Impact of assimilating surface salinity from SMOS on ocean circulation estimates

Köhl, Armin; Martins, Meike Sena; Stammer, Detlef

doi:10.1002/2014jc010040

Cited by 32 publications

(31 citation statements)

References 34 publications

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“…The model experiment using the above freshwater fluxes often generates a too strong halocline, thereby leading to negative MLD biases. Assimilation of the recent sea surface salinity observations from satellites is likely to reduce these biases (e.g., Köhl et al 2014;Toyoda et al 2015). Furthermore, better results from the reanalyses with ocean-atmosphere coupled models (ECDA and MOVE-C) suggest an advantage of these approaches (e.g., Fujii et al 2009) as they may eliminate some of the uncertainties associated with precipitation forcing from atmospheric analysis.…”

Section: Seasonal and Interannual Variations Of Mldsmentioning

confidence: 99%

Intercomparison and validation of the mixed layer depth fields of global ocean syntheses

et al. 2015

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5-7 (14-16) m with the vertical resolution of 25 (50) m when the criterion of potential density exceeding the 10-m value by 0.03 kg m −3 is used for the MLD estimation. Using the larger criterion (0.125 kg m −3 ) generally reduces the underestimations. In addition, positive biases greater than 100 m are found in wintertime subpolar regions when MLD criteria based on temperature are used. Biases of the reanalyses are due to both model errors and errors related to differences between the assimilation methods. The result shows that these errors are partially cancelled out through the ensemble averaging. Moreover, the bias in the ensemble mean field of the reanalyses is smaller than in the observation-only analyses. This is largely attributed to comparably higher resolutions of the reanalyses. The robust reproduction of both the seasonal cycle and interannual Abstract Intercomparison and evaluation of the global ocean surface mixed layer depth (MLD) fields estimated from a suite of major ocean syntheses are conducted. Compared with the reference MLDs calculated from individual profiles, MLDs calculated from monthly mean and gridded profiles show negative biases of 10-20 m in early spring related to the re-stratification process of relatively deep mixed layers. Vertical resolution of profiles also influences the MLD estimation. MLDs are underestimated by approximately This paper is a contribution to the special issue on Ocean estimation from an ensemble of global ocean reanalyses consisting of papers from the Ocean Reanalyses Intercomparsion Project (ORAIP), coordinated by CLIVAR-GSOP and GODAE OceanView. The special issue also contains specific studies using single reanalysis systems.

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Section: Seasonal and Interannual Variations Of Mldsmentioning

confidence: 99%

Intercomparison and validation of the mixed layer depth fields of global ocean syntheses

et al. 2015

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“…For examples, the data have been used to study tropical instability waves [Lee et al, 2012Yin et al, 2014], SSS associated with river plumes and marginal seas [e.g., Grodsky et al, 2012;Gierach et al, 2013;Zeng et al, 2014;Fournier et al, 2016], intraseasonal SSS variations associated with the Madden-Julian Oscillation [Grunseich et al, 2013;Guan et al, 2014;Li et al, 2015], mesoscale eddies [e.g., Reul et al, 2014b], Rossby waves [Menezes et al, 2014], and interannual variations associated with climate modes [e.g., Qu and Yu, 2014;Du and Zhang, 2015]. The data have also been used to improve ocean state estimation and seasonal climate prediction [e.g., Köhl et al, 2014;Vinogradova et al, 2014;Toyoda et al, 2015;Hackert et al, 2014]. SSS is being retrieved from NASA's Soil Moisture Active Passive (SMAP) to provide continuity of NASA's SSS measurements.…”

Section: Introductionmentioning

confidence: 99%

Consistency of Aquarius sea surface salinity with Argo products on various spatial and temporal scales

Lee

2016

Geophysical Research Letters

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Understanding the accuracies of satellite‐derived sea surface salinity measurements in depicting temporal changes and their dependence on spatiotemporal scales is important to applications, capability assessment, and future satellite mission design. This study quantifies the consistency between Aquarius Version 4 monthly gridded sea surface salinity (SSS) with two Argo‐based monthly gridded near‐surface salinity products for describing temporal changes on 1° × 1°, 3° × 3°, and 10° × 10° scales. Globally averaged standard deviation values for Aquarius‐Argo salinity differences on these three spatial scales are 0.16, 0.14, and 0.09 practical salinity unit (psu), compared to those between the two Argo products of 0.10, 0.09, and 0.04 psu. The consistency between Aquarius and Argo is similar to that between the two Argo products in the tropics for seasonal signals, and in the tropics and midlatitudes for nonseasonal signals. Therefore, the uncertainties of Argo products for various scales need to be considered in evaluating satellite SSS. Innovative satellite technologies are needed to improve high‐latitude satellite SSS measurements.

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“…Instead, the main aim here is to present and discuss the scales of SSS variability. We anticipate that results can improve the understanding of physical processes responsible for SSS changes, especially at short time and space scales, and they will be helpful for processing and understanding satellite SSS retrievals as well as for SSS assimilation studies [like e.g., Köhl et al ., ].…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal scales of sea surface salinity variability in the Atlantic Ocean

2015

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Space-time variability of SSS in the Atlantic Ocean (338S-808N) is analyzed using near-surface salinity observations from the period 1980-2013 jointly with the output from an eddy-resolving numerical ocean simulation. Results show a good agreement between in situ and model results in terms of spatial and temporal mean SSS patterns, geographically varying SSS variability, and spatial and temporal scales of SSS variability. A good agreement exists also for estimates of the amplitude and phase of the annual cycle of SSS with the model providing more spatial details of SSS variability, which cannot be resolved by observations, especially near ocean margins and in shelf areas. Dominant spatial and temporal scales of SSS variability are, respectively, between 100 and 250 km and between 30 and 70 days in most of the Atlantic when the annual cycle of the SSS is included. However, smaller-scale salinity features are also present, which show temporal decorrelation scales of only 3-5 days throughout the Atlantic. This fast variability must be considered when producing weekly averaged salinity products from satellite measurements.

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Impact of assimilating surface salinity from SMOS on ocean circulation estimates

Cited by 32 publications

References 34 publications

Intercomparison and validation of the mixed layer depth fields of global ocean syntheses

Intercomparison and validation of the mixed layer depth fields of global ocean syntheses

Consistency of Aquarius sea surface salinity with Argo products on various spatial and temporal scales

Spatial and temporal scales of sea surface salinity variability in the Atlantic Ocean

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