2013 World Ocean Atlas Aids High‐Resolution Climate Studies

Boyer, Tim; Garcia, Hernan E.; Locarnini, R. A.; Zweng, Melissa M.; Mishonov, Alexey V.; Reagan, James R.; Antonov, John I.; Baranova, O; Biddle, Matt; Johnson, Daphne R.; Paver, Christopher R.

doi:10.1002/2014eo410002

Cited by 114 publications

(166 citation statements)

References 7 publications

(7 reference statements)

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“…And the

δ S_{A}

uncertainty estimation from Pawlowicz et al() is taken as the Absolute Salinity anomaly approximation of non‐Baltic seas in the paper, i.e.,

u true(δ S_{A} true) \approx 0.003

g kg −1 for non‐Baltic seas. Moreover, from the latest World Ocean Atlas 2013 version 2 (hereinafter, “WOA13V2”) provided by NOAA (Boyer et al, ; NOAA/NODC, ), the decades‐average (1955–2012) climatological data (NOAA/NODC, ) of seawater Practical Salinity is used to analyze. WOA13V2 is a set of objectively analyzed climatological fields of in situ temperature, Practical Salinity, and other ecological parameters at standard depth levels for annual, seasonal, and monthly compositing periods for the world ocean.…”

Section: Uncertainties In Climatological Seawater Density Calculationsmentioning

confidence: 99%

Uncertainties in Climatological Seawater Density Calculations

Dai

Zhang

2018

JGR Oceans

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In most applications, with seawater conductivity, temperature, and pressure data measured in situ by various observation instruments e.g., Conductivity‐Temperature‐Depth instruments (CTD), the density which has strong ties to ocean dynamics and so on is computed according to equations of state for seawater. This paper, based on density computational formulae in the Thermodynamic Equation of Seawater 2010 (TEOS‐10), follows the Guide of the expression of Uncertainty in Measurement (GUM) and assesses the main sources of uncertainties. By virtue of climatological decades‐average temperature/Practical Salinity/pressure data sets in the global ocean provided by the National Oceanic and Atmospheric Administration (NOAA), correlation coefficients between uncertainty sources are determined and the combined standard uncertainties uctrue(ρtrue) in seawater density calculations are evaluated. For grid points in the world ocean with 0.25° resolution, the standard deviations of uctrue(ρtrue) in vertical profiles cover the magnitude order of 10−4 kg m−3. The uctrue(ρtrue) means in vertical profiles of the Baltic Sea are about 0.028kg m−3 due to the larger scatter of Absolute Salinity anomaly. The distribution of the uctrue(ρtrue) means in vertical profiles of the world ocean except for the Baltic Sea, which covers the range of true(0.004,0.01true) kg m−3, is related to the correlation coefficient rtrue(SA,ptrue) between Absolute Salinity SA and pressure p. The results in the paper are based on sensors' measuring uncertainties of high accuracy CTD. Larger uncertainties in density calculations may arise if connected with lower sensors' specifications. This work may provide valuable uncertainty information required for reliability considerations of ocean circulation and global climate models.

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“…And the

δ S_{A}

uncertainty estimation from Pawlowicz et al() is taken as the Absolute Salinity anomaly approximation of non‐Baltic seas in the paper, i.e.,

u true(δ S_{A} true) \approx 0.003

Section: Uncertainties In Climatological Seawater Density Calculationsmentioning

confidence: 99%

Uncertainties in Climatological Seawater Density Calculations

Dai

Zhang

2018

JGR Oceans

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“…Then, from January 1995 to December 2009, the model was forced on the ocean surface with 6-hourly atmospheric reanalysis version 1 by the National Centers for Environmental Prediction/National Centers for Atmospheric Research (NCEP/NCAR) (Kalnay et al, 1996), including precipitation, wind speed at 10 m, short-and long-wave radiation fluxes at the surface, air temperature and humidity at 2 m. The result of this simulation was used as the initial condition for the Argo data assimilation experiments starting on 1 January 2010. Figure 1 shows the mean state of temperature and salinity simulated by HYCOM from 1 January 1997 to 31 December 2008 and its comparison with the World Ocean Atlas 2013 Climatology (WOA13) (Boyer and Mishonov, 2013) along 25 • W for the upper 1000 m. WOA13 climatology shown in Fig. 1 spans the period from 1995 to 2012.…”

Section: Introductionmentioning

confidence: 99%

Argo data assimilation into HYCOM with an EnOI method in the Atlantic Ocean

et al. 2015

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Abstract. An ocean data assimilation system to assimilate Argo temperature (T ) and salinity (S) profiles into the HYbrid Coordinate Ocean Model (HYCOM) was constructed, implemented and evaluated for the first time in the Atlantic Ocean (78 • S to 50 • N and 98 • W to 20 • E). The system is based on the ensemble optimal interpolation (EnOI) algorithm proposed by Xie and Zhu (2010), especially made to deal with the hybrid nature of the HYCOM vertical coordinate system with multiple steps. The Argo T -S profiles were projected to the model vertical space to create pseudoobserved layer thicknesses ( p obs ), which correspond to the model target densities. The first step was to assimilate p obs considering the sub-state vector composed by the model layer thickness ( p) and the baroclinic velocity components. After that, T and S were assimilated separately. Finally, T was diagnosed below the mixed layer to preserve the density of the model isopycnal layers. Five experiments were performed from 1 January 2010 to 31 December 2012: a control run without assimilation, and four assimilation runs considering the different vertical localizations of T , S and p. The assimilation experiments were able to significantly improve the thermohaline structure produced by the control run. They reduced the root mean square deviation (RMSD) of T and S calculated with respect to Argo independent data in 34 and 44 %, respectively, in comparison to the control run. In some regions, such as the western North Atlantic, substantial corrections in the 20 • C isotherm depth and the upper ocean heat content towards climatological states were achieved.The runs with a vertical localization of p showed positive impacts in the correction of the thermohaline structure and reduced the RMSD of T (S) from 0.993 • C (0.149 psu) to 0.905 • C (0.138 psu) for the whole domain with respect to the other assimilation runs.

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“…The World Ocean Database (WOD) and International Comprehensive Ocean-Atmosphere Data Sets (ICOADS) are the world’s largest freely available marine databases. WOD comprise 16+ million globally distributed profiles, beginning with instrumental observations in 1772 through the present (Levitus, 2012; Boyer et al, 2014). A profile is the set of measurements of physical, hydrochemical, and plankton characteristics of seawaters on the surface and at various depths.…”

Section: Discussionmentioning

confidence: 99%

Layered patterns in nature, medicine, and materials: quantifying anisotropic structures and cyclicity

Smolyar

Bromage

Wikelski

2019

PeerJ

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Various natural patterns—such as terrestrial sand dune ripples, lamellae in vertebrate bones, growth increments in fish scales and corals, aortas and lamellar corpuscles in humans and animals—comprise layers of different thicknesses and lengths. Microstructures in manmade materials—such as alloys, perlite steels, polymers, ceramics, and ripples induced by laser on the surface of graphen—also exhibit layered structures. These layered patterns form a record of internal and external factors regulating pattern formation in their various systems, making it potentially possible to recognize and identify in their incremental sequences trends, periodicities, and events in the formation history of these systems. The morphology of layered systems plays a vital role in developing new materials and in biomimetic research. The structures and sizes of these two-dimensional (2D) patterns are characteristically anisotropic: That is, the number of layers and their absolute thicknesses vary significantly in different directions. The present work develops a method to quantify the morphological characteristics of 2D layered patterns that accounts for anisotropy in the object of study. To reach this goal, we use Boolean functions and an N-partite graph to formalize layer structure and thickness across a 2D plane and to construct charts of (1) “layer thickness vs. layer number” and (2) “layer area vs. layer number.” We present a parameter disorder of layer structure (DStr) to describe the deviation of a study object’s anisotropic structure from an isotropic analog and illustrate that charts and DStr could be used as local and global morphological characteristics describing various layered systems such as images of, for example, geological, atmospheric, medical, materials, forensic, plants, and animals. Suggested future experiments could lead to new insights into layered pattern formation.

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2013 World Ocean Atlas Aids High‐Resolution Climate Studies

Cited by 114 publications

References 7 publications

Uncertainties in Climatological Seawater Density Calculations

Uncertainties in Climatological Seawater Density Calculations

Argo data assimilation into HYCOM with an EnOI method in the Atlantic Ocean

Layered patterns in nature, medicine, and materials: quantifying anisotropic structures and cyclicity

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