scholarly journals SPATIAL AND TEMPORAL ANALYSIS OF SEA SURFACE SALINITY USING SATELLITE IMAGERY IN GULF OF MEXICO

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 219-223 ◽  
Author(s):  
S. Rajabi ◽  
M. Hasanlou ◽  
A. R. Safari
Keyword(s):  
Gulf Of Mexico ◽  
Temporal Analysis ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Loop Current ◽  
Surface Salinity ◽  
Spatiotemporal Resolution ◽  
Study Region ◽  
Distribution Maps ◽  
Saline Waters

The recent development of satellite sea surface salinity (SSS) observations has enabled us to analyse SSS variations with high spatiotemporal resolution. In this regards, The Level3-version4 data observed by Aquarius are used to examine the variability of SSS in Gulf of Mexico for the 2012-2014 time periods. The highest SSS value occurred in April 2013 with the value of 36.72 psu while the lowest value (35.91 psu) was observed in July 2014. Based on the monthly distribution maps which will be demonstrated in the literature, it was observed that east part of the region has lower salinity values than the west part for all months mainly because of the currents which originate from low saline waters of the Caribbean Sea and furthermore the eastward currents like loop current. Also the minimum amounts of salinity occur in coastal waters where the river runoffs make fresh the high saline waters. Our next goal here is to study the patterns of sea surface temperature (SST), chlorophyll-a (CHLa) and fresh water flux (FWF) and examine the contributions of them to SSS variations. So by computing correlation coefficients, the values obtained for SST, FWF and CHLa are 0.7, 0.22 and 0.01 respectively which indicated high correlation of SST on SSS variations. Also by considering the spatial distribution based on the annual means, it found that there is a relationship between the SSS, SST, CHLa and the latitude in the study region which can be interpreted by developing a mathematical model.

scholarly journals Sea Surface Salinity Estimation and Spatial-Temporal Heterogeneity Analysis in the Gulf of Mexico

2021 ◽  
Vol 13 (5) ◽  
pp. 881
Author(s):  
Zhiyi Fu ◽  
Fangfang Wu ◽  
Zhengliang Zhang ◽  
Linshu Hu ◽  
Feng Zhang ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
River Discharge ◽  
River Flow ◽  
High Accuracy ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Temporal Heterogeneity ◽  
Surface Salinity ◽  
Circular Distribution ◽  
Heterogeneity Analysis

As an important parameter to characterize physical and biogeochemical processes, sea surface salinity (SSS) has received extensive attention. Cubist is a data mining model, which can be well-suited to estimate and analyze SSS in the Gulf of Mexico (GOM) because it can reflect the SSS internal heterogeneity in the GOM—overall circular distribution, and the seasonality related to temperature and river discharge changes. Using remote sensing reflectance (Rrs) at 412, 443, 488 (490), 555, and 667 (670) nm and sea surface temperature (SST), a cubist model was developed to estimate SSS with high accuracy with the overall performance demonstrates a root mean square error (RMSE) of 0.27 psu and correlation coefficient of 0.97 of R2. The model divides the GOM area according to model rules into four sub-regions, which include estuary, nearshore, and open sea, reflecting the gradient distribution of SSS. The division of sub-regions and seasonal changes can be explained by the distribution of water bodies, river discharges, and local wind forces since it is obvious that the estuary region reaches the largest low-value area and spreads eastward with the monsoon in the spring when the river flow increases to the highest value. While the east to west wind in the non-summer monsoon period guides the plume westward, and the lowest river discharge in winter corresponds to the smallest low value area. After comparison with other statistical models, the cubist model showed satisfactory results in independent verification of cruise data, proving the estimation capability under different geographical conditions (such as estuaries and open seas) and seasons. Therefore, considering high accuracy and heterogeneity mining, the cubist-based model is an ideal method for coastal SSS estimation and spatial-temporal heterogeneity analysis, and can provide ideas for model construction for coastal areas with similar geographic environments.

scholarly journals Variability of Sea-Air Carbon Dioxide Flux in Autumn Across the Weddell Gyre and Offshore Dronning Maud Land in the Southern Ocean

2021 ◽  
Vol 7 ◽  
Author(s):  
Margaret Ojone Ogundare ◽  
Agneta Fransson ◽  
Melissa Chierici ◽  
Warren R. Joubert ◽  
Alakendra N. Roychoudhury
Keyword(s):  
Carbon Dioxide ◽  
Southern Ocean ◽  
Carbon Dioxide Flux ◽  
Weddell Sea ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Study Region ◽  
Entire Study ◽  
Weddell Gyre

Sea surface fugacity of carbon dioxide (fCO2ssw) was measured across the Weddell gyre and the eastern sector in the Atlantic Southern Ocean in autumn. During the occupation between February and April 2019, the region of the study transect was a potential ocean CO2 sink. A net CO2 flux (FCO2) of −6.2 (± 8; sink) mmol m–2 d–1 was estimated for the entire study region, with the largest average CO2 sink of −10.0 (± 8) mmol m–2 d–1 in the partly ice-covered Astrid Ridge (AR) region near the coast at 68°S and −6.1 (± 8) mmol m–2d–1 was observed in the Maud Rise (MR) region. A CO2 sink was also observed south of 66°S in the Weddell Sea (WS). To assess the main drivers describing the variability of fCO2ssw, a correlation model using fCO2 and oxygen saturation was considered. Spatial distributions of the fCO2 saturation/O2 saturation correlations, described relative to the surface water properties of the controlling variables (chlorophyll a, apparent oxygen utilization (AOU), sea surface temperature, and sea surface salinity) further constrained the interplay of the processes driving the fCO2ssw distributions. Photosynthetic CO2 drawdown significantly offsets the influence of the upwelling of CO2-rich waters in the central Weddell gyre and enhanced the CO2 sink in the region. FCO2 of −6.9 mmol m–2 d–1 estimated for the Weddell gyre in this study was different from FCO2 of −2.5 mmol m–2 d–1 in autumn estimated in a previous study. Due to low CO2 data coverage during autumn, limited sea-air CO2 flux estimates from direct sea-surface CO2 observations particularly for the Weddell gyre region are available with which to compare the values estimated in this study. This highlights the importance of increasing seasonal CO2 observations especially during autumn/winter to improving the seasonal coverage of flux estimates in the seasonal sea ice-covered regions of the Southern Ocean.

scholarly journals Tracking sea surface salinity and dissolved oxygen on a river-influenced, seasonally stratified shelf, Mississippi Bight, northern Gulf of Mexico

2018 ◽  
Vol 169 ◽  
pp. 25-33 ◽  
Author(s):  
Brian Dzwonkowski ◽  
Severine Fournier ◽  
John T. Reager ◽  
Scott Milroy ◽  
Kyeong Park ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Dissolved Oxygen ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Northern Gulf Of Mexico

scholarly journals Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model

2021 ◽  
Vol 13 (15) ◽  
pp. 2995
Author(s):  
Frederick M. Bingham ◽  
Severine Fournier ◽  
Susannah Brodnitz ◽  
Karly Ulfsax ◽  
Hong Zhang
Keyword(s):  
High Resolution ◽  
Time Window ◽  
Single Point ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Argo Floats ◽  
Statistical Measures ◽  
Salinity Difference

Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values being greater than satellites. RMSD is computed using an “all salinity difference” method that averages level 2 satellite observations within a given time and space window for comparison with Argo floats. RMSD values range from 0.08 to 0.18 depending on the space–time window and the satellite. This range gives an estimate of the representation error inherent in comparing single point Argo floats to area-average satellite values. The study has implications for future SSS satellite missions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS values.

scholarly journals Sea Surface Salinity Response to Tropical Cyclones Based on Satellite Observations

2021 ◽  
Vol 13 (3) ◽  
pp. 420
Author(s):  
Jingru Sun ◽  
Gabriel Vecchi ◽  
Brian Soden
Keyword(s):  
Tropical Cyclones ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Translation Speed ◽  
Salinity Stratification ◽  
Left Hand ◽  
In Situ Data ◽  
Salinity Response ◽  
The Right

Multi-year records of satellite remote sensing of sea surface salinity (SSS) provide an opportunity to investigate the climatological characteristics of the SSS response to tropical cyclones (TCs). In this study, the influence of TC winds, rainfall and preexisting ocean stratification on SSS evolution is examined with multiple satellite-based and in-situ data. Global storm-centered composites indicate that TCs act to initially freshen the ocean surface (due to precipitation), and subsequently salinify the surface, largely through vertical ocean processes (mixing and upwelling), although regional hydrography can lead to local departure from this behavior. On average, on the day a TC passes, a strong SSS decrease is observed. The fresh anomaly is subsequently replaced by a net surface salinification, which persists for weeks. This salinification is larger on the right (left)-hand side of the storm motion in the Northern (Southern) Hemisphere, consistent with the location of stronger turbulent mixing. The influence of TC intensity and translation speed on the ocean response is also examined. Despite having greater precipitation, stronger TCs tend to produce longer-lasting, stronger and deeper salinification especially on the right-hand side of the storm motion. Faster moving TCs are found to have slightly weaker freshening with larger area coverage during the passage, but comparable salinification after the passage. The ocean haline response in four basins with different climatological salinity stratification reveals a significant impact of vertical stratification on the salinity response during and after the passage of TCs.

scholarly journals Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models

2021 ◽  
Vol 13 (5) ◽  
pp. 831
Author(s):  
Jorge Vazquez-Cuervo ◽  
Chelle Gentemann ◽  
Wenqing Tang ◽  
Dustin Carroll ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Future Research ◽  
Surface Salinity ◽  
Cold Temperatures ◽  
Yukon River ◽  
Arctic Sea ◽  
The Arctic Ocean

The Arctic Ocean is one of the most important and challenging regions to observe—it experiences the largest changes from climate warming, and at the same time is one of the most difficult to sample because of sea ice and extreme cold temperatures. Two NASA-sponsored deployments of the Saildrone vehicle provided a unique opportunity for validating sea-surface salinity (SSS) derived from three separate products that use data from the Soil Moisture Active Passive (SMAP) satellite. To examine possible issues in resolving mesoscale-to-submesoscale variability, comparisons were also made with two versions of the Estimating the Circulation and Climate of the Ocean (ECCO) model (Carroll, D; Menmenlis, D; Zhang, H.). The results indicate that the three SMAP products resolve the runoff signal associated with the Yukon River, with high correlation between SMAP products and Saildrone SSS. Spectral slopes, overall, replicate the −2.0 slopes associated with mesoscale-submesoscale variability. Statistically significant spatial coherences exist for all products, with peaks close to 100 km. Based on these encouraging results, future research should focus on improving derivations of satellite-derived SSS in the Arctic Ocean and integrating model results to complement remote sensing observations.

scholarly journals Intercomparison of Global Sea Surface Salinity from Multiple Datasets over 2011–2018

2021 ◽  
Vol 13 (4) ◽  
pp. 811
Author(s):  
Hao Liu ◽  
Zexun Wei
Keyword(s):  
Time Scales ◽  
Correlation Coefficients ◽  
Dominant Mode ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Ocean Data Assimilation ◽  
Multiple Datasets ◽  
Different Time Scales

The variability in sea surface salinity (SSS) on different time scales plays an important role in associated oceanic or climate processes. In this study, we compare the SSS on sub-annual, annual, and interannual time scales among ten datasets, including in situ-based and satellite-based SSS products over 2011–2018. Furthermore, the dominant mode on different time scales is compared using the empirical orthogonal function (EOF). Our results show that the largest spread of ten products occurs on the sub-annual time scale. High correlation coefficients (0.6~0.95) are found in the global mean annual and interannual SSSs between individual products and the ensemble mean. Furthermore, this study shows good agreement among the ten datasets in representing the dominant mode of SSS on the annual and interannual time scales. This analysis provides information on the consistency and discrepancy of datasets to guide future use, such as improvements to ocean data assimilation and the quality of satellite-based data.

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