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 Matchup Characteristics of Sea Surface Salinity using a High-resolution Ocean Model

2021 ◽  
Author(s):  
Frederick Bingham ◽  
Severine Fournier ◽  
Susannah Brodnitz ◽  
Karly Ulfsax ◽  
Hong Zhang
Keyword(s):  
Time Window ◽  
Single Point ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Argo Floats ◽  
Statistical Measures ◽  
One Year ◽  
Salinity Difference

Sea surface salinity (SSS) satellite measurements are validated using in situ observations 8 usually made by surfacing Argo floats. Validation statistics are computed using matched values of 9 SSS from satellites and floats. This study explores how the matchup process is done using a high- 10 resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the 11 Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped 12 up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference 13 (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values 14 being greater than satellites. RMSD is computed using an “all salinity difference” method that av- 15 erages level 2 satellite observations within a given time and space window for comparison with 16 Argo floats. RMSD values range from 0.08 to 0.18 depending on the space-time window and the 17 satellite. This range gives an estimate of the representation error inherent in comparing single point 18 Argo floats to area-average satellite values. The study has implications for future SSS satellite mis- 19 sions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS 20 values.

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.

scholarly journals Validation of Aquarius sea surface salinity with in situ measurements from Argo floats and moored buoys

2014 ◽  
Vol 119 (9) ◽  
pp. 6171-6189 ◽  
Author(s):  
Wenqing Tang ◽  
Simon H. Yueh ◽  
Alexander G. Fore ◽  
Akiko Hayashi
Keyword(s):  
In Situ Measurements ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Argo Floats

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.

scholarly journals Copernicus Imaging Microwave Radiometer (CIMR) Benefits for the Copernicus Level 4 Sea-Surface Salinity Processing Chain

2019 ◽  
Vol 11 (15) ◽  
pp. 1818 ◽  
Author(s):  
Daniele Ciani ◽  
Rosalia Santoleri ◽  
Gian Luigi Liberti ◽  
Catherine Prigent ◽  
Craig Donlon ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Microwave Radiometer ◽  
Ocean Surface ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Optimal Interpolation ◽  
Surface Salinity ◽  
Processing Chain

We present a study on the potential of the Copernicus Imaging Microwave Radiometer (CIMR) mission for the global monitoring of Sea-Surface Salinity (SSS) using Level-4 (gap-free) analysis processing. Space-based SSS are currently provided by the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. However, there are no planned missions to guarantee continuity in the remote SSS measurements for the near future. The CIMR mission is in a preparatory phase with an expected launch in 2026. CIMR is focused on the provision of global coverage, high resolution sea-surface temperature (SST), SSS and sea-ice concentration observations. In this paper, we evaluate the mission impact within the Copernicus Marine Environment Monitoring Service (CMEMS) SSS processing chain. The CMEMS SSS operational products are based on a combination of in situ and satellite (SMOS) SSS and high-resolution SST information through a multivariate optimal interpolation. We demonstrate the potential of CIMR within the CMEMS SSS operational production after the SMOS era. For this purpose, we implemented an Observing System Simulation Experiment (OSSE) based on the CMEMS MERCATOR global operational model. The MERCATOR SSSs were used to generate synthetic in situ and CIMR SSS and, at the same time, they provided a reference gap-free SSS field. Using the optimal interpolation algorithm, we demonstrated that the combined use of in situ and CIMR observations improves the global SSS retrieval compared to a processing where only in situ observations are ingested. The improvements are observed in the 60% and 70% of the global ocean surface for the reconstruction of the SSS and of the SSS spatial gradients, respectively. Moreover, the study highlights the CIMR-based salinity patterns are more accurate both in the open ocean and in coastal areas. We conclude that CIMR can guarantee continuity for accurate monitoring of the ocean surface salinity from space.

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 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 Sea surface salinity subfootprint variability from a global high-resolution model

2021 ◽  
Author(s):  
Frederick Bingham ◽  
Susannah Brodnitz ◽  
Severine Fournier ◽  
Karly Ulfsax ◽  
Akiko Hayashi ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Observations ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Representation Error ◽  
Resolution Model ◽  
One Year ◽  
The One ◽  
Model Year

Subfootprint variability (SFV) is variability at a spatial scale smaller than the footprint of a sat-ellite, and cannot be resolved by satellite observations. It is important to quantify and understand as it contributes to the error budget for satellite data. The purpose of this study is to estimate the SFV for sea surface salinity (SSS) satellite observations. This is done using a high-resolution (1/48°) numerical model, the MITgcm, from which one year of output has recently become availa-ble. SFV, defined as the weighted standard deviation of SSS within the satellite footprint, was computed from the model for a 2°X2° grid of points for the one model year. We present maps of SFV for 40 and 100 km footprint size, display histograms of its distribution for a range of foot-print sizes and quantify its seasonality. At 100 km (40 km) footprint size, SFV has a mode of 0.06 (0.04). It is found to vary strongly by location and season. It has larger values in western bound-ary and eastern equatorial regions, and a few other areas. SFV has strong variability throughout the year, with generally largest values in the fall season. We also quantify representation error, the degree of mismatch between random samples within a footprint and the footprint average. Our estimates of SFV and representation error can be used in understanding errors in satellite obser-vation of SSS.

scholarly journals New insights of <i>p</i>CO<sub>2</sub> variability in the tropical eastern Pacific Ocean using SMOS SSS

2015 ◽  
Vol 12 (6) ◽  
pp. 4595-4625 ◽  
Author(s):  
C. W. Brown ◽  
J. Boutin ◽  
L. Merlivat
Keyword(s):  
High Resolution ◽  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Dissolved Inorganic Carbon ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Oceanic Circulation ◽  
Surface Salinity ◽  
Partial Pressures

Abstract. Complex oceanic circulation and air–sea interaction make the eastern tropical Pacific Ocean (ETPO) a highly variable source of CO2 to the atmosphere. Although the scientific community have amassed 70 000 surface partial-pressure of carbon dioxide (pCO2) datapoints within the ETPO region over the past 25 years, the spatial and temporal resolution of this dataset is insufficient to fully quantify the seasonal to inter-annual variability of the region, a region where pCO2 has been observed to fluctuate by >300 μatm. Upwelling and rainfall events dominate the surface physical and chemical characteristics of the ETPO, with both yielding unique signatures in sea surface temperature and salinity. Thus, we explore the potential of using a statistical description of pCO2 within sea-surface salinity-temperature space. These SSS/SST relationships are based on in-situ SOCAT data collected within the ETPO. This statistical description is then applied to high resolution (0.25°) SMOS sea surface salinity and OSTIA sea surface temperature in order to compute regional pCO2. As a result, we are able to resolve pCO2 at sufficiently high resolution to elucidate the influence various physical processes have on the pCO2 of the surface ETPO. Normalised (to 2014) oceanic pCO2 between July 2010 and June 2014 within the entire ETPO was 41 μatm supersaturated with respect to 2014 atmospheric partial pressures. Values of pCO2 within the ETPO were found to be broadly split between southeast and a northwest regions. The north west, central and South Equatorial Current regions were supersaturated, with wintertime wind jet driven upwelling found to be the first order control on pCO2 values. This contrasts with the southeastern/Gulf of Panama region, where heavy rainfall combined with rapid stratification of the upper water-column act to dilute dissolved inorganic carbon, and yield pCO2 values undersaturated with respect to atmospheric partial pressures of CO2.

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

&lt;p&gt;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.&lt;/p&gt;

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