suffers from the scarcity

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of observational data. Therefore, one of the aims of the World Ocean Circulation Experiment

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(WOCE) was to obtain a baseline dataset, including hydrographic sections across the Weddell

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Sea. The recent developments in modelling and observations make this an opportune time to

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revisit the performance of ocean models in polar regions and in particular in the Weddell Sea.

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In this paper, we test the performance of three global ocean general circulation models in the

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Weddell Sea and suggest possible reasons for the different model behaviours. First, we present

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the models used in the comparison, followed by a description of the observational datasets. The

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Weddell Sea hydrography and the sea ice cover in the models are compared between models

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and with observations in the fourth section. The discussion brings together the similarities and

57

differences of the models and we propose mechanisms leading to the model characteristics. At

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the end of the discussion, we present key points for improving OGCMs. The conclusion gives

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an overview of the benefits of each model and suggests the application where each model is

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most suitable.

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2

The Models

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For this study we use output of three global ocean models, one of them at two horizontal

63

resolutions. All are z-level models with partial bottom cells, and two include sea ice. This section

64

gives a brief overview of their characteristics, with the main details summarised in Table 1.

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2.1

OCCAM

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The Ocean Circulation and Climate Advanced Modelling (OCCAM) project model run by

67

the National Oceanography Centre, Southampton, is a global ocean general circulation model

68

(OGCM) of the Bryan-Cox-Semtner type (Bryan, 1969; Semtner, 1974; Cox, 1984) coupled with

69

a dynamic-thermodynamic sea ice model (Aksenov, 2002). The sea ice dynamics are based on an

70

elastic-viscous-plastic formalism. The thermodynamics use one vertical layer for snow and one

71

or two layers for thin and thick ice, respectively. The ice albedo is set to 0.66. Details are given

72

by Webb et al. (1998), Webb (2000) and Coward and de Cuevas (2005). The model exists in

73

different resolutions, both in the horizontal and the vertical. We use the eddy-permitting 1/4◦ -

74

and the eddy-resolving 1/12◦ -versions with 66 z-levels with partial step bottom layer (OCCAM

75

1/4 and OCCAM 1/12 respectively). This corresponds to a grid spacing of approximately 6

76

x 9.3 km at 50◦ S and 2.1 x 9.3 km at 77◦ S for OCCAM 1/12, 17.9 x 27.8 km at 50◦ S

77

and 6.2 x 27.8 km at 77◦ S for OCCAM 1/4, and layer thicknesses of 5 m at the surface

78

to 208 m for the deepest layer for both models. The model runs were forced using 6-hourly

79

winds and heat fluxes from the NCEP/NCAR reanalysis data (Kalnay et al., 1996; Large et al.,

80

1997), and monthly cloud fraction and solar radiation from the International Satellite Cloud

81

Climatology Project. Monthly precipitation is based on Microwave Sounding Unit data and

82

blended with Xie and Arkin (1997) observational data. The bathymetry is constructed from

83

Smith and Sandwell (1997) and DBDB5 (DBDB5, 1983). Sill depths of important straits are

84

checked and adjusted manually (Coward and de Cuevas, 2005). The runs for both resolutions

85

were initialised using WOCE Hydrographic Program Special Analysis Centre (SAC) climatology

86

(Gouretski and Jancke, 1996) for potential temperature (θ) and salinity (S) with additional data

87

for the Arctic Ocean. Sea ice in the Southern Ocean was set to 1.50 m thickness of sea ice and

88

0.15 m snow in all cells south of 65.25◦S with an ice concentration of 99% in each affected grid

89

cell. Both OCCAM 1/12 and OCCAM 1/4 were run for 20 years corresponding to January

90

1985 to December 2004 (runs 401 and 103, respectively).

91

2.2

ORCA025

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ORCA025 is a global configuration of the ocean model OPA (Oc´ean PArall´elis´e; Madec

93

(2006)) coupled with the Louvain la Neuve sea ice model LIM2 (Fichefet and Morales Maqueda,

94

1997). LIM2 is a dynamic-thermodynamic sea ice model with three layers (one for snow and

95

two for ice) and a viscous-plastic rheology. Ice albedo is parameterised following Shine and

96

Henderson-Sellers (1985) with modifications (Greenfell and Perovich, 1984). The model run

97

used for this study (run G70) was performed at the Laboratoire des Ecoulements G´eophysiques

98

et Industriels (LEGI) in Grenoble. For ORCA025, the global, tripolar Mercator grid has a

99

nominal horizontal resolution of 1/4◦ at the equator. In our study region, the grid spacing

100

therefore varies from 17.9 × 17.9 km at 50◦ S to 6.2 × 6.2 km at 77◦ S. The 46 vertical levels

101

have a thickness of 6 m at the surface increasing to 250 m for the lowest level. The model

102

uses partial bottom cells. The setup follows Barnier et al. (2006) except for the vertical mixing

103

where a modified version of the turbulent kinetic energy (TKE) parameterisation for the vertical

104

mixing coefficient was used that includes the effects of long waves, Langmuir cells and the

105

penetration of TKE in depth (Molines et al., 2006, updated 2007).

106

Run G70 was forced by the Drakkar Forcing Set No. 3 (DFS3; Molines et al. (2006, updated

107

2007); Brodeau et al. (submitted)). DFS3 is compiled from various sources. Long- and shortwave

108

radiation fields are from CORE (Coordinated Ocean-Ice Reference Experiments) forcing (Large

109

and Yeager, 2004). Precipitation south of 30◦ N is from the GXGXS (uncorrected CORE)

110

dataset and CORE north of 30◦ N. Air temperature, wind and air humidity are 6-hourly ERA40-

111

reanalysis fields (1958-2001) and ECMWF(2002-2004). River runoff is implemented from Dai

112

and Trenberth (2002) using a new dataset including coastal runoff. For a comparison of G70

113

with runs using the standard CORE forcing see Barnier et al. (2007). The model bathymetry is

114

regridded from ETOPO2 merged with the Bedrock Mapping Project data (Lythe and Vaughan,

115

2001) south of 72◦ S and GEBCO on the continental shelves (i.e. in depths shallower than 200

116

m). Additional smoothing and manual editing in key regions has been performed (Molines et al.,

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2006, updated 2007). θ and S were initialised from Levitus et al. (1998) in mid to low latitudes

118

and the Polar Science Center Hydrographic Climatology (PHC) v2.1 (Steele et al., 2001) in

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high latitudes. The initial sea ice state was taken from a previous run after 10 years of spin up

120

(Molines et al., 2006, updated 2007). The model is run for 47 years corresponding to the years

121

1958 to 2004.

122

2.3

TPAC

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The OGCM of the Tasmanian Partnership for Advanced Computing (TPAC) is based on

124

the Geophysical Fluid Dynamics Laboratory’s (GFDL) Modular Ocean Model (MOM), version

125

3.1. It is a primitive equation, ocean only model extending from 80◦ N to 80◦ S. Horizontal

126

resolution is 1/8◦ × 1/8◦ corresponding to 8.9 x 13.9 km at 50◦ S and 3.1 x 13.9 km at 77◦

127

S. In the vertical, the model has 24 z-levels with minimum layer thickness of 22 m at the

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surface and 489 m at the bottom, with partial bottom cells. Unusual for an eddy-resolving

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model (and different from OCCAM 1/12), TPAC includes isopycnal mixing by using the Gent

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and McWilliams (1990) parameterisation. Sea ice is not considered. More details are given by

131

Meijers et al. (2007).

132

For the run we use in this study, the model is forced by applying hemispheric wintertime

133

boundary conditions to which NCEP-R2 variability and trends (Kanamitsu et al., 2002) were

134

added. Sea surface temperature is constrained to the NOAA Optimum Interpolation Sea Surface

135

Temperature V2 (Reynolds et al., 2002). Surface salinity is adjusted by a salt flux calculated

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using the NCEP-R2 latent heat flux and precipitation rate. The time scale for both sea sur-

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face temperature and salinity restoring is 30 days. The model is initialised from the WOCE

138

Hydrographic Programme SAC atlas (Gouretski and Jancke, 1998) and Levitus (1982) data

139

in the Arctic. The model bathymetry is based on the TerrainBase dataset (Row et al., 1995).

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Integration time is 20 years, corresponding to 1982-2001, using asynchronous time stepping.

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3

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3.1

The observational datasets and extracted model output Hydrographic sections

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θ and S data were selected from hydrographic transects made in the Weddell Sea between

144

Joinville Island and Kapp Norvegia onboard RS Polarstern between 1989 and 1998 (Table 2; Fig.

145

1, Fahrbach et al. (2007)). They were designated section SR04 of the WOCE Repeat Sections

146

Program. Fahrbach et al. (2004) present details of the measurements and instruments as well

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as a description of the data. Using monthly mean fields, which correspond in time with the

148

months the CTD data were collected, model θ and S were bilinearly interpolated from the four

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nearest grid points onto the location of the CTD casts. As each model has a different vertical

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resolution, we then linearly interpolated the model data to fit the CTD depth grid (converted

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to depth from a 2 dbar pressure grid), thereby ensuring consistency between the models when

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calculating model to CTD differences and statistics.

153

Following Fahrbach et al. (2004) we calculate the area occupied on the sections by WDW,

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WSDW and WSBW, as well as the average θ, S and depth for each water mass. We use the

155

same criteria as Fahrbach et al. (2004): WDW is the water below the mixed layer with θ > 0◦

156

C, WSDW lies below that and has −0.7◦ C < θ ≤ 0◦ C, and WSBW is at the bottom of the

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water column with θ ≤ −0.7◦ C.

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In addition to the sections across the Weddell Sea Fahrbach et al. (2004) use for their

159

analysis, we include the section SR04e (April 1998) to maximise the available validation data.

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Since the section does not extend all the way onto the shelf in the eastern Weddell Sea, the

161

derived water mass properties (average θ, S, depth and occupied area) are biased and cannot

162

be compared directly with the results from the previous sections. For example, θ and S will

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be lower than in the other sections as water is transformed while flowing on and along the

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continental shelf and slope by interaction with ice shelves and sea ice formation.

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Observations in regions such as the Weddell Sea are often compromised by circumstances

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encountered during the cruise and by time limitations. Station spacing therefore varies between

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the different occupations of the SR04 section. When directly comparing a CTD section to the

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model section at the respective time, we extract and interpolate the model data onto exactly

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the set of CTD station positions of each of the five sections. For the timeseries of water mass

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properties we show the range of values for the four sets of stations excluding the station set of

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section SR04e. Representative of the five occupations of the SR04 transect, we present the full

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depth section and the θ − S diagram of the spring section SR04b in Figs. 2 and 3.

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3.2

Sea ice data

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Sea ice has been observed from satellites using passive microwave radiometry since the

175

1970s (e.g. Parkinson, 2004). For this study, we use monthly sea ice concentrations derived

176

using the Bootstrap algorithm from brightness temperatures measured by Special Sensor Mi-

177

crowave/Imagers (SSM/I) (Comiso, 1990, updated 2007), available from the National Snow

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and Ice Data Center. We restrict the region considered for extent and area calculations to the

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Weddell Sea, namely from the east coast of the Antarctic Peninsula and east of 58.5◦ W to

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10◦ W (Fig. 1a)). Ice extent is defined as the area of all grid boxes covered by ice whereas ice

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area is the area of the grid boxes covered by ice multiplied by the respective ice concentration.

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As satellites struggle with recognising very low concentrations, we use a threshold value of 15%,

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below which concentrations are not considered for calculations of ice extent and area.

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4

185

4.1

Results OCCAM 1/12

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OCCAM 1/12 captures the general features of the Weddell Sea hydrography (Figs. 2 and 3):

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The AASW lies above WDW below which θ decreases again with depth through WSDW and

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WSBW. There are, however, several deviations from the observations. In general, the depth of

189

the pycnocline and thereby the mixed layer is too deep and too cold in summer. In the CTD

190

sections, the surface mixed layer (ML; defined as the layer from the surface to the depth where

191

the gradient in potential density is greater than 0.02 kg m−3 per 10 db) extends down to 50 to

192

100 m in the central Weddell Sea (Fahrbach et al., 1992). In OCCAM 1/12, the average ML

193

depth in the deep Weddell Sea (i.e. bathymetry deeper than 4000 m) ranges from approximately

194

140 m at the start of the run to around 230 m after 15 model years and extends to 300 m in

195

the western part of the Weddell Sea.

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Beneath the mixed layer on the eastern side of the section, the modelled θ is colder then

197

the observations to a depth of 2500 m (Fig. 2). This signal can be attributed to the opening

198

of a polynya in the Maud Rise region shortly after the beginning of the model run. Following

199

the opening of the polynya in the OCCAM 1/12 sea ice cover in July 1985, a patch of water

200

characterised by a low WDW θmax appears in the eastern Weddell Sea in August 1985. The

201

polynya persists until early summer when the sea ice retreats. It recurs in 1986 and the WDW

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θmax decreases further. In the following model years, the cold water signal slowly moves around

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the Weddell Gyre.

204

Averaged over the entire WDW layer along the section, θ is higher than observed (Fig. 4).

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As the mixed layer is too thick, the area occupied by WDW is smaller than observed. Above

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the continental slope, both in the western and the eastern Weddell Sea, the downward slope

207

of the thermocline is less pronounced than in the observations leading to a core of water up to

208

1◦ C warmer situated above the 1000–3000 m isobaths. The cold water plume of newly formed

209

WSBW on the western continental slope which is visible in the CTD sections (Fahrbach et al.,

210

2001) is not resolved in the model (Fig. 2).

211

The area covered by WDW is underestimated in OCCAM 1/12 in all sections. After a

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decrease in WDW area percentage during the first five years of the model run, the amount

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of WDW increases again after 1990 but does not reach the initial level (Fig. 4). Average θ

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and S increase slightly during the run. The increase of the average depth is more pronounced.

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Contrary to WDW, the area percentage of WSDW increases in 1985-1990 and decreases after

216

that (Fig. 5). WSDW is overestimated at the cost of WDW. The area percentage of WSDW

217

remains greater than observed. Average θ and depth are fairly constant but S increases over

218

the model run. The area occupied by WSBW in OCCAM 1/12 is very close to observations,

219

as is the average depth. θ is on average about 0.03◦ C warmer in the model, excluding the

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last section SR04e (April 1998). For this section, the CTD data show much lower average θ

221

and S as the warmer and saltier eastern Weddell Sea is not included completely. Bottom water

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production in the southern and western Weddell Sea leads to an asymmetric distribution of

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WSBW with more WSBW in the western part. Although this pattern is present in OCCAM

224

1/12 as well, it is rather weak, which indicates underestimated bottom water formation. The

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total amount of WSBW along the section line fits the observations well though. It decreases

226

during the model run (Fig. 6). As for WDW and WSDW, the average WSBW S increases.

227

WSBW also becomes warmer and deeper.

228

The seasonal cycle is most visible in the sea ice cover (Figs. 7 and 8). In OCCAM 1/12,

229

the climatological minimum ice extent occurs in February, agreeing with observations, and the

230

maximum is in August, a month earlier than observed in the SSM/I climatology. Although the

231

annual cycle in the model agrees well temporally with the observations, the extent of the ice

232

cover is far overestimated by OCCAM 1/12 (Fig. 7 a). The ice extent at its minimum reaches

233

2.2 × 1012 m2 , almost 70% more than observed. Instead of a small area of high concentration of

234

sea ice in the southwestern Weddell Sea and near the Antarctic Peninsula, the ice spreads over

235

the entire Weddell Sea (Fig. 8). However, the summer ice cover is different in OCCAM 1/12

236

before and after 1994. In the first part of the run the ice extent reaches a summer minimum

237

of 5.5 × 1010 m2 in 1990 (Fig. 7 b). After 1994, the seasonal variations are small and ice

238

concentration and extent remain high even in summer. During winter, ice concentrations are

239

at 99%, the maximum value allowed in the model, over most of the Weddell Sea. Only at the

240

ice edge, which is too far north, and close to the coast at the tip of the Antarctic Peninsula

241

and the South Shetland Islands, is the ice less dense (Fig. 8). Concentrations remain too high

242

near the coast in the central Weddell Sea and prevent the formation of coastal polynyas such

243

as that observed in front of the Ronne-Filchner ice shelf (e.g. Renfrew et al., 2002).

244

4.2

OCCAM 1/4

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The representation of the central Weddell Sea in OCCAM 1/4 resembles that of OCCAM

246

1/12. The main water masses are present with similar properties to those of OCCAM 1/12

247

(Figs. 2–6) and their properties fit the observations well. As in OCCAM 1/12, the mixed layer

248

is too deep (Fig. 2) and gets deeper during the model run. During the first nine years of the

249

model run (1985-1993), OCCAM 1/4 is more saline in the mixed layer than OCCAM 1/12.

250

Also, while in OCCAM 1/12 the upper two layers experience a warming over the entire section

251

in summer during that period, OCCAM 1/4 does not show this behaviour. After 1994 the

252

surface layers warm significantly only twice (in summer 1999/2000 and 2001/2002), and on

253

these occasions the warming is visible in both resolutions but only over the continental shelf

254

and slope. In OCCAM 1/4, the surface layer is at or close to the freezing point at the times of

255

all sections.

256

The amounts of WDW (WSDW) in OCCAM 1/4 show only a small increase (decrease)

257

from 30 to 35% (58 to 57%) during the model run. Only WSBW significantly decreases: the

258

occupied area drops from 11% to below 7%, a reduction by more than a third of the initial

259

amount. The WSDW is slightly deeper in the lower resolution version, occupies less area along

260

the section than in OCCAM 1/12 and is warmer. WSBW is also deeper in OCCAM 1/4, but

261

θ, S and the amount of WSBW are similar to OCCAM 1/12.

262

The different behaviour of the two runs in the surface layer coincides with differences in

263

the ice cover. The seasonal cycle in the sea ice of OCCAM 1/4, although present, is far less

264

pronounced than in the satellite observations (Fig. 7 a). The climatological minimum ice extent

265

in February is 3.2 × 1012 m2 , a reduction of less than 30% of the August maximum. The

266

observations show a far larger seasonal cycle where in summer the ice cover is reduced by about

267

70% of the winter extent. As with the higher resolution version, OCCAM 1/4 produces dense

268

pack ice with maximum ice concentrations in winter (Fig. 8). In contrast to OCCAM 1/12, the

269

sea ice concentration remains high even in summer. As in the surface water properties, the two

270

versions of OCCAM become more similar in the sea ice evolution after 1994 (Figs. 7 a and 7 b).

271

The seasonal sea ice cycle in OCCAM 1/4 is consistent throughout the model run; the abrupt

272

change observed in OCCAM 1/12 does not appear. The polynya in the OCCAM 1/12 ice cover

273

in 1985 and 1986 does not appear in OCCAM 1/4 and the propagation of colder water around

274

the Weddell Sea as observed in OCCAM 1/12 is not visible in OCCAM 1/4.

275

4.3

ORCA025

276

ORCA025, the other 1/4◦ -model in our comparison, is generally too warm and, except in

277

the uppermost layer and above the shelves, slightly too saline (Figs. 2 and 3). In summer, the

278

surface layer is up to 2.8◦ C warmer than measured. At the shelf break, both temperature and

279

salinity are lower than in the CTD data. The modelled descent of the isolines starts further

280

away from the shelf over a deeper part of the continental slope and is less steep in the model.

281

Towards the end of summer (sections S04a and SR04e, not shown), the surface layer becomes

282

too saline. Overall, the salinity gradient from the surface to the salinity maximum in the WDW

283

is less steep than observed. The mixed layer depth is stable throughout the run at around 100

284

m. However, the average depths of the deeper water masses increase (Figs. 4-6). In ORCA025,

285

the average temperatures of WDW are 0.3◦ C too high. WDW warms substantially during the

286

first ten model years. The warming is slower during the rest of the run (Fig. 4). In ORCA025,

287

WDW occupies a larger area along the sections (on average 43.9% in the sections) than in the

288

CTD measurements (28.6%) and the average depth is 285 m deeper. Consequently, WSDW and

289

WSBW are deeper as well (Fig. 5,6). The WSDW salinity is overestimated by 0.005 (Fig. 5).

290

Average θ for WSDW matches the observations quite well, but the coldest deep water in the

291

model only just qualifies for WSBW and reaches a minimum of −0.719◦ C in the austral spring

292

section of 1989 (SR02). As in the OCCAM runs, the plume of newly formed WSBW along the

293

western continental shelf is not visible.

294

The Weddell Sea in ORCA025 becomes warmer during the model run. As the temperature

295

increases, the area occupied by WDW becomes larger and spreads deeper while the average

296

depth of WSDW and WSBW increases and the area occupied by WSBW decreases (Fig. 4-6).

297

At the same time, salinity increases too. Eventually, the entire water column is so warm that

298

from 1994 onwards no water fulfills Fahrbach et al. (2004)’s criterion for WSBW. The average

299

area, temperature and salinity of WSDW vary only a little.

300

The sea ice in ORCA025 is very different from the simulations by OCCAM (Fig. 8,7).

301

The timing of minimum and maximum ice coverage and the annual mean ice extent agree

302

with SSM/I data (Fig. 7). In winter, the ice concentrations are closer to the observations than

303

in both OCCAM versions (Fig. 8). However, although ORCA025 manages to simulate lower

304

concentrations close to the coast, they are still too high. The climatological maximum extent

305

is slightly overestimated but still below the values for OCCAM 1/12 and OCCAM 1/4. In

306

summer, almost all sea ice disappears and the ice extent decreases to 6.8 × 1010 m2 , an order

307

of magnitude less than observed. Only a very small amount of low concentration multi-year ice

308

remains in the southern Weddell Sea.

309

4.4

TPAC

310

TPAC is, like ORCA025, too warm (Figs. 2 and 3). At the surface, θ drops to below −1.4◦

311

C during September to December while during the rest of the year and particularly in late

312

summer and early autumn θ reaches values of more than −0.4◦ C. In the CTD data, θ remains

313

close to the freezing point at −1.8◦ C in the upper 10 m in all sections except for the summer

314

section SR04d where θ reaches 0.5◦ C near the Antarctic Peninsula (not shown). In the model,

315

cold and fresh Antarctic Surface Water occupies only the uppermost model layer and the mixed

316

layer is far too shallow at just over 30 m, which corresponds to the upper two model layers.

317

The surface layer and the waters below down to 150 m are too saline in all sections. Only over

318

the continental slope both in the eastern and western Weddell Sea is the model colder and

319

fresher than observed, indicating that the downward sloping of the isopycnals is too far away

320

from the coast. The plume of cold fresh water flowing down the western continental slope is

321

not reproduced.

322

TPAC is the only model in our comparison that is overall too saline in all of the sections.

323

While the maximum salinity (Smax ) in the CTD section is between 34.693 and 34.706 in the

324

WDW, even the average salinity in the WDW exceeds these values reaching 34.714 to 34.738

325

and Smax is 34.793 in section SR04b (Figs. 3 and 4). The WDW is on average more than 0.2◦

326

C warmer than observed. As the entire water column is too warm in TPAC, the area occupied

327

by WDW is also overestimated. The water warms further during the model run and the WDW

328

area percentage increases while WSDW decreases. WDW gets warmer and saltier but remains

329

slightly colder than in ORCA025. As the area occupied by WDW increases, so does the average

330

depth. The WSDW area decreases during the run (Fig. 5). The salinity of the deep water

331

corresponds to the observations, but θ is too high from the beginning of the run. The coldest

332

bottom water in TPAC is far too warm at −0.45◦ C (Fig. 3). Using Fahrbach et al. (2004)’s

333

criteria, TPAC does not simulate any WSBW at all (Fig. 6). As even in the first month of the

334

model run (January 1982) no WSBW is present, this suggests that the initialisation dataset is

335

too warm. TPAC shows a clear drift in both θ and S. The increase during the run is similar to

336

ORCA025 but occurs over a far shorter period of time (20 years cf. 47 years).

337

4.5

Error statistics

338

In order to quantify the model performance in reproducing the observed hydrography and

339

sea ice cover in the Weddell Sea, we calculated the basic statistical properties of observed

340

and modelled variability, correlation, and root mean square error (RMSE) for observed and

341

modelled θ and S along the sections, and sea ice extent and area in the Weddell Sea (Table 3).

342

The equations for the statistical variables are given in Table 3.

343

Figure 9 combines the various measures in a single diagram (Taylor, 2001). We use the CTD

344

measurements and the remote sensing sea ice observations as the reference datasets, marked

345

by the point labelled “reference” at standard deviation (σ) = 1 and correlation coefficient (R)

346

= 1. To include θ and S from all sections and the sea ice data, we use the normalised standard

347

deviation σnorm , that is σnorm = σdata /σobs for both model and observational data. The better

348

the correlation of the modelled data to the observations, the closer the corresponding model

349

marker will be to the x-axis. Similarly, the better the standard deviation (i.e. the variability)

350

fits the observed value, the closer the marker will be to the dashed black line marking the unit

351

circle. The red dotted semi circles mark how good the fit is taking into account equally both

352

correlation and agreement of variability. This “goodness of fit” corresponds to a centred pattern

353

root mean square error (RMSE), where a general bias in the model data is not considered.

354

The models are rather spread out on the diagram, i.e. they simulate some sections well

355

and some badly (Fig. 9). Generally, the models simulate θ along the sections better than S.

356

In OCCAM 1/12, the spatial variability of θ along the sections is close to the observations

357

and σnorm is close to 1. However, the patterns observed are not represented very well and R

358

is below 0.8 for all sections except S04a. The spatial distribution of S correlates better to the

359

observations but σnorm is higher and the RMSE larger. The seasonal cycle of the sea ice extent

360

is more poorly simulated than in OCCAM 1/4 and ORCA025 due to the offset of the annual

361

cycle.

362

OCCAM 1/4 matches the patterns and the distribution of the observed θ better than the

363

high resolution OCCAM 1/12. The RMSE of S varies between the sections from as low as 0.036

364

to a maximum of 0.61 (centred RMSE = 0.60). The temporal development of the sea ice extent

365

is closer to observations than in OCCAM 1/12, but the persistent summer ice cover and the

366

strong underestimation of the seasonality lead to very low σnorm (0.45).

367

Both for θ and S, the correlation is better in ORCA025 than in the other models: R varies

368

between 0.80 and 0.95. σnorm is larger though which suggests an overestimation of the spatial

369

variability in both variables. The centred pattern RMSE for θ is much lower than the standard

370

RMSE (difference of more then 0.1 in all sections) which confirms that ORCA025 is biased

371

towards high θ. The correlation of the sea ice extent in the model to the observations is very

372

high at R=0.98, but σnorm is well above 1 as well which is caused by the strong seasonality and

373

the underestimation of the summer sea ice in ORCA025.

374

TPAC shows the lowest correlations for θ and S of all the models. It also has the highest

375

standard RMSE for both variables. As in ORCA025, the centred RMSE for θ is much lower

376

than the standard RMSE, a sign of the warm bias of the model.

377

5

Discussion

378

Although all three models are global primitive equation z-level models, they behave very

379

differently. There are no systematic biases common to all the models in the region. Two res-

380

olutions of the same model (OCCAM) also produce different results. Nevertheless, OCCAM

381

1/4 and OCCAM 1/12 have common issues. Both model versions display a too deep and too

382

cold mixed layer (Fig. 2). This is linked to an extensive sea ice cover (Fig. 8). Very high ice

383

concentrations prevent heat uptake through solar radiation during the summer months, thus

384

preventing a warming of the ocean surface. In winter though, the lack of open water reduces

385

heat loss from the ocean to the atmosphere. In both OCCAM 1/12 and OCCAM 1/4, the first

386

effect seems to be stronger and helps to produce a very cold, deep mixed layer. The high ice

387

concentrations also reduce ice production. In the real oceans, ice formation in particular in

388

coastal polynyas (that do not appear in OCCAM at all) is an important factor as the process

389

is closely linked to salinification, HSSW formation (Renfrew et al., 2002; Foldvik et al., 2004)

390

and hence WSBW formation. The formation of coastal polynyas in OCCAM is compromised

391

by the weak wind forcing applied to both resolutions, and the missing offshore winds (Fig. 10).

392

A cold anomaly in the WDW θmax moving cyclonically around the Weddell Sea appears

393

only in OCCAM 1/12 and is not seen in the observations. The cold water is produced in a large

394

polynya in the Maud Rise region which is present in the model in the austral winter in 1985

395

and 1986, when the sea ice cover has diverged from the initial state. Travel times for anomalies

396

associated with the Antarctic Coastal Current around the entire perimeter of the Weddell Sea

397

are in the range of a few years (Beckmann et al., 1999; Schodlok et al., 2001). The cold water

398

signal which appears on the inner Weddell Sea side of the Antarctic Coastal Current travels

399

much slower in OCCAM 1/12. It takes two years for it to move from the Greenwich Meridian

400

to the hydrographic section at Kapp Norvegia. By the time corresponding to the last section

401

(SR04e, April 1998), the cold anomaly in OCCAM 1/12 has reached the western end of the

402

section at the Antarctic Peninsula. An indication of the weak Antarctic Coastal Current and

403

the slow circulation of the Weddell Gyre in both OCCAM versions is the small difference of sea

404

surface height (SSH) between the Antarctic Peninsula and the minimum SSH in the middle of

405

the gyre. Fig. 11 (upper panel) shows that OCCAM 1/12 and OCCAM 1/4 have a much weaker

406

gyre than ORCA025 and TPAC. This is due to the weak wind forcing in OCCAM compared

407

with the much stronger winds in ORCA025 (Fig. 10). The OCCAM wind stress is higher than

408

ORCA025’s in the central Weddell Sea while ORCA025 winds are much stronger north of 65◦

409

S and along the Antarctic coast The weak Weddell Gyre in OCCAM may contribute to the

410

extensive sea ice cover: the slow currents lead to long residence times of water in cold regions and

411

prevent faster heat transport into the Weddell Sea by inflow of warmer ACC water. Although

412

still weak, the Antarctic Coastal Current is faster in OCCAM 1/12 than in OCCAM 1/4 even

413

though the forcing is the same. The difference between the two resolutions could be explained by

414

the Gent and McWilliams (1990) parameterisation (GM90) used in OCCAM 1/4. GM90 is used

415

to remove baroclinic instabilities for numerical stability and, although the Antarctic Coastal

416

Current is mostly stable, GM90 reduces the horizontal density gradients across the current and

417

weakens the Coastal Current (Beckmann, pers. comm.). Also, the very dense and mostly land-

418

fast sea ice pack in OCCAM 1/4 with concentrations of > 95% reduces momentum transfer

419

from the wind to the upper ocean. The lower sea ice concentrations in the higher resolution

420

version allow for stronger wind forced currents.

421

OCCAM 1/12 also does not seem to have reached equilibrium in the Weddell Sea. While

422

the ACC transport through Drake Passage seems to be stable soon after the start of the model

423

run (Fig. 11b), the mixed layer depth in the central Weddell Sea continues to increase as does

424

the depth of θmax in the WDW. This indicates problems with the surface fluxes. OCCAM 1/4

425

on the other hand does appear to have reached equilibrium.

426

In ORCA025, the absence of multi-year ice facilitates too strong warming of the mixed

427

layer, the exact opposite to OCCAM. The warming is enhanced by a low baroclinic ACC

428

transport (Fig. 11 b). In the form of a feedback loop, the strength of the baroclinic ACC

429

transport is linked to the bottom water formation in the Weddell Sea: a weak ACC relates

430

to a weak meridional surface buoyancy gradient. This means that warmer surface water can

431

penetrate further south and thereby lateral heat transport into the Weddell Sea is enhanced

432

which reduces bottom water formation. This in turn reduces the stratification which weakens

433

the thermohaline forcing component of the ACC (Olbers and W¨ ubber, 1991; Cai and Baines,

434

1996). The initialisation of the model is a major reason for the missing summer sea ice: the final

435

state of a ten year spin up run has been used which had almost no sea ice left in the Weddell

436

Sea (J.-M. Molines, pers. comm.). The ice cover did not recover during the model run. One

437

likely explanation for this is the high sea surface temperature observed throughout the Weddell

438

Sea during the first month of the model run. In January 1958, the first month of the model run,

439

the surface temperature is only at the freezing point beneath existing sea ice; elsewhere in the

440

Weddell Sea it is up to 2◦ C. The almost complete loss of the highly reflective ice cover during

441

summer allows for large heat uptake by the ocean through solar radiation and therefore strong

442

warming. In winter, strong winds in the ECMWF forcing (Fig. 10) can quickly advect any

443

sea ice out of the Weddell Sea and thereby promote a faster decrease to the very low summer

444

concentrations. This export of freshwater would also explain the increase in salinity over the

445

ORCA025 model run.

446

In our comparison, TPAC is the only ocean model that is not coupled to a sea ice model.

447

Combined with the loose constraining of sea surface temperature and salinity to hemispheric

448

wintertime conditions, this can explain the high salinities. Excluding sea ice means that the

449

influence of sea ice formation, melt and drift on the freshwater budget is missing. The constraint

450

to wintertime conditions probably leads to overestimation of the surface salinities because the

451

input of freshwater due to sea ice melt in summer is ignored. The missing sea ice cycle also

452

affects the modification of shelf waters, so that dense water formation is heavily restricted and

453

bottom water missing. The baroclinic ACC transport decreases during the model run. This

454

allows increasing entrainment of warm water into the Weddell Sea, leading to the increasing

455

water temperatures, the increase of WDW and the decrease of WSDW. TPAC shows a large

456

drift which demonstrates the need for suitable forcing (possibly with stronger relaxation of sea

457

surface temperature and salinity to climatology) or coupling to a sea ice model.

458

Model validations using observations always struggle with the snapshot-like nature of most

459

observational data. Often, model results are compared with either observations from a single

460

point in time or a very short period (e.g. a hydrographic section), or long-term climatologies.

461

While the first approach neglects the fact that variability in the real world and in the model do

462

not necessarily coincide, the latter neglects the spread caused by exactly that variability. Using

463

repeat sections provides information on the temporal variability. Sampling the models at the

464

different sets of stations demonstrates variations due to station spacing. Hydrographic sections

465

are measured over several weeks and include short-term features such as eddies. Ocean models

466

cannot exactly reproduce the observations but aim to capture the level of natural variability

467

over the course of the model run. The models considered in this study manage to simulate a

468

certain level of variability which covers some of the changes seen in the observations, e.g. area

469

percentage, θ, and S of WDW in OCCAM 1/4, θ of WSDW in OCCAM 1/12, OCCAM 1/4 and

470

ORCA025, and WSBW area percentage and depth in OCCAM 1/12 and OCCAM 1/4 (Figs. 4,

471

5, and 6, respectively). They are, however, often in disagreement with the observed mean values.

472

This is most pronounced for the deep water masses and in ORCA025 and TPAC where both

473

models fail to reproduce the observed amounts and the characteristics of WSDW and WSBW.

474

During the entire model runs, the average depth of the WSDW layer was 200 to 400 metres

475

too deep, θ of WSDW in TPAC 0.1◦ C too warm, and WSBW completely missing in TPAC.

476

The WSBW layer in ORCA025 disappears after 38 years of the model run. Less drastically,

477

both OCCAM versions struggle as well and the WSBW in both resolutions is too fresh and too

478

warm. They do however simulate all major features of the Weddell Sea hydrography. Using the

479

repeat sections in relation to the entire model runs shows that the model biases are consistent

480

for each model, e.g. for all sections, the area percentage of WDW is too low in OCCAM 1/12

481

and too high in ORCA025, but there are exceptions, notably for section S04a.

482

483

Although the models are set up differently and use different forcings, general conclusions emerge by comparing their performances:

484

1. Increasing resolution is not a panacea. As noted by Barnier et al. (2006), model nu-

485

merics thoroughly tested at coarse resolutions may require reformulation for higher resolution.

486

Moving to higher resolution, both horizontally and vertically, can improve model simulations

487

significantly (Lee et al., 2002; Thoma et al., 2006). However at the level of resolution in eddy-

488

permitting and eddy-resolving models, better parameterisations and model numerics are just as

489

important. Examples are better advection schemes, improved and suitably chosen subgridscale

490

parameterisations, and use of partial bottom cells (Lee et al., 2002; Legg et al., 2008; Penduff

491

et al., 2007). It seems that the step from coarse 1◦ or 2◦ models to eddy-permitting models was a

492

much more vital one in terms of improving model hydrography and large-scale circulation than

493

going from eddy-permitting to eddy-resolving. The absence of major differences between our

494

OCCAM 1/12 and OCCAM 1/4 results demonstrate this. Within the range of resolutions in the

495

models presented in this paper, we do not find one to perform better than the others. Very high

496

resolution, such as OCCAM’s 1/12◦ or POP 1/10 as used by Maltrud and McClean (2005),

497

becomes important when examining processes that are strongly influenced by eddies, where

498

currently used subgrid parameterisations in eddy-permitting models are still insufficient (e.g.

499

Legg et al., 2006). In high latitudes however, model grids of nominally eddy-permitting models

500

such as the ORCA025 grid (Madec and Imbard, 1996) can provide high enough resolution to

501

include the effects of eddies.

502

2. The choice of the forcing datasets is important. Barnier et al. (2007) use various forcings

503

for the same model and show the large impact of the different datasets on the model simula-

504

tions. Their experiments led to the development of a coherent set of forcing variables (Brodeau

505

et al., submitted). In the Weddell Sea, errors in the freshwater and heat flux forcings can have

506

considerable impact on the ocean by altering sea ice production and melt and the density of

507

shelf waters. Demonstrating the sensitivity of the ocean to wind forcing, Saenko and Weaver

508

(2001) and Saenko et al. (2002) found that wind induced sea ice motion is important for the

509

formation of Antarctic Intermediate and Bottom Water and ocean ventilation. The differences

510

in our model simulations support the importance of realistic wind forcings. The weak NCEP

511

wind forcing is at least partly responsible for the build-up of the very dense sea ice pack and

512

the weak Weddell Gyre in the OCCAM runs. Gnanadesikan et al. (2004) demonstrated how

513

observations such as Levitus et al. (1998) used for restoring models did not allow deep water

514

masses to outcrop at the surface and hence restricted the formation of deep and bottom waters

515

in the Ross and Weddell Seas. Surface restoring of temperature and salinity cannot therefore

516

replace the accurate simulation of the underlying processes in order to produce the right water

517

masses.

518

3. Sea ice models need to be improved. The lack of a sea ice model in TPAC makes a

519

realistic simulation of the processes in the Weddell Sea difficult. The missing bottom water, the

520

warming of the entire water column, and the low salinities above the continental shelf and slope

521

show the necessity of the inclusion of sea ice formation and melt processes for a correct model

522

hydrography. The relaxation using salt flux derived from latent heat and precipitation is not

523

sufficient to ensure adequate bottom water formation without the sea ice processes. From the

524

ORCA025 run, we learn that the initialisation of the ice cover can impact the entire model run.

525

Kerr et al. (2009) analysed ocean model runs with and without a coupled sea ice model. They

526

found that the effect of the sea ice model on the ocean is complex. Having used a 1◦ model, they

527

conclude that both refined resolution and improved parameterisations of physical processes are

528

necessary to simulate ice-ocean interactions and the hydrography in the Weddell Sea. Hellmer

529

(2004) and Wang and Beckmann (2007) showed that including ice shelves in a coupled sea

530

ice-ocean model improves the simulation of the sea ice cover and alters the hydrography in the

531

Weddell Sea with global effects. None of the models discussed in this paper include ice shelves.

532

A previous model intercomparison study suggested biases specific to z-level models. As part

533

of the DYNAMO (Dynamics of the North Atlantic circulation) project, Willebrand et al. (2001)

534

compare three models at 1/3◦ horizontal resolution with different vertical coordinate schemes

535

(z-level, sigma and isopycnic). They find that the z-level model suffers from the steplike repre-

536

sentation of the topography combined with insufficient resolution (30 vertical levels). Combined

537

with inadequate numerical algorithms for advection and mixing, this leads to excessive diapyc-

538

nal mixing and poor representation of downslope flows. Similarly, investigating the performance

539

of two z-level models including an earlier version of OCCAM 1/4 in the Scotia Sea, Thorpe

540

et al. (2005) suggest that low vertical resolution, poor representation of the bathymetry, and

541

issues with the vertical mixing schemes lead to errors in the circulation and loss of Antarctic

542

Bottom Water. In this paper, we have shown that the new version of OCCAM 1/4 with the

543

higher vertical resolution and partial bottom cells is much improved and capable of simulating

544

Weddell Sea deep water masses. In ORCA025 and TPAC, however, with fewer vertical levels

545

and different vertical mixing schemes, WSBW is lost rapidly during the model run or is not

546

present at all, suggesting both the parameterisation and the vertical resolution are not suf-

547

ficient. All our models are too warm and too saline in the bottom layer of the Weddell Sea

548

suggesting that further improvements can be made with the representation of bottom water

549

formation processes including the simulation of cold overflows.

550

6

Conclusions

551

Comparing the output of three global OGCMs with observational data and with each other,

552

we find significant differences. The results of the same model with different horizontal resolution

553

(OCCAM 1/12 and OCCAM 1/4) are very similar but the higher resolution in OCCAM 1/12

554

and the different approach to vertical mixing lead to different behaviour in the upper ocean and

555

the sea ice model. Qualitatively, the results of ORCA025 and TPAC are close to each other but

556

very different from OCCAM. The mismatches between models and observations are genuine

557

and not an artefact of the snapshotlike nature of the hydrographic sections. The models are at

558

high enough resolution to reproduce some variability due to station location, but struggle with

559

the average values or fail to simulate the deep water masses correctly.

560

Ocean models are often used and developed to study specific areas or regions, for example

561

the North Atlantic overturning circulation or the Agulhas retroflection. They are tuned to

562

represent those regions well, while other regions might be less well simulated, as long as on a

563

global scale, results seem reasonable. As the models in this comparison produce very different

564

results for different parameters, it is not possible to choose “the best model”. Instead, the choice

565

should be made according to the application. We find that for analyses regarding deep water

566

mass properties, ORCA025 and TPAC perform poorly and OCCAM does best. However, the

567

seasonality of the sea ice cover and the surface of the ocean is very limited in OCCAM. The high

568

resolution of OCCAM 1/12 allows for much more detail as eddies are explicitly resolved and

569

topography is more precise. Thompson and Heywood (2008) demonstrate how higher resolution

570

in observations changes our understanding of processes in regions with narrow jets and the same

571

can apply to model analyses. For processes which are closely linked to the growth and retreat of

572

sea ice, for example assessing the spring phytoplankton bloom or air-sea CO2 fluxes, ORCA025

573

is more suitable. Problems can arise where the existence of multi-year ice is of importance.

574

Reasons for the different model performances include the use of different forcing datasets,

575

different initialisations and different parameterisations of vertical mixing and other subgridscale

576

processes. When using a global model for a regional study, the choice has to be made carefully;

577

our study has shown how models can be suitable for specific applications such as outflow of

578

bottom water, ocean-topography interaction, or ecological studies. It is difficult to say, which

579

improvements to OGCMs are most likely to be the most effective. Comparisons to previous

580

studies have shown the effect of better mixing and advection parameteristions and the benefit

581

of vertical resolution and partial bottom cells. Eddy-permitting OGCMs are being tested to

582

be used in the new generation of coupled climate models (e.g. Shaffrey et al., 2008, early

583

online release; WCRP, 2009). Keeping this in mind, we suggest that at least for global models

584

the focus should be on improving vertical mixing and horizontal advection schemes, better

585

parameterisations of processes at the ice-ocean interface and in the sea ice models, and on more

586

self-consistent and realistic forcing datasets, and not on increasingly higher resolutions.

587

Acknowledgements

588

We thank the modelling groups for their help and providing the model data: Andrew Coward

589

and the OCCAM team at the National Oceanography Centre, Southampton, the DRAKKAR

590

project team, esp. Jean-Marc Molines and Bernard Barnier at LEGI-Grenoble, and Jason

591

Roberts at ACECRC. Eberhard Fahrbach at the Alfred Wegener Institute made the hydro-

592

graphic data available. Nuno Nunes provided the ACC transports and Aike Beckmann valuable

593

advice. Comments from Robert Hallberg and two anonymous reviewers helped to improve the

594

manuscript. This work was funded by a EU Marie Curie Early Stage Training Fellowship in

595

Antarctic Air-Sea-Ice Science (FAASIS) by the European Commission Marie-Curie Actions at

596

the School of Environmental Sciences, University of East Anglia, and the British Antarctic

597

Survey, contract number MEST-CT-2004-514159.

598

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List of Tables 1

Summary of the main characteristics of the models under validation. References for the bathymetry datasets are given in the text.

29

773

2

Overview of WOCE hydrographic sections used for the model validation

774

3

Error statistics for ocean model - observation comparison following Taylor

775

(2001). RMSEs for θ, and sea ice extent are given in



C and m2 , respectively.

30

31

Table 1 Summary of the main characteristics of the models under validation. References for the bathymetry datasets are given in the text. Model

Ocean component

Vertical mixing scheme

Surface

salinity

Sea ice component

Horizontal and ver-

restoring OCCAM 1/12

Bryan-Cox-Semtner

(Coward and de

with free surface; eddy-

Cuevas, 2005)

type

No

isopycnic

mixing

scheme

resolving

Bathymetry source

tical Resolution

Relaxation of surface

Aksenov

(2002);

grid box salinity to

thermodynamic

with

dynamicelastic-

1/12◦ × 1/12◦ ;

Smith & Sandwell v6.2

66 vertical levels

with 72◦

DBDB5

south

Levitus et al. (1998)

viscous-plastic rheology (Semtner,

of

S; sill depths

at timescale of 30

1976; Hunke and Dukowicz, 1997)

checked manually

days OCCAM 1/4

As OCCAM 1/12; eddy-

Isopycnal mixing scheme

(Coward and de

permitting

(Griffies, 1998) with Gent

Cuevas, 2005)

As OCCAM 1/12

1/4◦ × 1/4◦ ;

As OCCAM 1/12

As for OCCAM 1/12

66 vertical levels

and McWilliams (1990) parameterisation

ORCA025 (Barnier

et

al.,

2006)

OPA9 (Madec, 2006): hy-

Modified TKE parameteri-

Relaxation to Levitus

LIM2

drostatic primitive equa-

sation

et al. (1998)/PHC at

Maqueda,

tion model with free sur-

timescale of 60 days

thermodynamic,

face; eddy permitting

for the upper two ver-

rheology (Hibler, 1979)

(Fichefet

and

1997):

Morales dynamic-

1/4◦ Mercator grid;

ETOPO2 with Bedrock

46 vertical levels

south of 72◦

viscous-plastic

GEBCO on the continental shelves; addi-

tical levels

tional smoothing, editing in key areas

TPAC (Meijers 2007)

et

al.,

MOM 3.1: primitive equa-

Isoneutral

tion model; eddy-resolving

form of

the

McWilliams

mixing

in

Gent

and

NCEP-R2 latent heat

(1990)

flux and precipitation

subgrid-scale mixing

Relaxation

rate, days

S and

using

timescale

30

No sea ice included

1/8◦ × 1/8◦ ; 24 vertical levels

TerrainBase

Table 2 Overview of WOCE hydrographic sections used for the model validation Section name (Cruise name)

Date

Colour in Fig. 1a

SR02 (ANT VIII/2)

12 Sep - 08 Oct 1989

dark blue

SR04b (ANT IX/2)

22 Nov - 15 Dec 1990

light blue

SR04d (ANT X/7)

18 Dec 1992 - 12 Jan 1993

dark green

S04a (ANT XIII/4)

20 Mar - 12 Apr 1996

red

SR04e (ANT XV/4)

01 Apr - 11 Apr 1998

light green

Table 3 Error statistics for ocean model - observation comparison following Taylor (2001). RMSEs for θ, and sea ice extent are given in ◦ C and m2 , respectively. Statistical parameter

θ

Model

S

Sea ice extent

SR02

SR04b

SR04d

S04a

SR04e

SR02

SR04b

SR04d

S04a

SR04e

OCCAM 1/12

0.71

0.79

0.77

0.82

0.63

0.83

0.87

0.84

0.89

0.83

0.75

OCCAM 1/4

0.74

0.81

0.74

0.83

0.82

0.79

0.81

0.77

0.82

0.84

0.88

ORCA025

0.84

0.93

0.88

0.89

0.80

0.95

0.93

0.92

0.88

0.90

0.98

TPAC

0.75

0.77

0.75

0.72

0.70

0.70

0.72

0.72

0.70

0.65

-

OCCAM 1/12

0.40

0.34

0.36

0.30

0.48

0.054

0.047

0.049

0.048

0.056

8.71 × 1011

RMSE =

OCCAM 1/4

0.39

0.33

0.39

0.30

0.33

0.061

0.041

0.057

0.036

0.056

9.76 × 1011

p1

ORCA025

0.44

0.41

0.422

0.43

0.54

0.046

0.049

0.045

0.056

0.053

7.12 × 1011

TPAC

0.50

0.50

0.50

0.48

0.59

0.068

0.073

0.061

0.082

0.089

-

OCCAM 1/12

0.39

0.33

0.35

0.30

0.47

0.053

0.047

0.049

0.047

0.056

7.39 × 1011

centred pattern RMSE =

OCCAM 1/4

0.39

0.33

0.39

0.30

0.33

0.060

0.041

0.057

0.036

0.055

6.96 × 1011

p1

ORCA025

0.32

0.28

0.30

0.32

0.39

0.046

0.049

0.044

0.056

0.052

6.07 × 1011

TPAC

0.35

0.34

0.35

0.37

0.42

0.068

0.073

0.060

0.082

0.088

-

OCCAM 1/12

1.02

1.05

1.12

0.98

1.03

1.17

1.65

1.25

1.88

0.89

0.92

OCCAM 1/4

1.07

1.10

1.17

1.03

1.00

1.17

1.31

1.23

1.39

0.89

0.45

ORCA025

1.11

1.33

1.27

1.32

1.17

1.38

1.76

1.40

1.98

1.13

1.50

TPAC

0.99

1.05

1.07

0.99

1.02

1.21

2.00

1.35

2.45

1.15

-

R= 1 1 N σx ×σy

Σ((x − x ¯)(y − y¯))

(x = obs.; y = model)

N

Σ(y − x)2

(x = obs.; y = model)

N

Σ((y − y¯) − (x − x ¯))2

(x = obs.; y = model)

normalised σ = σy /σx (σx =

p1

N

Σ(x − x ¯)2 )

(x = obs.; y = model)

776

777

List of Figures

1

a) CTD stations. Colour key to individual transects is given in Table 2. The

778

bathymetry shown is from Smith and Sandwell V10.1. The white dashed line

779

marks the western limit north of the Antarctic Peninsula for sea ice extent

780

calculations. b-d) model bathymetries: b) OCCAM 1/12 (dashed line: OCCAM

781

1/4 coastline in the Weddell Sea), c) ORCA025, d) TPAC.

782

2

Section SR04b, Nov-Dec 1990. Uppermost panel: CTD observations for θ (left)

783

and S (right). Below: differences between model and CTD for θ (left) and

784

S (right), from second panel to bottom panel: OCCAM 1/12, OCCAM 1/4,

785

ORCA025, and TPAC. The upper 500 m are expanded to allow for more detail.

786

The white lines on the x-axis indicate CTD station positions. The black lines

787

on the right hand side of the difference plots indicate the model depth levels.

788

3

790

4

35

θ - S diagram for section SR04b (Nov/Dec 1990). The dotted lines are contours of constant potential density anomaly (kg m−3 ).

789

34

36

Properties of WDW in OCCAM 1/12, OCCAM 1/4, ORCA025 and TPAC

791

(left to right) averaged along the SR04 section line. From top to bottom:

792

Percentage of area along section covered by WDW; average WDW θ; average

793

WDW S; average WDW depth. The grey shading indicates the variation due

794

to the different station spacing of the hydrographic sections (excluding the set

795

of stations of SR04e). The triangles indicate the values derived from the CTD

796

sections, the circles the values from the corresponding model sections. Filled

797

markers denote full sections, the open markers section SR04e which does not

798

extend over the entire eastern margin of the Weddell Sea. Note the extended

799

x-axis for ORCA025.

37

800

5

Fig. 4 but for WSDW.

38

801

6

Fig. 4 but for WSBW.

38

802

7

Sea ice extent in the Weddell Sea: a) climatological annual cycle (1988-2002),

803

and b) monthly means.

39

804

8

Climatological sea ice concentrations for the years 1988-2002 in February (upper

805

panel) and September (lower panel) for SSM/I observations, OCCAM 1/12,

806

OCCAM 1/4 and ORCA025 model results (TPAC does not include sea ice).

807

9

Taylor diagram for θ (+), S (⋄), and sea ice (∗) statistics. Dark blue markers

808

represent OCCAM 1/12, red markers OCCAM 1/4, grey markers ORCA025

809

and cyan markers TPAC.

810

10

from NCEP Reanalysis 2 for TPAC (from left to right). The NCEP Reanalysis

812

2 wind field is on the original 2◦ resolution grid instead of the TPAC model grid

813

because only snapshots of the forcing were stored during the model run. 11

40

Annual mean wind stress magnitude (N m−2 ) for OCCAM, ORCA025, and

811

814

39

40

a) Difference of sea surface height (SSH) between the tip of the Antarctic

815

Peninsula (63.45◦ S, 55◦ W) and the SSH minimum in the Weddell Sea. b)

816

Monthly mean baroclinic ACC transports through Drake Passage (referenced

817

to the bottom).

41

Fig. 1. a) CTD stations. Colour key to individual transects is given in Table 2. The bathymetry shown is from Smith and Sandwell V10.1. The white dashed line marks the western limit north of the Antarctic Peninsula for sea ice extent calculations. b-d) model bathymetries: b) OCCAM 1/12 (dashed line: OCCAM 1/4 coastline in the Weddell Sea), c) ORCA025, d) TPAC.

Fig. 2. Section SR04b, Nov-Dec 1990. Uppermost panel: CTD observations for θ (left) and S (right). Below: differences between model and CTD for θ (left) and S (right), from second panel to bottom panel: OCCAM 1/12, OCCAM 1/4, ORCA025, and TPAC. The upper 500 m are expanded to allow for more detail. The white lines on the x-axis indicate CTD station positions. The black lines on the right hand side of the difference plots indicate the model depth levels.

Fig. 3. θ - S diagram for section SR04b (Nov/Dec 1990). The dotted lines are contours of constant potential density anomaly (kg m−3 ).

Fig. 4. Properties of WDW in OCCAM 1/12, OCCAM 1/4, ORCA025 and TPAC (left to right) averaged along the SR04 section line. From top to bottom: Percentage of area along section covered by WDW; average WDW θ; average WDW S; average WDW depth. The grey shading indicates the variation due to the different station spacing of the hydrographic sections (excluding the set of stations of SR04e). The triangles indicate the values derived from the CTD sections, the circles the values from the corresponding model sections. Filled markers denote full sections, the open markers section SR04e which does not extend over the entire eastern margin of the Weddell Sea. Note the extended x-axis for ORCA025.

Fig. 5. Fig. 4 but for WSDW.

Fig. 6. Fig. 4 but for WSBW.

Fig. 7. Sea ice extent in the Weddell Sea: a) climatological annual cycle (1988-2002), and b) monthly means.

Fig. 8. Climatological sea ice concentrations for the years 1988-2002 in February (upper panel) and September (lower panel) for SSM/I observations, OCCAM 1/12, OCCAM 1/4 and ORCA025 model results (TPAC does not include sea ice).

Fig. 9. Taylor diagram for θ (+), S (⋄), and sea ice (∗) statistics. Dark blue markers represent OCCAM 1/12, red markers OCCAM 1/4, grey markers ORCA025 and cyan markers TPAC.

Fig. 10. Annual mean wind stress magnitude (N m−2 ) for OCCAM, ORCA025, and from NCEP Reanalysis 2 for TPAC (from left to right). The NCEP Reanalysis 2 wind field is on the original 2◦ resolution grid instead of the TPAC model grid because only snapshots of the forcing were stored during the model run.

Fig. 11. a) Difference of sea surface height (SSH) between the tip of the Antarctic Peninsula (63.45◦ S, 55◦ W) and the SSH minimum in the Weddell Sea. b) Monthly mean baroclinic ACC transports through Drake Passage (referenced to the bottom).

Validation of three global ocean models in the Weddell Sea

Click here to view linked References ...... 360 cycle. 361. OCCAM 1/4 matches the patterns and the distribution of the observed θ better than the. 362.

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