Abstract

The natural variability of the Weddell Sea variety of Antarctic Bottom Water (AABW) is examined in a long-term integration of a coupled climate model. Examination of passive tracer concentrations suggests that the model AABW is predominantly sourced in the Weddell Sea. The maximum rate of the Atlantic sector Antarctic overturning (ψatl) is shown to effectively represent the outflow of Weddell Sea deep and bottom waters and the compensating inflow of Warm Deep Water (WDW). The variability of ψatl is found to be driven by surface density variability, which is in turn controlled by sea surface salinity (SSS). This suggests that SSS is a better proxy than SST for post-Holocene paleoclimate reconstructions of the AABW overturning rate. Heat–salt budget and composite analyses reveal that during years of high Weddell Sea salinity, there is an increased removal of summertime sea ice by enhanced wind-driven ice drift, resulting in increased solar radiation absorbed into the ocean. The larger ice-free region in summer then leads to enhanced air–sea heat loss, more rapid ice growth, and therefore greater brine rejection during winter. Together with a negative feedback mechanism involving anomalous WDW inflow and sea ice melting, this results in positively correlated θS anomalies that in turn drive anomalous convection, impacting AABW variability. Analysis of the propagation of θS anomalies is conducted along an isopycnal surface marking the separation boundary between AABW and the overlying Circumpolar Deep Water. Empirical orthogonal function analyses reveal propagation of θS anomalies from the Weddell Sea into the Atlantic interior with the dominant modes characterized by fluctuations on interannual to centennial time scales. Although salinity variability is dominated by along-isopycnal propagation, θ variability is dominated by isopycnal heaving, which implies propagation of density anomalies with the speed of baroclinic waves.

1. Introduction

Antarctic Bottom Water (AABW) forms a major component of the global ocean thermohaline circulation. Originating around the periphery of Antarctica, AABW mixes with more saline and warmer Circumpolar Deep Water (CDW) as it spreads into the abyssal basins of the World Ocean (Mantyla and Reid 1983; Jacobs 2004). In the Atlantic sector, AABW further mixes with lighter water masses as it flows equatorward and reaches the North Atlantic, where interaction with North Atlantic Deep Water (NADW) occurs (see, e.g., Brix and Gerdes 2003). AABW variability influences the stability of the global overturning circulation and thus exerts an influence on the earth’s climate over long time scales. However, a better understanding of the spatial and temporal characteristics of AABW variability, especially on time scales beyond decades, is hampered by a lack of any extended observational record. Furthermore, at present, it is not feasible to directly measure the AABW overturning variability (see also Latif et al. 2004). The present study aims to provide insight into the evolution of AABW overturning and property anomalies on interannual to centennial time scales operating in a coupled climate model, thus completing a series of papers exploring the natural variability of Southern Ocean water masses (Rintoul and England 2002; Santoso and England 2004; Santoso et al. 2006). Here the focus is on the Atlantic sector AABW and the Weddell Sea variety of deep and bottom waters.

Sources of AABW include both the contribution from shelf waters at several sites around the Antarctic continental margin (Baines and Condie 1998) and the upwelling of CDW south of the Antarctic Circumpolar Current (ACC; see a review by Orsi et al. 1999). Despite significant input from the Ross Sea and other regions such as the Adelié land region (Rintoul 1998; Orsi et al. 1999), the Weddell Sea is regarded as the most prominent and active region for bottom water formation (e.g., Carmack 1977; Orsi et al. 1999). The process of AABW formation has been described in several previous studies (e.g., Foster and Carmack 1976; Baines and Condie 1998; Rintoul 1998; Orsi et al. 1999; Meredith et al. 2000; Foldvik et al. 2004); here we briefly describe the Weddell Sea variety. Upwelled CDW enters the Weddell Sea, where mixing with the overlying colder and fresher winter water occurs. Winter water exists year-round in the surface mixed layer as a remnant of the cold layer produced during sea ice formation (Foster and Carmack 1976). This modified CDW, now referred to as Warm Deep Water (WDW; Whitworth and Nowlin 1987), mixes further with the high-salinity southwestern Weddell Sea Shelf Water (−1.9°C, 34.7 psu) to form Weddell Sea Bottom Water (WSBW; −1.3°C, 34.65 psu). WSBW then mixes with the less dense CDW as it flows down the continental shelf to form Weddell Sea Deep Water (WSDW), gaining buoyancy to flow over sills as AABW (typical θS of ∼−0.4°C, 34.66 psu; Whitworth and Nowlin 1987). Nevertheless, the major ingredient of AABW is CDW, as suggested by the analysis of Foster and Carmack (1976), who showed that AABW sourced from the Weddell Sea is composed of ∼62.5% CDW, 25% shelf water, and 12.5% winter water (see also Whitworth et al. 1998).

Capturing AABW properties, formation, and pathways in general circulation models (GCMs) is a challenging task, requiring a realistic representation of shelf processes (including Antarctic sea ice and ice shelves), downslope flows, and convective overturning (Goosse et al. 2001; Doney and Hecht 2002; Stössel et al. 2002). To date, no climate model has been capable of simulating the correct properties of the abyssal oceans. However, considerable improvements have been achieved since the early model developments of Bryan (1969) and Cox (1984). For example, one of the most common deficiencies in climate models is that the deep ocean is not sufficiently dense, and deep and bottom waters are too fresh. Although Antarctic wintertime surface salinity adjustments can yield a better AABW representation in GCMs (e.g., England 1993), they generally equate to spurious air–sea freshwater fluxes (Toggweiler and Samuels 1995). Furthermore, the long ventilation time scales of AABW require a multicentury integration of a fully coupled GCM, currently only possible at coarse resolution. Explicit representation of key processes such as convection and bottom boundary currents is beyond the present-day class of models used to predict anthropogenic climate change (although some progress in the parameterization of bottom boundary layers has improved the representation of downslope flows; Doney and Hecht 2002). However, climate-scale models generally capture realistic net production rates of AABW (England et al. 2007, unpublished manuscript, hereafter EN07) and in some cases reasonable TS properties and chlorofluorocarbon uptake (e.g., Doney and Hecht 2002). In such cases, they can provide a meaningful way to examine the physics of long-term natural variability of AABW over interannual to centennial time scales.

There have been relatively few studies of the observed variability of AABW properties and its ingredients on seasonal to decadal time scales. Coles et al. (1996) found AABW cooling and freshening of 0.05°C and 0.008 psu along constant density surfaces in the Argentine Basin over the period 1980–89, accompanied by observable warming at abyssal depths. They suggested that this θS change might be linked to convective events in the Weddell Sea. Hogg and Zenk (1997) documented warming in the bottom waters of the Vema Channel of about 0.03°C, accompanied by a decrease in the northward bottom water transport. They proposed that these changes are a response to a reduction in bottom water production. This warming appears to have continued until 2005, as found by Johnson and Doney (2006), in the Brazil, South Georgia, and Argentine Basins. Warming of WDW and WSDW by 0.1°–0.2° and 0.05°C, respectively, was observed by Meredith et al. (2001) in the eastern Scotia Sea between 1995 and 1999. Changes in the WSDW properties at the shelves of the Weddell Sea and changes in the wind-driven gyre were both mentioned as possible causes for the warming. More recently, Fahrbach et al. (2004) documented θS fluctuations in the Weddell Sea between 1990 and 2002. A warming trend of WDW is observed from 1992 to 1998 at the prime meridian, which is consistent with that documented by Robertson et al. (2002). The warming trend is then followed by a cooling trend. Changes in the θ–S of WSDW and WSBW are also documented by Fahrbach et al. (2004), with an amplitude on the order of 0.01°–0.02°C and 0.001–0.002 psu, respectively. They proposed that these changes are caused by variations in atmospheric circulation in response to climate modes such as the Antarctic Circumpolar Wave (ACW) and the Southern Annular Mode (SAM), which can impact the inflow of ACC waters into the Weddell Sea.

Observational studies of low-frequency AABW variability up to centennial time scales are naturally absent given the short measurement record available. Modeling studies investigating AABW variability on interannual to decadal time scales have recently emerged, such as those by Stössel and Kim (1998; 2001) using a coupled sea ice–ocean GCM. Stössel and Kim (1998) found a 4-yr oscillation in AABW outflow and ACC transport confined within the Weddell Sea–Drake Passage region, generated internally by the sea ice–ocean system. By switching the wind forcing from monthly climatological to daily values, Stössel and Kim (2001) found a decadal mode associated with enhanced convection in the southern Weddell Sea, which they suggested could be induced by entrainment of anomalous CDW. The Stössel and Kim studies above imply the importance of an active dynamic–thermodynamic sea ice model for a meaningful investigation of high-latitude variability (see also Stössel et al. 1998). In this study, we extend the investigation of AABW variability and its mechanisms to a fully coupled global atmosphere–ice–ocean model integrated over multimillennial time scales.

The strength of the overturning cell emanating in the polar region of the Southern Ocean (Fig. 3a, far left) is widely used in modeling studies to represent the strength of AABW production (e.g., Drijfhout et al. 1996; Brix and Gerdes 2003). This cell strength is difficult to estimate from observations (Latif et al. 2004). Instead, volumetric analyses based on chlorofluorocarbon and mass budgets are generally used (Orsi et al. 1999). In this study, we will simply analyze variability in AABW formation rates via variability in the polar meridional overturning cell. We will also assess variability in AABW θS properties and how this relates to variability in production rates, and atmosphere–ice–ocean surface property fluxes.

Fig. 3.

(a) Global MOC averaged over 1000 model years. (b) Atlantic sector MOC. Solid (dashed) contours indicate positive (negative) overturning (Sv). The Antarctic overturning cell is highlighted using bold dashed contours. The mean positions of the σ41.50 and σ45.95 isopycnals are shown in (b) in bold contours. (c) Time series of the maximum magnitude of the Antarctic overturning cell for the global mean (gray) and the Atlantic sector (black). The magnitude of the overturning rate and its variability will be analyzed in this study. The horizontal dashed lines indicate one std dev above and below the long-term mean. (d) Time series of the maximum Atlantic sector Antarctic overturning calculated on the ρ3 vertical level (gray) and the z-level counterpart (black). The time snapshot in (d) coincides with the period shown in Fig. 6. Note that in (a) and (b) the MOC is derived from advection without inclusion of the GM terms.

Fig. 3.

(a) Global MOC averaged over 1000 model years. (b) Atlantic sector MOC. Solid (dashed) contours indicate positive (negative) overturning (Sv). The Antarctic overturning cell is highlighted using bold dashed contours. The mean positions of the σ41.50 and σ45.95 isopycnals are shown in (b) in bold contours. (c) Time series of the maximum magnitude of the Antarctic overturning cell for the global mean (gray) and the Atlantic sector (black). The magnitude of the overturning rate and its variability will be analyzed in this study. The horizontal dashed lines indicate one std dev above and below the long-term mean. (d) Time series of the maximum Atlantic sector Antarctic overturning calculated on the ρ3 vertical level (gray) and the z-level counterpart (black). The time snapshot in (d) coincides with the period shown in Fig. 6. Note that in (a) and (b) the MOC is derived from advection without inclusion of the GM terms.

The purpose of this paper is to provide an extensive analysis of AABW overturning and θ–S variability on interannual to centennial time scales in a long-term integration of a coupled climate model. Of particular interest is how the variability in AABW overturning and properties is influenced by surface θS conditions. The coupled model and its bottom water features are described in section 2. Section 3 investigates the link between AABW overturning and surface properties. The mechanisms of variability are investigated in section 4. In section 5, we assess the propagation of θS anomalies into the interior. Finally, the study is summarized in section 6.

2. AABW in the climate model

a. The climate model

The model used in this study is the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mark 2 10 000-yr integrated natural preindustrial CO2 coupled ocean–atmosphere–ice–land surface model. A full description of the model can be found in Gordon and O’Farrell (1997) and Hirst et al. (2000); here we only summarize briefly. We analyze 1000 yr of model data from the latter stages of the 10 000-yr run, by which time the model exhibits very minimal drift.

The atmospheric model is discretized on nine levels in a sigma coordinate system. The parameterization of land surface interactions follows the soil-canopy model of Kowalczyk et al. (1994). The sea ice model includes the cavitating fluid rheology of Flato and Hibler (1990), ice thermodynamics (Semtner 1976), and sea ice dynamics, thus allowing advection and divergence of sea ice by wind stress and ocean currents (see O’Farrell 1998 for details).

The ocean model is based on the Bryan–Cox code (Cox 1984) with a horizontal resolution of ∼5.6° longitude × 3.2° latitude, matching that of the atmospheric component. In the vertical, the model has 21 levels of irregular grid box thickness. The model captures major land masses and bottom bathymetric features (although topographic features are broader than observed because of coarse resolution). The Gent–McWilliams parameterization (GM; Gent and McWilliams 1990; Gent et al. 1995) is implemented with horizontal background diffusivity set at zero. The along-isopycnal mixing of Cox (1987) and Redi (1982) is implemented with an isopycnal tracer diffusivity of 1 × 107 cm2 s−1. Convective overturn is simulated by applying an enhanced vertical diffusivity in regions of static instability. Constant annual but seasonally varying air–sea flux adjustments (heat, freshwater, and wind stress) are included in the coupling between the ocean and atmosphere (and ocean and sea ice) to reduce long-term climate drift. There are no flux corrections applied between the atmosphere and sea ice components. The root-mean-square of the flux adjustment terms over the Southern Ocean south of 50°S is 33.5 W m−2 for heat and 0.69 m yr−1 for freshwater (Hirst et al. 2000). There is no Newtonian damping component to these flux adjustment terms.

The implementation of the GM eddy-induced mixing parameterization allows the elimination of background horizontal diffusivity in the model. This is worth mentioning because it has an important implication for AABW formation in the model. The inclusion of GM results in (i) a better subsurface stratification, (ii) colder and more saline (and therefore denser) deep waters because of the flattening of isopycnals, and (iii) denser downslope flows due to a lack of erosion by unrealistic horizontal diffusive fluxes (e.g., Hirst and McDougall 1996). The GM parameterization thus results in much reduced open-ocean convection, especially at high southern latitudes (Hirst et al. 2000). This effect is particularly desirable because more AABW tends to form via near-boundary convection adjacent to the Antarctic coast, exhibiting a closer correspondence to the real system. Furthermore, the reduction of Southern Ocean spurious convection in turn allows for smaller flux adjustment terms in the region. A comparison between this simulation and a more recent version without flux adjustment shows no obvious influence of the flux adjustment terms on the model’s climate variability (Hunt 2004). The favorable effects of GM on ocean water masses, together with the fully coupled nature of the model and its efficient computational cost for multimillenial integrations, allow us to implement the model for investigating large-scale AABW variability and its mechanisms on long time scales.

b. Modeled AABW

The spreading and ventilation pathways of AABW are illustrated in Fig. 1. This diagram shows passive tracer concentration at the bottommost cells at 50 and 150 yr after the release of tracer at the surface where it is set to 100% (after O’Farrell 2002). The bottom current velocities and bathymetry contours are also shown in Fig. 1. The tracer is at highest concentrations in the Weddell Sea, with a second weaker signal originating in the Ross Sea. Because the model AABW is predominantly produced in the Weddell Sea, we will focus our analyses on the Weddell Sea variety of AABW.

Fig. 1.

Passive tracer concentration at the model’s bottommost ocean grid boxes at (a) 50 and (b) 150 yr after release at the surface. The corresponding mean current velocities at the bottommost level are shown by the velocity vectors. The bottommost ocean grid boxes can correspond to the top of ridges, but they more generally track the abyssal oceans. The model bottom topography is presented by thick contours marking the 4000-m isobath and thin contours marking the 3000-m isobath.

Fig. 1.

Passive tracer concentration at the model’s bottommost ocean grid boxes at (a) 50 and (b) 150 yr after release at the surface. The corresponding mean current velocities at the bottommost level are shown by the velocity vectors. The bottommost ocean grid boxes can correspond to the top of ridges, but they more generally track the abyssal oceans. The model bottom topography is presented by thick contours marking the 4000-m isobath and thin contours marking the 3000-m isobath.

Generally speaking, the model’s advective time scales will be slower than observed, and the pathways of AABW ventilation will be broader compared to the real system. Nonetheless, as in observations, the tracer from the Weddell Sea flows north westward and eastward, spreading into the Argentine Basin and the Weddell-Enderby Plain, respectively (Orsi et al. 1999). The overflow of tracer into the Scotia Sea, reaching the Drake Passage, is also consistent with observations of WSDW (Naveira Garabato et al. 2002). However, it may be noted that there is excessive intrusion of tracer from the Cape Basin into the Angola Basin in the eastern Atlantic, in contrast to the real ocean where there is thought to be virtually no or only little bottom water of southern origin found in the Angola Basin (Reid 1989; Larqué et al. 1997). This discrepancy is likely due to the unresolved obstruction of the Walvis Ridge in the model. Furthermore, the tracer concentration minimum in the Brazil Basin likely results from the unresolved Vema and Hunter Channels, which would otherwise allow inflow of WSDW from the Argentine Basin (Larqué et al. 1997; Hogg et al. 1999). Eastward spreading of WSBW into the Indian Ocean is apparent with a decreasing tracer concentration due to mixing with the overlying CDW. The bottom flow from the Weddell Basin into the Mozambique and Crozet Basins is consistent with the findings of Mantyla and Reid (1995) and Haine et al. (1998). Although the model deep and bottom waters are not as saline or dense as in the real ocean (Hirst et al. 2000), the model overall reproduces key features of Weddell Sea deep and bottom water ventilation rates and pathways. This aspect of the model, coupled with its inexpensive computation, is what makes it relevant for studying the mechanisms driving AABW variability on long time scales and how this variability is transferred into the ocean interior.

The positive velocities at 68°S within the Weddell Sea (shown in Fig. 2) indicate WSDW/WSBW as the origin of AABW in the Atlantic sector. These water masses are contained underneath an isopycnal surface (labeled as σ41.50; solid contours in Figs. 2 and 3b) separating AABW from CDW. This isopycnal surface captures waters with relatively high tracer concentration as far north as the equator, thus satisfying the definition of AABW (i.e., waters sourced from the Antarctic surface that are eventually found in the abyssal oceanic basins). Note that σ41.50 is a “patched” potential density surface corresponding to ρ3 = 1041.50 kg m−3, locally referenced over five pressure levels: 0, 1000, 2000, 3000, and 4000 db (see Reid 1994 for details on the construction of patched density surfaces). A deeper potential density surface corresponding to ρ4 = 1045.95 kg m−3 (referenced to 4000 db; σ45.95 surface hereafter) is also shown in Fig. 2 (dashed contour) and Fig. 3b.

Fig. 2.

Meridional velocity along a circumpolar transect at 68.5°S. The mean position of the σ41.50 (σ45.95) isopycnal is marked by solid (dashed) contours (see text for definition of the σ41.50 and σ45.95 surfaces).

Fig. 2.

Meridional velocity along a circumpolar transect at 68.5°S. The mean position of the σ41.50 (σ45.95) isopycnal is marked by solid (dashed) contours (see text for definition of the σ41.50 and σ45.95 surfaces).

The outflow of WSDW/WSBW is compensated for by the inflow of WDW, which is embodied within the region of negative velocities above σ41.50 in the Weddell Sea (Fig. 2). This inflow–outflow regime constitutes the Antarctic meridional overturning circulation (MOC) in the Atlantic sector shown in Fig. 3b. The global meridional overturning in the model is shown in Fig. 3a, depicting the meridional cells of the World Ocean in a zonally integrated perspective. The strength of the negative Antarctic overturning cell south of 60°S is commonly taken to be the AABW formation rate in ocean GCMs (labeled in Fig. 3a). In the model, this overturning cell represents a maximum transport of up to 10.5 Sv (1 Sv ≡ 106 m3 s−1) with a mean of 8.5 Sv, at the lower end of the observed range of 5–15 Sv (e.g., Gill 1973; Carmack 1977; Jacobs et al. 1985; Orsi et al. 1999). The bottom water layer below σ41.50 (shown in Fig. 3b) captures the Atlantic sector of the Antarctic cell and the lower portion of the Atlantic sector abyssal cell to the north. It may be noted that the northward penetration of AABW into the North Atlantic, as suggested by the abyssal cell in Fig. 3b, is too far north in the model because of the weak formation of lower North Atlantic Deep Water—a common problem in ocean GCMs (England and Holloway 1998). The time series of the Atlantic sector overturning is compared to that of the global Antarctic overturning cell in Fig. 3c, demonstrating that AABW in the model is predominantly sourced in the Weddell Sea. The variance of the Atlantic sector Antarctic overturning accounts for about 70% of the total variability of global AABW production.

Figure 4 demonstrates that waters on σ45.95 are generally more rapidly ventilated, colder, and more saline than those on σ41.50. Specifically, Fig. 4a shows a scatterplot of tracer concentration along σ41.50 and along the bottommost cells against the tracer concentration along σ45.95 in the Atlantic sector. Relative to tracer concentrations on σ45.95, σ41.50 contains lower concentration when the tracer concentration on σ45.95 is lower than 75%. The two layers exhibit comparable concentrations when the concentration is higher than 75%. The opposite holds for the bottommost cells, with σ45.95 less ventilated as it overlies the bottom grid cells. This suggests that the model σ41.50 isopycnal captures the WSDW whereas σ45.95 marks the WSBW layer, both of which are well ventilated adjacent to Antarctica. Away from the Antarctic margin, the ventilation on σ41.50 decreases whereas the bottom layers are more rapidly flushed by the northward spreading Weddell Sea bottom waters, which are cold and saline (Figs. 4b,c).

Fig. 4.

(a) Tracer concentration at 150 yr after release, (b) potential temperature, and (c) salinity for the Atlantic sector on σ41.50 vs that on σ45.95 (dots) and for the bottommost model level vs σ45.95 concentration (crosses). WSBW can be taken to lie on σ45.95, whereas WSDW lies on σ41.50. See text for further details.

Fig. 4.

(a) Tracer concentration at 150 yr after release, (b) potential temperature, and (c) salinity for the Atlantic sector on σ41.50 vs that on σ45.95 (dots) and for the bottommost model level vs σ45.95 concentration (crosses). WSBW can be taken to lie on σ45.95, whereas WSDW lies on σ41.50. See text for further details.

3. Variability of Weddell Sea Bottom Water

a. A meridional overturning representation of outflow and sinking

In this section, we demonstrate that the maximum rates of the Atlantic sector Antarctic overturning (denoted ψatl hereafter; see Fig. 3) can be adopted to conveniently represent the sinking and outflow rates of the Weddell Sea bottom waters in the model. In this case, examining the variability of sinking and outflow rates of Weddell Sea deep and bottom waters is equivalent to examining the variability of ψatl.

The outflow of WSDW/WSBW can be represented by the integral of meridional velocities (υ) within the sector 65°W–3°E at 68°S (Fig. 2) calculated as

 
formula

where zσ is the mean depth of σ41.50 below 1500 m, R is the radius of the earth, dz is the thickness of grid box at a model depth level, and is the longitudinal width of the model grid. The integral in Eq. (1) purposefully includes both northward and southward velocities to ensure that any local recirculations are not added to the net outflow diagnostic. Similarly, the inflow of WDW is calculated as the integral of meridional velocities from the surface to above the depth of σ41.50:

 
formula

The integral of downward velocities south of 68°S is also computed and is maximum at 1500-m depth, where ψatl is most rapid. Table 1 summarizes the mean values and ranges of the above integrated transports as well as their correlation coefficients against ψatl. The average value of the above integral transports is 8.0 Sv, with a range of ∼4 Sv. The correlations in Table 1 suggest that high ψatl corresponds to high sinking and outflow of Weddell Sea deep and bottom waters, leading to high inflow of WDW. These correlations of near unity over a 1000-yr time series imply that ψatl is an accurate estimate of Weddell Sea deep and bottom water transports in the model. For the rest of this study, we use ψatl as a direct measure of WSDW/WSBW formation and outflow in the model. Although this is certainly valid for the Atlantic sector, the global meridional overturning may not necessarily be a good estimate of the total production of AABW (EN07). A simple but accurate diagnosis of AABW outflow, both in models and observations, remains a topic of ongoing research.

Table 1.

The mean and range of transports within the Weddell Sea. The term ψatl is the magnitude of the Antarctic overturning cell shown in Fig. 3b. The transports are calculated within the proximity of the Weddell Sea (between 3°E and 65°W). “Sinking at 1500 m” refers to the sinking of bottom water defined as the integral of downward velocities south of 68°S at 1500-m depth where ψatl is most rapid. “Outflow” refers to the integral of meridional velocities at 68°S underneath the σ41.50 surface and below 1500-m depth [see Eq. (1); refer to Fig. 2], and “inflow” refers to the integral of meridional velocities from the surface to the depth of the σ41.50 surface. The maximum correlation coefficients against the Atlantic sector Antarctic overturning are shown with the indicated time lags (years) in brackets. A positive time lag indicates ψatl leading the specified variable.

The mean and range of transports within the Weddell Sea. The term ψatl is the magnitude of the Antarctic overturning cell shown in Fig. 3b. The transports are calculated within the proximity of the Weddell Sea (between 3°E and 65°W). “Sinking at 1500 m” refers to the sinking of bottom water defined as the integral of downward velocities south of 68°S at 1500-m depth where ψatl is most rapid. “Outflow” refers to the integral of meridional velocities at 68°S underneath the σ41.50 surface and below 1500-m depth [see Eq. (1); refer to Fig. 2], and “inflow” refers to the integral of meridional velocities from the surface to the depth of the σ41.50 surface. The maximum correlation coefficients against the Atlantic sector Antarctic overturning are shown with the indicated time lags (years) in brackets. A positive time lag indicates ψatl leading the specified variable.
The mean and range of transports within the Weddell Sea. The term ψatl is the magnitude of the Antarctic overturning cell shown in Fig. 3b. The transports are calculated within the proximity of the Weddell Sea (between 3°E and 65°W). “Sinking at 1500 m” refers to the sinking of bottom water defined as the integral of downward velocities south of 68°S at 1500-m depth where ψatl is most rapid. “Outflow” refers to the integral of meridional velocities at 68°S underneath the σ41.50 surface and below 1500-m depth [see Eq. (1); refer to Fig. 2], and “inflow” refers to the integral of meridional velocities from the surface to the depth of the σ41.50 surface. The maximum correlation coefficients against the Atlantic sector Antarctic overturning are shown with the indicated time lags (years) in brackets. A positive time lag indicates ψatl leading the specified variable.

The MOC is often also viewed in the density–latitude plane. The model’s Atlantic sector MOC in density coordinates was calculated for the full 1000-yr period and compared to the latitude–depth ψatl diagnostic. As demonstrated in Fig. 3d for an arbitrary 200-yr record, the time series of AABW formation in density coordinates (ψatl|σ) is highly significantly correlated to the z-level overturning (ψatl), with a correlation coefficient (r) of 0.86, in which ψatl leads ψatl|σ by 1 yr. However, the ψatl|σ metric is more weakly correlated to the meridional transport metrics of Eqs. (1) and (2) (r ≈ 0.6) than when using ψatl (r ≈ 0.9). We thus find it more suitable to employ the z-level overturning diagnostic of bottom water production in this study.

b. Overturning variability linked to surface buoyancy

The extent to which variability in surface buoyancy impacts the rates of deep and bottom water outflow and formation is assessed here. Figure 5 shows lagged-correlation maps in which surface density leads ψatl by 2 and 4 yr, as well as the 0-yr lag analysis. Significant correlations occur largely in the Weddell Sea, with some additional regions of high correlation to the west of the Antarctic Peninsula. The highest correlation (r = 0.72) is seen in the central Weddell Sea when surface density leads ψatl by about 3 yr. This equates to an increase in surface density being followed by an enhanced overturning a few years later. Taking a spatial average over the region indicated in Fig. 5, the time series of the sea surface salinity SSSwed and temperature SSTwed are plotted against ψatl in Fig. 6. It is apparent that variations in ψatl are accompanied by fluctuations in SSSwed and SSTwed in such a way that anomalously saline and warm surface waters lead to a more vigorous overturning of deep and bottom waters. The positive correlation between the overturning and each of sea surface salinity (SSS), sea surface temperature (SST), and surface density implies that SSS controls density, and thus overturning, because SST would be negatively correlated to both overturning and density if it were the driving component.

Fig. 5.

Lagged correlations between sea surface density (SSD) and ψatl in which surface density leads ψatl by the indicated lag in years. The correlation maps focus on the Weddell Sea region. The correlations are based on 200 yr of model data. Correlations above ≈0.2 are significant at the 95% confidence level.

Fig. 5.

Lagged correlations between sea surface density (SSD) and ψatl in which surface density leads ψatl by the indicated lag in years. The correlation maps focus on the Weddell Sea region. The correlations are based on 200 yr of model data. Correlations above ≈0.2 are significant at the 95% confidence level.

Fig. 6.

Time series of the ψatl vs (a) SSS and (b) SST, spatially averaged over the Weddell Sea as indicated by the box in Fig. 5 (denoted as SSSwed and SSTwed hereafter).

Fig. 6.

Time series of the ψatl vs (a) SSS and (b) SST, spatially averaged over the Weddell Sea as indicated by the box in Fig. 5 (denoted as SSSwed and SSTwed hereafter).

The power spectrum of ψatl is presented in Fig. 7a, which shows spectral peaks at periods over interannual to centennial time scales. It can be seen in Fig. 7b that SSS also exhibits spectral peaks similar to those of ψatl on decadal to centennial time scales. On the other hand, the only spectral peaks of SST that coincide with those of SSS and ψatl are the 21-yr and 32-yr periodicities (Fig. 7c). Furthermore, it is noted that the correlations for ψatl are higher against SSSwed (r = 0.78) than SSTwed (r = 0.52), with ψatl lagging surface θS by 3 yr. This suggests that SSS is a better proxy for the AABW overturning cell for paleoclimatic reconstructions.

Fig. 7.

Power spectral density of (a) ψatl, (b) SSSwed, and SSTwed. The dashed curve indicates the fitted red-noise spectrum at the 90% confidence level.

Fig. 7.

Power spectral density of (a) ψatl, (b) SSSwed, and SSTwed. The dashed curve indicates the fitted red-noise spectrum at the 90% confidence level.

The surface density perturbation signals are transmitted to the bottom depth as illustrated in Fig. 8a, showing lagged correlations between ψatl and the spatially averaged density from the surface to the deepest model level. A similar correlation analysis for θS (Figs. 8b,c) reveals positive (negative) θS anomalies created at the surface a few years prior to an enhanced (weakened) overturning. Although salinity is positively correlated to ψatl at all depth levels (Fig. 8b), θ is negatively correlated below the mixed layer (Fig. 8c). This is because cooling (warming) in the interior is a result of enhanced (reduced) convection, advecting more (less) cold water from the surface layer into the interior in response to the surface density increase (decrease). The vertical transmission of surface density and salinity anomalies into the interior is confirmed by Figs. 8d,e, showing lagged correlations between density and salinity at the surface and those at depth. However, the correlation pattern for temperature (Fig. 8f) is in contrast to that shown in Fig. 8c because the associated convective overturning events are driven by surface salinity anomalies, not temperature anomalies, which results in a stronger subsurface–interior connection.

Fig. 8.

(top) Lagged correlation between the spatially averaged (a) density, (b) salinity, and (c) temperature in the Weddell Sea vs ψatl. (bottom) Lagged correlation between the surface and the regional depth profile of (d) density, (e) salinity, and (f) temperature. Only correlation values exceeding ±0.2 (above the 95% confidence level) are contoured. Positive correlations are shown in solid contours and gray shading. Negative correlations are shown in dashed contours. Negative (positive) time lags indicate that the first mentioned variables lead (lag) the latter. The tracer variables are spatially averaged over the region indicated in Fig. 5.

Fig. 8.

(top) Lagged correlation between the spatially averaged (a) density, (b) salinity, and (c) temperature in the Weddell Sea vs ψatl. (bottom) Lagged correlation between the surface and the regional depth profile of (d) density, (e) salinity, and (f) temperature. Only correlation values exceeding ±0.2 (above the 95% confidence level) are contoured. Positive correlations are shown in solid contours and gray shading. Negative correlations are shown in dashed contours. Negative (positive) time lags indicate that the first mentioned variables lead (lag) the latter. The tracer variables are spatially averaged over the region indicated in Fig. 5.

The θS anomaly distributions at various depths in connection with overturning variability are depicted in Fig. 9. Here we separate the θS anomalies that correspond to years of anomalously high ψatl from those occurring during anomalously low ψatl, at the time lags at which the correlation of density and ψatl is at a maximum. Years of anomalously high and low ψatl are defined as those when ψatl anomalies exceed one standard deviation above and below the long-term mean. These θS anomalies are presented in αθ′–βS′ space in Fig. 9 so that the dominance of θ versus S in controlling the density variations can be assessed. A similar analysis can be found in Santoso et al. (2006; see their appendix). However, here we consider deviations from the 1000-yr mean as denoted by θ′, S′, in contrast to Santoso et al. (2006) who considered year-to-year changes (θt, St). It can be seen that the θS anomaly distributions during low ψatl years are almost the mirror image of the anomalies for high ψatl years. Thus, we limit the following discussion to the case of high overturning anomalies.

Fig. 9.

The θ–S anomalies at various fixed depths in αθ–βS′ space at the indicated time lags of anomalously high (dark dots) and low (light dots) ψatl. The time lags are determined by the maximum lagged correlations of the density at the specified depth vs ψatl. The composite averages are shown by the thick dark and light lines. The abbreviations WF, WS, CS, and CF indicate the signs of the θ–S anomalies, corresponding to warming–freshening, warming–salination, cooling–salination, and cooling–freshening, respectively. The dashed line at a 45° angle indicates perfect density compensation of θS variations (i.e., Rρ = 1). Details of the analysis can be found in the appendix of Santoso et al. (2006). The dashed line at Rρ = −1 at a 135° angle indicates maximum density instability (i.e., Rρ = 1).

Fig. 9.

The θ–S anomalies at various fixed depths in αθ–βS′ space at the indicated time lags of anomalously high (dark dots) and low (light dots) ψatl. The time lags are determined by the maximum lagged correlations of the density at the specified depth vs ψatl. The composite averages are shown by the thick dark and light lines. The abbreviations WF, WS, CS, and CF indicate the signs of the θ–S anomalies, corresponding to warming–freshening, warming–salination, cooling–salination, and cooling–freshening, respectively. The dashed line at a 45° angle indicates perfect density compensation of θS variations (i.e., Rρ = 1). Details of the analysis can be found in the appendix of Santoso et al. (2006). The dashed line at Rρ = −1 at a 135° angle indicates maximum density instability (i.e., Rρ = 1).

At the surface (Fig. 9a), the density anomalies are distributed in the warming–salination regime below the absolute density compensation line where Rρ = 1. The average of the density ratios Rρ = αθ′/(βS′) is ≈0.1. This implies the dominance of surface salinity increase over warming on the positive density perturbations that ultimately set vigorous overturning. In the interior, such as at 410, 2125, and 4375 m (Figs. 9b–d), the distribution of density anomalies leaks into the cooling regime (below Rρ = 0). This illustrates the effect of convective adjustment as described above. It may be noted that the anomaly distribution becomes more dispersed with increasing depth, intruding into the freshening regime occasionally. However, in general the composite anomalies shown in Fig. 9 suggest that years of high bottom water formation and outflow are initiated by salination and warming of surface waters, which then leads to anomalous convection, resulting in cooler and saltier bottom waters at fixed depths. The opposite holds during years of low bottom water production. The composites of θS variations at constant density levels do not show such coherent patterns (not shown), likely due to spatial aliasing (refer to section 5 for description of the spatial patterns of the θS anomalies).

4. Mechanisms of variability

It was demonstrated in section 3b that fluctuations in Weddell Sea Bottom Water production are associated with salinity-driven surface density perturbations accompanied by positively correlated SST and SSS anomalies (see also Figs. 6a,b). To reveal the mechanisms involved in setting these variations in SSS and SST, surface heat and salt budget analyses are conducted. First note that the freshwater flux into the ocean in the coupled model can be seen as an equivalent negative salt flux into the ocean. For brevity, this “equivalent salt flux” is hereafter simply referred to as the “salt flux.” Comparisons of the standard deviations of the budget terms spatially averaged over the Weddell Sea (see the boxed region in Fig. 5) are presented in Tables 2 and 3. Net surface heat and salt fluxes dominate the annual-mean SST and SSS variability. Shortwave radiation (Qsolar) is found to dominate the surface heat flux variability, whereas the surface freshwater flux is dominated by variations in the sea ice meltwater rate. This dominance of Qsolar and sea ice meltwater is robust at all time scales, as confirmed by heat–salt budget analyses on data filtered with various bandpass frequencies (not shown). Our analysis suggests that variations in sea ice coverage play a crucial role in regulating Weddell Sea surface water density, via its direct link to variations in solar heat flux and the sea ice meltwater/brine rejection rate.

Table 2.

Standard deviation of the annual-mean surface heat budget terms over 1000 yr, spatially averaged over the region indicated in Fig. 5.

Standard deviation of the annual-mean surface heat budget terms over 1000 yr, spatially averaged over the region indicated in Fig. 5.
Standard deviation of the annual-mean surface heat budget terms over 1000 yr, spatially averaged over the region indicated in Fig. 5.
Table 3.

Standard deviation of surface salinity budget terms. The terms are calculated as for those in Table 2. For consistency of units, evaporation and precipitation have been converted to equivalent salt fluxes (psu s−1). Note that the salt flux into the ocean referred to here is equivalent to a negative freshwater flux in the coupled model.

Standard deviation of surface salinity budget terms. The terms are calculated as for those in Table 2. For consistency of units, evaporation and precipitation have been converted to equivalent salt fluxes (psu s−1). Note that the salt flux into the ocean referred to here is equivalent to a negative freshwater flux in the coupled model.
Standard deviation of surface salinity budget terms. The terms are calculated as for those in Table 2. For consistency of units, evaporation and precipitation have been converted to equivalent salt fluxes (psu s−1). Note that the salt flux into the ocean referred to here is equivalent to a negative freshwater flux in the coupled model.

The composite means of sea ice concentration, ice–ocean salt flux, and solar heat flux for years of high-salinity Weddell Sea surface water are shown in Fig. 10. The high-salinity years are those when the spatially averaged salinity within the indicated region exceeds one standard deviation above the long-term mean. Our focus here is on SSS because salinity, not temperature, regulates surface density perturbations in the Weddell Sea bottom water formation region (section 3b). Seasonal effects are shown by presenting the annual-mean, summer (averaged over December–March), and winter (June–September) composites. The composites for the low-SSS-anomaly years are not displayed as they are more or less the mirror image of the high-salinity composites. Accordingly, our discussion will focus on the results of the high-salinity composite analysis.

Fig. 10.

Composite maps of (left column) annual mean, (middle column) summer, and (right column) winter mean anomalies of (a) ice concentration, (b) ice-ocean salt flux, and (c) shortwave radiative flux, calculated based on the high-salinity years (i.e., when SSSwed exceeds one std dev unit above the mean). In (a) and (b), positive sign indicates an increase in ice concentration and ice-ocean salt fluxes, respectively. In (c), positive sign indicates an increased solar radiation (i.e., increased heating of the ocean). The sea ice salt flux into the ocean referred to here can be equivalently interpreted as a negative freshwater flux.

Fig. 10.

Composite maps of (left column) annual mean, (middle column) summer, and (right column) winter mean anomalies of (a) ice concentration, (b) ice-ocean salt flux, and (c) shortwave radiative flux, calculated based on the high-salinity years (i.e., when SSSwed exceeds one std dev unit above the mean). In (a) and (b), positive sign indicates an increase in ice concentration and ice-ocean salt fluxes, respectively. In (c), positive sign indicates an increased solar radiation (i.e., increased heating of the ocean). The sea ice salt flux into the ocean referred to here can be equivalently interpreted as a negative freshwater flux.

Surprisingly, the annual-mean anomalies in the region of interest are dominated by summertime variability (Fig. 10). This is supported by the observations of Zwally et al. (2002), who documented larger long-term changes in sea ice coverage in summer than in winter over the period 1979–98. During years of high Weddell Sea surface salinity, summer ice coverage is anomalously low (Fig. 10a), meltwater input is low1 (Fig. 10b), and incoming solar radiation is anomalously high (Fig. 10c). Anomalies during winter are, in contrast, generally weak within the Weddell Sea, apart from higher than average brine rejection in the southwest region (Fig. 10b, right). The anomalous pattern of shortwave radiation absorbed by the ocean over summer (Fig. 10c, middle) coincides with that of low sea ice concentration, confirming the link between sea ice coverage and surface heat fluxes in regulating SST variability. The winter months, however, exhibit little anomalous sea ice concentration (Fig. 10a, right), yet they are accompanied by positive ice-ocean salt flux anomalies (Fig. 10b, right). This counterintuitive result will be explained below.

Figure 11 presents the composites of the annually averaged SSS, SST, wind stresses, and surface net salt flux for both the high- and low-salinity composite means. Periods of unusually high surface salinity (Fig. 11a) and temperature (Fig. 11b) are accompanied by strengthened westerlies (Fig. 11c) and anomalous katabatic winds (Fig. 11d), which drive sea ice northward in the western flank of the Weddell Gyre (Fig. 10a, left-hand side; see also Uotila et al. 2000; Harms et al. 2001). This explains the reduction in sea ice concentration in the west during summer (Fig. 10a, middle) and the increase in sea ice to the north in winter (Fig. 10a, right). Consequently, the removal of sea ice over the region reduces the amount of ice available for melting in summer, thus explaining the low meltwater flux shown in Fig. 10b (middle). The overall reduction in the summer sea ice coverage allows enhanced ocean cooling by latent and sensible heat fluxes (not shown), although the warming by enhanced incoming solar radiation still dominates. Approaching winter, enhanced atmospheric cooling over the larger than normal ice-free area leads to higher than average brine rejection (Fig. 10b), with the ice concentration itself merely recovering to normal wintertime levels (Fig. 10a, right). This indicates that a negative ice-ocean feedback loop is limiting wintertime ice anomalies despite substantial variations in the summertime ice coverage and seasonal ice-ocean salt fluxes. The ice-ocean salt flux composite mean dominates the net air– or ice–ocean salt flux anomaly (Fig. 11e) as demonstrated in Table 3. The mechanism described above is illustrated by the schematic diagram shown in Fig. 14.

Fig. 11.

Composite maps of annual-mean (a) SSS, (b) SST, (c) zonal wind stress, (d) meridional wind stress, and (e) net air–ice-ocean salt flux based on the (left) high and (right) low Weddell Sea salinity years. The region in the Weddell Sea used to define high- and low-salinity years is indicated.

Fig. 11.

Composite maps of annual-mean (a) SSS, (b) SST, (c) zonal wind stress, (d) meridional wind stress, and (e) net air–ice-ocean salt flux based on the (left) high and (right) low Weddell Sea salinity years. The region in the Weddell Sea used to define high- and low-salinity years is indicated.

Fig. 14.

Life cycle of the Weddell Sea overturning anomalies linked to surface salinity and temperature as described in the text (section 4). (a) Anomalously strong westerlies and katabatic winds drive enhanced sea ice drift, resulting in a reduction of sea ice available for summer melting, thus creating a positive ice-ocean salt flux anomaly and increased absorption of solar radiation into the ocean. (b) As winter approaches, the larger than normal ice-free area exposed to atmospheric cooling leads to higher than average brine rejection, driving stronger overturning. Conversely, at other times, weaker wind forcing leads to low overturning as illustrated in (c) and (d). In this way, surface variability initiates an internal negative feedback oscillation. A half-oscillation period is illustrated by the strengthening of bottom water outflow (b) approximately 5 yr after the generation of surface anomalies, leading to stronger inflow of WDW [also seen in (b)], which then leads to (c) enhanced heat injection into the mixed layer about 1 yr later. This results in (d) enhanced sea ice melting and thus weaker bottom water outflow and WDW inflow, which makes up the second half of the oscillation. This internal negative feedback loop generates an overturning oscillation on interdecadal time scales (see also Fig. 13).

Fig. 14.

Life cycle of the Weddell Sea overturning anomalies linked to surface salinity and temperature as described in the text (section 4). (a) Anomalously strong westerlies and katabatic winds drive enhanced sea ice drift, resulting in a reduction of sea ice available for summer melting, thus creating a positive ice-ocean salt flux anomaly and increased absorption of solar radiation into the ocean. (b) As winter approaches, the larger than normal ice-free area exposed to atmospheric cooling leads to higher than average brine rejection, driving stronger overturning. Conversely, at other times, weaker wind forcing leads to low overturning as illustrated in (c) and (d). In this way, surface variability initiates an internal negative feedback oscillation. A half-oscillation period is illustrated by the strengthening of bottom water outflow (b) approximately 5 yr after the generation of surface anomalies, leading to stronger inflow of WDW [also seen in (b)], which then leads to (c) enhanced heat injection into the mixed layer about 1 yr later. This results in (d) enhanced sea ice melting and thus weaker bottom water outflow and WDW inflow, which makes up the second half of the oscillation. This internal negative feedback loop generates an overturning oscillation on interdecadal time scales (see also Fig. 13).

It is interesting to note that an annular pattern appears in the zonal wind stress composites of high- and low-salinity years (Fig. 11c), suggesting an influence of the Southern Annular Mode in forcing SST and SSS variations in the Weddell Sea. An EOF analysis conducted on the annually averaged zonal wind stress (τx) exhibits a zonally symmetric pattern as its dominant mode, accounting for 26% of the total τx variance (Fig. 12a). The corresponding principal component (PC) time series extracted from the analysis (Fig. 12b) is correlated to SSSwed and SSTwed with only a modest correlation coefficient of ≈0.27 but with a 1000-yr time series; this is well above the 99% confidence level, with the PC time series leading by 1 yr. The power spectrum of the PC time series exhibits significant signals above the background noise with peak periods of ∼8 and 30 yr (Fig. 12c), thus contributing to the interannual to interdecadal variations in SSSwed and SSTwed. It is likely that other modes of climate variability, such as the model’s ACW and Pacific–South America (PSA) modes, may also influence the variability; however, further investigation on this topic is beyond the scope of the present study. What is apparent from the above analyses is that sea ice variability plays a key role in Weddell Sea salt and heat content variations, which ultimately drive fluctuations in bottom water formation and outflow rates in the Atlantic sector of the model.

Fig. 12.

EOF analysis of the annual-mean zonal wind stress (τx) showing (a) the spatial map of the first EOF mode and (b) the principal component of the first mode (PC1) accounting for 26.4% of the total τx variance. (c) The power spectrum of PC1 with the fitted white-noise background spectrum at the 95% confidence level.

Fig. 12.

EOF analysis of the annual-mean zonal wind stress (τx) showing (a) the spatial map of the first EOF mode and (b) the principal component of the first mode (PC1) accounting for 26.4% of the total τx variance. (c) The power spectrum of PC1 with the fitted white-noise background spectrum at the 95% confidence level.

Although it is evident that direct atmospheric forcing controls variations in SSS and SST via sea ice variability, there is also evidence of an internal feedback mechanism that modulates the quasiperiodicities observed in ψatl and SSS (refer to Fig. 7). A lagged correlation analysis presented in Fig. 13 explains a negative feedback mechanism involving sea ice, SSS, and the Antarctic overturning (ψatl), as implied by the opposite signs of the correlation coefficients at negative and positive time lags. The correlation analysis is presented based on the raw and bandpass filtered time series to isolate interdecadal signals with periods in the range of 10 to 50 yr. It can be seen that the negative feedback becomes more apparent with the time series filtered to retain only signals of 10–33-yr periodicities. Positive correlations at the positive time lags indicate that the ice-to-ocean salt flux (Hice) leads an increase in ψatl by about 5 yr (Fig. 13a) via higher surface salinity (Fig. 13b), which in turn generates higher-density surface waters (section 3b), leading to enhanced ψatl (Fig. 13c). The negative correlations at negative time lags in Fig. 13a imply that an increase in ψatl (see schematic diagram in Fig. 14b) leads to a reduction in Hice (or equivalently an increase in meltwater; Fig. 14d) with a time lag of about 2 yr. This is because an increase in overturning causes an increased inflow of WDW (see Table 1; Fig. 14b), which provides heat from the subsurface to melt sea ice, thus causing lower SSS (Fig. 14d). This leads to a weakening of overturning, which then reduces the amount of heat injected under the sea ice, creating a higher-salinity anomaly at the surface (Fig. 14b). This negative feedback cycle continues, linking variations in sea ice and overturning via sea surface salinity variability.

Fig. 13.

Lagged correlation of (a) ψatl vs ice-ocean salt flux, (b) ice-ocean salt flux vs SSSwed, and (c) SSSwed vs ψatl, using the raw data (thin curve) and data filtered with a bandpass period of 10–50 yr (gray curve) or 10–33 yr (thick curve). The dashed horizontal lines are the corresponding 95% significance level coefficients for the bandpass-filtered analysis. The dotted line is the 95% significance level for the raw time series analysis. Negative (positive) time lags indicate that the first mentioned variable leads (lags) the latter variable.

Fig. 13.

Lagged correlation of (a) ψatl vs ice-ocean salt flux, (b) ice-ocean salt flux vs SSSwed, and (c) SSSwed vs ψatl, using the raw data (thin curve) and data filtered with a bandpass period of 10–50 yr (gray curve) or 10–33 yr (thick curve). The dashed horizontal lines are the corresponding 95% significance level coefficients for the bandpass-filtered analysis. The dotted line is the 95% significance level for the raw time series analysis. Negative (positive) time lags indicate that the first mentioned variable leads (lags) the latter variable.

5. Propagation of θS anomalies

We have explored how the Atlantic sector of the Antarctic overturning (ψatl) fluctuates with surface properties in the Weddell Sea (section 3b) and the mechanisms that give rise to this variability (section 4). We now shift our attention to the patterns and propagation of θS anomalies into the abyssal Atlantic Ocean. For this purpose, we conduct analyses on σ41.50 as this density surface extends from the Weddell Sea source region to the Atlantic equatorial region. A standard deviation analysis of θS along σ41.50 reveals the largest magnitude variability of ∼0.15°C, 0.01 psu at the Weddell Sea outflow region (Fig. 15). The magnitude of variability is considerably reduced northward into the abyssal Atlantic and eastward into the Indian Ocean. A complex empirical orthogonal function (CEOF) analysis is conducted to extract the spatial and temporal characteristics of various modes of θS variability along σ41.50. Because θS features vary coherently along isopycnal surfaces, it is sufficient to just present the CEOF analysis of salinity on σ41.50. The propagation of θS anomalies is depicted in the CEOF maps in Fig. 16, which present the three leading CEOF modes accounting for 76.5% of the total S variance. These modes are well separated according to the North rule (North et al. 1982). The temporal characteristics of the modes are represented by the principal component time series shown in Fig. 17, together with their power spectra. For comparison, the CEOF temporal characteristics of S variability on a deeper isopycnal surface (σ45.95; equivalent to σ3 ≈ 41.54 kg m−3) are also shown in Fig. 17.

Fig. 15.

Standard deviation of θS on the σ41.50 isopycnal surface. A logarithmic scale has been applied to the color scheme to enhance signal visibility in the interior.

Fig. 15.

Standard deviation of θS on the σ41.50 isopycnal surface. A logarithmic scale has been applied to the color scheme to enhance signal visibility in the interior.

Fig. 16.

(a) Spatial maps of the three leading complex EOF modes of salinity shown at 90° phase intervals along σ41.50. The first (CEOF1), second (CEOF2), and third (CEOF3) modes account for 42.4%, 21.9%, and 12.2% of salinity variance, respectively. An equivalent CEOF analysis of θ shows similar modal structure and so is not shown here. (b) Spatial phase angle CEOF1–3 indicating the direction of phase propagation of each mode from 0° to 360°. Apparent phase discontinuities occur because the phase is defined only between 0° and 360°. The horizontal and vertical lines shown in the CEOF1 map (top row, middle column) mark the position of the transects for the Hovmöller diagrams shown in Figs. 18 and 19. The box in the middle and bottom rows (middle column) indicates the Weddell Sea region.

Fig. 16.

(a) Spatial maps of the three leading complex EOF modes of salinity shown at 90° phase intervals along σ41.50. The first (CEOF1), second (CEOF2), and third (CEOF3) modes account for 42.4%, 21.9%, and 12.2% of salinity variance, respectively. An equivalent CEOF analysis of θ shows similar modal structure and so is not shown here. (b) Spatial phase angle CEOF1–3 indicating the direction of phase propagation of each mode from 0° to 360°. Apparent phase discontinuities occur because the phase is defined only between 0° and 360°. The horizontal and vertical lines shown in the CEOF1 map (top row, middle column) mark the position of the transects for the Hovmöller diagrams shown in Figs. 18 and 19. The box in the middle and bottom rows (middle column) indicates the Weddell Sea region.

Fig. 17.

(left) Principal component time series of CEOF1–3 shown for σ41.50 (black) and σ45.95 (gray). (right) The corresponding power spectral density (PSD) of the principal component time series with an estimated autoregressive-1 background spectrum at the 90% confidence level (dashed curves). A log10 scale is used in frequency to give more weight to the higher-frequency signals and the power spectra are multiplied by frequency to preserve variance.

Fig. 17.

(left) Principal component time series of CEOF1–3 shown for σ41.50 (black) and σ45.95 (gray). (right) The corresponding power spectral density (PSD) of the principal component time series with an estimated autoregressive-1 background spectrum at the 90% confidence level (dashed curves). A log10 scale is used in frequency to give more weight to the higher-frequency signals and the power spectra are multiplied by frequency to preserve variance.

The CEOF analysis (Fig. 16) shows that θS anomalies emitted in the Weddell Sea propagate eastward and then northward into the Atlantic Ocean. The CEOF spatial patterns on σ45.95 show similar patterns to those in Fig. 16 and thus are not shown. CEOF-1 shows a broad region of high variance extending deep into the interior, whereas the higher CEOF modes capture more intense variance close to the surface, where decadal–interdecadal signals are more energetic (Fig. 17). Because of the slow integrative effects of isopycnal mixing, the low-frequency surface variability (Fig. 7) is best preserved as θS anomalies propagate into the ocean interior, while higher modes are damped. This is more apparent for the near centennial time-scale signals in Figs. 7a,b, which are picked up by CEOF-1 (Fig. 17). Inspecting Fig. 17 further, fluctuations on interannual to interdecadal time scales are more prominent on σ45.95 than on σ41.50, where signals of centennial and longer time scales become evident. Also apparent in the PC time series of Fig. 17 (left column) is the fact that the magnitude of θS variability is larger on σ45.95. This is because the deeper bottom waters are more rapidly ventilated than the upper deep waters (see section 2) where mixing with CDW takes place. Indeed, the apparent ∼330-yr signal on σ41.50 matches the dominant time scale of θS variability in the CDW layer (Santoso et al. 2006).

The advective time scale of the propagating θS anomalies along isopycnals is depicted in the Hovmöller diagram presented across 62°S on σ41.50 (Figs. 18a,b; see inset for the zonal transect location). The space–time gradients of the anomalies suggest that the traveling signals cover around 125° longitude over ∼50 yr. At 62°S, this corresponds to a speed of ∼0.4 cm s−1, comparable to the ocean current speeds in the region. Similar diagrams are also presented for the θS anomalies propagating along isobars (Figs. 18d,e), referenced to the long-term averaged depths of the σ41.50 surface. The steep gradient of S anomalies along the σ41.50 isopycnal (Fig. 18a) is also apparent in Fig. 18d, and the sign of the along-isopycnal and along-isobar S anomalies are also in phase. This suggests that salinity variability is dominated by along-isopycnal anomalies. However, this is not the case for θ anomalies, which are related to the effect of isopycnal displacements (i.e., heave). This is evident in the close resemblance between the θ anomaly patterns along isobars (Fig. 18e) and the σ41.50 depth anomalies (hσ) shown in Fig. 18c, both in terms of their phase and space–time gradients. The fact that temperature variability is dominated by isopycnal heaving is further demonstrated by the high mean correlation coefficient of 0.84 between θ|z and h|σ across 62°S, with θ|z leading h|σ by ∼4 yr in the Weddell Sea region.

Fig. 18.

Hovmöller diagram of (a), (b) θS anomalies along the isopycnal, (c) isopycnal depth anomalies, and (d), (e) θS anomalies along isobars on σ41.50 along a zonal transect at 62°S from 60°W to 87°E (see bottom right inset for the transect location). The along-isobar θS anomalies are calculated as deviations from the 1000-yr mean along the mean isopycnal depth of σ41.50.

Fig. 18.

Hovmöller diagram of (a), (b) θS anomalies along the isopycnal, (c) isopycnal depth anomalies, and (d), (e) θS anomalies along isobars on σ41.50 along a zonal transect at 62°S from 60°W to 87°E (see bottom right inset for the transect location). The along-isobar θS anomalies are calculated as deviations from the 1000-yr mean along the mean isopycnal depth of σ41.50.

The rising and deepening of the σ41.50 surface (Fig. 18c) illustrates density anomalies that can be seen propagating eastward from 31°W to 25°E before intercepting a westward wave propagation. Inspection of the gradients of the westward signals suggests that the time taken to travel 45° of longitude is about 5 yr. This implies a speed of up to ∼1.5 cm s−1, which is roughly 3 times the theoretical speed of unforced baroclinic Rossby waves given by c = βgH0/f2, where f is the Coriolis parameter, β the meridional derivative of f, H0 the depth of the σ41.40 surface, and g′ the reduced gravity, calculated using the difference between the average densities above and below σ41.50. A scaling analysis yields typical values of the following parameters in the south Indian sector: g′ = 0.003 m s−2, f = −1.3 × 10−4 s−1, β = 1.14 × 10−11 m−1 s−1, and H0 = 2500 m, which yields c ≈ 0.5 cm s−1 at 60° latitude, matching the background current velocity. As shown by Qiu et al. (1997), fast baroclinic waves traveling higher than twice the theoretical speed are expected be found in subpolar regions of the Southern Hemisphere. In contrast, the speed of the eastward propagating hσ signals in the Atlantic sector is on the order of 0.5 cm s−1, which is comparable to the mean velocity of the background current at that depth.

The northward propagation of θS anomalies into the abyssal Atlantic is further masked by baroclinic wave propagation as shown by the Hovmöller diagrams of θS and hσ anomalies along a meridional section crossing the Argentine Basin (Fig. 19; see inset for the transect location). The signature of the fast baroclinic waves is apparent in the patterns of the along-isobar anomalies (Figs. 19d,e). It is worth mentioning that the northward propagation of the high-frequency signals appears to shut down at about 50°S. This is because the anomalies join the ACC eastward as they flow to the north. Northward propagation into the Argentine Basin mainly involves the low-frequency components via mixing and wave propagation as they meet a southeastward recirculation (not shown). This is also implied from the fact that the tracer concentration on σ41.50 is about 30% in this region after 150 yr of release, compared to more than 50% at the bottommost level (see Fig. 4 for comparison). Nonetheless, the similarity in the frequencies of the along-isopycnal and along-isobar θS anomalies and hσ anomalies (Fig. 19c) suggests that they are linked at the source region and set by a common mechanism (section 4). This also implies that the isopycnal heaving in the interior is a signature of baroclinic wave propagation initiated by the density perturbation in the Weddell Sea.

Fig. 19.

As in Fig. 18, but for the meridional transect shown in the bottom right inset.

Fig. 19.

As in Fig. 18, but for the meridional transect shown in the bottom right inset.

To establish the link between θS anomalies in the interior and the Antarctic overturning (ψatl), the variables shown in Fig. 18 are correlated against ψatl at various time lags (Fig. 20). An increase in ψatl leads to a widespread cooling along isobars (Fig. 20b) and shoaling of isopycnals (Fig. 20e) across ∼100° of longitude over ∼20 yr. On the other hand, we see a dipole structure of S anomalies along isobars (Fig. 20a) and of θS anomalies along the σ41.50 isopycnal (Figs. 20c,d). Hence, an increase in ψatl is associated with warmer and higher salinity water along the isopycnal within the Weddell Sea. The along-isopycnal cooling and freshening further east correspond to periods of anomalously weak overturning. Similar patterns are captured along the meridional transect as presented in Fig. 19; however, they are apparent only south of 40°S. North of this latitude, the θS variability is over time scales that are too long to be statistically resolved by this analysis. The difference in ψatl correlation patterns with θS on isobars and isopycnals suggest that care should be taken when associating θS anomalies in the interior to fluctuations in AABW formation and outflow.

Fig. 20.

Lagged correlation of ψatl vs (a), (b) Sθ at the mean depth of the σ41.50 isopycnal surface, (c), (d) Sθ on σ41.50, and (e) the depth of the σ41.50 surface along the zonal transect shown in Fig. 18. Positive (negative) correlations are shown by solid (dashed) contours. Positive time lags indicate ψatl leading the respective hydrographic variables.

Fig. 20.

Lagged correlation of ψatl vs (a), (b) Sθ at the mean depth of the σ41.50 isopycnal surface, (c), (d) Sθ on σ41.50, and (e) the depth of the σ41.50 surface along the zonal transect shown in Fig. 18. Positive (negative) correlations are shown by solid (dashed) contours. Positive time lags indicate ψatl leading the respective hydrographic variables.

Finally, it is worth mentioning that the magnitude of the maximum decadal θS changes along isopycnals in the model is on the order of 0.1°C and 0.01 psu in the Weddell Sea and 0.01°C and 0.001 psu in the Atlantic interior near 43°S. These are comparable to the magnitude of θS changes on decadal time scales found in observations (Coles et al. 1996; Meredith et al. 2001; Fahrbach et al. 2004) and the study by Stössel and Kim (2001).

6. Summary and conclusions

The natural variability of AABW has been analyzed in a coupled climate model. Examination of passive tracer concentrations suggests that the model AABW is predominantly sourced in the Weddell Sea, with weak contribution from the Ross Sea and insignificant bottom water formation in the Adélie Land region (the latter in contrast to observations). This deficiency is common in coarse-resolution GCMs and likely results from inadequate representation of dense water overflow, convective processes, and surface boundary conditions in that sector of the Southern Ocean. In contrast, the model successfully reproduces key features of bottom water pathways in the Atlantic sector. The focus of the present study is therefore on the Atlantic sector AABW, sourced by WSDW and WSBW.

The Atlantic sector Antarctic overturning was shown to effectively approximate Weddell Sea deep and bottom water transport rates in the model. Thus, we examined variability in ψatl to investigate the variability of WSDW/WSBW formation and outflow. The overturning variability is tightly linked to surface density anomalies in the Weddell Sea, with strong and weak phases of ψatl characterized by composite patterns that are mirror images of each other. During phases of strong overturning anomalies, sea surface density first increases approximately 2 yr prior to the increase in overturning and then continues to increase with depth over the next decade. The increase in surface density is accompanied by positive θS anomalies, indicating that salinity variations control the fluctuations in the overturning, not temperature. The spectral peaks of ψatl were shown to closely match those of SSS over various time scales but coincided with SST at only 20- and 30-yr periods. This implies that the Weddell Sea surface salinity, rather than temperature, should be used in paleo-reconstructions of AABW variability, akin to SST for the North Atlantic thermohaline circulation as shown by Latif et al. (2004). It should be noted, however, that the amount of salt required to form AABW must also vary over much longer time scales as a function of the mean global ocean salinity, which is in turn related to the net volume (and sea level) of the ocean. Thus, this AABW–SSS link has more direct implications for the use of surface salinity in reconstructing AABW formation rates for the past 7000 yr, when the global sea level has been near its present-day value (Fleming et al. 1998).

The changes in convection triggered by surface salinity anomalies transmit the salinity signal to depth. However, temperature anomalies below the mixed layer are of opposite sign than those at the surface. This is a result of convective processes injecting cold and freshwater downward. It is noted that although surface density is ultimately controlled by salinity, the dominance of salinity on density variations at depth is moderated while temperature variations become more apparent. Nonetheless, the surface salinity anomalies prevail at depth, but with a reduced amplitude owing to damping by convective mixing. A similar vertical structure of θS variability on interdecadal time scales was found in an idealized ocean-only model forced by surface mixed boundary conditions (Arzel et al. 2006). Arzel et al. (2006) explain that the anomalous upward injection of warm and saline waters enhances the positively correlated surface θS anomalies and, thus, the growth of density anomalies (i.e., through a positive feedback mechanism). However, no explanation is offered for what drives the periodic oscillation from positive to negative anomalies in their model. The presence of a sea ice component in our model provides a source of freshwater flux anomalies coupled to subsurface heating of WDW inflow. The increased inflow of WDW following vigorous overturning provides surface heat anomalies, inducing sea ice melting and thus the anomalous freshening of surface waters. The overturning is then reduced, leading to a suppressed inflow of WDW and inhibiting sea ice melting. This internal negative feedback loop spikes overturning oscillations on interdecadal time scales (see Figs. 13 and 14).

Direct atmospheric forcing was found to play an important role in generating positively correlated θS anomalies in the Weddell Sea, thus initiating the internal negative feedback mechanism described above. The dominance of the solar heat flux and ice-ocean equivalent salt flux was revealed by a heat and salt budget analysis of the region. By employing composite analyses, we deduced that enhanced westerlies drive enhanced sea ice drift, resulting in a reduction of sea ice available for summer melting. This results in a positive ice-ocean salt flux anomaly and a larger ice-free area for increased absorption of solar radiation by the ocean. Approaching winter, the larger than normal ice-free area exposed to atmospheric cooling leads to higher than average ice regrowth and ensuing brine rejection. This mechanism results in higher temperature and salinity in the region, leading to years of enhanced overturning. The processes described above were summarized by the schematic shown in Fig. 14, which can be naturally induced by known climate modes. Interestingly, a signature of the SAM was revealed by the composite patterns of the zonal winds during years of Weddell Sea salinity anomalies. Here, positive SAM events correspond to increased Weddell Sea surface salinity and generally an increased sea ice extent (see also Hall and Visbeck 2002; Sen Gupta and England 2006). A modest but significant correlation between Weddell Sea surface salinity and the characteristic time series of the SAM in zonal winds implies that SAM events contribute to the model’s bottom water variability.

It is of particular importance to interpret θS changes in the interior in association with overturning fluctuations. To approach this issue, we first presented CEOF analyses of θS anomalies along an isopycnal surface that intercepts the upper layer of WSDW. The propagation of anomalies was then investigated further along cross sections within the South Atlantic. The CEOF analyses reveal a θS dipole pattern emerging in the Weddell Sea on various time scales. The anomalies propagate eastward and then northward into the Atlantic. Fluctuations on interannual to interdecadal time scales are noted to be more prominent in deeper layers because the deeper bottom waters are more rapidly ventilated. Along isobars, salinity variability was shown to be dominated by along-isopycnal propagation of S anomalies. On the other hand, θ variability is dominated by signatures of isopycnal displacements exhibiting propagation of density anomalies with the speed of baroclinic waves (see also Stössel and Kim 2001). A lagged-correlation analysis between θS against the overturning reveals a basin-scale uniform θ anomaly pattern, in contrast to the dipole pattern of salinity. An increase in the overturning is associated with a widespread cooling and shoaling of isopycnals, higher salinity in the western central Weddell Sea, and lower salinity in the eastern Weddell Sea.

Finally, we note that although the mechanisms described here could potentially be the dominant mechanisms driving AABW variability in the real system, some of the results are likely to be sensitive to model parameters such as the resolution and mixing parameterization employed. For instance, the propagation of anomalies would likely be faster in higher-resolution models because ocean currents and topographic features are better resolved. Inclusion of a parameterization of downslope flows could also potentially affect the time scales of variability and further enhance the role of surface waters on AABW variability. Ideally, we would have used a fully coupled global climate model with sufficiently high resolution to explicitly resolve these processes and coastal polynyas, which is not computationally feasible at present. Nonetheless, our study highlights, in the broadest sense, the close links among sea ice, surface salinity, and bottom water variability via atmospheric forcing and internal feedback mechanisms. Our study on AABW variability is an advance on previous studies, given the fully coupled nature of the model combined with its very long integration time.

The link between Antarctic sea ice variability and Southern Hemisphere climate modes has received increased attention in recent years (e.g., Fichefet et al. 2003; Liu et al. 2004; Lefebvre et al. 2004; Sen Gupta and England 2006). Our study demonstrates the interplay between air–sea and ice–sea fluxes, sea surface temperature–salinity, and internal oceanic advection in setting the magnitude and time scales of AABW variability. It will be important to continue this effort to understand how regional to global climate modes control Antarctic sea ice and bottom water variability, particularly as atmospheric greenhouse gas concentrations continue to rise.

Acknowledgments

The authors thank Mark Collier for preparing the model data output and Barrie Hunt and Tony Hirst for access to the 10 000-yr climate model simulations. Siobhan O’Farrell is gratefully acknowledged for providing the passive tracer data used in Figs. 1 and 4. The authors also thank Steve Rintoul, Neil Holbrook, and Siobhan O’Farrell for their helpful comments. Comments and suggestions by two anonymous reviewers helped improve the manuscript. This research was supported by the Australian Research Council and the Australian Antarctic Science Program.

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Footnotes

Corresponding author address: Agus Santoso, Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia. Email: a.santoso@unsw.edu.au

1

The mean ice–ocean equivalent salt flux in summer is negative (i.e., because of melting of sea ice), except over a small region in the eastern Weddell Sea, so the positive ice–ocean salt flux anomaly in Fig. 10b implies anomalously low meltwater input.