Power Spectral Density of the Error in a Sediment Archived Proxy Timeseries

Usage

ProxyErrorSpectrum(
  nu = NULL,
  tau_s,
  tau_b,
  tau_p,
  tau_r,
  T,
  delta_t,
  N,
  n.k,
  clim.spec.fun,
  clim.spec.fun.args,
  sig.sq_a,
  sig.sq_c,
  nu_a,
  nu_c,
  phi_a,
  phi_c,
  delta_phi_c,
  sigma.meas,
  sigma.ind,
  sigma.cal,
  n.nu.prime = 1000,
  ...
)

Arguments

nu: frequency
tau_s: sediment slice thickness in years (layer.width / sedimentation rate)
tau_b: timescale of bioturbation (bioturbation depth / sedimentation rate) (L/sr)
tau_p: length of proxy carrier "growth season" (< 12 months)
tau_r: width of moving average filter that represents the "interpreted" resolution of the timeseries
T: length of a finite time series, e.g. 1e04
delta_t: sampling frequency of the sediment core / climate timeseries
N: number of signal carriers (e.g. individual foraminifera)
n.k: the number of aliasers used when estimating the error spectrum of the stochastic climate signal. Defaults to 15, does not normally need to be adjusted.
clim.spec.fun: a function to return climate power spectral density as a function of frequency, nu
clim.spec.fun.args: arguments to the named climate power spectrum function
sig.sq_a: variance of precessionary amplitude modulation
sig.sq_c: variance of the seasonal cycle
nu_a: 1/tau_a = frequency of the orbital variation, e.g. for precession 1/23e03 yrs
nu_c: 1/1 (yr) = frequency of the seasonal cycle
phi_a: phase of precessionary amplitude modulation relative to centre of finite timeseries of length T
phi_c: phase of carrier growth season relative to the maximum of the seasonal cycle
delta_phi_c: Uncertainty in the phase of the signal carrier production. A value between 0 and 2Pi.
sigma.meas: the standard deviation of the per sample measurement error
sigma.ind: the standard deviation of error between individuals (e.g. foraminifera) e.g. due to "vital effects" or calcification depth
sigma.cal: the 1-sigma (standard deviation) of the calibration error in the same units as the proxy
n.nu.prime: number of discrete frequencies at which to evaluate the PSD of the error

Value

a dataframe of frequencies and spectral power

Examples

spec.pars <- GetSpecPars("Mg_Ca", tau_p = 4 / 12, delta_phi_c = pi)
spec.obj <- do.call(ProxyErrorSpectrum, spec.pars)
#> Warning: Rounding T to 10100 so that T is an odd integer multiple of delta_t
PlotSpecError(spec.obj)
#> Warning: There were 4 warnings in `summarise()`.
#> The first warning was:
#> ℹ In argument: `max.spec = max(spec, na.rm = TRUE)`.
#> ℹ In group 9: `component = "Climate"` and `ax.grp = "nu == 0"`.
#> Caused by warning in `max()`:
#> ! no non-missing arguments to max; returning -Inf
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 3 remaining warnings.
#> Joining with `by = join_by(component, ax.grp)`
#> Warning: log-10 transformation introduced infinite values.
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?