gsw package provides an R implementation of the Gibbs
SeaWater toolbox for the calculation of seawater properties, based on the GSW-C
framework^[This version of GSW-R is based on GSW-C as it existed on 2021-07-06
in github commit 9c10670e89fce906da2cebce3399d73c054e769e; see
https://github.com/TEOS-10/GSW-C.]. This vignette outlines how to use
alone or as part of the
oce package [@kelley_oce_2021].
In recent years, thermodynamic considerations have led to improved formulae for
the calculation of seawater properties
important component of which is the Gibbs-SeaWater (GSW) toolbox
gsw package is an R version of GSW, which may
be used independently or within the more general
This vignette sketches how to use
gsw. Readers are assumed to be familiar
with oceanographic processing, and at least somewhat familiar with GSW. A good
resource for learning more about GSW is
http://www.teos-10.org, which provides technical
manuals for the Matlab version of GSW
along with white papers and links to the growing peer-reviewed literature on the
gsw framework uses function wrappers that connect R with the C version of
the Gibbs Seawater library. This yields high processing speed. By minimizing
transliteration errors, it also increases reliability. In a further effort to
increase reliability, GSW-R makes tests against the check values provided on
the webpages that document GSW-Matlab.
By design, the documentation of
gsw functions is spare, amounting mainly to an
explanation of function arguments and return values, with most other details
being provided through hyperlinks to the GSW reference documentation. The idea
is to avoid duplication and to encourage users to consult the technical
materials linked to the GSW functions mimicked in
gsw. The GSW system is
somewhat complex, and analysts owe it to themselves to learn how it works, and
also to develop an appreciation for its scientific context by consulting
various documents at http://www.teos-10.org,
including expansive white papers and pointers to the growing peer-reviewed
options(keep.source=TRUE, width=60, prompt=' ', continue=' ', oceEOS="unesco")
Suppose a water sample taken at pressure (For practical reasons,
beyond SI to incorporate oceanographic units, such as decibars for pressure.)
100 dbar, longitude 188E and latitude 4N, reveals Practical Salinity 35 and
in-situ temperature 10$^\circ$C (ITS-90). Then the Absolute Salinity may be
calculated as follows.
library(gsw) SA <- gsw_SA_from_SP(SP=35, p=100, longitude=188, latitude=4)
r SA [g/kg], which can then be used to
calculate Conservative Temperature as follows.
CT <- gsw_CT_from_t(SA=SA, t=10, p=100)
The above yields
r CT [$^\circ$C]. Readers familiar with
GSW will recognize the function and argument names, and are likely to find the
other functions needed for their work among the roughly sixty functions that
oce plotting functions have an argument named
eos that can be set to
"unesco" to get the older seawater formulation, or to
get the newer one. For example, the
section dataset provided by
oce holds a
sequence of CTD casts in the North Atlantic. Individual casts may be selected
by index, so a TS diagram of the station at index 100 (south of Cape Cod in 4000
m of water) can be plotted as follows.
library(oce) data(section) ctd <- section[["station", 100]] Slim <- c(34.8, 37.0) Tlim <- c(0, 25) par(mfcol=c(2,2)) plotTS(ctd, Slim=Slim, Tlim=Tlim, eos="unesco") plotTS(ctd, Slim=Slim, Tlim=Tlim, eos="gsw") plot(ctd[["SA"]] - ctd[["salinity"]], ctd[["z"]], xlab="Practical Salinity - Absolute Salinity", ylab="Depth [m]") plot(ctd[["CT"]] - ctd[["theta"]], ctd[["z"]], xlab="Conservative Temp. - Potential Temp.", ylab="Depth [m]")
Most hydrography-related functions of
oce provide this
eos argument for
selecting the seawater formulation. This includes functions for plotting and
for calculating. In addition, most of the objects within
oce have accessors
that can return temperature and salinity in either the
UNESCO or GSW scheme.
For example, the ratio of Conservative Temperature to
potential temperature $\theta$ for all the CTD profiles in
f <- section[["CT"]] - section[["theta"]] hist(f, main="", breaks=100, xlab="CT-theta")
A salinity comparison is constructed with
f <- section[["SA"]] - section[["salinity"]] hist(f, main="", breaks=100, xlab="Absolute Salinity - Practical Salinity")
An examination of worldwide spatial patterns is also informative, with the following producing such a graph.
library(oce) data("levitus", package="ocedata") SSS <- levitus$SSS dim <- dim(SSS) ll <- expand.grid(lon=levitus$longitude, lat=levitus$latitude) SA <- gsw_SA_from_SP(levitus$SSS, 0, ll$lon, ll$lat) per <- 100 * (1 - levitus$SSS / SA) imagep(levitus$longitude, levitus$latitude, per, col=oceColorsJet, zlim=quantile(per, c(0.001, 0.999), na.rm=TRUE)) title(expression("Percent difference between " * S[A] * " and " * S[P]))
Note the use of quantile-specified scales for the images, the colour mappings of which would otherwise be controlled by isolated low-saline waters, yielding little to see in the wider expanses of the world ocean; for a broader context, see e.g. @mcdougall_getting_2020.
options(prompt='> ', continue='+ ', oceEOS="unesco")
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