Satellite Sea Surface Salinity Research Articles

Characterizing the California Current System through Sea Surface Temperature and Salinity

Characterizing temperature and salinity (T-S) conditions is a standard framework in oceanography to identify and describe deep water masses and their dynamics. At the surface, this practice is hindered by multiple air–sea–land processes impacting T-S properties at shorter time scales than can easily be monitored. Now, however, the unsurpassed spatial and temporal coverage and resolution achieved with satellite sea surface temperature (SST) and salinity (SSS) allow us to use these variables to investigate the variability of surface processes at climate-relevant scales. In this work, we use SSS and SST data, aggregated into domains using a cluster algorithm over a T-S diagram, to describe the surface characteristics of the California Current System (CCS), validating them with in situ data from uncrewed Saildrone vessels. Despite biases and uncertainties in SSS and SST values in highly dynamic coastal areas, this T-S framework has proven useful in describing CCS regional surface properties and their variability in the past and in real time, at novel scales. This analysis also shows the capacity of remote sensing data for investigating variability in land–air–sea interactions not previously possible due to limited in situ data.

Drivers of Laptev Sea interannual variability in salinity and temperature

Abstract. Eurasian rivers provide a quarter of total fresh water to the Arctic, maintaining a persistent fresh layer that covers the surface Arctic Ocean. This freshwater export controls Arctic Ocean stratification, circulation, and basin-wide sea ice concentration. The Lena River supplies the largest volume of runoff and plays a key role in this system, as runoff outflows into the Laptev Sea as a particularly shallow plume. Previous in situ and modelling studies suggest that local wind forcing is a driver of variability in Laptev sea surface salinity (SSS) but there is no consensus on the roles of Lena River discharge and sea ice cover in contributing to this variability or on the dominant driver of variability. Until recently, satellite SSS retrievals were insufficiently accurate for use in the Arctic. However, retreating sea ice cover and continuous progress in satellite product development have significantly improved SSS retrievals, giving satellite SSS data true potential in the Arctic. In this region, satellite-based SSS is found to agree well with in situ data (r>0.8) and provides notable improvements compared to the reanalysis product used in this study (r>0.7) in capturing patterns and variability observed in in situ data. This study demonstrates a novel method of identifying the dominant drivers of interannual variability in Laptev Sea dynamics within reanalysis products and testing if these relationships appear to hold in satellite-based SSS, sea surface temperature (SST) data, and in situ observations. The satellite SSS data firmly establish what is suggested by reanalysis products and what has previously been subject to debate due to the limited years and locations analysed with in situ data; the zonal wind is the dominant driver of offshore or onshore Lena River plume transport. The eastward wind confines the plume to the southern Laptev Sea and drives alongshore transport into the East Siberian Sea, and westward wind drives offshore plume transport into the northern Laptev Sea. This finding is affirmed by the strong agreement in SSS pattern under eastward and westward wind regimes in all reanalyses and satellite products used in this study, as well as with in situ data. The pattern of SST also varies with the zonal wind component and drives spatial variability in sea ice concentration.

Missing Argo Float Profiles in Highly Stratified Waters of the Amazon River Plume

Abstract In the western tropical Atlantic Ocean close to the Amazon plume, a large loss rate of Argo-float profiles took place, that is, instances of profiles that should have happened but were not transmitted. We find that APEX and SOLO floats were not ascending to the surface in the presence of low surface practical salinity, typically on the order of 32.5 or less, because of limitations on the surface buoyancy range for those floats. This results in an overall loss of profiles from these floats that is on the order of 6% averaged over the year, with a peak of 12% in July. We also find aborted descents/incorrect grounding detections for ARVOR/PROVOR floats when surface salinity is low and the descending float reaches a strong halocline (2.6% of all the profiles in the June–August season). Altogether, the whole Argo set includes a maximum loss rate of roughly 6% in July. We find a pattern of loss that fits the surface salinity seasonal cycle and the occurrence of low surface salinity investigated from a high-resolution daily satellite salinity product in 2010–21. The agreement is even better when considering surface density instead of surface salinity, with the temperature contribution to density inducing a shift in the maximum occurrence of these events by 1 month relative to the cycle of very low salinity events. Because of changes in the float technology, the loss rate that targets the lowest surface salinities was very large until 2010, with an overall decrease afterward. Significance Statement In the western tropical Atlantic Ocean, some Argo floats were not able to ascend or descend with very low surface salinity, because of buoyancy limitations for some float types and false bottom detection on others. In this region, for surface practical salinity smaller than 32.5, this resulted in a loss of close to one-half of the Argo profiles during the last 20 years. Altogether, this undersampling of the lowest surface salinities by Argo floats modifies the upper-ocean salinity seasonal cycle, as well as longer-term trends portrayed in Argo data–based products. Furthermore, in this region, care must be taken when validating satellite salinity data with Argo data or when adjusting satellite sea surface salinity data to in situ data products.

Quality Evaluation of Satellite Sea Surface Salinity Products in the Pacific Ocean

Sea surface salinity (SSS) is one of the most important variables in ocean dynamics and atmospheric climate. The launch of three salinity satellites, Aquarius, SMAP and SMOS, has greatly expanded the global sea surface salinity data field. The latest ocean surface salinity (CCI+SSS) fusion project fully utilizes the satellite data from SMOS, supplemented by SMAP and Aquarius satellite data, to merge into the CCI fusion product. In this paper, the data accuracy of the four SSS products, Aquarius, SMAP, SMOS and CCI, is evaluated in terms of both average deviation and root mean square error (RMSE) by comparing with the in situ SSS monthly gridded EN4.2.2 dataset in the Pacific Ocean. The results show that compared with the in situ EN4.2.2 data, the average deviation and root mean square error of the four SSS products are relatively small in the Pacific subtropical region, while the errors are generally higher in the cold water and offshore regions. Overall, the CCI product has the smallest average deviation and RMSE in the Pacific Ocean, which is the best performance among the four products.

Contribution of satellite sea surface salinity to the estimation of liquid freshwater content in the Beaufort Sea

Abstract. The hydrography of the Arctic Ocean has experienced profound changes over the last 2 decades. The sea ice extent has declined by more than 10 % per decade, and its liquid freshwater content has increased mainly due to glaciers and sea ice melting. Further, new satellite retrievals of sea surface salinity (SSS) in the Arctic might contribute to better characterizing the freshwater changes in cold regions. Ocean salinity and freshwater content are intimately related such that an increase (decrease) in one entails a decrease (increase) in the other. In this work, we evaluate the freshwater content in the Beaufort Gyre using surface salinity measurements from the satellite radiometric mission Soil Moisture and Ocean Salinity (SMOS) and TOPAZ4b reanalysis salinity at depth, estimating the freshwater content from 2011 to 2019 and validating the results with in situ measurements. The results highlight the underestimation of the freshwater content using reanalysis data in the Beaufort Sea and a clear improvement in the freshwater content estimation when adding satellite sea surface salinity measurements in the mixed layer. The improvements are significant, with up to a 70 % reduction in bias in areas near the ice melting. Our research demonstrates how remotely sensed salinity can assist us in better monitoring the changes in the Arctic freshwater content and understanding key processes related to salinity variations that cause density differences with potential to influence the global circulation system that regulates Earth's climate.

Salinity Inversion of Flat Sea Surface Based on Deep Neural Network

The distribution and change of sea surface salinity (SSS) have an important influence on the sea dynamic environment, marine ecological environment, global water cycle, and global climate change. Satellite remote sensing is the only practical way to continuously observe SSS over a wide area and for a long period of time. The salinity retrieval model of flat sea surface, which primarily includes empirical model and iterative model, is the key to retrieving satellite SSS products. The empirical models have high computational efficiency but low inversion accuracy, while the iterative models have high inversion accuracy but low computational efficiency. In order to reconcile the contradiction between the computational efficiency and inversion accuracy of existing models, this paper proposes a universal deep neural network (DNN) model architecture and corresponding training scheme, and provides 3 DNN models with extremely high computational efficiency and high inversion accuracy. The inversion error range, the root mean square error (RMSE), and the mean absolute error (MAE) of the DNN models on 311,121 sets of data have decreased by more than 40 times, 150 times, and 150 times, respectively, compared to the empirical model. The computational efficiency of the DNN models on 420,903 sets of data has improved by more than 100,000 times compared to the iterative model. Therefore, the algorithm developed in this paper can effectively solve the contradiction between the computational efficiency and inversion accuracy of existing models, and provide a theoretical support for high-precision and high-efficiency salinity inversion research.

The Salinity of Coastal Waters as a Bellwether for Global Water Cycle Changes

AbstractWhile the global water cycle has been studied previously based on land and open ocean studies, here we use coastal satellite sea surface salinity (SSS) data to show that in global aggregate, SSS variations near coasts are strongly correlated with global water cycle variability driven by El Niño Southern Oscillation (ENSO). This is a significant finding as we demonstrate that open ocean SSS variability is not as sensitive to ENSO and global water cycle variability as the coastal oceans at interannual timescales. Aggregated global coastal SSS could therefore be used as a proxy for detection of changes in the large‐scale cycling of water between the oceans and continents. Moreover, we identify major potential “hotspots” on land and in the coastal ocean that tend to drive global coastal salinity variability, and which may consequently be most sensitive to future physical and biological impacts of water cycle changes on the coastal oceans.

An observing system experiment framework for the tropical Indian Ocean salinity: A case study using a constellation of three satellites

In this study impact of assimilating Sea Surface Salinity (SSS) from multi-satellites (SMOS, Aquarius and SMAP) on numerical ocean model simulations in the north Indian Ocean has been analysed under the observing system experiment (OSE) framework. Daily data sets of Aquarius, SMAP and SMOS, which were available for a common period of April–May 2015, are used to constrain the ocean model using ensemble optimal interpolation technique. Apart from the control simulation in which satellite data were not assimilated, a total of seven assimilation experiments using different combinations of satellite SSS were conducted. The impact of assimilation experiments is analysed by comparing the model-simulated variables with in situ observations. Assimilating satellite SSS results in a reduction in Root Mean Square Error (RMSE) in SSS (∼ 54%) and also in subsurface salinity (∼ 21%) over the control run. The impact of assimilating SMAP observations is maximum on model simulations with the errors reducing by ∼ 54%. Subsurface salinity improvement is better with three satellites with ∼31% improvement in RMSE in the halocline region, which was ∼11% more than single satellite assimilation. Assimilation of SSS also resulted in improved simulations of the model surface, subsurface temperature and mixed layer depth. Model results show the ability of SSS observations to complement other ocean observation networks. One important observation from this study is that while the impact of assimilating SSS observations from a single satellite was on par with the impact of assimilating SSS observations from two or three satellites in correcting simulated surface salinity, assimilation from more than one satellite had a larger impact in the salinity of deeper layers of the ocean.

Quantification of Aquarius, SMAP, SMOS and Argo-Based Gridded Sea Surface Salinity Product Sampling Errors

Evaluating and validating satellite sea surface salinity (SSS) measurements is fundamental. There are two types of errors in satellite SSS: measurement error due to the instrument’s inaccuracy and problems in retrieval, and sampling error due to unrepresentativeness in the way that the sea surface is sampled in time and space by the instrument. In this study, we focus on sampling errors, which impact both satellite and in situ products. We estimate the sampling errors of Level 3 satellite SSS products from Aquarius, SMOS and SMAP, and in situ gridded products. To do that, we use simulated L2 and L3 Aquarius, SMAP and SMOS SSS data, individual Argo observations and gridded Argo products derived from a 12-month high-resolution 1/48° ocean model. The use of the simulated data allows us to quantify the sampling error and eliminate the measurement error. We found that the sampling errors are high in regions of high SSS variability and are globally about 0.02/0.03 psu at weekly time scales and 0.01/0.02 psu at monthly time scales for satellite products. The in situ-based product sampling error is significantly higher than that of the three satellite products at monthly scales (0.085 psu) indicating the need to be cautious when using in situ-based gridded products to validate satellite products. Similar results are found using a Correlated Triple Collocation method that quantifies the standard deviation of products’ errors acquired with different instruments. By improving our understanding and quantifying the effect of sampling errors on satellite-in situ SSS consistency over various spatial and temporal scales, this study will help to improve the validation of SSS, the robustness of scientific applications and the design of future salinity missions.

Remote Sensing of the Subtropical Front in the Southeast Pacific and the Ecology of Chilean Jack Mackerel Trachurus murphyi

The Subtropical Front (STF) plays a key role in the ecology of Chilean jack mackerel Trachurus murphyi. Nonetheless, there are few remote sensing studies of the STF in the open Southeast Pacific, and almost all of them have been conducted by satellite oceanographers in Russia and Ukraine to support respective large-scale fisheries of jack mackerel in this region. We reviewed these studies that documented long-term seasonal and interannual variability of the STF from sea surface temperature (SST) and sea surface height (SSH) data. We also mapped the STF from satellite sea surface salinity (SSS) data of the SMOS mission (2012–2019). The Subtropical Front consists of two fronts–North and South STF about 500 km apart–that border the Subtropical Frontal Zone (STFZ) in-between. The STF is density-compensated, with spatially divergent manifestations in temperature and salinity. In the temperature field, the STF extends in the WNW to ESE direction in the Southeast Pacific. In the salinity field, the STFZ appears as a broad frontal zone, extending zonally across the entire South Pacific. Three major types of satellite data-SST, SSH, and SSS-can be used to locate the STF. The SSH data is most advantageous with regard to the jack mackerel fisheries, owing to the all-weather capability of satellite altimetry and the radical improvement of the spatial resolution of SSH data in the near future. Despite the dearth of dedicated in situ studies of the South Pacific STFZ, there is a broad consensus regarding the STFZ being the principal spawning and nursing ground of T. murphyi and a migration corridor between Chile and New Zealand. Major data/knowledge gaps are identified, and key next steps are proposed to mitigate the data/knowledge gaps and inform fisheries management.

Deviations of satellite-measured sea surface salinity caused by environmental factors and their regional dependence

Satellite measurements have largely improved sea surface salinity (SSS) observations in the recent decade, including Soil Moisture Ocean Salinity (SMOS), Aquarius/Satélite de Aplicaciones Científicas (SAC) D, and Soil Moisture Active-Passive (SMAP). This study evaluates the deviations between the satellite SSS products and the in-situ observations (ΔS) and analyses their relationships with the environmental factors, including sea surface temperature (SST), precipitation, and wind speed. The level 3 satellite SSS generally correlates well with Argo-observed SSS but differs in the middle and high latitudes and the tropical convergence zones. The smallest ΔS appears in the subtropical open oceans, where the sea surface features warm SST, rare precipitation, and low wind speed. The variations in mean |ΔS| for the three satellites show a robust dependence on SST, precipitation, and wind, with large ΔS (outside 5%–95% interval) in particular tending to occur in areas of cold SST, heavy rainfall, and high wind speeds. The most pronounced SSS deviations occur in the middle and high latitudes (beyond 40°) due to cold SST. In particular, heavy rainfall in the subpolar regions and strong westerly winds in the mid-latitudes also make the satellite SSS measurements less accurate. In the tropical convergence zones, large SSS deviations are mainly related to heavy rainfall, showing significant surface freshening. The fresh biases related to rainfall are more significant in Level 2 SSS than in Level 3, due to the short period of the convective rainfall. In addition, surface freshening is more obvious in low wind speed scenarios, showing that the fresh skin layer induced by rain is more common on the calm ocean surface.

Meltwater Lenses Over the Chukchi and the Beaufort Seas During Summer 2019: From In Situ to Synoptic View

AbstractWe investigate the Chukchi and the Beaufort seas in the Arctic Ocean, where salty and warm Pacific Water flows in through the Bering Strait and interacts with the sea ice, contributing to its summer melt. Thanks to in situ measurements recorded by two saildrones deployed during summer 2019 and to refined sea ice filtering in satellite L‐band radiometric data, we demonstrate the ability of satellite sea surface salinity (SSS) observed by Soil Moisture and Ocean Salinity and Soil Moisture Active and Passive to capture SSS freshening induced by sea ice melt. We refer to these freshening events as meltwater lenses (MWL). The largest MWL observed by the saildrones during this period occupied a large part of the Chukchi shelf, with a SSS freshening reaching −5 practical salinity scale, persisting for up to 1 month. This MWL restricted the transfer of air‐sea momentum to the upper ocean, as illustrated by measured wind speed and vertical profiles of currents. With satellite‐based sea surface temperature, satellite SSS provides a monitoring of the different water masses encountered in the region during summer 2019. Using sea ice concentration and estimated Ekman transport, we analyze the spatial variability of sea surface properties after the sea ice edge retreat over the Chukchi and the Beaufort seas. The two MWL captured by the saildrones and the satellite measurements resulted from different dynamics. Over the Beaufort Sea, the MWL evolution followed the meridional sea ice retreat whereas, in the Chukchi Sea, a large persisting MWL was generated by advection and subsequent melting of a sea ice filament.

Coastal Summer Freshening and Meltwater Input off West Greenland from Satellite Observations

Coastal waters off west Greenland are strongly influenced by the input of low salinity water from the Arctic and from meltwater from the Greenland Ice Sheet. Changes in freshwater content in the region can play an important role in stratification, circulation, and primary production; however, investigating salinity variability in the region is challenging because in situ observations are sparse. Here, we used satellite observations of sea surface salinity (SSS) from the Soil Moisture and Ocean Salinity mission produced by LOCEAN and by the Barcelona Expert Center (SMOS LOCEAN and SMOS BEC) and from the Soil Moisture Active Passive mission produced by the Jet Propulsion Laboratory (SMAP JPL) as well as by Remote Sensing Systems (SMAP RSS) to investigate how variability in a narrow coastal band off west Greenland is captured by these different products. Our analyses revealed that the various satellite SSS products capture the seasonal freshening off west Greenland from late spring to early fall. The magnitudes of the freshening and of coastal salinity gradients vary between the products however, being attenuated compared to historical in situ observations in most cases. The seasonal freshening off southwest Greenland is intensified in SMAP JPL and SMOS LOCEAN near the mouth of fjords characterized by large inputs of meltwater near the surface, which suggests an influence of meltwater from the Greenland Ice Sheet. Synoptic observations from 2012 following large ice sheet melting revealed good agreement with the spatial scale of freshening observed with in situ and SMOS LOCEAN data. Our analyses indicate that satellite SSS can capture the influence of meltwater input and associated freshwater plumes off coastal west Greenland, but those representations differ between products.

Comparative Analysis between Sea Surface Salinity Derived from SMOS Satellite Retrievals and in Situ Measurements

Validating Sea Surface Salinity (SSS) data has become a key component of the Soil Moisture Ocean Salinity (SMOS) satellite mission. In this study, the gridded SMOS SSS products are compared with in situ SSS data from analyzed products, a ship-based thermosalinograph and a tropical moored buoy array. The comparison was conducted at different spatial and temporal scales. A regional comparison in the Baltic Sea shows that SMOS slightly underestimates the mean SSS values. The influence of river discharge overrides the temperature in the Baltic Sea, bringing larger biases near river mouths in warm seasons. The global comparison with two Optimal Interpolated (OI) gridded in situ products shows consistent large-scale structures. Excluding regions with large SSS biases, the mean ΔSSS between monthly gridded SMOS data and OI in situ data is −0.01 PSU in most open sea areas between 60°S and 60°N, with a mean Root Mean Square Deviation (RMSD) of 0.2 PSU and a mean correlation coefficient of 0.50. An interannual tendency of mean ΔSSS shifting from negative to positive between satellite SSS and in situ SSS has been identified in tropical to mid-latitude seas, especially across the tropical eastern Pacific Ocean. A comparison with collocated buoy salinity shows that on weekly and interannual scales, the SMOS Level 3 (L3) product well captures the SSS variations at the locations of tropical moored buoy arrays and shows similar performance with in situ gridded products. Excluding suspicious buoys, the synergetic analysis of SMOS, SMAP and gridded in situ products is capable of identifying the erroneous data, implying that satellite SSS has the potential to act as a real-time 27 Quality Control (QC) for buoy data.

Satellite-Observed Time and Length Scales of Global Sea Surface Salinity Variability: A Comparison of Three Satellite Missions

Sea surface salinity (SSS) observations from Aquarius, Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) satellite missions are compared to characterize the time and length scales of SSS variability globally. Overall, there is general agreement between the global patterns of the time and length scales of SSS variability estimated from the three satellite missions. The temporal scales of SSS variability vary from more than 90 days in the tropics to ~15 days in the Southern Ocean. The very short temporal scales (close to the Nyquist period) in some parts of the ocean are probably due to the high level of noise in the satellite data or the high noise-to-signal ratio. The longest temporal scales are observed along the South Pacific Convergence Zone (SPCZ) and in the central and western tropical Pacific. These areas are also related to the strongest ENSO-related signal in SSS. The processes governing the SSS variability and distribution are also non-stationary, such that the scales determined over different observation periods may differ. Dominant spatial scales of SSS variability are generally the longest (up to 150 km) in the tropics and the shortest (<60 km) in the subpolar regions. The distribution of the dominant spatial scales is not simply latitudinal but exhibits a more complex spatial pattern. In the tropics, there is slight east-west and inter-hemispheric asymmetry observed in the Pacific but absent in the other two oceans. The analysis also reveals that the length scales of SSS variability are highly anisotropic in the tropics (the zonal scales are generally shorter than the meridional ones) and become more isotropic towards higher latitudes. Regional differences in the estimates of the scales from the three satellite SSS datasets may arise due to differences in the observation duration, spatial resolution and/or different level of noise.

Retrieving Mediterranean Sea Surface Salinity Distribution and Interannual Trends from Multi-Sensor Satellite and In Situ Data

Sea surface salinity (SSS) is one of the Essential Climate Variables (ECVs), defined by the Global Climate Observing System (GCOS). Salinity is modified by river discharge, land run-off, precipitation, and evaporation, and it is advected by oceanic currents. In turn, ocean circulation, the water cycle, and biogeochemistry are deeply impacted by salinity variations. The Mediterranean Sea represents a hot spot for the variability of salinity. Despite the ever-increasing number of moorings and floating buoys, in situ SSS estimates have low coverage, hindering the monitoring of SSS patterns. Conversely, satellite sensors provide SSS surface data at high spatial and temporal resolution, complementing the sparseness of in situ datasets. Here, we describe a multidimensional optimal interpolation algorithm, specifically configured to provide a new daily SSS dataset at 1/16° grid resolution, covering the entire Mediterranean Sea (Med L4 SSS). The main improvements in this regional algorithm are: the ingestion of satellite SSS estimates from multiple satellite missions (NASA’s Soil Moisture Active Passive (SMAP), ESA’s Soil Moisture and Ocean Salinity (SMOS) satellites), and a new background (first guess), specifically built to improve coastal reconstructions. The multi-sensor Med L4 SSS fields have been validated against independent in situ SSS samples, collected between 2010–2020. They have also been compared with global weekly Copernicus Marine Environment Monitoring Service (CMEMS) and Barcelona Expert Centre (BEC) regional products, showing an improved performance. Power spectral density analyses demonstrated that the Med L4 SSS field achieves the highest effective spatial resolution, among all the datasets analysed. Even if the time series is relatively short, a clear interannual trend is found, leading to a marked salinification, mostly occurring in the Eastern Mediterranean Sea.

Increasing stratification as observed by satellite sea surface salinity measurements

Changes in the Earth’s water cycle can be estimated by analyzing sea surface salinity. This variable reflects the balance between precipitation and evaporation over the ocean, since the upper layers of the ocean are the most sensitive to atmosphere–ocean interactions. In situ measurements lack spatial and temporal synopticity and are typically acquired at few meters below the surface. Satellite measurements, on the contrary, are synoptic, repetitive and acquired at the surface. Here we show that the satellite-derived sea surface salinity measurements evidence an intensification of the water cycle (the freshest waters become fresher and vice-versa) which is not observed at the in-situ near-surface salinity measurements. The largest positive differences between surface and near-surface salinity trends are located over regions characterized by a decrease in the mixed layer depth and the sea surface wind speed, and an increase in sea surface temperature, which is consistent with an increased stratification of the water column due to global warming. These results highlight the crucial importance of using satellites to unveil critical changes on ocean–atmosphere fluxes.

Satellite and In Situ Sampling Mismatches: Consequences for the Estimation of Satellite Sea Surface Salinity Uncertainties

Validation of satellite sea surface salinity (SSS) products is typically based on comparisons with in-situ measurements at a few meters’ depth, which are mostly done at a single location and time. The difference in term of spatio-temporal resolution between the in-situ near-surface salinity and the two-dimensional satellite SSS results in a sampling mismatch uncertainty. The Climate Change Initiative (CCI) project has merged SSS from three satellite missions. Using an optimal interpolation, weekly and monthly SSS and their uncertainties are estimated at a 50 km spatial resolution over the global ocean. Over the 2016–2018 period, the mean uncertainty on weekly CCI SSS is 0.13, whereas the standard deviation of weekly CCI minus in-situ Argo salinities is 0.24. Using SSS from a high-resolution model reanalysis, we estimate the expected uncertainty due to the CCI versus Argo sampling mismatch. Most of the largest spatial variability of the satellite minus Argo salinity is observed in regions with large estimated sampling mismatch. A quantitative validation is performed by considering the statistical distribution of the CCI minus Argo salinity normalized by the sampling and retrieval uncertainties. This quantity should follow a Gaussian distribution with a standard deviation of 1, if all uncertainty contributions are properly taken into account. We find that (1) the observed differences between Argo and CCI data in dynamical regions (river plumes, fronts) are mainly due to the sampling mismatch; (2) overall, the uncertainties are well estimated in CCI version 3, much improved compared to CCI version 2. There are a few dynamical regions where discrepancies remain and where the satellite SSS, their associated uncertainties and the sampling mismatch estimates should be further validated.

Influence of Nonseasonal River Discharge on Sea Surface Salinity and Height

AbstractRiver discharge influences ocean dynamics and biogeochemistry. Due to the lack of a systematic, up‐to‐date global measurement network for river discharge, global ocean models typically use seasonal discharge climatology as forcing. This compromises the simulated nonseasonal variation (the deviation from seasonal climatology) of the ocean near river plumes and undermines their usefulness for interdisciplinary research. Recently, a reanalysis‐based daily varying global discharge data set was developed, providing the first opportunity to quantify nonseasonal discharge effects on global ocean models. Here we use this data set to force a global ocean model for the 1992–2017 period. We contrast this experiment with another experiment (with identical atmospheric forcings) forced by seasonal climatology from the same discharge data set to isolate nonseasonal discharge effects, focusing on sea surface salinity (SSS) and sea surface height (SSH). Near major river mouths, nonseasonal discharge causes standard deviations in SSS (SSH) of 1.3–3 practical salinity unit (1–2.7 cm). The inclusion of nonseasonal discharge results in notable improvement of model SSS against satellite SSS near most of the tropical‐to‐midlatitude river mouths and minor improvement of model SSH against satellite or in‐situ SSH near some of the river mouths. SSH changes associated with nonseasonal discharge can be explained by salinity effects on halosteric height and estimated accurately through the associated SSS changes. A recent theory predicting river discharge impact on SSH is found to perform reasonably well overall but underestimates the impact on SSH around the global ocean and has limited skill when applied to rivers near the equator and in the Arctic Ocean.

Error Characterization of Satellite SSS Products Based on Extended Collocation Analysis

Triple collocation analysis is an effective method for assessing the error of observation systems with independent random error. However, this error independence is often violated, and systems show clear error correlations. Whereas many efforts have been devoted, the cause and determination of this error correlation are still challenging. In this work, we reveal that the error correlation is actually a signal observed by some systems, and taking this signal as the error correlation between systems will bias the error estimation results. Therefore, we define it as the representativeness signal instead of the representativeness error. Based on the multiscale signal approach and extended collocation analysis, we discuss the cause of this representativeness signal and its effect on sea surface salinity (SSS) data validation by synthesized experiments, satellite, buoy, and climatology products. The results suggest that the representativeness signal may vary with the different behaviors of observed variables. For SSS, the representativeness signal lies in satellite data with medium resolutions and similar representativeness. However, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> and climatology data, which have different resolutions compared with satellites, do not show apparent representativeness signals with satellite data. The data collocation procedure also affects the representativeness signal, which decreases with increasing collocation intervals. The extended collocation analysis can estimate the error of SSS products in most cases but may not provide robust estimation when the collocation data pairs are limited.