The subpolar North Atlantic (SPNA) is a region with prominent
decadal variability that has experienced remarkable warming and cooling
trends in the last few decades. These observed trends have been preceded by
slow-paced increases and decreases in the Labrador Sea density (LSD), which
are thought to be a precursor of large-scale ocean circulation changes. This
article analyses the interrelationships between the LSD and the wider North
Atlantic across an ensemble of coupled climate model simulations. In
particular, it analyses the link between subsurface density and the deep
boundary density, the Atlantic Meridional Overturning Circulation (AMOC),
the subpolar gyre (SPG) circulation, and the upper-ocean temperature in the
eastern SPNA.
All simulations exhibit considerable multidecadal variability in the LSD and
the ocean circulation indices, which are found to be interrelated. LSD is
strongly linked to the strength of the subpolar AMOC and gyre circulation, and
it is also linked to the subtropical AMOC, although the strength of this
relationship is model-dependent and affected by the inclusion of the Ekman
component. The connectivity of LSD with the subtropics is found to be
sensitive to different model features, including the mean density
stratification in the Labrador Sea, the strength and depth of the AMOC, and
the depth at which the LSD propagates southward along the western boundary.
Several of these quantities can also be computed from observations, and
comparison with these observation-based quantities suggests that models
representing a weaker link to the subtropical AMOC might be more
realistic.
Introduction
The North Atlantic Ocean is a key component in Earth's climate through, for
example, its role in redistributing heat and in taking up excess heat and
carbon from the atmosphere. It is also a region that has varied
significantly in the past. This is particularly true for the North Atlantic
subpolar gyre, which has varied significantly on multidecadal timescales
across a range of different variables (Häkkinen and Rhines, 2004;
Holliday et al., 2020; Reverdin, 2010; Robson et al., 2018b). Basin-mean sea
surface temperature (SST) over the North Atlantic has also been observed to
vary on multidecadal timescales (Schlesinger and Ramankutty, 1994) and has
been linked to a range of important climate impacts, including hurricane
numbers and rainfall in monsoon regions (Knight et al., 2006; Monerie et
al., 2019; Zhang and Delworth, 2006). The North Atlantic is also expected to
change significantly in the future due to the effects of climate change and
consequently produce substantial climate impacts on the surrounding regions
(Sutton and Hodson, 2005; Woollings et al., 2012). On decadal timescales, it
is the interaction between natural variability and externally forced changes
that will shape how the Atlantic region's climate will evolve. Therefore, in
order to improve predictions of the North Atlantic, it is imperative that we
improve our understanding of the processes that control decadal-timescale
changes in this region.
It has generally been thought that changes in the ocean circulation,
particularly the Atlantic Meridional Overturning Circulation (AMOC), have
played a significant role in shaping the Atlantic multidecadal variability
(AMV; Knight et al., 2005). In particular, changes in the strength of the
AMOC and its related ocean heat transports have been shown to control
multidecadal internal variability in a range of coupled climate models
(Danabasoglu, 2008; Dong and Sutton, 2005; Jungclaus et al., 2005; Ortega et
al., 2011, 2015). The proposed mechanisms to explain the multidecadal
variability involve interplays between the North Atlantic Oscillation (NAO),
North Atlantic Deep Water (NADW) formation, the boundary currents, the Gulf
Stream and gyre circulations, and the horizontal density gradients (e.g. Joyce and Zhang, 2010; Polyakov et al., 2010; Ba et al., 2013; Nigam et al.,
2018; Zhang et al., 2019). Changes in the AMOC and the wider ocean circulation
have indeed been used to explain the observed changes in the subpolar North
Atlantic (SPNA) on decadal and longer timescales (Moat et al., 2019). In
particular, the SPNA underwent a rapid warming and salinification in the mid-1990s before a decadal-timescale cooling and freshening started in 2005,
which is consistent with decadal to multidecadal variability of the AMOC
(Robson et al., 2012, 2013, 2016). The recent cooling has been linked to
climate impacts over the continents, including heat waves (Duchez et al.,
2016), through an effect on the position of the jet stream (Josey et al.,
2018). A long-term relative cooling of the SPNA since ∼ 1850
has also been attributed to a centennial weakening of the AMOC (Caesar et
al., 2018; Rahmstorf et al., 2015), an AMOC reduction that most CMIP6 model
projections predict to continue in the future (Weijer et al., 2020).
However, a lack of direct observations of the strength of the AMOC and the
ocean circulation more generally have hindered our ability to make a direct
attribution of recent changes.
In order to understand the aforementioned changes in the SPNA on
multidecadal timescales many authors have turned to indirect measurements
of the AMOC. One particular proxy for AMOC strength that has received some
focus recently involves density anomalies at depth in the western SPNA or
Labrador Sea region. In climate models, density anomalies in the western
SPNA are a key predictor of density anomalies further south on the western
boundary and hence of the AMOC strength via thermal wind balance (Hodson
and Sutton, 2012; Ortega et al., 2017; Robson et al., 2014, 2016).
Observations show considerable decadal variability in subsurface density
anomalies; density anomalies in the western SPNA and Labrador Sea between
∼ 1000 and 2500 m increased significantly, peaked in
∼ 1995, and subsequently declined (Robson et al., 2016;
Yashayaev and Loder, 2016). Therefore, these density anomalies have been
interpreted as indicating that the AMOC peaked in the middle to late 1990s and
then declined, consistent with the warming and then cooling of the eastern
SPNA (Hermanson et al., 2014; Ortega et al., 2017; Robson et al., 2016).
Time series of subsurface density anomalies in the western SPNA are also
consistent with other proxies for AMOC strength, including sea-level-based
proxies (McCarthy et al., 2015; Sutton et al., 2018), sediment based proxies
(Thornalley et al., 2018), and upper-ocean heat content fingerprints (Caesar
et al., 2018; Zhang, 2008). Furthermore, the decline in the AMOC suggested by
the above proxies is also consistent with the observed AMOC decline at
26∘ N since 2004 (Smeed et al., 2018) and with the changes in the AMOC
seen in ocean data assimilation systems (Jackson et al., 2016, 2019).
Therefore, there is confidence that large-scale changes in North Atlantic
Ocean circulation have occurred over the past few decades and that they have
had a significant impact on upper-ocean heat content.
Although there is consistency across proxies for AMOC changes in the North
Atlantic, there are considerable gaps in our understanding and major
uncertainties to overcome. For example, the development of subsurface
density proxies has been investigated so far with just a few models (Ortega et
al., 2017; Robson et al., 2014). However, there is considerable spread
across climate models in the simulations of AMOC mean state and variability
(Reintges et al., 2017; Zhang and Wang, 2013) and also in the latitudinal
coherence of AMOC anomalies (Li et al., 2019; Roberts et al., 2020; Hirschi
et al., 2020), which might reflect different roles of deep density anomalies
in the western SPNA for the AMOC, as well as different interplays between the
subpolar and subtropical gyre contributions (Zou et al., 2020). Models also
do not realistically resolve many key features of the AMOC, most notably the
overflows, and this affects the subsurface stratification downstream and on
the western boundary (Zhang et al., 2011). Significant
uncertainty also remains for other important processes. For example, it is not yet clear
whether the recent changes in the SPNA are an ocean response to buoyancy
forcing or whether mechanical wind forcing has shaped the recent observed
changes (Robson et al, 2016; Piecuch et al., 2017). Local surface fluxes are
also likely to explain a significant proportion of the recent cooling (Josey
et al, 2018). Subsurface density anomalies are not just a proxy for the
AMOC, but also more generally for buoyancy-forced (or thermohaline) circulation
changes, including gyre changes (Ortega et al., 2017; Yeager, 2015).
Finally, the AMOC variability is also thought to respond to local wind
forcing on a range of timescales, especially at lower latitudes (Polo et
al., 2014; Zhao and Johns, 2014), which could disrupt or “mask” the
influence of subsurface density anomalies as they propagate further south.
There is also considerable uncertainty in how and where subsurface density
anomalies are formed in the SPNA and how they are related to the AMOC. In
observations and models, most water transformation associated with the AMOC
occurs within the SPNA, particularly in the eastern SPNA
(Desbruyères et al., 2019; Grist et al., 2014; Langehaug et al., 2012).
However, decadal changes in subsurface density anomalies in the western SPNA have often been linked to buoyancy forcing and changes in deep
convection in the Labrador Sea or to changes in the volume of Labrador Sea
Water production (Yashayaev and Loder, 2016; Yeager and Danabasoglu, 2014).
Many studies have also reported that the basin-wide AMOC in ocean-only and
coupled models is sensitive to heat flux or buoyancy forcing in the Labrador
Sea (Kim et al., 2020; Ortega et al., 2011, 2017; Xu et al., 2019; Yeager
and Danabasoglu, 2014). Indeed, idealized experiments have shown that
persisting positive NAO phases can strengthen the AMOC by fostering deepwater formation via increased surface cooling in the Labrador Sea, thus
inducing changes in the zonal density gradient (Delworth and Zeng, 2016; Kim
et al., 2020) and thermal wind responses. However, the real link between
deep convection, deepwater formation, and density anomalies at depth in the
Labrador Sea is complex and not fully understood (Katsman et al., 2018).
Observations suggest that very little water transformation and deepwater
formation actually occur in the Labrador Sea (Pickart and Spall 2007;
Lozier et al., 2019). Indeed, recently it has been shown that the Labrador
Sea (i.e. OSNAP west) has played a very minor role in the interannual variability
observed so far across the whole OSNAP line (Lozier et al., 2019), with the
Irminger Sea playing a more dominant role. The Irminger Sea is a region that
in some models controls the AMOC and SPNA variability and that is
especially sensitive to advective processes (Ba et al., 2013) and Arctic
overflows (Fröb et al., 2016). Moreover, ocean-only models appear to
significantly overestimate the amount of deep water formed within the
Labrador Sea, with likely implications for coupled models (Li et al., 2019).
These inconsistencies raise the question of whether models are simulating
the right relationships.
In this study we will address some of the above uncertainties by performing
a multi-model analysis of the North Atlantic in coupled climate models. We
focus on the question of how robust the relationship is between subsurface
Labrador Sea density anomalies and the basin-wide Atlantic Ocean circulation
on decadal timescales. We also address the question of whether Labrador Sea
density can robustly induce density changes over the western continental
slope and generate a geostrophic response in the meridional circulation
(Bingham and Hughes, 2009; Roussenov et al., 2008). Shedding new light on
these links is important to, among other reasons, determine to what
extent the RAPID measurements represent the variability of the basin-wide
AMOC cell and to identify the models that can produce more reliable
predictions and projections of the SPNA. For this, we will specifically assess
the connection between subsurface density and AMOC at high and
low latitudes via the western boundary. Furthermore, we will determine
whether models consistently support an impact of AMOC changes on the SPNA
upper-ocean temperatures and, if not, investigate why. Our primary aim is to
provide, for the first time in a multi-model context, a broad
characterization of these relationships using consistent analysis frameworks
and tools, documenting the uncertainty. The reasons for the uncertainty
in the relationships will also be explored, establishing links to key
model climatological properties that could eventually be exploited as
emergent constraints. We intentionally do not explore in detail how
subsurface density anomalies are formed in these models and leave this for
further study.
The paper is organized as follows. Section 2 describes the experiments and
methods. Labrador Sea density and its link to the ocean circulation and
the wider North Atlantic are explored across the multi-model ensemble in
Sect. 3. The characteristics of the intermodel spread in the previous
relationships are explored in Sect. 4. Section 5 presents the main
conclusions of this study and discusses its implications.
Experiments and methods
Here we provide an overview and brief description of the models used in this
study and provide some statistical considerations for the intermodel
comparison.
Experiment selection
For the multi-model analysis, we use the preindustrial control simulations
(picontrol) from the fifth phase of the Coupled Model Intercomparison
Project (CMIP5; Taylor et al., 2012), in which forcing values of greenhouse gases (GHGs),
aerosols, ozone, and solar irradiance are fixed to 1850 levels. We chose to
use control over historical simulations to focus exclusively on internal
variability and benefit from the more robust statistics that the long
preindustrial experiments provide. Furthermore, we avoid the forced trends
present in the historical experiments, which can lead to correlations that
are difficult to interpret objectively (Tandon and Kushner, 2015). From the
CMIP5 ensemble, we only use models in which 3D fields of ocean
temperature and salinity, as well as the streamfunctions of meridional
overturning circulation and/or the barotropic circulation, were available.
A total of 20 different models meet this condition. Their main characteristics and
number of simulation years have been summarized in Table 1. Most of the
models have a nominal horizontal resolution in the ocean close to
1∘ and therefore cannot resolve the effects of eddies. Menary
et al. (2015) have shown for these same model simulations that the effective
horizontal resolution can be higher over the Labrador Sea due to the
non-regular grids. Effective resolutions over the Labrador Sea area range
from 0.21∘ in the GC2 model to 1.1∘ in
GISS-E2-R, GISS-E2-R-CC, and CanESM2, with these differences determining to a
large extent the mean state model biases and the dominant drivers (i.e. salinity or temperature) of the Labrador Sea density changes.
Complementing these simulations, we also consider two control experiments
with eddy-permitting resolutions. Specifically, we use a present-day control
simulation (i.e. with fixed radiative forcing levels from the year 1990) of the
HiGEM model, with a nominal horizontal resolution in the ocean of
1/3∘ and of 0.83∘ latitude × 1.25∘ longitude in the atmosphere (Shaffrey et al., 2009), and a preindustrial
control of HadGEM3-GC2 (hereafter, GC2; Ortega et al., 2017) with a nominal
resolution in the ocean of 1/4∘ (ORCA025) and N216 in the
atmosphere (i.e. approximately 60 km in the mid-latitudes). The GC2
simulation is the same one employed for previous analyses of Labrador
Sea variability in Robson et al. (2016) and Ortega et al. (2017). Note that
we will assume that the present-day control in HiGEM can be compared with
the other preindustrial simulations due to the large uncertainty the latter
show in their climatological biases; so, for the sake of simplicity, we
will only refer to preindustrial control experiments from now on. Figure 1 demonstrates that this assumption is reasonable, since the mean
Labrador Sea stratification in HiGEM is very similar to that in the other
models.
As an observationally constrained reference, this study also includes the
assimilation run from DePreSys3, a decadal prediction system from the
Met Office based on GC2 (Dunstone et al., 2016). In the ocean, the
assimilation is performed through a strong nudging (10 d relaxation
timescale) towards the full fields of a three-dimensional objective
temperature and salinity analysis (Smith and Murphy, 2007). Since it covers
a comparatively shorter period (1960–2013) and therefore different
timescales than the control experiments, its comparison with the other
simulations will be done with caution, in particular regarding the indices
of the large-scale Atlantic circulation, for which other assimilation
products show important discrepancies (Karspeck et al., 2015), thus
highlighting significant uncertainty. For evaluation purposes, we also use
EN4.2.1 (Good et al., 2013), an objective analysis of monthly temperature
and salinity 3D observations developed at the Met Office.
List of the models used for this study, their characteristics, and
those of their picontrol simulations. For further details on the CMIP5 model
configurations and components, please refer to Table 9A1 in Flato et al. (2013) and references therein.
Model IDLong × lat ocean resolution (number of vertical levels)LengthKey variables availableHadGEM3-GC21/4∘×1/4∘ (75 levels)311 yearsAMOC, SPGSI, LSD, NOHTHiGEM1/3∘×1/3∘ (40 levels)341 yearsAMOC, SPGSI, LSD, NOHTACCESS1-01∘× 1∘ enhanced near Equator and high latitudes (50 levels)500 yearsSPGSI, LSD, NOHTACCESS1-31∘× 1∘ enhanced near Equator and high latitudes (50 levels)500 yearsSPGSI, LSD, NOHTCCSM41.125∘× 0.27–0.64∘ (60 levels)1051 yearsAMOC, SPGSI, LSDCESM1-BGC1.125∘× 0.27–0.64∘ (60 levels)500 yearsAMOC, LSDCESM1-CAM51.125∘× 0.27–0.64∘ (60 levels)319 yearsAMOC, LSDCESM1-FASTCHEM1.125∘× 0.27–0.64∘ (60 levels)222 yearsAMOC, LSDCESM1-WACCM1.125∘× 0.27–0.64∘ (60 levels)200 yearsAMOC, LSDCNRM-CM50.7∘× 0.7∘ (42 levels)850 yearsAMOC, SPGSI, LSDCanESM21.4∘× 0.93∘ (40 levels)996 yearsAMOC, SPGSI, LSDFGOALS-g21∘× 1∘ with 0.5∘ meridional in the tropical region (30 levels)700 yearsAMOC, LSDFGOALS-s21∘× 1∘ with 0.5∘ meridional in the tropical region (30 levels)501 yearsSPGSI, LSD, NOHTGFDL-ESM2G1∘× 0.85∘ (63 levels)500 yearsSPGSI, LSDGISS-E2-R1.25∘× 1∘ (32 levels)550 yearsAMOC, LSDGISS-E2-R-CC1.25∘× 1∘ (32 levels)251 yearsAMOC, LSDMPI-ESM-LR1.5∘× 1.5∘ (40 levels)1000 yearsAMOC, SPGSI, LSDMPI-ESM-MR0.4∘× 0.4∘ (40 levels)1000 yearsAMOC, SPGSI, LSDMPI-ESM-P1.5∘× 1.5∘ (40 levels)1156 yearsAMOC, SPGSI, LSDMRI-CGCM31∘× 0.5∘ (51 levels)500 yearsAMOC, LSD, NOHTNorESM1-M1.125∘× 1.125∘ (53 levels)501 yearsAMOC, SPGSI, LSD, NOHTNorESM1-ME1.125∘× 1.125∘ (53 levels)252 yearsAMOC, SPGSI, LSD, NOHTMethodological considerations
Density values are computed from 3D salinity and potential temperature
fields using the International Equation of State of Seawater (EOS-80) and
are referenced to the level of 2000 dbar (σ2) to give stronger
emphasis to the deepwater properties.
Statistical significance of correlation coefficients is assessed following a
two-tailed Student's t test that takes into account the series'
autocorrelation to correct the sample size, reducing the degrees of freedom
of a series to its effective value (Bretherton et al., 1999).
Because our goal is to provide further insight into the suggested
relationships established from observed trends in the North Atlantic (e.g. Robson et al., 2016), all statistical analyses in this study exploring the
relationships between variables and associated lags are based on 10-year
running trends. This is analogous to the calculation of a typical 10-year
running mean, but computing a linear trend instead over each 10-year period
and keeping the slope value. Note also that our main results remain similar
if decadal running means are applied instead (not shown), as both are
alternative approaches to concentrate on the low-frequency variability.
Running trends also have the particular advantage of not being sensitive to
long-term drifts, which are still present (and can be important for some
simulations and variables) when running means are computed. To illustrate
how decadal running trends represent low-frequency variability and how they
compare with the decadal running means, both have been included in
Fig. 2b (solid thick lines vs. dashed thin lines) for an index
of Labrador Sea density.
Labrador Sea density as an index of multidecadal North Atlantic variability
This section explores the potential of Labrador Sea density as a proxy for
the ocean circulation changes in the North Atlantic. As in our previous
studies (Ortega et al., 2017; Robson et al., 2016), the indices that we will
define herein represent waters within the Labrador Sea and not those that
are necessarily formed in the region (e.g. Labrador Sea Water). Since
Labrador Sea variability is affected by different processes (e.g. vertical
mixing, Arctic–Atlantic overflows, sea ice interactions) that can be
represented differently in the models in both time and space, we
characterize its variability over a relatively broad box (60–35∘ W, 50–65∘ N; blue box in Fig. 1a) that also includes part of the Irminger Sea region. Note that
over this large area, EN4.2.1 shows the weakest density stratification in
the North Atlantic (characterized in Fig. 1a as the density
difference between 1000 m and the surface).
Labrador Sea density across models
A first indicator of potential model discrepancies is Labrador Sea
stratification, which can lead to differences in the representation of deep
ocean convection (i.e. weaker density stratifications will facilitate
mixing, fostering convection activity, and vice versa for stronger density
stratifications). Figure 1b–d illustrate the intermodel
differences with the vertical profile of the spatially averaged Labrador Sea
temperature, salinity, and density. The largest discrepancies are seen for
temperature. Most models present their warmest waters at the surface, and
temperatures decrease sharply to minimum values around 100 m and increase
again at deeper levels, reaching uniform conditions after approx 300 m.
However, the location and magnitude of this temperature minimum and the two
maxima are highly variable. It is important to note that the profile for one
of the models, MRI-CGCM3, is noticeably different to the others, with a
subsurface minimum more than 2∘ colder than for any other
model. In terms of salinity, the general profile is more coherent across
models, with minimum salinity at the surface that progressively increases
with depth and attains uniform values after 500 m. Density stratification
seems to be determined by salinity, as their two vertical profiles show
similar features. This similarity includes exceptionally strong density and
salinity stratification in MRI-CGCM3 compared with the other models. This
stratification is so strong that it precludes the occurrence of deep
convection (not shown). Because of this, MRI-CGCM3 is an outlier for many of
the metrics used in the paper and has been excluded from the subsequent
analyses to facilitate the interpretation of our results. We also note that
the profiles for the two eddy-permitting models (green and orange lines in
Fig. 1b, d) lie within the spread of the CMIP5 models,
indicating that resolution (at least to eddy-permitting spatial scales) does
not drastically change stratification in the region. The DePreSys3
assimilation run closely matches the stratification in EN4.2.1, which
supports the DePreSys assimilation run as a reasonable observation-constrained
reference for the models. The comparison of both observation-based datasets
with the rest of simulations suggests that, in the subsurface, all models
are too warm and most of them are too salty; these two biases have a
competing effect on the mean subsurface density. Because of these cancelling
effects, several models show a comparatively better representation of the
subsurface densities when compared to EN4.2.1 and DePreSys3. This
compensation of model shortcomings for temperature and salinity is clearly
illustrated in HiGEM, which shows remarkable agreement with EN4.2.1 below
500 m.
To represent the characteristic interannual variability of Labrador Sea
densities (hereafter referred to as LSD for consistency with previous work),
we perform an empirical orthogonal function (EOF; Storch and Zwiers, 1999)
analysis and extract the leading mode for the spatially averaged annual
means of LSD (Fig. 2a), as in Ortega et al. (2017). For all
simulations the first EOF of LSD exhibits a vertical structure with density
values that are largest at or near the surface and gradually decrease with depth.
Thus, this first EOF typically reflects situations in which the density
stratification, as described by the climatological vertical profile in
Fig. 1d, is weakened or strengthened, which happens when the
corresponding principal component takes positive and negative values,
respectively. Some intermodel discrepancies are evident, in particular
regarding the depths at which the maximum density values are found, which can
happen between the surface and 500 m. Despite these differences, the
dominant timescales of LSD variability seem to coincide between models. For
example, Fig. 2b illustrates the first principal component of
LSD (PC1-LSD) for GC2 and HiGEM, showing clear multidecadal
variability in both cases. Furthermore, Fig. 2c shows the Fourier spectrum
analysis of the annual PC1-LSD values, and most models show enhanced PC1-LSD
variability for periodicities between 5 and 30 years.
In addition to the PC1-LSD index we consider a deep LSD index as introduced
in Robson et al. (2016). The deep LSD index is defined as the 1000–2500 m vertical mean of the spatially averaged density over the same region as
PC1-LSD. We now compare how both indices represent the low-frequency changes
in LSD, which are described in this paper as decadal running trends. A lead–lag
correlation between the decadal trends in both PC1-LSD and deep LSD indices
shows that they are strongly correlated in all models. However, some
differences emerge when considering the lag of maximum correlation
(Fig. 2d). This comparison might indicate, once again, that
decadal variability of subsurface density is concentrated at different
depths in different models. It is also possible that both indices are
sensitive to changes in deepwater formation in different locations (e.g. Irminger or GIN seas), which could hence affect the depth and maximum lag
of the correlations. Nevertheless, we adopt PC1-LSD for the rest of the
analyses, as it has the advantage of adjusting in each model to the depths
at which density variability is more prominent.
(a) Climatological density (computed as σ2 at all depth
levels) difference between the subsurface (1000 m) and surface in the North
Atlantic in the observational dataset EN4.2.1 (Good et al., 2013). The
reference period to compute the climatology is 1960–2013. The grey box
(32–10∘ W and 47–63∘ N)
encloses the region where the ESPNA-T700 index in Fig. 4d is
computed. (b–d) Climatological mean of the spatially averaged Labrador Sea
(60–35∘ W, 50–65∘ N; blue box in a) temperature,
salinity, and density as a function of depth in the simulation ensemble, the
DePreSys3 assimilation run, and EN4.2.1. The magenta (cyan) bars on the
vertical axis correspond to the depths that have been used to define the
vertical stratification Labrador Sea indices. The horizontal orange lines by
the North American coast represent the location of the latitudinal
cross sections in Figs. 10 and 11. For
each model and dataset the climatology is computed for its whole length
except for EN4.2.1, which is computed for the overlap period with the
DePreSys3 assimilation run.
(a) First empirical orthogonal function (EOF) as a function of depth
of the spatially averaged LSD in all the preindustrial experiments and in
the DePreSys3 assimilation run. The percentage of variance explained by this
mode in each model is included in brackets in the legend (for the CMIP5
runs, this represents the mean value across the ensemble). Because the sign
of an EOF is arbitrary, it has been adjusted for all models (together with
the sign of the respective principal component) so that both represent an
increase in density stratification. (b) Associated principal component of the
spatially averaged LSD (PC1-LSD) in the two high-resolution experiments. The
thin solid lines represent the raw yearly resolved PC1-LSD time series, the
thin dashed lines their respective 10-year running means, and the thick (and
slightly darker) lines their associated 10-year running trends (centred
around the last year of the decade over which the trend is computed). (c) Normalized Fourier spectra of the PC1-LSD index in each of the preindustrial
simulations. The black thick line represents a red noise process with the
same first autoregressive (AR1) coefficient as PC1-LSD in GC2, and the
dashed line sets the 95 % confidence interval of this red noise process.
No major differences are found when using HiGEM's AR1 coefficient instead.
The red vertical line highlights the 10-year periodicity to separate the
interannual from the decadal to multidecadal timescales. (d) Lead–lag
correlations between the decadal trends in PC1-LSD and those in the deep
LSD index from Robson et al. (2016), defined as the 1000–2500 m average
density in the box 60–35∘ W, 50–65∘ N. Positive lags
indicate that PC1-LSD leads the changes in deep LSD. Full dots denote
correlation values exceeding a 95 % confidence level based on a Student's
t test that takes into account the series autocorrelation.
Labrador Sea density linkages to the ocean circulation
The link between PC1-LSD and other ocean circulation indices in the North
Atlantic is now examined. Three indices are considered: the AMOC at two
different latitudes of 26∘ N (i.e. the same latitude as the RAPID
array) and 45∘ N to capture the typical variability of the
subpolar AMOC and an index of the subpolar gyre strength. The AMOC indices
are computed as the maximum of the North Atlantic overturning circulation at
any depth. Furthermore, the Ekman component is removed to focus on the slow
wind-forced and the thermohaline-driven (i.e. the only one that can be
influenced by the PC1-LSD directly) AMOC changes. To compute the Ekman
component, we vertically integrate the Ekman velocities (after introducing a
depth-uniform return flow to ensure no net meridional mass transport)
following Eq. (6) in Baehr et al. (2004) with a fixed Ekman layer depth of 50 m. This Ekman component is then removed at each depth level, prior to
the calculation of the AMOC indices. The subpolar gyre strength is computed
as an average of the North Atlantic barotropic streamfunction in the
Labrador Sea region (60–35∘ W, 50–65∘ N), where the
gyre strength is usually maximum. Since the SPG circulation is cyclonic and
therefore associated with negative barotropic streamfunction values, the
subpolar gyre strength index (SPGSI) is multiplied by -1 so that an
intensification of the gyre corresponds to a positive value of the index.
The Fourier spectra of the raw ocean circulation indices (Fig. 3) show that, similar to the PC1-LSD, all three indices have strong
multidecadal variability, with the largest differences with respect to
PC1-LSD emerging for timescales between 10 and 30 years, for which the
spectral power is comparatively weaker. Important differences are also seen at 50-year and longer timescales, for which the ocean circulation indices
appear to have enhanced variability with respect to PC1-LSD. Similar
spectra, but with enhanced variance at short timescales and reduced variance
at the longest timescales, are obtained for the AMOC indices when the Ekman
component is kept (Supplement Fig. S1), which suggests that
the low-frequency processes dominate the total AMOC variability.
(a–c) Fourier spectra in the picontrol ensemble for the indices
AMOC45, AMOC26, and SPGSI. Red noise spectra corresponding to a first-order autoregressive process fit to GC2 indices are provided as a reference.
Figure 4a shows that decadal trends in PC1-LSD are associated
with trends in the AMOC at 45∘ N (AMOC45). Nevertheless, there is
some intermodel spread regarding the lag of maximum correlation, which
ranges between 0 and 2 years (with PC1-LSD leading), although both variables
are in phase for the majority of models. The AMOC at 26∘ N (AMOC26) is also positively related to PC1-LSD, with PC1-LSD leading AMOC26
by 3 years on average (Fig. 4b). However, the average
correlation between PC1-LSD and AMOC26 is weaker, and the spread in the
magnitude and lag of the maximum correlation is larger than for AMOC45.
Therefore, it appears that the link to the subtropics is weaker than for
45∘ N and that AMOC coherence between subpolar latitudes and the
subtropics in coupled models is model-dependent. This weaker link of PC1-LSD
to the subtropical AMOC is not surprising, as the LSD anomalies need to
propagate over a longer distance along the western boundary, allowing model
differences in the representation of ocean currents and gyres to impact the
timing and magnitude of the maximum correlations. The reasons for the spread
in the relationship between PC1-LSD and AMOC26 are explored in Sect. 4. A
strong relationship is also found between PC1-LSD trends and those in SPGSI
(Fig. 4c), which are of similar order as for AMOC45. Thus, overall,
PC1-LSD is a good proxy for the large-scale ocean circulation in the
subpolar North Atlantic and can also be a precursor for a fraction of the
AMOC variability in the subtropical Atlantic.
(a) Lead–lag correlations across the picontrol ensemble between the
PC1-LSD index and the maximum AMOC streamfunction at 45∘ N after
the Ekman transport is removed (AMOC45). Correlations are based on 10-year
running trends. Significance is assessed as in Fig. 2d and is
indicated with a circle. For positive lags, PC1-LSD leads. (b–c) The same as
in (a) but between PC1-LSD and the maximum AMOC streamfunction at 26∘ N after the Ekman transport is removed (AMOC26) and the subpolar gyre
strength index (SPGSI).
PC1-LSD is also a good precursor of the full AMOC variability (i.e. including the Ekman transport), although the wind-induced fluctuations
associated with the Ekman component can introduce differences in the lags of
the maximum AMOC vs. PC1-LSD correlations (Supplement Fig. S2). This different lag can be explained by the fact that when the Ekman
component is included, the AMOC contains a signal that is instantaneously
driven by basin-scale surface wind anomalies (such as those driven by the NAO) that are,
ultimately, also linked to the heat loss in the subpolar North Atlantic,
which induces a delayed influence on the PC1-LSD (Ortega et al., 2017).
Hence, including Ekman can lead to counterintuitive relationships in some
models, in which the AMOC appears to lead the PC1-LSD changes. Also, in the
particular case of GC2, the interference of the two signals (i.e. the
subtropical Ekman and the delayed PC1-LSD) renders the correlations in
Supplement Fig. S2d insignificant, masking out the real
influence of PC1-LSD on the subtropics. For those reasons and to ease the
interpretation of the lagged relationships, the rest of the analysis is
exclusively focused on the AMOC indices without Ekman.
(a) Maximum correlation (for any lag between 0 and 10 years) between
the AMOC45 index (after the Ekman transport is removed) and Labrador Sea
densities as a function of depth for all the simulations. Coloured dots
indicate correlations that are significant at the 95 % confidence level.
(b–c) The same as in (a) but between the AMOC26 index and LSD and between the
SPGSI and LSD, respectively.
The role of PC1-LSD as a precursor of the AMOC is further supported by a
parallel analysis in Fig. 5, looking at the maximum
correlation between the decadal AMOC trends and those in Labrador Sea
density as a function of depth, with the latter leading the AMOC by up to 10 years. Figure 5 reveals that the strongest link between the
Labrador Sea densities and the AMOC, both at 45 and 26∘ N, occurs
in its first 1000 m, the same levels at which the first EOF of LSD shows the
maximum loadings (Fig. 2a), which confirms the
appropriateness of using PC1-LSD to represent the ocean circulation. The
same analysis also supports a strong link between SPGSI and LSD, although in
that case the largest correlations usually happen at deeper levels (between
1000 and 2000 m). Note that the main conclusions drawn from PC1-LSD are
also valid for the deep LSD index; however, the intermodel differences are
larger in the cross-correlations with the AMOC indices (Supplement Fig. S3). This difference could reflect the fact that the deep LSD
index is more sensitive to other influences, like the Arctic overflows
(Ortega et al., 2017), which can be very differently represented across
models. Overall, the PC1-LSD index seems to be a better choice to describe
multidecadal North Atlantic variability in multi-model comparisons, as it
selects the key depths for each model. However, PC1-LSD is mostly focused on
near-surface levels and therefore likely represents mostly Labrador Sea
forced variability. Other indices describing densities at deeper levels
might be preferable to compare Labrador Sea Water of different origins
across models and to evaluate its realism against observations.
(a) Lead–lag correlations across the picontrol ensemble between the
PC1-LSD index and the vertically averaged top 700 m temperatures in the
eastern subpolar gyre (ESPNA-T700; grey box in Fig. 1a).
Correlations are based on 10-year running trends. Significance is assessed
as in Fig. 2d and is indicated with a circle. For positive lags,
PC1-LSD leads. (b–c) The same as in (a) but between the North Atlantic
Oscillation (NAO; defined as the standardized difference in sea level
pressure between the closest grid points to Azores and Reykjavik) and the
ESPNA-T700 and between the NAO and the PC1-LSD, respectively. In these two
cases, for negative lags the NAO leads.
Labrador Sea density linkages to the wider North Atlantic
Previous studies based on the GC2 picontrol simulation have suggested LSD to
also be a potential predictor of widespread cooling events in the eastern
SPNA, like the observed cooling over 2005 to 2014 (Robson et al., 2016;
Ortega et al., 2017). We therefore continue our exploration of the PC1-LSD
index by investigating its link to the eastern SPNA in the multi-model
ensemble. To explore this link we introduce a new index that represents the
mean potential temperature in the eastern SPNA region (32–10∘ W, 47–63∘ N) averaged over the top 700 m of the ocean (ESPNA-T700). Lead–lag correlations between the decadal
trends in PC1-LSD and this index (Fig. 6a) show that there
is a coherent relationship between the two variables across models, with
PC1-LSD increases (decreases) being consistently followed by ESPNA-T700
warmings (coolings). Nevertheless, there are intermodel differences
concerning the magnitude and lag of the strongest positive correlations,
revealing important uncertainty in the relationship. The spread in the
PC1-LSD vs. ESPNA-T700 relationship is thus reminiscent of the spread found
between PC1-LSD and AMOC26, which suggests that they might be related. We
also note significant negative correlations when ESPNA-T700 leads PC1-LSD by
2–4 years that might be explained by the opposing (and nearly concomitant)
impacts that the NAO exerts on both variables (Fig. 6b, c).
Positive NAO phases and associated surface buoyancy forcing (Lozier et al.,
2008) lead in first instance to negative SSTs (Barrier et al., 2014; Lohmann
et al., 2009) and an almost simultaneous cooling in ESPNA-T700 (Fig. 6b). In comparison, on the western side of the SPNA, positive NAO
phases contribute to reduce vertical density stratification, favouring
convection and a more positive LSD index (Robson et al., 2016), which in the
models lags the NAO by 2–3 years (Fig. 6c). The fact that
correlations between NAO and ESPNA-T700 are weaker than between PC1-LSD and
ESPNA-T700 suggests that the ocean might also be playing an additional role
(besides the NAO) in controlling the ESPNA temperatures.
(a) Lead–lag correlations in a subset of the picontrol experiments
between the PC1-LSD index and the ocean heat transport across the
45∘ N transect (OHT45N). Note that the ocean heat content is only
available for five models of the CMIP5 ensemble. Correlations are based on
10-year running trends. (b) The same as in (a) but only in HiGEM for the
different terms of the OHT45N. For positive lags, PC1-LSD leads.
The link between PC1-LSD and the ESPNA could be explained through an
influence of the PC1-LSD on the meridional ocean heat transport. This link
is now investigated in two eddy-permitting simulations (Fig. 7) and five CMIP5 models for which the ocean heat transport
fields are publicly available. In the two high-resolution experiments and
two of the CMIP5 ones the decadal trends in the meridional ocean heat
transport at 45∘ N (OHT45) are strongly linked to those in
PC1-LSD. This is a similar relationship to the one previously found in
Fig. 4 between PC1-LSD and both the AMOC45 and SPGSI, but in
this case with PC1-LSD leading with a slightly longer lead time. The other
CMIP5 experiments support a weaker, yet significant, link and a
longer lag between OHT45 and PC1-LSD. Altogether, Fig. 7a
confirms that PC1-LSD is a good precursor of the changes in the meridional
ocean heat transport, although with some differences across models which
might reflect a different representation of certain processes. The
contributions of two different processes to this delay are further
investigated in HiGEM, for which OHT was decomposed online at each
time step into vertical and horizontal heat transports (as in Bryan, 1969) that can be respectively interpreted as the “overturning” (i.e. characterized by the zonal mean transport) and “gyre” (i.e. characterized
by variations from the zonal mean transport) components (Robson et al.,
2018a). While the overturning contribution (OHT45over) increases in
phase with the AMOC45, SPGSI, and PC1-LSD changes (Fig. 7b),
the increase in the gyre component (OHCgyre) starts 4 years later.
That lag could be the time required in HiGEM for the propagation of mean
and/or anomalous temperatures from the southern to the northern branch of
the SPG.
Characteristics of the intermodel spread in the subpolar to subtropical AMOC
This section investigates which particular climatological model features are
linked to the large intermodel spread in the PC1-LSD vs. AMOC26
relationships. The most relevant model features thus identified will improve
our process understanding and can eventually be used to identify which
models are most realistic and, in turn, can deliver more reliable
projections of the future changes in the North Atlantic.
Figure 8 shows that models that simulate a stronger and deeper
climatological AMOC (both at 45 and 26∘ N) tend to
have a stronger correlation between PC1-LSD and the subtropics. All these
linear relationships between climatological AMOC strength and depth as well as the
PC1-LSD vs. AMOC26 connectivity are significant at the 95 % confidence
level. These climatological AMOC values (without Ekman) can be put in
context with those from RAPID observations and DePreSys3. RAPID
observational uncertainties have been considered by including the mean
values over three different non-overlapping periods (i.e. 2004–2007,
2008–2012, and 2013–2016; dotted lines in Fig. 8). The
scatterplots show that the majority of models whose climatological AMOC26
lies within the RAPID/DePreSys3 climatological spread have a relatively weak
link between PC1-LSD and AMOC26, although some models supporting a strong
link are also included or remain close to the RAPID/DePreSys3 values.
However, caution is recommended before defining emerging constraints
because models and observations are not directly comparable for numerous
reasons. For example, both RAPID and DePreSys3 cover shorter periods than
the simulations and relate to different background forcing conditions
(present day vs. preindustrial), which might imply different mean states
(Thornalley et al., 2018). Also, climatological values of the AMOC26
strength are notably weaker in DePreSys3 than in RAPID, a difference that is
not explained by the different temporal periods covered by each dataset (not
shown) and that implies that DePreSys3 might also be underestimating the real
AMOC45 strength. This underestimation could be larger than shown in
Fig. 8, as evidence suggests that RAPID calculations from mooring
arrays might be underestimating the AMOC strength by ∼ 1.5 Sv
(Sinha et al., 2018).
(a, b) Scatterplot of the maximum cross-correlation value in
Fig. 4b between PC1-LSD and AMOC26 against the climatological
AMOC45 and AMOC26 means, respectively. All AMOC indices refer to the values
after the Ekman transport signal is removed. The maximum correlations are
based on 10-year running trends and always happen when PC1-LSD leads the
AMOC26 index. Colours indicate the depth at which the climatological AMOC
maximum occurs. The correlation coefficient between the maximum PC1-LSD
correlation and the climatological mean AMOC is shown in the top left corner
in black. The analogous correlation but against the depth of the
mean climatological AMOC is shown in magenta. The presence of an asterisk indicates
that the correlation is significant at the 95 % confidence level. The
dashed grey vertical lines mark the climatological AMOC strength value in
the DePreSys3 assimilation run. The orange vertical lines indicate the
climatological value from RAPID observations (Smeed et al., 2018) from 2004
to 2016 (dashed) and in three non-overlapping sub-periods of 4 years
(dotted).
A potentially important factor behind the intermodel spread in Fig. 4b is the mean density stratification in the Labrador Sea.
Figure 9 suggests that, indeed, the PC1-LSD vs. AMOC26 spread is
partly influenced by the density stratification in this region. Models that
have a weaker density stratification (here defined as the difference between
the top 100 m and the average between 500 and 1000 m), and thus favour deeper
convection in the Labrador Sea, generally exhibit a stronger link between
PC1-LSD and AMOC26. This result is robust for other stratification indices
based on different depth levels (See Supplement Fig. S4).
Differences in density stratification across models can be due to a
combination of different factors, from differences in the local buoyancy
fluxes (driven by differences in the atmospheric circulation), to
differences in the representation of the Arctic overflows, which are
parameterized in some models (e.g. the CESM family; Danabasoglu et al.,
2010) and explicitly resolved in others. No robust link between the PC1-LSD
vs. AMOC relationship and both temperature and salinity stratification in the
Labrador Sea has been found. It is also worth mentioning that all models
except CanESM2 are more weakly stratified in the Labrador Sea than the
observations (represented herein by the DePreSys3 assimilation run and
EN4.2.1). Hence, the real link of LSD to the AMOC26 may not be as strong
as some models suggest.
(a) Scatterplot of the maximum cross-correlation value in Fig. 4b between PC1-LSD and AMOC26 (without the Ekman component) against
the climatological mean of the Labrador Sea temperature stratification index
(computed as the difference of the vertical means in the levels 0–100 m minus the vertical means in the levels 500–1000 m; see Fig. 1). The maximum
correlations are based on 10-year running trends. The correlation
coefficient between the two metrics is shown in the top left corner. The
presence of an asterisk indicates that the correlation is significant at the
95 % confidence level. Colours indicate the lag at which the maximum
correlation between PC1-LSD and AMOC26 is obtained. The grey (blue) vertical
lines depict the mean stratification value in the DePreSys3 assimilation run
(EN4.2.1). In both cases, their overlap period is used to compute the
climatology (i.e. 1960–2013). (b–c) The same as in (a) but for the Labrador Sea
salinity and density (defined as σ2), respectively.
Another key aspect of the PC1-LSD vs. AMOC26 connectivity is the western
boundary density (WBD). Indeed, boundary density is critical to the
mechanism through which LSD influences the AMOC at lower latitudes. Positive
(negative) LSD anomalies propagate equatorward following this boundary, and
as they do so they strengthen (weaken) the zonal density gradient,
triggering a thermal wind response that accelerates (decelerates) the AMOC.
In the following we investigate differences in the propagation of boundary
densities across models and if these differences can affect the intermodel
PC1-LSD vs. AMOC26 spread. Figure 10 focuses on the two
high-resolution simulations, wherein important differences already manifest.
It represents the in-phase correlations of PC1-LSD with the density fields
(defined as σ2) near the western boundary at four different
longitudinal transects: 57 (cutting across the Labrador Sea),
45, 35, and 26∘ N. In both models, the
depth of the maximum correlation near the continental shelf is coherent
across latitudes. However, in HiGEM these occur at deeper levels (1000 to
3000 m) compared to GC2 (1000 to 2000 m), and the difference is especially
clear at 35∘ N, where the highest correlations occur at
∼ 2000 m in HiGEM, while they are only at 1000 m in GC2. Similar depth
differences are also found at 26∘ N but with slightly weaker
correlations. In addition to the difference in the depth of the maximum
correlation between HiGEM and GC2, there are differences in the vertical
structure between the two models. For example, at 35∘ N in GC2,
density anomalies on the western boundary form a tripole (low correlation
above and below the maximum correlation at ∼ 1000 m), but in
HiGEM the density anomalies form a dipole (Fig. 10g). We note
some differences in bathymetry at this latitude (which is steeper in HiGEM),
which might partly explain some of the differences in terms of the density
correlation structure.
Figure 11 shows that the diversity in the depth of these
boundary densities is even more evident when including the CMIP5 models. The
depth of the maximum correlation between PC1-LSD and the western boundary
density at the four latitudinal sections relates linearly (and significantly
at the 95 % confidence level) across models to their PC1-LSD vs. AMOC26
correlation. In this case, models exhibiting maximum correlations with the
WBDs at deeper levels generally show stronger links between PC1-LSD and the
subtropical AMOC. In DePreSys, our observationally constrained reference
(dashed grey lines in Fig. 11), these maximum correlations
tend to occur at relatively shallow levels when compared with the
multi-model ensemble. We have also checked if models with stronger
correlations with the WBDs (as represented by the PC1-LSD and WBD maximum
correlations at every latitudinal section) also support a stronger link
between the PC1-LSD and the AMOC, but this linearity assumption only holds
true at 57∘ N (correlations in magenta in Fig. 11).
This suggests that the depth along which WBDs propagate southward and/or
the vertical structure of anomalies are the key aspects to understand and
potentially narrow down the spread.
(a) In-phase correlation in GC2 between the PC1-LSD index and the
density fields across a zonal section at 57∘ N located in the
vicinity of the western Atlantic boundary. Thin dashed contours enclose
areas where the correlation significance exceeds the 95 % confidence
level. Correlations are based on 10-year running trends. (b–d) The same as in
(a) but for zonal sections at 45, 35, and
26∘ N. (d–h) The same as in (a–d) but for HiGEM.
Conclusions and discussion
This article has explored, in a multi-model context, the linkages between
subsurface density in the subpolar North Atlantic (SPNA) and the ocean
circulation further south. In particular, it has explored the role of
Labrador Sea density (LSD) in driving western boundary density anomalies
(WBD) and the ocean circulation, as well as the impact on upper-ocean temperature
changes in the SPNA. The analysis was based on two control simulations with
eddy-permitting models (a preindustrial one with HadGEM3-GC2 and a present-day one with HiGEM) and on 20 CMIP5 preindustrial experiments. Furthermore,
where possible these characteristic model features have been computed in
observational datasets and in a simulation assimilating
observations. The major findings are listed below.
All the simulations show clear multidecadal variability in Labrador Sea
density. There is also a close link between LSD and the strength of the
subpolar Atlantic Ocean circulation, with positive density anomalies leading
to a strengthening of the Atlantic Meridional Overturning Circulation (AMOC)
at 45∘ N and the subpolar gyre (SPG) circulation.
The relationship between anomalous LSD and the strength of the AMOC at
26∘ N – the latitude of the RAPID array measurements – is also
positive in the simulations, but there are significant intermodel
differences in both the strength of the relationship and the lag of maximum
correlation. This uncertainty implies that the connectivity of LSD to the
subtropics and latitudinal AMOC coherence is model-dependent.
The connectivity between anomalies in LSD and the AMOC at 26∘ N is
sensitive to different model features, including the strength and depth of
the climatological AMOC maximum, the mean density stratification in the
Labrador Sea, and the depths at which the LSD propagates southward along the
western boundary. Stronger LSD connectivity to the subtropics tends to
occur in models with a stronger and deeper AMOC, weaker Labrador Sea
stratification, and western boundary density propagating at deeper levels.
Observationally derived constraints of the model-based relationships tend to
suggest that the link between LSD and the subtropical AMOC is weak. This
suggests that observations of the AMOC via RAPID may not be representative of
the basin-wide buoyancy-forced AMOC variability. However, caution is advised
because simulations and observations are not directly comparable, so
significant uncertainty remains in constraining the relationship between LSD
and the subtropical AMOC.
The multi-model ensemble also supports a significantly lagged relationship
between LSD and the upper-ocean temperature in the eastern SPNA, in line
with previous studies linking LSD to the recently observed changes in the
North Atlantic. However, models disagree regarding the strength of the link
(correlations between 0.3–0.7) and the maximum lag (3 to 10 years).
We have shown that, in coupled climate models at least, subsurface density
anomalies in the western SPNA are an important predictor of the wider North
Atlantic ocean circulation and upper-ocean temperature in the SPNA. This
importance for the ocean circulation is especially clear at the latitudes of
the SPNA itself. Given the important role of the wind in driving lower-latitude AMOC anomalies and the range of processes through which wind can act on
the AMOC (Duchez et al., 2014a, b; Kanzow et al., 2010; Polo et al.,
2014; Zhao and Johns, 2014), it is not surprising that the relationship
between LSD and the AMOC at 26∘ N is much weaker. Nevertheless, the
reasons behind the large spread in these relationships across models are not
so clear.
(a) Scatterplot of the maximum cross-correlation value in
Fig. 4b between PC1-LSD and AMOC26 (without the Ekman
component) against the depth at which the maximum correlations at any lag
between PC1-LSD and the WBD at 57∘ N occur. The maximum
correlations are based on 10-year running trends. The correlation
coefficient between the two metrics is shown in black the top left corner.
Likewise, another correlation coefficient is shown in magenta, computed
between the PC1-LSD and AMOC26 maximum correlation and the PC1-LSD and WBD
at 57∘ N maximum correlation. The presence of an asterisk
indicates that the correlation is significant at the 95 % confidence
level. Colours indicate the maximum correlation between PC1-LSD and the WBD.
The grey vertical lines depict the corresponding depth of maximum
correlation for the DePreSys3 assimilation run. (b–d) The same as in (a) but for
the WBD at 45, 35, and 26∘ N, respectively.
We have tried to constrain this uncertainty by looking at a range of
observed metrics that may explain the spread in the correlation strength,
including the density anomalies on the western boundary, the stratification
of the Labrador Sea, and the mean AMOC strength. Overall, these constraints
point to a relatively weak relationship between LSD and the AMOC at
26∘ N on decadal timescales (i.e. r∼0.4) in the
real world. However, there are many reasons why this number is still very
uncertain, and further work is needed to assess its validity. A caveat of
this study is that the simulations and observation-based datasets employed
are not directly comparable, as they differ in the background radiative
forcing levels, the length of the period used to compute the climatologies,
and even the way some indices, like the AMOC, are computed. We also
recognize that there is large uncertainty within the observationally derived
metrics. For instance, the assimilation run in DePreSys3, which is used to
constrain relationships, clearly underestimates the mean AMOC strength at
26∘ N with respect to RAPID (see Fig. 8b) and
therefore might also be underestimating the AMOC at higher latitudes. Our
findings might also be limited by model deficiencies. There is emerging
evidence that current models underestimate AMOC and North Atlantic
variability on decadal timescales (Roberts et al., 2013; Cheung et al.,
2017), which can degrade decadal predictability in the region and even lead
to overly weak linkages between the AMOC and the AMV (Yan et al., 2018). The
AMV is indeed a mode of variability that also shows important differences
across models in terms of different aspects like its periodicity, amplitude, spatial
structure, and climate footprints (Medhaug and Furevik, 2011; Zhang and Wang
2013; Kavvada et al., 2013); these are intermodel differences that could be partly
connected to those reported herein for the PC1-LSD vs. AMOC relationships.
Models also tend to generally underestimate the depth of the return flow,
and this may still affect how density anomalies project on the basin-wide
AMOC. It has also been argued that ocean-only models produce too much deep
water in the western basin and Labrador Sea (i.e. Li et al., 2019), and
recent observations even challenge the prevailing view from models that
Labrador Sea convection dominates the AMOC variability (Koenigk and Brodeau,
2017), suggesting that the key deepwater formation occurs in the Irminger
Sea a few hundred kilometres northeast of the Labrador Sea (Lozier et al.,
2019). Therefore, further in-depth study is warranted to narrow down the
uncertainty in the real AMOC and PC1-LSD relationship.
Most of the models considered in this study have relatively coarse
resolution, including non-eddying oceans (≥1∘×1∘), which means that they might be missing some key dynamics for the AMOC
(Johnson et al., 2019) that could be important to represent realistic
linkages. The current analysis also includes two models at eddy-permitting
resolution (HadGEM3-GC2 and HiGEM), whose relationships lie within the
spread of those in the coarser models. However, it could be that higher
resolution is needed (e.g. enabling mesoscale eddies at subpolar latitudes)
to identify substantial differences (Hirschi et al., 2020; Johnson et al.,
2019). A recent analysis based on HadGEM3-GC3.1 (a later version of
HadGEM3-GC2) configured at different horizontal resolutions has shown that
long-standing model biases affecting the North Atlantic are reduced at
eddy-resolving resolution (1/12∘×1/12∘ in the ocean)
and that the strength of the AMOC, the boundary currents, and the northward
heat transport is higher than for the coarser resolutions (Hirschi et al.,
2020; Roberts et al., 2019). High-resolution coupled models also generally
support the new view from OSNAP observations that the largest fraction
of AMOC variability (on sub-annual to decadal timescales) originates at the
eastern SPNA (Hirschi et al., 2020). Eddy-resolving resolutions have also
been shown in a multi-model study (Roberts et al., 2020) to represent the
AMOC response at 26∘ N differently in future projections, leading
to stronger declines than in non-eddying simulations that are mostly
associated with a weakening in the Florida Current. Roberts et al. (2020)
also compare the meridional coherence of the AMOC, which does not seem to
be resolution-dependent; this is a result that is in line with another multi-model
comparison between non-eddying and eddy-permitting simulations (Li et al.,
2019).
Despite the current limitations in the models considered for this study, it
is important to highlight the fact that they provide a rather consistent picture of a
chain of relationships in the North Atlantic that is able to explain some of
the recent observed trends (Robson et al., 2016). This paper has broadly
characterized this behaviour and highlighted the uncertainty. These
relationships are also consistent with the mechanisms proposed by Yeager and
Robson (2017) to explain high levels of predictive skill in the SPNA on
decadal timescales. Our analysis has also helped to identify specific
metrics (such as LSD stratification and the depth of the boundary density)
that could be used as emergent constraints for future projections, i.e. to
subset the simulations expected to more realistically represent the future
changes in the region. Having a more realistic subpolar gyre stratification
under present-day conditions has been shown in CMIP5 simulations to increase
the probability of a future collapse in convection (Sgubin et al., 2017),
which would lead to a widespread SPG cooling. It remains to be tested if
similar conclusions can be drawn from eddy-resolving simulations.
Code availability
The main scripts used in the analysis and other
supporting information that may be useful to reproduce the results of this
article are archived at the Barcelona Supercomputing Center and will be
shared upon request by the corresponding author.
Data availability
Outputs from the CMIP5 simulations can be downloaded from
the corresponding ESGF node: https://esgf-node.llnl.gov/projects/cmip5/ (last access: April 2021, ESGF, 2021). EN4 observations used in this
study correspond to version 2.1 of the dataset, available at https://www.metoffice.gov.uk/hadobs/en4/download-en4-2-1.html (last access: April 2021, UK Met Office, 2021). Outputs
from the GC2, HiGEM, and DePreSys3 simulations are available upon request to
the corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/esd-12-419-2021-supplement.
Author contributions
PO, JIR, and RTS conceived the study, which was
later discussed and refined with the other co-authors. MM downloaded and
processed the CMIP5 data, computing the main climate indices. PO led the
analysis and, together with JIR, prepared the paper with contributions
from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank the UK Met Office for providing the model data from
GC2 used in this study and all the research centres that contributed to
CMIP5 and made their data available. The HiGEM control was created using the ARCHER UK National Supercomputing Service. This work used JASMIN, the UK collaborative data analysis facility, and was largely supported by the
NERC projects “Dynamics and Predictability of the Atlantic Meridional
Overturning and Climate Project” (DYNAMOC, NE/ M005127/1, and NE/M005097/1) and “Wider
Impacts of Subpolar North Atlantic Decadal Variability on the Ocean and
Atmosphere” (WISHBONE, NE/T013516/1, and NE/T013540/1). Pablo Ortega's work was additionally supported
by the Spanish Ministry of Economy, Industry and Competitiveness through the
Ramon y Cajal grant (RYC-2017-22772). Jon I. Robson was additionally supported by the
NERC ACSIS programme, and Rowan T. Sutton was supported by NERC via the National Centre for Atmospheric
Science (NCAS). Leon Hermanson was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. Steve G. Yeager acknowledges support from the National Science Foundation (NSF) research grant OCE-2040020 and from the International Laboratory for High-Resolution Earth System Prediction (iHESP). Adam Blaker, Joel Hirschi, and Babblu Sinha were additionally supported by the NERC project ACSIS (NE/N018044/1).
Financial support
This research has been supported by the Natural Environment Research Council (grant nos. M005127/1, NE/M005097/1, NE/T013516/1, NE/T013540/1, and NE/N018044/1) and the Ministerio de Economía, Industria y Competitividad, Gobierno de España (grant no. RYC-2017-22772).
Review statement
This paper was edited by Gerrit Lohmann and reviewed by two anonymous referees.
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