Sea surface salinity (SSS) is a critical parameter in the Arctic Ocean for a variety of reasons, e.g., since its variation controls seawater density variations at cold temperatures [1
]. SSS is essential to study Arctic and global ocean circulation, freshwater distribution and transports, mixing, water-mass formation, ocean heat content, biogeochemical cycles [3
], sea-ice formation, and ice cover persistence [8
], especially with the changes currently affecting the Arctic Ocean [9
]. In turn, all these phenomena have implications for ocean–ice–atmosphere interactions and thus, climate and weather, including potential teleconnections with lower latitudes.
In-situ data are sparse in the Arctic basin, which limits our knowledge of salinity patterns in the Arctic Ocean. On the other hand, modeling tools have been widely used to study the Arctic Ocean. However, the lack of in-situ measurements makes it difficult to validate and constrain models. Recent advances in the observation of SSS from space offer the potential to provide measurements of the ice-free Arctic Ocean on synoptic (a few days) and longer time scales. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission [12
] was launched in 2009 and has provided SSS data since then. The National Aeronautics and Space Administration (NASA) Aquarius/SAC-D mission was launched in 2011 and provided SSS data from mid-2011 until a hardware failure occurred mid-2015 [14
]. Finally, the NASA Soil Moisture Active Passive (SMAP) mission [15
] has provided SSS measurements since mid-2015. While SMAP has a spatial resolution of about 40 km and a 2–3-day temporal repeat, SMOS has a spatial resolution of about 45–50 km and 3-day repeat, while Aquarius has a lower spatial resolution of 100–150 km and a longer temporal repeat of 7 days.
All three SSS missions are based on L-band radiometry, which is a limitation at high latitudes due to the much lower sensitivity to salinity in cold waters [16
], resulting in much larger uncertainties in polar oceans than at lower latitudes [16
]. As shown in Reference [17
], such sensitivity drops from 0.5 K/g of salt per kg of seawater to 0.3 K/g of salt per kg of seawater, when sea surface temperature (SST) decreases from 15 °C to 5 °C. However, Arctic SSS variability (both spatial and seasonal) is large [18
], and therefore, L-band SSS may still have reasonable capability to detect significant Arctic changes. The paucity of in-situ salinity measurements in the ice-free Arctic Ocean presents a great challenge to the evaluation of satellite SSS in the Arctic Ocean. A few recent studies have attempted to assess the quality of several satellite SSS products compared with in-situ measurements. However, what is still lacking is a systematic evaluation of the full suite of commonly used satellite SSS products in the Arctic Ocean in terms of their consistency among one another and the consistency with in-situ data. The authors of Reference [19
] performed a comparison of SMOS and Aquarius products with in-situ measurements and models for the north Atlantic region up to 80°N. They found a root mean square difference (RMSD) of 0.9. However, they did not perform any comparison inside the Arctic Basin. The authors of Reference [20
] presented a comparison of three different Aquarius SSS products and one SMOS SSS product in the polar and subpolar oceans. When the products are compared with in-situ measurements, they found RMSD values ranging from 0.33 to 0.89. Overall, the products agree well, although with large biases of 1. Further, the majority of their in-situ data were in the subpolar oceans south of the Arctic Ocean. The authors of Reference [21
] assessed the accuracy of only the SMAP SSS product produced by the Jet Propulsion Laboratory (JPL) in the Arctic Ocean through a comparative analysis with in-situ salinity data from Argo floats, ships, gliders, and in-field campaigns. Their results show a RMSD between in-situ and SMAP JPL SSS lower than 1.2 north of 65°N. The authors of Reference [22
] presented a new method to retrieve SMOS SSS maps in the Arctic Ocean. They used an extensive database of Argo and thermosalinographs (TSG) data to assess the quality of their product dedicated to the Arctic Ocean. They found that the major features of interannual SSS variations observed by the TSG are also captured by SMOS. They also found a standard deviation of the difference between the new SMOS SSS maps and Argo SSS that ranges from 0.25 to 0.35. Finally, a recent study from Reference [23
] assessed the quality of only two SMOS SSS products in the Arctic Ocean from 2011 to 2013 by comparing these products to in-situ data. They found that in the Beaufort Sea, the SMOS product dedicated to the Arctic has the smallest bias (about 0.6) and the smallest RMSD (2.6).
Several studies have demonstrated the potential scientific capabilities and applications for satellite SSS observations at high latitudes. The authors of Reference [24
] studied the interannual variability of the Ob’ and Yenisei River plume dynamics using multi-satellite observations. Aquarius SSS and Aqua MODIS chlorophyll-a data for 2011–2014 permitted identification of the river plume and its extension. The authors of Reference [25
] used SMOS SSS data along with ocean color data in the Beaufort Sea to discriminate freshwater sources and demonstrated that SSS estimates can be used to identify riverine freshwater plumes. More recently, the authors of Reference [21
] demonstrated the capability of SMAP to capture interannual SSS variability in the Kara Sea associated with the Ob’ and Yenisei rivers. The increasing amount of studies using satellite SSS observations in the Arctic Ocean show the need to thoroughly assess the quality and the capability of satellite SSS measurements in this region.
To our knowledge, Reference [20
] is the only study that compared several satellite SSS products with each other. However, they only compared satellite data with a few TSG transects within the Arctic Ocean, the majority of their in-situ data being in subpolar seas. This study also did not include any SMAP product and did not provide diagnostics, such as dependence on temperature and sea-ice concentration. The importance of SSS for Arctic Ocean research and the lack of a systematic evaluation of satellite SSS in the Arctic Ocean motivated the main objective of this study: to perform a systematic evaluation of the commonly used satellite SSS products from all available satellite SSS missions, both among the products and against a more extensive suite of in-situ measurements in the Arctic Ocean. A main goal is to quantify the strengths and limitations of satellite SSS on various spatial and time scales. Another goal is to provide feedback to the various satellite SSS teams so as to improve the retrievals and the consistency among different products.
The paper is organized as follows. In Section 2
, we provide a description of the satellite and in-situ SSS datasets. The results of the intercomparison between the different satellite SSS observations and the comparison between satellite and in-situ salinity measurements are presented in Section 3
. Finally, the findings and broader implications are discussed in Section 4
a,b show the WOA13 annual mean and standard deviation salinity respectively, over the domain, the Arctic Ocean, on top of which are represented the areas of interest discussed in this study. We first compare several commonly used SSS products with each other in the Arctic Ocean to examine their consistency. As the satellite SSS from different missions cover different time periods, we separate the analysis into two periods: August 2011 to June 2015 (referred to as 2011–2015 hereafter for simplicity) that is common for Aquarius and SMOS data, and April 2015 to December 2017 (referred to as 2015–2017 hereafter for simplicity) that is common for SMOS and SMAP data. Figure 2
shows the spatial distribution of the time mean SSS during these two periods for the six satellite SSS products: SMOS BEC, SMOS LOCEAN, Aquarius, and Aquarius CAP for 2011–2015 and SMOS BEC, SMOS LOCEAN, SMAP RSS, and SMAP JPL for 2015–2017. This time mean is calculated with all the data available, whether there is one or several data available in a pixel through the whole period due to the presence of sea-ice for part of the year.
These maps show that the ice masks can be quite different among the products. In particular, the mask used in SMAP JPL data is more permissive than the other products (i.e., allowing SSS retrievals closer to ice edges). In contrast, the mask used in SMAP RSS is the most conservative among all products (i.e., excluding more SSS retrievals near ice edges). These maps also show the qualitative, large-scale consistency of the time-mean SSS fields among different SSS products and with the climatology salinity based on in-situ data from WOA13, as shown in Figure 1
a. For example, the inflow of saltier Pacific waters through the Bering Strait separates the freshwater waters in the Beaufort Sea to the east and in the East Siberian Sea to the west. Both of these seas are significantly affected by river discharges. All products (except for Aquarius CAP) show a relatively sharp transition of higher salinity from the Barents Sea to lower salinity in the Kara and Laptev Seas. This reflects the influence of river discharge on the Kara and Laptev Seas and the intrusion of saltier subpolar North Atlantic waters through the Barents Sea (Aquarius CAP excluded the retrievals in the Kara Sea and Laptev Sea, so it does not show such a transition). In Baffin Bay, most products show lower SSS on western and northern parts because of Arctic Ocean outflow and river discharge, and higher salinity on the eastern and southern sides because of the intrusion of the saltier subpolar North Atlantic waters through the West Greenland Current. Despite the similarity in large-scale patterns discussed above, there are regional differences among the products. For example, SMOS LOCEAN retrievals are saltier in fresh regions, such as the Beaufort gyre, the Siberian shelf, and the Baffin Bay. The salty bias (higher than 36) observed along the east coast of Greenland is due to sea-ice contamination (personal communication from J. Boutin). Salty bias observed in SMOS LOCEAN could also be due to the use of a different dielectric constant model [39
]. These differences will inform the retrieval teams to identify the underlying causes so as to improve the SSS retrievals at high latitudes.
The maps of temporal standard deviation (STD) of SSS for each of the two periods for the different products are presented in Figure 3
. The standard deviation is calculated when at least 10 data values are available in a pixel through the whole period. In this figure, we chose different color scales for various products in order to visualize the spatial patterns. The temporal variability of SMOS BEC SSS is much smaller than those of other products. This is probably because in the SMOS BEC product, an objective analysis scheme with radii of 321 km, 267 km, and 175 km are applied [22
]. This produces a smoothing which does not allow for capturing the small-scale variability. A new binned product is being developed at BEC under the ESA contract Arctic+, which aims at better capturing the SSS small-scale features. In contrast, the temporal variability of SMAP JPL SSS is much larger than those of other products, most likely due to the more permissive algorithm allowing retrievals near ice edges and land that are more subject to variability (e.g., runoff, sea-ice melt, and formation). However, most products, as well as the WOA13 product (Figure 1
b), exhibit similar regions of high variability such as the Kara Sea, the Siberian shelf, near the mouth of the Mackenzie River, Baffin Bay, and Eastern Greenland, and the magnitudes of the STDs in these regions vary significantly among the products.
To examine consistency among different SSS products for the Arctic Ocean as a whole, we computed the monthly time series of average SSS within the Arctic Ocean (Figure 4
a,b). The average was calculated for latitudes above 65°N over the ice-free ocean grid points that are common to all products for 2011–2015 (Figure 4
a) and 2015–2017 (Figure 4
b). These time series exhibit a seasonal cycle with minimum SSS during August–September every year (corresponding to the ice melt season) and maximum SSS during approximately December–June (corresponding to the ice freezing period). The time series also show the excellent consistency among different products at monthly time scales with regard to the interannual variation of minimum SSS: for example, the minimum SSS values during the falls of 2011 and 2012 are lower than those in 2013 and 2014. The minimum SSS values in 2017 are lower than those in 2015 and 2016. These differences do not particularly correlate with the Arctic-wide time series of September mean sea-ice extent (e.g., https://nsidc.org/arcticseaicenews/2019/09/arctic-sea-ice-reaches-second-lowest-minimum-in-satellite-record/
), indicating that SSS responds to a variety of forcings from river discharge, sea-ice growth and melt, net precipitation, and ocean circulation, however this is beyond the scope of this study. The monthly spread among products (grey shadings) is about 0.43 to 0.5, which is 4 to 8 times smaller than the magnitude of the average seasonal cycle (2–4). Aquarius CAP is excluded from this figure as there is no retrieval in the Kara and Laptev Seas, affecting the seasonal and interannual variability. Therefore, the different SSS products have relatively good “signal-to-noise” ratio in representing the seasonal cycle and interannual variability of SSS averaged over the Arctic ice-free oceans.
We next compare each satellite SSS product with available in-situ data in the Arctic Ocean (Figure 5
). Only observations that can be collocated with SSS satellite data (see Section 2.2
for the collocation method) are represented, i.e., there are no satellite SSS measurements retrieved under the ice, within 40 to 150 km from land (depending on the satellite product) or when sea-ice concentration or sea-ice fraction within a satellite footprint exceeds the threshold considered for each SSS product (see Data section). This map shows the striking sparseness of in-situ data in the Arctic Ocean. Over the 2011–2017 period, most of the Arctic Ocean was not sampled by in-situ instruments, only 40% of the Arctic Ocean sampled by SMAP JPL (above 65°N and considering SMAP JPL ice and land mask) has also been sampled by at least one in-situ instrument over 2011–2017. In most places sampled, the numbers of measurements are single digit, within these 40% of the area sampled, about 46% contain less than 10 in-situ observations. Some places in the Bering Strait, the Beaufort gyre, and the Barents Sea have about 50–100 measurements over the seven years. Baffin Bay and the northern Atlantic Ocean are regions that are routinely sampled with more than 500 measurements per 36 km grid cell over these seven years. The paucity of in-situ measurements poses a significant challenge to the evaluation of satellite SSS.
We first investigated the capability of satellite SSS to depict salinity gradients along TSG transects (Figure 6
). We considered three different transects among the 98 transects collected. These three transects were the longest and the ones crossing the largest gradients within the two periods 2011–2015 and 2015–2017. The first transect corresponds to salinity measured all along the Arctic basin from the 19 May 2013, the second one from Denmark to northern Baffin Bay and back starting on the 31 July 2013 and the third one, also from Denmark to Baffin Bay and back, stating on the 1 June 2016. The sea-ice fraction or sea-ice concentration provided by the satellite SSS products along the transects are shown under each plot. In the three cases presented here, large gradients along the transects were well retrieved by the satellites when not contaminated by ice. Table 1
gathers all the statistics from each of the three transects. We computed in particular the correlations and the root mean square difference (RMSD) between each satellite product (when available) and in-situ measurement. Correlation coefficients are larger than 0.9 and RMSD values range from 0.2 to 2.7 for the first transect (Figure 6
b, Table 1
). Correlations are between 0.6 and 0.97 for the second transect and RMSD range from 0.4 to 1.4 (Figure 6
d, Table 1
). For the third and last transect, correlations are about 0.5 to 0.9 and RMSD range from 0.5 to 1.1 (Figure 6
f, Table 1
). RMSD values significantly drop for SMOS LOCEAN, SMOS BEC, and Aquarius when only considering ice fraction or sea-ice concentration values lower than 0.5% for the first transect, as well as for SMOS LOCEAN for the second and third transect (Table 1
). The correlations are all significant with p
-values lower than 0.05.
In the following, we compare each satellite SSS product with all the available in-situ data to examine their respective consistency with the in-situ data. Figure 7
shows the maps of RMSD between each satellite product and in-situ data per 1° bin as well as the scatter plot between each satellite SSS product and in-situ SSS. Because SMOS retrieved data from the whole study period 2011 to 2017, and because SMOS BEC provides daily data (with a 9-day running mean window), more colocations between in-situ and satellite are available for SMOS BEC (Figure 7
a). SMOS LOCEAN on the other hand, only retrieves data every 4 days, so fewer colocations are available for SMOS LOCEAN than for SMOS BEC (Figure 7
b). SMAP JPL is only available from 2015 to 2017 but has a relatively permissive ice mask, so more colocations are also available (Figure 7
f). The RMSD between satellite and in-situ is generally larger in the Arctic Ocean than in the subpolar North Atlantic Ocean and Barents Sea, likely due to the larger gradients caused by runoff, sea-ice melting and formation, and sampling differences between in-situ and satellite observations, especially large in highly variability regions (Figure 7
]). Also, the larger uncertainty of satellite SSS in the colder Arctic Ocean is a factor for larger differences between satellite and in-situ in the Arctic Ocean compared to the subpolar region.
The scatter plots between satellite and in-situ show that at salinities lower than 25, SMOS BEC and LOCEAN tend to overestimate SSS (Figure 2
a,b,e,f; Figure 7
g,h). Because of their less permissive ice masks, both Aquarius CAP and SMAP RSS do not retrieve as fresh SSS as other products (lower than 25) (Figure 7
k). SMAP JPL tends to underestimate lower SSS significantly (lower than 25), this might be due to the contaminations by land and sea-ice signals (Figure 7
l). From the scatter plots between each satellite SSS product and in-situ salinity, we compute the bias, RMSD, and correlations between satellite and in-situ SSS. We also compute the standard error by normalizing the standard deviation of each satellite product compared with in-situ observations by the square root of the number of samples. All the statistics are shown in Table 2
. The bias between SMOS BEC and in-situ observations are relatively small compared to other products (0.08 against –0.1 to –0.6). RMSD is smaller for Aquarius CAP compared to other products, about 0.8 against 1.2 to 3.4 (Table 2
). Correlation coefficients are large for all products, from 0.7 to 0.9, and are significant with p
-values lower than 0.05. The standard error between satellite and in-situ is between 0.5 and 2.4. These statistics include both the uncertainties of satellite SSS and the effect of sampling differences between satellite SSS and in-situ measurements.
In order to better understand the impact of temperature and sea-ice on satellite SSS validation, in Figure 8
a–f we show the average temperature (in color) per 1 × 1 bin of satellite against in-situ salinity. The largest observed differences between satellite and in-situ SSS are often associated with temperatures lower than 5 °C, especially for SMAP JPL, SMOS BEC, and SMOS LOCEAN. This shows (1) the retrieval errors in cold waters, but also (2) the impact of natural variability of SSS that is not well represented by the lack of ground truth measurements. Indeed, the SSS variability in the North Atlantic Ocean and Barents Sea where waters are warm and salty is lower compared to the higher variability in the Arctic basin, in fresh and cold waters, due to influences of runoff, ice melt, and formation. A similar analysis is done for sea-ice fraction (for Aquarius CAP and SMAP RSS) or sea-ice concentration (for SMAP JPL). In Figure 9
a–c, we show the average sea-ice fraction or sea-ice concentration in percentage (color) per 1 × 1 bin of satellite against in-situ SSS. However, sea-ice fraction or concentration data are only available in Aquarius CAP, SMAP RSS, and SMAP JPL products. Because it allows more retrievals near ice edges and land, SMAP JPL exhibits the largest differences with in-situ, especially at fresher salinities. In Figure 9
, we can see that the largest differences between satellite and in-situ SSS occur for larger sea-ice fraction or concentration (on average, for about 0.3% sea-ice fraction or concentration). In summary, the largest differences between in-situ and satellite are associated with lower temperature and larger sea-ice fraction or concentration.
To provide further quantitative information on the impacts of temperature and sea-ice fraction or concentration on the in-situ-satellite salinity comparisons, we analyzed the bias and RMSD between satellite and in-situ SSS as a function of the minimum temperature considered (Figure 10
). There is no consistent decrease of bias as temperature increases, although two of the six products (Aquarius and SMOS LOCEAN) do have systematically lower biases as temperature increases (Figure 10
a). However, there is a consistent reduction of RMSD across all products as temperature increases (Figure 10
b). In other words, the STD decreases consistently as temperature increases. The implication is that biases in the satellite SSS are not necessarily related to the temperature dependence of L-band radiometric sensitivity to SSS. These results are informative to retrieval teams to investigate the factors influencing the biases. Also, increasing efforts should be dedicated to improve the dielectric constant models for cold waters.
We next investigated the relationships of biases and RMSD of the satellite SSS with maximum sea-ice fraction or concentration within the satellite footprint considered (Figure 11
). As sea-ice fraction or concentration values are only available for SMAP JPL and Aquarius, we only considered these products. Note that sea-ice fraction values are also available for SMAP RSS; however, because SMAP RSS SSS are only retrieved when the sea-ice fraction within the satellite footprint is below 0.1%, we were not able to analyze the impact of the sea-ice contamination threshold on the comparison between SMAP RSS and in-situ SSS, the threshold being too low. For Aquarius CAP, RMSD between satellite and in-situ slightly increase from 0.5 to 1 with the sea-ice fraction threshold (Figure 11
b), while the bias is constant (Figure 11
a). For SMAP JPL, RMSD increase significantly above 0.5% of sea-ice concentration from 1.5 to 2.8 (Figure 11
b), showing that considering SSS in pixels where there is more than 0.5% of sea-ice might not be reasonable. The bias however decreases from –0.7 to –0.4 (Figure 11
Compared to previous studies, our in-situ database is much more extensive within the Arctic Ocean. In-situ data in other studies were generally biased more toward subpolar seas and the North Atlantic Ocean. One result of this new focus is that discrepancies between in-situ and satellite SSS are expected to be relatively large, owing to cold temperatures and ice contamination. In fact, we found satellite versus in-situ RMSD of 0.7–1.7 for SMOS and Aquarius, while References [19
] and [20
] found values generally lower than 1. Similarly, we found RMSD values of 1.5 and 3.3 for SMAP JPL and SMOS BEC matchups, while References [21
] and [22
] found values of 1.2 and 0.35. However, biases found in this study are between −0.6 and 0.5, which is comparable to 0.6 for SMOS in the Beaufort Sea found in Reference [23
] and lower than 1, found in Reference [20
] in subpolar seas.
Our study also underlines the challenges of evaluating satellite SSS, given the paucity of in-situ measurements. Enhancement of the in-situ observing system in the Arctic Ocean (e.g., Reference [40
]) is important for improving satellite SSS for the Arctic Ocean. Improved satellite SSS in turn increases the sampling and coverage of the Arctic Ocean SSS field significantly, paving the way for an effective in-situ-satellite observing system for the Arctic Ocean. It is important to note that the statistics reported here for the comparison of the satellite SSS and in-situ measurements should not be interpreted as the uncertainties of the satellite SSS alone. This is because the statistics also include the effects of sampling differences between satellite SSS and in-situ measurements. Satellite SSS represent the averages within satellite footprints (ranging from about 40 km for SMOS and SMAP to 150 km for Aquarius), whereas in-situ measurements are point-wise. In regions with strong small-scale gradients (e.g., near river plumes and sea-ice edges), this difference in spatial sampling can cause significant apparent discrepancies between satellite SSS and in-situ measurements (e.g., Reference [36
]). In the Arctic Ocean, where the dynamic scales are small compared with lower latitude oceans, the effect of spatial sampling differences could be further enhanced. Near-surface salinity stratification, that we have not explored in this study, can also cause differences between satellite SSS (representing the average over the top 1 cm of the ocean) and in-situ measurements (typically at the depths of meters) [36
]. Unfortunately, there are insufficient in-situ salinity measurements in the upper few meters in the Arctic Ocean to examine the possible effect of the near-surface stratification. The sub-footprint horizontal SSS gradient and the vertical stratification of salinity in the Arctic Ocean are not well understood. Also, colocations between weekly satellite observations and instantaneous in-situ measurements are affected by temporal mismatch. A process experiment in the future may shed light on these processes, using, for example, a high-resolution model in which colocations with in-situ data are free from sampling differences.
Some consistent features observed by the satellite SSS products can be used to evaluate ocean and climate models, for instance, the seasonal and interannual variability of the SSS averaged over the ice-free Arctic Ocean. On the other hand, the discrepancies among different satellite SSS products provide guidance to users of the current generation of satellite SSS to evaluate the suitability of specific applications and the robustness of the results from such applications. Finally, our results provide important feedback to the satellite SSS retrieval teams to work collaboratively to improve the products (e.g., investigating the cause for and reducing the biases, sea-ice corrections). Indeed, more in-situ data are needed in order to better evaluate SSS products near sea-ice edges (1%–3% ice fraction up to 15%) during different seasons based on level 2 SSS data. A strategy within the different retrieval teams is needed to develop in-situ campaign in the Arctic to allow a more robust validation of satellite SSS products in the Arctic.