# The Life Cycle of the Low Salinity Lenses at the Surface of the Arctic Ocean

Clement van Straaten, Camille Lique, Nicolas Kolodziejcyk

► To cite this version:

Clement van Straaten, Camille Lique, Nicolas Kolodziejcyk. The Life Cycle of the Low Salinity Lenses at the Surface of the Arctic Ocean. Journal of Geophysical Research. Oceans, 2025, 130 (4), e2024JC021699 (18p.). 10.1029/2024JC021699. hal-05063285

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Submitted on 22 May 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# JGR Oceans

## RESEARCH ARTICLE

10.1029/2024JC021699

**Key Points:**

* Arctic surface low salinity lenses are detected and tracked in a high resolution regional ocean-sea ice model

* Lenses are ubiquitous and primarily formed during summer due to sea ice melt, river discharge, and eddies

* Throughout their week-to-month life, lenses experience larger summer sea ice melt flux and winter sea ice formation than their surroundings

**Supporting Information:**

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

**Correspondence to:**

C. Van Straaten,
clement.vanstraaten@univ-brest.fr

**Citation:**

Van Straaten, C., Lique, C., & Kolodziejcyk, N. (2025). The life cycle of the low salinity lenses at the surface of the Arctic Ocean. *Journal of Geophysical Research: Oceans, 130*, e2024JC021699.
https://doi.org/10.1029/2024JC021699

Received 9 AUG 2024
Accepted 15 MAR 2025

**Author Contributions:**

**Methodology:** Clément Van Straaten

**Software:** Camille Lique

**Supervision:** Camille Lique,
Nicolas Kolodziejcyk

**Validation:** Camille Lique,
Nicolas Kolodziejcyk

**Writing – original draft:**
Clément Van Straaten

**Writing – review & editing:**
Camille Lique, Nicolas Kolodziejcyk

© 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.

# The Life Cycle of the Low Salinity Lenses at the Surface of the Arctic Ocean

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

Clément Van Straaten¹ ID, Camille Lique¹ ID, and Nicolas Kolodziejcyk¹

¹Laboratoire d'Océanographie Physique et Spatiale (LOPS), University Brest, CNRS, Ifremer, IRD, IUEM, Plouzané, France

**Abstract** In the Arctic Ocean, coherent low salinity anomalies, known as lenses, are often observed at the surface and are thought to result from the input of large amounts of freshwater from sea ice melting and river runoff. In this study, we perform a systematic detection of these lenses and track their displacements in a 21-year simulation performed with a high resolution ocean-sea ice regional model of the Arctic in order to gain a better understanding of their life cycle. Two thirds of the lenses are formed during summer, in response to sea ice melt, river discharge, or are colocated with mesoscale eddies. They are then able to persist for weeks to months, traveling long distance across the basin as their characteristic surface salinity anomalies get eroded through vertical processes. Throughout their life, the lenses are associated with larger sea ice melting flux during summer and concentrate stronger sea ice formation than their surroundings by up to 50%. Over the 21-year period, the number of lenses has increased by ~2%/year over the Arctic Ocean, and the formation locations have shifted following the retreat of the sea ice edge in regions such as Greenland, Barents, and Chukchi seas. Our results suggest that these localized, intermittent and coherent lenses may be important for the large scale Arctic dynamics and the ocean-sea ice interaction.

**Plain Language Summary** In the cold Arctic Ocean, seawater salinity controls the ocean currents and how the ocean interacts with sea ice. Surface salinity observations have revealed the presence of lenses fresher than typical seawater. As salinity measurements are scarce in the Arctic, we detect and track the lenses in a regional high resolution model to better understand the origin and importance of these lenses. Overall, the lenses tend to appear close to the coasts between April and September and die between August and April in the deep Arctic basin or outside the Arctic. Their lifespan depends on their size, salinity, and how much sea ice covers them. Lenses influence sea ice: during summer, they concentrate the sea ice melting, whereas during winter, they favor sea ice freezing above them. Over the 21-year period, both the number and size of lenses have grown. This trend suggests that as the Arctic becomes ice-free in summer, lenses will become more common.

## 1. Introduction

Over the last decades, the rapid and large reduction of the Arctic sea ice cover is one of the most striking sig- natures of climate change in the Arctic region (Masson-Delmotte et al., 2021), leading to an additional input of low-salinity water at the ocean surface due to the increased sea ice melt (referred to as a freshwater input in the following, although we acknowledge that it is not strictly freshwater as the sea ice salinity is typically around 4–6 pss). Moreover, as the Arctic transitions toward a seasonal ice cover, the melting season lengthens and the marginal ice zone (MIZ) spreads drastically (Haine & Martin, 2017), resulting in intensified air-sea ice-ocean interactions. In addition, the Arctic warming amplifies the freshwater cycle with increased precipitation and river discharge into the surface of the Arctic Ocean (Bintanja, 2018).

Overall, the additional surface freshwater input resulting from the changes in sea ice conditions and freshwater cycle are of particular importance in the Arctic Ocean as they affect the ocean salinity of the surface layer, which primarily controls the ocean stratification and thus the ocean dynamics (Carmack, 2007). As such, gaining a better understanding of the spatial and temporal distribution of the Arctic surface salinity is of utmost importance if we are to better comprehend the Arctic ocean dynamics, the freshwater budget and exports to the lower latitudes, the ocean-sea ice interactions, and better predict their future evolution.

During summer, sea ice melting releases a large amount of freshwater both under sea ice and in the ice free regions (Steele & Ermold, 2015), which strongly modulates the salinity and the depth of the shallow surface mixed layer (~5–30 m Peralta-Ferriz & Woodgate, 2015). The presence of meltwater layers (which typically have the same
depth as the mixed layer) results in a large near-surface salinity-dominated stratification (Dewey et al., 2017). These strongly stratified layers have large impacts on the air-sea and ice-ocean interactions. For instance, it can act as a barrier between the atmosphere and the ocean surface, impeding the momentum flux to the ocean, and the heat release from the mixed layer, thus resulting in a significant amount of solar radiation and heat trapped below the meltwater layer or the mixed layer (Timmermans, 2015). In addition, the persistence of meltwater lenses at the surface could locally favor the refreezing of sea ice during the following winter (Crews et al., 2022). We note that thinner meltwater layers (typically on the order of 0.1–1.0 m) are also routinely observed at the Arctic surface (Smith et al., 2023) but they are distinct in scale and typical salinity from the features of interest in the present paper.

Despite their potential importance for the Arctic dynamics, there are only a few localized events of melt and associated low sea surface salinity (SSS) signature documented and studied in the recent literature. Dewey et al. (2017) have examined the 1-D vertical dynamics of a low salinity layer close to the sea ice edge in the Beaufort Gyre. Supply et al. (2022) have used satellite L-Band observations of SSS to document how sea ice melt can result in localized strong salinity anomalies (up to 5 pss) that spread over tens of kilometers and can survive over a few weeks. In the Southern Ocean, fine scale and eddy dynamics associated with sea ice melt have been observed from gliders (e.g., Biddle & Swart, 2020). Moreover, observed low SSS anomalies are likely not solely the signature of intense sea ice melt: they can result from river runoff over the Arctic shelves (Matsuoka et al., 2016; Tarasenko et al., 2021) or can be the SSS signature of surface eddies commonly observed in the Arctic (Cassianides et al., 2021; Kozlov et al., 2019). In contrast with the heat fluxes, the freshwater fluxes are, by nature, localized and intense; therefore, they result in ubiquitous coherent and buoyant low SSS signature at the ocean surface.

Although some large negative SSS anomalies are widely encountered in the Arctic, we are still lacking a comprehensive picture of their importance at the scale of the Arctic Basin. This requires a description of their 3-D dynamics, including their regional variability and coherence, their life cycle, their impact on sea ice melting/ formation, and their importance for the variations of Arctic freshwater budget on a seasonal timescale.

In this context, the objective of this study is to provide a comprehensive description of the coherent low SSS anomalies in the Arctic, and their evolution over the recent period. To that aim, we analyze outputs from a simulation performed with a high-resolution regional numerical model, conducting a detection and tracking of coherent low SSS anomalies (hereafter referred to as “lenses”) in order to determine their characteristics and their temporal and spatial evolution.

The remainder of this paper is structured as follows. Section 2 briefly presents the numerical model and simulation analyzed in this study as well as the detection and tracking methods. In Section 3, we illustrate the life cycle of a typical single lens before generalizing to all the lenses detected in the model outputs (Section 4). Section 5 provides a discussion of the importance of the lenses for the Arctic dynamics. Conclusions are given in Section 6.

## 2. Data and Methods

### 2.1. Numerical Model and Simulation

Our analysis uses a simulation performed with the high-resolution regional Arctic-North Atlantic model configuration named CREG12 (Canadian Regional; Dupont et al., 2015). It is based on the NEMO 3.6 (Madec, G. and the NEMO System Team, 2016) and LIM 3.5 (Rousset et al., 2015) numerical models for the ocean and sea ice components, respectively. The configuration covers the Arctic Basin and part of the North Atlantic (down to 27°N). It has 75 vertical levels with 8 levels in the first 10 m of the water column. The model grid is distorted close to the North Pole, thus the horizontal resolution is not constant in the Arctic Ocean but varies between 3 and 5 km, meaning that baroclinic eddies are resolved everywhere in the Arctic except on the shallow shelves (Meneghello et al., 2021; Regan et al., 2020). Parameterizations include a Laplacian mixing of temperature and salinity along isopycnals and a horizontal biharmonic viscosity. Vertical mixing is based on a turbulence closure scheme (TKE) (Blanke & Delecluse, 1993), and its numerical implementation follows closely the description made by Allende et al. (2024) (their control simulation). The representation of tidal mixing effects is included in a comprehensive parameterization of mixing by breaking internal tides and lee waves (de Lavergne et al., 2019).

Initial conditions are taken from the World Ocean Atlas 2009 climatology of temperature and salinity. The initial sea ice thickness and concentration are taken from a long global ORCA12 simulation performed by the Drakkar group (Treguier et al., 2014). Along the lateral open boundaries, monthly mean conditions (comprising 3D
<figure><img src="image_3.png" /><figcaption>**Figure 1.** (a) Map showing the 5 regions of interest of our analysis: the Barents Sea, the Greenland Sea, the Deep Basins, the Siberian Seas, and the Beaufort/Chuckchi Sea. The full domain considered in our analysis corresponds to the 5 regions plus the domain colored in gray and covers 15,929,554 km². (b) Map of the mean SSS over 1994–2014. (c) Map of the SSS anomaly on 4 February 2008. The yellow contours delineate the detected lenses on that date with their barycenter indicated by a colored cross. (d) Map of the SSS anomaly threshold used for the lens detection. In panels (a, b), the white contours indicate the 500 and 2,000 m isobaths.</figcaption></figure>

velocities, temperature and salinity, and sea ice thickness and concentration) taken from the same ORCA12 simulation are applied. The simulation is forced with the latest version of the Drakkar Forcing Set (DFS 5.2, which is an updated version of the atmospheric forcing described in Brodeau et al. (2010)). Interannual monthly varying inputs from the river and ice sheet runoffs are taken from Hu et al. (2019) and include the large and increasing contribution from Greenland ice sheet melt.

The simulation covers the period from 1979 to 2014 and is described by Talandier and Lique (2021). An extended evaluation of the ocean and sea ice conditions in the Arctic Basin can be found by Regan et al. (2020) and Barton et al. (2022). In particular, the simulation was shown to capture well the observed variability of the Beaufort Gyre (including the changes of circulation and stratification), and the water mass properties and surface frontal dynamics observed in the Barents Sea. In the following, we focus on the final 21-year period starting in 1994 to allow for an initial spin-up of the ocean and sea ice conditions. Our analysis is based on the 5-day average outputs.

## 2.2. Lens Definition and Detection Method

Our method of lens detection is solely based on their associated signature in SSS. The model SSS averaged over 1994–2014 is shown in Figure 1b. It reveals large contrasts in the Arctic Basin with very fresh surface waters on the Siberian shelves (~20 pss), and a gradient toward saltier values on the Atlantic side. To perform our detection, we first build a SSS climatological year. The 73 5-day mean outputs per year are averaged across the 21 years to
obtain a 5-day varying climatological SSS field. The SSS anomaly is then estimated as a difference from this climatology at each grid point. An example of the SSS anomaly map obtained for 4 February 2008 is shown in Figure 1c. This anomaly definition allows us to take into account the large seasonal cycle in SSS. In contrast, considering anomalies against a long-term mean or an annual mean would introduce a large seasonal bias in our detection particularly on the shallow shelves, where SSS lowers strongly in summer in response to sea ice melt and river discharge, which would results in the detection of very large lenses spreading over most of the shelves. Second, we define a lens as a closed contour of negative SSS anomaly stronger than a given threshold. Figure 1c shows that the SSS anomaly exhibits some large spatial variations on that date, varying between -3 and 3 pss depending on the region considered. To account for the large regional variability, we choose to use a spatially variable threshold for our detection, defined as the value corresponding to the 5% quantile of the SSS distribution at each grid point over the full period. The spatially averaged threshold is around -1.8 pss but varies from below -1 to -4 pss from the Nordic seas to close to the Arctic river mouths, respectively (Figure 1d). Overall, the absolute value threshold tends to be larger in regions characterized by the weaker mean SSS (Figure 1b), which are also the regions with the largest SSS standard deviations (as high as 3 pss; not shown). Moreover, we have performed some sensitivity analysis to the choice of the threshold (by testing several quantile values of 2% and 10%; see Figure S1 in Supporting Information S1), which results in variations in the number of detected lenses by up to 50% (with larger differences found in the number of winter births). However, the spatial and temporal distributions of the lens field remain coherent regardless of the quantile chosen for the detection. The choice of a 5% quantile appears to be a good compromise to capture only the extreme anomalies while detecting enough events for our analysis to be statistically robust, although we acknowledge that this choice is somewhat arbitrary. Applying this method results in the detection of numerous lenses at each time step with varying sizes and persistence timescales. As we are primarily interested in the long-lived coherent lenses, we further apply two criteria to eliminate the most intermittent features from our detection: we only consider (a) lenses larger than 800 km² (corresponding roughly to 50 grid points) and (b) lenses surviving for at least 15 days (i.e., 3 consecutive model outputs based on the tracking algorithm described in the following section). The smaller and most intermittent lenses are thus not considered in our analysis, but our results are not strongly affected by these criteria. Indeed, the total surface covered by the lenses varies by less than 2% when considering the lenses of all sizes and lifespan (see Figures S1 and S2 in Supporting Information S1).

Once detected, we further assign the position of the center of the lens to the barycenter of the closed contour (Figure 1c). The properties of a given lens correspond to the average within the closed contour. To contrast the conditions within a lens and in its surrounding region, we compare the horizontally averaged properties within the lens with the spatial average over the larger region where the lens is located. To that aim, we split up the Arctic Basin into 5 main regions roughly based on bathymetry (largely following the 500 m isobath) and geographical regions (Figure 1a). The full domain used in our analysis encompasses the 5 regions in addition to the region shown in gray (Figure 1a).

## 2.3. Lens Tracking Method

To gain a deeper insight into the life cycle of the detected lenses, we develop a tracking method based on the movement of the position of the lens barycenter between two consecutive time steps (corresponding to two consecutive 5-day mean model outputs). For a given lens detected at one time step, we thus search for a lens at the following time step with similar characteristics. Two conditions are applied to connect two lenses detected at two consecutive time steps:

* In the following model output, 5 days later, we search for the closest lens with a barycenter that is within 80 grid points (which is roughly 320 km) of the initial barycenter. This distance primarily corresponds to the displacement and deformation of the lenses given their relatively slow displacement by a weak background flow (at a few cm/s, or roughly 10 km over 5 days, consistent with the weak surface velocities generally observed in the Arctic; Doglioni et al., 2023).

* We further require a minimum overlap of 10% of the grid cells between the lens detected at the two time steps. This condition is useful to improve the tracking of the smaller lenses that do not deform much over one time step. Sensitivity to the choice of this condition (set to 5% or 15%) results in changes of the number of detected lenses by less than 10% (see Table S1 in Supporting Information S1).
In some instances, the lenses can merge or split between two consecutive time steps. We thus introduce an additional categorization filter classifying lenses into three categories: merged, split, and new. Initially, all the lenses are labeled as “new.” In order to identify all the lenses resulting from a splitting event at a time step (t), we search for the lenses born at that time step (t) with a center located within the contour of a lens detected at the time step (t + 1). For the case with more than one lens center found within the contour, we have two options: (a) if the detected lenses at (t + 1) cannot be connected to the lens detected at (t) based on the above criteria, the lenses detected at (t + 1) are labeled as “split”; (b) if the properties of the lenses detected at (t + 1) allow us to connect them with the lens detected at (t), then the lens with the closest center at (t + 1) is assigned to be the same lens as the initial one at (t) while the other lens or lenses are labeled as “split.” To detect the merging events, we reverse the process. At a time step (t), we search for each lens if more than one lens center was found within the contour at the previous time step (t − 1). In this case, the lens is labeled as “merged.”

3. Life Cycle of a Single Lens Crossing the Arctic Basin

We start by examining the case of one lens detected with our method and tracked for nearly 2 years by our al-
gorithm. Among all the lenses detected in our simulation, this one stands out by its size, longevity, and journey. Its
examination is useful to exemplify the characteristics of a lens throughout its life cycle. The lens first appears on
22 September 1996 in the Laptev Sea, where it is characterized by an SSS anomaly of up to −5.3 pss and a size of
85,000 km² (Figures 2a and 2d). On 6 November 1996, it experiences a split into two lenses, after which we
continue to track the one with the closest center from the initial lens' center (first vertical line in Figures 2d–2h). It
is then advected within the transpolar drift and reaches the interior of the Eurasian Basin by 3 August 1996, where
it is associated with a local SSS anomaly larger than −2 pss (Figure 2b). Going forward in time, it reaches north of
Greenland where it is last seen on 19 June 1998 in the form of a small closed contour (11,101 km²; Figure 2c).
This means that, in total, the lens has traveled approximately 2,000 km in 2 years, corresponding to an average
advection speed of 3–3.5 km per day, which is roughly consistent with the ocean surface geostrophic velocities
found in this region (Doglioni et al., 2023).

We further examine the evolution of mean properties of the conditions within the lens that we compare with the conditions found in the surrounding region where the lens is located (the definition of the regions is shown in Figure 1a). Note that, on 21 December 1996, the lens changes the region to which it belongs (going from the Siberian Seas to the Deep Basins), inducing a discontinuity when examining the properties within and around the lens (second vertical line in Figures 2d–2h). As it forms in September 1996, the lens is covered by thick (∼1.5 m) and concentrated (∼90%) sea ice (Figure 2e). At the time of its formation, the surface freshwater flux from sea ice melt and freezing is positive indicating a period of freezing. This means that the lens is not resulting from sea ice melt. Given the proximity to the mouth of the Lena river and its large freshwater discharge including in winter (Feng et al., 2021), it is likely that the lens arises from the river plume advected on the shelves.

Throughout its 2-year lifespan, as it is advected under sea ice, the net surface freshwater flux averaged over the surface of the lens (corresponding to the net precipitation and the freshwater from sea ice melt and freezing) as well as the mean sea ice thickness and concentration within the lens follow the large-scale seasonal cycle of the sea ice pack, with thicker and concentrated sea ice in summer and sea ice formation in winter (Figures 2d and 2e). It is interesting to note that there are no significant differences in sea ice-induced freshwater flux between the regions inside and around the lens (Figures 2d and 2e) nor in sea ice conditions (except in summer 1997 when a concentration difference of 10% is visible). In contrast, sea ice is consistently thinner within the lens than the mean condition found in the deep basins (by about 30–80 cm). This is likely because the region north of Greenland with larger amount of thicker multiyear sea ice biases the mean over the large region toward larger values.

The time-evolution of the salinity profile averaged over the lens reveals the very fresh conditions found in the top 5–10 m when the lens is formed and the very strong surface stratification resulting from it (Figure 2f). This vertical extension of the lens corresponds largely to the mixed layer depth (MLD), defined as the depth with a density difference of 0.1 kg.m⁻³ from the surface density (Peralta-Ferriz & Woodgate, 2015). As the lens evolves, the surface stratification weakens and the MLD deepens again, meaning that the fresh lens expands over a deeper depth range but remains confined within the mixed layer. We then look at the evolution of the temperature and salinity profiles, contrasting again the average within the lens and in the surrounding regions (Figures 2f and 2g). As expected from the definition of a lens, the salinity within the mixed layer is fresher inside the lens by up to 3 pss at beginning of its life. The fresher surface also results in a stronger stratification over the surface layer, and thus a
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Maps of salinity anomalies on (a) 22 September 1996; (b) 3 August 1997; (c) 19 June 1998. The yellow contour shows the lens analyzed in Section 3, (d) Time series of the lens size (black line; right axis) and the net surface freshwater flux averaged within the lens (yellow dashed line; left axis) and over the region where the lens is detected (yellow dotted line; left axis). (e) Time series of the sea ice concentration and thickness averaged within the lens (blue and green dashed lines, respectively) and over the region where the lens is detected (blue and green dotted lines, respectively). Hovmöller diagrams of the salinity averaged profiles within the lens (f), and of the difference of salinity (g) and temperature (h) profiles averaged within the lens minus over the surrounding region where the lens is detected. The red dashed and dotted lines indicate the MLD within the lens and over the region where the lens is detected, respectively. On panels d–h, the first vertical gray dashed lines on 6 November 1996 indicate the lens splitting and the second one on 21 December 1996 indicates the change of the region where the lens is detected (see Figure 1a for the definition of the regions).</figcaption></figure>

thinner mixed layer, that is particularly pronounced as the lens evolves in the deep basins (by up to 40 m). The stronger stratification inside the lens is also associated with a warm anomaly intensified below the mixed layer, corresponding to the presence of a Near Surface Temperature Maximum (NSTM; Jackson et al., 2010).
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Spatial distribution of the density of lenses detected over the period 1994–2014 computed for boxes of 100 km × 100 km. The gray contours indicate the 500 and 2,000 m isobaths.</figcaption></figure>

The lens experiences a negative sea ice-induced freshwater flux between May and September 1997, corresponding to local sea ice melting (Figure 2d). As a response, the lens gets larger again, reaching about 350,000 km² (Figures 2b and 2d). During that period, the mixed layer shoals to the surface, both in and around the lens, and the differences of salinity and temperature between the interior of the lens and its surrounding decreases to less than 2 pss and 0.1°C, respectively.

During the second year of the lens lifespan, the differences between the properties within the lens and around become progressively smaller, although the mixed layer remains fresher inside the lens, and more heat is trapped below the mixed layer within the lens (with a temperature difference between 0.1 and 0.3°C; Figure 2h).

# 4. Temporal and Spatial Distribution of the Lens Field

## 4.1. Statistical Description

Over the 1994–2014 period, we detect and track a total of 8,969 lenses. Figure 3 shows the spatial distribution of the number of lenses detected per box of 100 km × 100 km. Densities are typically higher closer to the coasts and over the continental shelves particularly on the Siberian shelves where it peaks at 80 lenses per 100 × 100 km². Large densities are also visible over the Greenland, Barents, and Chukchi seas, whereas the density largely decreases in the Deep Basins and gets close to zero in the Canadian Basin and around the North Pole.

We characterize the lens field by looking at the probability density functions (PDF) of some key properties of the lenses at their birth (the first time step when a lens is detected) as well as their overall age at death (Figure 4) for
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Probability density function per region of (a) the size of the lenses (bin size of 384 km²); (b) the age of the lenses at their death (bin size of 5 days); (c) the difference between the salinity anomaly and the salinity threshold averaged within the lenses (bin size of 0.02 pss); (d) MLD averaged within the lenses (bin size of 1 m). Note that here we only consider the new lenses.</figcaption></figure>

the different regions shown on Figure 1a. Irrespective of the region we consider, the PDF of the size at birth peaks at 800 km² (which is the smallest lens size captured by our detection method) and decreases rapidly when considering larger sizes, although one lens is as large as 4,690,000 km². This exceptionally large lens was formed in August 2007 (a summer characterized by a strong sea ice decline) and resulted from the merging of multiple small lenses to scale up to roughly the size of the Arctic Basin. Most of the lenses survive for 15 days (which is again the minimum possible surviving period), although some lenses can survive over longer periods. Indeed, 10% of the lenses are tracked for more than 60 days and 2.5% for more than 105 days. We also examine the PDF of the mean salinity anomaly (compared to the detection threshold) averaged in the lens contour at their birth (Figure 4c). Regardless of the region considered, the distribution peaks in the first few salinity bin (between -0.03 and -0.05 pss), which accounts for between 5% and 15% of the lenses depending on the region. The distribution
then gradually decreases toward larger anomalies reaching approximately 1% for -0.5 pss. The only exception is the Siberian Seas, where the distribution peaks at -0.11 pss and 5%. Interestingly, the PDF of the mean MLD within the lenses at their birth (which corresponds roughly to the thickness of the lenses, and is thus used for simplicity to characterize the vertical extension of the lenses) exhibits some large differences across the regions (Figure 4d). In the Siberian Seas, the lens MLDs range between 0 and 10 m, and the distribution peaks between 2 and 3 m with a probability at 20%. The Barents, Chukchi, and Beaufort seas show a similar peak toward shallow mixed layers albeit less pronounced. In contrast, the PDF of the lens MLDs in the Deep Basins and the Greenland Sea exhibits a very wide range of values with no pronounced peak in the distribution. These regional differences reflect the temporal and spatial variations of the MLD across the Arctic Basin with very shallow mixed layers found over most of the Arctic shelves in summer (Peralta-Ferriz & Woodgate, 2015; Supply et al., 2023).

It is interesting to note that we could not find a significant correlation between the different quantities discussed above. Conceptually, one may have thought that larger lenses or lenses associated with a stronger salinity anomaly at their birth would tend to survive longer. This does not appear to be the case, suggesting that the conditions encountered by the lenses during their lifetime are more important than their initial properties in determining their ability to persist.

## 4.2. Seasonal Variability of the Lens Birth and Properties

As discussed in the introduction, previous studies have suggested that most of the low SSS lenses found at the surface of the Arctic Ocean are resulting from strong sea ice melt events (Crews et al., 2022; Supply et al., 2022). One could thus expect a strong seasonality of the number of lens births and possibly of the lens properties. To quantify such a seasonality, we start by coloring the positions of the centers of the lenses by their month of birth (Figure 5), considering separately the lenses categorized as new (4,599 lenses), split (2,545 lenses), and merged (1,825 lenses). At first sight, the maps reveal that, regardless of their category, lenses can be formed throughout the year and not only during the sea ice melt season. Regions with the largest number of lens birth logically correspond to regions with the highest density of lenses (Figure 3), suggesting that lenses are most often remaining close to their birth location. These regions also correspond to regions with the largest number of split and merging events, suggesting a certain degree of randomness in the processes resulting in splitting and merging. However, a closer look also reveals that, in the Deep Basins, there are more lenses originating from a split than new lenses, whereas in the Greenland Sea, there are nearly as many split lenses as new ones. This increased number of split events in these two regions is interesting as these two regions are also characterized by contrasting sea ice conditions (and hence contrasted momentum and buoyancy surface fluxes), suggesting that split events are not directly determined by the local sea ice conditions.

To further quantify the seasonality of the lens life cycle, we estimate the number of lens births for the different regions (Figure 6). The largest number of births occurs in the Greenland Sea (more than 1,600 births, or 33%) followed by the Siberian Seas (29%) and the Barents Sea (19%). Note that alleviating the effect of considering regions of various sizes (Figure 1a) does not modify the predominance of the Greenland Sea. Overall lenses form preferentially on the shelves rather than in the interior of the Arctic Basin. Considering all the regions, the overall picture is that the largest number of births occur during summer (37%) particularly between June and August, which coincides with the sea ice melt season. Regions that are fully ice covered during winter (the Deep Basins, and the Beaufort-Chukchi and Siberian seas) exhibit a further reduced number of winter births.

We then examine the number of lens deaths per region that we compare to the number of births in the same region (Figure 6). For a given region, a positive imbalance between the number of births compared to the deaths suggest that a number of lenses are advected outside of the region. In all the regions but the Deep basins, the number of births exceed the number of deaths by about 10%. In total over the full period, 375 lenses are lost in our count (less than 4% of the total), meaning that they are advected outside of the domain covered by our 5 regions of interest.

In the Barents, Greenland, and Siberian seas, the seasonality of the deaths follow closely the seasonality of the births with again more deaths in summer/fall (63%) than in winter/spring. This is likely the results of the short persistence of the lenses and traveled distances in these regions. In the deep basins and the Beaufort/Chukchi seas, the seasonality of the births and deaths differs. The number of deaths in these regions outweigh the number of births during the freezing season (October to May), whereas the tendency reverses during the melting season. This could be due to several factors: (a) a large number of lenses advected to these regions; (b) river runoff in these regions is small, and thus lenses there are more likely to be formed as a response to sea ice melt; and (c) the
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Maps of the position of the barycenter at their birth of each lenses classified as (a) new, (b) split, and (c) merged. The colorbar indicates the month of birth.</figcaption></figure>

presence of thicker sea ice in these regions during winter may result in a larger ocean surface stress that may enhance the dissipation (and thus the death) of the lenses.

To better explain the different life cycles of the lenses depending on their properties and the conditions found in the region where they are evolving, we now estimate the mean seasonal cycle per region of the key properties of the lenses: size, age, salinity anomaly, MLD as well as the sea ice conditions found on top of the lenses and the differences compared to the surrounding regions (Figure 7).

Comparing the seasonal cycle in the different regions, the deep basins stand out. There, the lenses are consistently larger (up to $10^5$ km$^2$), older (up to 250 days), and associated with a weaker SSS anomaly (around 0.5 pss below the local threshold) than in the other regions. The largest lenses are found in summer (July–August), which corresponds to the youngest lenses and the largest salinity anomaly. This suggests that, on average in this region, large lenses associated with strong salinity anomalies are predominantly formed in summer (Figure 6), and that the lenses may tend to shrink and lose slowly their SSS signature as they evolve in the region throughout the year. The other regions exhibit lenses with more similar sizes and ages and a somewhat weaker seasonality. In contrast, the mean salinity anomaly compared to the detection threshold is stronger in summer everywhere (up to -1 pss), when the lenses are predominantly formed. This suggests again that the salinity anomaly tends to erode over time.

The MLD averaged within the lenses (which again corresponds roughly to the vertical extension of the lenses) exhibits a seasonal cycle in all the regions (Figure 7), which follows roughly the seasonality documented in the different regions of the background environment (Peralta-Ferriz & Woodgate, 2015). It is thus expected to find deeper mixed layers in the Barents and Greenland seas, where deep convection occurs during winter, and where the stratification is not solely driven by the salinity variations (Almeida et al., 2023; Barton et al., 2022). In contrast, in the ice-covered regions, where the salinity determines the density at first order, a negative SSS anomaly associated with a lens may likely result in a strengthening of the surface stratification and thus a shoaling of the mixed layer. This explains the very shallow mixed layer found in the lenses in the deep basins and in the Beaufort/Chukchi and Siberian Seas.

In the Beaufort, Chukchi, and Barents seas, sea ice tends to be thinner in winter and spring and thicker in summer and fall within the lens than in their surrounding (Figures 7e and 7g). The difference can reach up to 60 cm in the Beaufort/Chukchi seas in October, which represents a ~30% difference. We acknowledge, however, that these differences may reflect both the specific behavior of the lenses and the spatial variations of the sea ice thickness across the regions, as lenses are not evenly distributed in space (Figures 3 and 6). The Barents and Greenland seas are largely ice free, at least for part of the year, but the thickness differences remain substantial throughout the year. Moreover, the thickness differences are of the same order of magnitude as the mean sea ice thickness within the lenses. This suggests some interplay between the lenses and the sea ice formation and melting in these regions. This latter idea is reinforced when comparing the surface freshwater flux associated with sea ice melt and freezing averaged within the lens and in the surrounding regions (Figures 7f and 7h). The striking picture is that, in all the regions, the flux is strongly intensified within the lenses with a difference that can reach as high
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Number of births (left column) and deaths (right column), for the three types of lenses altogether, per month and for the different regions. Note that 375 lenses are advected outside of the total domain covered by the 5 regions of interest and are thus not counted as “dead” in a given region.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 7.** Monthly mean of (a) the lens sizes, (b) the lens ages, (c) the difference between the SSS anomaly averaged within each lens and the local detection threshold, (d) the MLD averaged within the lenses, (e) the sea ice thickness averaged within the lenses, (f) the freshwater flux associated with the sea ice melting and freezing averaged within the lenses, (g) and (h) the differences between the quantities shown in (e) and (f) and the average over the region where the lens is detected. Here, we consider all the timesteps and thus a given lens is counted at all the timesteps it can be detected. Note that for b–d–e–f–g–h, we compute a weighted mean by considering the various sizes of the lenses.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Maps of the position of the barycenter at the birth of the “new” lenses. The colorbar indicates the year of birth.</figcaption></figure>

as 50%. Consistently, there is both more melt and more freezing occurring within the lenses than in the surrounding regions. This suggests that the presences of the lenses could be important for the pan-Arctic evolution of the sea ice conditions. This is explored further in Section 5.

### 4.3. Interannual Variability of the Lens Distribution

So far, we have examined the evolution of the lens field over a mean seasonal cycle over the period 1994–2014. However, this period was also characterized by strong changes affecting both the ocean and sea ice conditions (Carmack et al., 2016; Meier & Stroeve, 2022) that could also affect the properties of the lens field.

Figure 8 shows a map of the birth location of the new lenses colored this time by their birth's year (we only show the “new” lenses here). The map reveals that lenses are formed throughout the full period. Some regional differences are however visible. In the Greenland Sea, there seems to be more births on the western side of the basin in recent years, following roughly the position of the sea ice edge, whereas there are more births in the 1990s and early 2000s east of the Barents Sea Opening. This changes extend also over the Barents Sea and may be related to the strong sea ice loss in this region and the shift from a β-ocean (where density is mostly determined by salinity) toward an α-ocean (where density is mostly determined by temperature) (Barton et al., 2020) in which SSS anomalies are less prone to form and persist. In contrast, the number of births seems to increase over time in the deep basins, and somewhat in the Beaufort/Chukchi and Siberian seas. There, a significant increase in bottom melt was reported by Perovich and Richter-Menge (2015) over the period, which may explain part of the trend.

We now produce a time series of the number of lenses detected at each time step and of the corresponding surface they occupy (Figure 9). As expected, the two time series exhibit a strong seasonal cycle with a minimum in winter and spring when the lenses are only covering 2%–3% of the Arctic surface. This contrasts with a peak occurring at the end of the melting season both for the number of lenses and the surface they cover (typically around 5%–10% of the Arctic surface).
<figure><img src="image_11.png" /><figcaption>**Figure 9.** (a) Count of the number of lenses, at each time step over the full domain from 1994 to 2014. The orange line shows the 12-month running mean; (b) Surface covered by lenses at each time step in percent of the surface of the full domain (which covers 15,929,554 km²).</figcaption></figure>

Over the 21-year period, the number of lenses exhibits a small but significant increasing linear trend of 1 lens per year, whereas the total surface does not show a significant trend. In addition to this trend, the two times series are affected by a large interannual variability that largely modulates the amplitude of the seasonal cycle, which varies between 30 and 70 lenses and between 5% and 25% for the number of lenses and surface covered, respectively. It is also interesting to note that the interannual variations of the two time series are not correlated. For instance, the largest number of lenses (97 lenses) is found in summer 2004, but the surface covered by the lenses at that time is only around 12%. The largest surface covered by the lenses amounts to 27% in summer 2007, which is also a summer characterized by a record low of the Arctic sea ice extent (Giles et al., 2008). However, in contrast, there is no marked extrema in 2012 (the year with the lowest extent ever recorded by satellite observations). This suggests that the lens occurrence is not directly determined by the amount of sea ice melt in a given year. This is discussed in more details in the following section.

# 5. Discussion

## 5.1. Processes Important for the Lens Formation

To gain some further insights on the mechanisms that play a role in the formation of the lenses, we first examine the connection between the surface forcing and the birth of the lenses. To that aim, we cluster the new lenses by the sign of the freshwater flux associated with sea ice averaged over them at their birth. A third category is added in our clustering for the new lenses with no sea ice on top of them as they form. The three clusters account for different parts of the total number of lenses (963, 2,022 and 1,614 lenses for the category corresponding to a freezing, melting and no freshwater flux, respectively), and the clusters exhibit some clear spatial structures (Figure 10a). First, lenses associated with a zero surface flux are generally found at lower latitudes, and in regions
<figure><img src="image_12.png" /><figcaption>**Figure 10.** (a) Maps of the position of the barycenter at their birth of the new lenses clustered by the sign of their associated surface freshwater flux resulting from the sea ice processes. (b) Surface Eddy Kinetic Energy (EKE) average over 1994–2014. Here, the reference is taken as the long term mean over the full period.</figcaption></figure>

that are largely ice free (e.g., the Labrador and Irminger seas, and the eastern side of the Nordic Seas). In addition, these types of lenses are also formed along the Arctic coastlines (likely in summer) albeit with a smaller occurrence. Second, lenses from the two other clusters (associated with either a freezing flux or a melting flux) can be found in all the regions of the Arctic, although there is a larger number of lenses with a melting flux in the Barents and Nordic seas. The large number of lenses belonging to the “freezing flux” cluster may seem counterintuitive at first, as these lenses appear in a period when the SSS increases under the influence of brine rejection. In these cases, it is likely that the negative SSS anomaly associated with these lenses result from a negative anomaly in sea ice formation (relative to the climatological seasonal cycle of sea ice). It is also interesting to note that lenses found very close to the coastlines are part of the three clusters. This likely highlight the role of the river runoff in the formation of some of the lenses close to the river mouths. Indeed, the river plumes are advected offshore over the shelves, keeping their low salinity signature (Clark & Mannino, 2022; Matsuoka et al., 2016). However, sourcing the lens generation in these regions to the runoff of a specific river remains challenging, as the extension of the river plumes largely varies in response to the large-scale atmospheric and oceanic conditions (Morison et al., 2012). Moreover, the birth of some of the lenses may arise from the combination of several factors that act altogether to generate a low SSS anomaly.

Further, we note that there is a striking similarity between the regions associated with high levels of Eddy Kinetic Energy (EKE) at the surface (Figure 10b), and the regions with a higher number of lenses (Figure 3). This similarity is even stronger when considering the lenses that are not associated with any sea ice melt or freezing (Figure 10a), but also all along the East Greenland Current, suggesting that part of the lenses could coincide with the passage of eddies. Indeed, it is well known that Arctic eddies are able to trap water masses in their core from their region of formation and advect these anomalies over long distances (Cassianides et al., 2023; Pnyushkov et al., 2018). A cross-tracking of the lenses and the eddies could help to explore this mechanism in further detail but is beyond the scope of the current paper.

Last, we note that we have tried to determine the important processes resulting in the death of the lenses. One logical candidate would have been the surface stress (arising from both the wind and the sea ice drift) that could contribute to mix away the SSS anomaly associated with the lenses. However, comparing the stress over the lenses at their death against the stress experience throughout their life did not reveal any significant difference (not shown). Rather, we found some tendency of the lens' size to decrease over time, suggesting that lenses tend to erode gradually over time rather than being mixed away due to one strong event, although we acknowledge some large variations in the behavior of the individual lenses.

## 5.2. Lens Contribution to the Arctic Freshwater Flux and Budget

The clustering performed above reveals that 65% of the new lens birth occurs in presence of a surface freshwater flux resulting from sea ice processes. Moreover, as they evolved through time, lenses are associated with
<figure><img src="image_13.png" /><figcaption>**Figure 11.** Time series of the total surface freshwater flux due to sea ice freezing and melt integrated over the full domain (in green and blue, respectively) and integrated only over the lenses (in orange and red, respectively).</figcaption></figure>

significant differences in sea ice thickness and surface freshwater flux compared to the regions where they are found (Figures 7g and 7h). To gain some insights on the contribution of all the lenses to the Arctic freshwater budget, we estimate the cumulated freshwater flux due to sea ice melt and freezing occurring within the lenses that we compare to the total sea ice melt and freezing over the full domain (Figure 11). We choose to show the melting and freezing independently as, during winter, both sea ice melt and formation can happen depending on the location we consider. Overall, the amount of freshwater due to sea ice formation within the lenses remain small compared to the total (5% at most). In contrast, a significant part of the total freshwater flux associated with sea ice melt occurs with the lenses. The lens contribution reaches at least 20% every summer and peaks as high as 50% during summers 2005 and 2007. This is much larger than the surface covered by the lenses (Figure 9b) although the two numbers are not directly comparable as the total surface is computed for the full domain and not only for the ice-covered region. However, Figure 11 suggests that a significant part of the ice melt occurs within the lenses in the Arctic Ocean, including in cases when the lenses are not formed in response to the sea ice melt. This is for instance the case of the lens described in Section 3.

**6. Summary and Conclusions**

In the Arctic Basin, the ocean surface is affected by numerous types of forcing capable of strongly modulating the SSS, including sea ice melting and freezing as well as large and localized river discharges. As a result, low SSS anomalies are generated. In the literature, these anomalies are often referred to as meltwater layers (Crews et al., 2022) or meltwater lenses (Supply et al., 2022), as only the SSS anomalies resulting from intense and localized sea ice melt were considered previously. Here, we expand these analyses by considering any low salinity coherent anomaly found at the Arctic surface regardless of their origin.

Based on the analysis of simulation outputs from a high resolution Arctic ocean-sea ice model, we have performed a systematic detection of these lenses and have tracked their displacements in order to gain a better understanding of their life cycle. Our detection and tracking algorithms depend on a number of parameters, including a choice of a minimum size and lifespan and a threshold for the SSS anomaly. Amongst this parameters, the largest sensitivity of our results relates to the choice of this threshold (a quantile of the SSS anomaly distribution). The choice made here aims at capturing enough extreme events to obtain a statistically robust description of these events, while focusing only on these events. Nonetheless, over the period 1994–2014, we are able to detect and track a total of 8,969 lenses that are found largely along the high Arctic coastlines and in the Nordic, Irminger and Labrador seas. Many of the lenses are formed in summer (37% in July–September), although we detect some births throughout the year. Although 90% of the lenses do not survive longer than 60 days and thus die close to their birth location, some can survive for several months and be advected away from the birth location with the background flow. This is consistent with the characteristics of the lenses observed in summer by satellite (Supply et al., 2022) that were able to survive for weeks to months. In our simulation, we find that lenses and their associated salinity anomalies
are confined to the shallow mixed layer, which is often less than 5 m deep. Lenses are associated with SSS anomalies of up to 3 pss compared to a mean climatological year. In contrast, our simulation is not able to capture the thinner meltwater lenses observed during MOSAIC (Smith et al., 2023), that are stronger but also more confined to the surface (less than 1 m) and thus too thin to be captured by the model and its vertical resolution. Moreover, the model vertical mixing scheme (Blanke & Delecluse, 1993) likely tends to diffuse over the mixed layer any surface anomalies rather than maintaining strong gradient within the mixed layer, which is a common deficiency of the Arctic simulations based on the NEMO model (Allende et al., 2024). However, the model is able to capture the formation of a near surface temperature maximum that tends to appear below the mixed layer and tend to trap a significant amount of heat and can survive for a several months and potentially modulate any future sea ice formation (Jackson et al., 2010; Steele et al., 2011). Lenses are also found more commonly in regions characterized by high level of surface EKE, suggesting that part of the SSS anomalies we detect could correspond to eddies. However, one needs to remember that the model is not high resolution enough to fully capture the mesoscale activity on the Arctic shelves, meaning that the number of lenses associated with eddies is likely underestimated in our study.

We further find some strong connections between the lenses and sea ice. First, 65% of the lenses are generated by either some strong sea ice melt or by less than usual sea ice formation (that results in a negative SSS anomaly compared to the climatology). Second, throughout their life cycle, lenses are on average associated by a local anomaly of the sea ice thickness and they consistently encounter both more freezing during winter and more melting during summer compared to their surroundings. The local anomalies are particularly noticeable in the Greenland and Barents seas, where the differences of freezing freshwater flux on top of the lenses and average over a larger region can be as high as half of the total fluxes, suggesting that sea ice formation occurs disproportionately over the lenses in these areas.

Once formed, some lenses can travel long distances. More than half of the lenses detected here experience a splitting event during their life time, and another 25% experience a merging with one or more other lens. The examination of a few cases suggests that lenses and their associated SSS anomalies tend to be gradually eroded over time through vertical mixing, similar to what was found by Dewey et al. (2017) in the seasonal ice zone of the Beaufort Sea. However, future studies are required to fully quantify the importance of the different processes contributing to the lens erosion. In particular, resolving mesoscale eddies over the Arctic shelves would likely affect the life cycle of the lenses, and as small scale features could contribute to mix away the lenses. A better understanding to the important driver of both the generation and decay of the lenses should also allow for an improved detectiondata availability statement in the future based on for example, more physical parameters representative of their evolution.

Overall our results suggest that these lenses mediate a significant part of the freshwater flux in the Arctic Ocean. They may thus be important for the large scale Arctic dynamics and the ocean-sea ice interplay. As the Arctic transitions toward a seasonally ice-free ocean, the increase of the river runoff and the sea ice melt (Carmack et al., 2016) as well as the possible intensification of the eddy activity (Li et al., 2024) could potentially result in more frequent formations of these features in the future Arctic.

## Data Availability Statement

A full description of the simulation used in this study (CREG12. L75-REF08 Canadian) as well as all the information required to produce the model output are available in open access at https://doi.org/10.5281/zenodo.5789520 and at Talandier & Lique, 2021. It includes the configuration files, the links to boundary conditions, atmospheric forcing, and initialization files. The lenses dataset is available in netCDF format at https://doi.org/10.5281/zenodo.15040769 (2025).

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## Acknowledgments

Clément Van Straaten is supported by a PhD fellowship from CNES. Camille Lique and Nicolas Kolodziejczyk are supported by funding from the CLIMARC TIC project funded by the “PPR Océan et Climat—France 2030” (contract ANR-22-POCE-0005). We acknowledge the European Space Agency’s Climate Change Initiative (ESA CCI) for providing funding to develop the activities of this research under contract number 4000123663/18/I-NB, Phase 2 of the Sea Surface Salinity CCI + R&D project, as specified in proposal reference ARG-003-130-OPT. The pan-Arctic simulation was performed using HPC resources from the French GENCI-CINES center (Grant 2018-A0050107420). This study was also supported by CNES TOSCA SMOS OCEAN project. We are particularly grateful to Claude Talandier who has set up the configuration and produced the simulation and to Alexandre Supply for useful discussions at the early stage of the study. We also thank the two anonymous reviewers for their comments which helped to improve the manuscript.
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