Sea Surface Salinity Measurements Research Articles

Gap-filling techniques applied to the GOCI-derived daily sea surface salinity product for the Changjiang diluted water front in the East China Sea

Abstract. The spatial and temporal resolutions of contemporary microwave-based sea surface salinity (SSS) measurements are insufficient. Thus, we developed a gap-free gridded daily SSS product with higher spatial and temporal resolutions, which can provide information on short-term variability in the East China Sea (ECS), such as the front changes by Changjiang diluted water (CDW). Specifically, we conducted gap-filling for daily SSS products based on the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 1 km (0.01°), using a machine learning approach during the summer seasons from 2015 to 2019. The comparison of the Soil Moisture Active Passive (SMAP), Copernicus Marine Environment Monitoring Service (CMEMS), and Hybrid Coordinate Ocean Model (HYCOM) SSS products with the GOCI-derived SSS over the entire SSS range showed that the SMAP SSS was highly consistent, whereas the HYCOM SSS was the least consistent. In the < 31 psu range, the SMAP SSS was still the most consistent with the GOCI-derived SSS (R2=0.46; root mean squared error: RMSE = 2.41 psu); in the > 31 psu range, the CMEMS and HYCOM SSS products showed similar levels of agreement with that of the SMAP SSS. We trained and tested three machine learning models – the fine trees, boosted trees, and bagged trees models – using the daily GOCI-derived SSS as output, including the three SSS products, environmental variables, and geographical data. We combined the three SSS products to construct input datasets for machine learning. Using the test dataset, the bagged trees model showed the best results (mean R2=0.98 and RMSE = 1.31 psu), and the models that used the SMAP SSS as input had the highest level. For the dataset in the > 31 psu range, all the models exhibited similarly reasonable performances (RMSE = 1.25–1.35 psu). The comparison with in situ SSS data, time series analysis, and the spatial SSS distribution derived from models showed that all the models had proper CDW distributions with reasonable RMSE levels (0.91–1.56 psu). In addition, the CDW front derived from the model gap-free daily SSS product clearly demonstrated the daily oceanic mechanism during the summer season in the ECS at a detailed spatial scale. Notably, the CDW front in the zonal direction, as captured by the Ieodo Ocean Research Station (I-ORS), moved approximately 3.04 km d−1 in 2016, which is very fast compared with the cases in other years. Our model yielded a gap-free gridded daily SSS product with reasonable accuracy and enabled the successful recognition of daily SSS fronts at the 1 km level, which was previously not possible with ocean color data. Such successful application of machine learning models can further provide useful information on the long-term variation of daily SSS in the ECS. The gridded gap-free SSS dataset at 0.01°×0.01° spatial resolution is freely available at https://doi.org/10.22808/DATA-2023-2 (Shin et al., 2023).

Estimation of sea surface salinity from MODIS-Aqua data in the northern South China Sea

Sea Surface Salinity (SSS) is a key variable in physical oceanography and biogeochemical research. Microwave remote sensing technology has been used to monitor SSS in open ocean areas. However, the limited spatial resolution and suboptimal precision exhibited by microwave remote sensing derived SSS data have led to the omission of crucial oceanic information, consequently constraining its utilization. In this study, an artificial neural network (ANN) model for the regional SSS inversion was constructed utilizing in situ SSS measurements in the northern South China Sea and remote sensing sea surface temperature and reflectance at wavelengths of 412, 443, 488, 555 and 667 nm derived from MODIS-Aqua satellite Level 2 data. The model showed a good performance of root mean square error (RMSE) of 0.32 and 0.34 for the model training and testing datasets, with mean bias (MB) of 0 and 0.01, and correlation coefficients (R2) reaching 0.93. The model validation indicated that the ANN model reproduced the spatial distribution of SSS highly consistent with the measured SSS (RMSE = 2.75, MB = −1.67, R2 = 0.95), demonstrating its strong applicability in the NSCS. In contrast to other local SSS inversion models, our ANN model clearly showed the fine-scale SSS feature under the intrusion of Pearl River plume near to Dongsha Island with the help of anticyclonic-cyclonic eddies. This prediction closely aligned with the observations from the Soil Moisture Active Passive SSS dataset. The ANN model was applied to MODIS-Aqua data to generate the monthly SSS from 2003 to 2022. A decreasing SSS trend in SSS of 0.02 psu y−1 during 2005–2013 and 2017–2020 and a slightly increasing SSS trend of 0.01 psu y−1 from 2013 to 2017 were observed. Further analysis shows the low-frequency variability of SSS is significantly correlated with the Pacific Decadal Oscillation.

Salinity Fronts in the South Atlantic

Monthly climatology data for salinity fronts in the South Atlantic have been created from satellite SMOS sea surface salinity (SSS) measurements taken from 2011–2019, processed at the Barcelona Expert Center of Remote Sensing (BEC), and provided as high-resolution (1/20°) daily SSS data. The SSS fronts have been identified with narrow zones of enhanced horizontal gradient magnitude (GM) of SSS, computed using the Belkin–O’Reilly algorithm (BOA). The SSS gradient fields generated by the BOA have been log-transformed to facilitate feature recognition. The log-transformation of SSS gradients markedly improved the visual contrast of gradient maps, which in turn allowed new features to be revealed and previously known features to be documented with a monthly temporal resolution and a mesoscale (~100 km) spatial resolution. Monthly climatologies were generated and analyzed for large-scale open-ocean SSS fronts and for low-salinity regions maintained by the Rio de la Plata discharge, Magellan Strait outflow, Congo River discharge, and Benguela Upwelling. A 2000 km-long triangular area between Africa and Brazil was found to be filled with regular quasi-meridional mesoscale striations that form a giant ripple field with a 100 km wave length. South of the Tropical Front, within the subtropical high-salinity pool, a trans-ocean quasi-zonal narrow linear belt of meridional SSS maximum (Smax) was documented. The meridional Smax belt shifts north–south seasonally while retaining its well-defined linear morphology, which is suggestive of a yet unidentified mechanism that maintains this feature. The Subtropical Frontal Zone (STFZ) consists of two tenuously connected fronts, western and eastern. The Brazil Current Front (BCF) extends SE between 40 and 45°S to join the subantarctic front (SAF). The STFZ trends NW–SE across the South Atlantic, seemingly merging with the SAF/BCF south of Africa to form a single front between 40 and 45°S. In the SW Atlantic, the Rio de la Plata plume migrates seasonally, expanding northward in winter (June–July) from 39°S into the South Brazilian Bight, up to Cabo Frio (23°S) and beyond. The inner Plata front moves in and out seasonally. Farther south, the Magellan Strait outflow expands northward in winter (June–July) from 53°S up to 39–40°S to nearly join the Plata outflow. In the SE Atlantic, the Congo River plume spreads radially from the river mouth, with the spreading direction varying seasonally. The plume is often bordered from the south by a quasi-zonal front along 6°S. The diluted Congo River water spreads southward seasonally down to the Angola–Benguela Front at 16°S. The Benguela Upwelling is delineated by a meridional front, which extends north alongshore up to 20°S, where the low-salinity Benguela Upwelling water forms a salinity front, which is separate from the thermal Angola–Benguela Front at 16°S. The high-salinity tropical water (“Angola water”) forms a wedge between the low-salinity waters of the Congo River outflow and Benguela Upwelling. This high-salinity wedge is bordered by salinity fronts that migrate north–south seasonally.

Ocean Variability in the Costa Rica Thermal Dome Region from 2012 to 2021

Satellite ocean color and sea surface temperature (SST) observations from 2012 to 2021 and sea surface salinity (SSS) measurements from the Soil Moisture Active Passive (SMAP) mission from 2015 to 2021 are used to characterize and quantify the seasonal and interannual variability in the physical, optical, and biological sea surface features in the Costa Rica Thermal Dome (CRTD) region. High-resolution climatology and the seasonal variability in SST, SSS, and ocean color properties are produced. Chlorophyll-a (Chl-a) concentration, SST, and SSS show these three properties are linked with similar spatial patterns and seasonal variations, i.e., elevated Chl-a concentrations, match the depressed SST and increased SSS and vice versa. This reflects that the physical driving force is the same for these three ocean properties and implies that nutrient supply associated with the physical processes is the major driver for the seasonal biological variability. The interannual changes in Chl-a, SST, and SSS also show that these three ocean properties are consistent among themselves. The positive (negative) Chl-a anomaly generally occurs with negative (positive) SST anomaly and enhanced (reduced) SSS. The in situ measurements evidently show that the subsurface ocean dynamics in the upper 100 m controls the sea surface variability for Chl-a, SST, and SSS. We report that no significant enhancement of Chl-a is observed in the CRTD region during the central Pacific (CP)-type 2020–2021 La Niña event, while Chl-a changes are significant in the other three of four ENSO events between 2012 and 2021. Furthermore, the difference in Chl-a variability driven by the CP-type ENSO and eastern Pacific (EP)-type ENSO is further discussed.

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.

Implication of Errors in Space-Based Sea Surface Salinity Measurement on Ocean State Estimation: An Observing System Simulation Experiment for the Tropical Indian Ocean

Implication of Errors in Space-Based Sea Surface Salinity Measurement on Ocean State Estimation: An Observing System Simulation Experiment for the Tropical Indian Ocean

A Warming Southern Gulf of Mexico: Reconstruction of Anthropogenic Environmental Changes From a Siderastrea siderea Coral on the Northern Coast of Cuba

AbstractThe Gulf of Mexico is a vital region for the Atlantic Meridional Overturning Circulation (AMOC), that fuels the exchange of heat between the tropics and the polar regions. A weakening of the AMOC would have dire consequences for the planet. First observations and ocean models show that this process has already started. Very limited knowledge of the components that are part of the AMOC such as the Loop Current (LC) make it difficult to understand its dynamics as well as changes in strength or temperature since the onset of the Industrial Revolution. Currently, there are no continuous in situ sea surface temperature or salinity measurements for the southeastern Gulf of Mexico or reconstruction attempts for this region, showing the necessity for high‐resolution climate archives. A Siderastrea siderea coral core was retrieved from the northwestern Cuban coast and used as a sub‐seasonally resolved sea surface temperature and hydroclimate archive. The approach is based on skeletal δ18O, and trace and minor element contents show an increase in temperature over 160 years since 1845 of 2.6–3.3°C. A possible stagnation of the warming trend set in after the 1980s, indicating a potential weakening of the Loop Current. Impacts in sea surface salinity such as El Niño events in the Pacific region can still be detected in the Gulf of Mexico as decreases in salinity in 1998 from the reconstructed δ18OSW coral record. In situ measurements remain crucial to understand the dynamics in the LC and its influence on the AMOC.

Impact of Giant Iceberg A68A on the Physical Conditions of the Surface South Atlantic, Derived Using Remote Sensing

AbstractGiant icebergs release cold, fresh meltwater as they drift, perturbing the physical conditions of the surface ocean. This study uses satellite‐derived sea surface salinity and temperature measurements to explore the physical impact of supergiant iceberg A68A between September 2020 and June 2021. During A68A's drift through the Scotia Sea in austral spring, gradual but persistent edge‐wasting contributed to a freshening of several psu extending hundreds of kilometers ahead of the iceberg, whilst the cooling signal was more pronounced in the iceberg's wake. The magnitude of the physical perturbation intensified during A68A's breakup near South Georgia. Several large meltwater lenses surrounding the descendant icebergs displayed temperature anomalies of up to −4.5°C, whilst the salinity measurements indicated a surface (skin‐depth) anomaly regularly exceeding order −10 psu. The perturbations stretched at times >1,000 km and persisted for >2 months following A68A's melt in April 2021.

Soil Moisture and Sea Surface Salinity Derived from Satellite-Borne Sensors

AbstractThe monitoring of soil moisture and sea surface salinity over the Earth has been profoundly enhanced during the last thirteen years due to a new generation of satellite sensors. L-band radiometry is currently the only technology providing direct measurements of soil moisture, insensitive to surface roughness and distribution of elements in the soil, and the only technology the only technology for measuring that allows us to measure sea surface salinity from space. The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions resolve global and local variability with a spatial resolution of approximately 43 km, a swath width close to 1000 km, and a sampling time, for each mission, of at least twice every 3 days. These resolutions and samplings can be increased by either merging data from the two sensors, and with complementary information gathered from other passive or active sensors, or with in situ information at higher spatial resolution. Numerous scientific studies based on the use of this new type of measurement have led to a better understanding and constraint of the processes governing the variability of the water cycle, ocean circulation and the Earth's climate. The continuity of measurements, and the increased spatial and radiometric resolution is critical for fulfilling scientific needs. Future L-band radiometry missions currently being planned in Europe (the Copernicus Imaging Microwave Radiometer), and in China (the Ocean Salinity mission) should provide better constraints on auxiliary parameters by combining multiple frequencies, but they will not have improved spatial resolution beyond SMOS and SMAP. The temporal continuity with SMOS and SMAP will likely not be ensured. In parallel, new concepts are being developed to increase spatial resolution of both land and ocean parameters.

Matchup Strategies for Satellite Sea Surface Salinity Validation

Satellite validation is the process of comparing satellite measurements with in-situ measurements to ensure their accuracy. Satellite and in-situ sea surface salinity (SSS) measurements are different due to instrumental errors (IE), retrieval errors (RE), and representation differences (RD). In real-world data, IE, RE, and RD are inseparable, but validations seek to quantify only instrumental and retrieval error. Our goal is to determine which of four methods comparing in-situ and satellite measurements minimizes RD most effectively, which includes differences due to mismatches in the location and timing of the measurement, as well as representation error caused by the averaging of satellite measurements over a footprint. IE and RE were obviated by using simulated Argo float, and L2 NASA/SAC-D Aquarius, NASA·SMAP, and ESA·SMOS data generated from the high-resolution ECCO (Estimating the Climate and Circulation of the Oceans) model SSS data. The methods tested include the all-salinity difference averaging method (ASD), the N closest method (NCLO), which is an averaging method that is optimized for different satellites and regions of the ocean, and two single salinity difference methods—closest in space (SSDS) and closest in time (SSDT). The root mean square differences (RMSD) between the simulated in-situ and satellite measurements in seven regions of the ocean are used as a measure of the effectiveness of each method. The optimization of NCLO is examined to determine how the optimum matchup strategy changes depending on satellite track and region. We find that the NCLO method marginally produces the lowest RMSD in all regions but invoking a regionally optimized method is far more computationally expensive than the other methods. We find that averaging methods smooth IE, thus perhaps misleadingly lowering the detected instrumental error in the L2 product by as much as 0.15 PSU. It is apparent from our results that the dynamics of a particular region have more of an effect on matchup success than the method used. We recommend the SSDT validation strategy because it is more computationally efficient than NCLO, considers the proximity of in-situ and satellite measurements in both time and space, does not smooth instrumental errors with averaging, and generally produces RMSD values only slightly higher than the optimized NCLO method.

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.

Simulation Framework and Case Studies for the Design of Sea Surface Salinity Remote Sensing Missions

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> -band microwave radiometers have now been used to measure sea surface salinity (SSS) from space for over a decade with the SMOS, Aquarius, and SMAP missions, and it is expected that the launch of the CIMR mission in the later half of this decade will ensure measurement continuity in the near future. Beyond these missions, it is useful to consider how future missions can be designed to meet different scientific objectives and performance requirements as well as to fit within different cost spaces. In this article, we present a software simulator for remote sensing measurements of ocean state capable of generating L1- and L2- equivalent data products for an arbitrary spacecraft mission including multifrequency fixed-pointing or scanning microwave radiometers.This simulator is then applied to case studies of SSS measurement over selected areas of interest, including the Gulf Stream, Southern Ocean, and Pacific tropical instability wave regions. These simulations illustrate how different design choices concerning receiver bandwidth and revisit time can improve the detection of SSS features in these regions from the mesoscale to the seasonal scale.

Comparison of SMOS, SMAP and In Situ Sea Surface Salinity in the Gulf of St. Lawrence

ABSTRACT The Gulf of St. Lawrence (GSL) is an Eastern Canada semi-enclosed sea under the influence of the freshwater discharge from the Great Lakes – St. Lawrence River drainage basin. Studying the variability of oceanographic conditions in the GSL under a changing climate is important for ecosystem and fisheries management. To supplement the available in situ sea surface salinity (SSS) measurements with satellite SSS data, this study compares all available years of Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) satellite SSS to in situ SSS observations. Despite the relatively cold water and proximity to land and sea ice, the satellite SSS is able to capture the interannual variability and annual cycle of SSS in the GSL, with correlations ranging from 0.80 to 0.85 in the Southern GSL, and 0.22 to 0.77 in the Northern GSL. All satellite SSS data products were able to detect the very low salinity year of 2017 in the Southern GSL.

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.

GOCI-II based sea surface salinity estimation using machine learning for the first-year summer

ABSTRACT Estimation of sea surface salinity (SSS) using the Geostationary Ocean Color Imager-II (GOCI-II) measurements in the East China Sea (ECS) was conducted from July to September of 2021, when the first-year observations are available after GOCI-II launch in 2020. The SSS in the ECS is mainly affected by the Changjiang River plume, which varies from under 20–35 psu, and the discharged freshwater disperses from the river mouth towards Jeju Island, Korea. For the SSS estimation, a multi-layer perceptron neural network (MPNN) was employed to train the nonlinear processes of GOCI-II spectral measurements as inputs and the SSS of Soil Moisture Active Passive (SMAP) as the target. Because GOCI-II has four additional spectral bands (380, 510, 620 and 709 nm) compared to the bands in the first generation of GOCI (413, 443, 490, 555, 660 and 680 nm), we developed a new MPNN algorithm and analysed (1) how much these new spectral measurements increase SSS accuracy, and (2) how the enhanced spatial and temporal resolution of GOCI-II make SSS features different from GOCI. The first results showed that the root mean square error (RMSE) and coefficient of determination (R 2) were 0.68 psu and 0.92, respectively. Furthermore, R 2, when compared with in-situ measurements at the Ieodo Ocean Research Station (I-ORS), increased as much as 0.23 (0.20–0.43) for the 10-band model, which performed much better than previous 6-band model. The second result suggests how the improved spatial features at more frequent SSS measurements can be utilized to avoid low salinity intrusion into the aquafarming sites off the coast of Korea. More importantly, we elucidated why the MPNN algorithm performs better than conventional SSS estimation methods by comparing various optical properties with SSS variation; thus, the newly developed model can provide SSS not only in ECS but also low salinity water near southwest Korean coasts at hourly spatial resolution of 250 m.

Spatial and temporal variability of the physical, carbonate and CO2 properties in the Southern Ocean surface waters during austral summer (2005-2019)

In situ measurements of sea surface temperature (SST), salinity (SSS), Total Alkalinity (AT) and Total Carbon (CT) were obtained during austral summer (mid-February to mid-March) from 2005 to 2019 in the Southern Ocean (SO), along a transect between Hobart, Tasmania and Dumont d’Urville French Antarctic Station. The studied transect is divided in four regions from North to South: the Subtropical Zone (STZ), the Subantarctic Region (SAR), the Antarctic Region (AAR) and the Coastal Antarctic Zone (CAZ). Latitudinal distribution of measured SST, SSS, AT, CT as well as calculated pH, CO2 parameters (seawater fugacity of CO2 (fCO2sw), difference between seawater and atmospheric fCO2 (ΔfCO2), CO2 flux (FCO2)) and satellite-derived Chlorophyll a (Chl-a) are discussed. We show that the variability of physical and carbonate parameters in the STZ and north of the SAR are related to the mesoscale activity. In the CAZ, the freshwater inputs from sea-ice melting strongly impact the variability of all parameters. The comparison between physical and carbonate parameters highlights that AT and CT are directly related to the latitudinal variability of SST and SSS. Study of the CO2 parameters shows that the transect is a sink of CO2 during February and March, with a mean FCO2 of −4.0 ± 2.8 mmol m−2 d−1. The most negative values of FCO2 are found in the STZ and SAR north of 50°S and in the AAR south of 62°S, where biological activity is high. New simple empirical relationships are developed for AT from SST and SSS and for CT using SST, SSS and atmospheric fCO2 (fCO2atm) for the austral summer in the studied area. Using high resolution SSS and SST from the SURVOSTRAL program, trends of AT and CT are determined in the SAR and the AAR from 2005 to 2019. SST, SSS and AT increase over this period in the SAR, which might be explained by the southward migration of the Subtropical Front. In the AAR, no clear trend is detected. CT increases by 1.0 ± 0.2 and 0.8 ± 0.3 μmol kg−1 yr−1 in the SAR and AAR respectively. The trend in the AAR is attributed to the increase in anthropogenic CO2 emissions in the atmosphere while, in the SAR, hydrographic changes also contribute to the increase. Using the coefficient associated with fCO2atm in the equation of CT, we estimate the impact of atmospheric CO2 increase on CT at 1.18 ± 0.14 μmol kg−1 yr−1 and 1.07 ± 0.13 μmol kg−1 yr−1 in the SAR and AAR respectively. Decreases in pH are observed in both regions (−0.0018 ± 0.0001 and −0.0026 ± 0.0003 yr−1 in the SAR and AAR respectively), indicating the sensitivity of surface waters in the area towards the development of ocean acidification processes under rising anthropogenic emissions.

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.

Measurement of SST and SSS Using Frequencies in the Range 0.3–2.0 GHz

AbstractWide bandwidth radiometer systems that make measurements at multiple frequencies in the range from 300 MHz to 2 GHz have been proposed to address parameters important for understanding issues in the cryosphere associated with climate change such as ice sheet thickness and temperature. It is also possible with such a system to retrieve sea surface salinity (SSS), which is important for understanding the impact of climate change on ocean circulation at high latitude. In contemporary sensors for retrieving SSS, such as on Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), sea surface temperature (SST), another parameter important for understanding ocean circulation and necessary in the retrieval of salinity, is treated as an ancillary parameter obtained from an independent source. However, both SSS and SST have peaks in sensitivity below 1 GHz; and it has been shown that measurements at multiple frequencies in this portion of the spectrum can take advantage of this peak in sensitivity to improve the accuracy of the retrieval of SSS. In this manuscript, it will be shown that there is also the potential to retrieve SST and, in cold water, the possibility for improved accuracy over existing retrievals.

Study of the Real-Time Onboard Radio Frequency Interference Detection and Mitigation Strategy for MICAP L-Band Radiometer

Microwave Imager Combined Active and Passive (MICAP), which is a package of active and passive microwave instruments including L/C/K-band radiometers and L-band scatterometer, has been chosen as one of the primary payloads for the Chinese Ocean Salinity Mission (COSM). The L-band one-dimensional synthetic aperture radiometer (L-Rad) is the key part of MICAP to measure sea surface salinity (SSS). As radio frequency interference (RFI) has been reported as a severe threat to L-band radiometry, it can be foreseen that MICAP L-Rad will encounter RFI contaminations unavoidably. Due to the conflict between the huge transmission data volume and the limited downlink budget, it is challenging for MICAP L-Rad to transmit all rapidly sampled sub-band data products and conduct on-ground RFI processing like Soil Moisture Active Passive (SMAP) microwave radiometer. The onboard real-time RFI detection and mitigation strategy is proposed accordingly for MICAP L-Rad. It is designed based on the distributed digital processing structure and the synthetic-aperture feature. The proposed RFI processing strategy is evaluated and verified by the system simulations and hardware experiment, demonstrating its RFI detection capability and low false alarm rate performance.

Enhanced Sea Surface Salinity Estimates Using Machine-Learning Algorithm With SMAP and High-Resolution Buoy Data

Despite the recent advances in satellite-based L-band microwave radiometry and retrieval algorithms to provide a unique capability to measure sea surface salinity (SSS) from space and explore its utility for understanding mesoscale dynamics, global ocean circulation, vertical mixing, evaporation rates and climate oscillations, SSS retrieval from L-band microwave radiometric data from the NASA-SMAP (Soil Moisture Active Passive) mission is often biased with systematic errors on larger temporal, spatial scales and random errors on short-time and length scales. To improve the SSS retrieval from SMAP data, we developed a robust algorithm based on a machine-learning approach with high-resolution in-situ data from the Ocean Moored Buoy Network in the Northern Indian Ocean (OMNI), which includes the Arabian Sea (AS) and Bay of Bengal (BoB)). The new algorithm was rigorously trained, tested and validated using the in-situ SSS time series measurements from the AS and BoB. Several sensitive variables were examined and used to improve the SSS estimates &#x2013; such as radiometric and ancillary data from the satellite observations, sea-surface wind and precipitation from the ERA5 data, and SSS/ SST from the OMNI buoy measurements. The OMNI time-series measurements provided the spatially averaged satellite products to characterize the variability and gross features of SSS in BoB and AS waters on weekly, monthly, seasonal and annual time scales. The systematic validation of SMAP SSS products on a range of spatio-temporal scales showed that the new algorithm improved the SSS estimates by more than 15% in open ocean waters and 25% in river-discharge and precipitation-dominated regions in BoB and AS, when compared to the standard (operational) algorithm. Further analysis demonstrated that the new algorithm reduced clear biases and significant anomalies in SMAP SSS retrievals in the regions of river runoff, surface-freshened ocean and intense tropical cyclones, and captured synoptic/mesoscale SSS features and their seasonal variations in the North Indian Ocean.