scholarly journals Sea surface salinity short-term variability in the tropics

Ocean Science ◽  
2021 ◽  
Vol 17 (5) ◽  
pp. 1437-1447
Author(s):  
Frederick M. Bingham ◽  
Susannah Brodnitz
Keyword(s):  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Satellite Measurement ◽  
Validation Process ◽  
Short Term ◽  
Surface Salinity ◽  
Using Data ◽  
Short Term Variability

Abstract. Using data from the Global Tropical Moored Buoy Array, we study the validation process for satellite measurement of sea surface salinity (SSS). We compute short-term variability (STV) of SSS, variability on timescales of 2–17 d. It is a proxy for subfootprint variability over a 100 km footprint as seen by a satellite measuring SSS. We also compute representation error, which is meant to mimic the SSS satellite validation process where footprint averages are compared to pointwise in situ values. We present maps of these quantities over the tropical array. We also look at seasonality in the variability of SSS and find which months have maximum and minimum amounts. STV is driven at least partly by rainfall. Moorings exhibit larger STV during rainy periods than during non-rainy ones. The same computations are also done using output from a high-resolution global ocean model to see how it might be used to study the validation process. The model gives good estimates of STV, in line with the moorings, although tending to have smaller values.

scholarly journals Sea Surface Salinity Short Term Variability in the Tropics

2021 ◽  
Author(s):  
Frederick Bingham ◽  
Susannah Brodnitz
Keyword(s):  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Satellite Measurement ◽  
Validation Process ◽  
Short Term ◽  
Surface Salinity ◽  
Using Data ◽  
Short Term Variability

Abstract. Using data from the Global Tropical Moored Buoy Array we study the validation process for satellite measurement of sea surface salinity (SSS). We compute short-term variability (STV) of SSS, variability on time scales of 5–14 days. It is meant to be a proxy for subfootprint variability as seen by a satellite measuring SSS. We also compute representation error, which is meant to mimic the SSS satellite validation process where footprint averages are compared to pointwise in situ values. We present maps of these quantities over the tropical array. We also look at seasonality in the variability of SSS and find which months have maximum and minimum amounts. STV is driven at least partly by rainfall. Moorings exhibit larger STV during rainy periods than non-rainy ones. The same computations are also done using output from a high-resolution global ocean model to see how it might be used to study the validation process. The model gives good estimates of STV, in line with the moorings, though tending to have smaller values.

scholarly journals Assessment of Aquarius Sea Surface Salinity

2018 ◽  
Vol 10 (9) ◽  
pp. 1341 ◽  
Author(s):  
Hsun-Ying Kao ◽  
Gary Lagerloef ◽  
Tong Lee ◽  
Oleg Melnichenko ◽  
Thomas Meissner ◽  
...  
Keyword(s):  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
General View ◽  
Surface Salinity ◽  
Validation Data ◽  
Accuracy Requirement ◽  
Salinity Data ◽  
Mean Square Errors

Aquarius was the first NASA satellite to observe the sea surface salinity (SSS) over the global ocean. The mission successfully collected data from 25 August 2011 to 7 June 2015. The Aquarius project released its final version (Version-5) of the SSS data product in December 2017. The purpose of this paper is to summarize the validation results from the Aquarius Validation Data System (AVDS) and other statistical methods, and to provide a general view of the Aquarius SSS quality to the users. The results demonstrate that Aquarius has met the mission target measurement accuracy requirement of 0.2 psu on monthly averages on 150 km scale. From the triple point analysis using Aquarius, in situ field and Hybrid Coordinate Ocean Model (HYCOM) products, the root mean square errors of Aquarius Level-2 and Level-3 data are estimated to be 0.17 psu and 0.13 psu, respectively. It is important that caution should be exercised when using Aquarius salinity data in areas with high radio frequency interference (RFI) and heavy rainfall, close to the coast lines where leakage of land signals may significantly affect the quality of the SSS data, and at high-latitude oceans where the L-band radiometer has poor sensitivity to SSS.

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 The Role of Averaging for Improving Sea Surface Salinity Retrieval from the Soil Moisture and Ocean Salinity (SMOS) Satellite and Impact of Auxiliary Data

2007 ◽  
Vol 24 (2) ◽  
pp. 255-269 ◽  
Author(s):  
Sabine Philipps ◽  
Christine Boone ◽  
Estelle Obligis
Keyword(s):  
Soil Moisture ◽  
Sensitivity Study ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
Auxiliary Data ◽  
Additional Information ◽  
Ocean Salinity

Abstract Soil Moisture and Ocean Salinity (SMOS) was chosen as the European Space Agency’s second Earth Explorer Opportunity mission. One of the objectives is to retrieve sea surface salinity (SSS) from measured brightness temperatures (TBs) at L band with a precision of 0.2 practical salinity units (psu) with averages taken over 200 km by 200 km areas and 10 days [as suggested in the requirements of the Global Ocean Data Assimilation Experiment (GODAE)]. The retrieval is performed here by an inverse model and additional information of auxiliary SSS, sea surface temperature (SST), and wind speed (W). A sensitivity study is done to observe the influence of the TBs and auxiliary data on the SSS retrieval. The key role of TB and W accuracy on SSS retrieval is verified. Retrieval is then done over the Atlantic for two cases. In case A, auxiliary data are simulated from two model outputs by adding white noise. The more realistic case B uses independent databases for reference and auxiliary ocean parameters. For these cases, the RMS error of retrieved SSS on pixel scale is around 1 psu (1.2 for case B). Averaging over GODAE scales reduces the SSS error by a factor of 12 (4 for case B). The weaker error reduction in case B is most likely due to the correlation of errors in auxiliary data. This study shows that SSS retrieval will be very sensitive to errors on auxiliary data. Specific efforts should be devoted to improving the quality of auxiliary data.

Ocean Salinity Aspects of the Ningaloo Niño

2021 ◽  
pp. 1
Author(s):  
Yaru Guo ◽  
Yuanlong Li ◽  
Fan Wang ◽  
Yuntao Wei
Keyword(s):  
Fresh Water ◽  
Large Scale ◽  
Southern Oscillation ◽  
Regional Climate ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Near Surface ◽  
Ocean Salinity

AbstractNingaloo Niño – the interannually occurring warming episode in the southeast Indian Ocean (SEIO) – has strong signatures in ocean temperature and circulation and exerts profound impacts on regional climate and marine biosystems. Analysis of observational data and eddy-resolving regional ocean model simulations reveals that the Ningaloo Niño/Niña can also induce pronounced variability in ocean salinity, causing large-scale sea surface salinity (SSS) freshening of 0.15–0.20 psu in the SEIO during its warm phase. Model experiments are performed to understand the underlying processes. This SSS freshening is mutually caused by the increased local precipitation (~68%) and enhanced fresh-water transport of the Indonesian Throughflow (ITF; ~28%) during Ningaloo Niño events. The effects of other processes, such as local winds and evaporation, are secondary (~18%). The ITF enhances the southward fresh-water advection near the eastern boundary, which is critical in causing the strong freshening (> 0.20 psu) near the Western Australian coast. Owing to the strong modulation effect of the ITF, SSS near the coast bears a higher correlation with the El Niño-Southern Oscillation (0.57, 0.77, and 0.70 with Niño-3, Niño-4, and Niño-3.4 indices, respectively) than sea surface temperature (-0.27, -0.42, and -0.35) during 1993-2016. Yet, an idealized model experiment with artificial damping for salinity anomaly indicates that ocean salinity has limited impact on ocean near-surface stratification and thus minimal feedback effect on the warming of Ningaloo Niño.

Colder and smaller : 10 years of observations of surface salinity by SMOS, Aquarius and SMAP to study mesoscale eddies in the Southern Ocean

2020 ◽  
Author(s):  
Audrey Hasson ◽  
Cori Pegliasco ◽  
Jacqueline Boutin ◽  
Rosemary Morrow
Keyword(s):  
Soil Moisture ◽  
Southern Ocean ◽  
European Space Agency ◽  
Mesoscale Eddy ◽  
Mesoscale Eddies ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
Sst Anomalies

<p>Since 2010, space missions dedicated to Sea Surface Salinity (SSS) have been providing observations with almost complete coverage of the global ocean and a resolution of about 45 km every 3 days. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission was the first orbiting radiometer to collect regular SSS observations from space. The Aquarius and SMAP (Soil Moisture Active-Passive) missions of the National Aeronautics and Space Administration (NASA) then reinforced the SSS observing system between mid-2011 and mid-2015 and since mid-2015, respectively.</p><p>Using the most recent SSS Climate Change Initiative project dataset merging data from the 3 missions, this study investigates the SSS signal associated with mesoscale eddies in the Southern Ocean. Eddies location and characteristics are obtained from the daily v3 mesoscale eddy trajectory atlas produced by CLS. SSS anomalies along the eddies journey are computed and compared to Sea Surface Temperature (SST) anomalies (v4 Remote Sensing Systems) as well as the SubAntarctic Front (SAF) position (CTOH, LEGOS). The vertical structure of the eddies is further investigated using profiles from colocated Argo autonomous floats.<span> </span></p><p>This study highlights a robust signal in SSS depending on both the eddies rotation (cyclone/anticyclone) and latitudinal position with respect to the SAF. Moreover, this dependence is not found in SST. These observations reveal oceanic the interaction of eddies with the larger scale ocean water masses. SSS and SST anomalies composites indeed show different patterns either bi-poles linked with horizontal stirring of fronts, mono-poles from trapping water or vertical mixing changes, or a mix of the two.</p><p>This analysis gives strong hints for the erosion of subsurface waters, such as mode waters, induced by enhanced mixing caused by the deep-reaching eddies of the southern ocean.</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>

Impact of improved Ocean initial condition in Climate Forecast System (CFSv2) Hindcast run

2020 ◽  
Author(s):  
Samir Pokhrel ◽  
Hasibur Rahaman ◽  
Hemantkumar Chaudhari ◽  
Subodh Kumar Saha ◽  
Anupam Hazra
Keyword(s):  
Monsoon Rainfall ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Climate Forecast ◽  
Surface Currents ◽  
Forecast System ◽  
Surface Salinity ◽  
Climate Forecast System ◽  
The Indian Ocean

<p>IITM provides seasonal monsoon rainfall forecast using modified CGCM CFSv2. The present operational CFSv2 initilized with the INCOIS-GODAS ocean analysis based on MOM4p0d and 3DVar assimilation schemes. Recently new Ocean analysis GODAS-Mom4p1 using Moduler Ocean Model (MOM) upgraded physical model MOM4p1 is generated. This analysis has shown improvement in terms of subsurface temperature, salinity , current as well as sea surface temperature (SST), sea surface salinity (SSS) and surface currents over the Indian Ocean domain with respect to present operational INCOIS-GODAS analysis (Rahaman et al. 2017;Rahman et al. 2019). This newly generated ocean analysis is used to initialize NCEP Climate Forecast System (CFSv2) for the retrospective run from 2011 to 2018. The simulated coupled run has shown improvement in both oceanic as well atmospheric parameters. The more realistic nature of coupled simulations across the atmosphere and ocean may be promising to get better forecast skill.</p>

scholarly journals The MULTI OBSERVATIONS Thematic Assembly Centre of the Copernicus Marine Environment Monitoring Service

2021 ◽  
Author(s):  
Stephanie Guinehut ◽  
Bruno Buongiorno Nardelli ◽  
Trang Chau ◽  
Frederic Chevallier ◽  
Daniele Ciani ◽  
...  
Keyword(s):  
Data Fusion ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Environment Monitoring ◽  
Surface Salinity ◽  
In Situ Observations ◽  
Marine Environment Monitoring ◽  
Monitoring Service

<p>Complementary to ocean state estimate provided by modelling/assimilation systems, a multi observations-based approach is available through the MULTI OSERVATIONS (MULTIOBS) Thematic Assembly Center (TAC) of the European Copernicus Marine Environment Monitoring Service (CMEMS).</p><p>CMEMS MULTIOBS TAC proposes products based on satellite & in situ observations and state-of-the-art data fusion techniques. These products are fully qualified and documented and, are distributed through the CMEMS catalogue (http://marine.copernicus.eu/services-portfolio). They cover the global ocean for physical and biogeochemical (BGC) variables. They are available in Near-Real-Time (NRT) or as Multi-Year Products (MYP) for the past 28 to 36 years.</p><p>Satellite input observations include altimetry but also sea surface temperature, sea surface salinity as well as ocean color. In situ observations of physical and BGC variables are from autonomous platform such as Argo, moorings and ship-based measurements. Data fusion techniques are based on multiple linear regression method, multidimensional optimal interpolation method or neural networks.</p><p>MULTIOBS TAC provides the following products at global scale:</p><ul><li>3D temperature, salinity and geostrophic current fields, both in NRT and as MYP;</li> <li>2D sea surface salinity and sea surface density fields, both in NRT and as MYP;</li> <li>2D total surface and near-surface currents, both in NRT and as MYP;</li> <li>3D vertical current as MYP;</li> <li>2D surface carbon fields of CO<sub>2</sub> flux (fgCO<sub>2</sub>), pCO<sub>2</sub> and pH as MYP;</li> <li>Nutrient vertical distribution (including nitrate, phosphate and silicate) profiles as MYP;</li> <li>3D Particulate Organic Carbon (POC) and Chlorophyll-a (Chl-a) fields as MYP.</li> </ul><p>Furthermore, MULTIOBS TAC provides specific Ocean Monitoring Indicators (OMIs), based on the above products, to monitor the global ocean 3D hydrographic variability patterns (water masses) and the global ocean carbon sink.</p>

scholarly journals The Antarctic Circumpolar Wave in a global, high-resolution, coupled ice-ocean model

2001 ◽  
Vol 33 ◽  
pp. 539-544 ◽  
Author(s):  
Yuxia Zhang ◽  
Albert J. Semtner
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Phase Speed ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Sea Ice Extent ◽  
Antarctic Circumpolar Wave ◽  
The Antarctic

AbstractThe Antarctic Circumpolar Wave (ACW) is identified by White and Peterson (1996) as anomalies in sea-level pressure, meridional wind stress (MWS), sea-surface temperature (SST) and sea-ice extent (SIE) propagating eastward over the Southern Ocean. In this study, the ACW is examined using a global coupled ice-ocean model with an average horizontal grid size of 1/4°. The model is forced with 1979−93 daily average atmospheric data from the European Centre for Medium-range Weather Forecasts (ECMWF) re-analysis (ERA). The sea-ice model includes both dynamics and thermodynamics, and the ocean model is a primitive-equation, free-surface, z-coordinate model. Both standing and propagating oscillations are present in ERA surface net heat-flux (NHF) and MWS anomalies. The ocean and ice respond to such atmospheric forcing with similar standing and propagating oscillations. For the propagating mode, SIE, SST and sea-surface salinity anomalies propagate eastward with a period of about 4−5 years and take about 8−9 years to encircle the Antarctic continent. Thus, the simulated ACW is a wavenumber-2 phenomenon which agrees with the ACW identified by White and Peterson (1996). The correctly simulated strength of the Antarctic Circumpolar Current, which governs the phase speed of oceanic anomalies, in our high-resolution model is essential for obtaining the observed wavenumber-2 ACW mode in the ocean. The ACW signature is also present in ocean temperature and salinity anomalies down to about 1000 m depth with similar eastward-propagating speed. The anomalies in the interior ocean are more coherent and intense over the Pacific and Atlantic sectors than over the Indian sector. Northward (southward) MWS anomalies, northward (southward) SIE anomalies, cold (warm) SST anomalies and saltier (fresher) than normal salinity anomalies are in phase, while less (more) than normal NHF is 90° out of phase with them, indicating the ACW in sea ice and ocean is a response to that in the atmosphere.

Sign in / Sign up

Export Citation Format

Share Document