scholarly journals Interannual Variability of the Lena River Plume Propagation in 1993–2020 during the Ice-Free Period on the Base of Satellite Salinity, Temperature, and Altimetry Measurements

2021 ◽  
Vol 13 (21) ◽  
pp. 4252
Author(s):  
Vladislav R. Zhuk ◽  
Arseny Alexandrovich Kubryakov
Keyword(s):  
Interannual Variability ◽  
Chemical Properties ◽  
River Plume ◽  
Sea Surface Salinity ◽  
Arctic Seas ◽  
Surface Salinity ◽  
Lena River ◽  
In Situ Data ◽  
The North ◽  
Infrared Measurements

The Lena River plume significantly affects the thermohaline, optical and chemical properties of the eastern Arctic seas. We use sea surface salinity (SSS), temperature (SST), and altimetry measurements to study features of the Lena plume propagation during 1993–2020. A comparison of Soil Moisture Active Passive (SMAP) SSS measurements with in situ data obtained using the flow-through system in oceanographic surveys in 2018–2019 demonstrates good coincidence with correlation ~ 0.96 and RMSD ~ 1 psu. The SMAP data were used to reconstruct the plume evolution in 2015–2020 and to identify three main types of Lena plume propagation, which are mainly related to the variability of dominant zonal wind direction: «northern»—the plume moves to the north from the delta up to 78° N; «eastern»—the plume moves eastward along the Siberian coast up to 180° E; «mixed» between two main types. Brackish plume waters were characterized by increased temperature and sea level, which provides the opportunity for studying the Lena plume dynamics using satellite altimetry and infrared measurements. These data were analyzed to study the interannual variability of plume propagation during the ice-free period of 1993–2020. The obtained results show that the «northern» type is observed twice more often than the «eastern» one, but the «eastern» type has intensified since 2010.

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.

Impact of mesoscale eddies on salinity and CO2 ocean parameters in the western tropical Atlantic in February 2020

2021 ◽  
Author(s):  
Léa Olivier ◽  
Jacqueline Boutin ◽  
Nathalie Lefèvre ◽  
Gilles Reverdin ◽  
Peter Landschützer ◽  
...  
Keyword(s):  
Dissolved Inorganic Carbon ◽  
Salinity Gradient ◽  
Sea Surface Salinity ◽  
Amazon River ◽  
Tropical Atlantic ◽  
Surface Salinity ◽  
The North ◽  
Brazil Current ◽  
North Brazil

<p>Large oceanic eddies are formed by the retroflection of the North Brazil Current (NBC) near 8°N in the western tropical Atlantic. The EUREC<sup>4</sup>A-OA/Atomic cruise took place in January - February 2020, and extensively documented two NBC rings. The NBC flows northward across the Equator and pass the mouth of the Amazon River, entraining fresh and nutrient-rich water along its nearshore edge. From December to March, the Amazon river discharge is low but a freshwater filament stirred by a NBC ring was nevertheless observed. The strong salinity gradient can be used to delineate the NBC ring during its initial phase and its westward propagation. Using satellite sea surface salinity and ocean color associated to in-situ measurements of salinity, temperature, dissolved inorganic carbon, alkalinity and fugacity of CO<sub>2</sub> we characterize the salinity and biogeochemical signature of NBC rings.</p>

scholarly journals Sea-surface salinity variations in the Northern Caribbean Sea across the mid-Pleistocene transition

2010 ◽  
Vol 6 (3) ◽  
pp. 1229-1265
Author(s):  
S. Sepulcre ◽  
L. Vidal ◽  
K. Tachikawa ◽  
F. Rostek ◽  
E. Bard
Keyword(s):  
Temperature Gradient ◽  
Planktonic Foraminifera ◽  
Caribbean Sea ◽  
Meridional Overturning Circulation ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Inter Tropical Convergence Zone ◽  
The North ◽  
The Difference

Abstract. This study aimed at documenting climate changes in tropical area in response to the Mid-Pleistocene Transition (MPT) by reconstructing past hydrologic variations in the Northern Caribbean Sea and its influence on the stability of the Atlantic Meridional Overturning Circulation (AMOC) during the last 940 kyr. Using core MD03-2628, we estimated past changes in sea surface salinity (SSS) using Δδ18O, the difference between the modern and the past δ18O of seawater (obtained by combining alkenone thermometer data with the δ18O of the planktonic foraminifera Globigerinoides ruber (white) and corrected for ice-sheet volume effects). Today, the lowest SSS values in the studied area are associated with the northernmost location of the Inter-Tropical Convergence Zone (ITCZ). The Δδ18O record exhibits glacial/interglacial cyclicity with higher values during all glacial periods spanning the last 940 kyr, indicating increased SSS. At a longer timescale, the Δδ18O exhibits a shift toward lower values for interglacial periods during the last 450 kyr, when compared to interglacial stages older than 650 kyr. A rise in SSS during glacial stages may be related to the southernmost location of the ITCZ, which is induced by a steeper interhemispheric temperature gradient and associated with reduced northward cross equatorial oceanic transport. Therefore, the results suggest a permanent link between the tropical salinity budget and the AMOC during the last 940 kyr. Following the MPT, lower salinities during the last five interglacial stages indicate a northernmost ITCZ location, forced by changes in the interhemispheric temperature gradient that is associated with the poleward position of Southern Oceanic Fronts that amplified the transport of heat and moisture to the North Atlantic. These processes may have contributed to amplification of the climate cycles that followed the MPT.

Meridional Distribution of Surface CO2 along 67°E of the Indian Ocean

2020 ◽  
Author(s):  
Dong-Jin Kang ◽  
Sang-Hwa Choi ◽  
Daeyeon Kim ◽  
Gyeong-Mok Lee
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Seawater ◽  
Surface Salinity ◽  
The North ◽  
The Indian Ocean ◽  
North And South

<p>Surface seawater carbon dioxide was observed from 3 °S to 27 °S along 67 °E of the Indian Ocean in April 2018 and 2019. Partial pressure of CO<sub>2</sub>(pCO<sub>2</sub>) in the surface seawater and the atmosphere were observed every two minutes using an underway CO2 measurement system (General Oceanics Model 8050) installed on R/V Isabu. Surface water temperature and salinity were measured as well. The pCO<sub>2</sub> was measured using Li-7000 NDIR. Standard gases were measured every 8 hours in five classes with concentrations of 0 µatm, 202 µatm, 350 µatm, 447 µatm, and 359.87 µatm. The fCO<sub>2</sub> of atmosphere remained nearly constant at 387 ± 2 µatm, but the surface seawater fCO<sub>2</sub> peaked at about 3 °S and tended to decrease toward the north and south. The distribution of fCO<sub>2</sub> in surface seawater according to latitude tends to be very similar to that of sea surface temperature. In order to investigate the factors that control the distribution of fCO<sub>2</sub> in surface seawater, we analyzed the sea surface temperature, sea surface salinity, and other factors. The effects of salinity are insignificant, and the surface fCO<sub>2</sub> distribution is mainly controlled by sea surface temperature and other factors that can be represented mainly by biological activity and mixing.</p>

High-resolution sea surface salinity and temperature in coastal areas from Sentinel-2 and Copernicus Marine in situ data

2020 ◽  
Author(s):  
Encarni Medina-Lopez
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
In Situ Data ◽  
The Neural Network ◽  
Sentinel 2

<p>The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m x 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82% and 84% for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 C and 0.4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.</p>

scholarly journals Estimating temporal and spatial variation of ocean surface <i>p</i>CO<sub>2</sub> in the North Pacific using a self-organizing map neural network technique

2013 ◽  
Vol 10 (9) ◽  
pp. 6093-6106 ◽  
Author(s):  
S. Nakaoka ◽  
M. Telszewski ◽  
Y. Nojiri ◽  
S. Yasunaka ◽  
C. Miyazaki ◽  
...  
Keyword(s):  
Neural Network ◽  
North Pacific ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Self Organizing Map ◽  
Surface Salinity ◽  
The North ◽  
The North Pacific ◽  
Self Organizing

Abstract. This study uses a neural network technique to produce maps of the partial pressure of oceanic carbon dioxide (pCO2sea) in the North Pacific on a 0.25° latitude × 0.25° longitude grid from 2002 to 2008. The pCO2sea distribution was computed using a self-organizing map (SOM) originally utilized to map the pCO2sea in the North Atlantic. Four proxy parameters – sea surface temperature (SST), mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS) – are used during the training phase to enable the network to resolve the nonlinear relationships between the pCO2sea distribution and biogeochemistry of the basin. The observed pCO2sea data were obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies (NIES). The reconstructed pCO2sea values agreed well with the pCO2sea measurements, with the root-mean-square error ranging from 17.6 μatm (for the NIES dataset used in the SOM) to 20.2 μatm (for independent dataset). We confirmed that the pCO2sea estimates could be improved by including SSS as one of the training parameters and by taking into account secular increases of pCO2sea that have tracked increases in atmospheric CO2. Estimated pCO2sea values accurately reproduced pCO2sea data at several time series locations in the North Pacific. The distributions of pCO2sea revealed by 7 yr averaged monthly pCO2sea maps were similar to Lamont-Doherty Earth Observatory pCO2sea climatology, allowing, however, for a more detailed analysis of biogeochemical conditions. The distributions of pCO2sea anomalies over the North Pacific during the winter clearly showed regional contrasts between El Niño and La Niña years related to changes of SST and vertical mixing.

scholarly journals Evidence of intense climate variation and reduced ENSO activity from δ<sup>18</sup>O of <i>Tridacna</i> 3700 years ago

2019 ◽  
Author(s):  
Yue Hu ◽  
Xiaoming Sun ◽  
Hai Cheng ◽  
Hong Yan
Keyword(s):  
Seasonal Variation ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Tropical Ocean ◽  
Xisha Islands ◽  
The North

Abstract. Tridacna is the largest marine bivalves in the tropical ocean, and its carbonate shell can shed light on high-resolution paleoclimate reconstruction. In this contribution, δ18Oshell was used to estimate the climatic variation in the Xisha Islands of the South China Sea. We first evaluate the sea surface temperature (SST) and sea surface salinity (SSS) influence on modern rehandled monthly (r-monthly) resolution Tridacna gigas δ18Oshell. The obtained results reveal that δ18Oshell seasonal variation is mainly controlled by SST and appear insensitive to local SSS change. Thus, the δ18O of Tridacna shells can be roughly used as a proxy of the local SST: a 1 ‰ δ18Oshell change is roughly equal to 4.41 °C of SST. R-monthly δ18O of a 40-year Tridacna squamosa (3673 ± 28 BP) from the North Reef of Xisha Islands was analyzed and compared with the modern specimen. The difference between the average δ18O of fossil Tridacna shell (δ18O = −1.34 ‰) and modern Tridacna specimen (δ18O = −1.15 ‰) probably implies a warm climate with roughly 0.84°C higher in 3700 years ago. The seasonal variation in 3700 years ago was slightly decreased compared with that suggested by the instrument data, and the switching between warm and cold-seasons was rapid. Higher amplitude in r-monthly and r-annual reconstructed SST anomalies implies an enhanced climate variability in this past warm period. Investigation of the El Ninõ-Southern Oscillation (ENSO) variation (based on the reconstructed SST series) indicates a reduced ENSO frequency but more extreme El Ninõ events in 3700 years ago.

scholarly journals SURATLANT: a 1993–2017 surface sampling in the central part of the North Atlantic subpolar gyre

2018 ◽  
Vol 10 (4) ◽  
pp. 1901-1924 ◽  
Author(s):  
Gilles Reverdin ◽  
Nicolas Metzl ◽  
Solveig Olafsdottir ◽  
Virginie Racapé ◽  
Taro Takahashi ◽  
...  
Keyword(s):  
Decadal Variability ◽  
Dissolved Inorganic Carbon ◽  
Deep Ocean ◽  
Sea Surface Salinity ◽  
Total Alkalinity ◽  
Surface Sampling ◽  
Surface Salinity ◽  
Carbonate System ◽  
Data Set ◽  
The North

Abstract. This paper presents the SURATLANT data set (SURveillance ATLANTique). It consists of individual data of temperature, salinity, parameters of the carbonate system, nutrients, and water stable isotopes (δ18O and δD) collected mostly from ships of opportunity since 1993 along transects between Iceland and Newfoundland (https://doi.org/10.17882/54517). We discuss how the data are validated and qualified, their accuracy, and the overall characteristics of the data set. The data are used to reconstruct seasonal cycles and interannual anomalies, in particular of sea surface salinity (SSS); inorganic nutrients; dissolved inorganic carbon (DIC); and its isotopic composition δ13CDIC, total alkalinity (At), and water isotope concentrations. Derived parameters such as fCO2 and pH are also estimated. The relation between salinity and At is estimated from these data to investigate the possibility to replace missing At when estimating other parameters of the carbonate system. When examining the average seasonal cycle in the deep ocean, in both these data with other climatologies, we find a period of small seasonal change between January and late April. On the Newfoundland shelf and continental slope, changes related with spring stratification and blooms occur earlier. The data were collected in a period of multi-decennial variability associated with the Atlantic multi-decadal variability with warming between 1994 and 2004–2007, and with the recent cooling having peaked in 2014–2016. We also observe strong salinification in 2004–2009 and fresher waters in 1994–1995 as well as since 2010 south of 54° N and in 2016–2017 north of 54° N. Indication of multi-decadal variability is also suggested by other variables, such as phosphate or DIC, but cannot be well resolved seasonally with the discrete sampling and in the presence of interannual variability. As a whole, over the 24 years, the ocean fCO2 trend (+1.9 µatm yr−1) is close to the atmospheric trend and associated with an increase in DIC (+0.77 µmol kg−1 yr−1). The data also revealed a canonical pH decrease of −0.0021 yr−1. There is also a decrease in δ13CDIC between 2005 and 2017 (in winter, −0.014 ‰ yr−1, but larger in summer, −0.042 ‰ yr−1), suggesting a significant anthropogenic carbon signal at play together with other processes (mixing, biological activity).

scholarly journals Comparison of Satellite-Derived Sea Surface Temperature and Sea Surface Salinity Gradients Using the Saildrone California/Baja and North Atlantic Gulf Stream Deployments

2020 ◽  
Vol 12 (11) ◽  
pp. 1839 ◽  
Author(s):  
Jorge Vazquez-Cuervo ◽  
Jose Gomez-Valdes ◽  
Marouan Bouali
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic ◽  
Gulf Stream ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Temporal Sampling ◽  
The North

Validation of satellite-based retrieval of ocean parameters like Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) is commonly done via statistical comparison with in situ measurements. Because in situ observations derived from coastal/tropical moored buoys and Argo floats are only representatives of one specific geographical point, they cannot be used to measure spatial gradients of ocean parameters (i.e., two-dimensional vectors). In this study, we exploit the high temporal sampling of the unmanned surface vehicle (USV) Saildrone (i.e., one measurement per minute) and describe a methodology to compare the magnitude of SST and SSS gradients derived from satellite-based products with those captured by Saildrone. Using two Saildrone campaigns conducted in the California/Baja region in 2018 and in the North Atlantic Gulf Stream in 2019, we compare the magnitude of gradients derived from six different GHRSST Level 4 SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and two SSS (JPLSMAP, RSS40km) datasets. While results indicate strong consistency between Saildrone- and satellite-based observations of SST and SSS, this is not the case for derived gradients with correlations lower than 0.4 for SST and 0.1 for SSS products.

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