the Latter Again Is More Conductive to the Former What Does This Mean

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Published: fourteen Nov 2017

Electrical electrical conductivity of the global bounding main

Tim P. Boyer³,
Takuto Minami⁴,
Melissa M. Zwengⁱⁱⁱ &
James R. Reagan^three,v

Earth, Planets and Space book 69, Article number:156 (2017) Cite this commodity

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Abstract

The electrical electrical conductivity of the bounding main is a key parameter in the electrodynamics of the World Organisation. This parameter is involved in a number of applications ranging from the calibration of in situ ocean flow meters, through extensions of traditional induction studies, and into quite new opportunities involving the remote sensing of ocean flow and properties from space-borne magnetometers such as carried aboard the three satellites of the Swarm mission launched in 2013. Here, the first sea conductivity data gear up calculated direct from observed temperature and salinity measurements is provided. These information depict the globally gridded, three-dimensional mean conductivity as well as seasonal variations, and the statistics of spatial and seasonal variations are shown. This "climatology" data gear up of ocean conductivity is offered every bit a standard reference similar to the ocean temperature and salinity climatologies that take long been available.

Introduction

The electric electrical conductivity of the ocean is a fundamental parameter in the electrodynamics of the Earth Arrangement. It affects electrodynamic processes and the appreciable electromagnetic fields inside the ocean just likewise throughout the Globe's diverse components extending from the interior to the upper atmosphere. Noesis of the sea'southward electrical conductivity is therefore required in studies of these processes and estimation of these fields.

The electrical conductivity referred to in this commodity is specifically the frequency-independent proportionality constant $\sigma $ relating electrolytic conduction current density ${\mathbf {J}}$ to the electric field ${\mathbf {E}}'$ as measured in a frame moving with the fluid (the units of conductivity are Siemens/meter (i.due east., S/m)). This human relationship is expressed as Ohm's law ${\mathbf {J}}=\sigma {\mathbf {E}}'$. Using the Lorentz transformation, this tin likewise be written every bit ${\mathbf {J}}=\sigma \left( {\mathbf {East}}+{\mathbf {u}}\times {\mathbf {B}}\correct) $, where ${\mathbf {E}}, {\mathbf {u}}$, and ${\mathbf {B}}$ are the electric field, fluid velocity, and magnetic field measured in the common rotating frame of the Earth. Maxwell's equations provide further relationships to consummate a airtight set of equations from which general solutions tin be found, simply Ohm's law alone establishes the importance of $\sigma $ in opportunities attempting to infer components of $\sigma , {\mathbf {J}}, {\mathbf {East}}, {\mathbf {u}}, {\mathbf {B}}$ from an incomplete gear up of observations.

A wide variety of oceanographic instruments infer flow velocity from in situ measurements of the electric field (see Tyler et al. 1997; Szuts 2012 for review fabric) or, more accurately, the divergence in electric potential between two points which, depending on the experimental design, may exist separated by distances ranging from centimeters to hundreds of kilometers. The inference of flow from electric field measurements requires estimates of the electrical conductivity distributed over a region typically much larger than can be co-sampled in the same experiment with conductivity sensors. Hence, a database for accurately prescribing the conductivity distribution immediately improves the calibration of a wide diverseness of oceanographic flow meters.

Since long, processes in the upper atmosphere and magnetosphere have been inferred from observed fluctuations in the geomagnetic field. Electric currents excited by dynamical processes in these regions cast magnetic fields reaching land and satellite magnetic observatories. Simply these magnetic fluctuations also induce electric currents in the oceans and World, and and then the observed magnetic fields are due to a combination of the primary and induced electric currents. Accurate inference of the processes in the upper atmosphere and magnetosphere therefore depends on accurate noesis of the conductivity distribution in the oceans and drape.

Until relatively recently, about observations of the geomagnetic field were made on land. Since 1999, however, a number of long-term, low-orbit magnetic survey missions (Oersted, Gnaw, SAC-C) take provided unprecedented resolution, particularly over the oceans and other regions poorly covered by country observatories (e.one thousand., Olsen et al. 2006). In 2013, iii low-orbit satellites were launched in the start mission (Swarm, eastward.g., Friis-Christensen et al. 2008) involving multiple space-borne magnetic observatories aimed to farther increase the item in the observed magnetic fields. This new epoch of satellite magnetic surveys has opened new opportunities, some extending from the applications described above, and some quite novel. Many of these opportunities involve interpretation of large-calibration, relatively weak fluctuations in the World'south magnetic field that in plow involve electric currents (either principal or induced) in the ocean (e.g., meet Kuvshinov 2008 for review). Hence, much of the modernistic evolution in geomagnetic studies has become dependent on the accurateness in modeling oceanic electric currents, and the priority for better understanding the distribution of electrical conductivity in the ocean has risen.

In the post-obit two subsections, the physics of ocean electrical conductivity is described, and a description of the methods used in previous estimates of this parameter is given. The methodology of this report is presented in "Methodology" section, and the results are presented in "Climatology of body of water electrical conductivity" section. In "Apply of climatology to assess errors in previous assumptions" department, a cursory description of errors associated with previous electrical conductivity estimates is provided, a word is included in "Conclusion" section, and data on obtaining the data is given in "Availability of data and materials" section. This information ready of the long-term (1981–2010) climatological mean shall exist referred to hither as the "climatology," consequent with nomenclature for other ocean data.

Conductivity of seawater

Within the parameters of Earth's oceans, the electric electrical conductivity of seawater depends on temperature, salinity, and to a much smaller degree pressure (depth). Salts such as sodium chloride (NaCl) disassociate in water to form cations ($\hbox {Na}^{+}$) and anions ($\hbox {Cl}^{-}$) that migrate in the presence of an electric field, thereby producing an electric electric current. It is then piece of cake to understand that the conductivity $\sigma $ increases with the concentration of dissolved salts (salinity S). The conductivity also increases with increasing temperature T, and this may be reasonably associated with an increase in mobility of the ions. Although the increase of $\sigma $ with T and Due south may be hands understood intuitively, this does non immediately provide much indication of the distribution of $\sigma $ in the ocean, even for oceanographers familiar with the distributions of T and Due south. Even the typical vertical contour of $\sigma $ is non easily anticipated because of opposing tendencies in the dependencies of conductivity and density on T,S. While $\sigma $ increases with both T and S, the water density $\rho $ increases with S simply decreases with T. With possible exceptions that are volumetrically rare (e.g., surf zones), the oceans are stably stratified and $\rho $ increases with depth. This alone, nonetheless, does not immediately provide intuition on the profile of $\sigma $ or whether the profile is even monotonic with depth. It seems so to be the case that quantitative and fifty-fifty qualitative understanding of the distribution of body of water electrical conductivity must be obtained through original analyses of observations, rather than through reference to other well-described ocean parameters.

Global conductivity in previous studies

In early studies, and even continuing to recent work (e.g., Tyler et al. 2003; Kuvshinov et al. 2006; Schnepf et al. 2014; Sabaka et al. 2015), the electrical conductivity of the ocean has often been causeless to exist compatible. In some applications involving idealized or model comparing studies, this may exist justified but information technology is also clear that a reliable global description or gridded data fix of the observed ocean conductivity was non available. Tyler et al. (1997) and several subsequent studies extending to the present (eastward.g., Irrgang et al. 2016a, b) included a description of the iii-dimensional ocean conductivity as obtained from the temperature, salinity, and pressure variables in a global ocean circulation model. While the conductivity obtained this mode is expected to be dynamically consistent, information technology remains unclear how realistic the description is and, well-nigh importantly, how well it agrees with the large fix of scattered observations.

Climatology information sets for other ocean parameters (e.1000., temperature, salinity, density) have long been available and used in a broad diversity of oceanographic applications ranging from observational studies to body of water modeling. But a climatology data set for conductivity has non previously been bachelor and structure of such requires a major effort. Climatologies of ocean temperature and salinity, for case, endeavor to represent the well-nigh reliable gridded information sets that can be constructed from the big set up of historical observations taken using a diversity of methods and instrumentation. Electrical conductivity depends nonlinearly on temperature and salinity, and the separate temperature/salinity climatologies are synthetic from quite unlike sample distributions. Conductivity should and so exist calculated from co-observed temperature and salinity.

Alternatively, 1 may summate a description of conductivity straight from climatologies of temperature and salinity. Such an approach was used to obtain the electrical conductivity in Manoj et al. (2006), and these data have been adopted in later studies (e.thousand., Kuvshinov 2008; Sabaka et al. 2015; Schnepf and Kuvshinov 2015; Grayver et al. 2016). While this may reasonably provide a quick approach to obtaining a gridded electrical conductivity data set, a major weakness is that the realism is immediately suspect and the uncertainties are not easily assessed. The but published study and then far to include gridded electrical conductivity calculated from observations is the Tyler tidal magnetic field simulation in Sabaka et al. (2016). The latter data (a preliminary information set of that presented in this study) used data from the longer range 1978–2012, and this date range was adapted to 1981–2010 in this study to follow the fourth dimension span convention of other climatologies.

As described, previous descriptions of global bounding main conductivity are either recognizably simplistic, or their caste of realism is difficult to assess. Of grade with the climatology provided here, the errors associated with previous approaches can at present be addressed, and a discussion toward this is included in "Employ of climatology to assess errors in previous assumptions" section.

Methodology

In this department, the algorithm used for calculating seawater electric conductivity from temperature, salinity, and pressure is commencement presented, and and then the data serving as input is described.

Algorithm for calculating electric conductivity

The electric electrical conductivity ($\sigma $) of seawater became one of the fundamental oceanographic parameters in the 1950s with the increased use of high-precision electric electrical conductivity bridges for salinity determinations. After near 70 years of using the dilution theory provided by Knudsen et al. (1902), the practical salinity (Due south) was defined on the Practical Salinity Scale of 1978 (PSS-78) in terms of the electrical conductivity ratio to the reference, and its human relationship with $\sigma $, temperature ($t_{68}$, IPTS-68), and force per unit area (p, cipher dbar at one atmospheric pressure) was incorporated in the Equation of Land for Seawater (EOS-eighty; e.m., Fofonoff and Millard 1983; Fofonoff 1985). Although the international thermodynamic equation of seawater 2010 (TEOS-10; IOC et al. 2010) superseded EOS-80, the relationship in EOS-80 associated with the conductivity is still valid. In this study, we use gsw_C_from_SP in the Gibbs Seawater (GSW) Oceanographic Toolbox (McDougal and Barker 2011; IOC et al. 2010) to calculate $\sigma $ from S,p, and the temperature on ITS-ninety, T (TEOS-10 is used with no conversions betwixt temperature scales; if data were in IPTS-68 or ITS-xc, we used it directly). In both EOS-80 and TEOS-10, the conductivity, $\sigma $, is calculated using the relationship,

$$\brainstorm{aligned} \sigma (South,t_{68},p)=\sigma (35,15\,^{\circ }\mathrm{C},0\;\mathrm{dbar})\cdot R, \finish{aligned}$$

(1)

where R is the solution of

$$\brainstorm{aligned} r_{t}(t_{68})\cdot R_{t}\cdot \left( 1+\frac{C_{p}(p)}{A(t_{68})\cdot R+B(t_{68})}\right) -\,R=0, \end{aligned}$$

and $R_{t}$ is obtained by solving the following equation:

$$\begin{aligned} \left\{ \sum _{i=0}^{5}\left( a_{i}+\frac{t_{68}-xv}{one+m(t_{68}-fifteen)}b_{i}\right) (R_{t})^{i/2}\right\} -\,South=0. \cease{aligned}$$

(two)

Here, $r_{t}(t_{68})=\sum _{i=0}^{4}c_{i}t_{68}^{i}, B(t_{68})=i+d_{1}t_{68}+d_{ii}t_{68}^{ii}, A(t_{68})=d_{3}+d_{iv}t_{68}$, and $C_{p}(p)=e_{1}p+e_{2}p^{2}+e_{3}p^{iii}$, while $k, a_{i}, b_{i}, c_{i}, d_{ane\ldots four},$ and $e_{1\ldots 3}$ are empirical coefficients.

The reference electrical conductivity is $\sigma (35,15\,^{\circ }\mathrm{C},0\;\mathrm{dbar})=iv.29140$ (S/m). Between the 2 temperature scales, IPTS-68 and ITS-90, T is linked to $t_{68}$ by the relationship, $t_{68}= ane.00024 \cdot T$. While Eq. (1) is valid only in the range of $two<Due south<42$, GSW Toolbox adopts the method of Hill et al. (1986) for the range of $0<S<two$. Refer to Fofonoff (1985) and IOC et al. (2010) for more details about Eq. (1).

Concurrent salinity, temperature, and pressure level measurements used to calculate conductivity come from the Earth Ocean Database (future WOD). Descriptions of data sources for this drove of oceanographic profile data, instrumentation, temporal and spatial distributions, measurement accuracies, and quality command procedures can be found in Boyer et al. (2013). Only concurrent salinity and temperature profiles taken during or after 1981 were used for consistency in the definition of salinity (PSS-78). Pressure was used when reported; otherwise, it was calculated from reported depth measurements. Although most salinity values in this fourth dimension period are derived from conductivity measurements, the conductivity was not usually reported, hence the need to back calculate conductivity. The same procedures as outlined in Zweng et al. (2013) were followed to calculate objectively analyzed climatological hateful fields of conductivity for the period 1981–2010 at 102 standard depth levels from the surface to 5500 yard depth. Briefly, one-degree gridded mean values of electrical conductivity at each standard depth were compiled later on back adding, subject to quality command procedures. An objective analysis technique (Cressman 1959; Barnes 1964) was employed to alter each one-caste mean based on the difference between a commencement-estimate field and the compiled hateful of each one-degree square within a given radius of influence. The start-gauge field for the annual (all-data) field was a basin specific zonal average. The kickoff-guess field for the four seasonal fields (Wintertime = January–March, etc.) was the almanac climatological hateful. The get-go-guess for each calendar month was the appropriate seasonal climatological mean. Monthly fields only extend to 1500 m, every bit there is little almanac cycle (and thin data) below that level. Annual and seasonal fields extend to 5500 thousand depth.

Climatology of ocean electrical conductivity

In this study, the temporal mean sea conductivity data for the global ocean have been calculated following the method described in "Methodology" section. We proceed hither to describe characteristics of this data fix which is available at https://www.nodc.noaa.gov/OC5/woa13/.

The temporal mean conductivity $\sigma =\sigma (\text{ longitude, } \text{ latitude, } {-\,z) }$ is a three-dimensional assortment, with each value representing a cell volume which varies with breadth and depth ($-\,z$). The volumetrically weighted mean electrical conductivity of the global ocean is 3.31 ± 0.23 S/thou.

In Fig. 1, we come across that the values and variability of the conductivity at the sea surface are much larger than the volume quantities. There is a potent latitudinal variation that primarily tracks that of temperature. Merely in fresh inland seas and locations well-nigh the mouths of rivers, conductivity primarily depends on salinity. The salinity dependence is quite farthermost in the Arctic because the temperature is relatively abiding (almost freezing), at that place are many large rivers supplying freshwater, and alkali rejection/salt dilution during ice formation/cook makes the variability of salinity high. Because evaporation exceeds precipitation over the Mediterranean (and more often than not the Atlantic), both salinity and conductivity are elevated compared to other locations at like temperatures.

The depth-averaged electrical conductivity is shown in Fig. 2. Away from warm, shallow areas and the freshwater of rivers and inland seas, one sees that the depth-averaged conductivity is remarkably constant over most of the global ocean. This is an immediate indication that most of the variability seen in Fig. one does non extend very deep into the sea. The related depth integral (i.e., the depth-averaged electrical conductivity multiplied by the ocean depth) is referred to every bit the conductance (S) and is also shown in Fig. 2. The globally averaged profile of conductivity with depth is shown in Fig. iii, together with similar profiles for T,Due south.

To characterize the three-dimensional beliefs of the conductivity, we follow an approach used in oceanography in which T,South are used as plotting coordinates. Considering a water mass' T,S properties are primarily set by processes occurring at the sea surface, the T,South coordinates are often useful in identifying the location of origin of the water mass. In Fig. 4, a scatter plot of the conductivity data is shown using T,S (and depth, $-\,z$) equally coordinates. The value of the electrical conductivity $\sigma \left( T,Due south,-\,z\right) $ is shown past the color scale. Several projections onto planar surfaces are included and used toward the following clarification.

Consider the project onto the $-\,z=6000$ m surface (as seen in the superlative panel of Fig. 4). Several betoken clusters with low S coordinate correspond to inland seas (Caspian, Black, Baltic) and the Arctic. Excluding these regions, we magnify this figure (bottom panel) and see that aside from a cluster with high T,S (the Mediterranean), near of the global ocean electrical conductivity is remarkably uniform at depth (meet also the Boosted file 3 which shows a pic of conductivity surfaces from the surface to seafloor).

Above, the spatial variability of the temporally averaged conductivity has been described. Now we shall depict the temporal variability. One should note that even the fourth dimension-averaged conductivity climatology tin can be useful in understanding and predicting temporal variability caused by fluid motion. Figures 3 and four testify that in that location are typically large vertical gradients near the surface. Therefore, vertical fluid movement (e.g., internal waves, convection) can cause temporal variability in the electrical conductivity measured at a given location. Although the horizontal gradients in conductivity are much smaller than the vertical gradients, persistent advection by body of water currents associated with eddies, waves, and tides can likewise pb to of import temporal fluctuations in conductivity. The expected amplitudes of these fluctuations depend very much on the features considered, just a uncomplicated approximate is $\Delta \sigma \sim |{\mathbf {u}}\cdot \nabla \sigma |\Delta t$ , where $\Delta \sigma $ is the amplitude of the anomaly, $|{\mathbf {u}}\cdot \nabla \sigma |$ is the amplitude of the dot production of menses velocity ${\mathbf {u}}$ (which may consider vertical or horizontal components) and the gradient of the electrical conductivity, and $\Delta t$ is the fourth dimension scale for the process. On the large scales relevant to the applications described in this commodity, the almost predominant temporal fluctuation to look is due to the seasonal changes in the fluxes of heat and freshwater across the ocean boundaries. For the purposes of the big-calibration climatology described here, we shall use the seasonal changes to narrate the temporal variability in the sea'south conductivity. In Fig. 5, the surface values are seen to vary $\sim \,ane$ S/m over the seasonal cycle, the fluctuations traceable to expected seasonal variations in heat and freshwater fluxes. The large-scale seasonal variations at the surface show, at mid and low latitudes, a ascendant dependence on hemispheric warming/cooling rather than salinity. Regionally (notably the Arctic) conductivity fluctuations can show stiff dependence on river runoff.

Equally shown in Additional file 1: Fig. S1, nosotros encounter that the depth-averaged temporal fluctuations are typically extremely small $\sim \,0.01$ S/chiliad. The respective fluctuation in terms of conductance is $\sim l$ S. This is consistent with the expectation that most of the seasonal variations in the surface fluxes of rut and freshwater do not penetrate below the top few hundred meters of the sea.

At that place are ii sources of uncertainty to consider in the conductivity climatologies. The climatological mean value is a single value representing the mean over the given time period at each one-degree grid box. However, there is variation around that mean, both natural variation and that caused by measurement dubiety. The measurement accuracy for a standard Seabird 911+ CTD instrument is 0.00003 S/m for electrical conductivity and 0.001C for temperature. In do, due to bounding main conditions, CTD calibration, and measurement procedures, the incertitude in electrical conductivity may be higher. It is hard to separate natural variations from measurement uncertainty. So the standard error of the mean represents the combined uncertainty of the mean value for each depth at each one-degree grid box. The global mean of the standard error is 0.02 S/m at the surface and 0.003 South/m at chiliad m depth for monthly climatological mean fields and $0.04 \pm 0.03\,\hbox {S/yard}, 0.004 \pm 0.004$ S/yard at the surface and 1000 yard, respectively, for the annual climatological mean field. The almanac hateful field has a higher standard error, equally expected, as it encompasses the unabridged seasonal cycle. The 2d measure out of dubiousness is the difference betwixt the observed mean and the objectively analyzed mean at each one-degree grid box. This is a measure out of the incertitude introduced in the objective assay procedure. It is not an independent mensurate from the standard fault of the mean, equally the natural variations and discrepancies in measurement accuracy play a role in the differences between values in nearby filigree boxes and hence are partially responsible for the difference between observed and analyzed mean fields. For monthly fields at the surface, the global mean observed minus analyzed is approximately $0.003 \pm 0.08$ S/grand, while at 1000 thousand the value is $0.0004 \pm 0.02$ Due south/m. For the almanac time period at the surface, the global hateful observed minus analyzed deviation is $0.006 \pm 0.09$ S/m at the surface and $0.0005 \pm 0.01$ Southward/k. All values are for the 1981–2010 climatological mean fields of electrical conductivity. Full fields of standard error of the mean and observed minus analyzed difference are provided with the climatological mean fields of electrical conductivity at https://www.nodc.noaa.gov/OC5/woa13/.

Apply of climatology to appraise errors in previous assumptions

As described in "Introduction" section, a weakness in previously assumed ocean conductivity distributions was the difficulty in assessing the realism or associated errors in the estimates. With the climatology provided hither, ane may now assess these errors. Although some comments and comparisons are included here, it should be noted that with the climatology data now available there is little reason to justify the continued use of simplistic assumptions. The word here then serves but to address errors due to assumptions used in previous applications. As this is non the goal of this paper, the give-and-take hither is kept cursory. Considering accuracy in the previous applications can depend on non just the values of the conductivity but too the gradients and integrals, it is also clear that such an error assessment would remain incomplete unless performed by the original investigators using the new conductivity information provided.

As described in "Methodology" section, we calculated the gridded conductivity data set by beginning computing conductivities from concurrently measured in situ T,South,p and then conducting the objective analysis confronting the irregularly distributed conductivity data. One can much more easily obtain a gridded conductivity data ready by directly calculating conductivity from existing T,S,p climatologies, every bit conducted in Manoj et al. (2006). Yet, the latter conductivity data demand not exist representative nor even realistic.

The climatological T,Due south data in the World Ocean Atlas 2013 (Boyer et al. 2013; Zweng et al. 2013) correspond spatial/temporal distributions of observed T,South, but the distributions of observed T,S data are somewhat different because some observations reported only one of either T or South. While climatologies of either T or S can be obtained through objective analyses of the respective T,S information available, problems tin can arrive when one attempts to combine these information derived from different temporal/spatial distributions of original data. This is especially a business organization in the case here where the calculation of conductivity involves a nonlinear dependence on T,Due south. The latter approach may indeed give conductivity values that are neither representative nor realistic. At the centre of this matter is the fact that the formulae for calculating conductivity from T,S require that the T,South measurements are taken at the same time and location. The approach in this study restores this requirement by creating conductivity climatologies from ancillary, co-located T,S observations rather than the candy T,S climatological products.

Several previous studies treated the ocean every bit having a compatible conductivity of value iii.2 S/m (Tyler et al. 2003; Kuvshinov et al. 2006; Schnepf et al. 2014; Sabaka et al. 2015). This can be compared with the global mean of 3.31 S/1000 described in "Climatology of ocean electric conductivity" section. One as well sees in Fig. 2 that supposition of a compatible value is quite crude every bit at that place are both regional and zonal departures. About notably, the Arctic, with its large river runoff, has much lower conductivity due to the reduction in salinity, and a global-scale reduction due to the lower temperatures at high latitudes is also apparent.

While it tin be immediately appreciated that the electrical conductivity data presented here are significantly more realistic than the assumption of compatible ocean conductivity, the improvement over the data previously calculated from T,S climatologies is less immediate to describe and is therefore included in Additional file 2: Supporting Information to this paper. In brief, conductance calculated in the two methods (or fifty-fifty with the compatible conductivity assumption) appears very similar over almost of the ocean, simply this is primarily simply reflecting the mutual bounding main depth used in all cases. Examination of the gradients of conductance and its inverse show larger fractional differences in the methods. Finally, a better test of the differences (in terms of testing the ocean conductivity distribution assumed rather than mutual integration limits) is in comparing the depth-averaged conductivity and its gradients (as shown in Additional file 3: Supporting Data). In this latter case, there are large differences between the two methods.

In summary, the results here support arcadian ocean conductivity assumptions every bit an approximation (compatible conductivity multiplied by realistic ocean depth gives a reasonable conductance distribution, and one may even obtain a reasonable depth-averaged conductivity bold a mutual electrical conductivity depth profile appropriately representing the global averaged beliefs). The electrical conductivity distribution as calculated in Manoj et al. (2006) may similarly serve the same level of approximation but too shows spurious features such that it is recommended that utilise of these data is discontinued in favor of the information presented here in applications attempting to represent the realistic electrical conductivity of the ocean.

Determination

The generation and analyses of the conductivity climatology data gear up take shown that the spatial and seasonal variability is remarkably pocket-sized, at least when compared with the conductivity in other components of the Earth System which may typically prove orders of magnitude variations in both space and time. The ocean appears to be the only large-calibration component of the Earth System with such highly predictable electrical conductivity, and the data presented here provide a standard reference. This stability and predictability of the ocean conductivity is very important in a number of consecration and motional induction applications where naturally occurring magnetic fields (driven by external fields or flow) may be used to infer flow and electrical parameters in the ocean and solid Earth. In some case (e.yard., tides) where the forces ultimately driving oceanic electrical currents are besides highly predictable, the ocean may provide the most predictable naturally occurring large-scale sounding source on Earth. In this case, the data presented here may exist used to quantify the stability of such sources.

While the conductivity of the ocean may be more than stable and predictable than other components of the Globe System, the climatology presented also shows interesting spatial and temporal variations. Immediately apparent are the effects of seasonal heating and river runoff.

Quantifying errors in past studies due to simplistic assumptions for the electrical conductivity is necessarily incomplete as accuracy tin can depend on non only the electrical conductivity values used but also the gradients and integrals. Some reassurance in past simplistic assumptions is provided here in that the spatial variability is seen to be relatively modest and/or described past roughly like depth profiles (hence, arcadian assumptions are not equally bad as they could first seem). Just in attempting to correspond more realistic sea conductivity, information technology is recommended that apply of electrical conductivity every bit calculated in Manoj et al. (2006) be discontinued in favor of the results provided here. Futurity studies that intentionally proceed to apply idealized or ocean model conductivity distributions may too at present draw from the statistics and data provided here.

References

Barnes SL (1964) A technique for maximizing details in numerical weather map analysis. J Appl Meteorol iii(4):396–409

Article Google Scholar
Boyer TP, Antonov JI, Baranova OK, Garcia HE, Johnson DR, Mishonov AV, O'Brien TD, Seidov D, Smolyar I, Zweng MM et al (2013) Earth Bounding main Database 2013. In: Levitus S, Mishonov A (eds) Tech. Ed. NOAA Atlas NESDIS, vol 74
Cressman GP (1959) An operational objective assay system. Monday Weather condition Rev 87(10):367–374

Article Google Scholar
Fofonoff NP, Millard RC (1983) Algorithms for computation of fundamental properties of seawater. UNESCO Technical Papers in Marine Science 44
Fofonoff NP (1985) Physical backdrop of seawater: a new salinity scale and equation of country for seawater. J Geophys Res Oceans (1978–2012) 90(C2):3332–3342

Article Google Scholar
Friis-Christensen E, Luhr H, Knudsen D (2008) Swarm—an Earth observation mission investigating geospace. Adv Space Sci 41:210–216

Commodity Google Scholar
Grayver AV, Schnepf NR, Kuvshinov AV, Sabaka TJ, Manoj C, Olsen Due north (2016) Satellite tidal magnetic signals constrain oceanic lithosphere–asthenosphere boundary. Sci Adv 2(9):e1600798. https://doi.org/10.1126/sciadv.1600798, http://advances.sciencemag.org/content/2/9/e1600798
Hill KD, Dauphinee T, Forest DJ (1986) The extension of the applied salinity calibration 1978 to low salinities. IEEE J Ocean Eng 11(1):109–112

Article Google Scholar
IOC, SOR, IAPSO (2010) The international thermodynamic equations of seawater-2010: calculation and utilise of thermodynamic properties. UNESCO
Irrgang C, Saynisch J, Thomas M (2016a) Ensemble simulations of the magnetic field induced by global bounding main circulation: estimating the uncertainty. J Geophys Res Oceans. https://doi.org/10.1002/2016/C011633

Google Scholar
Irrgang C, Saynisch J, Thomas 1000 (2016b) Impact of variable seawater electrical conductivity on motional consecration simulated with an ocean general circulation model. Body of water Sci 12(1):129–136

Commodity Google Scholar
Knudsen M, Forch C, Sorensen S (1902) Bericht uber die chemische und physikalische Untersuchung des Seewassers und die Aufstellung der neuen hydrographischen Tabellen, Kommission zur wissenschaftlichen Untersuchung der deutschen Meere
Kuvshinov A (2008) 3-D global induction in the oceans and solid globe: recent progress in modeling magnetic and electric fields from sources of magnetospheric, ionospheric and oceanic origin. Surv Geophys 29(2):139–186

Commodity Google Scholar
Kuvshinov A, Junge A, Utada H (2006) 3-D modelling the electric field due to bounding main tidal flow and comparison with observations. Geophys Res Lett 33(6):L06314 (239–241)

Commodity Google Scholar
Manoj C, Kuvshinov A, Maus S, Luhr H (2006) Body of water circulation generated magnetic signals. Earth Planets Space 58(4):429–437. https://doi.org/x.1186/BF03351939

Article Google Scholar
McDougal TJ, Barker PM (2011) Getting started with TEOS-ten and the Gibbs Seawater (GSW) Oceanographic Toolbox, SCOR/IAPSO WG127. ISBN 978-0-646-55621-5
Olsen N, Lühr H, Sabaka TJ, Mandea M, Rother G, Tøffner-Clausen 50, Choi Southward (2006) CHAOS—a model of the Globe'south magnetic field derived from CHAMP, Ørsted, and SAC-C magnetic satellite data. Geophys J Int 166(1):67–75

Article Google Scholar
Sabaka TJ, Olsen N, Tyler RH, Kuvshinov A (2015) CM5, a pre-Swarm comprehensive geomagnetic field model derived from over 12 twelvemonth of CHAMP, Orsted, SAC-C and observatory data. Geophys J Int 200(3):1596–1626

Article Google Scholar
Sabaka TJ, Tyler RH, Olsen N (2016) Extracting ocean-generated tidal magnetic signals from swarm data through satellite gradiometry. Geophys Res Lett 43:3237–3245

Commodity Google Scholar
Schnepf NR, Kuvshinov A (2015) Can nosotros probe the electrical conductivity of the lithosphere and upper mantle using satellite tidal magnetic signals? Geophys Res Lett 42:3233–3239

Article Google Scholar
Schnepf NR, Manoj C, Kuvshinov A, Toh H, Maus S (2014) Tidal signals in ocean-bottom magnetic measurements of the Northwestern Pacific: observation versus prediction. Geophys J Int 198(2):1096–1110

Article Google Scholar
Szuts ZB (2012) Using motionally-induced electric signals to indirectly measure bounding main velocity: instrumental and theoretical developments. Prog Oceanogr 96(ane):108–127

Article Google Scholar
Tyler RH, Sanford TB, Oberhuber JM (1997) Geophysical challenges in using large-scale ocean-generated EM fields to determine the ocean flow. J Geomagn Geoelectr 49(11–12):1351–1372

Commodity Google Scholar
Tyler RH, Maus South, Lühr H (2003) Satellite observations of magnetic fields due to body of water tidal period. Scientific discipline 299(5604):239–241

Article Google Scholar
Zweng MM, Reagan JR, Antonov JI, Locarnini RA, Mishonov AV, Boyer TP, Garcia HE, Baranova OK, Johnson DR, Seidov D, Biddle MM (2013) World Ocean Atlas 2013. In: Levitus Southward, Mishonov A (eds) Tech. Ed. NOAA Atlas NESDIS, vol 2: Salinity, vol 74

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Authors' contributions

All authors contributed to the design, implementation, and writing of this study, with the post-obit leading tasks: RHT was involved in conception/blueprint of written report, analyses of gridded data, and writing; TPB was involved in design and objective analyses of observations; TM was involved in description and implementation of TEOS conversion formulae; MMZ was involved in quality control of information; JRR was involved in quality control and generation of supporting material comparison study. All authors read and approved the final manuscript.

Acknowledgements

RHT acknowledges support from the NASA Globe Surfaces and Interiors Program. This report was partially supported past NOAA grant NA14NES4320003 (Cooperative Institute for Climate and Satellites—CICS) at the University of Maryland/ESSIC. Boosted support was provided by the Body of water Observing and Monitoring Segmentation, Climate Program Part, National Oceanic and Atmospheric Assistants, U.Southward. Department of Commerce. The authors wish to give thanks Trevor McDougall for helpful discussions.

Competing interests

The authors declare that they take no competing interests.

Availability of information and materials

The conductivity climatological data presented here are available at http://www.nodc.noaa.gov/OC5/woa13/.

Consent for publication

Not applicable.

Ethics blessing and consent to participate

Not applicable.

Funding

Funding support for this work includes the NASA Earth Surface and Interiors Program, the Japan Society for the Promotion of Science, and NOAA.

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Author information

Affiliations

Geodesy and Geophysics Laboratory, Code 61A, NASA Goddard Infinite Flying Center, Greenbelt, Dr., 20771, Usa

Robert H. Tyler
Astronomy Section, Academy of Maryland, College Park, Medico, United states

Robert H. Tyler
National Centers for Environmental Data (NCEI), National Oceanic and Atmospheric Administration (NOAA), Washington, DC, The states

Tim P. Boyer, Melissa M. Zweng & James R. Reagan
Earthquake Research Institute, The University of Tokyo, ane-1-i Yayoi, Bunkyo-ku, Tokyo, 113-0032, Nippon

Takuto Minami
Cooperative Institute for Climate and Satellites (CICS), Earth System Science Interdisciplinary Center (ESSIC) University of Maryland, College Park, MD, USA

James R. Reagan

Respective author

Correspondence to Robert H. Tyler.

Additional file

40623_2017_739_MOESM1_ESM.pdf

Additional file one: Figure S1. Depth-averaged conductivity anomaly (relative to almanac hateful) for each of the four seasons. The total range in the data are, respectively, −0.660–−0.556 (S/g), −0.471–−0.317 (S/m), −0.511–−0.721 (Due south/m), −0.502–−0.594 (S/m).

40623_2017_739_MOESM2_ESM.docx

Boosted file 2. Supplement document comparing results hither with results from a previous study.

40623_2017_739_MOESM3_ESM.avi

Boosted file iii. Picture of bounding main conductivity showing frames moving from surface to sea floor.

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Cite this article

Tyler, R.H., Boyer, T.P., Minami, T. et al. Electrical conductivity of the global ocean. Earth Planets Space 69, 156 (2017). https://doi.org/ten.1186/s40623-017-0739-7

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Received: 22 August 2017
Accustomed: 25 October 2017
Published: 14 November 2017
DOI : https://doi.org/10.1186/s40623-017-0739-7

Keywords

Electrical conductivity
Conductance
Body of water
Climatology

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