High‐resolution climatology of the northeast Atlantic using Data‐Interpolating Variational Analysis (Diva)

Troupin, Charles; Machín, Francisco; Ouberdous, Mohamed; Sirjacobs, Damien; Barth, Alexander; Beckers, Jean-Marie

doi:10.1029/2009jc005512

Cited by 76 publications

(108 citation statements)

References 55 publications

(54 reference statements)

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“…Only this section of the AzC presents a seasonal variation of its meridional position (see also Siedler and Finke, 1993). This removes inconsistency between the reported 2 • northward shift of the front of the AzC in winter, obtained from a coarse-resolution study by Stramma and Siedler (1988), and a recently demonstrated stability (within 0.25 • limit) of the AzC axis, integrated over the full length of the current (Lázaro et al, 2013).…”

Section: Seasonal Variability In the Medtrans Climatologymentioning

confidence: 93%

“…2) complements the existing gridding methods (WOA09, WOA13, Schmidtko et al, 2013, Troupin et al, 2010 and the resulting climatic fields provide additional details on temperaturesalinity distributions (described in Sect. 3).…”

Section: Bashmachnikov Et Al: Temperature-salinity Distribution Imentioning

confidence: 99%

“…Among other climatologies covering the study region, we consider AMA (Roemmich and Gilson, 2009), MIMOC (Schmidtko et al, 2013) and DIVA (Troupin et al, 2010). AMA climatology is based exclusively on ARGO data.…”

Section: Bashmachnikov Et Al: Temperature-salinity Distribution Imentioning

confidence: 99%

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Temperature–salinity distribution in the northeastern Atlantic from ship and Argo vertical casts

et al. 2015

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Abstract. The present study defines new interpolation functions for hydrological data. These functions are applied to generate climatological maps of temperature-salinity distribution with a 25 m depth interval and a 30 km space interval (MEDTRANS data set). The data undergo rigorous initial data quality control, having passed several filtering procedures. The gridding is done on neutral density surfaces, which allows better representation of thermohaline fronts for the same gridding radius. The multi-pass Barnes optimum interpolation procedure with spatially variable size of the gridding window is used. The shape of the window accounts for the dominant along-isobath direction of water mass transport over steeply sloping topography. A local ratio of topographic to planetary β-effects is used to define the shape of the window as a function of the relative importance of the topographic influence. The N /f ratio is applied to account for the baroclinic compensation decay of the topographic influence on water mass transport with the distance from the bottom. The gridded fields are available at the website of the Centre of Oceanography of the University of Lisbon (http://co.fc.ul.pt/en/data).The MEDTRANS climatology gives more details of the distribution of water characteristics in the subtropical northeastern Atlantic than other alternative climatologies and is able to reproduce a number of dynamic features described in the literature: the acceleration in the meanders of the Azores current; the cyclonic gyre in the Gulf of Cadiz; and the splitting and separation of the Mediterranean Water (MW) outflow in two veins near the Gorringe and Galicia banks. Seasonal climatologies, computed for the warm (May-October) and cold (November-April) seasons, reveal stronger zonal extension of the upper ocean patterns during the warm season, as compared to the cold one.

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Section: Seasonal Variability In the Medtrans Climatologymentioning

confidence: 93%

Section: Bashmachnikov Et Al: Temperature-salinity Distribution Imentioning

confidence: 99%

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Temperature–salinity distribution in the northeastern Atlantic from ship and Argo vertical casts

et al. 2015

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“…Static interpolation approaches (e.g., optimal interpolation [Gandin, 1965;Reynolds and Smith, 1994], objective mapping [Wong et al, 2003;Böhme and Send, 2005;Böhme et al, 2008], and Data-Interpolating Variational Analysis [Troupin et al, 2010[Troupin et al, , 2012Korablev, 2014 in addition, exploit modeled physics and provide temporally and spatially varying 4-dimensional analysis fields. The former approaches need a scale representing the mean field, while the latter, in addition, needs spatial and temporal scales representing the anomaly field to fully exploit the information embedded in in-situ data.…”

Section: Resultsmentioning

confidence: 99%

Decorrelation scales for Arctic Ocean Hydrography. Part I: Amerasian Basin

Sumata¹,

Kauker²,

Kärcher³

et al. 2017

Preprint

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Abstract. Any use of observational data for data assimilation requires adequate information of their representativeness in space and time. This is particularly important for sparse, non-synoptic data, which comprise the bulk of oceanic in-situ observations in the Arctic. To quantify spatial and temporal scales of temperature and salinity variations, we estimate the autocorrelation function and associated decorrelation scales for the Amerasian Basin of the Arctic Ocean. For this purpose, we compile historical measurements from 1980 to 2015. Assuming spatial and temporal homogeneity of the decorrelation scale in the basin interior (abyssal plain area), we calculate autocorrelations as a function of spatial distance and temporal lag. The examination of the functional form of autocorrelation in each depth range reveals that the autocorrelation is well described by a Gaussian function in space and time. We derive decorrelation scales of 150 ~ 200 km in space and 100 ~ 300 days in time. These scales are directly applicable to quantify the representation error, which is essential for use of ocean insitu measurements in data assimilation. We also describe how the estimated autocorrelation function and decorrelation scale should be applied for cost function calculation in a data assimilation system.

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“…The present scale estimates pose a requirement from a basin-scale data assimilation on a sampling strategy. Static interpolation approaches (e.g., optimal interpolation (Gandin, 1965;Reynolds and Smith, 1994), objective mapping (Wong et al, 2003;Böhme and Send, 2005;Böhme et al, 2008), and data-interpolating variational analyses (Troupin et al, 2010(Troupin et al, , 2012Korablev, 2014) exploit statistical information of data to derive a mean analysis field. Data assimilation approaches, in addition, exploit modeled physics and provide temporally and spatially varying fourdimensional analysis fields.…”

Section: Discussionmentioning

confidence: 99%

Decorrelation scales for Arctic Ocean hydrography – Part I: Amerasian Basin

et al. 2018

View full text Add to dashboard Cite

Abstract. Any use of observational data for data assimilation requires adequate information of their representativeness in space and time. This is particularly important for sparse, non-synoptic data, which comprise the bulk of oceanic in situ observations in the Arctic. To quantify spatial and temporal scales of temperature and salinity variations, we estimate the autocorrelation function and associated decorrelation scales for the Amerasian Basin of the Arctic Ocean. For this purpose, we compile historical measurements from 1980 to 2015. Assuming spatial and temporal homogeneity of the decorrelation scale in the basin interior (abyssal plain area), we calculate autocorrelations as a function of spatial distance and temporal lag. The examination of the functional form of autocorrelation in each depth range reveals that the autocorrelation is well described by a Gaussian function in space and time. We derive decorrelation scales of 150-200 km in space and 100-300 days in time. These scales are directly applicable to quantify the representation error, which is essential for use of ocean in situ measurements in data assimilation. We also describe how the estimated autocorrelation function and decorrelation scale should be applied for cost function calculation in a data assimilation system.

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High‐resolution climatology of the northeast Atlantic using Data‐Interpolating Variational Analysis (Diva)

Cited by 76 publications

References 55 publications

Temperature–salinity distribution in the northeastern Atlantic from ship and Argo vertical casts

Temperature–salinity distribution in the northeastern Atlantic from ship and Argo vertical casts

Decorrelation scales for Arctic Ocean Hydrography. Part I: Amerasian Basin

Decorrelation scales for Arctic Ocean hydrography – Part I: Amerasian Basin

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