Sensor Corrections for Sea-Bird SBE-41CP and SBE-41 CTDs

Johnson, Gregory C.; Toole, John M.; Larson, Nordeen

doi:10.1175/jtech2016.1

Cited by 72 publications

(100 citation statements)

References 8 publications

(17 reference statements)

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“…Filtered and unfiltered spectra for the DIMES profiles sampled in Drake Passage are shown in Figs. 2a,b. Both CTDs and XCTDs are subject to salinity spiking caused by the differing time responses of the temperature and conductivity sensors (Johnson et al 2007;Gargett and Garner 2008). To minimize the effects of this spiking for both instruments, we utilized an iterative procedure similar to the approach used by Johnson et al (2007) to estimate the lag between sensor pairs by minimizing the cross-spectral phase and maximizing the squared coherence between temperature and conductivity (R. Todd 2012, personal communication).…”

Section: ) Data Preprocessing and Noise Estimationmentioning

confidence: 99%

An Assessment of Density-Based Finescale Methods for Estimating Diapycnal Diffusivity in the Southern Ocean

Frants

Damerell

Gille

et al. 2013

Journal of Atmospheric and Oceanic Technology

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Finescale estimates of diapycnal diffusivity k are computed from CTD and expendable CTD (XCTD) data sampled in Drake Passage and in the eastern Pacific sector of the Southern Ocean and are compared against microstructure measurements from the same times and locations. The microstructure data show vertical diffusivities that are one-third to one-fifth as large over the smooth abyssal plain in the southeastern Pacific as they are in Drake Passage, where diffusivities are thought to be enhanced by the flow of the Antarctic Circumpolar Current over rough topography. Finescale methods based on vertical strain estimates are successful at capturing the spatial variability between the low-mixing regime in the southeastern Pacific and the high-mixing regime of Drake Passage. Thorpe-scale estimates for the same dataset fail to capture the differences between Drake Passage and eastern Pacific estimates. XCTD profiles have lower vertical resolution and higher noise levels after filtering than CTD profiles, resulting in XCTD k estimates that are, on average, an order of magnitude higher than CTD estimates. Overall, microstructure diffusivity estimates are better matched by strain-based estimates than by estimates based on Thorpe scales, and CTD data appear to perform better than XCTD data. However, even the CTD-based strain diffusivity estimates can differ from microstructure diffusivities by nearly an order of magnitude, suggesting that density-based fine-structure methods of estimating mixing from CTD or XCTD data have real limitations in low-stratification regimes such as the Southern Ocean.

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Section: ) Data Preprocessing and Noise Estimationmentioning

confidence: 99%

An Assessment of Density-Based Finescale Methods for Estimating Diapycnal Diffusivity in the Southern Ocean

Frants

Damerell

Gille

et al. 2013

Journal of Atmospheric and Oceanic Technology

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“…12b, d). This may be due to small calibration errors associated with our use of ITP level-2 (i.e., not the fully calibrated level-3) data (see Krishfield et al, 2008b;Johnson et al, 2007). In order to incorporate as many data as possible, we have included all available ITP level-2 data, where level-3 data are not yet available.…”

Section: Decorrelation Scalementioning

confidence: 99%

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

et al. 2018

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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 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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“…The procedure includes identifying bad pressure points, checking for density inversions, testing for surface pressure drifts, checking for salinity drifts, and applying a thermal lag correction. The thermal lag correction is tailored for the EM-APEX floats following the method of Johnson et al (2007), and values for the correction parameters are a 5 0.023 and t 5 25.0 s. The quality control of the EM-APEX float velocity data is described in more detail in Phillips and Bindoff (2014).…”

Section: ) Quality Controlmentioning

confidence: 99%

Internal Waves and Mixing near the Kerguelen Plateau

Meyer

Polzin

Sloyan

et al. 2015

Journal of Physical Oceanography

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In the stratified ocean, turbulent mixing is primarily attributed to the breaking of internal waves. As such, internal waves provide a link between large-scale forcing and small-scale mixing. The internal wave field north of the Kerguelen Plateau is characterized using 914 high-resolution hydrographic profiles from novel Electromagnetic Autonomous Profiling Explorer (EM-APEX) floats. Altogether, 46 coherent features are identified in the EM-APEX velocity profiles and interpreted in terms of internal wave kinematics. The large number of internal waves analyzed provides a quantitative framework for characterizing spatial variations in the internal wave field and for resolving generation versus propagation dynamics. Internal waves observed near the Kerguelen Plateau have a mean vertical wavelength of 200 m, a mean horizontal wavelength of 15 km, a mean period of 16 h, and a mean horizontal group velocity of 3 cm s 21 . The internal wave characteristics are dependent on regional dynamics, suggesting that different generation mechanisms of internal waves dominate in different dynamical zones. The wave fields in the Subantarctic/Subtropical Front and the Polar Front Zone are influenced by the local small-scale topography and flow strength. The eddy-wave field is influenced by the large-scale flow structure, while the internal wave field in the Subantarctic Zone is controlled by atmospheric forcing. More importantly, the local generation of internal waves not only drives largescale dissipation in the frontal region but also downstream from the plateau. Some internal waves in the frontal region are advected away from the plateau, contributing to mixing and stratification budgets elsewhere.

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Sensor Corrections for Sea-Bird SBE-41CP and SBE-41 CTDs

Cited by 72 publications

References 8 publications

An Assessment of Density-Based Finescale Methods for Estimating Diapycnal Diffusivity in the Southern Ocean

An Assessment of Density-Based Finescale Methods for Estimating Diapycnal Diffusivity in the Southern Ocean

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

Internal Waves and Mixing near the Kerguelen Plateau

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