Summer and winter surveys of the Subtropical Front of the southeastern Indian Ocean 1997–1998

James, Charles; Tomczak, Matthias; Helmond, I.; Pender, Lindsay

doi:10.1016/s0924-7963(02)00199-9

Cited by 31 publications

(42 citation statements)

References 19 publications

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“…this area as suggested by James et al (2002). The SAF is well followed by PC1 with some discrepancies: PC1 contours surround the Crozet Archipelago and get wider southeast of the Campbell Plateau and in the east Pacific and Argentine Basin.…”

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confidence: 63%

A Linear Decomposition of the Southern Ocean Thermohaline Structure

Pauthenet

Roquet

Madec

et al. 2017

Journal of Physical Oceanography

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The thermohaline structure of the Southern Ocean is deeply influenced by the presence of the Antarctic Circumpolar Current (ACC), where water masses of the World Ocean are advected, transformed, and redistributed to the other basins. It remains a challenge to describe and visualize the complex 3D pattern of this circulation and its associated tracer distribution. Here, a simple framework is presented to analyze the Southern Ocean thermohaline structure. A functional principal component analysis (PCA) is applied to temperature u and salinity S profiles to determine the main spatial patterns of their variations. Using the Southern Ocean State Estimate (SOSE), this study determines the vertical modes describing the Southern Ocean thermohaline structure between 5 and 2000 m. The first two modes explain 92% of the combined u-S variance, thus providing a surprisingly good approximation of the thermohaline properties in the Southern Ocean. The first mode (72% of total variance) accurately describes the north-south property gradients. The second mode (20%) mostly describes salinity at 500 m in the region of Antarctic Intermediate Water formation. These two modes present circumpolar patterns that can be closely related with standard frontal definitions. By projecting any given hydrographic profile onto the SOSE-based modes, it is possible to determine its position relative to the fronts. The projection is successfully applied on the hydrographic profiles of the WOCE SR3 section. The Southern Ocean thermohaline decomposition provides an objective way to define water mass boundaries and their spatial variability and has useful application for comparing model output with observations.

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confidence: 63%

A Linear Decomposition of the Southern Ocean Thermohaline Structure

Pauthenet

Roquet

Madec

et al. 2017

Journal of Physical Oceanography

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“…This study was conducted at 4 sites: Cape Gantheaume (36°04' S, 137°27' E) and Cape du Couedic (36°03' S, 136°42' E) on Kangaroo Island, North Neptune Island (35°13' S, 136°03' E) and Liguanea Island (34°59' S, 135°37' E) (Fig. 1) (James et al 2002, Kostianoy et al 2004. North Neptune Island and Liguanea Island are the furthest colonies from the continental slope and the STF (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The STF was mapped using mean monthly SST temperatures. In April, the STF was identified as the location of a 14°C isotherm at the sea surface (James et al 2002, Tomczak et al 2004. In June, the STF was defined as the location of the 12°C sea surface isotherm.…”

Section: Methodsmentioning

confidence: 99%

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Colony-specific foraging areas of lactating New Zealand fur seals

Baylis

Page

Goldsworthy

2008

Mar. Ecol. Prog. Ser.

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“…This of course may be an accurate reflection of the state of the ocean; a strong subsurface front can exist without a marked surface thermal signature. Strong winds and mixing may break down the surface structure and increased solar insolation in the summer stratifies the water column resulting in an isothermal top layer that may become decoupled from and mask the sub-surface structure below (James et al 2002). Alternatively it may be the case that gradient changes have been blurred through temporal smoothing in a composite image.…”

Section: Quality Flags and Weightingsmentioning

confidence: 99%

A New Statistical Modeling Approach to Ocean Front Detection from SST Satellite Images

Hopkins

Challenor

Shaw

2010

Journal of Atmospheric and Oceanic Technology

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Ocean fronts are narrow zones of intense dynamic activity that play an important role in global ocean-atmosphere interactions. Owing to their highly variable nature, both in space and time, they are notoriously difficult features to adequately sample using traditional in-situ techniques. In this paper we propose a new statistical modelling approach to detecting and monitoring ocean fronts from AVHRR SST satellite images that builds on the 'front following' algorithm of Shaw and Vennell (2000). Weighted local likelihood is used to provide a smooth, non-parametric description of spatial variations in the position, mean temperature, width and temperature change of an individual front within an image. Weightings are provided by a Gaussian kernel function whose width is automatically determined by likelihood crossvalidation. The statistical model fitting approach allows estimation of the uncertainty of each parameter to be quantified, a capability not possessed by other techniques. The algorithm is shown to be robust to noise and missing data in an image, problems that hamper many of the existing front detection schemes. The approach is general and could be used with other remotely sensed data sets, model output or data assimilation products.1

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Summer and winter surveys of the Subtropical Front of the southeastern Indian Ocean 1997–1998

Cited by 31 publications

References 19 publications

A Linear Decomposition of the Southern Ocean Thermohaline Structure

A Linear Decomposition of the Southern Ocean Thermohaline Structure

Colony-specific foraging areas of lactating New Zealand fur seals

A New Statistical Modeling Approach to Ocean Front Detection from SST Satellite Images

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