# Antarctic Bottom Water contraction drives abyssal ocean warming along SAMBA-West line (34.5°S) in the Argentine basin

Daniel M.C. Santos, Tiago C Biló, Dante C Napolitano, Renellys C Perez, Paulo S Polito, Jonathan Gula, Shenfu Dong, Edmo J.D. Campos, Olga T Sato

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Daniel M.C. Santos, Tiago C Biló, Dante C Napolitano, Renellys C Perez, Paulo S Polito, et al. Antarctic Bottom Water contraction drives abyssal ocean warming along SAMBA-West line (34.5°S) in the Argentine basin. Deep Sea Research Part I: Oceanographic Research Papers, 2026, 227, pp.104627. 10.1016/j.dsr.2025.104627. hal-05464720

HAL Id: hal-05464720

https://hal.univ-brest.fr/hal-05464720v1

Submitted on 19 Jan 2026

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
1

2

# AABW Contraction Drives Abyssal Ocean Warming Along SAMBA-West Line in the Argentine Basin

3

4

5

Daniel M. C. Santos¹,*, Tiago C. Bilóª,ᵇ, Dante C. Napolitanoᶜ, Renellys C. Perezᵈ, Paulo S. Polito¹, Jonathan Gulaᶜ,ᵉ, Shenfu Dongᵈ, Edmo J. D. Campos¹, Olga T. Sato¹

6

## Abstract

7

8

9

10

11

12

13

14

15

16

17

18

19

We present an updated assessment of abyssal temperature trends in the Argentine Basin using expanded hydrographic and moored observations from the SAMBA-West line, along with a high-resolution numerical simulation output. The study addresses three questions: (1) how does the abyssal flow crossing the line connect to the broader basin-scale circulation? (2) what is the spatial distribution of the abyssal warming along the line? (3) what are the mechanisms driving the observed changes? Results show that SAMBA-West sits at a closed circulation region in the northwestern portion of the Argentine Basin with a dynamically complex junction of deep boundary currents and recirculation pathways. A coherent and statistically significant warming trend is found across most of the array and vertically throughout the AABW, primarily due to vertical contraction of the AABW layer, likely reflecting reduced formation or export.

20

21

**Keywords:** Abyssal Warming, Antarctic Bottom Water (AABW), Argentine Basin, Deep Ocean Circulation, SAMBA-West Array

<footnote>ªSchool of Marine Sciences, University of Maine, College Avenue,168, Orono, 04469, ME, USA</footnote>

<footnote>ᵇClimate Change Institute, University of Maine, College Avenue,168, Orono, 04469, ME, USA</footnote>

<footnote>ᶜUniv. Brest, CNRS, IRD, Ifremer, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, Rue Dumont d'Urville, Plouzané, 29280, BZH, France</footnote>

<footnote>ᵈAtlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, Rickenbacker Causeway, Miami, 4301, FL, USA</footnote>

<footnote>ᵉInstitut Universitaire de France, boulevard Saint-Michel, Paris, 75005, Île-de-France, France</footnote>

<footnote>*Corresponding author. Instituto Oceanográfico da Universidade de São Paulo, Praça do Oceanográfico, 191, São Paulo, 05508-120, SP, Brasil
Email address: daniel.melo.santos@alumni.usp.br (Daniel M. C. Santos)</footnote>
22

# 1. Introduction

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

Occupying up to 40% of the ocean volume below 4000 m (Johnson, 2008), the Antarctic Bottom Water (AABW) is the deepest, densest, and coldest water mass in the global ocean. This water mass is formed at four main sites around Antarctica: the Ross Sea, Weddell Sea, Prydz Bay, and Adelie Land (Purkey et al., 2018). Its physical properties and formation rates result from the winter atmospheric forcings and mixing of colder and denser Antarctic Shelf Water with lighter and warmer Circumpolar Deep Water over the Antarctic slope (Orsi et al., 1999). Among these regions, the Weddell Sea is especially important because it is the primary source of the AABW that ventilates the Atlantic Ocean.

Once formed, approximately 90% of Weddell Sea AABW enters the Atlantic Ocean (Solodoch et al., 2022), where it feeds the abyssal limb of the Atlantic Meridional Overturning Circulation (AMOC). In the Argentine Basin, AABW flows through the Malvinas Channels from the Georgia Basin (Georgi, 1981), circulates anticyclonically around the Zapiola Ridge, and exits mainly through the Vema Channel (Fig. 1; Smythe-Wright and Boswell, 1998; McDonagh et al., 2002). While its general pathways are described (e.g., Coles et al., 1996), the AABW circulation within the basin remain mostly unknown.

In the Argentine Basin, the AABW is clearly distinguished from its overlying deep waters. The AABW core has a potential temperature $\theta$ of about -0.10°C and a salinity $S = 34.67$ (Valla et al., 2018). Its upper boundary, defined by $\gamma = 28.27 \text{ kg m}^{-3}$ (or equivalently the $\theta = 0.0°C$ isotherm), marks the interface with the Lower Circumpolar Deep Water (LCDW). The LCDW, with core properties $\theta = 0.72°C$ and $S = 34.72$, occupies the layer between AABW and the North Atlantic Deep Water (NADW), with its upper boundary ($\gamma = 28.10 \text{ kg m}^{-3}$) closely following the 2.0°C isotherm.

The export of AABW from the Argentine Basin occurs mainly through the Vema Channel, situated at its northern edge. This narrow passage is the principal conduit for AABW leaving the Argentine Basin and entering the Brazil Basin, making it a key chokepoint for abyssal volume transport (Hogg and Zenk, 1997; Hogg et al., 1982; Hogg and Owens, 1999; Zenk and Hogg, 1996; Morozov et al., 2018). Transport estimates range from $4.15 \pm 1.27 \text{ Sv}$ (Hogg et al., 1982) to $1.7 \pm 1.1 \text{ Sv}$ when accounting for compensating southward flow (McDonagh et al., 2002). This makes the Vema Channel not only a critical pathway for abyssal circulation but also a natural site for detecting long-term changes in AABW properties.

Indeed, evidence of such changes have accumulated over the past decades. Historical Conductivity-Temperature-Depth (CTD) profiles from 1972 to 2006 revealed a consistent warming of waters with potential temperature $\theta \le 0.2°C$ (Zenk and Morozov, 2007). That warming persisted throughout 2010 (Zenk and Visbeck, 2013), reaching approximately $2.6 \text{ m}^\circ\text{C yr}^{-1}$ by 2020 (Campos et al., 2021). Beyond the channel, the abyssal western Atlantic has shown a basin-scale warming trend that varies meridionally (Johnson and Purkey, 2024), which appears to be linked to a long-term reduction in AABW volume (Purkey and
<figure><img src="image_3.png" /></figure>

Figure 1: (a): Schematic of the main pathways of the AABW (black arrows). Colored diamonds mark Site BB (green), Site C (blue), Site CC (orange), and Site D (red) of the SAMBA-West line. Background represents the bottom topography (m) from General Bathymetric Chart of the Oceans (GEBCO). (b): Zoom-in of the region outlined by the black box in panel (a).

Johnson, 2012; Zhou et al., 2023; Biló et al., 2024). The Argentine Basin reflects this broader pattern, with Coles et al. (1996) reporting a drastic reduction in the densest portion of the AABW between 1980 and 1987, followed by abyssal warming in 1989. Later studies confirmed a general warming of waters deeper than 2000 m between 1989 and 2014 (Johnson et al., 2014), and a mean warming rate of about 2.1 m°C yr⁻¹ for waters from 4500 to 6000 dbar (Johnson, 2022).

Despite a substantial body of work that documented abyssal warming trends across the Atlantic Ocean, direct estimates of AABW transformation along its primary pathways remain limited. The western component of the South Atlantic MOC Basin-wide Array, hereafter SAMBA-West line, located in the northwestern Argentine Basin along 34.5°S (Fig. 2a), is strategically positioned to address this gap. It sits astride the Brazil-Malvinas Confluence to the south and the Vema Channel to the north, designed to capture the full extent of the southward-flowing Deep Western Boundary Current (DWBC) while also monitoring the spread of the AABW. Meinen et al. (2020) analyzed ocean bottom temperature records from pressure-equipped inverted echo sounder (PIES) and
CTD casts along this line and found a consistent warming trend of AABW from 2009 to 2019, with rates comparable to those observed in the Vema Channel and throughout the broader Argentine Basin.

<figure><img src="image_4.png" /><figcaption>Figure 2: Panel (a): Location of the moored instruments and hydrographic stations maintained at the SAMBA-West array. The colored shading represents the bathymetry of the study region from GEBCO. Diamonds mark CTD-PIES locations. Colored diamonds mark the PIES/CPIES locations: Site AA (white), Site BB (green), Site C (blue), Site CC (orange), and Site D (red). Circles mark CTD-only stations. Panel (b): Number of profiles that successfully captured AABW at site BB (green), site C (blue), site CC (orange), site D (red), and at other stations (white). Triangles mark the cruises in which LADCP data were present, stars mark the MSM60 data and the new SAMBA data. Panel (c): Colored lines represent the time series during which data of PIES/CPIES are available. Panel (d): Vertical section of time-averaged θ from CTD data. White line represents the upper limit of AABW (θ = 0.0°C). Green, blue, and red diamonds mark sites BB, C, CC, and D locations, respectively. Panel (e): Same as (d), but for the GIGATL6 numerical simulation output.</figcaption></figure>

Monitoring water mass properties with sufficient accuracy to detect long-term changes remains a challenge due to the small magnitude of temperature and salinity fluctuations at depth (e.g., Biló et al., 2024; Johnson and Purkey, 2024; Zhou et al., 2023). Moorings provide high temporal resolution but are typically limited to single near-bottom instruments (e.g., Meinen et al., 2020; Campos et al., 2021) strongly influenced by local processes. Hydrographic sections, in contrast, capture the full vertical structure and large-scale perspective of the water column but lack temporal coverage. These limitations underscore the importance of strategically located observing systems, such as the SAMBA-West array, which combines moorings with repeated hydrography.

Taking advantage of these complementary datasets, we revisit and expand
the analysis conducted by Meinen et al. (2020) by integrating mooring and hydrographic data to take advantage of the strengths of both sampling methods. In addition, we use output from a high-resolution regional model to address three key questions: (1) how does the abyssal flow crossing the SAMBA-West line connect to the broader basin-scale circulation? (2) what is the spatial distribution of the abyssal warming along the line? (3) what are the mechanisms driving the observed changes?

## 2. Data

### 2.1. *Observations*

Our analysis is focused on temperature and velocity observations along the SAMBA-west line (Fig. 2a). Temperature data were collected from sensors housed inside PIES and PIES equipped with single-point acoustic current meters (CPIES), as well as from CTD profiles. Velocity measurements were obtained from a Lowered Acoustic Doppler Current Profiler (LADCP), attached to the CTD frame and from CPIES. In addition, salinity profiles obtained from CTD casts were used as a supporting variable to help decompose the observed temperature trends into heave and spiciness components.

The CTD data come from a series of repeated hydrographic cruises that have supported long-term monitoring of the SAMBA-West line near 34.5°S (Fig. 2a). Those surveys have been conducted with quasi-annual frequency, totaling 15 cruises since 2009 (Fig. 2b). In addition to the repeated section, we also analyzed data from a trans-basin cruise conducted aboard the R/V *Maria S. Merian* in January 2017 (MSM60; Karstensen et al., 2019) that followed the same latitude as the SAMBA-West line, for a total of 16 cruises.

The number of CTD casts sampling down to the AABW varied between cruises, ranging from 1 to 11 stations per cruise (Fig. 2b). For this study, we selected the four locations with the highest total number of casts: 48.5°W (14 profiles), 47.5°W (14 profiles), 46°W (7 profiles), and 44.5°W (15 profiles). These stations correspond to sites commonly referred in the literature and in Figure 2 as sites BB, C, CC, and D, respectively (Chidichimo et al., 2023, 2021; Valla et al., 2019, 2018; Meinen et al., 2020, 2017). All four locations are situated in the deep or abyssal region of the basin, with depths of 4165 m (site BB), 4565 m (site C), 4750 m (site CC), and 4780 m (site D). LADCP velocity data are available from six cruises, the years in which LADCP measurements were performed (Fig. 2b).

Both CTD and LADCP measurements extend from the surface down to approximately 10 m above the seafloor, with the maximum sampled pressure for each cast shown in the Supplementary Figure S1. These measurements are grouped into three categories: (1) historical SAMBA data: published by Meinen et al. (2020) for CTD and by Valla et al. (2019) for LADCP, covering August 2009 to July 2019; (2) MSM60 data: from the single trans-basin cruise in January 2017 (Karstensen et al., 2019); and (3) new SAMBA data: from two recent cruises in August and December 2022, designated as SAMBA19 and SAMBA20
as part of the SAMBA-West monitoring line (Santos, 2024a,b,c,d). Together, the inclusion of the MSM60 and the new SAMBA data provides an extension of the historical SAMBA time series by about 20%. The CTD and LADCP instrumentation and processing protocols are detailed in the supplementary information.

The PIES and CPIES instruments were deployed at five distinct locations along the SAMBA-West line: PIES at sites C and D, and CPIES at sites AA (-50.5°W), BB, and CC. They were moored approximately 1 m above the seafloor and operated between 2009 and 2019. The duration of each time series is shown in Figure 2c. Although site CC was equipped with a CPIES, its temperature record could not be calibrated due to the lack of CTD casts at this location and the velocity record was unusable because of an instrument malfunction; thus, Site CC data were not included in this study. Due to its local depth, only velocity data from site AA were used, exclusively for validation purposes. All instruments recorded hourly measurements, which are grouped into two datasets: (1) historical SAMBA data, published by Meinen et al. (2020), covering sites C and D; and (2) new SAMBA data, covering sites AA and BB from 2013 to 2019 (Santos, 2025).

## 2.2. Numerical model

Complementary to the *in situ* observations, we incorporate a high-resolution ocean model output (GIGATL6) to gain insight into the regional mean flow and fill the spatial gaps between observation sites. We used 5-day averages of velocity and $\theta$ from March 2005 to January 2012 (e.g., Qu et al., 2021; Ruan et al., 2021; Tagliabue et al., 2022; Vic et al., 2022; Schubert et al., 2025). The velocity outputs were further used to compute stream function ($\psi$) fields following the methodology of Li et al. (2006), providing a comprehensive picture of the circulation patterns.

GIGATL6 is based on the Coastal and Regional Ocean Community model (CROCO; Auclair et al., 2025) developed upon the Regional Oceanic Modeling System (ROMS; Shchepetkin and McWilliams, 2005). GIGATL6 spans the Atlantic Ocean at a nominal horizontal resolution of 6 km with 50 vertical levels that follow the seafloor topography, sourced from the Global Bathymetry and Elevation Data at 30 Arc Seconds Resolution (Becker et al., 2009). The model configuration includes initial and boundary conditions from the Simple Ocean Data Assimilation (SODA) project version 2.2.4 (Carton and Giese, 2008), atmospheric forcing from the hourly Climate Forecast System Reanalysis (Saha et al., 2010), and a k-ε turbulence closure scheme for vertical mixing parameterization (Umlauf and Burchard, 2003), and tidal forcing derived from TPXO7.2 and GOT99.2b (Ray, 1999).

To assess the model's reliability, we compared GIGATL6 velocities with CPIES measurements from 2012 to 2016 at sites BB and AA (Fig. 2a). CPIES instruments provide hourly velocity records, allowing a robust comparison. Site BB is the primary focus, while site AA serves as an additional validation point. Hourly GIGATL6 velocities were used for consistency, and statistics were computed over 2008–2011 at the deepest model level.
Table 1 summarizes the key statistics of the horizontal velocity components. The comparison shows that GIGATL6 captures the main flow features at both validation sites, reproducing the overall magnitude, direction, and variability of the observed currents. Deviations in vector angles are likely related to bathymetric effects in the model. Despite these differences, the general agreement provides confidence that GIGATL6 can be used to complement the *in situ* observations, filling spatial gaps and enabling a more complete analysis of the near-bottom circulation patterns. Detailed interpretation and discussion of the velocities are presented in the Results section 3.

<table><caption>Table 1: Comparison of velocity statistics between CPIES, GIGATL6 (1-hour average)</caption><thead><tr><th>Variable</th><th>Dataset</th><th>Statistic</th><th>AA</th><th>BB</th><th>C</th><th>CC</th><th>D</th></tr></thead><tbody><tr><td rowspan="4">u [m s<sup>-1</sup>]</td><td rowspan="2">CPIES</td><td>Mean</td><td>0.015</td><td>0.055</td><td></td><td></td><td></td></tr><tr><td>Std</td><td>0.064</td><td>0.150</td><td></td><td></td><td></td></tr><tr><td rowspan="2">GIGATL6</td><td>Mean</td><td>0.020</td><td>0.036</td><td>0.008</td><td>-0.007</td><td>-0.013</td></tr><tr><td>Std</td><td>0.062</td><td>0.137</td><td>0.111</td><td>0.064</td><td>0.061</td></tr><tr><td rowspan="4">v [m s<sup>-1</sup>]</td><td rowspan="2">CPIES</td><td>Mean</td><td>0.010</td><td>0.005</td><td></td><td></td><td></td></tr><tr><td>Std</td><td>0.054</td><td>0.047</td><td></td><td></td><td></td></tr><tr><td rowspan="2">GIGATL6</td><td>Mean</td><td>0.013</td><td>0.014</td><td>0.023</td><td>0.012</td><td>0.003</td></tr><tr><td>Std</td><td>0.046</td><td>0.043</td><td>0.052</td><td>0.048</td><td>0.041</td></tr><tr><td rowspan="2">Angle [°]</td><td rowspan="2">CPIES</td><td>Mean</td><td>31.89</td><td>5.07</td><td></td><td></td><td></td></tr><tr><td>Std</td><td>33.10</td><td>20.53</td><td>69.8</td><td>119.8</td><td>166.29</td></tr></tbody></table>

Although the simulation dynamics are consistent with available observations, it is important to note that GIGATL6 was not designed specifically for this study, and we are taking advantage of an existing output. Consequently, while the model captures the overall flow patterns, it tends to overestimate abyssal temperatures relative to observed profiles, which is a common issue of the current Ocean General Circulation Models (Heuzé, 2020). This bias is evident in the isotherm defining the upper limit of the AABW, which lies approximately 350 m higher in the model than in the observations (Fig. 2d,e). Hence, site BB is within the AABW in the observations, but above it in the model. To account for this discrepancy, a correction based on the root mean square error (RMSE) between GIGATL6 output and the time-averaged *in situ* $\theta$ field was applied (see Supplementary Information).

### 3. SAMBA-West connectivity

To contextualize the abyssal temperature trends along the SAMBA-West line, we examine the time-mean velocity and transport patterns previously described in the literature. The general structure of the abyssal flow across the SAMBA-West array is relatively well understood, but its integration with the broader circulation within the Argentine Basin remains less clear.
In the Argentine Basin, Coles et al. (1996) proposed several circulation pathways for AAPW using a thermal wind proxy, providing a schematic circulation in which the AAPW enters the basin through the Malvinas Channels. From there, part of the flow follows the isobaths westward, while another portion goes direct to the north to form an anticyclonic flow around the Zapiola Ridge. The westward branch subsequently turns northward, where it splits: one segment continues into the Vema Channel, contributing to AAPW export to the north, while the other recirculates cyclonically to the south, reaching the Zapiola gyre (see Figure 3 of Coles et al., 1996).

Analyzing the SAMBA-West array, Valla et al. (2019) found that west of ∼48°W, the abyssal flow is largely influenced by the southward-flowing DWBC. East of this longitude, the flow becomes more complex, with the presence of alternating northward and southward jets (see Figure 1 of Valla et al., 2019). The presence of these unexpected southward velocities within the AAPW layer motivated hypotheses of alternative AAPW pathways near the SAMBA-West line, as suggested by Meinen et al. (2017).

This schematic circulation closely aligns with the mean abyssal flow pattern reproduced by GIGATL6 (Fig. 3a). The model captures key features, including the anticyclonic flow around the Zapiola Ridge, the northward flow along the western portion of the basin and the cyclonically recirculation to the south. In addition to this, another interesting feature is well reproduced by the model: the southward flow centered at 47–46°W (Valla et al., 2019). This feature lies between Sites C and CC (Fig. 3b), is likely steered by local topography, and may explain the observed southward flow of AAPW at this location.

Some unique features are also reproduced by the model, the most notable being the closed circulation in the northwestern portion of the Argentine Basin, where the $\psi$ field is mainly positive (Fig. 3a). In this region, waters reaching Site D originate mainly from the basin interior. After crossing this site, they turn toward Site CC (Fig. 3b) and subsequently feed the southward flow described by Valla et al. (2019). Site C appears to be located at the confluence of waters arriving from the west and from the north, associated with the large counterclockwise circulation north of the SAMBA-West line, before turning southward. The model also shows that most of the waters crossing the Vema Channel come from the basin interior, although a smaller contribution originates from the northwestern sector of this area (Fig. 3b). Site BB lies just at the edge of this big closed region, where on average, waters seem to come from the south of the basin (Fig. 3a).

In reality, Site BB appears to be a particularly unique location. It is positioned near the limit of AAPW, where the 0°C isotherm intersects the bathymetry (Fig. 2d), and in dynamical terms it shows extreme variability: the standard deviation of the velocity components exceeds the mean flow by factors of 3.8 (u) and 3.0 (v). Site C shows a similar pattern, with ratios of 13.0 (u) and 2.2 (v). Velocities at BB are also highly anisotropic, with u being 2.5 times larger than v and a standard deviation 3.1 times greater, while at C, u is 2.8 times smaller than v but with a standard deviation 2.1 times larger, when looking to the model outputs. Such variability at BB has been attributed to
<figure><img src="image_5.png" /></figure>

Figure 3: Panel (a): Time-averaged horizontal velocity vectors (black arrows) and streamfunction field (contours and colored shading) from the deepest layer of the GIGATL6 simulation, masked at 3800 dbar. Panel (b): Zoom-in of the region outlined by the black box in panel (a). Panel (c): Time-averaged zonal velocity from *in situ* observations. Panel (d): Time-averaged zonal velocity from the GIGATL6 simulation (red shading indicates eastward velocity, and blue indicates westward velocity). Panel (e): Time-averaged meridional velocity from *in situ* observations. Panel (f): Time-averaged meridional velocity from the GIGATL6 simulation (red indicates northward velocity and blue indicates southward velocity). Diamonds indicate PIES deployment sites: green for site BB, blue for site C, orange for site CC, and red for site D. Magenta thick line represents the upper limit of AABW ($\theta = 0.0^\circ\text{C}$).
the passage of westward-propagating vortices consistent with Rossby wave-like features (Meinen et al., 2017).

The GIGATL6 simulation, together with *in situ* measurements (Fig. 3c-f), clarifies the relationship between the SAMBA-West transect and the broader flow patterns in the Argentine Basin, including its connection to the Vema Channel. The model supports earlier findings (McDonagh et al., 2002; Coles et al., 1996), while also brings new interpretations of the main pathways of the AABW. The highly intricate bathymetry of the region (Fig. 1b) appears to play a central role in shaping abyssal circulation. In terms of water-mass structure, Sites C, CC, and D form a coherent group, reflecting the contraction of the coldest AABW layers, which is examined in detail in the following sections. Dynamically, however, site BB is more closely related to site C, with both locations characterized by velocity variability that far exceeds the mean flow. While these advances refine the description of the mean abyssal circulation, a dedicated study is still required to fully understand the dynamics along the SAMBA-West line.

## 4. Abyssal warming

In this section, we revisit the analysis conducted by Meinen et al. (2020), expanding it by adding the SAMBA new data and the MSM60 data. These datasets extend the CTD time series along the SAMBA-West line through 2022 and increase the number of assessed PIES sites from two to three. We also expand the number of CTD sampling sites from two to four, providing a more detailed spatio-temporal characterization of the region occupied by the AABW between site BB (48.5°W) and site D (44.5°W). By combining these datasets, we can take advantage of the high vertical resolution of the CTDs and the high temporal resolution of the PIES moorings, allowing a more complete assessment of abyssal temperature trends.

Although the PIES temperature records are calibrated using concurrent near-bottom CTD measurements and thus are not fully independent (see Supplementary Information), the comparison between the two datasets still supports the reliability of the CTD-derived trends (Tab. 2). The PIES data, due to their high temporal resolution, provide time series with greater degrees of freedom resulting in a more statistically robust 95% Confidence Interval (CI). This reinforces the validity of the CTD-derived trends, despite their lower temporal resolution and relatively smaller number of data-points.

Hence, one important caveat is that CTD-based trend analysis is limited by relatively sparse temporal sampling. According to Meinen et al. (2012), achieving a temperature trend accuracy of 0.01°C per decade (therefore approximately 0.001°C per year) at the 95% confidence level requires sampling approximately once per year or more frequently. The hydrographic cruises used in this study satisfy this requirement, but our observational time series is at the threshold of this criterion. As such, uncertainties of this magnitude are expected and should be taken into account when interpreting the results, particularly at site BB.
<table><caption>Table 2: Locations of the PIES Moorings and CTD Casts, Along With Basic Statistics of the Observed Temperatures from 2009 to 2019</caption><thead><tr><th></th><th>Equipment</th><th>Latitude (°S)</th><th>Longitude (°W)</th><th>Mean</th><th>Standard deviation</th><th>Trend (°C per year)</th><th>CI 95% (°C per year)</th></tr></thead><tbody><tr><td rowspan="2">site BB</td><td>PIES*</td><td>34.5</td><td>48.5</td><td>0.1939</td><td>0.0193</td><td>0.0048</td><td>0.0063</td></tr><tr><td>CTD</td><td>34.5</td><td>48.5</td><td>0.2023</td><td>0.0181</td><td>-0.0008</td><td>0.0027</td></tr><tr><td rowspan="2">site C</td><td>PIES</td><td>34.5</td><td>47.5</td><td>0.2229</td><td>0.0078</td><td>0.0022</td><td>0.0015</td></tr><tr><td>CTD</td><td>34.5</td><td>47.5</td><td>0.2234</td><td>0.0089</td><td>0.0021</td><td>0.0012</td></tr><tr><td rowspan="2">site CC</td><td>PIES</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CTD</td><td>34.5</td><td>46.0</td><td>0.2469</td><td>0.0065</td><td>0.0022</td><td>0.0018</td></tr><tr><td rowspan="2">site D</td><td>PIES</td><td>34.5</td><td>44.5</td><td>0.2398</td><td>0.0063</td><td>0.0019</td><td>0.0014</td></tr><tr><td>CTD</td><td>34.5</td><td>44.5</td><td>0.2472</td><td>0.0082</td><td>0.0019</td><td>0.0011</td></tr></tbody></table>

Before presenting the CTD-derived trends, the dataset was subjected to additional quality control (see Supplementary Information). Outliers were removed using a threshold of 1.5 times the interquartile range (IQR), which led to the exclusion of site BB data from the October 2017 cruise (SAMBA14). In addition, the PIES record at site BB contained a major gap between 2017 and 2018 (Fig. 2c). To estimate the temperature trend at this site, we therefore calculated the difference between the mean values of the two uninterrupted segments of the time series, considering only periods with active instrument records.

The inclusion of the 2022 cruises confirms that the warming trend previously identified at sites C and D (Meinen et al., 2020) persists beyond 2019 (Fig. 4 and Tab. 2). A similar positive trend is now evident at site CC, spanning the period from 2013 to 2022. In contrast, site BB shows a slight overall cooling. However, if the analysis is restricted to the CTD record beginning in 2013, it indicates a relative warming of abyssal waters, which is reinforced by the PIES dataset (not shown).

Despite this agreement between the CTD and PIES records, the temperature trend at site BB is not significantly different from zero in any data set (Tab. 2). This likely arises because site BB lies on the edge of the AABW domain, where interactions with adjacent water masses could enhance variability. At this location, the standard deviation of the CTD temperature record is roughly 20 times larger than its trend, and for the PIES record, about four times larger, which helps explain the lack of statistical significance. In contrast, the other sites, which are more centrally located within the AABW layer experience lower background variability, exhibiting robust and statistically significant warming trends (Tab. 2).

Overall, the warming trend along the SAMBA-West line is robust and consistent at all sites except BB. This is particularly noteworthy at site CC, which, despite having roughly half as many CTD data points as the other sites, still shows a comparable warming signal, but with a wider 95% CI, likely due to fewer degrees of freedom. These trends are statistically identical to those observed in other regions of the Argentine Basin such as in the southeast portion of the Argentine Basin with rates of 2–4 m°C yr⁻¹ (Johnson et al., 2014) and within the

<footnote>* Defined using the difference of the average temperature of each individual period</footnote>
<figure><img src="image_6.png" /></figure>

Figure 4: Temperature records from PIES (red lines) alongside temperatures from the deepest bin of each CTD cast (gray circles) at site BB (panel a), site C (panel b), site CC (panel c), and site D (panel d). Gray lines represent the linear regression of the CTD data, and the shaded areas indicate the 95% confidence intervals.
Vema Channel with rates of about 2.6 m°C yr⁻¹ (Campos et al., 2021), further reinforcing the connection between the western portion of the SAMBA-West line, the Vema Channel and the northwestern portion of the Argentine Basin, as discussed in Section 3.

Building on the near-bottom observations, we next examine how temperature trends vary with depth. The θ trends along pressure levels are not statistically significant within the LCDW (Fig. 5a, c, e, g). Below this layer, however, the AABW exhibits a general warming (Fig. 5b, d, f, h), with trends significantly different from zero at the bottommost portion of sites C and D. At site CC, the bottommost portion of the profile also shows significant warming, but the confidence intervals at this location are wider than the previous and nearly reaches the 0.0 m°C yr⁻¹ limit, likely due to the limited number of degrees of freedom. Interestingly, site BB shows a reversal in the deepest part of the AABW, with negative temperature trends. Despite having a comparable number of data points to sites C and D, none of the trends at site BB are statistically significant, which limits our ability to draw firm conclusions about the temperature change at that location.

<figure><img src="image_7.png" /></figure>

Figure 5: Potential temperature (θ) trends as a function of pressure (p) at site BB (panel a), site C (panel c), site CC (panel e), and site D (panel g). The dashed line indicates the mean pressure interface between the LCDW and the AABW, with its values annotated below it. Gray shading indicates the 95% confidence intervals. Blue boxes highlight the zoomed-in regions shown in panels (b), (d), (f), and (h), corresponding to panels (a), (c), (e), and (g), respectively.
In summary, the $\theta$ profiles at sites C, CC, and D consistently show significant warming in the bottommost AABW layers, reinforcing the view of a basin-scale warming signal that extends across the Argentine and Brazil basins (Campos et al., 2021; Meinen et al., 2020; Johnson and Purkey, 2024). This spatial coherence strengthens confidence that the observed changes reflect large-scale processes rather than local variability. In contrast, site BB emerges as an exception: despite comparable sampling size, its record lacks statistically significant trends and even suggests localized cooling in the deepest layer (Fig. 5b). This probably reflects its boundary position, where the high variability obscures long-term signals. Together, these results highlight a robust warming of the AABW in the northwestern Argentine Basin, while also highlighting the importance of site-specific variability when interpreting regional abyssal trends.

## 5. Warming drivers

To determine if the abyssal warming rates are associated with intrinsic changes of water mass properties or with vertical movements of isopycnals, we decompose the $\theta$ tendency along pressure levels into spiciness and heave components (Bindoff and Mcdougall, 1994), as:

$$ \left. \frac{\Delta\theta}{\Delta t} \right|_p \approx \left. \frac{\Delta\theta}{\Delta t} \right|_{\gamma} - \left. \frac{\Delta p}{\Delta t} \right|_{\gamma} \left( \frac{\partial\theta}{\partial p} \right), \qquad (1) $$

where $|_p$ denotes changes along isobaric levels and $|_\gamma$ denotes changes along isopycnal levels. While spiciness [first term in the RHS of (1)] represents $\theta$ variability along isopycnal surfaces, the heave [second term in the RHS of (1)] component represents $\theta$ changes due to the vertical displacement of isopycnals (Häkkinen et al., 2016). The latter is a combination of two mechanisms: the deepening or shallowing of isopycnals ($\frac{\Delta p}{\Delta t}|_\gamma$), and the mean background thermal gradient ($\frac{\partial\theta}{\partial p}$). To define the $\gamma$ levels, we create an evenly spaced vertical grid of $\gamma$ for each location with resolution of 0.0005 kg m$^{-3}$. Then, we linearly interpolated $p$ and $\theta$ profiles onto the $\gamma$ grid. Finally, we quantified its trends as the slope of a linear least-square fit.

The accuracy of this approximation is strongly dependent on the number of available data points. At all sites, data loss due to vertical interpolation affects the reconstruction of the $\theta$ trend, particularly in the deepest portions of the profiles, where downward motions of near bottom isopycnals force them out of the observed region at certain times (see dotted lines in Figure 6). Site BB shows the weakest agreement overall, likely due to both significant data loss and elevated temperature variability, which can affect the background thermal gradient ($\frac{\partial\theta}{\partial p}$) and reduce the accuracy of the decomposition.

The heave–spiciness decomposition (Fig. 6) reveals that heave is the primary driver of abyssal temperature changes along the SAMBA-West line. At most locations, volume contraction appears to overcome isopycnal transformation effects in setting the observed warming trends within the AABW. However, a
<figure><img src="image_8.png" /></figure>

Figure 6: Panel (a): Potential temperature ($\theta$) trend (gray line) and its approximation (yellow line), decomposed into heave (red) and spiciness (blue) components at site BB. The dashed line indicates the mean pressure interface between the LCDW and the AABW, with its values annotated below it. The dotted line shows the number of data points used in the calculation. Panel (c): Same as (a), but for site C. Panel (e): Same as (a), but for site CC. Panel (g): Same as (a), but for site D. Panels (b), (d), (f), and (h) show zoomed-in views of the blue-boxed regions from panels (a), (c), (e), and (g), respectively. Trends calculated with less than four sample points are masked.
closer examination of different water masses and locations reveals a more com-
plex and spatially variable picture.

At site BB (Fig. 6a), the $\theta$ trend is negative throughout most of the LCDW domain, where heave oscillates between small negative and positive values and spiciness exhibits larger magnitudes. This results in a predominantly negative trend extending from above 3750 dbar to approximately 3900 dbar. Below this pressure level and as the magnitude of heave increases with depth, the $\theta$ trends begin to show positive values as well. Specifically, the upper portion of the AABW (Fig. 6b) shows a warming trend, while the deeper layers exhibit cooling, driven by the decrease in the magnitude of the heave component with depth.

At site C (Fig. 6c), within the portion of the profile typically associated with the LCDW, both the spiciness and heave components contribute to a cooling trend, acting in the same direction. This cooling is primarily driven by the heave component, which dominates the $\theta$ trend. Below these depths (at approximately 4100 dbar), heave becomes increasingly positive and in consequence the trends shift to positive, leading to a warming signal in the depths occupied by the AABW (Fig. 6d).

At site CC (Fig. 6e), the overall pattern within the LCDW domain shows $\theta$ trends predominantly negative. Similar to site BB, this negative trend is driven by both spiciness and heave components, with heave fluctuating between small negative and positive values. As heave increases in magnitude with depth, the $\theta$ trend shifts to positive values near 4150 dbar with heave emerging as the dominant contributor to the observed $\theta$ changes (Fig. 6f).

At site D (Fig. 6g), the trend components exhibit a distinct relation com-
pared to those at sites BB, C, and CC above 4150 dbar, within the LCDW
domain. Heave and spiciness components act in opposite directions: heave con-
tributes to warming, while spiciness induces cooling. In contrast, $\theta$ trends in the
AABW domain (Fig. 6h) follow the pattern observed at the other sites, with
heave largely driving the trends.

Although not dominant, a consistent spiciness signal across all sites sug-
gests widespread freshening of both AABW and LCDW layers. However, a
key limitation of (1) is that spiciness is directly tied to salinity changes, since
temperature changes along constant density levels implies in salinity changes as
well, and salinity trends are not statistically significant at the 95% confidence
level (Supplementary Figure S3). Despite that, the sum of the heave and spici-
ness components generally reproduces the observed $\theta$ trends (Fig. 6, yellow and
gray lines), providing confidence in the decomposition of the temperature signal
into spiciness and heave contributions.

Up to this point, we have examined the observed $\theta$ trends and shown that they are primarily driven by the vertical contraction of abyssal water masses. We now take a closer look at this contraction using both *in situ* observations and numerical simulations to assess the extent of AABW layer thinning along the SAMBA-West line and farther east.
434

# 6. AABW contraction

435

436

437

438

439

440

441

To better visualize the vertical displacement of isotherms, we analyzed trends in isotherm depth above the bottom ($h$) as a function of $\theta$. Before calculating these trends, we generated an evenly spaced vertical $\theta$ grid for each site, considering the maximum and minimum $\theta$ values of each time series, with a resolution of 0.01°C. We then linearly interpolated the corresponding $h$ profiles onto these $\theta$ grids. Finally, the $h$ trend at each $\theta$ level (Fig. 7 a, b, c, and d) was estimated by computing the slope via linear least-squares fit.

<figure><img src="image_9.png" /></figure>

Figure 7: Trends in isotherm depth above the bottom ($h$) as a function of potential temperature ($\theta$) at site BB (panel a), site C (panel b), site CC (panel c), and site D (panel d), from *in situ* data. Shaded areas indicate the 68% and 95% confidence intervals. Colored circles mark the trend of the -0.05°C isotherm. Horizontal dashed line marks the boundary between the AABW and LCDW layers ($\theta$ = 0.0°C). Panel (e): shows the trend of the -0.05°C isotherm along 34.5°S. Colored circles represent *in situ* data, the bold black line shows the trend from the GIGATL6 simulation output, the shaded area between gray lines indicates the 95% confidence interval.

442

443

444

445

446

The analysis of the $h$ trends provides insight not only into temporal changes at specific $\theta$ levels but also into the shifts among waters from one $\theta$ class to another. As outlined by Johnson et al. (2014), layer thickness changes can be inferred from how isotherm depths vary with $\theta$. If isotherms fall ($\frac{\Delta h}{\Delta t} < 0$) as $\theta$ increases, the layer is thinning. If they rise ($\frac{\Delta h}{\Delta t} > 0$) with increasing $\theta$,
the layer is thickening. When isotherm depth remains constant with respect to temperature ($\frac{\Delta h}{\Delta t} = 0$), the layer thickness is unchanged.

In Figure 7, we show observed *h* trends for a range of isotherms across the SAMBA-West transect and fill the gaps between sites using outputs from the GIGATL6 simulation. However, we highlight that the utilized simulation covers a relatively short time period, what limits the robustness of comparisons with the observations. At site BB (Fig. 7a), the profile shows no significant trends throughout most of the water column, with fluctuations that averaged produce a mean trend around 0.0 m yr⁻¹.

At sites C and CC (Fig. 7b and c), water between approximately 0.0°C and 1.0°C, and 0.1°C and 1.0°C, respectively, show positive *h* trends. At site C, this upward movement is relatively uniform across the *θ* classes, whereas site CC exhibits greater variability. Below 0.0°C, both sites display negative trends: waters between roughly -0.15°C and 0.0°C show a downward displacement of isotherms, with the rate of descent increasing, until a maximum value of about 10 m yr⁻¹ at Site C and 15 m yr⁻¹ at Site CC, as *θ* decreases. For waters colder than approximately -0.15°C, the pattern reverses, with trends becoming less negative with decreasing temperature.

At site D (Fig. 7d), negative *h* trends are observed throughout the entire profile. This pattern contrasts with sites C and CC in the temperature range between approximately 0.0°C and 1.0°C, where those sites exhibited positive trends. However, as temperature decreases, the patterns at site D start to resemble those observed at sites C and CC. Below 0.0°C, waters between roughly -0.15°C and 0.0°C show a downward displacement of isotherms, with the rate of descent increasing, as *θ* decreases raching a maximum value of about 12 m yr⁻¹. For waters colder than approximately -0.15°C, the trend again becomes less negative as temperature decreases. This overall pattern reflects the dominance of the heave term at these sites (Fig. 6c, e, and g). At site D, *h* remains negative throughout the profile because the heave contribution is consistently positive. At sites C and CC, by contrast, the heave term changes sign, driving the corresponding shift in *h* trends from positive to negative.

Thus, at sites C, CC, and D, the AAPW layer ($\theta < 0.0°C$), exhibits similar trend sign variations along the water column. This pattern is characterized by a downward expansion of the relatively warmer portion of the AAPW and a corresponding contraction of the colder segment, producing a characteristic “V-shape” in the profiles (Fig. 7b, c, and d). Such changes suggest a thinning of the coldest AAPW layers, which is partially offset by a thickening of the warmest layers. Notably, the result for $\theta = -0.05 °C$ at site CC is statistically significant at the 68% CI, while site D achieves significance at the 95% CI, and site C does not reach statistical significance. This pattern closely resemble those reported by Johnson et al. (2014) in the southern Argentine Basin. This similarity supports the robustness of the observed results and indicates potential regional connectivity among these sites and the rest of the basin.

In contrast, trends within the assessed portion of the LCDW layer (0.0°C < $\theta < 1.0°C$) show little to no sign change throughout the water column at all sites. This consonance suggests that changes in layer thickness within this part
of the LCDW are not balanced across different temperature ranges, with the
entire assessed portion tending to lose or gain thickness uniformly.

We selected the isotherm of -0.05°C as a case study and compared the *h* trends from *in situ* observations and model simulations (Fig. 7e). The model captures the observed trends: *h* rises in the westernmost portion of the transect, reaching slightly positive values around 48°W. Note that the *θ* trend in Site BB (Fig. 6a), reverses from positive (warming) to negative values at its very bottom portion, while spiciness remains negative. This indicates that this cooling is primarily driven by the heave term, leading to a local expansion of AABW near 48°W.

Overall, however, the dominant signal across the SAMBA-West line is one of contraction. This contraction is consistent with the broader hypothesis that weakened production of dense waters in the Weddell Sea has slowed the lower limb of the AMOC. Abrahamsen et al. (2019) showed that reductions in the northward export of WSDW to the Scotia Sea are closely linked to a reduction in the underlying WSBW. Zhou et al. (2023) documented a long-term decline in WSBW volume driven by reduced sea-ice formation in the southern Weddell Sea, partly associated with interdecadal wind variability linked to the Inter-decadal Pacific Oscillation. This combination of reduced WSBW formation and diminished export of WSDW provides a plausible mechanism for the contraction observed along the SAMBA-West line. Consistent with this interpretation, Biló et al. (2024) reported a (12 ± 5)% weakening in northward AABW transport at 16°N between 2000 and 2020, suggesting that the contraction signal propagates well beyond the South Atlantic. Taken together, these studies provide multiple, independent lines of evidence linking the contraction observed at SAMBA-West to processes in the Weddell Sea, even if the precise mechanisms and propagation pathways remain to be fully understood.

# 7. Summary and Conclusions

In this study, we analyzed different datasets and a numerical simulation to address three key questions: (1) how does the abyssal flow crossing the SAMBA-West line connect to the broader basin-scale circulation? (2) what is the spatial distribution of the abyssal warming along the line? (3) what are the mechanisms driving the observed changes?

We address the first question with a high-resolution regional simulation output. SAMBA-West array is located in a closed circulation region in the north-western portion of the Argentine Basin, marked by positive $\psi$ values, where the abyssal circulation over the transect appears strongly influenced by bathymetry. Site BB, in particular, lies at the edge of this large closed circulation region and, in terms of dynamics, seems more closely related to Site C, with both locations characterized by velocity variability that far exceeds the mean flow, while Sites CC and D exhibit similar flow characteristics. Waters crossing Sites CC, C, and D primarily originate from the central part of this circulation, whereas at Site BB they appear to come from the southwestern portion of the basin.
Building on this, our investigation of the second question reveals that the expanded temperature dataset confirms the warming trend through 2022 at sites C and D. Despite having fewer CTD profiles, site CC shows a vertical warming pattern similar to sites C and D within the AABW domain, reinforcing the spatial uniformity of this signal across the eastern portion of the SAMBA-West line. In contrast, site BB, which is located at both at the edge of the AABW horizontal domain and of the region marked by positive values of $\psi$ at the northwestern Argentine Basin, exhibits high temperature variability and no statistically significant long-term trend, which likely reflects its unique position among the assessed sites. These results point to a consistent abyssal warming signal across the eastern portion of the SAMBA-West line.

Finally, in addressing the third question, the heave-spiciness decomposition indicates that heave, rather than water mass modification along isopycnals, is the dominant mechanism behind the observed abyssal warming along the SAMBA-West line. This suggests that vertical displacement of isopycnals is the main driver of the temperature trends. At sites C, CC, and D, this contraction is characterized by a thinning of the coldest, densest AABW layers, partially compensated by a thickening of warmer layers above. These changes are consistent with reduced formation or export of AABW to the region.

Although a dedicated study is still required to directly connect the contraction signal at the SAMBA-West line to its Weddell Sea source and to fully understand the drivers of this connection, including potential propagation mechanisms such as internal waves (Masuda et al., 2010), several independent lines of evidence support this link. This contraction is consistent with the broader hypothesis that weakened production of dense waters in the Weddell Sea has slowed the lower limb of the Atlantic meridional overturning circulation (Zhou et al., 2023; Meredith et al., 2014; Abrahamsen et al., 2019). However, attribution of its drivers remains challenging: data are sparse both at the AABW formation sites and along the pathways that connect them to the SAMBA-West line, leaving interpretations at the level of conjecture, despite the growing evidence for a basin-wide contraction signal.

Taken all together, these results provide robust evidence of an ongoing abyssal warming in the Argentine Basin, driven primarily by the contraction of abyssal waters. In this context, the SAMBA-West array emerges as a crucial observational location for tracking these changes, especially given its position at one of the main AABW pathways. As such, continued and more frequent sampling at this location, through recurrent hydrographic cruises and the deployment of abyssal moorings, is critical to better capture changes of the abyssal water masses, in special at Site BB, and to deepen our understanding of the mechanisms driving AABW variability and its broader implications for the global climate system. In parallel, dedicated numerical simulations targeting abyssal dynamics and AABW trends are needed to complement the GIGATL6 results and provide insight into the evolution of abyssal water masses.
579

## 8. CRediT Authorship Contribution Statement

580

**Daniel M. C. Santos**: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, and Writing – original draft. **Tiago C. Biló**: Conceptualization, Formal analysis, Software, Methodology, and Writing-Review & Editing. **Dante C. Napolitano**: Conceptualization, Formal analysis, Software, and Writing-Review & Editing. **Renellys C. Perez**: Conceptualization, Data curation, Writing-Review & Editing, Funding acquisition. **Paulo S. Polito**: Methodology, Validation, and Writing-Review & Editing. **Jonathan Gula**: Software, Data Curation, Writing-Review & Editing. **Shenfu Dong**: Writing-Review & Editing, Funding acquisition. **E. Campos**: Funding acquisition. **Olga T. Sato**: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, and Writing-Review & Editing.

581

582

583

584

585

586

587

588

589

590

591

592

## 9. Acknowledgments

593

594

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

This work was supported by FAPESP [2021/09317-3, 2017/09659-6], and financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. T. C. B., R. P., and S. D. gratefully acknowledge the funding from the NOAA's Global Ocean Monitoring and Observing program (FundRef number 100007298); NOAA's Climate Program Office, Climate Observations and Monitoring, and Climate Variability and Predictability programs under NOFO NOAA-OAR-CPO-2021-2006389 with additional NOAA Atlantic Oceanographic and Meteorological Laboratory support. This research was carried out in part under the auspices of the Cooperative Institute for Marine and Atmospheric Studies, a cooperative institute of the University of Miami and the National Oceanic and Atmospheric Administration (NOAA), cooperative agreement NA 20OAR4320472. D. C. N. acknowledge support from Région Bretagne, the French National Agency for Research through the EUREC4A-OA (ANR-19-JPOC-0004-05) and the CROSS-ROAD (ANR-21-CE01-0011) projects. J.G. was supported by the French National Agency for Research (ANR) through the project DEEPER (ANR-19-CE01-0002-01). GIGATL simulations were performed using HPC resources from GENCI-TGCC (Grants 2024-A0170112051) and from DATARMOR of “Pôle de Calcul Intensif pour la Mer” at Ifremer Brest, France. We thank the captains and crew of R/V Puerto Deseado, R/V Alpha Crucis, R/V Maria S. Merian, and R/V Austral who supported our work at sea. We acknowledge the Brazilian Navy, SeCIRM, Petróleo Brasileiro S.A. (PETROBRAS), and the Agência Nacional do Petróleo (ANP) for enabling the field campaign of the Brazilian vessels through the Cooperation Term SIGITEC 2018/00451-6 and 2018/00452-2.

617

## 10. Data Availability Statement

618

619

The hydrographic and LACDP data from the cruises SAM02 (08/2009) until SAM18 (07/2019) are available at https://www.aoml.noaa.gov/ftp/pub/
phod/pub/SAM/hydrographic_data/. The LACDP data of the cruises SAM19 (08/2022) and SAM20 (12/2022) are available at: https://doi.org/10.5281/zenodo.11149277 (?) and https://doi.org/10.5281/zenodo.11108848 (?), respectively. The hydrographic data of the cruises SAM19 and SAM20 are available at: https://doi.org/10.5281/zenodo.11149213 (?) and https://doi.org/10.5281/zenodo.11148978 (?), respectively. The hydrographic data of the MSM60 cruise are available at: https://doi.org/10.1594/PANGAEA.915898 (Karstensen, 2020b) and the LADCP data at: https://doi.org/10.1594/PANGAEA.915879 (Karstensen, 2020a). CPIES temperature record from site C and D are available at: www.aoml.noaa.gov/phod/research/moc/samoc/sam/ (Meinen et al., 2020). PIES temperature record from site BB are available at: https://doi.org/10.5281/zenodo.15365408 (?). GEBCO data is available at: https://www.gebco.net/data_and_products/gridded_bathymetry_data/. The source code of the simulation used in this study is available at https://doi.org/10.5281/zenodo.4948523 Gula et al. (2021).

## 11. Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the authors used ChatGPT to improve the readability and language of the manuscript. After using this tool, the authors reviewed and edited the content as needed and assume full responsibility for the content of the published article.

## References

Abrahamsen, E.P., Meijers, A.J., Polzin, K.L., Naveira Garabato, A.C., King, B.A., Firing, Y.L., Sallée, J.B., Sheen, K.L., Gordon, A.L., Huber, B.A., et al., 2019. Stabilization of dense Antarctic water supply to the Atlantic Ocean overturning circulation. Nature Climate Change 9, 742–746.

Auclair, F., Benshila, R., Bordois, L., Boutet, M., Brémond, M., Caillaud, M., Cambon, G., Capet, X., Debreu, L., Ducousso, N., Dufois, F., Dumas, F., Ethé, C., Gula, J., Hourdin, C., Illig, S., Jullien, S., Corre, M.L., Gac, S.L., Gentil, S.L., Lemarié, F., Marchesiello, P., Mazoyer, C., Morvan, G., Nguyen, C., Penven, P., Person, R., Pianezze, J., Pous, S., Renault, L., Roblou, L., Sepulveda, A., Theetten, S., Schaefer, A.L., Treillou, S., Valat, S., 2025. Coastal and regional ocean community model. URL: https://doi.org/10.5281/zenodo.15064113, doi:10.5281/zenodo.15064113.

Becker, J., Sandwell, D., Smith, W., Braud, J., Binder, B., Depner, J., Fabre, D., Factor, J., Ingalls, S., Kim, S., et al., 2009. Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. Marine Geodesy 32, 355–371.
Biló, T.C., Perez, R.C., Dong, S., Johns, W.E., Kanzow, T., 2024. Weakening of the Atlantic Meridional Overturning Circulation Abyssal Limb in the North Atlantic, in: 2024 Ocean Sciences Meeting, AGU.

Bindoff, N.L., Mcdougall, T.J., 1994. Diagnosing climate change and ocean ventilation using hydrographic data. Journal of Physical Oceanography 24, 1137-1152.

Campos, E.J., Van Caspel, M.C., Zenk, W., Morozov, E.G., Frey, D.I., Piola, A.R., Meinen, C.S., Sato, O.T., Perez, R.C., Dong, S., 2021. Warming trend in Antarctic Bottom Water in the Vema Channel in the South Atlantic. Geophysical Research Letters 48, e2021GL094709.

Carton, J.A., Giese, B.S., 2008. A reanalysis of ocean climate using Simple Ocean Data Assimilation (SODA). Monthly weather review 136, 2999-3017.

Chidichimo, M.P., Perez, R.C., Speich, S., Kersalé, M., Sprintall, J., Dong, S., Lamont, T., Sato, O.T., Chereskin, T.K., Hummels, R., et al., 2023. Energetik overturning flows, dynamic interocean exchanges, and ocean warming observed in the South Atlantic. Communications Earth & Environment 4, 10.

Chidichimo, M.P., Piola, A.R., Meinen, C.S., Perez, R., Campos, E.J.D., Dong, S., Lumpkin, R., Garzoli, S., 2021. Brazil Current Volume Transport Variability during 2009-2015 from a long-term moored array at 34.5 S. Journal of Geophysical Research: Oceans 126, e2020JC017146.

Coles, V.J., McCartney, M.S., Olson, D.B., Smethie Jr, W.M., 1996. Changes in Antarctic Bottom Water properties in the western South Atlantic in the late 1980s. Journal of Geophysical Research: Oceans 101, 8957-8970.

Georgi, D.T., 1981. Circulation of bottom waters in the southwestern South Atlantic. Deep Sea Research Part A. Oceanographic Research Papers 28, 959-979.

Gula, J., Theetten, S., Cambon, G., Roullet, G., 2021. Description of the GIGATL simulations. URL: https://doi.org/10.5281/zenodo.4948523, doi:10.5281/zenodo.4948523.

Häkkinen, S., Rhines, P.B., Worthen, D.L., 2016. Warming of the global ocean: Spatial structure and water-mass trends. Journal of Climate 29, 4949-4963.

Heuzé, C., 2020. Antarctic bottom water and North Atlantic deep water in CMIP6 models. Ocean Science Discussions 2020, 1-38.

Hogg, N., Biscaye, P., Gardner, W., Schmitz Jr, W.J., 1982. On the transport and modification of Antarctic Bottom Water in the Vema Channel.

Hogg, N.G., Owens, W.B., 1999. Direct measurement of the deep circulation within the brazil basin. Deep Sea Research Part II: Topical Studies in Oceanography 46, 335-353.
696 Hogg, N.G., Zenk, W., 1997. Long-period changes in the bottom water flowing through Vema Channel. Journal of Geophysical Research: Oceans 102, 15639–15646.

699 Johnson, G.C., 2008. Quantifying Antarctic bottom water and North Atlantic deep water volumes. Journal of Geophysical Research: Oceans 113.

701 Johnson, G.C., 2022. Antarctic Bottom Water Warming and Circulation Slow-down in the Argentine Basin From Analyses of Deep Argo and Historical Ship-board Temperature Data. Geophysical Research Letters 49, e2022GL100526.

704 Johnson, G.C., McTaggart, K.E., Wanninkhof, R., 2014. Antarctic Bottom Water temperature changes in the western South Atlantic from 1989 to 2014. Journal of Geophysical Research: Oceans 119, 8567–8577.

707 Johnson, G.C., Purkey, S.G., 2024. Refined estimates of global ocean deep and abyssal decadal warming trends. Geophysical Research Letters 51, e2024GL111229.

710 Karstensen, J., 2020a. Lowered ADCP data during MARIA S. MERIAN cruise MSM60/1. URL: https://doi.org/10.1594/PANGAEA.915879, doi:10.1594/PANGAEA.915879.

713 Karstensen, J., 2020b. Physical oceanography (CTD) during Maria S. Merian cruise MSM60/1. URL: https://doi.org/10.1594/PANGAEA.915898, doi:10.1594/PANGAEA.915898.

716 Karstensen, J., Speich, S., Asdar, S., Berbel, G.B.B., Branlard, L., Carvalho, A., Chidichimo, M.P., Cotrim da Cunha, L., da Silva Calixto, A., Eds-gren, C., Guerrero, R., Hummels, R., Jones, S., Kersale, M., Lebehot, A., Marshall, T., Mohale, N., Rogge, A., Sato, O.T., Schrandt, J., Stöven, T., Sutti Otera, B., 2019. Seamount observatory and samoc overturning, cruise no. msm60, january 04 - february 01, 2017, cape town southafrica - montevideo uruguay. URL: https://www.tib.eu/de/suchen/id/awi%3A8afb37850edb33cd39ff6bf1a0d55438bac05410, doi:10.2312/cr_msm60.

724 Li, Z., Chao, Y., McWilliams, J.C., 2006. Computation of the streamfunction and velocity potential for limited and irregular domains. Monthly weather review 134, 3384–3394.

727 Masuda, S., Awaji, T., Sugiura, N., Matthews, J.P., Toyoda, T., Kawai, Y., Doi, T., Kouketsu, S., Igarashi, H., Katsumata, K., et al., 2010. Simulated rapid warming of abyssal North Pacific waters. Science 329, 319–322.

730 McDonagh, E.L., Arhan, M., Heywood, K.J., 2002. On the circulation of bottom water in the region of the Vema Channel. Deep Sea Research Part I: Oceanographic Research Papers 49, 1119–1139.
733 Meinen, C.S., Garzoli, S.L., Perez, R.C., Campos, E., Piola, A.R., Chidichimo, M.P., Dong, S., Sato, O.T., 2017. Characteristics and causes of deep western boundary current transport variability at 34.5 s during 2009–2014. Ocean Science 13, 175–194.

737 Meinen, C.S., Perez, R.C., Dong, S., Piola, A.R., Campos, E.E.J., 2020. Observed ocean bottom temperature variability at four sites in the northwestern Argentine Basin: Evidence of decadal deep/abyssal warming amidst hourly to interannual variability during 2009–2019. Geophysical Research Letters 47, e2020GL089093.

742 Meinen, C.S., Piola, A.R., Perez, R., Garzoli, S., 2012. Deep Western Boundary Current transport variability in the South Atlantic: Preliminary results from a pilot array at 34.5 S. Ocean Science 8, 1041–1054.

745 Meredith, M.P., Jullion, L., Brown, P.J., Naveira Garabato, A.C., Couldrey, M.P., 2014. Dense waters of the weddell and scotia seas: recent changes in properties and circulation. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, 20130041.

749 Morozov, E., Frey, D., Campos, E., 2018. Flow of antarctic bottom water in the vema channel. a review. 11, 94–102.

751 Orsi, A.H., Johnson, G.C., Bullister, J.L., 1999. Circulation, mixing, and production of Antarctic Bottom Water. Progress in Oceanography 43, 55–109.

753 Purkey, S.G., Johnson, G.C., 2012. Global contraction of Antarctic Bottom Water between the 1980s and 2000s. Journal of Climate 25, 5830–5844.

755 Purkey, S.G., Smethie Jr, W.M., Gebbie, G., Gordon, A.L., Sonnerup, R.E., Warner, M.J., Bullister, J.L., 2018. A synoptic view of the ventilation and circulation of Antarctic Bottom Water from chlorofluorocarbons and natural tracers. Annual review of marine science 10, 503–527.

759 Qu, L., Thomas, L., Gula, J., 2021. Bottom mixing enhanced by tropical storm-generated near-inertial waves entering critical layers in the Straits of Florida. Geophysical Research Letters 48, e2021GL093773.

762 Ray, R.D., 1999. A global ocean tide model from TOPEX/POSEIDON altimeter: GOT99. 2. National Aeronautics and Space Administration, Goddard Space Flight Center.

765 Ruan, X., Wenegrat, J.O., Gula, J., 2021. Slippery bottom boundary layers: The loss of energy from the general circulation by bottom drag. Geophysical Research Letters 48, e2021GL094434.

768 Saha, S., Moorthi, S., Pan, H.L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., et al., 2010. The NCEP climate forecast system reanalysis. Bulletin of the American Meteorological Society 91, 1015–1058.
772 Santos, D.M.C., 2024a. CTD data - SAM19 cruise. URL: https://doi.org/10.5281/zenodo.11149213, doi:10.5281/zenodo.11149213.

774 Santos, D.M.C., 2024b. CTD data - SAM20 cruise. URL: https://doi.org/10.5281/zenodo.11148978, doi:10.5281/zenodo.11148978.

776 Santos, D.M.C., 2024c. LADCP data - SAM19 cruise. URL: https://doi.org/10.5281/zenodo.11149277, doi:10.5281/zenodo.11149277.

778 Santos, D.M.C., 2024d. LADCP data - SAM20 cruise. URL: https://doi.org/10.5281/zenodo.11108848, doi:10.5281/zenodo.11108848.

780 Santos, D.M.C., 2025. CPIES temperature data - Site BB. URL: https://doi.org/10.5281/zenodo.15365408, doi:10.5281/zenodo.15365408.

782 Schubert, R., Gula, J., Capó, E., Damien, P., Molemaker, M.J., Vic, C., McWilliams, J.C., 2025. The ocean flows downhill near the seafloor and recirculates upward above. Nature Communications 16, 5873.

785 Shchepetkin, A.F., McWilliams, J.C., 2005. The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean modelling 9, 347–404.

788 Smythe-Wright, D., Boswell, S., 1998. Abyssal circulation in the Argentine Basin. Journal of Geophysical Research: Oceans 103, 15845–15851.

790 Solodoch, A., Stewart, A., Hogg, A.M., Morrison, A., Kiss, A., Thompson, A., Purkey, S., Cimoli, L., 2022. How does antarctic bottom water cross the southern ocean? Geophysical Research Letters 49, e2021GL097211.

793 Tagliabue, A., Lough, A.J., Vic, C., Roussenov, V., Gula, J., Lohan, M.C., Resing, J.A., Williams, R.G., 2022. Mechanisms driving the dispersal of hydrothermal iron from the northern Mid Atlantic Ridge. Geophysical Research Letters 49, e2022GL100615.

797 Umlauf, L., Burchard, H., 2003. A generic length-scale equation for geophysical turbulence models.

799 Valla, D., Piola, A.R., Meinen, C.S., Campos, E., 2018. Strong mixing and recirculation in the northwestern Argentine Basin. Journal of Geophysical Research: Oceans 123, 4624–4648.

802 Valla, D., Piola, A.R., Meinen, C.S., Campos, E., 2019. Abyssal transport variations in the Southwest South Atlantic: first insights from a long-term observation array at 34.5 S. Geophysical Research Letters 46, 6699–6705.

805 Vic, C., Hascoët, S., Gula, J., Huck, T., Maes, C., 2022. Oceanic mesoscale cyclones cluster surface Lagrangian material. Geophysical Research Letters 49, e2021GL097488.
808 Zenk, W., Hogg, N., 1996. Warming trend in Antarctic bottom water flowing into the Brazil Basin. Deep Sea Research Part I: Oceanographic Research Papers 43, 1461–1473.

811 Zenk, W., Morozov, E., 2007. Decadal warming of the coldest Antarctic Bottom Water flow through the Vema Channel. Geophysical Research Letters 34.

813 Zenk, W., Visbeck, M., 2013. Structure and evolution of the abyssal jet in the Vema Channel of the South Atlantic. Deep Sea Research Part II: Topical Studies in Oceanography 85, 244–260.

816 Zhou, S., Meijers, A.J., Meredith, M.P., Abrahamsen, E.P., Holland, P.R., Silvano, A., Sallée, J.B., Østerhus, S., 2023. Slowdown of Antarctic Bottom Water export driven by climatic wind and sea-ice changes. Nature climate change 13, 701–709.