scholarly journals Validating Salinity From SMAP With Saildrones and Research Vessel Data During EUREC4A-OA/ATOMIC

2021 ◽  
Author(s):  
Kashawn Hall ◽  
Alton Daley ◽  
Shanice Whitehall ◽  
Sanola Sandiford ◽  
Chelle Leigh Gentemann
Keyword(s):  
Climate Models ◽  
Weather Prediction ◽  
Ocean Model ◽  
Sea Surface Salinity ◽  
Small Scale ◽  
Surface Salinity ◽  
Hybrid Coordinate Ocean Model ◽  
Salinity Variability ◽  
Ocean Atmosphere ◽  
Remote Sensing Systems

The 2020 Elucidating the role of clouds-circulation coupling in climate - Ocean-Atmosphere (EUREC4A-OA) and Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) campaigns sought to improve the knowledge of the interaction between clouds, convection and circulation and their function in our changing climate. The campaign consisted of numerous research technologies, some of which are relatively novel to the scientific community. In this study we used a saildrone uncrewed surface vehicle to validate satellite and modelled sea surface salinity (SSS) products in the Western Tropical Atlantic. These products include the Soil Moisture Active Passive (SMAP) Jet Propulsion Laboratory (JPL), SMAP Remote Sensing Systems (RSS), and Hybrid Coordinate Ocean Model (HYCOM). In addition to the validation, we investigated a fresh tongue south east of Barbados. The saildrones accurately depicted the salinity conditions and all satellite and modelled products performed well in areas that lacked small-scale salinity variability. However, SMAP RSS 70 km outperformed its counterparts in areas with small submesoscale irregularities while RSS 40 km was better at identifying small irregularities in salinity such as a fresh tongue. These results will allow researchers to make informed decisions regarding the most ideal product for their application and aid in the improvement of mesoscale and submesoscale SSS products, which can lead to the refinement of numerical weather prediction (NWP) and climate models.

scholarly journals Assessing Temporal Aliasing in Satellite-Based Surface Salinity Measurements

2012 ◽  
Vol 29 (9) ◽  
pp. 1391-1400 ◽  
Author(s):  
Nadya T. Vinogradova ◽  
Rui M. Ponte
Keyword(s):  
Ocean Model ◽  
Sea Surface Salinity ◽  
Western Boundary ◽  
Boundary Current ◽  
Surface Salinity ◽  
Aliasing Error ◽  
Salinity Variability ◽  
Global Mapping ◽  
Salinity Data

Abstract The Aquarius/Satelite de Aplicaciones Cientificas-D (SAC-D) salinity remote sensing mission is intended to provide global mapping of sea surface salinity (SSS) fields over the next few years. Temporal and spatial averages of the satellite salinity retrievals produce monthly mean fields on 1° grids with target accuracies of 0.2 psu. One issue of relevance for the satellite-derived products is the potential for temporal aliasing of rapid fluctuations into the climate (monthly averaged) values of interest. Global daily SSS fields from a data-assimilating, eddy-resolving Hybrid Coordinate Ocean Model (HYCOM) solution are used to evaluate whether the potential aliasing error is large enough to affect the accuracy of the SSS retrievals. For comparison, salinity data collected at a few in situ stations over the tropical oceans are also used. Based on the HYCOM daily series, over many oceanic regions, a significant part of the total salinity variability is contributed by rapid fluctuations at periods aliased in the satellite retrievals. Estimates of the implicit aliasing error in monthly mean salinity estimates amount to 0.02 psu on average and >0.1 psu in some coastal, tropical, western boundary current, and Arctic regions. Comparison with in situ measurements suggests that HYCOM can underestimate the effect at some locations. While local aliased variance can be significant, the estimated impact of aliasing noise on the overall Aquarius system noise is negligible on average, when combined with effects of other instrument and geophysical errors. Effects of aliased variance are strongest at the shortest periods (<6 months) and become negligible at the annual period.

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.

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 Variational data assimilation with superparameterization

2015 ◽  
Vol 2 (2) ◽  
pp. 513-536 ◽  
Author(s):  
I. Grooms ◽  
Y. Lee
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Climate Models ◽  
Low Cost ◽  
Ocean Model ◽  
Variational Data Assimilation ◽  
Scale Model ◽  
Small Scale ◽  
New Applications ◽  
Lorenz 96

Abstract. Superparameterization (SP) is a multiscale computational approach wherein a large scale atmosphere or ocean model is coupled to an array of simulations of small scale dynamics on periodic domains embedded into the computational grid of the large scale model. SP has been successfully developed in global atmosphere and climate models, and is a promising approach for new applications. The authors develop a 3D-Var variational data assimilation framework for use with SP; the relatively low cost and simplicity of 3D-Var in comparison with ensemble approaches makes it a natural fit for relatively expensive multiscale SP models. To demonstrate the assimilation framework in a simple model, the authors develop a new system of ordinary differential equations similar to the two-scale Lorenz-'96 model. The system has one set of variables denoted {Yi}, with large and small scale parts, and the SP approximation to the system is straightforward. With the new assimilation framework the SP model approximates the large scale dynamics of the true system accurately.

scholarly journals Bay of Bengal Sea surface salinity variability using a decade of improved SMOS re-processing

2020 ◽  
Vol 248 ◽  
pp. 111964 ◽  
Author(s):  
V.P. Akhil ◽  
J. Vialard ◽  
M. Lengaigne ◽  
M.G. Keerthi ◽  
J. Boutin ◽  
...  
Keyword(s):  
Bay Of Bengal ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Salinity Variability

scholarly journals Small-Scale Variability in Sea Surface Salinity and Implications for Satellite-Derived Measurements

2013 ◽  
Vol 30 (11) ◽  
pp. 2689-2694 ◽  
Author(s):  
Nadya T. Vinogradova ◽  
Rui M. Ponte
Keyword(s):  
Sampling Error ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Small Scale ◽  
Surface Salinity ◽  
Ocean Data Assimilation ◽  
Ocean Salinity ◽  
Mean Square Difference

Abstract Calibration and validation efforts of the Aquarius and Soil Moisture and Ocean Salinity (SMOS) satellite missions involve comparisons of satellite and in situ measurements of sea surface salinity (SSS). Such estimates of SSS can differ by the presence of small-scale variability, which can affect the in situ point measurement, but be averaged out in the satellite retrievals because of their large footprint. This study quantifies how much of a difference is expected between in situ and satellite SSS measurements on the basis of their different sampling of spatial variability. Maps of sampling error resulting from small-scale noise, defined here as the root-mean-square difference between “local” and footprint-averaged SSS estimates, are derived using a solution from a global high-resolution ocean data assimilation system. The errors are mostly <0.1 psu (global median is 0.05 psu), but they can be >0.2 psu in several regions, particularly near strong currents and outflows of major rivers. To examine small-scale noise in the context of other errors, its values are compared with the overall expected differences between monthly Aquarius SSS and Argo-based estimates. Results indicate that in several ocean regions, small-scale variability can be an important source of sampling error for the in situ measurements.

Observed year-to-year sea surface salinity variability in the Bay of Bengal during the 2009–2014 period

2014 ◽  
Vol 65 (2) ◽  
pp. 173-186 ◽  
Author(s):  
Akurathi Venkata Sai Chaitanya ◽  
Fabien Durand ◽  
Simi Mathew ◽  
Vissa Venkata Gopalakrishna ◽  
Fabrice Papa ◽  
...  
Keyword(s):  
Bay Of Bengal ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Salinity Variability

scholarly journals Ocean processes at the Antarctic continental slope

2014 ◽  
Vol 372 (2019) ◽  
pp. 20130047 ◽  
Author(s):  
Karen J. Heywood ◽  
Sunke Schmidtko ◽  
Céline Heuzé ◽  
Jan Kaiser ◽  
Timothy D. Jickells ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Continental Shelf ◽  
Continental Slope ◽  
Climate Models ◽  
Water Mass ◽  
Ocean Model ◽  
Shelf Break ◽  
Small Scale ◽  
Exchange Processes ◽  
The Antarctic

The Antarctic continental shelves and slopes occupy relatively small areas, but, nevertheless, are important for global climate, biogeochemical cycling and ecosystem functioning. Processes of water mass transformation through sea ice formation/melting and ocean–atmosphere interaction are key to the formation of deep and bottom waters as well as determining the heat flux beneath ice shelves. Climate models, however, struggle to capture these physical processes and are unable to reproduce water mass properties of the region. Dynamics at the continental slope are key for correctly modelling climate, yet their small spatial scale presents challenges both for ocean modelling and for observational studies. Cross-slope exchange processes are also vital for the flux of nutrients such as iron from the continental shelf into the mixed layer of the Southern Ocean. An iron-cycling model embedded in an eddy-permitting ocean model reveals the importance of sedimentary iron in fertilizing parts of the Southern Ocean. Ocean gliders play a key role in improving our ability to observe and understand these small-scale processes at the continental shelf break. The Gliders: Excellent New Tools for Observing the Ocean (GENTOO) project deployed three Seagliders for up to two months in early 2012 to sample the water to the east of the Antarctic Peninsula in unprecedented temporal and spatial detail. The glider data resolve small-scale exchange processes across the shelf-break front (the Antarctic Slope Front) and the front's biogeochemical signature. GENTOO demonstrated the capability of ocean gliders to play a key role in a future multi-disciplinary Southern Ocean observing system.

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