August 27-29, 2018
Santa Rosa, CA USANASA Physical Oceanography Program sponsored the Ocean Salinity Science Team and Salinity Continuity Processing meeting held at Remote Sensing Systems in August 2018. The primary objectives of this meeting were to review recent work on sea surface salinity products and their applications, discuss improvements in SMAP satellite SSS retrieval, and develop a strategy for integrating SSS into the next Decadal Survey for Earth Science and Applications from Space.Agenda

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Manaster, A., Meissner, T., and Wentz., F. [27-Aug-18]. The most recent versions of SMAP L-Band radiometer salinity retrievals are the Jet Propulsion Lab Version 4 (JPL V4) and the Remote Sensing Systems Version 3 (RSS V3) L2 and L3 salinity data. These two products represent the latest round of improvements to SMAP salinity measurements. In this study JPL V4 and RSS V3 salinities are compared at various spatial and temporal scales and differences between the two are examined. By cataloging the similarities and differences between the two products, we can further work towards even more accurate and convergent SMAP salinity retrievals. Kilic, L., Prigent, C., Aires, F., Boutin, J., Heygster, G., and Meissner, T. [29-Aug-18]. Surface characterization from satellites is required to understand, monitor and predict the general circulation of the ocean and atmosphere, the interaction between the surface and the atmosphere, as well as the water and energy cycles. With more than 70% cloud coverage at any time, visible and infrared satellite observations only provide limited information. Regardless of the cloud cover, passive microwaves offer ocean and sea ice information such as Sea Surface Temperature (SST), Ocean Wind Speed (OWS) even under extreme conditions, Sea Surface Salinity (SSS), Sea Ice Concentration (SIC) or thin Sea Ice Thickness (SIT). These products are particularly important for polar regions that are very vulnerable to climate change. Up to now all these oceanic/sea ice parameters estimated from passive microwave observations are limited in spatial resolution and/or accuracy. Jacob, M.M., Jones, L., Drushka, K., and Asher, W. [28-Aug-18]. When rain falls over the ocean, it produces a vertical salinity profile that is fresher at the surface. This fresh water will be mixed downward by turbulent diffusion through gravity waves and the wind stress (Boutin et. al., 2014), which dissipates over a few hours until the upper layer (1-5 m depth) becomes well mixed. Therefore, there will be a transient bias between the bulk salinity, measured by in situ instruments, and the satellite-measured SSS (representative of the first cm of the ocean depth). Jiang, Y. [28-Aug-18]. The Amazon river discharges about 15% of all the fresh water into the oceans by all the rivers of the world. Seasonal fresh water discharge from Amazon river is shown by the lower sea surface salinity (SSS) plume in the Atlantic Ocean. Both RSS SMAP SS v2.0 Level 3 dataset and Tropical Rainfall Measuring Mission (TRMM) Precipitation dataset spanning from 2015 to current are used for the analysis and comparison. SSS average from 55°W to 45°W in longitude and from 2°S to 10°N latitude region presents the seasonal variation with lowest SSS around summer and maximum around winter season which is lagged by about three months from the TRMM precipitation average data. Asher, W., Drushka, K., Jacobs, M.M., and Jones, W.L. [28-Aug-18]. Rain-induced freshening of the top meter of the ocean can cause an error in validating satellite salinity measurements with in situ salinity data measured at depths below the surface fresh layer. In order to determine the importance of this error, the Rain-Impact Model (RIM) was developed (Santos-Garcia et al., 2014). RIM is a semi-empirical model that estimates the rain-induced freshening between the surface and a depth of 1 m using global rain products and a one-dimensional model of vertical diffusion in the upper ocean. Melnichenko, O., Hacker, P., Meissner, T., Wentz., F., and Potemra, J. [28-Aug-18]. To address the need for a consistent, continuous, long-term, high-resolution sea surface salinity (SSS) dataset for ocean research and applications, a trial SSS analysis is produced in the eastern tropical Pacific from multi-satellite observations. The new SSS data record is a synergy of data from two NASA satellite missions. The beginning segment, covering the period from September 2011 to June 2015, utilizes Aquarius SSS data and is based on the optimum interpolation analysis (OI SSS) developed at the University of Hawaii. Schanze, J., Kao, H-Y., Springer, S.R., Lagerloef, G.S.E., Carey, D., and Thompson, E.J. [27-Aug-18]. Here, we present a brief update on ongoing salinity science and validation at Earth and Space Research. With the transition from Aquarius to SMAP, it is particularly important to understand the differences in how surface salinity features are observed by these radiometers to create a continuous salinity data record spanning seven years. Furthermore, regular validation of SMAP is crucial to understand limitations and advances in the retrieval algorithms, and to assess the quality of the observations. Boutin, J., Vergely, J.L., Supply, A., Khvorostyanov, D., and Tarot, S. [27-Aug-18]. Salinity observing satellites have the potential to monitor river fresh-water plumes mesoscale spatio-temporal variations better than any other observing system. In the case of the Soil Moisture and Ocean Salinity (SMOS) satellite mission, this capacity was hampered due to the contamination of SMOS data processing by strong land-sea emissivity contrasts. Based on the internal consistency of SMOS Sea Surface Salinity retrieved in various locations across swath, a correction was developed to mitigate SMOS systematic errors in the vicinity of continents and seasonally-varying latitudinal systematic errors (Boutin et al. 2018). Brown, S. and Misra, S. [27-Aug-18]. In this talk, we will present results comparing residual systematic biases in SMAP to those that remained in the Aquarius V5 product. The aim is to identify common and non-common errors that will lead to a better understanding of their origin. Common errors can be traced to the processing algorithms and model functions, while non-common errors will have their origin in the individual instrument processing. We will analyze seasonal and regional biases over time. We will also present a detailed analysis of the noise in the SMAP product and the spatial correlation. Unlike Aquarius, SMAP is an imaging sensor. It is important to understand the spatial correlation of the noise that results from employing along track averaging of the calibration measurements as well as resampling the footprints. We will show the spatial correlation as a function of scale length which we expect to benefit future updates to the processing algorithms. Dinnat, E.P., Le Vine, D.M., Boutin, J., and Meissner, T. [27-Aug-18]. We present comparisons between satellite sea surface salinity (SSS) products from the SMOS, Aquarius and SMAP missions and assess some of the possible reasons for the observed differences. We also compare satellite products to in situ observations. The comparisons focus on SSS large scale spatial patterns, temporal variability at regions of reference and statistical distribution. We also assess the dependence of SSS differences (between satellite products and between satellite and in situ) to sea surface temperature (SST), which has been an ongoing issue in the previous product versions. Vazquez-Cuervo, J., Fournier, S., Dzwonkowski,B., and Reager, J.T. [27-Aug-18]. Four satellite derived sea surface salinity (SSS) datasets are evaluated in the Gulf of Mexico (GoM), three from NASA's Soil Moisture Active Passive (SMAP) mission and one from the European Space Agency's Soil Moisture Ocean Salinity (SMOS) mission. The first two SMAP products are from Remote Sensing Systems (REMSS) with different resolutions of 40 km and 70 km, while the third, a 40 km product, is produced by the Jet Propulsion Laboratory. All products are compared against in situ data from buoys and the World Ocean Database (WOD) in the GoM, a coastal/semi-enclosed basin strongly influence by river discharge. Fournier, S., Lee, T., and Steele, M. [27-Aug-18]. In this work, we present an assessment of the quality of the different satellite SSS products against multiple in situ datasets and we provide an intercomparison of these different satellite SSS products. Zhou, Y., Lang, R., Dinnat, E., and Le Vine, D.M. [27-Aug-18]. A transmission-type TM mode resonant cavity has been employed to determine the seawater dielectric constant at L-band accurately (Lang et al. 2016). Based on the dielectric constant data, a dielectric model function has been generated and applied to retrieve the seawater surface salinity from the Aquarius satellite data (Zhou et al. 2017). The comparison between the retrieved salinity and the in situ data, however, indicated that more accuracy is still needed, especially at low temperature, for the dielectric measurements of seawater. D'Addezio, J.M. and Bingham, F.M. [28-Aug-18]. Submesoscale resolving simulations of the North Arabian Sea and western Pacific using the Navy Coastal Ocean Model (NCOM) for 2016 were examined to estimate sea surface salinity (SSS) subfootprint variability (SFV). Footprint sizes of 10 km, 20 km, 40 km, and 100 km were tested. The high-resolution model grids were partitioned into regularly spaced, larger subdomains to simulate each footprint size. SFV was quantified by the annual median SSS standard deviation within each of the respective subdomains. deCharon, A. and Vazquez, J. [29-Aug-18]. As a NASA pathfinder mission dedicated to public engagement, Aquarius made significant strides in broadening interest in salinity beyond the scientific community. Using lessons learned from this experience, we will help the Ocean Salinity Science Team (OSST) contribute to NASA's strategic communications goals in specific and measurable ways. By doing so, the OSST will collectively contribute to broader ocean and climate literacy efforts. Dinnat, E.P., Le Vine, D.M., De Amici, G., and Piepmeier, J. [29-Aug-18]. We report on the science requirements and technical definition of a next-generation spaceborne instrument for ocean salinity remote sensing. The new sensor will be designed to improve retrievals in cold water and enhance applications closer to the shores where there are important interactions between land, ocean and ice. With the advent of the SMOS and Aquarius instruments in 2010, it has become possible to monitor global sea surface salinity (SSS) on a weekly to monthly basis. These sensors have also shown the increased complexity of retrieving SSS in the cold water of the high latitudes and close to land and ice boundaries. These limitations have hindered the application of space-borne SSS observations to the study of important processes such as ocean freshening due to ice melt and river outflow, especially in the high latitude and in narrow costal currents. Bayler, E. [28-Aug-18]. NOAA/NCEP has been running real-time operational ocean data assimilation systems since 2004. NOAA continues to pursue satellite sea-surface salinity (SSS) data for its operational models, specifically the National Weather Service's (NWS) real-time Real-Time Ocean Forecast System (RTOFS) and the real-time seasonal-interannual Global Ocean Data Assimilation System (GODAS), the ocean component of NOAA's coupled Climate Forecast System (CFS). NOAA's ocean data assimilation systems and core ocean models are both in transition. Clarke, A.J. and Zhang, X. [28-Aug-18]. If intense rain falls uniformly at a rate of 50mm/hour over a circular lake of radius a, and there is no evaporation, then after 1 hour the sea level would rise by 5cm. But how is the sea level rise distributed over a similar circular region if the rain falls over the open ocean? Analytical results for this idealized rainfall with rate R provide physical insight about how the size of the ocean response depends on the rainfall rate, its duration, and its horizontal scale. Zhu, J., Ren, L., Kumar, A., Murtugudde, R., and Xie, P. [28-Aug-18]. In this presentation, we will give an update of our project supported by NASA's Ocean Salinity Science Team, by focusing on the first two tasks: (1) role of SSS in MJO predictability, and (2) development of sub-monthly SSS products. Soldo, Y., de Matthaeis, P., and Le Vine D.M. [27-Aug-18]. This talk will present examples of the SMAP RFI maps and research being done on RFI detection and mitigation in support of the SMAP salinity project. Lee, T. and Fournier, S. [28-Aug-18]. Variations of sea surface salinity (SSS) in the southeast Asian Seas (SEAS) have important implications to ocean circulation and climate variability. Systematic monitoring of salinity changes in the SEAS region has been extremely challenging due to the complicated geometry and other factors. This has hindered our understanding of freshwater changes in the SEAS region, the relationships with climate variability (e.g., monsoon, El Niño-Southern Oscillation, and Indian Ocean Dipole), and the potential implications to climate predictions. Menezes, V.V. [28-Aug-18]. The Arabian Sea exhibits the highest salinity of the Indian Ocean with values above 36.5 (psu), being a source of salt for the entire basin in several time scales. Despite the Arabian Sea to be a central region to understand the salinity dynamics of the Indian Ocean, the time scales of variability of sea surface salinity (SSS) in this region is not fully known due to the historical lack of in situ observations in basin scale with relatively high temporal resolution. In the present work, Aquarius and SMAP satellite and Argo SSS data are used to characterize these scales. Misra, S. and Brown, S. [27-Aug-18]. We will present a summary of the SMAP radiometer calibration activities undertaken by the SMAP science team and project team for the recent Version 4.0 data release. SMAP recently completed three years of operation in space with a fairly stable daily average NEDT of 0.96 K and an overall drift of less than 0.1K. Sabia, R., Reul, N., Guimbard, S., Mecklenburg, S., and Laur, H. [29-Aug-18]. The SMOS Pilot Mission Exploitation Platform (Pi-MEP) for Salinity is an ESA initiative to support and widen the uptake of Soil Moisture and Ocean Salinity (SMOS) mission data over the ocean. Melnichenko, O., Hacker, P., and Meissner, T. [27-Aug-18]. The Soil Moisture Active Passive (SMAP) satellite based version V2.0 SSS data delivered by Remote Sensing Systems (RSS) are evaluated against in situ salinity from Argo buoy measurements. To assess the effect of smoothing, two versions of the data have been evaluated- the so-called 40-km and 70-km resolution products. The difference statistics are computed by comparing Argo buoy measurements for a given week with SSS values at the same locations obtained by interpolation of the corresponding Level-3 SSS maps. Bingham, F. [28-Aug-18]. Using the SPURS-1 and SPURS-2 datasets, I calculated the subfootprint variability (SFV) of sea surface salinity (SSS). The SFV was defined as the weighted standard deviation of SSS within a 50 km distance of the central mooring for each field campaign. Data for SPURS-1 included drifters, shipboard TSGs, wavegliders, etc. For SPURS-2 the calculation was only done using waveglider data. Separate calculations were done for each field campaign using weekly mooring records, plus a high resolution ROMS model run. Yu, L. [28-Aug-18]. The subtropical surface salinity maximum (SSS-max), the net evaporation maximum, and the subtropical underwater (STUW) are intertwined with each other and with the wind-driven gyre circulation of the upper ocean. The evaporation-minus-precipitation (E-P) flux is a forcing for SSS-max, and the SSS-max is the source water in the subduction process that forms the STUW. Since neither the E-P flux nor the subduction process can be observed directly, satellite observations of the SSS-max provide not only a useful additional gauge on the change of ocean water cycle but also a window into the water mass exchange between the ocean surface and the permanent pycnocline. Vazquez-Cuervo, J. and Tsontos, V. [29-Aug-18]. This talk presents a summary of the archival process for Aquarius and the support and distribution of NASA Salinity data going forward. Aquarius V 5.0 end-of-mission data was archived and released publicly in December 2017. The Phase F Aquarius mission artifact preservation and closeout task were completed in July 2018. Fore, A., Yueh, S., Tang, W., and Hayashi, A. [27-Aug-18]. The Soil Moisture Active Passive (SMAP) mission was launched January 31st, 2015. It is designed to measure the soil moisture over land using a combined active / passive L-band system. Due to the Aquarius mission, L-band model functions for ocean winds and salinity are already mature and have been directly applied to the SMAP mission. In contrast to Aquarius, the higher resolution and scanning geometry of SMAP allows for wide-swath ocean winds and salinities to be retrieved. In this talk we present the SMAP Sea Surface Salinity (SSS) dataset and algorithm. Tang, W., Yueh, S., Yang, D., Fore, A., and Hayashi, A. [27-Aug-18]. Sea surface salinity (SSS) links various components of the Arctic freshwater system. SSS responds to freshwater inputs from river discharge, sea ice change, precipitation and evaporation, and oceanic transport through the open straits of the Pacific and Atlantic oceans. However, in situ SSS data in the Arctic Ocean are very sparse and insufficient to depict the large-scale variability. With improved land correction, the JPL SMAP SSS algorithm is able to retrieve SSS in ice-free regions within 35 km of the coast. Wentz, F., Meissner, T., and Manaster, A. [27-Aug-18]. This presentation discusses the resampling and land contamination correction in the RSS SMAP Salinity Version 3 release. We will provide an analysis showing the degradation of the SMAP salinity retrievals as function of distance from the coast with and without applying the land contamination correction, for both the 40-km and the 70-km salinity products. Meissner, T., Wentz, F., and Manaster, A. [27-Aug-18]. This presentation gives a detailed account of the RSS Version 3 SMAP SSS release, which is scheduled for summer 2018. We discuss the major steps of the SMAP salinity retrieval algorithm, including updates and improvements from the Version 2 release and we compare with the algorithm of the Aquarius Version 5 release. Ruiz-Etcheverry, L.A., Maximenko, N., and Melnicenko, O. [28-Aug-18]. The Equatorial Atlantic Ocean is a region dominated by the seasonal trade winds and Inter Tropical Convergence Zone (ITCZ). It is also marked by the existence of a strong sea surface temperature (SST) front due to the formation of the equatorial cold tongue. These features are believed to have strong effects on the atmospheric circulation in the region and thus climate. Little, however, is known about the salinity front that became possible to resolve only with the release of high-resolution satellite salinity products. In this study, we use sea surface salinity (SSS) observations from the Aquarius satellite and from the reanalysis model GLORYS to investigate the spatial structure, temporal variability, and driving mechanisms of the frontal SSS feature in the equatorial Atlantic. Kao, H-Y. and Schanze, J. [27-Aug-18]. The Aquarius validation data system (AVDS) was developed to monitor and evaluate the Aquarius data quality from version to version. AVDS provided the Aquarius matchup with in situ observations and was useful to examine the global, regional, seasonal and long-term salinity biases in Aquarius data. To continue the salinity data validation from Aquarius to SMAP, AVDS was revised into salinity validation data system (SVDS) based on the data format and the design of the SMAP satellite. SVDS will be used to closely evaluate the SMAP salinity data quality in each version. In this talk, we present the results of the SVDS for the most recent version of SMAP-SSS. Supply, A., Boutin, J., and Reverdin, G. [28-Aug-18]. Two L-Band (1.4GHz) microwave radiometer missions, the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) currently provide salinity measurements in the first centimetre below the sea surface. At this depth, salinity is very sensitive to precipitations that dominate variability at small temporal scale, except in river plumes (Amazon, Mississippi, etc.). The rain-related freshening observed with SMOS and SMAP is very consistent between 50°S and 50°N once the salinity error associated to each instrument is take into account. Liu, W.T. and Xie, X. [28-Aug-18]. Ocean surface carbon dioxide partial pressure governs major variability of the ocean uptake of atmospheric carbon dioxide. The up-take mitigates greenhouse warming and increases ocean acidification; acidification is harmful to marine life. We have produced daily maps of the partial pressure and pH. pH is a logarithm scale of the concentration of hydrogen ion in a solution; more acidic solutions have lower value and neutral is 7. Our data reveal that water freshens up the ocean (lower surface salinity), dilutes the carbon in the ocean, causes lower partial pressure and higher pH (lower acidity).