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 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 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 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.

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 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 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 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.

Variability of Intraseasonal Oscillations and Synoptic Signals in Sea Surface Salinity in the Bay of Bengal

2019 ◽  
Vol 32 (20) ◽  
pp. 6703-6728 ◽  
Author(s):  
Corinne B. Trott ◽  
Bulusu Subrahmanyam ◽  
Heather L. Roman-Stork ◽  
V. S. N. Murty ◽  
C. Gnanaseelan
Keyword(s):  
Soil Moisture ◽  
River Runoff ◽  
Bay Of Bengal ◽  
Southwest Monsoon ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Intraseasonal Oscillations ◽  
Salinity Data

Abstract Intraseasonal oscillations (ISOs) significantly impact southwest monsoon precipitation and Bay of Bengal (BoB) variability. The response of ISOs in sea surface salinity (SSS) to those in the atmosphere is investigated in the BoB from 2005 to 2017. The three intraseasonal processes examined in this study are the 30–90-day and 10–20-day ISOs and 3–7-day synoptic weather signals. A variety of salinity data from NASA’s Soil Moisture Active Passive (SMAP) and the European Space Agency’s (ESA’s) Soil Moisture and Ocean Salinity (SMOS) satellite missions and from reanalysis using the Hybrid Coordinate Ocean Model (HYCOM) and operational analysis of Climate Forecast System version 2 (CFSv2) were utilized for the study. It is found that the 30–90-day ISO salinity signal propagates northward following the northward propagation of convection and precipitation ISOs. The 10–20-day ISO in SSS and precipitation deviate largely in the northern BoB wherein the river runoff largely impacts the SSS. The weather systems strongly impact the 3–7-day signal in SSS prior to and after the southwest monsoon. Overall, we find that satellite salinity products captured better the SSS signal of ISO due to inherent inclusion of river runoff and mixed layer processes. CFSv2, in particular, underestimates the SSS signal due to the misrepresentation of river runoff in the model. This study highlights the need to include realistic riverine freshwater influx for better model simulations, as accurate salinity simulation is mandatory for the representation of air–sea coupling in models.

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