Improvements to a global ocean data assimilation system through the incorporation of Aquarius surface salinity data

Toyoda, Takahiro; Fujii, Yosuke; Kuragano, Tsurane; Matthews, Janice R.; Abe, Hiroto; Ebuchi, Naoto; Usui, Norihisa; Ogawa, Koji; Kamachi, Masafumi

doi:10.1002/qj.2561

Cited by 27 publications

(18 citation statements)

References 36 publications

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“…It can also reconstruct cross correlations between different parameters. For example, sea surface salinity data have an impact on subsurface temperature [ Toyoda et al ., ]. Such an impact is difficult to assess in the statistical method as shown in this paper because cross‐parameter correlation feature is difficult to estimate from in situ observations.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the Argo network using statistical space‐time scales derived from satellite altimetry data

2015

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This study evaluates capability of the Argo observation network for monitoring ocean variation, especially for eddy‐scale variation, by using an optimum interpolation (OI) procedure. Sea surface dynamic height anomalies (DHAs) are derived from Argo temperature and salinity profile data, and DHA fields are obtained by the OI based on the space‐time correlation scales estimated from along‐track sea level anomaly (SLA) data by satellite altimetry. The DHA fields are compared with the SLA fields derived from the same OI applied to the along‐track SLA data. The results show that the equatorial Kelvin waves and tropical instability waves are well captured by Argo floats. Eddies are also monitored effectively in the subtropical western North Pacific. The OI results of DHA do not agree well with those of SLA in the high latitudes. A simple test of the space‐time OI analysis shows that more than six data in the e‐folding domain, where the correlation coefficient of ocean variation is above e−1, are required for the reliable analysis with 99% confidence level. Argo floats provide sufficient number of observations for the reliable analysis in the low latitudes and some areas in the North Pacific. Two to three times more Argo data would be required in most of midlatitudes and much more in high latitudes for capturing eddy‐scale variation.

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Section: Discussionmentioning

confidence: 99%

Evaluation of the Argo network using statistical space‐time scales derived from satellite altimetry data

2015

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“…The difference between the forecast and the satellite SSS can be 5 times larger than the misfit between the forecast and near surface Argo salinity. Nevertheless, several studies (Lee et al, 2012;Reul et al, 2013;Toyoda et al, 2015) show that SSS measured from space can bring new information.…”

Section: Oses For Evaluating Satellite Sss Data Impacts During the Elmentioning

confidence: 99%

Observing System Evaluation Based on Ocean Data Assimilation and Prediction Systems: On-Going Challenges and a Future Vision for Designing and Supporting Ocean Observational Networks

et al. 2019

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This paper summarizes recent efforts on Observing System Evaluation (OS-Eval) by the Ocean Data Assimilation and Prediction (ODAP) communities such as GODAE OceanView and CLIVAR-GSOP. It provides some examples of existing OS-Eval methodologies, and attempts to discuss the potential and limitation of the existing approaches. Observing System Experiment (OSE) studies illustrate the impacts of the severe decrease in the number of TAO buoys during 2012-2014 and TRITON buoys since 2013 on ODAP system performance. Multi-system evaluation of the impacts of assimilating satellite sea surface salinity data based on OSEs has been performed to demonstrate the need to continue and enhance satellite salinity missions. Impacts of underwater gliders have been assessed using Observing System Simulation Experiments (OSSEs) to provide guidance on the effective coordination of the western North Atlantic observing system elements. OSSEs are also being performed under H2020 AtlantOS Fujii et al.Observing System Evaluation project with the goal to enhance and optimize the Atlantic in-situ networks. Potential of future satellite missions of wide-swath altimetry and surface ocean currents monitoring is explored through OSSEs and evaluation of Degrees of Freedom for Signal (DFS). Forecast Sensitivity Observation Impacts (FSOI) are routinely evaluated for monitoring the ocean observation impacts in the US Navy's ODAP system. Perspectives on the extension of OS-Eval to coastal regions, the deep ocean, polar regions, coupled data assimilation, and biogeochemical applications are also presented. Based on the examples above, we identify the limitations of OS-Eval, indicating that the most significant limitation is reduction of robustness and reliability of the results due to their system-dependency. The difficulty of performing evaluation in near real time is also critical. A strategy to mitigate the limitation and to strengthen the impact of evaluations is discussed. In particular, we emphasize the importance of collaboration within the ODAP community for multi-system evaluation and of communication with ocean observational communities on the design of OS-Eval, required resources, and effective distribution of the results. Finally, we recommend further developing OS-Eval activities at international level with the support of the international ODAP (e.g., OceanPredict and CLIVAR-GSOP) and observational communities.

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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%

“…10. Surface salinity observations from satellites will possibly contribute to the improvements of the analyses in these regions (e.g., Toyoda et al, 2015). Also, observations by the mooring array are required to be maintained in order to keep or enhance the quality of the reanalyses/syntheses.…”

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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Improvements to a global ocean data assimilation system through the incorporation of Aquarius surface salinity data

Cited by 27 publications

References 36 publications

Evaluation of the Argo network using statistical space‐time scales derived from satellite altimetry data

Evaluation of the Argo network using statistical space‐time scales derived from satellite altimetry data

Observing System Evaluation Based on Ocean Data Assimilation and Prediction Systems: On-Going Challenges and a Future Vision for Designing and Supporting Ocean Observational Networks

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

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