1. Introduction
The Labrador Basin, a semienclosed basin in the northwestern North Atlantic (Fig. 1), is one of the few locations in the global ocean where deep convection occurs (Marshall and Schott 1999). Cold air outbreaks during winter remove buoyancy from the weakly stratified water column, triggering convective mixing to depths of 1000–2000 m (Lazier et al. 2002; Yashayaev 2007; Yashayaev and Loder 2017). During convection, the deep ocean becomes ventilated and sequestrates carbon and oxygen from the atmosphere, with important implications for global climate change and marine biological activities (Sabine et al. 2004; Pérez et al. 2013; Koelling et al. 2017). The product of convective mixing is the Labrador Sea Water (LSW), an intermediate-depth water mass with characteristically low salinity, low potential vorticity, and high oxygen content (Fig. 2; Talley and McCartney 1982; Koelling et al. 2017). After formation, LSW is exported from the Labrador Basin to the other subpolar basins, as well as equatorward as part of the lower limb of the Atlantic meridional overturning circulation (AMOC).
Many modeling and paleoceanographic studies have stressed the importance of the Labrador Sea convection to the AMOC transport, with enhanced convection leading to a strengthened AMOC on decadal and longer time scales (Biastoch et al. 2008; Danabasoglu et al. 2012; Thornalley et al. 2018; Zhang et al. 2019; Li et al. 2019; Yeager et al. 2021). For example, according to sensitivity experiments in a suite of ocean circulation models (1/2°–1/12°), Biastoch et al. (2008) have shown that positive AMOC anomalies emerge 1–2 years after the onset of intensified LSW production on decadal time scales. However, estimates of the overturning in the Labrador Sea based on hydrographic transects are quite small [2–5 Sv (1 Sv ≡ 106 m3 s−1); Pickart and Spall 2007; Hall et al. 2013], even in the early 1990s when intensified convection occurred. More recently, direct moored measurements from the Overturning in the Subpolar North Atlantic Program (OSNAP) have shown that the overturning east of Greenland (15–17 Sv) far outweighs the contribution from the Labrador Sea since 2014 (2–3 Sv; Lozier et al. 2019; Li et al. 2021; Fu et al. 2023), another period with intensified convection (Yashayaev and Loder 2017). Collectively, these observation-based studies suggest an overall weak overturning response to convection in the Labrador Sea. The discrepancy between models and observations may be attributed to the common salinity biases in models that significantly impact the simulated density structure in the basin and therefore the overturning strength (Jackson and Petit 2023; Zou et al. 2020a). On the other hand, the Labrador Sea convection may have remote and delayed impacts on the AMOC that are not captured by the observational time scales. For example, using a high-resolution ocean circulation model (1/20°), Böning et al. (2023) suggest that enhanced convection may contribute to overturning east of Greenland by the spreading and entrainment of the dense LSW into the Irminger Sea.
In steady state, the overturning transport in density coordinate along a section equals the diapycnal (i.e., across density surface) volume flux (or the diapycnal transformation) within the basin enclosed by the section and the coastal boundaries (Walin 1982; Speer and Tziperman 1992; Marsh 2000; Grist et al. 2010). Thus, an alternative way to evaluate the contribution of Labrador Sea convection to the AMOC is to estimate the diapycnal volume flux in the basin. A number of studies have estimated the diapycnal transformation induced by air–sea buoyancy flux in the North Atlantic (Speer et al. 1995; Marsh 2000; Myers and Donnelly 2008; Grist et al. 2009, 2014; Desbruyeères et al. 2019). In particular, using an atmospheric reanalysis product, Petit et al. (2020) calculated the mean surface-induced diapycnal transformation during August 2014–May 2016 in the Labrador Sea, which was 1.5 ± 0.7 Sv at σθ =27.70 kg m−3. This number compares favorably to the overturning transport at the same isopycnal over the same time period (2.1 ± 0.3 Sv; Petit et al. 2020) along OSNAP West (Fig. 1), suggesting a dominant role of surface forcing in transforming dense waters that compose the overturning circulation. However, given the short time period considered and the level of uncertainty, it is possible that the system is not in steady state, and other processes, such as diapycnal mixing, play a role.
What is also uncertain is the formation rate of the LSW density layer (see Haine et al. 2008 and Garcia‐Quintana et al. 2019 for reviews) and its forcing attributions. Previous estimates using different approaches, including those based on chlorofluorocarbon inventories (Smethie and Fine 2001; Rhein et al. 2002; LeBel et al. 2008), air–sea flux calculations (Speer and Tziperman 1992; Marsh 2000; Khatiwala et al. 2002; Myers and Donnelly 2008), numerical models (Böning et al. 1996; Marsh et al. 2005; Garcia‐Quintana et al. 2019; Yeager et al. 2021), and inverse methods (Mackay et al. 2020), vary significantly—from 2 to 11 Sv. In addition to the distinct methods used, this large range may also be attributed to the inconsistent definitions of the density range of the LSW layer and the different time periods considered among the studies. Estimates based on numerical simulations are also sensitive to the model resolution and configuration (Garcia‐Quintana et al. 2019). In fact, as pointed out by Haine et al. (2008), many studies did not provide uncertainty estimates for LSW layer formation rates, making it difficult to draw a robust conclusion. Furthermore, past studies mostly focused on formation in response to surface buoyancy forcing. However, OSNAP observations have suggested a possible conversion of the overflow layer waters into the LSW layer (Zou et al. 2020a), probably induced by mixing, which adds another possible forcing mechanism for LSW formation.
So far, much of the attention on transformation and formation has been paid on the diapycnal processes because of their direct linkage to the overturning circulation in density space. However, water masses may experience important thermohaline changes with compensating impacts on density, which cannot be illustrated by density coordinate. For example, using the 21-month observations at OSNAP West, Zou et al. (2020a) reported that the mean maximum diathermal (i.e., across temperature surface) and diahaline (i.e., across salinity surface) transformations were 11–14 Sv, about 3–4 times greater than the maximum diapycnal transformation (3 Sv) in the Labrador Sea, suggesting significant density compensation by the thermal and haline anomalies. The strong diathermal and diahaline transformations are reflected by the stark contrasts in temperature and salinity between the inflow and outflow across the upper Labrador Sea. As shown in Fig. 2, relatively warm and salty Irminger Water (IW) flows into the basin at ∼500 m via the West Greenland Current (WGC; Pacini et al. 2020). By the time the boundary current exits the basin, where it is known as the Labrador Current (LC), it becomes much colder and fresher. This property change has been attributed to the lateral exchange of heat and salt between the boundary current and the basin interior (Cuny et al. 2002; de Jong et al. 2014, 2016), as well as to the convective overturning that takes place within the boundary in response to surface buoyancy loss (Pickart et al. 1997, 2002; Brandt et al. 2007; Palter et al. 2008; MacGilchrist et al. 2020). In particular, the boundary convective overturning may directly bring surface fresh and cold waters of Arctic/Greenland origin down to a few hundreds of meters, resulting in thermohaline anomalies in the boundary current. Using an idealized simulation, a recent study has shown that the cold and fresh anomalies in the boundary current can be attributed to surface heat loss for the former and mixing with freshwater along the Greenland and Labrador shelves for the latter (Bebieva and Lozier 2023). Lacking from these studies is an analysis of observational data that quantitatively links each possible forcing mechanism to the diathermohaline (i.e., across temperature and salinity surfaces) volume fluxes.
The diagnostic framework for water mass transformation (WMT) and formation in thermohaline coordinates has been developed and applied both globally and regionally (Groeskamp et al. 2014a,b; Mackay et al. 2018, 2020; Evans et al. 2014, 2023). This framework establishes an unambiguous linkage between the velocity field and the thermohaline forcing that includes surface heat and freshwater fluxes, as well as diffusive heat and salt fluxes in the interior (Groeskamp et al. 2014a). Thus, the framework is particularly useful in understanding the driving mechanisms for the diathermohaline volume fluxes. For example, using observational and ocean reanalysis datasets, Mackay et al. (2020) and Evans et al. (2023) evaluated volume fluxes in thermohaline coordinates in an extended region from the subpolar North Atlantic to the Bering Strait and revealed the respective role of surface heat flux and interior mixing in driving the thermohaline changes in this area.
With a focus on the Labrador Sea, the goals of this study are to estimate the water mass transformation and formation rates in both density and thermohaline coordinates and to assess their quantitative attributions to the thermohaline forcing from an observational perspective. To this end, we combine moored measurements at OSNAP West and the Davis Strait, gridded hydrographic datasets, and atmospheric reanalysis products to conduct volume budget analysis in both density and thermohaline coordinates during the time period from August 2014 to August 2019.
2. Data and methods
a. Moored measurements at OSNAP West
Monthly gridded temperature, salinity, and velocity data along the OSNAP West section from August 2014 to August 2019 are used in this study (Fu et al. 2023). This time period is chosen since it covers five full seasonal cycles during the OSNAP observational period. It is worth noting that our study period follows intense convective activity in the winters of 2014–16 (Yashayaev and Loder 2017) and thus offers the opportunity to evaluate water mass changes associated with strong convection. The OSNAP product are obtained by objective analysis that incorporates various sources of data, including mooring measurements mainly within the boundary currents (Fig. 2), Argo profiles, satellite altimetry, shipboard hydrographic transects, and World Ocean Atlas climatology. The horizontal resolution is <25 km, and the vertical resolution is 20 m. The gridded velocity field at OSNAP West allows for a net transport of −1.6 ± 0.2 Sv across the section to account for the southward throughflow across the Davis Strait (section 2b). More information about the OSNAP data and methodology can be found in Lozier et al. (2019) and Li et al. (2017, 2021).
b. Moored measurements at the Davis Strait
At the Davis Strait, which is the northern boundary of the Labrador Basin (Fig. 1), we use the monthly objectively mapped product from the Davis Strait observing system (Curry et al. 2014). The product contains salinity, temperature, and along-strait velocity measured by moorings and seagliders across the strait between Baffin Island and Greenland. The horizontal resolution of the gridded product varies from 1 to 8 km, and the vertical resolution is 4 m in the upper 370 and 10 m at greater depths. Unfortunately, the temporal span of the data, which is from 2004 to 2010, does not overlap with the time period of the OSNAP measurements. Here, we assume that the multiyear averaged transport during 2004–10 is representative of the mean over the OSNAP time period at the Davis Strait. This assumption and its limitations should be kept in mind when considering the results presented in this study. As shown in Fig. S1 in the online supplemental material, the mean volume transport through the Davis Strait is −1.6 Sv (Curry et al. 2014).
c. Gridded hydrographic datasets
Monthly temperature and salinity fields during August 2014–August 2019 from three observation-based datasets are used. The first dataset is the Multiobservation Global Ocean ARMOR3D Level-4 dataset (Guinehut et al. 2012; Mulet et al. 2012). In the top 1500 m, gridded temperature and salinity are obtained from a combination of synthetic profiles, derived from a vertical projection of satellite data via a multiple linear regression method, and in situ measurements through optimal interpolation. The in situ measurements include profiles from Argo profiling floats, XBT, CTD, and mooring measurements. The property fields below 1500 m are based on World Ocean Atlas 2018 seasonal climatology. We use the multiyear reprocessed monthly temperature and salinity. The dataset has a horizontal grid of 1/4° and 50 depth levels from 0 to 5500 m.
The second dataset used is the In Situ Analysis System (ISAS) (Nicolas et al. 2021), which is based on measurements from Argo, Deep Argo, and other types of in situ measurements. The optimal interpolation method is applied to obtain the gridded product. The version used in this study is ISAS17, which has 187 standard depth levels from 0 to 5500 m and a horizontal grid of 0.5°. We use delayed mode monthly time series from August 2014 to December 2017 (Gaillard et al. 2016). For the months from January 2018 to August 2019, we use ISAS20_ARGO, which has the same vertical and horizontal resolutions, but only incorporate Argo and Deep Argo data in the gridded fields.
Finally, we use the objective analysis product of EN4 from the Met Office Hadley Center (Good et al. 2013). The product incorporates data from the World Ocean Database 13, Global Temperature and Salinity Profile Project, Argo profiling floats, and additional Arctic data. Here, we use monthly time series from version EN4.2.2 with Gouretski and Reseghetti (2010) corrections. The dataset has a horizontal grid of 1° and 42 depth levels from 5 to 5350 m.
d. Atmospheric reanalysis products
Monthly air–sea heat and freshwater fluxes of three atmospheric reanalysis products are used to estimate the surface-forced water mass transformation over the Labrador Sea: the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) product (∼1.8°; Kalnay et al. 1996), the fifth major global reanalysis produced by ECMWF (ERA5; 30 km; Poli et al. 2016), and the Japanese 55-yr Reanalysis (JRA-55; 1.25°; Ebita et al. 2011). The heat fluxes include the net longwave and shortwave radiation fluxes and the latent and sensible heat fluxes. The freshwater fluxes include evaporation and precipitation. All these fluxes span the time period of August 2014–August 2019.
e. Water mass definitions
In this study, layers are defined between isopycnals where the mean diapycnal transformation reaches a local maximum/minimum (Fig. 4a). The water mass contained in each density layer is specified by its characteristic properties at the OSNAP West section (Table 1). Specifically, we define the upper layer as the density layer between 27 and 27.7 kg m−3, where waters are generally warm and highly stratified (Fig. 2). This layer contains both relatively freshwater (
Density layers and water masses defined in this study. The upper, intermediate, and overflow layers are defined in terms of mean potential density
The intermediate layer is defined as the density layer between 27.7 and 27.8 kg m−3. In the basin interior and near the Labrador coast, this intermediate layer is filled with LSW, which is characterized by the lowest potential vorticity (≤6 × 10−12 m−1 s−1) and relatively low salinity (
f. Volume budget for transformation in σθ coordinate
The water mass transformation framework was introduced by Walin (1982) and has been widely applied thereafter (e.g., Speer and Tziperman 1992; Speer et al. 1995; Brambilla et al. 2008; Myers and Donnelly 2008; Badin et al. 2010; Petit et al. 2020). Following these previous studies, we evaluate the transformation with respect to σθ coordinate. The method is briefly summarized below.
The residual term
g. Volume budget for formation in σθ coordinate
h. Volume budget in S–θ coordinates
We follow Mackay et al. (2018, 2020) for the construction of volume budget in thermohaline (S–θ) coordinates. In their study, the thermohaline formation rate was estimated using a regional thermohaline inverse method, which was originally based on a global thermohaline inverse method developed by Groeskamp et al. (2014a). Here, the thermohaline formation rate is directly estimated from observational and atmospheric reanalysis datasets.
The GSθ in Eq. (9) represents the diathermohaline volume flux, and GSθ = (G|S,θ±Δθ/2, G|θ,S±ΔS/2) (Mackay et al. 2018). Here, positive G|S,θ±Δθ/2 denotes salty-to-fresh transformation across S surface between θ ± Δθ/2 surfaces, and positive G|θ,S±ΔS/2 denotes warm-to-cold transformation across θ surface between S ± ΔS/2 surfaces (Fig. 3). The gradient operator is defined as
i. The mean and uncertainty estimates
The monthly transformation rate induced by surface flux (
Finally, the mean formation rate
3. Results in σθ coordinate
a. Mean transformation
The volume budget associated with the mean water mass transformation in σθ space during August 2014–August 2019 is shown in Fig. 4a. The mean volume change (
At densities less than 27 kg m−3,
The strongest transformation in the basin interior occurs at 27.7 kg m−3, where
The remaining 36% of the light-to-dense transformation at 27.7 kg m−3 is accomplished by
At isopycnals greater than 27.8 kg m−3, outcroppings at the sea surface occur less frequently, and
b. Mean formation
The convergence (divergence) of transformation between isopycnals gives the water mass formation (destruction) rate. Here, we discuss the mean formation rates in three density layers defined in Table 1. In the upper layer (27–27.7 kg m−3), there is net destruction over the 5-yr time period, with
In the intermediate layer (27.7–27.8 kg m−3), a transformation convergence is induced by a light-to-dense transformation of 3.6 Sv at 27.7 kg m−3 and a dense-to-light transformation of 1.3 Sv at 27.8 kg m−3, resulting in a formation rate of 4.9 ± 1.5 Sv in this layer. Importantly, 63% of the transformation convergence is induced by mixing (
To summarize, in section 3, we have estimated the mean transformation and formation rates with respect to σθ space in the Labrador Basin during August 2014–August 2019. The maximum light-to-dense transformation rate is 3.6 ± 1.2 Sv and is achieved at 27.7 kg m−3. Both surface buoyancy loss and interior mixing are important in driving the transformation, with a stronger contribution from the former (64%) than the latter (36%). Convergence of transformations in the LSW density layer 27.7–27.8 kg m−3 leads to a production rate of 4.9 ± 1.5 Sv, 63% of which (3.1 ± 1.5 Sv) is accomplished by interior mixing, with the remaining 37% (1.8 ± 0.3 Sv) attributed to surface buoyancy forcing. These results underscore the importance of mixing on the diapycnal transformation and formation associated with the LSW layer.
4. Results in S–θ coordinates
a. Mean thermohaline formation
To further understand the physical processes driving the thermal and haline anomalies in the Labrador Sea, we evaluate the water mass formation rates with respect to the thermohaline (S–θ) coordinates. The mean thermohaline formation rate (
More quantitatively, the mean warm-to-cold transformation (
To further understand the physical processes responsible for the abovementioned thermohaline volume changes, we next quantify the contributions from interior mixing and surface flux to the thermohaline formation rates.
b. Mixing-induced thermohaline formation
The mean thermohaline formation rate induced by interior mixing is revealed by
Apparently, the saltier and warmer waters of [34.9–35, 4°–5°C] are the IW carried into the Labrador Basin by the West Greenland Current, and the fresher and colder waters of [34.8–34.9, 3°–4°C] are the ULW and LSW in the interior. When the West Greenland Current encounters the steep topography near Cape Desolation, it becomes barotropically and/or baroclinically unstable, generating coherent Irminger Rings (e.g., Eden and Böning 2002; Lilly et al. 2003; Bracco et al. 2008; Rieck et al. 2019; Gou et al. 2023), noncoherent mesoscale (de Jong et al. 2016), and submesoscale features (Tagklis et al. 2020). These submesoscale and mesoscale features are shown to facilitate lateral exchange of heat and salt between the warm, salty boundary and the cold, fresh interior in the northern Labrador Basin (Cuny et al. 2002; Palter et al. 2008; de Jong et al. 2016; Georgiou et al. 2021; Tagklis et al. 2020). Here, we further show, from a quantitative perspective, the importance of lateral exchange along isopycnals, in facilitating water mass transformations in terms of their thermohaline anomalies. This result is consistent with Mackay et al. (2020), who applied the regional thermohaline inverse method on observation-based datasets and found isopycnal mixing as the primary pathway for the newly convected LSW to enter the boundary current.
It is interesting to note in Fig. 7a that mixing also drives thermohaline volume changes at fresher isohalines (<34.7). Specifically, it leads to a formation of warmer waters between 3° and 4°C and a destruction of colder waters between 2° and 3°C (Fig. 7b), with little change in salinity (Fig. 7c). According to a preliminary analysis on the seasonal variability of transformation (not shown), we find that this mixing-induced transformation primarily occurs in the winter months (January–March). To further illustrate the potential process responsible for this cold-to-warm transformation, we locate waters with properties of [34.6–34.7, 3°–4°C] and [34.6–34.7, 2°–3°C] (black diamonds in Fig. 7a). As shown in Fig. 8b, the occurrences of the two sets of [S, θ] based on ARMOR3D overlap along the rim of the convection site in the central basin and along the western boundary. In ISAS and EN4, the overlapping region is more concentrated toward the western boundary of the basin (Figs. S3 and S4). The depth range over which both sets of [S, θ] are present is shallower than 400 m (not shown).
Based on the geographic locations and the fact that the cold-to-warm transformation primarily occurs in winter, we surmise that the mixing is associated with eddies and filaments developed at the sharp front between the wintertime convective patch and the buoyant surrounding waters (Jones and Marshall 1997; Xu et al. 2018). One of the candidates is the convective eddies, which are characterized by cold and fresh anomalies analogous to the newly convected waters (Lilly et al. 2003; Chanut et al. 2008). These eddies have been suggested to be the major driver of rapid stratification after convection in the central Labrador Sea according to a high-resolution ocean circulation model (Rieck et al. 2019). In addition, the unstable Labrador Current has also been shown to generate eddies known as the boundary current eddies (Chanut et al. 2008; Rieck et al. 2019). Both types of eddies, along with other small-scale filaments, may act to restratify the convective interior and result in a cold-to-warm (thus dense-to-light) transformation. Similar dense-to-light transformation along the rim of the convection site is shown in a modeling study by Xu et al. (2018).
By comparing Fig. 7 with Fig. 6, we can see that the volume changes induced by the restratifying eddies are not reflected in the total thermohaline volume changes. This implies that surface forcing acts to drive volume changes opposite to those induced by mixing, which is further illustrated in the next section.
c. Surface-induced thermohaline formation
The mean thermohaline volume change induced by surface flux is shown in Fig. 9. Because surface freshwater flux plays a negligible role (Fig. 9c; Petit et al. 2020; Bebieva and Lozier 2023), the surface-induced volume change is primarily driven by surface heat flux, and thus, the transformation occurs in thermal space. Specifically, the strongest warm-to-cold transformation
5. Discussion and conclusions
Using a combination of moored measurements, hydrographic datasets, and atmospheric reanalysis products, we investigate the mean water mass transformation and formation rates with respect to potential density σθ and thermohaline (S–θ) coordinates in the Labrador Basin during August 2014–August 2019. Our major conclusions are summarized in Fig. 10.
In σθ coordinate, surface buoyancy loss (2.3 ± 0.2 Sv) and diapycnal mixing (1.3 ± 1.2 Sv) collectively lead to a maximum light-to-dense transformation of 3.6 ± 1.2 Sv at 27.7 kg m−3 (Fig. 10b). This light-to-dense volume flux is responsible for the volume destruction of 5.0 ± 1.2 Sv in the upper layer (27–27.7 kg m−3) and the volume formation of 4.9 ± 1.5 Sv in the intermediate layer (27.7–27.8 kg m−3) that contains LSW. Importantly, 63% of the formation rate in this intermediate layer is attributed to diapycnal mixing (3.1 ± 1.5 Sv), which transforms waters from both the upper layer (1.3 ± 1.2 Sv) and the overflow layer (1.8 ± 1.0 Sv) into the intermediate layer. The remaining 37% (1.8 ± 0.3 Sv) of the formation rate is accomplished by surface buoyancy loss acting on the convective area in the central-western Labrador Sea. These results highlight the importance of mixing in driving the diapycnal transformation and formation associated with the LSW density layer.
By investigating transformation and formation in S–θ coordinates, we are able to distinguish volume fluxes between water masses having the same density but different temperature and salinity. The most pronounced diathermohaline transformation (∼10 Sv) is from the warmer, saltier IW into the colder, fresher ULW and LSW. The transformation is found to primarily occur along constant density surfaces (centered at 27.7 kg m−3) and is attributed to mesoscale activities west of Greenland that efficiently exchange properties between the boundary current and basin interior. This conclusion differs from that of an idealized modeling study, which attributes the diathermohaline transformation to the combined effects of surface flux and mixing within the boundary current system (Bebieva and Lozier 2023). Specifically, using a three-layer model, the authors show that cold anomalies in the boundary current are produced by direct surface heat loss, while fresh anomalies result from mixing with fresh shelf water along the Greenland and Labrador coasts. While it is clear that surface heat loss and fresh shelf water input are ultimately responsible for the cold and fresh anomalies in the basin, how they collectively generate the observed thermohaline structure in the Labrador Sea is unresolved. Given the limits imposed by observational datasets, this resolution might best be accomplished with output from comprehensive ocean models.
In addition, we identify counteractive diathermal volume fluxes induced by surface heat flux and interior mixing. Specifically, surface heat loss results in a warm-to-cold transformation of 5.6 ± 0.3 Sv at 3°C in the southwestern Labrador Sea, which is significantly counteracted by a cold-to-warm transformation induced by mixing surrounding the convective patch. The mixing, which is important for restratifying the mixed layer, is likely associated with the eddies, such as convective eddies and boundary current eddies, and filaments developed at the unstable front along the rim of the convection site.
One caveat of the study is that the contribution from interior mixing is estimated as a residual in the volume budget and its associated physical process is only implied. An explicit quantification of mixing and other unresolved processes in terms of their contributions to the water mass transformation would require further investigations with numerical models. In addition, because the results presented in this study cover a period of intense convection, we are not in a position to understand how they may change during periods of weak convection. While we anticipate similar transformation and formation processes to occur, their magnitudes would likely be smaller (e.g., Myers and Donnelly 2008). Extended OSNAP observations and analyses will yield this answer in time. Finally, we acknowledge that our assumption that the mean transport at the Davis Strait during 2004–10 is representative of the mean during the study period of 2014–19 adds some uncertainty to our results.
Results from this observation-based work, along with those from previous studies, highlight the advantage of viewing water mass changes in thermohaline coordinates. It is shown, from a quantitative perspective, that the thermohaline properties and volumes of the deep-water mass in the Labrador Basin are influenced not only by surface flux but also by mixing that occurs both along and across density surfaces. Our results have important implications for modeling deep-water and AMOC change. First of all, they underscore the necessity to resolve or reasonably parameterize small-scale mixing in the models in order to adequately simulate deep-water properties and formation. Second, the large thermohaline anomalies and their compensating effect on density imply significant impacts from both heat and freshwater forcings in the Labrador Sea. Incorrect representation of either forcing may result in property (especially salinity) biases in climate models, which are directly related to the simulated AMOC strength and its meridional connectivity (Heuzé 2021; Jackson and Petit 2023). Overall, we believe that results from this work provide important observational constraints for modeling deep-water evolution, which is key for predicting the AMOC’s response to a warming climate.
Acknowledgments.
This work was supported by the National Natural Science Foundation of China (Grant 42376005) and the National Key Research and Development Program of China (2023YFF0805102). T. Petit was supported by the UKRI-NERC SNAP-DRAGON (NE/T013494/1) project. M. S. Lozier acknowledges support from the Physical Oceanography Program of the U.S. National Science Foundation (OCE-1948335). Gratitude is extended to L. Chafik for helpful conversations on the transformation analysis and Y. Fu for tests with OSNAP gridded products. This study has been conducted using E.U. Copernicus Marine Service Information (https://doi.org/10.48670/moi-00052). OSNAP data were collected and made freely available by the OSNAP (Overturning in the Subpolar North Atlantic Program) project and all the national programs that contribute to it (http://www.o-snap.org).
Data availability statement.
All data used in this study are publicly available. Gridded data from the OSNAP can be downloaded from https://www.o-snap.org/data-access/. Gridded product at the Davis Strait is downloaded from https://iop.apl.washington.edu/downloads.php. ARMOR3D dataset is accessed at https://doi.org/10.48670/moi-00052. EN4 dataset is accessed at https://www.metoffice.gov.uk/hadobs/en4/download-en4-2-2.html. ISAS dataset is downloaded from https://www.seanoe.org/data/00412/52367/. ERA5 is downloaded from https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. NCEP/NCAR can be accessed at https://www.ncep.noaa.gov. JRA-55 is downloaded from https://rda.ucar.edu/datasets/ds628.1/.
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