# Weakening of the Arctic Water Outflow From the Barents Sea and Consequences on the Fram Strait Warming

François Challet, R. P. Raj, S Chatterjee, Christophe Herbaut, A. Bonaduce

► To cite this version:

François Challet, R. P. Raj, S Chatterjee, Christophe Herbaut, A. Bonaduce. Weakening of the Arctic Water Outflow From the Barents Sea and Consequences on the Fram Strait Warming. Journal of Geophysical Research. Oceans, 2025, 10.1029/2024JC021054. http://dx.doi.org/10.1029/2024JC021054

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Distributed under a Creative Commons Attribution 4.0 International License
JGR Oceans

RESEARCH ARTICLE

10.1029/2024JC021054

**Key Points:**

* The outflow exiting the Barents Sea south of Bear Island has significantly weakened since the 1990s

* The variability of this outflow is linked to wind forcing and sea ice imports from the central Arctic

* Reduced cold water input from the northern Barents Sea generates significant warming in surface waters in the Fram Strait

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

F. Challet, francois.challet@locean.ipsl.fr

**Citation:**

Challet, F., Raj, R. P., Chatterjee, S., Herbaut, C., & Bonaduce, A. (2025). Weakening of the Arctic water outflow from the Barents Sea and consequences on the Fram Strait warming. *Journal of Geophysical Research: Oceans, 130*, e2024JC021054. https://doi.org/10.1029/2024JC021054

Received 28 FEB 2024
Accepted 31 MAR 2025

**Author Contributions:**

**Conceptualization:** F. Challet, R. P. Raj

**Data curation:** F. Challet

**Investigation:** F. Challet, R. P. Raj, S. Chatterjee, C. Herbaut

**Methodology:** F. Challet, R. P. Raj, S. Chatterjee, C. Herbaut

**Software:** F. Challet

**Supervision:** R. P. Raj

**Visualization:** F. Challet

**Writing – original draft:** F. Challet

**Writing – review & editing:** R. P. Raj, S. Chatterjee, C. Herbaut, A. Bonaduce

© 2025. The Author(s). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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# Weakening of the Arctic Water Outflow From the Barents Sea and Consequences on the Fram Strait Warming

F. Challet<fnref n="1" />,<fnref n="2" /> ID, R. P. Raj<fnref n="1" /> ID, S. Chatterjee³,⁴ ID, C. Herbaut<fnref n="2" />, and A. Bonaduce<fnref n="1" /> ID

**Abstract** Atlantic waters are transported to the Arctic Ocean mainly through the Fram Strait and the Barents Sea. Anomalous warm temperatures at the Fram Strait are generally interpreted as the consequence of a stronger and warmer West Spitsbergen Current (WSC) transporting the Atlantic Water (AW) northwards or as the propagation of warm anomalies across the Nordic Seas. In this study, we suggest that warm anomalies in the Fram Strait are also linked to a weakening of the westward flowing colder Arctic waters from the northern Barents Sea to the Svalbard region. Using a suite of satellite, in situ and reanalysis data, we propose two processes that could explain the weakening of this outflow during the recent decades. While direct wind forcing contributes to part of the observed weakening of the outflow, a reduction of the sea ice import from the central Arctic to the Barents Sea could impact the salinity in the northern Barents Sea, modify the meridional density gradient and further weaken the outflow. This phenomenon is eventually discussed in the context of long term trends and linked to the enhanced sea surface warming observed in the Fram Strait.

**Plain Language Summary** An important part of the transport of warm and salty Atlantic Water to the Arctic Ocean occurs through the Fram Strait. Warm ocean anomalies in that region are generally interpreted as the consequence of a warmer and stronger current bringing more warm waters from the north Atlantic and the Nordic Seas. In this study, we suggest that colder waters exiting from the northern Barents Sea mix with Atlantic Waters before they reach the Fram Strait and that the weakening of the transport of this cold water input could be responsible for warm anomalies downstream. We propose that changes in the wind patterns could directly impact the outflow, but also reduce the sea ice and freshwater import from the central Arctic to the northern Barents Sea, which is expected to further reduce the intensity of the outflow. This mechanism could be linked to the accelerated warming observed in the Fram Strait region during the past decades.

## 1. Introduction

The Arctic Ocean is warming nearly four times faster than the rest of the globe under the influence of climate change (Overland et al., 2019; Rantanen et al., 2022) and the warming reaches its maximum in the Barents Sea where sea ice is retreating at the largest rate (Barton et al., 2018; Lind et al., 2018; Onarheim & Årthun, 2017; Rieke et al., 2023; Screen & Simmonds, 2010). Despite an inflow of Pacific water through the Bering Strait, most of the oceanic heat input into the Arctic is supplied by the warm (>2°C) and relatively salty Atlantic Water (AW) entering the Arctic via the Fram Strait and the Barents Sea. After circulating around the Eurasian, Makarov and Canadian Basins of the Central Arctic and losing its heat, most of these cooled AW branches return to the Nordic Seas via the western Fram Strait (Carmack et al., 2015; Rudels et al., 2015).

Figure 1 portrays major ocean currents in the region. Warm and saline waters are transported from the North Atlantic through the Nordic Seas via the two branches of the Norwegian Atlantic Current (NwAC). The two branches (eastern and western) diverge further when reaching the Vøring Plateau (Orvik & Niiler, 2002) and border the eastern and western parts of the Lofoten Basin (LB). Further downstream of the LB, the eastern and western branches respectively form the West Spitsbergen Current (WSC) and the Atlantic Water recirculation branch towards the west, as they reach the Fram Strait. Before continuing as the WSC, part of the eastern branch of the NwAC flows into the Barents Sea through the Barents Sea Opening (BSO). Studies have shown that water fractionation between the Fram Strait and the BSO is more complex (Furevik, 2001), with surface waters from the western branch of the LB also reaching the Barents Sea (Broomé et al., 2021). North of this eastward flow, the presence of a westward current in the Barents Sea along the Bear Island shelf can also be noted

<footnote n="1">¹Nansen Environmental and Remote Sensing Center and Bjerknes Center for Climate Research, Bergen, Norway,</footnote>

<footnote n="2">²Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques, LOCEAN/IPSL, Sorbonne</footnote>

<footnote>Université, CNRS, IRD, MNHN, Paris, France, ³Indian Institute of Tropical Meteorology (IITM), Ministry of Earth Sciences,</footnote>

<footnote>Pune, India, ⁴National Centre for Polar and Ocean Research (NCPOR), Ministry of Earth Sciences, Goa, India</footnote>
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Main circulation features in the Nordic and Barents Seas (inspired from Strand et al. (2020)). The red arrows show the Atlantic Water pathways and the blue arrows show the Arctic Water pathways. The thick dashed red and blue arrows represent the respective contributions of recirculating AW and ArW to the Barents Sea outflow (BSOut). The 200 and 2,000 m isobaths are represented in black. The thick black segments indicate the locations of the Fram Strait and Barents Sea Opening mooring lines. The black box indicates the area used for the definition of the outflow weakening index (OWI) in Table 1.</figcaption></figure>

(Heukamp et al., 2023; Ingvaldsen et al., 2004). This current flows along the polar front (Barton et al., 2018), a steep Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) gradient which separates the Atlantic domain to the south from the Arctic Waters (ArW) present on the northern shelves. The role and water mass distribution of this outflow south of Bear Island is relatively complex, with ArW from the north-eastern Barents Sea circulating westwards on the Spitsbergen bank and at the shelf break (Heukamp et al., 2023; Strand et al., 2020), AW recirculating around the Bear Island trough before exiting the Barents Sea (Skagseth, 2008), and dense waters formed in the Barents Sea and exported towards the Nordic Seas (Årthun et al., 2011). In total, the volume transport in the Barents Sea outflow is close to 1.0 Sv on average (Årthun et al., 2011; Lien et al., 2013). Throughout this paper, this outflow will be abbreviated as BSOut. All these currents tend to follow the main bathymetric features, as is usually observed at high latitudes (Blindheim & Rey, 2004; Isachsen et al., 2003; Nøst & Isachsen, 2003).

The variability of the heat transported through the Fram Strait and Barents Sea is of great scientific interest. One example is the significant warming trend in the WSC, west of Svalbard, since 1997 and the strong temperature anomaly during 2006–2007 (Beszczynska-Möller et al., 2012). Several studies have proposed mechanisms to explain such anomalies. One of the explanations relies on a strengthening of the Greenland Sea Gyre driven by low-pressure anomalies, which would intensify the northward current to the Fram Strait (Chatterjee et al., 2018). Other processes could involve the propagation of warm anomalies across the Nordic Seas (Holliday et al., 2008; Skagseth et al., 2008). In the Barents Sea, rapid increases of AW temperature of 1°C since 2000 have been documented (Årthun et al., 2012; Lind et al., 2018), with a corresponding salinity increase of 0.1 psu (Lind et al., 2018). This northward expansion of the AW influence in the Arctic, often described as “Atlantification” (Barton et al., 2018), has mostly been linked to evolutions in the AW transport through the BSO (Årthun et al., 2012).

Yet, studies suggest that the variability of the heat and volume transports in the NwAC may not fully explain the warming events and trends observed in the Fram Strait and the Barents Sea (Herbaut et al., 2017). Chafik et al. (2015) suggested that while a positive NAO phase mainly strengthened the NwAC, the WSC mostly
responded to anticyclonic winds over the Spitsbergen Bank. Such winds over Svalbard could alter the circulation in the Fram Strait, shifting the recirculation branch in central Fram Strait towards the north (McPherson et al., 2023), and also impact the Barents Sea. In particular, Lien et al. (2013) showed that cyclonic winds over Svalbard had modified the pathways of AW to the Arctic, with more AW entering through the BSO, and significantly weakened the Barents Sea outflow (BSOut)—or even reversed the flow—several times between 2003 and 2005. And more recently, using model results, Heukamp et al. (2023) suggested that fast warming along the polar front in the Barents Sea could disturb local wind patterns and weaken the ArW outflow from the Barents Sea towards the Fram Strait.

In this study, we focus on the role of the BSOut on the heat exchange between the Barents Sea and the Fram Strait. We combine satellite, in situ observations and atmospheric/ocean reanalyses to understand the variability and trend in the BSOut volume transport and its role on the temperature anomalies and trends in the Fram Strait. Section 2 describes the data sets and defines the indices used in this study. Section 3.1 shows that the BSOut variability can be estimated using local sea level anomalies as a proxy. Section 3.2 investigates the main drivers of the BSOut variability and its impacts at the Fram Strait section. Section 3.3 analyzes the weakening trend in the BSOut intensity. Section 4 summarizes the main findings and discusses their possible implications on the accelerated warming in the Fram Strait and the northern Barents Sea.

## 2. Data and Methods

In the study, Sea Level Anomalies (SLA) from satellite altimetry data were used to calculate anomalies in the geostrophic surface currents and to investigate the variability of the oceanic circulation. They are distributed by the Copernicus Marine Service (Copernicus Marine Service, 2023a) after merging the L3 along-track measurements from various altimeter missions and interpolation on a regular grid (0.25° horizontal resolution). They cover the ice-free regions of the Nordic and Barents Seas throughout the year.

Sea surface temperatures (SST) are obtained from Multi Observation Global Ocean ARMOR3D L4 analysis at a 0.25° regular grid (Copernicus Marine Service, 2023b; Guinehut et al., 2012; Mulet et al., 2012). A sea surface salinity (SSS) and sea surface density (SSD) product from CMEMS with a resolution of 0.25° was also used (Consiglio Nazionale delle Ricerche, 2023). It was obtained through a multivariate optimal interpolation algorithm relying on Soil Moisture and Ocean Salinity (SMOS) satellite images and in situ salinity measurements combined with satellite SST information (Buongiorno Nardelli et al., 2016; Droghei et al., 2016, 2018; Sammartino et al., 2022). The product was developed by the Consiglio Nazionale delle Ricerche (CNR). It should be noted that SSS is a challenging variable to measure remotely as the sensitivity of brightness temperature measured by satellites is low for cold polar waters and the signal may be contaminated by the sea-ice to ocean transition.

Finally, data sets for the wind, the sea level pressure (SLP), and the sea ice cover (SIC) were extracted from the ERA5 reanalysis, the latest climate reanalysis produced by ECMWF, which provides data on many atmospheric, land-surface and sea-state parameters on a regular latitude-longitude grid with a resolution of 0.25° (Hersbach et al., 2023). All data sets have monthly resolution and cover the time-period 1993–2020 (28 years).

In situ data includes temperature data from the AWI/NPI moorings in the Fram Strait (Beszczynska-Möller et al., 2012; Von Appen et al., 2019). It was interpolated over the 1997–2016 period using a two-step kriging method (described in more details in Challet et al. (2022) and Geyer et al. (2023)). The gaps in the data were first filled using the temporal and spatial correlations between the moorings. The resulting temperatures were then interpolated on a regular grid with 31 depths, a horizontal resolution of 0.5°, and monthly time steps. The final product was quality-checked using CTD observations. Salinity measurements are scarcer in the mooring array, especially below 250 m, and their poorer quality does not allow for a reliable analysis of the salinity variability (Beszczynska-Möller et al., 2012). They were therefore left aside for this study.

Bathymetry data were provided by the General Bathymetric Chart of the Oceans (GEBCO) Grid (Nippon Foundation-GEBCO, 2022).

Results from the above observational and reanalysis data were compared to the output of a sea ice-ocean model, which is based on NEMO (Nucleus for European Modelling of the Ocean) ocean model version 3.6 (Madec, 2008) coupled to the LIM3 sea ice model (Rousset et al., 2015). The equations are discretized on 75 vertical levels with thicknesses varying from 1 m in the top layer to roughly 200 m at the deepest model level. The model grid has a horizontal resolution varying from 10 km in the Arctic Ocean to 25 km at the equator. It is
integrated from 1979 to 2017, driven by 6-hourly surface atmospheric fields from the ERA-I reanalysis (Dee et al., 2011). More details about the model configuration and the forcing can be found in Herbaut et al. (2017).

## 2.1. Indices Definition

There is not enough in situ data available to quantify the transports induced by the BSOut. Though a mooring line was deployed by the Institute of Marine Research in the BSO between 1997 and 2017 (Ingvaldsen, 2020; Ingvaldsen et al., 2004), the northernmost mooring is located at 73.3°N, south of the Bear Island shelf slope where the BSOut is located. So the first step is to create a proxy of the volume transport in the BSOut. This proxy is defined as the mean SLA meridional gradient between 73.5° and 75°N and 17° and 24°E, which corresponds to the shelf break south of Bear Island. Gradients are first calculated meridionally at each longitude grid point, and the resulting values are then averaged zonally. The gradient is considered positive for higher SLA values to the north. As this paper focuses on the weakening of the BSOut, we consider the sign-reversed outflow proxy, which is thereafter referred to as the outflow weakening index (OWI). The use of this index is validated in Section 3.1 thanks to a comparison with the output of the NEMO-LIM3 model.

To investigate the potential drivers and consequences of the changes in the BSOut, we calculate the regressions of the SLP, wind stress curl, SSS, SST, SSD, and SIC on the normalized OWI. The significance of regression maps and sections is tested using two-sided Student t-tests (Von Storch, 1998). The number of degrees of freedom used in the t-tests is estimated from the calculation of autocorrelations in detrended time series for all quantities (Santer et al., 2000). Since the OWI has a strong positive trend (which corresponds to a negative trend in the BSOut volume transport), it is first detrended for the analysis of interannual variability (Section 3.2), and the trends are then analyzed in Section 3.3. To understand the correlations between the atmospheric and oceanic changes and the BSOut variability, we also define indices for the wind stress curl and SIC on the Spitsbergen bank and for the SST along the western Spitsbergen shelf in the Fram Strait (later on referred to as the WSC index) from the observation and reanalysis data. Indices for the sea ice transport into the northern Barents Sea, the net freshwater flux from the ocean to the sea ice (due to sea ice melt and formation) and the BSOut volume transport are defined from the model outputs. Definitions of all these indices are summarized in Table 1. Each index is formed as follows: (a) The seasonal cycle at each grid point is calculated and data are deseasoned to estimate the anomalies. (b) At each time step, the anomalies are averaged spatially over the indicated box, to obtain time series. (c) Each time series is detrended and filtered using a 13-month running mean. (d) The resulting time series is normalized (divided by its standard deviation).

After validation of the model's ability to replicate the main features of the BSOut variability in Section 3.1, it is used to check the consistency of the mechanisms proposed throughout the article, in particular the roles of sea ice import into the Barents Sea and sea ice melt on the salinity anomalies. This analysis is further detailed in Supporting Information S1.

## 2.2. Trend Analysis

To analyze long-term changes in the circulation we calculate the trends in SLA, SLP, winds, SSS, SST, SSD, and SIC over the region. In each case, the trends are obtained by calculating the regressions of the different quantities on the linear trend of the OWI. The average contribution of the sea level rise over the study region is subtracted from the map showing the SLA trend. The resulting maps are then compared to understand the processes responsible for the observed trends. The significance of the trends is tested using the method detailed in Santer et al. (2000).

## 2.3. Calculation of the Steric and Mass Contributions to the SLA

In the model, the steric contribution to the SLA is calculated using the formula (Siegismund et al., 2007):

$$h = \int_{z_{\max}}^{0} \left( \frac{\rho_{0}}{\rho} - 1 \right) dz$$
<table><caption>**Table 1**
*Definition of the Indices Used in the Article*</caption><thead><tr><th>Index name</th><th>Physical variable</th><th>Geographical definition</th><th>Data source</th><th>Physical meaning</th></tr></thead><tbody><tr><td rowspan="2">Outflow weakening index (OWI)</td><td>Sea level anomaly meridional gradient</td><td>73.5°–75°N</td><td>Satellite altimetry</td><td>Weakening of the volume transport out of the Barents Sea</td></tr><tr><td>Wind stress curl anomaly</td><td>17°–24°E</td><td>Reanalysis (ERA5)</td><td>Cyclonic wind forcing over the Spitsbergen bank</td></tr><tr><td rowspan="2">SIC index</td><td>Sea ice cover anomaly</td><td>75°–77°N</td><td>Reanalysis (ERA5)</td><td>Sea ice cover anomaly in the northern Barents Sea</td></tr><tr><td>Sea surface temperature anomaly</td><td>10°–30°E</td><td>Observations (ARMOR3D)</td><td>Sea surface temperature anomaly on the western Spitsbergen shelf</td></tr><tr><td rowspan="2">WSC index</td><td>Sea ice volume transport anomaly (model)</td><td>77°–79°N</td><td>NEMO-LIM3 model</td><td>Sea ice volume transport anomaly into the northern Barents Sea</td></tr><tr><td>Net freshwater flux anomaly from the sea ice to the ocean (model)</td><td>6°–14°E</td><td>NEMO-LIM3 model</td><td>Contribution of sea ice formation and melt to the salinity anomalies</td></tr><tr><td>BSOut transport index</td><td>Volume transport anomaly (model)</td><td>Bathymetry shallower than 1,000 m Through the section between (79°N, 28°E) and (76°N, 40°E)</td><td>NEMO-LIM3 model</td><td>Volume transport anomaly in the BSOut. Positive index values correspond to a weaker transport</td></tr></tbody></table>

*Note.* All indices are calculated by averaging the physical variable of the second column over the geographical area defined in the third column. All indices are normalized before comparison.
where $\rho$ is the seawater density, $\rho_0$ is a reference density chosen at 1028 kg/m$^3$ and $z_{max}$ is the depth of the ocean floor. The mass contribution is calculated as the difference between total SLA and the steric contribution. $h$ is very sensitive to water depth variations. To remove this influence of the water depth gradient, the temporal mean of $h$ is subtracted at each grid point, before studying the variability.

# 3. Results and Discussion

## 3.1. Validation of the SLA Proxy

Figure 2a shows the comparison of OWI indices derived using SLA gradient from satellite altimetry (blue curve) and the model (orange curve). The correlation between these two indices (detrended and 13-month-filtered) is $R = 0.53$ ($p < 0.05$), indicating a reasonable comparison between the indices in terms of interannual variability. Further, the model succeeds in replicating the BSOut weakening trend (in Figure 2a the trend of the observed OWI is $0.090 \text{ yr}^{-1}$ and that of the model index is $0.064 \text{ yr}^{-1}$) and most BSOut weakening events although the timing and intensity of these events are less accurate. Since there is no observed volume transport data in the BSOut, the relevance of using the OWI as a proxy to the volume transport anomalies is confirmed by comparing OWI and volume transport time series (BSOut transport index in Table 1) from the model. Figure 2b suggests a good comparison between these two indices with a correlation of $0.79$ ($p < 0.05$).

Figures 2c and 2d provide a more complete vision of the outflow composition and variability in the model. Dense waters are defined as waters with density anomalies larger than 27.9 kg/m$^3$, in-line with Årthun et al. (2011), and less dense waters are split between recirculating AW and ArW following a 3.5°C temperature threshold (colder waters are categorized as ArW). No significant correlations are found between the variability of the transport of these three water masses, though Figure 2d suggests a decreasing trend in the ArW contribution. The mean outflow volume transport is close to 0.7 Sv on average over the study period, with large-amplitude variations between 0.4 and 1.0 Sv (Figure 2d). Figure 2c shows that in periods of weaker BSOut (1995, 2001, 2007, 2013), the net volume transport north of 73°N nearly cancels out and even temporarily reverses sign in 2015–2017, as a net inflow is found along the Spitsbergen Bank slope. This echoes the outflow reversal events over time scales of a few months described by Lien et al. (2013).

Figure 2e presents the average zonal velocity at the BSOut location in the model. North of 73.5°N, the modeled BSOut has a mostly barotropic structure, which is consistent with the ability of the SLA-based OWI to replicate most of the variability in the volume transport (Figure 2b). Between 73° and 73.5°N however, the outflow has a more baroclinic structure and is bottom-intensified. Figure 2f presents the regression of zonal velocity anomalies on the OWI and shows that the anomalies contributing to the variability of the BSOut volume transport are found north of 73.5°N, where the outflow is barotropic. These anomalies have themselves a mostly barotropic structure. A regression on the normalized model transport shown in Figure 2b would give a similar result (not shown).

This model analysis therefore validates our approach of using SLA gradients to estimate the transport variability at the Bear Island shelf break, and further suggests that the BSOut has a large variability between periods of relatively strong and quasi null exports.

## 3.2. Drivers of the Interannual Variability in the BSOut and Consequences on the Fram Strait

### 3.2.1. Direct Wind Forcing

The normalized OWI is again presented in Figure 3a in two versions: the unfiltered index (monthly resolution) in orange and the 13-month-filtered index in blue. It shows a positive trend (significant at $p < 0.05$), indicating that the BSOut has significantly weakened since the mid-1990s. In addition to the trend, several periods of anomalously weak outflow, corresponding to positive OWI values, can be identified, with typical temporal extents of 1–3 years, in particular in 1994–1995, in 1999–2000, in 2007–2008, in 2012–2013 and 2015–2017.

The regression of the detrended SLA on the detrended unfiltered OWI, presented in Figure 3b, shows that such weakening events are associated with anomalously low SLA over all the Spitsbergen bank and high SLA in the southern Barents Sea. In the Fram Strait, anomalous geostrophic currents are oriented southwards along the Spitsbergen shelf, which corresponds to a decrease in the volume transport into the Arctic via the Fram Strait.

Figures 3c and 3d show the SLP and wind stress curl patterns associated with a weakening of the BSOut. The impact of wind forcing on the oceanic circulation of the region has been shown to be of great importance by many
<figure><img src="image_4.png" /><figcaption>**Figure 2.** (a) Comparison between the 13-month-filtered OWI as defined from satellite measurements in Table 1 and the OWI calculated with the SLA model output. (b) Comparison between the OWI calculated from the SLA model output and the normalized modeled transport south of Bear Island (calculated at 20°E between 73.5° and 75°N). (c) Net volume transports at 20°E between 73° and 75°N in the model. Positive values are associated with a transport into the Barents Sea. Dense waters (density anomalies larger than 27.9 kg/m³) are shown in green and less dense waters are separated between recirculating AW (warmer than 3.5°C) in orange and ArW (colder than 3.5°C) in blue. (d) Breakdown of the outflow volume transport at 20°E between 73° and 75°N in the model. (Inflows are excluded.) (e) Mean zonal velocity section in the model (calculated between 1993 and 2017). Outflowing waters are represented in blue. (f) Regression of the zonal velocity anomalies on the detrended OWI defined from the model SLA. The solid contour indicates where the mean zonal velocity is zero in (e). The hatches show the areas where the results are significant at *p* < 0.05.</figcaption></figure>

studies (e.g., Chafik et al., 2017; Chatterjee et al., 2018). Consistent with Lien et al. (2013) and Chafik et al. (2015), we find a negative SLP anomaly and an associated strong cyclonic wind stress curl above Spitsbergen bank during periods of BSOut weakening. Such wind stress curl anomalies, linked with southward wind anomalies in the Fram Strait, could potentially also induce the weakening of the northward flow of theWSC. Ekman transport anomalies induced by northerly wind anomalies push surface waters away from the Svalbard
<figure><img src="image_5.png" /><figcaption>**Figure 3.** (a) Unfiltered and 13-month-filtered OWI (altimetry). Regressions of (b) the SLA, (c) the sea level pressure (SLP) and (d) the wind stress curl on the detrended unfiltered OWI. The arrows in (b) and (d) respectively show the regressions of the geostrophic surface currents and 10 m wind velocities on the detrended OWI. The contours in (c) show isolines of SLP anomalies. The hatches show the regions where the results are significant at *p* < 0.05. The framed region in (b) (resp. (d)) indicates the area over which the OWI (resp. the wind stress curl index) was calculated.</figcaption></figure>

coast creating a negative SLA there. A similar link between cyclonic or anticyclonic wind anomalies over Svalbard and the intensity of the WSC was found in model studies by both Heukamp et al. (2023) and McPherson et al. (2023).

The correlation analysis between the weakening of the BSOut and the wind stress curl, presented in Figure 4, confirms the role of wind stress curl as a significant (though not necessarily predominant) driver of the BSOut intensity. A correlation of 0.39 (*p* < 0.05) is found between the two indices in Figure 4a at zero-lag. The correlation is higher in the model, as presented in Figure 4c. The correlation between the unfiltered signals peaks at 0.56 at 0-lag but drops significantly at a lag of 1 month, which suggests that the wind should mostly have a direct impact on the BSOut volume transport at high frequencies (around a month - see Text S2 and Figure S2 in Supporting Information S1). However, few observed weakening events in Figure 4a occur simultaneously with maxima in the wind stress curl (e.g., in 1995) while other events occur months—or even more than a year—after a strong maximum in the wind stress curl index (the 2007–2008 weakening event occurs after a prolonged period of strong wind stress curl which started in 2005). In the next section, we will therefore propose another mechanism which could play a complementary role in the BSOut variability.

### 3.2.2. Sea Ice Import Variability

Sea levels anomalies, and the associated anomalies in the geostrophic surface currents, are impacted by both mass contribution and steric contribution. While the wind-induced Ekman transport moves water masses horizontally at the surface, buoyancy fluxes redistribute mass vertically, thus impacting the pressure gradients and the currents via the thermal wind relation. The impact of winds on the mass component of the sea level in the Nordic Seas region has been shown recently by Mangini et al. (2023), who linked the mass component with the intensity of along-slope wind stress in shelf regions, consistently with Ekman theory. Along the Norwegian coast, they were
<figure><img src="image_6.png" /><figcaption>**Figure 4.** (a) Comparison between the 13-month-filtered detrended OWI (altimetry) and the wind stress curl index (ERA5). (b) Lagged correlation analysis between the unfiltered and 13-month-filtered time series. For positive lags, the OWI lags the wind stress curl index. Information about the index definitions is presented in Table 1. (c) Lagged correlations in the model between the detrended wind stress curl index and the detrended BSOut volume transport. The blue curves in (b) and (c) show the correlations between the unfiltered time series, while the orange curves show the correlations between the 13-month-filtered time series. The dashed lines in (b) and (c) indicate the *p* = 0.05 significance levels for the two time series.</figcaption></figure>

found to explain 50%–60% of the variance of the mass component. But the results from Section 3.2.1 suggest that the BSOut variability may not be explained by wind forcing only. We now investigate the influence of the changes in the steric contribution to SLA on the BSOut variability. We hypothesize that it is mostly influenced by salinity anomalies induced by variable sea ice imports into the Barents Sea.

Figure 5a presents the comparison in the model between the SLA difference across the Bear Island shelf break used to define the OWI, and the steric and mass contributions to these anomalies. When applying a 13-month running mean, the correlation between the total SLA difference and the steric contribution reaches 0.87 and the variabilities of the two signals display very similar amplitudes and trends (Figure 5a). The correlation with the mass contribution in Figure 5a is also significant, but lower (*R* = 0.65). This result suggests that density gradients across the polar front can play an important role to explain the BSOut weakening events. Figures 5b and 5c show the regressions of SSS and SSD on the detrended OWI based on observational data, and Figure S3a in Supporting Information S1 shows a similar salinity regression obtained from model data. Both figures feature positive anomalies in most of the northern Barents Sea. The regions with the highest significance in Figure 5b (*p* < 0.1) are found just south of Storbanken. The lower significance levels in Figures 5b and 5c compared to the model might
<figure><img src="image_7.png" /><figcaption>**Figure 5.** (a) Total SLA difference and steric and mass contributions to the SLA difference between 73.5° and 75°N at 20°E in the model. Positive values correspond to lower SLA at 75°N. Both time series are filtered using a 13-month running mean. Regressions of (b) the SSS anomaly and (c) the SSD anomaly on the detrended OWI, based on satellite observation. The hatches show the regions where the results are significant at *p* < 0.1.</figcaption></figure>

be linked to the larger uncertainties of the salinity and density observational products (Buongiorno Nardelli et al., 2016). The pattern correlation between the salinity and density maps in Figures 5b and 5c is 0.94, as the low surface density is mainly due to the presence of freshwater. The density increase in the northern Barents Sea matches the negative SLA in Figure 3b, which also supports the contribution of meridional density gradients to the SLA and surface geostrophic current anomalies. Density gradients and geostrophic currents are linked by the thermal wind relation, which states that vertical velocity gradients should be induced where or when horizontal density gradients are present. This generally results in baroclinic velocity anomalies. However, studies have found that, in the presence of a steep topography as is the case at the Bear Island shelf break, the baroclinic and barotropic modes should be coupled (Allen, 1975; Mertz & Wright, 1992; Yeager, 2015). This implies that the density gradients of Figure 5d could translate into barotropic velocity anomalies. This is what happens in the model where both velocity anomalies associated with anomalous wind forcing and salinity anomalies have a mostly barotropic structure (see Figure 2f and Figure S1 in Supporting Information S1).

The origin of the salinity anomalies of Figure 5b and their link with anomalous SIC are investigated in Figure 6. The regression of the SIC anomalies on the detrended OWI presented in Figure 6a shows that BSOut weakening events are generally associated with negative SIC anomalies in the northern Barents Sea, and in particular close to the polar front, where significant positive SSS and SSD anomalies were found in Figure 5. A similar SIC regression pattern was obtained from model data (Figure S3c in Supporting Information S1). This contribution of SIC is further supported by Figure 6c, which illustrates the high correlations between detrended SIC anomalies and the OWI when SIC anomalies lead by up to 6 months (R = 0.53 between the two time series for lags between 0 and 6 months). The northern Barents Sea is a region characterized by a net sea ice import from the Central Arctic, and SSS anomalies are thus strongly influenced by the variability of these imports (Lind et al., 2018). Besides, salinity anomalies on time scales larger than a year can hardly be explained with a static vision (without sea ice imports and exports) of sea ice formation and melt, as freshwater fluxes between the sea ice and the ocean would then equilibrate over the whole seasonal cycle. We thus argue that the link between the salinity anomalies of Figure 5b and SIC anomalies of Figure 6a can be explained by a decrease of sea ice import into the northern
<figure><img src="image_8.png" /><figcaption>**Figure 6.** (a) Regression of the sea ice cover (SIC) anomaly (ERA5) on the detrended OWI (altimetry) at lag 0. (b) Regression of the sea ice transport per model grid cell anomalies on the detrended OWI in the model. The arrows show the direction of the anomalies and the color corresponds to their module. The black line shows where sea ice transport anomalies are calculated. The hatches in (a) and (b) show the regions where the results are significant at *p* < 0.05. (c) Comparison between the detrended 13-month-filtered wind stress curl index (ERA5) and the sign-reversed SIC index (ERA5)—calculated in the region framed in (a)). (d) Comparison between the detrended 13-month-filtered OWI (altimetry) and the sign-reversed SIC index (ERA5). (e–g) Lagged correlations in the model between the detrended net SIC import into the Barents Sea, the detrended net freshwater exchange between the ocean and sea ice and the detrended BSOut volume transports. The blue (resp. orange) curves show the correlations with the unfiltered (resp. 13-month filtered) time series. The dashed lines in the bottom panels indicate the *p* = 0.05 significance level.</figcaption></figure>
Barents Sea in the months preceding BSOut weakening events. This is supported by an analysis using the model presented in Figure 6b, which shows that a weaker outflow is associated with a decrease in sea ice import into the region. The correlation between the 13-month-filtered sea ice import and BSOut transport indices reaches 0.70 at a 1-month lag in the model (Figure 6e). The correlations are much lower when considering the unfiltered signals, which suggests that sea ice imports mostly impact the BSOut variability at low frequencies (several months). Figures 6f and 6g highlight that the link between sea ice transport and the BSOut volume transport is associated with changes in the freshwater input from sea ice melt: anomalous sea ice import is tightly linked with anomalous meltwater input into the ocean (R = 0.90 at a 1-month lag between the filtered time series of Figure 6f), which eventually impact the BSOut transport via a change in the steric contribution (Figure 6g). The lag between SIC and BSOut transport anomalies, though longer in observational data compared to the model, may be explained by the different seasonalities of sea ice import and melt, as sea ice is mostly imported during winters and melt is maximum during springs (although some melt occurs year round along the polar front due to the proximity of warm AW). At a lower order, positive salinity anomalies could also be enhanced by increased atmosphere-ocean interaction allowed by the reduced SIC, which would induce a stronger mixing in the upper layers of the ocean and a strengthened AW ventilation bringing more salt to the surface (Lind et al., 2018; Rainville et al., 2011).

The existence of a link between anomalously low sea ice import into the Barents Sea and the wind stress curl anomalies of Figure 3d is examined next. The impact of wind forcing on sea ice transport into the northern Barents Sea is well established (Efstathiou et al., 2022; Herbaut et al., 2015; Lien et al., 2021). The sea ice is usually imported into the Barents Sea from the north around Franz Joseph Land and/or from the east north of Novaya Zemlya. Lien et al. (2021) showed that westerly winds over the Barents Sea lead to a reduction of sea ice import from the east while prevalence of southerly winds reduce sea ice import from the north. The atmospheric forcing presented in Figures 3c and 3d features steady southerly and southwesterly wind anomalies in the Barents Sea, and is therefore expected to cause a reduction of the sea ice import into the region. Figure 6d confirms this result and illustrates the temporal correlation (R = 0.63) between wind forcing and SIC in the Barents Sea. Most important events of strong cyclonic wind forcing over the Spitsbergen Bank (2000, 2005, 2009, 2012, 2016) were followed generally one month later by a minimum in the local SIC (Figure 6c). Interestingly, wind patterns such as that of Figure 3d could therefore both induce a BSOut weakening on short time scales (less than a month) due to an anomalous Ekman transport pushing water away from the Bear Island shelf, and over longer time scales (a few months) by reducing sea ice imports into the Barents Sea and eventually impacting the density gradients across the polar front.

### 3.2.3. Impact on Temperature Anomalies in the Fram Strait

The temperatures of the BSOut waters have a vertically homogeneous structure but vary significantly when moving on and off the shelf. Lien et al. (2016) showed that their representation in ocean models gives a vast range of results (see their Figure 6 for temperature sections obtained from different ocean models and interpolation of in situ data). Their analysis also suggests that these temperatures remain between 0°C and 4°C. This is generally colder than the temperatures found in the WSC, which often reach 4–5°C in the upper 200 m, and up to 7°C during summers (McPherson et al., 2023; Von Appen et al., 2016), but potentially warmer than the those found between 600 and 900 m in the Fram Strait (temperatures mostly between 0°C and 1°C; Beszczynska-Möller et al., 2012). The impact of variations in the BSOut on the Fram Strait temperatures is therefore not straightforward, but it seems plausible that the BSOut has a cooling effect close to the surface. The contribution of ArW from the shelf south-west of Spitsbergen is already known to shelter some of the west Spitsbergen fjords from AW (Strzelewicz et al., 2022).

Figure 7a shows the regression of SST on the detrended OWI based on observations, when the SST anomalies lag the OWI by 5 months. It features a strong warm anomaly in the Barents Sea which could mostly result from decreased SIC which precedes periods of weaker BSOut. This illustrates the difficulty of attributing a unique causality to the observed signals as several couplings and feedbacks may likely be involved. In this particular case, the observed warming in the Barents Sea cannot be interpreted as a consequence of the BSOut weakening, but is rather a simultaneous consequence of the decreased sea ice import, which impacts the outflow on one hand via the mechanism described in Section 3.2.2, while inducing a surface warming via the well-known albedo feedback on the other. Still, a warm anomaly is also visible around the Spitsbergen bank, along the path of the BSOut, and west of Svalbard, following the topography of the western Spitsbergen shelf. This signal is consistent with a warming in the Fram Strait caused by decreased cold water input close to the surface. Another possible
<figure><img src="image_9.png" /><figcaption>**Figure 7.** (a) Regression of the detrended SST (observations) on the detrended OWI (altimetry). The SST lags the OWI by 5 months. The hatches show the statistically significant anomalies (*p* < 0.05). (b) Comparison between the detrended 13-month-filtered SST anomaly in the Fram Strait (in the framed region in (a)) and the OWI. (c) Lagged correlation analysis between the two time series. The Fram Strait SST lags the OWI for positive lag values. The dashed line indicates the *p* = 0.05 significance level.</figcaption></figure>

cause of the surface warming west of Svalbard could be an upwelling of warm AW induced by the alongshore wind forcing presented in Figure 3d, as the associated Ekman transport pushes surface waters offshore.

The WSC index (defined in Table 1), shown in Figure 7b, provides an indicator of the SST anomaly on the shelf and slope west of Svalbard. It is well-correlated with the OWI (*R* > 0.55 for lags between 0 and 10 months, with a maximum correlation of 0.61 for a 5-month lag), which suggests that the strength of the BSOut is a dominant feature to explain the variability of surface temperatures close to the west Spitsbergen shelf. The correlations between the detrended wind stress curl index and the detrended WSC index are slightly weaker (with a maximum of *R* = 0.52 when the WSC index lags the wind stress curl index by 2 months - not shown). Coastal upwelling could thus be a secondary driver of the surface warming west of Svalbard.

Figure 8 shows regressions of interpolated observed ocean temperatures, at the Fram Strait mooring section located at 78°50′N, on the OWI for lags of 2, 5 and 8 months. It suggests that the warm anomaly induced by decreased cold water input from the BSOut mostly remains confined to the upper 100 m of the section. It is mostly found close to the shelf (around 8°E in Figure 8) in the first few months (and likely extends beyond the mooring line which stops at 8.7°E) and peaks 3 months after the BSOut weakening. The anomaly then extends on top of the
<figure><img src="image_10.png" /><figcaption>**Figure 8.** (a–c) Regressions on the detrended OWI (altimetry) of the temperature anomalies of the interpolated Fram Strait mooring section with lags of 2, 5, and 8 months between the OWI and the temperature anomalies in the Fram Strait. (d) Correlations between the Fram Strait temperature anomalies east of 6°E (zonally averaged for each depth) and the detrended OWI (altimetry) for increasing lags. The outflow weakening leads to positive lag values. The hatches show the results significant at *p* < 0.1.</figcaption></figure>

westward AW recirculation branch (Figure 8b). Between 5 and 8 months after the BSOut weakening, a persistent cold anomaly starts to appear between 600 and 900 m in the eastern Fram Strait (between 6° and 8°E in Figure 8), while the warm surface anomaly starts to weaken. The relatively low significance levels (*p* = 0.1) obtained in Figure 8 may be linked to the uncertainties in the interpolation method used on the Fram Strait mooring data (Challet et al., 2022), and higher significance levels are obtained in the model (Figure 9). The sequence of surface warming (0–100 m) at lags of 2–5 months followed by an intermediate water cooling (600–900 m) at lags of 6–12 months is illustrated in Figure 8d. Significant anticorrelations of −0.3 are found between water temperatures at 750 m east of 6°E in the Fram Strait and the OWI lagged by 7–12 months. When applying a 13-month running mean to the OWI and Fram Strait temperatures, the anticorrelations reach −0.6.

We hypothesize that this cold anomaly in intermediate waters could be related to the dense outflow waters transported out of the Barents Sea directly above the sea floor. These relatively dense waters have temperatures between 2°C and 3°C (Lien et al., 2016; Skagseth, 2008), which is substantially warmer than the 0°C–1°C of water masses found at 750 m in the eastern Fram Strait. They are therefore expected to have a warming effect in the Fram Strait if they sink to 750 m and are transported northwards, after flowing out of the Barents Sea at depths close to 450 m and with density anomalies of 28–28.1 kg/m³ (Ingvaldsen et al., 2004). Such density anomalies are larger than the density anomalies of 27.96–28 kg/m³ found in the AW layer in the WSC (Beszczynska-Möller et al., 2012) and compare well with density anomalies found at 750 m in the Fram Strait (close to 28.06 kg/m³, Marnela et al., 2013). A weakening of the BSOut may therefore reduce this warming effect on intermediate waters and create a cold anomaly propagating to the Fram Strait. Since meridional currents in the WSC are stronger closer to the surface - around 15–20 cm/s in the first 200 m close to the shelf break and around 5–10 cm/s at 750 m (Beszczynska-Möller et al., 2012), it seems consistent to see the warm surface anomaly in the Fram Strait months before the cold anomaly in intermediate waters.
<figure><img src="image_11.png" /><figcaption>**Figure 9.** (a) Regression of the temperature anomalies in the model at 78°50′N in the Fram Strait on the OWI at a lag of 3 months. The hatches show the results significant at *p* < 0.05. (b) Average meridional velocity section at 78°50′N in the model. Positive velocities are oriented northwards. Regressions of the temperature anomalies at 300 m on the detrended OWI in the model at lags of (c) 0 month and (d) 3 months. The temperature anomalies lag the OWI for positive lag values. (e and f) Same at 950 m. The dashed lines in (a) show the depths of the regression maps.</figcaption></figure>

The question of the causality between the BSOut volume transport anomalies and the temperature anomalies at the Fram Strait is further investigated using the model in Figure 9. The model replicates the pattern of warm anomalies overlying cold anomalies in the months following high OWI values (Figure 9a), although warm anomalies have a stronger amplitude and a deeper extent than in Figure 8 (they extend down to 600 m east of 6°E). The lag between the arrivals of warm and cold anomalies at the Fram Strait is also lower in the model, as both can be detected around 3 months after the BSOut weakening. These differences may be impacted by the Fram Strait velocity structure in the model, presented in Figure 9b. Due to the model's relatively low resolution, the WSC is very broad, too far west and not fast enough compared to observations and higher resolution models of the Fram
<figure><img src="image_12.png" /><figcaption>**Figure 10.** (a) Sea ice cover (SIC) and (b) wind stress curl trends over the Nordic and Barents Seas between 1993 and 2020 (ERA5). The black arrows represent the winds associated with the wind stress curl trend. The hatches indicate the regions where the SIC and wind stress curl trends are significant (at *p* = 0.05).</figcaption></figure>

Strait (Wekerle et al., 2017). The regression maps of temperature anomalies at 300 m (Figure 9c) and 950 m (Figure 9d) show the emergence of continuous warm and cold anomaly patterns between Bear Island and the eastern Fram Strait in the months following the BSOut weakening, which supports the existence of a causal link between the BSOut volume transport and the Fram Strait temperature anomalies.

### 3.3. Weakening Trend of the BSOut

#### 3.3.1. Impact of the Barents Sea Ice Loss on the Local Winds

In this section, the impact of the processes described above as an accelerator of the warming in the region is investigated. SIC acts as a boundary between the atmosphere and the ocean, limiting the exchanges of heat and momentum between the two. In Section 3.2, we showed how atmospheric parameters forced sea ice changes. But the impact of sea ice changes on the atmosphere is also important, and it is a topic of growing interest, especially since the Barents Sea has experienced one of the most rapid declines in SIC (Barton et al., 2018; Lind et al., 2018; Onarheim & Årthun, 2017). A recent model study (Heukamp et al., 2023) suggested that sea ice loss in the Barents Sea should result in a local increase of heat loss from the ocean to the atmosphere. The rise of warming air in the lower atmosphere should induce a near-surface atmospheric convergence and in turn reduce the SLP. This would impact the local winds, generating cyclonic anomalies around the region of higher sea ice loss. In Section 3.2.2, we explained that such a wind forcing is expected to reduce sea ice imports into the Barents Sea (and therefore the SIC), which could set up a positive feedback loop between atmospheric forcing and the Barents Sea SIC ("wind-sea ice feedback"). The resulting changes in the local winds could contribute to the weakening of the BSOut. Additionally, if the BSOut weakening trend implies an increased occurrence of reversed flow in the BSOut (net inflow into the Barents Sea) as described by Lien et al. (2013), the presence of AW further north in the Barents Sea should also reduce the SIC.

To discuss the contribution of such a feedback on the BSOut weakening trend, we present the SIC and wind stress curl trends, obtained from the ERA5 atmospheric reanalysis in Figure 10. The analysis of SLP data over the study period does not allow us to conclude the significant SLP decrease in the northern Barents Sea, where the ice loss is most pronounced (see Figure 10a): the p-values for the SLP trend remain close to 0.15 over most of the region (not shown). However, significant trends are obtained around Svalbard when considering the wind stress curl (Figure 10b). Like in Figure 3d, they feature a cyclonic trend over the shelf southwest of Svalbard, which could impact the intensity of the BSOut as was shown in Section 3.2.1, and an anticyclonic trend over the Greenland Gyre, which could reduce the intensity of the WSC via the mechanism described by Chatterjee et al. (2018). The extent of significant wind stress curl trend on the Spitsbergen Bank is nevertheless quite reduced in Figure 9b, compared to the extent of anomalies of Figure 3d, which suggests that changes in the surface Ekman transports may only play a minor role in the BSOut weakening trend (visible in Figures 2 and 3a). The mass contribution to the SLA difference across the polar front, calculated in the model and presented in Figure 5b, shows no significant trend and also supports the argument of a second order role of changes in direct wind forcing - and of the “wind-sea ice feedback”—on the BSOut weakening trend.
<figure><img src="image_13.png" /><figcaption>**Figure 11.** Observed SLA (the mean trend showing sea level rise is removed) and surface geostrophic currents trends (altimetry). The hatches indicate the regions where the local SLA trend is significant at *p* < 0.05.</figcaption></figure>

### 3.3.2. Contribution of Decreasing Sea Ice Imports Into the Barents Sea

Figure 11 presents the trends in local SLA and geostrophic currents over the study period. The most visible feature is the contrast between the positive SLA trend in the southern Barents Sea and the negative SLA trend in the northern Barents Sea, resulting in a weakening of the BSOut. A significant decrease in the intensity of the WSC is also observed, consistent with the wind stress curl trend presented in Figure 10b. Among the two processes described in Section 3.2 - cyclonic wind forcing anomalies over Svalbard and reduced sea ice import into the Barents Sea modifying the density gradient -, the latter should play a more important role to explain the observed trend. This is supported by the calculation of the steric contribution to the OWI presented in Figure 5a, as both the total SLA difference and the steric contribution show similar trends (+0.94 mm/yr for the total SLA difference and +0.74 mm/yr for the steric contribution). Using hydrographic observations since 1970, Lind et al. (2018) showed that a strong decline of the sea ice import into the northern Barents Sea took place from the mid-2000s: they found that the mean 2010–2015 sea ice area import (resp. volume import) was around 40% (resp. 60%) lower than the 1979–2009 average. Figure 12 shows the trends and correlation between the SIC and BSOut indices. The two indices are very well correlated, with maximum values of *R* > 0.78 for lags between 0 and 5 months, and they present comparable trends. In particular, both the SIC and BSOut weakening trends may have intensified after 2004, over the period when Lind et al. (2018) found a decrease in sea ice import. Figures 12c and 12d also show the trends in SSS and SSD. The results confirm that these trends were associated with a strong salinity increase in the northern Barents Sea, consistent with the lower sea ice import and freshwater input. Figure 12c also shows positive salinity trends in the Nordic Seas, along the NwAC, as documented by Holliday et al. (2008) along the AW pathway, as well as in the Greenland Gyre and on the Greenland shelf. There, the sea ice export has been found to decrease since 1992 (Sreen et al., 2020). Figure 12c finally shows a significant freshening trend in the southern Barents Sea, which could also contribute to the change of density gradient across the polar front. The origin of this freshening trend is however beyond the scope of this work.

In the cold waters of the northern Barents sea, the higher surface salinity leads to a higher surface density (Figure 12d), while the surface density in the southern Barents Sea is found to decrease over the years due to a pronounced surface warming (Figure 12e) and freshening (Figure 12c). As shown in Section 3.2, this change in the meridional density gradient should contribute to the observed weakening of the BSOut along the polar front. The significant anticorrelation coefficient between surface density (Figure 12d) and SLA (Figure 11) trends in the Barents Sea supports this contribution (the pattern correlation, in other words the correlation between the geographical variations of the SSD and SLA trends, is *R* = -0.75 east of 17°E).
<figure><img src="image_14.png" /><figcaption>**Figure 12.** (a) Comparison between the 13-month-filtered sea ice cover (SIC) index (ERA5) and BSOut index (altimetry—the OWI is the sign-reversed BSOut index). This panel is similar to Figure 6c, but is sign-reversed and not detrended. The dashed lines indicate the trends between 1993 and 2003 and between 2004 and 2018. (b) Lagged correlation between the SIC and BSOut indices. The BSOut index lags the SIC index for positive lag values. Correlations are significant at *p* < 0.05 for all lag values. Calculated trends in (c) SSS, (d) SSD, and (e) SST over the period 1993–2020 (remote sensing and observations). The hatches show the region where the trend is significant at *p* < 0.05.</figcaption></figure>

**4. Summary and Conclusions**

In this article, we show that the variability of the BSOut can be linked to wind-induced Ekman transport on short time scales, and to changes in the density gradient across the polar front on long time scales. These two processes are summarized in Figure 13. The existence of the causal relations in the chain of events linking sea ice import anomalies to the BSOut volume transport was further investigated using an ocean-sea ice model, which confirmed the existence of important correlations or lagged correlations between them. Reversed or more complex causal
<figure><img src="image_15.png" /><figcaption>**Figure 13.** Summary diagram of the mechanisms involved in the weakening of the BSOut.</figcaption></figure>

links may however still be possible, for example, when a net inflow is found at the BSOut location, bringing potentially more heat and salt to the northern Barents Sea. We argue here that the variability of the BSOut is in fact tightly linked with the ongoing Atlantification of the region.

Atlantification in the northern Barents Sea can be understood as the imbalance between the competing influences of increasingly warm and salty AW transported from the south through the Nordic Seas and cold and fresh ArW from the north (Lind et al., 2018). The BSOut, which circulates along the polar front in the Barents Sea, is - at least partially - driven by the contrasts between these two water masses, and is therefore a symptom of the ongoing Atlantification trend in the northern Barents Sea. Conversely, it may also constitute a barrier to limit the AW input into the northern Barents Sea: in periods of BSOut weakening, an AW inflow branch may form at the Bear Island shelf break, which could accelerate the transition of the northern Barents Sea to a warmer, saltier and less stratified state. The BSOut would then act as a switch between two states of the northern Barents Sea: (a) a mostly sea ice-covered "Arctic state" with a circulation at the polar front containing the AW to the south of the Barents Sea before recirculating back to the Nordic Seas; and (b) an almost ice-free "Atlantified state" where AW inflows are found along the polar front, affecting the northern Barents Sea.

In the WSC, the weakening trend in the BSOut may also accelerate the transition of the North Svalbard from a seasonally ice-free state to an annually ice-free state (Athanase et al., 2020). It should modify the Fram Strait vertical temperature structure with an enhanced surface warming along the west Spitsbergen shelf and a reduced warming—or even a cooling—in the intermediate waters. Though the whole region is warming, the SST trends presented in Figure 12e confirm that the fastest warming is observed around Svalbard, and more specifically along the West Spitsbergen shelf break. There, the SST warming trend reaches 1°C/decade. The heat concentrated in the surface layers may be lost more easily downstream of the Fram Strait, as the AW enters the Eurasian Basin following the topography of the continental slope north of Svalbard. Future studies will therefore be needed to understand the links between the BSOut and the Atlantification of the North Svalbard and the northern Barents Sea, in order to anticipate the future sea ice decline in the Eurasian Arctic.

## Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

## Data Availability Statement

The satellite altimetry data and sea surface temperature, salinity and density products can be downloaded from the Copernicus Marine Service website (Consiglio Nazionale delle Ricerche, 2023; Copernicus Marine Service, 2023a, 2023b). The SLP, wind and sea ice concentration can be downloaded from the Copernicus Climate data store (Hersbach et al., 2023). Raw temperature data from the AWI/NPI moorings in the Fram Strait can be found in De Steur (2019, 2021) and Von Appen et al. (2019). The model outputs can be found on zenodo (Herbaut et al., 2024).
Acknowledgments

The work presented in this article was also
made possible thanks to the help and
support of Laurent Bertino (Nansen
Environmental and Remote Sensing
Center, Bergen). This work was granted
access to HPC resources of CINES under
allocation A0120100239 attributed by
GENCI. A. Bonaduce and R. P. Raj are
respectively supported by the Sea Level
Budget Closure CCI+ project and the Sea
Surface Salinity CCI+ project funded by
the European Space Agency. This work
benefited from the comments and
suggestions of two anonymous reviewers,
which helped to improve the manuscript.

References

Allen, J. S. (1975). Coastal trapped waves in a stratified ocean. *Journal of Physical Oceanography*, 5(2), 300–325. https://doi.org/10.1175/1520-0485(1975)005<0300:CTWIAS>2.0:CO;2

Årthun, M., Eldevik, T., Smedsrud, L. H., Skagseth, Ø., & Ingvaldsen, R. B. (2012). Quantifying the influence of Atlantic heat on Barents Sea ice variability and retreat. *Journal of Climate*, 25(13), 4736–4743. https://doi.org/10.1175/jcli-d-11-00466.1

Årthun, M., Ingvaldsen, R., Smedsrud, L., & Schrum, C. (2011). Dense water formation and circulation in the Barents Sea. *Deep-Sea Research, 58*(8), 801–817. https://doi.org/10.1016/j.dsr.2011.06.001

Athanase, M., Provost, C., Pérez-Hernández, M. D., Sennéchael, N., Bertosio, C., Artana, C., et al. (2020). Atlantic water modification north of Svalbard in the Mercator physical system from 2007 to 2020. *Journal of Geophysical Research: Oceans, 125*(10), 2169–9275. https://doi.org/10.1029/2020JC016463

Barton, B. I., Lenn, Y., & Lique, C. (2018). Observed Atlantification of the Barents Sea causes the Polar Front to limit the expansion of winter sea ice. *Journal of Physical Oceanography*, 48(8), 1849–1866. https://doi.org/10.1175/JPO-D-18-0003.1

Beszczynska-Möller, A., Fahrbach, E., Schauer, U., & Hansen, E. (2012). Variability in Atlantic Water temperature and transport at the entrance to the Arctic Ocean, 1997–2010. *ICES Journal of Marine Science*, 69(5), 852–863. https://doi.org/10.1093/icesjms/fss056

Blindheim, J., & Rey, F. (2004). Water-mass formation and distribution in the Nordic Seas during the 1990s. *ICES Journal of Marine Science*, 61(5), 846–863. https://doi.org/10.1016/j.icesjms.2004.05.003

Broomé, S., Chafik, L., & Nilsson, J. (2021). A satellite-based Lagrangian perspective on Atlantic Water fractionation between Arctic gateways. *Journal of Geophysical Research: Oceans*, 126(11), e2021JC017248. https://doi.org/10.1029/2021JC017248

Buongiorno Nardelli, B. B., Droghei, R., & Santoleri, R. (2016). Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data. *Remote Sensing of Environment*, 180, 392–402. https://doi.org/10.1016/j.rse.2015.12.052

Carmack, E., Polyakov, I., Padman, L., Fer, I., Hunke, E., Hutchings, J., et al. (2015). Toward quantifying the increasing role of oceanic heat in sea ice loss in the new Arctic. *Bulletin of the American Meteorological Society*, 96(12), 2079–2105. https://doi.org/10.1175/BAMS-D-13-00177.1

Chafik, L., Nilsen, J. E. Ø., & Dangendorf, S. (2017). Impact of North Atlantic teleconnection patterns on Northern European sea level. *Journal of Marine Science and Engineering*, 5(3), 43. https://doi.org/10.3390/jmse5030043

Chafik, L., Nilsson, J., Skagseth, Ø., & Lundberg, P. (2015). On the flow of Atlantic water and temperature anomalies in the Nordic Seas toward the Arctic Ocean. *Journal of Geophysical Research: Oceans*, 120(12), 7897–7918. https://doi.org/10.1002/2015JC011012

Challet, F., Geyer, F., Bertino, L., & Raj, R. P. (2022). Physical oceanography of the Fram Strait: Improving general understanding and models through the use of mooring interpolation products, *NERSC Technical Report 420* (p. 57).

Chatterjee, S., Raj, R. P., Bertino, L., Skagseth, Ø., Ravichandran, M., & Johannessen, O. M. (2018). Role of Greenland Sea gyre circulation on Atlantic Water temperature variability in the Fram Strait. *Geophysical Research Letters*, 45(16), 8399–8406. https://doi.org/10.1029/2018GL079174

Consiglio Nazionale delle Ricerche. (2023). Multi observation global ocean sea surface salinity and sea surface density [Dataset]. E.U. *Copernicus Marine Service Information (CMEMS)*. Marine Data Store (MDS). https://doi.org/10.48670/moi-0005

Copernicus Marine Service. (2023a). Global ocean gridded L4 sea surface heights and derived variables reprocessed 1993 ongoing [Dataset]. E.U. *Copernicus Marine Service Information (CMEMS)*. Marine Data Store (MDS). https://doi.org/10.48670/moi-00148

Copernicus Marine Service. (2023b). Multi observation global ocean 3D temperature salinity height geostrophic current and MLD [Dataset]. E.U. *Copernicus Marine Service Information (CMEMS)*. Marine Data Store (MDS). https://doi.org/10.48670/moi-00052

Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011). The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. *Quarterly Journal of the Royal Meteorological Society*, 137(656), 553–597. https://doi.org/10.1002/qj.828

De Steur, L. (2019). Moored current meter data from the western Fram Strait 1997–2009 [Dataset]. *Norwegian Polar Institute*. https://doi.org/10.21334/npolar.2019.8bb85388

De Steur, L. (2021). Moored current meter and hydrographic data from the Fram Strait Arctic Outflow Observatory since 2009 [Dataset]. *Norwegian Polar Institute*. https://doi.org/10.21334/npolar.2021.c4d80b64

Droghei, R., Buongiorno Nardelli, B., & Santoleri, R. (2016). Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface. *Journal of Atmospheric and Oceanic Technology*, 33(6), 1211–1223. https://doi.org/10.1175/JTECH-D-15-0194.1

Droghei, R., Buongiorno Nardelli, B., & Santoleri, R. (2018). A new global sea surface salinity and density dataset from multivariate observations (1993–2016). *Frontiers in Marine Science*, 5, 1–13. https://doi.org/10.3389/fmars.2018.00084

Efstathiou, E., Eldevik, T., Årthun, M., & Lind, S. (2022). Spatial patterns, mechanisms, and predictability of Barents Sea ice change. *Journal of Climate*, 35, 2961–2973. https://doi.org/10.1175/JCLI-D-21-0044.1

Furevik, T. (2001). Annual and interannual variability of Atlantic Water temperatures in the Norwegian and Barents Seas: 1980–1996. *Deep Sea Research Part I: Oceanographic Research Papers*, 48(2), 383–404. https://doi.org/10.1016/S0967-0637(00)00050-9

Geyer, F., Gopalakrishnan, G., Sagen, H., Cornuelle, B., Challet, F., & Mazloff, M. (2023). Data assimilation of range- and depth-averaged sound speed from acoustic tomography measurements in Fram Strait. *Journal of Atmospheric and Oceanic Technology*, 40(9), 1023–1036. https://doi.org/10.1175/JTECH-D-22-0132.1

Guinehut, S., Dhomps, A.-L., Larnicol, G., & Le Traon, P.-Y. (2012). High resolution 3D temperature and salinity fields derived from in situ and satellite observations. *Ocean Science*, 8(5), 845–857. https://doi.org/10.5194/os-8-845-2012

Herbaut, C., Blaizot, A.-C., & Houssais, M.-N. (2024). Arctic-North Atlantic ocean-sea ice model: 1/4° resolution [Dataset]. *Zenodo*. https://doi.org/10.5281/zenodo.10630713

Herbaut, C., Houssais, M.-N., Close, S., & Blaizot, A.-C. (2015). Two wind-driven modes of winter sea ice variability in the Barents Sea. *Deep Sea Research Part I: Oceanographic Research Papers*, 106, 97–115. https://doi.org/10.1016/j.dsr.2015.10.005

Herbaut, C., Houssais, M.-N., Close, S., & Blaizot, A.-C. (2017). On the spatial coherence of the Atlantic Water inflow across the Nordic Seas. *Journal of Geophysical Research: Oceans*, 122(5), 4346–4363. https://doi.org/10.1002/2016JC012566

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., et al. (2023). ERA5 monthly averaged data on single levels from 1940 to present [Dataset]. *Copernicus Climate Change Service (C3S) Climate Data Store (CDS)*. https://doi.org/10.24381/cds.f17050d7

Heukamp, F. O., Kanzow, T., Wang, Q., Wekerle, C., & Gerdes, R. (2023). Impact of cyclical wind anomalies caused by massive winter sea ice retreat in the Barents Sea on Atlantic Water transport toward the Arctic: A model study. *Journal of Geophysical Research: Oceans*, 128(3), e202JC019045. https://doi.org/10.1029/2022JC019045

Holliday, N. P., Hughes, S. L., Bacon, S., Beszczynska-Möller, A., Hansen, B., Lavín, A., et al. (2008). Reversal of the 1960s to 1990s freshening trend in the northeast North Atlantic and Nordic Seas. *Geophysical Research Letters*, 35(3), L03614. https://doi.org/10.1029/2007GL032675
Ingvaldsen, R. (2020). Mooring data from the Barents Sea Opening - Atlantic Water inflow [Dataset]. https://doi.org/10.21335/NMDC-1838527821

Ingvaldsen, R. B., Asplin, L., & Loeng, H. (2004). Velocity field of the western entrance to the Barents Sea. *Journal of Geophysical Research*, 109(C3), C03021. https://doi.org/10.1029/2003JC001811

Isachsen, P. E., LaCasce, J. H., Mauritzen, C., & Häkkinen, S. (2003). Wind-driven variability of the large-scale recirculating flow in the Nordic Seas and Arctic Ocean. *Journal of Physical Oceanography*, 33(12), 2534–2550. https://doi.org/10.1175/1520-0485(2003)033<2534:WVOTLR>2.0.CO;2

Lien, V., Vikebø, F., & Skagseth, Ø. (2013). One mechanism contributing to co-variability of the Atlantic inflow branches to the Arctic. *Nature Communications*, 4(1), 1488. https://doi.org/10.1038/ncomms2505

Lien, V. S., Hjøllo, S. S., Skogen, M. D., Svendsen, E., Wehde, H., Bertino, L., et al. (2016). An assessment of the added value from data assimilation on modelled Nordic Seas hydrography and ocean transports. *Ocean Modelling*, 99, 43–59. https://doi.org/10.1016/j.oceemod.2015.12.010

Lien, V. S., Raj, R. P., & Chatterjee, S. (2021). Modelled sea-ice volume and area transport from the Arctic Ocean to the Nordic and Barents Seas. *Journal of Operational Oceanography*, 14, S10–S17.

Lind, S., Ingvaldsen, R. B., & Furevik, T. (2018). Arctic warming hotspot in the northern Barents Sea linked to declining sea-ice import. *Nature Clim Change*, 8(7), 634–639. https://doi.org/10.1038/s41558-018-0205-y

Madec, G. (2008). NEMO ocean engine, version 3.0, *Note du Pôle de modélisation de l'Institut Pierre-Simon Laplace* (Vol. 27, p. 217).

Mangini, F., Bonaduce, A., Chafik, L., Raj, R., & Bertino, L. (2023). Detection and attribution of intra-annual mass component of sea-level variations along the Norwegian coast. *Scientific Reports*, 13(1), 15334. https://doi.org/10.1038/s41598-023-40853-8

Marnela, M., Rudels, B., Houssais, M.-N., Beszczynska-Möller, A., & Eriksson, P. B. (2013). Recirculation in the Fram Strait and transports of water in and north of the Fram Strait derived from CTD data. *Ocean Science*, 9(3), 499–519. https://doi.org/10.5194/os-9-499-2013

McPherson, R. A., Wekerle, C., & Kanzow, T. (2023). Shifts of the recirculation pathways in central Fram Strait drive Atlantic intermediate water variability on Northeast Greenland shelf. *Journal of Geophysical Research: Oceans*, 128(10), e2023JC019915. https://doi.org/10.1029/2023JC019915

Mertz, G., & Wright, D. G. (1992). Interpretations of the JEBAR Term. *Journal of Physical Oceanography*, 22(3), 301–305. https://doi.org/10.1175/1520-0485(1992)022<0301:JOTJT>2.0.CO;2

Mulet, S., Rio, M.-H., Mignot, A., Guinehut, S., & Morrow, R. (2012). A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. *Deep Sea Research Part II: Topical Studies in Oceanography*, 77–80(0), 70–81. https://doi.org/10.1016/j.dsr2.2012.04.012

Nippon Foundation-GEBCO. (2022). General Bathymetric Chart of the Oceans (GEBCO) Grid [Dataset]. https://doi.org/10.5285/e0f0bb80-ab44-2739-e053-6c86abc0289c

Nøst, O. A., & Isachsen, P. E. (2003). The large-scale time-mean ocean circulation in the Nordic Seas and Arctic Ocean estimated from simplified dynamics. *Journal of Marine Research*, 61(2), 175–210. https://doi.org/10.1357/00224003322005069

Onarheim, I. H., & Arthun, M. (2017). Toward an ice-free Barents Sea. *Geophysical Research Letters*, 44, 8387–8395. https://doi.org/10.1002/2017GL074304

Orvik, K. A., & Niiler, P. (2002). Major pathways of Atlantic water in the northern North Atlantic and Nordic Seas toward Arctic. *Geophysical Research Letters*, 29(19), 1896. https://doi.org/10.1029/2002GL015002

Overland, J., Dunlea, E., Box, J. E., Corell, R., Forsius, M., Kattsov, V., et al. (2019). The urgency of Arctic change. *Polar Science*, 21, 6–13. https://doi.org/10.1016/j.polar.2018.11.008

Rainville, L., Lee, C. M., & Woodgate, R. A. (2011). Impact of wind-driven mixing in the Arctic Ocean. *Oceanography*, 24(3), 136–145. https://doi.org/10.5670/oceanog.2011.65

Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., et al. (2022). The Arctic has warmed nearly four times faster than the globe since 1979. *Communications Earth & Environment*, 3(1), 168. https://doi.org/10.1038/s43247-022-00498-3

Rieke, O., Arthun, M., & Dörr, J. S. (2023). Rapid sea ice changes in the future Barents Sea. *The Cryosphere*, 17(4), 1445–1456. https://doi.org/10.5194/tc-17-1445-2023

Rousset, C., Vancoppenolle, M., Madec, G., Fichefet, T., Flavoni, S., Barthélemy, A., et al. (2015). The Louvain-La-Neuve sea ice model LIM3.6: Global and regional capabilities. *Geoscientific Model Development*, 8(10), 2991–3005. https://doi.org/10.5194/gmd-8-2991-2015

Rudels, B., Korhonen, M., Schauer, U., Pisarev, S., Rabe, B., & Wisotzki, A. (2015). Circulation and transformation of Atlantic water in the Eurasian Basin and the contribution of the Fram Strait inflow branch to the Arctic Ocean heat budget. *Progress in Oceanography*, 132, 128–152. https://doi.org/10.1016/j.pocean.2014.04.003

Sammartino, M., Aronica, S., Santoleri, R., & Nardelli, B. B. (2022). Retrieving Mediterranean sea surface salinity distribution and interannual trends from multi-sensor satellite and in situ data. *Remote Sensing*, 14(10), 2502. https://doi.org/10.3390/rs14102502

Santer, B. D., Wigley, T. M. L., Boyle, J. S., Gaffen, D. J., Hnilo, J. J., Nychka, D., et al. (2000). Statistical significance of trends and trend differences in layer-average atmospheric temperature time series. *Journal of Geophysical Research*, 105(D6), 7337–7356. https://doi.org/10.1029/1999JD901105

Screen, J., & Simmonds, I. (2010). The central role of diminishing sea ice in recent Arctic temperature amplification. *Nature*, 464(7293), 1334–1337. https://doi.org/10.1038/nature09051

Siegismund, F., Johannessen, J., Orange, H., Mork, K. A., & Korablev, A. (2007). Steric height variability in the Nordic Seas. *Journal of Geophysical Research*, 112(C12), C12010. https://doi.org/10.1029/2007JC004221

Skagseth, Ø. (2008). Recirculation of Atlantic Water in the western Barents Sea. *Geophysical Research Letters*, 35(11), L11606. https://doi.org/10.1029/2008GL033785

Skagseth, Ø., Furevik, T., Ingvaldsen, R., Loeng, H., Mork, K. A., Orvik, K. A., & Ozhigin, V. (2008). Volume and heat transports to the Arctic Ocean via the Norwegian and Barents Seas. In R. R. Dickson, J. Meincke, & P. Rhines (Eds.), *Arctic–Subarctic Ocean Fluxes*. Springer. https://doi.org/10.1007/978-1-4020-6774-7_3

Sreen, G., de Steur, L., Divine, D., Gerland, S., Hansen, E., & Kwok, R. (2020). Arctic sea ice volume export through Fram Strait from 1992 to 2014. *Journal of Geophysical Research: Oceans*, 125(6), e2019JC016039. https://doi.org/10.1029/2019JC016039

Strand, E., Bagøien, E., Edwards, M., Broms, C., & Klevjer, T. (2020). Spatial distributions and seasonality of four Calanus species in the Northeast Atlantic. *Progress in Oceanography*, 185, 102344. https://doi.org/10.1016/j.pocean.2020.102344

Strzelewicz, A., Przyborska, A., & Walczowski, W. (2022). Increased presence of Atlantic Water on the shelf south-west of Spitsbergen with implications for the Arctic fjord Hornsund. *Progress in Oceanography*, 200, 102714. https://doi.org/10.1016/j.pocean.2021.102714

Von Appen, W., Schauer, U., Hattermann, T., & Beszczynska-Möller, A. (2016). Seasonal cycle of mesoscale instability of the West Spitsbergen Current. *Journal of Physical Oceanography*, 46(4), 1231–1254. https://doi.org/10.1175/JPO-D-15-0184.1
Von Appen, W.-J., Beszczynska-Möller, A., Schauer, U., & Fahrbach, E. (2019). Physical oceanography and current meter data from moorings F1-F14 and F15/F16 in the Fram Strait, 1997-2016 [Dataset]. PANGAEA. https://doi.org/10.1594/PANGAEA.900883

Von Storch, H. (1998). *Statistical analysis in climate research/Hans von Storch and Francis W. Zwiers*. Cambridge University Press.

Wekerle, C., Wang, Q., von Appen, W.-J., Danilov, S., Schourup-Kristensen, V., & Jung, T. (2017). Eddy-resolving simulation of the Atlantic Water circulation in the Fram Strait with focus on the seasonal cycle. *Journal of Geophysical Research: Oceans*, 122(11), 8385–8405. https://doi.org/10.1002/2017JC012974

Yeager, S. (2015). Topographic coupling of the Atlantic Overturning and gyre circulations. *Journal of Physical Oceanography*, 45(5), 1258–1284. https://doi.org/10.1175/JPO-D-14-0100.1