Anomaly of a covariate averaged over a box

Averages a covariate over a lon/lat box in each time step, then subtracts a reference to give an anomaly. The result is one number per time step, broadcast to every row, so it behaves like a climate index rather than a map.

Usage

box_anomaly(
  env_dat,
  var,
  box,
  reference = c("climatology", "record", "none"),
  name = NULL
)

Arguments

env_dat

an sf POINT object with one row per location and time step, as datamatch's access functions return

var

covariate column to average

box

named list with xmin, xmax, ymin, ymax, in degrees

reference

"climatology" (the default) removes a separate mean per calendar month, so only departures from the usual conditions for that month survive. "record" removes one mean over the whole series, leaving the seasonal cycle in. "none" returns the box mean itself.

name

name for the new column

Value

env_dat with an anomaly column added, in the units of var

Details

This is the simplest of the region-scale indices and often the most robust. It asks whether conditions in a place were unusual, without needing velocities or endmembers, so it survives on products where the other methods cannot run.

What it cannot tell you

A box mean says conditions changed, not that water moved. A fresh anomaly in the eastern Gulf of Maine is consistent with more Scotian Shelf inflow, and also with local runoff, rainfall, or ice melt. Where a transport across a section measures the crossing directly, this measures its most visible consequence and asks you to supply the interpretation.

See also

eastern_gom_salinity(), derived_indices()

Examples

if (FALSE) { # \dontrun{
env <- box_anomaly(env, "SSS", box = list(
  xmin = -68.5, xmax = -66.5, ymin = 43.0, ymax = 44.5
))
} # }