# Stratification and overturning circulation are intertwined controls on ocean heat uptake efficiency in climate models

Linus Vogt, Jean-Baptiste Sallée, Casimir de Lavergne

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Linus Vogt, Jean-Baptiste Sallée, Casimir de Lavergne. Stratification and overturning circulation are intertwined controls on ocean heat uptake efficiency in climate models. Ocean Science, 2025, 21 (3), pp.1081-1103. 10.5194/os-21-1081-2025. hal-05172914

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# Stratification and overturning circulation are intertwined controls on ocean heat uptake efficiency in climate models

Linus Vogt<fnref n="1" />,<fnref n="2" />, Jean-Baptiste Sallée<fnref n="1" />, and Casimir de Lavergne<fnref n="1" />

**Correspondence:** Linus Vogt (linus.vogt@nyu.edu)

Received: 5 November 2024 – Discussion started: 13 November 2024
Revised: 19 March 2025 – Accepted: 25 March 2025 – Published: 20 June 2025

**Abstract.** The global ocean takes up over 90 % of the excess heat added to the climate system due to anthropogenic emissions, thereby buffering climate change at Earth's surface. A key metric for quantifying the role of the oceanic processes removing this heat from the atmosphere and storing it in the ocean is the ocean heat uptake efficiency (OHUE), defined as the amount of ocean heat uptake per degree of global surface warming. Despite the importance of OHUE, there remain substantial uncertainties concerning the physical mechanisms controlling its magnitude in global climate model simulations: ocean mixed-layer depth, Atlantic Meridional Overturning Circulation (AMOC) strength, and upper-ocean stratification strength have all been previously proposed as controlling factors.

In this study, we analyze model output from an ensemble of 28 climate models from the Coupled Model Intercomparison Project, Phase 6 (CMIP6), in order to resolve these apparently divergent explanations. We find that stratification in the mid-latitude Southern Ocean is a key model property setting the value of OHUE due to its influence on Southern Ocean overturning. The previously proposed role of the AMOC in OHUE is explained by a link between stratification model biases in the subpolar North Atlantic and the Southern Ocean. Our analysis thus reconciles previous attempts at explaining controls on OHUE and highlights the importance of interlinked model biases across variables and geographical regions.

## 1 Introduction

The global ocean buffers anthropogenic climate change by taking up excess heat and carbon from the atmosphere. Since the preindustrial era, over 90 % of the additional heat that has entered the Earth system as a result of changes in Earth's radiative balance has been stored in the ocean (von Schuckmann et al., 2020; Forster et al., 2021). This ocean heat uptake (OHU) is a key process determining the sensitivity of the climate system to external perturbations, in particular to radiative forcing from increased atmospheric greenhouse gas concentrations.

More than half of the observed increase in ocean heat content (OHC) is concentrated in waters shallower than 700 m depth (von Schuckmann et al., 2020). Under increased radiative forcing, anomalous air-sea heat fluxes enter the ocean through its surface and quickly warm its mixed layer on seasonal to interannual timescales, whereas the deep ocean (below 2000 m depth) is more isolated from the atmosphere and is warmed on timescales of decades to centuries (Cheng et al., 2022). Heat is fluxed towards the deep ocean through a multitude of processes, e.g., subduction from the mixed layer (Marzocchi et al., 2021), mean downwelling flows and vertical mixing (Exarchou et al., 2015), and (sub)mesoscale eddy processes contributing notably to isopycnal mixing (Gregory, 2000; Morrison et al., 2016).

A key metric for quantifying the efficiency of these processes at hiding heat from the atmosphere under transient climate change is the OHU efficiency (OHUE), defined as the rate of global OHU per degree of global mean surface warming (e.g., Gregory and Mitchell, 1997; Gregory et al.,

<footnote n="1">¹Sorbonne Université, CNRS/IRD/MNHN, Laboratoire d'Océanographie et du Climat Expérimentations et Approches Numériques (LOCEAN), Paris, France</footnote>

<footnote n="2">²Courant Institute of Mathematical Sciences, New York University, New York, NY, USA</footnote>
2024) (W m⁻² K⁻¹):

$$ \text{OHUE} = \text{OHU}/\Delta T, \qquad (1) $$

where OHU is the increase in OHC relative to preindustrial levels expressed as a flux of energy per unit global surface area, and ΔT is the global mean surface air temperature anomaly relative to preindustrial levels.

In global climate model (GCM) simulations of transient climate change, OHUE estimates span a factor of 2 across different models (Gregory et al., 2024) due to intermodel spread in both OHU (e.g., Vogt et al., 2024) and transient surface warming projections (e.g., Meehl et al., 2020). In an attempt to determine the source of this uncertainty and to find potential observational constraints on OHUE, previous studies have proposed a number of oceanic metrics to control OHUE in GCMs participating in successive phases of the Coupled Model Intercomparison Project (CMIP; Eyring et al., 2016). High-latitude ocean mixed-layer depths were first identified as a possible control of transient warming rates in the ocean and atmosphere using the CMIP3 ensemble (Boé et al., 2009). Subsequently, the strength of the Atlantic Meridional Overturning Circulation (AMOC) in the preindustrial baseline climate was found to correlate well with OHUE across CMIP5 multimodel ensembles (Kostov et al., 2014; Winton et al., 2014) as well as across parameter perturbation ensembles (Romanou et al., 2017; Saenko et al., 2018) and initial-condition ensembles (He et al., 2017), each based on a single model. However, the actual amount of anomalous heat entering the North Atlantic and subducted by the AMOC is small compared to the OHU in the mid-latitude Southern Ocean (Frölicher et al., 2015; Cheng et al., 2022) in a historical context. This is explained by aerosol-induced cooling in the North Atlantic and higher subduction rates in the Southern Ocean (Williams et al., 2024). Furthermore, OHUE actually decreases when the AMOC strengthens under transient forcing (Stolpe et al., 2018). Gregory et al. (2024) thus postulated that the correlation between the AMOC and OHUE may originate from a common dependence on a third factor that would characterize the preindustrial ocean state of a model and influence both the AMOC and OHUE.

A promising candidate that potentially controls both the AMOC and OHUE is the strength of the upper-ocean stratification (Kuhlbrodt and Gregory, 2012), i.e., the density difference between the upper and deeper ocean, which is the main reason for the deep ocean's relative isolation from other parts of the climate system. Because large-scale ocean currents and smaller-scale mixing processes occur preferentially along isopycnal surfaces, stratification impedes the exchange of properties between the upper and deep oceans (e.g., McDougall et al., 2014). Recent studies have highlighted the impact of upper-ocean stratification on OHUE in GCMs. Bourgeois et al. (2022) constrained oceanic heat and carbon uptake in the Southern Ocean using observed and CMIP6-simulated stratification profiles in the region between 30 and 55° S. Similarly, Liu et al. (2023) underscored the importance

of salinity stratification in influencing OHUE in the CMIP6 model and used global sea surface salinity observations to estimate OHU efficiency through an emergent constraint. Finally, Newsom et al. (2023) showed that the depth of the global pycnocline, used as a metric to quantify upper-ocean stratification, is strongly correlated with OHUE across the CMIP5 and CMIP6 models and across a parameter perturbation ensemble of a single model.

It remains unclear, however, how to reconcile these proposed OHUE controls based on AMOC strength, mixed-layer depth (MLD), and stratification. This is not least due to the fact that these variables are interconnected: a deeper mixed layer translates into reduced stratification and vice versa, and North Atlantic MLD and stratification condition the AMOC (Jackson et al., 2023; Nayak et al., 2024). Furthermore, climate model biases can be linked between remote regions of Earth (Wang et al., 2014; Luo et al., 2023), complicating the analysis and interpretation of regional climate metrics in GCMs. For instance, the extratropical oceans, in particular the subpolar North Atlantic and the Southern Ocean, have an outsized role in ventilating the global ocean and storing heat and carbon (Frölicher et al., 2015; Shi et al., 2018). In these regions, the stratification is directly related to the large-scale global ocean circulation since the upper and deep oceans are connected via upwelling isopycnals (Kuhlbrodt et al., 2007; Kamenkovich and Radko, 2011; Morrison et al., 2022). A potential link between Southern Ocean and subpolar North Atlantic stratification could therefore provide insight into the control of upper-ocean stratification on OHUE in GCMs.

In this study, we use an ensemble of CMIP6 models under idealized CO₂ forcing as well as a global ocean state estimate in order to analyze the intermodel relationships and biases in upper-ocean properties (stratification and mixed-layer depth) and meridional overturning metrics (AMOC and Southern Ocean overturning strength), together with their combined influence on OHUE.

In particular, we aim to answer the following questions:

- In which oceanic regions does stratification control OHUE?

- How do biases in temperature and salinity stratification differ in their control on OHUE?

- What explains the positive correlation between AMOC strength and OHUE across CMIP6 models?

- What is the role of meridional overturning in the Southern Ocean for OHUE?

The remainder of this article is organized as follows. In Sect. 2, we present the data and methods used in this study. In Sect. 3, we analyze the dependence of OHUE on upper-ocean properties and meridional overturning metrics from both a global perspective (Sect. 3.1) and a local perspective
(Sect. 3.2). In Sect. 4, we then present the intermodel relationships between these upper-ocean properties on the one hand and the meridional overturning metrics on the other hand. In Sect. 5, we analyze the ensemble mean and intermodel spread of historical stratification and its bias relative to observations, including a link between GCM stratification biases in the Southern Ocean and the subpolar North Atlantic (Sect. 5.2). Finally, in Sect. 6, we offer a schematic picture of all of the major intermodel relationships explored in this study and conclude by answering the four questions posed above.

## 2 Methods

### 2.1 CMIP6 model output

We use model output from a set of 28 climate models from 14 modeling centers run in three CMIP6 experiments: a baseline experiment with preindustrial forcings (piControl experiment), a historical scenario with realistic forcing from 1850 to 2014 (historical experiment), and a perturbed scenario forced by an idealized atmospheric CO₂ increase of 1% per year during 150 years (1pctCO₂ experiment). We use one ensemble member per model, with the 1pctCO₂ run branching off from the piControl run (Table A1). All model outputs used for the analysis (principally ocean potential temperature and ocean salinity) are regridded onto a regular 1° × 1° latitude–longitude grid in order to allow the calculation of local intermodel correlations at each grid cell. Anomalies of variables in the 1pctCO₂ experiment relative to the piControl run are calculated by subtracting the appropriate piControl period from the 1pctCO₂ data; since piControl runs are extended over the 150-year period of the 1pctCO₂ experiment, this method removes the effect of model drift.

### 2.2 Calculation of ocean variables

Ocean heat content per unit volume is defined as OHC = $ρ_0C_pθ$, where $ρ_0 = 1035 \text{ kg m}^{-3}$ is a reference density, $C_p = 3992 \text{ J kg}^{-1} \text{ K}^{-1}$ is a reference heat capacity (as defined in TEOS-10, e.g., Griffies et al., 2016), and $θ$ is the potential temperature. Global OHU in the 1pctCO₂ experiment is then calculated as the time derivative of the three-dimensional integral of the OHC anomaly relative to the preindustrial state.

OHUE is defined as in Gregory et al. (2024): the total OHU divided by 1.5 times the global mean sea surface temperature anomaly at years 60–80 in the 1pctCO₂ run, which is the 20-year period around the time of CO₂ doubling relative to the preindustrial.

The AMOC strength is calculated using the overturning streamfunction variables in latitude–depth coordinates from the CMIP6 output and is defined as the streamfunction maximum in the Atlantic basin at 26.5° N and below 500 m depth.

Stratification is defined as the squared buoyancy frequency $N^2$ integrated in depth between 0 and 1500 m, resulting in units of meters per square second. The squared buoyancy frequency $N^2$ is calculated using the TEOS-10 software toolbox (McDougall and Barker, 2011). The depth of 1500 m is chosen to encompass the mixed layer as well as the internal pycnocline (Gnanadesikan, 1999; Klocker et al., 2023). The main results of this study are tested with different values of this maximal depth (spanning a range from 400 to 2500 m) and will be shown to be only weakly sensitive to this particular choice. The stratification is further decomposed into contributions from temperature and salinity, according to

$$N^2 = N_T^2 + N_S^2 = \frac{g}{\rho_0} \left( -\alpha \frac{\partial \theta}{\partial z} + \beta \frac{\partial S}{\partial z} \right), \quad (2)$$

where g is gravity, $ρ_0$ is a reference density, $α$ is the thermal expansion coefficient, $β$ is the haline contraction coefficient, and S is salinity. The sum of these two terms reproduces the total $N^2$ exactly.

Potential density for use in the definitions of mixed-layer depth and the Southern Ocean upper overturning cell below is computed from ocean potential temperature and salinity using the TEOS-10 software toolbox (McDougall and Barker, 2011).

Mixed-layer depth is defined as the minimum depth where the monthly potential density $σ_0$ deviates by $0.03 \text{ kg m}^{-3}$ from its value at 5 m depth (de Boyer Montégut et al., 2004). For consistency, this definition is used even for models that have the MLD variable mlotst available as part of their CMIP output.

To calculate the strength of the upper Southern Ocean overturning cell, we first calculate the time-mean overturning streamfunction in latitude–density coordinates from time-mean meridional ocean velocity and potential density referenced to 2000 dbar ($σ_2$) (e.g., Farneti et al., 2015):

$$\bar{\Psi}_{\text{SO}}(y, \sigma_2) = - \int dx \int_{-H}^{\bar{z}(x, y, \sigma_2)} \bar{v}(x, y, z') dz' dx, \quad (3)$$

where x, y, and z are longitude, latitude, and depth; $H(x, y)$ is the depth of the ocean bottom; v is the residual mean meridional mass transport (CMIP variable vmo, including resolved and parameterized transport); and $\bar{z}(x, y, \sigma_2)$ is the local depth of the isopycnal $σ_2$. The strength of the upper cell $M_{\text{SO}}$ is then defined as the time-mean streamfunction maximum within the $1034 \text{ kg m}^{-3} < \sigma_2 < 1038 \text{ kg m}^{-3}$ density range and between 35 and 40° S.

For a complementary quantification of Southern Ocean overturning, we compute the surface flux water mass transformation (SFWMT), a measure of overturning inferred from surface buoyancy fluxes, following, e.g., Jackson and Petit (2023). The SFWMT is the derivative of the surface buoyancy flux into the Southern Ocean south of 30° S with respect
to density:

$$ \Psi(\sigma_2) = \frac{\partial}{\partial \sigma_2} B_A(\sigma_2), \qquad (4) $$

where the surface buoyancy flux into the area A is a sum of heat and freshwater terms:

$$ B_A(\sigma_2) = \int dA' \left( -\alpha \frac{Q}{C_p} - \beta \frac{sW}{1-s} \right). \qquad (5) $$

In this equation, s is the non-dimensional sea surface salinity, and W is the surface freshwater flux (CMIP variable wfo) (kg m⁻² s⁻¹). As a single measure of Southern Ocean overturning strength inferred from surface buoyancy fluxes, we choose the difference

$$ M_{WMT} = \max_{\sigma_2} \Psi - \min_{\sigma_2} \Psi. \qquad (6) $$

## 2.3 Observation-based data

For comparison of model fields with observationally constrained data, we use potential temperature and salinity data from the ECCO Version 4 global state estimate (ECCO Consortium et al., 2024; Forget et al., 2015) with data coverage from 1992 to 2017. To calculate stratification strength and MLD, the ECCO output fields are regridded and processed in the same way as the CMIP6 model output.

## 2.4 Intermodel empirical orthogonal function analysis

An empirical orthogonal function (EOF) algorithm (Dawson, 2016) is applied to two-dimensional model fields to construct intermodel EOF patterns, expressed as the correlation across models between the principal component value and the input field at each grid cell. This corresponds to a standard EOF analysis but with the variance maximized by each EOF being measured across models instead of in time (e.g., Hu et al., 2020). The model fields used as input to the EOF analysis are preindustrial upper-ocean stratification and mixed-layer depth averaged over the time period in the preindustrial run corresponding to the first 150 years of the 1pctCO₂ experiment used to determine OHUE in each model.

For the EOF analysis of preindustrial mixed-layer depth (Fig. A8), a number of outlier models with extreme values of the first principal component were identified and removed from the analysis in order to facilitate interpretation. For this, the EOF algorithm was iteratively applied five times to the preindustrial annual mean MLD fields of all of the models, and the model with the most extreme value of the first principal component was removed, leaving a total of 23 models for the final EOF output. For all other non-EOF analyses in this study, the full set of 28 models is used.

## 2.5 Classification of vertical stratification profiles

An unsupervised ocean profile classification algorithm (Maze et al., 2017; Maze, 2020) is applied to vertical profiles

of $N_T^2$ and $N_S^2$ to obtain a pre-specified number of eight representative classes characterized by the shape and amplitude of temperature and salinity stratification profiles. As input to the classification procedure, the preindustrial time-mean $N_T^2$ and $N_S^2$ profiles are pooled together from all of the grid cells and all of the models.

# 3 Global and local controls on ocean heat uptake efficiency

We begin by investigating the main proposed controls on OHUE in our set of 28 CMIP6 GCMs in the preindustrial state. These variables belong to two categories: upper-ocean properties (i.e., stratification and mixed-layer depth) and meridional overturning strength (i.e., AMOC, $M_{SO}$, and $M_{WMT}$).

## 3.1 Global controls on OHUE

We first establish how the two upper-ocean properties are related to OHUE in the global mean (Fig. 1a–b). Preindustrial global mean upper-ocean stratification is not significantly correlated with OHUE at the $p = 0.05$ level across our ensemble of 28 CMIP6 models (Fig. 1a). In contrast, preindustrial global mean MLD is positively correlated with OHUE, with a linear correlation coefficient of $r = 0.56$ (Fig. 1b); i.e., models with a deeper global mean mixed layer tend to have a higher OHUE.

Turning now to the three overturning strength metrics (Fig. 1c–e), preindustrial AMOC strength is positively correlated across models with OHUE (Fig. 1c, $r = 0.61$). This is consistent with previous findings, but we obtain a smaller correlation coefficient for our ensemble of 28 CMIP6 models than for the mixed-model ensemble of Gregory et al. (2024), which included 19 CMIP5 models and 14 CMIP6 models ($r = 0.81$). A slightly stronger relationship is found for the Southern Ocean upper cell (Fig. 1d): $M_{SO}$ and OHUE are also positively correlated ($r = 0.64$). The MRI-ESM2-0 model is an outlier with high OHUE but only moderate $M_{SO}$; removing this model from the linear fit results in a correlation of $r = 0.86$. As an alternative to the overturning metric $M_{SO}$ computed in latitude–density coordinates, we also consider the Southern Ocean overturning strength inferred from surface buoyancy fluxes, i.e., $M_{WMT}$ (Fig. 1e). This metric is not significantly correlated with OHUE at the $p = 0.05$ level in our model ensemble ($r = 0.39$ and $p = 0.08$).

## 3.2 Local upper-ocean controls on OHUE

The fact that global mean upper-ocean stratification is not significantly correlated with OHUE across models may at first sight appear to contradict previous findings highlighting the importance of stratification for OHUE (Liu et al., 2023; Newsom et al., 2023; Bourgeois et al., 2022). This is because globally averaged stratification and MLD are relatively crude
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Proposed controls on ocean heat uptake efficiency (OHUE). Scatterplot between OHUE and (a) preindustrial global mean upper-ocean (0–1500 m) stratification ($N^2$), (b) preindustrial global mean mixed-layer depth (MLD), (c) preindustrial mean AMOC strength, (d) Southern Ocean upper-cell strength, and (e) inferred Southern Ocean surface buoyancy flux overturning. In panels (c)–(e), only a subset of models is included due to output availability (see Table A1).</figcaption></figure>

bulk measures of the simulated upper-ocean state. We now therefore extend this analysis to the local level by considering intermodel correlations between global OHUE and the two upper-ocean variables at each model grid cell (Fig. 2).

Figure 2a shows the intermodel correlation coefficient between OHUE and local preindustrial annual mean upper-ocean (0–1500 m) stratification. Unlike global average stratification (Fig. 1a), local stratification is significantly anti-correlated with OHUE in several locations. Significant correlations ($p < 0.05$) are found in two primary regions: the subpolar North Atlantic Ocean and the mid-latitude South-

ern Ocean. In both regions, the correlation is negative, indicating that models with greater (more stable) preindustrial stratification in these regions have a lower OHUE. This is consistent with previous studies showing a strong link between OHUE and upper-ocean stratification in the mid-latitude Southern Ocean (Bourgeois et al., 2022; Liu et al., 2023). In the Southern Ocean, significant negative correlations are found, particularly in the Pacific and Indian sectors, whereas the signal in the South Atlantic Ocean is less widespread. This zonally asymmetric pattern is consistent with the geography of Subantarctic Mode Water formation
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Local upper-ocean controls on OHUE. Maps of the inter-model Pearson correlation coefficient across 28 CMIP6 models between OHUE and local preindustrial annual mean (a) upper-ocean (0–1500 m) stratification and (b) mixed-layer depth. Stippling indicates the region where the least-squares linear regression slope is not significantly different from zero ($p \ge 0.05$, Wald test with a t distribution). In panel (a), regions where the bathymetry is less than 1500 m deep are shaded in grey.</figcaption></figure>

(McCartney, 1979; Hanawa and Talley, 2001) and subduction (Sallée et al., 2010). Apart from these two regions, a smaller patch of significant negative correlations is found in the eastern tropical Pacific. These patterns are partly dependent on the choice of the depth range over which the squared buoyancy frequency $N^2$ is integrated (Fig. A1). The negative correlation in the subpolar North Atlantic is present for all depth choices from 0–400 to 0–2500 m, but the negative correlation in the mid-latitude Southern Ocean is absent for the 0–400 m stratification and only emerges gradually for the 0–1500 m and deeper depth ranges. This suggests that the aspect of subpolar North Atlantic stratification that is important for OHUE strength is already set in the top 400 m (i.e., the surface ocean mixed layer), while in the Southern Ocean almost the entire water column matters for OHUE there. The decomposition of stratification into its temperature and salinity contributions (Eq. 2) shows that the subpolar North Atlantic control on OHUE is due to salinity stratification, whereas temperature stratification in this region is positively correlated with OHUE (Fig. A1). In the Southern Ocean, both temperature and salinity contribute to the negative correlation with OHUE (Fig. A1), and only their combination to total stratification results in the broad-scale signal found across the Southern Ocean in Fig. 2a.

An analogous analysis of the local preindustrial annual mean MLD is shown in Fig. 2b. Significant positive correlations are found in the subpolar North Atlantic as well as at low latitudes in all of the ocean basins; higher OHUE is thus associated with deeper mixed layers in these regions. However, in contrast to stratification, there are no significant correlations between MLD and OHUE in the mid-latitude Southern Ocean.

The relationships shown in Figs. 1 and 2 still hold when averaging all variables over years 60–80 in the 1pctCO2 experiment instead of the preindustrial experiment, but they tend to be weaker (not shown). This bolsters our results and our approach of focusing on preindustrial controls on OHUE.

## 4 Upper-ocean controls on meridional overturning

In the previous section, we found significant intermodel correlations with OHUE, not only for meridional overturning metrics (Fig. 1c, d), but also for regional upper-ocean properties (Fig. 2). It is therefore worthwhile investigating the potential links between these two categories of variables across the model ensemble, i.e., between stratification and MLD on the one hand and overturning metrics on the other hand, as shown in Fig. 3.

The left column of Fig. 3 shows the intermodel correlations between local preindustrial mean upper-ocean stratification and the preindustrial AMOC, $M_{SO}$ and $M_{WMT}$. Preindustrial AMOC strength is anticorrelated with subpolar North Atlantic total stratification and weakly positively correlated with total stratification in the western Pacific (Fig. 3a). While the signal in the western Pacific is unclear and due to both temperature and salinity stratification, the negative correlation in the subpolar North Atlantic can be attributed to salinity stratification, since the temperature contribution has the opposite sign (Fig. A2b–c).

The Southern Ocean upper-cell strength, $M_{SO}$, computed in latitude–density coordinates, is anticorrelated with the total stratification, mostly in the Southern Ocean at the latitudes of the Antarctic Circumpolar Current (ACC; Fig. 3c). This can mostly be attributed to temperature stratification (Fig. A2e), which has significant negative correlations extending up to the subtropical latitudes of the Pacific and Indian oceans.

The Southern Ocean upper-cell strength $M_{WMT}$ inferred from surface buoyancy fluxes is also negatively correlated with total stratification in the Southern Ocean, and its correlations are higher and extend over a greater surface area (Fig. 3e) than for the upper cell computed in latitude–density coordinates. However, for this metric, the intermodel link to stratification can be attributed solely to salinity stratification (Fig. A2i), while temperature stratification shows no significant correlation with $M_{WMT}$ in any of the major ocean basins (Fig. A2h). This is consistent with the regional hydrography, since the stratification in this region is mostly representative
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Local upper-ocean controls on meridional overturning strength in CMIP6. Left column (a, c, e): maps of the intermodel Pearson correlation coefficient across 28 CMIP6 models between local preindustrial annual mean upper-ocean (0–1500 m) stratification and (a) preindustrial mean AMOC strength, (c) Southern Ocean upper-cell strength, and (e) inferred Southern Ocean surface buoyancy flux overturning. Right column (b, d, f): as in the left column but for the local preindustrial annual mean mixed-layer depth.</figcaption></figure>

of the density difference between the surface ocean and the Circumpolar Deep Water (CDW) below, because the conversion of CDW into lighter water is mostly due to surface freshwater fluxes (Abernathey et al., 2016; Pellichero et al., 2018) and is qualitatively given here by $M_{WMT}$.

We now turn to the links between these overturning strength metrics and the local preindustrial mean MLD, as shown in the right column of Fig. 3. AMOC strength is positively correlated with MLD in the subpolar North Atlantic as well as at tropical latitudes in all of the ocean basins. This closely resembles the pattern found for the MLD–OHUE link

in Fig. 2b, which is a point to which we will return in our conclusions (Sect. 6).

For the two Southern Ocean overturning metrics $M_{SO}$ and $M_{WMT}$, a potential link to MLD is overall much less clear than for the AMOC. While $M_{SO}$ is positively correlated with MLD in some regions in the tropical and subtropical Pacific, it is negatively correlated with MLD along the Polar Front in the Southern Ocean. Furthermore, the Southern Ocean overturning metric inferred from surface buoyancy fluxes, $M_{WMT}$, exhibits no large-scale regions of significant correlations with MLD. It is possible that links between the South-
ern Ocean overturning circulation and the local MLD in the CMIP6 ensemble are more difficult to identify than for the AMOC in the North Atlantic, since Southern Ocean water mass formation and subduction locations vary across models (Sallée et al., 2013a, b).

# 5 Stratification model bias and intermodel spread

## 5.1 Ensemble mean stratification and bias relative to observations

Although we found global mean stratification to be unrelated to OHUE (Fig. 1), there are significant links between regional stratification and OHUE in the subpolar North Atlantic and the mid-latitude Southern Ocean (Fig. 2a). In addition, stratification in each of these regions is in turn related to the AMOC and the Southern Ocean overturning, respectively (Fig. 4). Potential model biases in these regions would thus have direct implications for OHUE. Beyond the previous analysis of intermodel relationships between variables, it is thus also insightful to investigate the mean state, the intermodel spread, and the bias relative to observations of simulated upper-ocean stratification; this is shown in Fig. 4.

The ensemble mean total stratification (Fig. 4a) has a distinct Equator-to-pole gradient, with a highly stratified water column in the tropics and its lowest stratification in the Southern Ocean and subpolar North Atlantic. Consequently, the largest relative intermodel spread in the total stratification (Fig. 4b) is found in regions with low stratification commonly associated with deep convection: the Weddell and Ross seas in the Southern Hemisphere and the subpolar North Atlantic and Nordic Seas in the Northern Hemisphere, where the intermodel standard deviation is larger than 50% of the ensemble mean. Compared to the ECCO state estimate, the CMIP6 ensemble is too stratified over most of the ocean (Fig. 4c), especially in the equatorial Pacific and Atlantic, where the bias reaches values of up to 10% of the ensemble mean, and in the mid-latitude Southern Ocean.

The temperature contribution to stratification dominates the magnitude and pattern of the ensemble mean total stratification at low to middle latitudes (Fig. 4d), while the mean salinity contribution is responsible for stabilizing the high-latitude oceans (Fig. 4g). This is a consequence of the nonlinear equation of state for seawater, which diminishes the influence of temperature on density in cold water (Roquet et al., 2015). Relative to the average total stratification, there is a larger intermodel spread in salinity stratification than in temperature stratification (Fig. 4e, h), especially in the high-latitude Southern Ocean around Antarctica and in the North Atlantic subpolar gyre and Nordic Seas. Despite its subordinate role in setting the mean global stratification, the salinity contribution is thus a deciding factor in the intermodel spread in total stratification. Furthermore, salinity stratification also dominates the model bias relative to the state es-

timate (Fig. 4i), with relatively large positive salinity stratification biases in the Southern Ocean and subpolar North Atlantic, while temperature stratification biases are small in magnitude, except for a negative bias in the Atlantic basin (Fig. 4f). It should be recalled that the biases documented here are those of the CMIP6 ensemble mean; individual model biases may differ.

## 5.2 Regional coherence of stratification intermodel links

The fact that OHUE is unrelated to global mean stratification (Fig. 1a) and instead is sensitive to stratification in disconnected regions of both the Northern Hemisphere and Southern Hemisphere (Fig. 2a), which additionally exhibit common biases relative to observations (Fig. 4), motivates a closer analysis of the intermodel spread in regional stratification patterns.

An intermodel EOF analysis of the model ensemble's preindustrial annual mean stratification patterns reveals two principal modes of intermodel spread (Fig. 5), which together explain 55% of the intermodel variance (the third leading mode only explains 5.6% of the variance). The first EOF (Fig. 5a) explains 39% of the intermodel variance and consists of a broadly uniform large-scale coherence including the Pacific and Indian basins and the Southern Ocean but with no signal in the North Atlantic. This means that, to first order, model biases in preindustrial stratification in the Pacific, Indian, and Southern oceans tend to covary across models, whereas the North Atlantic stratification varies independently. The first-order independence of North Atlantic stratification from other regions can also be seen from an unsupervised classification of vertical stratification profiles (Fig. A7), where the North Atlantic is associated with a stratification profile not found in any other ocean basin or in the Southern Hemisphere. The same pattern as in the first EOF can be seen when considering the preindustrial intermodel correlation of local stratification with global mean stratification (Fig. 5b). Global mean stratification is correlated with local stratification across the Pacific, Indian, and Southern oceans, but not in the North Atlantic. This shows that the principal component associated with the first EOF (Fig. 5a) is strongly correlated with the global mean stratification (Fig. A3). The pattern of the first EOF is potentially also linked to the strength of the subtropical gyres and subtropical overturning cells in the Pacific and Atlantic.

The second EOF (Fig. 5c) explains 16% of the intermodel variance in preindustrial stratification. It mainly consists of a coherence including the mid-latitude Southern Ocean, subpolar North Atlantic, and eastern tropical Pacific, and a signal of opposite sign in the western tropical Pacific. This suggests that, to second order, preindustrial stratification model biases in the Southern Ocean and subpolar North Atlantic tend to be linked. Although these two regions are geographically far apart, they are physically con-
<figure><img src="image_6.png" /></figure>

**Figure 4.** Ensemble mean stratification and bias relative to observations. (a) CMIP6 ensemble mean total historical stratification integrated over the 0–1500 m depth range. (b) Intermodel coefficient of variation (ratio of the ensemble standard deviation to the ensemble mean) of the total stratification. (c) Bias in the total stratification between the CMIP6 ensemble mean and the ECCO state estimate. (d–f) As in panels (a)–(c) but for temperature stratification. (g–i) As in panels (a)–(c) but for salinity stratification. For both the model ensemble and the state estimate, stratification is averaged over the historical period 1992–2017.

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

**Figure 5.** Regional coherence of the intermodel stratification spread. Panels (a) and (c) show the first and second modes of the intermodel EOF analysis of the preindustrial annual mean upper-ocean stratification (see the Methods section). The violin plots in panels (a) and (c) show the ensemble distribution of the normalized loadings for each EOF. In panel (c), stippling indicates areas with surface density in the range $25.75 \text{ kg m}^{-3} < \sigma_0 < 27 \text{ kg m}^{-3}$. Panels (b) and (d) show the intermodel correlation between the local preindustrial stratification and either (b) the global mean preindustrial stratification or (d) the subpolar North Atlantic mean preindustrial stratification. The subpolar North Atlantic region used in panel (d) is indicated by the blue contour.
nected by the outcropping of the same isopycnals in the range
$25.75 \text{ kg m}^{-3} < \sigma_{0} < 27 \text{ kg m}^{-3}$, as indicated by the stippling
of sea surface density in Fig. 5c. This link is further illus-
trated by the intermodel correlation of local stratification
with stratification averaged over the subpolar North Atlantic
(indicated by the contour in Fig. 5d). Apart from a trivial pos-
itive correlation in the subpolar North Atlantic itself, we find
a circumpolar band of positive intermodel correlation in the
mid-latitude Southern Ocean.

Further EOF modes are not explored in detail here since
they each explain less than 6% of the intermodel variance.
Still, the three subsequent EOFs all have a signal of the
same sign in the Southern Ocean and subpolar North At-
lantic (Fig. A4), strengthening the intermodel link between
these regions that was found in the second EOF.

The distinct role of temperature and salinity stratification
in setting these patterns of intermodel spread can be seen
when applying the EOF analysis to temperature and salin-
ity stratification separately (Fig. A5). It is apparent that the
first two intermodel EOFs in total stratification (Fig. 5a, c) re-
semble the first two EOFs of salinity stratification (Fig. A5b,
d). In contrast, the first EOF of temperature stratification
(Fig. A5a) consists of a broad low- to mid-latitude pat-
tern that includes the North Atlantic, and the second EOF
(Fig. A5c) shows an approximate hemispheric dipole sig-
nal with opposite signs between the Southern Ocean and the
Northern Hemisphere oceans. This implies that intermodel
spreads in patterns of salinity stratification are decisive for
setting the patterns of total stratification, which in turn con-
trol OHUE (Fig. 2a).

It is also interesting to note that temperature and salinity
stratifications ($N_T^2$ and $N_S^2$) do not vary independently across
the model ensemble: intermodel biases in temperature and
salinity stratification tend to compensate for each other in
the high-latitude Southern Ocean and in the North Atlantic,
meaning that models with strong salinity stratification tend
to have weak temperature stratification at these locations and
vice versa (Fig. A6a). In addition, a difference in total strati-
fication between two models tends to coincide with a differ-
ence in salinity stratification of the same sign across almost
all of the global ocean (Fig. A6c), while temperature strati-
fication is only positively correlated with total stratification
over the low- to mid-latitude oceans (Fig. A6b). These find-
ings partly explain the success of the emergent constraint by
Liu et al. (2023) between sea surface salinity as a proxy for
$N_S^2$ and OHUE.

# 6 Discussion and conclusions

## 6.1 Schematic summary of principal intermodel relationships between variables controlling OHUE

The schematic in Fig. 6 summarizes the intermodel relation-
ships found in this study between local upper-ocean strat-

<figure><img src="image_8.png" /><figcaption>**Figure 6.** Schematic illustrating the intermodel links between key ocean properties. Arrows indicate the identified physically based intermodel relationships, and the dashed arrow labeled “(e)” indicates the unclear relationship between $M_{WMT}$ and OHUE. The letter labels correspond to subsections in the Discussion section (Sect. 6).</figcaption></figure>

ification, local mixed-layer depth, various meridional over-
turning strength metrics, and OHUE. We now summarize our
findings for the most important connections, depicted as ar-
rows and labeled with lowercase letters in Fig. 6.

(a) Subpolar North Atlantic stratification ($N_{\text{N.Atl.}}^2$) and
Southern Ocean stratification ($N_{\text{SO}}^2$)

We have identified a coherent pattern of intermodel spread
in preindustrial stratification linking the subpolar North At-
lantic and the mid-latitude Southern Ocean (Fig. 5c, d). Al-
though this mode of intermodel variability only explains
16% of the intermodel variance in preindustrial stratification
(compared to 39% for the leading mode), it is key to driv-
ing differences in OHUE between models. Indeed, the load-
ings of this second EOF are correlated with OHUE across
the model ensemble (Pearson *r* = 0.57 and *p* < 0.05). This
pattern of North Atlantic–Southern Ocean coherence is also
found in the intermodel correlation between total preindus-
trial stratification and OHUE (Fig. 2a) and in the ensemble
mean bias of historical total and salinity stratification with
respect to observations (Fig. 4c, i).

The physical link between stratification in the mid-latitude
Southern Ocean and the subpolar North Atlantic is illustrated
by the outcropping of the same isopycnals in these two re-
gions (Fig. 5c). In both regions, permanent stratification is
dominated by the internal pycnocline of the global ocean,
which separates the shallow northward and deep southward
limbs of the AMOC (Gnanadesikan, 1999; Klocker et al.,
2023). An interhemispheric connection via the AMOC has
also been shown to explain common temperature biases of
CMIP6 models in the Southern Ocean (Luo et al., 2023). Cer-
tain characteristics of the subpolar North Atlantic can thus be
proxies for those of the Southern Ocean and vice versa.
(b) Southern Ocean stratification ($N_{SO}^2$) and upper-cell strength ($MOC_{SO}$)

Southern Ocean stratification impacts the strength of the Southern Ocean upper overturning cell $M_{SO}$ computed in latitude–density coordinates (Fig. 3c). However, this correlation is relatively weak ($r < 0.6$ at most locations) and its spatial pattern is rather discontinuous albeit consistent with the documented regions of water mass formation feeding the upper overturning cell (eastern Indian and eastern Pacific basins in the latitude range 40–60° S; e.g., Sallée et al., 2010).

(c) Southern Ocean stratification ($N_{SO}^2$) and upper-cell strength inferred from surface buoyancy fluxes ($MOC_{WMT}$)

The upper-cell strength inferred from surface buoyancy fluxes, $M_{WMT}$, was used as an alternative measure of Southern Ocean overturning. It is impacted by stratification across the Southern Ocean and from latitudes of the ACC up to the subtropics (Fig. 3g), with higher correlations than for the alternative metric $M_{SO}$.

(d) Upper-cell strength ($MOC_{SO}$) and OHUE

The strength of the Southern Ocean upper overturning cell $M_{SO}$ computed in latitude–density coordinates is correlated well with OHUE (Fig. 1d), and when ignoring the outlier model MRI-ESM2-0, the correlation coefficient ($r = 0.86$) is much higher than that for the AMOC ($r = 0.61$). In a different model ensemble, Gregory et al. (2024) found a correlation coefficient between the AMOC and OHUE of $r = 0.83$.

(e) Upper-cell strength inferred from surface buoyancy fluxes ($MOC_{WMT}$) and OHUE

The upper-cell strength inferred from surface buoyancy fluxes, $M_{WMT}$, was found to be insignificantly correlated with OHUE ($r = 0.39$ and $p = 0.08$).

(f) Subpolar North Atlantic stratification ($N_{N.Atl.}^2$) and AMOC

Preindustrial upper-ocean stratification in the subpolar North Atlantic is anticorrelated with preindustrial AMOC strength (Fig. 3a). This is consistent with theoretical understanding and modeling results from previous studies, which have shown that AMOC strength in CMIP6 is influenced by North Atlantic stratification (Nayak et al., 2024), especially in the Labrador Sea and due to salinity stratification (Jackson et al., 2023; Lin et al., 2023; Jackson and Petit, 2023). This is because stratification in this region inhibits the formation of North Atlantic Deep Water, which feeds the southward branch of the AMOC, mostly via open-ocean deep convection in these models (Heuzé, 2021).

(g) AMOC and low-latitude mixed-layer depth ($MLD_{low-lat}$)

Preindustrial AMOC strength is positively correlated with preindustrial MLD in the subpolar North Atlantic as well as at the low latitudes in all ocean basins (Fig. 3b). Subpolar North Atlantic MLD is a proxy for deep convection (Jackson and Petit, 2023; Heuzé, 2021), and its connection to AMOC strength is consistent with process understanding and is related to point (f) above (Jackson et al., 2023).

However, the reason for the link between the AMOC and low-latitude mixed-layer depths is unclear. Since significant positive correlations are found not only in the Atlantic but also extend across the Pacific and Indian basins, it is possible that this relationship is not directly caused by a physical mechanism but rather the spatial coherence of intermodel MLD spread, analogous to the stratification in Sect. 5.2. Indeed, an intermodel EOF analysis applied to preindustrial annual mean MLD reveals a first-order coherence between subpolar North Atlantic MLD and global MLDs including the tropics (Fig. A8a), with the second- and third-order EOFs separately containing the variance of the high and low latitudes (Fig. A8b–c).

(h) Low-latitude mixed-layer depth ($MLD_{low-lat}$) and OHUE

Preindustrial mixed-layer depth at the low latitudes is positively correlated with OHUE (Fig. 2b). One hypothesis to explain this is that the mixed-layer depth at these latitudes quantifies the thermal capacity of the ocean, since most of the radiative forcing is applied to the ocean surface at these latitudes (Gregory et al., 2024) and deeper mixed layers have a higher heat capacity. Furthermore, since sea surface temperatures are high and vertical temperature gradients are sharp at the low latitudes, the modeled mixed-layer depth there may be sensitive to the parameterization of vertical mixing of heat in these models. The representation of this mixing also impacts OHUE (Newsom et al., 2023), possibly contributing to the link between low-latitude MLD and OHUE.

## 6.2 Synthesis

We are now in a position to answer the questions posed in the Introduction section of this study.

### 6.2.1 In which oceanic regions does stratification control OHUE?

The key regions where preindustrial stratification controls OHUE are the subpolar North Atlantic and the mid-latitude Southern Ocean (Fig. 2a). These two regions are linked via the second-order mode of the intermodel stratification spread (Fig. 5), and they are precisely the regions where ensemble mean historical stratification is biased high (Fig. 4c) due to biased salinity stratification (Fig. 4i). This is consistent with
the findings of Liu et al. (2023), who showed that CMIP6 models tend to overestimate salinity stratification, particularly in these regions (their Fig. 3a), and that salinity stratification approximated by sea surface salinity can be used to constrain OHUE. Our results demonstrate that it is possible that only the Southern Ocean stratification has a direct effect on OHUE through its influence on the large-scale overturning circulation (Fig. 3d-i). The subpolar North Atlantic stratification could be anticorrelated with OHUE due to its connection with Southern Ocean stratification (Fig. 5) rather than a direct influence on OHUE. This would be consistent with previous findings showing that the actual amount of anomalous heat entering the North Atlantic and subducted by the AMOC is small compared to the OHU in the midlatitude Southern Ocean (Frölicher et al., 2015; Cheng et al., 2022) and that changes in the strength of OHUE and the AMOC under transient forcing are uncorrelated (Stolpe et al., 2018). The direct link between OHUE and Southern Ocean stratification, rather than North Atlantic stratification, is illustrated further by a comparison of the upper-ocean stratification definition used here with the pycnocline depth index defined by Newsom et al. (2023) (Fig. A9). This near-global (60° S–60° N) pycnocline depth index has been shown to nicely constrain OHUE (Newsom et al., 2023), and we show here that it is strongly anticorrelated with local stratification in the Southern Ocean but not in the subpolar North Atlantic (Fig. A9a).

## 6.2.2 How do biases in temperature and salinity stratification differ in their control on OHUE?

Salinity stratification biases in CMIP6 play a dominant role in OHUE for several reasons. First, the intermodel spread in total stratification in key regions is dominated by the spread in salinity stratification (Fig. 4h). Second, salinity stratification sets the spatial patterns of intermodel stratification spread as determined by the intermodel EOF analysis (Figs. 5 and A5). Finally, the pattern of the bias of CMIP6 ensemble mean stratification with respect to the ECCO state estimate is driven by the bias in salinity stratification (Fig. 4c, i). This is consistent with the dominant role of salinity stratification in OHUE found by Liu et al. (2023). However, temperature stratification also plays a role, in particular for setting the mean strength of the global total stratification.

## 6.2.3 What explains the positive correlation between AMOC strength and OHUE across CMIP6 models?

AMOC strength is directly controlled by subpolar North Atlantic stratification. The positive correlation of AMOC with OHUE can be explained by two factors: (i) North Atlantic stratification is connected to Southern Ocean stratification, physically via the internal pycnocline (separating the shallow northward and deep southward limbs of the global overturning)

and statistically via the second EOF of the intermodel stratification spread. We argue that Southern Ocean stratification in turn influences OHUE via the overturning circulation. (ii) Both the AMOC and OHUE are related to low-latitude MLD as a proxy for thermal capacity.

These two factors represent the upper and lower branches connecting the AMOC to OHUE in the schematic in Fig. 6, and presumably they both contribute to the positive correlation between the AMOC and OHUE. Our analysis thus supports the hypothesis that the AMOC is not the mechanism actively controlling OHUE (Gregory et al., 2024). This hypothesis concurs with the observation that the amount of heat entering the North Atlantic and subducted by the AMOC is relatively small compared to Southern Ocean OHU (Frölicher et al., 2015; Cheng et al., 2022), due to aerosol-induced cooling in the North Atlantic and higher subduction rates in the Southern Ocean (Williams et al., 2024).

## 6.2.4 What is the role of meridional overturning in the Southern Ocean for OHUE?

Our results indicate that the AMOC might not be the ocean circulation directly affecting OHUE by transporting heat into the ocean interior and that, instead, it is the Southern Ocean upper overturning cell which has a direct impact on OHUE. However, the link between Southern Ocean stratification and OHUE via Southern Ocean overturning is difficult to quantify. The connection between Southern Ocean stratification and Southern Ocean overturning is clearest when using an overturning metric inferred from surface buoyancy fluxes ($M_{WMT}$, Fig. 3c, e), but the link between Southern Ocean overturning and OHUE is only significant when using an overturning metric calculated directly from meridional velocities in latitude–density coordinates (Fig. 1d, e). The two Southern Ocean overturning metrics $M_{SO}$ and $M_{WMT}$ are uncorrelated across the model ensemble and have distinct advantages and disadvantages. Although $M_{SO}$ directly quantifies the strength of the upper overturning cell actively transporting heat into the ocean interior, it is not a perfect measure of subduction across the Southern Ocean (Sallée et al., 2012). Indeed, subduction occurs at different latitudes and densities around the Southern Ocean and across members of the CMIP6 ensemble, such that it is difficult to obtain an accurate measure of Southern Ocean subduction rates from CMIP6 output. The $M_{WMT}$ metric instead quantifies the total upwelling in the Southern Ocean via surface buoyancy fluxes but does not include the effect of mixing, which plays an important role in the Southern Ocean overturning circulation (Sallée et al., 2013b; Evans et al., 2018).

The foregoing discussion highlights the practical difficulties in quantifying Southern Ocean vertical transports in a large multimodel ensemble. By contrast, subduction in the subpolar North Atlantic is more straightforward to quantify using the AMOC streamfunction, and this partly explains the relative success of AMOC strength as a metric for quantifying
fying ocean overturning rates in models and for correlation with climate metrics such as OHUE (Kostov et al., 2014; Gregory et al., 2024). More detailed output variables in future model intercomparisons that allow us to characterize regional subduction or ventilation rates would be instrumental in better pinning down physical controls of ocean heat and carbon uptake.

## Appendix A

<table><caption>Table A1. The CMIP6 models used in this study.</caption><thead><tr><th>Model</th><th>piControl</th><th>lpctCO2</th><th>historical</th><th>Data available for AMOC/M<sub>SO</sub>/M<sub>WMT</sub></th><th>Reference</th></tr></thead><tbody><tr><td>CanESM5</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/Y/Y</td><td>Swart et al. (2019b)</td></tr><tr><td>CanESM5-CanOE</td><td>rliplf2</td><td>rliplf2</td><td>rliplf2</td><td>Y/Y/Y</td><td>Swart et al. (2019a)</td></tr><tr><td>CMCC-CM2-SR5</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/Y/Y</td><td>Lovato and Peano (2020)</td></tr><tr><td>CMCC-ESM2</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/Y/Y</td><td>Lovato et al. (2021)</td></tr><tr><td>CNRM-CM6-1</td><td>rliplf2</td><td>rliplf2</td><td>rliplf2</td><td>Y/Y/Y</td><td>Voldoire (2018)</td></tr><tr><td>CNRM-CM6-1-HR</td><td>rliplf2</td><td>rliplf2</td><td>rliplf2</td><td>Y/N/Y</td><td>Voldoire (2019)</td></tr><tr><td>CNRM-ESM2-1</td><td>rliplf2</td><td>rliplf2</td><td>rliplf2</td><td>Y/Y/Y</td><td>Seferian (2018)</td></tr><tr><td>ACCESS-ESM1-5</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/Y/Y</td><td>Ziehn et al. (2019)</td></tr><tr><td>ACCESS-CM2</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/Y/Y</td><td>Dix et al. (2019)</td></tr><tr><td>EC-Earth3</td><td>rliplf1</td><td>r3ilpf1</td><td>rliplf1</td><td>N/Y/N</td><td>EC-Earth Consortium (2019a)</td></tr><tr><td>EC-Earth3-CC</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/Y/Y</td><td>EC-Earth Consortium (2020b)</td></tr><tr><td>EC-Earth3-Veg</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/Y/N</td><td>EC-Earth Consortium (2019b)</td></tr><tr><td>EC-Earth3-Veg-LR</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>N/Y/N</td><td>EC-Earth Consortium (2020a)</td></tr><tr><td>IPSL-CM6A-LR</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/Y/Y</td><td>Boucher et al. (2018)</td></tr><tr><td>MIROC6</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/Y/Y</td><td>Tatebe and Watanabe (2018)</td></tr><tr><td>HadGEM3-GC31-LL</td><td>rliplf1</td><td>rliplf3</td><td>rliplf3</td><td>Y/Y/Y</td><td>Ridley et al. (2018)</td></tr><tr><td>HadGEM3-GC31-MM</td><td>rliplf1</td><td>rliplf3</td><td>rliplf1</td><td>Y/N/Y</td><td>Ridley et al. (2019)</td></tr><tr><td>UKESM1-0-LL</td><td>rliplf2</td><td>rliplf2</td><td>rliplf2</td><td>Y/Y/Y</td><td>Tang et al. (2019)</td></tr><tr><td>MPI-ESM1-2-HR</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/Y/Y</td><td>Jungclaus et al. (2019)</td></tr><tr><td>MPI-ESM1-2-LR</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/Y/Y</td><td>Wieners et al. (2019)</td></tr><tr><td>MRI-ESM2-0</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/Y/Y</td><td>Yukimoto et al. (2019)</td></tr><tr><td>GISS-E2-1-G</td><td>rliplf1</td><td>rliplf1</td><td>rliplf2</td><td>N/Y/N</td><td>NASA/GISS (2018)</td></tr><tr><td>CESM2</td><td>rliplf1</td><td>rliplf1</td><td>r4ilpf1</td><td>Y/N/N</td><td>Danabasoglu (2019b)</td></tr><tr><td>CESM2-WACCM</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/N/N</td><td>Danabasoglu (2019a)</td></tr><tr><td>NorESM2-LM</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/Y/N</td><td>Seland et al. (2019)</td></tr><tr><td>NorESM2-MM</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/Y/Y</td><td>Bentsen et al. (2019)</td></tr><tr><td>GFDL-CM4</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/N/Y</td><td>Guo et al. (2018)</td></tr><tr><td>GFDL-ESM4</td><td>rliplf1</td><td>rliplf1</td><td>rliplf1</td><td>Y/N/Y</td><td>Krasting et al. (2018)</td></tr></tbody></table>
<figure><img src="image_9.png" /></figure>

**Figure A1.** Maps of the intermodel Pearson correlation coefficient between OHUE and the local preindustrial annual mean total (left column), temperature (middle column), and total stratification (right column) across 28 CMIP6 models, with stratification in the depth ranges (a–c) 0–400 m, (d–f) 0–750 m, (g–i) 0–1000 m, (j–l) 0–1500 m, (m–o) 0–2000 m, and (p–r) 0–2500 m. Stippling indicates the region where the linear slope is not significantly different from zero (*p* ≥ 0.05, Wald test with a *t* distribution). Regions where the bathymetry is less than 1500 m deep are shaded in grey.
<figure><img src="image_10.png" /></figure>

**Figure A2.** Intermodel relation between stratification and overturning cells. (a–c) Intermodel correlation between the preindustrial 0–1500 m stratification and the AMOC for the total (a), temperature (b), and salinity (c) stratifications. (d–f) As in panels (a)–(c) but for the Southern Ocean upper cell in density coordinates. (g–i) As in panels (a)–(c) but for the Southern Ocean overturning strength inferred from surface buoyancy fluxes (see the Methods section). Note that the first column of this figure is the same as the first column of Fig. 3 in the main text.

<figure><img src="image_11.png" /></figure>

**Figure A3.** Scatterplot of the first principal component of the intermodel EOF analysis of the preindustrial stratification (see Sect. 5.2) and the global mean preindustrial stratification for each CMIP6 model.
<figure><img src="image_12.png" /><figcaption>**Figure A4.** Modes 3 to 5 of the intermodel EOF analysis of the preindustrial annual mean upper-ocean stratification.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Figure A5.** (a, c) First and second modes of the intermodel EOF analysis of the preindustrial annual mean upper-ocean temperature stratification. (b, d) As in the left column but for the salinity stratification.</figcaption></figure>
<figure><img src="image_14.png" /></figure>

**Figure A6.** (a) Map of the intermodel correlation between preindustrial local 0–1500 m temperature stratification and salinity stratification. (b) Same as panel (a) but between total stratification and temperature stratification. (c) Same as panel (a) but between total stratification and salinity stratification.

<figure><img src="image_15.png" /></figure>

**Figure A7.** Classification of vertical stratification profiles. (a) Map showing the geographical locations of the identified classes. (b–d) Median vertical stratification profiles of each class (for total, temperature, and salinity stratification).
<figure><img src="image_16.png" /></figure>

**Figure A8.** EOF analysis of the preindustrial MLD. The first three modes of the intermodel EOF analysis of the preindustrial annual mean MLD are shown after removing five outlier models (see the Methods section).

<figure><img src="image_17.png" /></figure>

**Figure A9.** Intermodel correlation across 28 CMIP6 models between the pycnocline depth metric defined by Newsom et al. (2023) and the local preindustrial annual mean (a) total, (b) temperature, and (c) salinity stratification.
**Code and data availability.** All of the model output and observational data used in this study are freely available. The CMIP6 model output is available at https://esgf-node.llnl.gov/projects/cmip6/ (Earth System Grid Federation, 2025). The data from the ECCO state estimate are available at https://www.ecco-group.org/products-ECCO-V4r4.htm (ECCO Consortium et al., 2024).

The processed data and Python code used to produce the figures in this study are available at https://doi.org/10.5281/zenodo.15085902 (Vogt, 2025).

**Author contributions.** LV conceived the study, performed the data processing and analysis, and wrote the initial draft. JBS and CdL were responsible for supervision and funding acquisition. All of the authors contributed to interpreting the results and revising the paper.

**Competing interests.** The contact author has declared that none of the authors has any competing interests.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

**Acknowledgements.** The authors thank Juliette Mignot, Ric Williams, and Robin Waldman for the discussions. The authors thank the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and the ESGF.

**Financial support.** This research has been supported by the European Union's Horizon 2020 research and innovation program (grant no. 821001).

**Review statement.** This paper was edited by Katsuro Katsumata and reviewed by Timothée Bourgeois and one anonymous referee.

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