knitr::opts_chunk$set( collapse = TRUE, comment = "#>", dpi = 300, fig.path = "man/figures/README-", out.width = "100%" )
psem implements the Proxy Spectral Error Model described in the discussion papers:
A spectral approach to estimating the timescale-dependent uncertainty of paleoclimate records – Part I: Theoretical concept. https://doi.org/10.5194/cp-16-1469-2020
A spectral approach to estimating the timescale-dependent uncertainty of paleoclimate records – Part II: Application and interpretation. https://doi.org/10.5194/cp-17-825-2021
Please contact Dr Andrew Dolman <andrew.dolman@awi.de>, Prof. Thomas Laepple <tlaepple@awi.de>, or Dr Torben Kunz <tkunz@awi.de>, at the Alfred-Wegener-Institute, Helmholtz Centre for Polar and Marine Research, Germany, for more information.
This work was supported by German Federal Ministry of Education and Research (BMBF) as Research for Sustainability initiative FONA through the PalMod project (FKZ: 01LP1509C).
psem can be installed directly from github
if (!require("remotes")) { install.packages("remotes") } remotes::install_github("EarthSystemDiagnostics/psem")
library(psem)
# PSD Climate example.lat <- 45 clim.spec.ex1 <- ModelSpectrum( freq = NULL, latitude = example.lat, variable = "temperature", beta = 1 ) p.clim.spec.ex1 <- PlotModelSpectrum(clim.spec.ex1) p.clim.spec.ex1
seasonal.amp <- AmpFromLocation( longitude = 0, latitude = example.lat, proxy.type = "degC", depth.upr = 0, depth.lwr = -50 )
orbital.pars <- RelativeAmplitudeModulation( latitude = example.lat, maxTimeKYear = 23, minTimeKYear = 1, bPlot = FALSE )
# sediment accumulation rate for the core ex.sed.acc.rate <- 10 spec.pars.ex1 <- GetSpecPars( proxy.type = "Mg_Ca", T = 1e04, delta_t = 100, tau_r = 100, sig.sq_a = orbital.pars$sig.sq_a, sig.sq_c = seasonal.amp$sig.sq_c, tau_b = 1000 * 10 / ex.sed.acc.rate, tau_s = 1000 * 1 / ex.sed.acc.rate, N = 30, tau_p = 7/12, phi_c = 0, delta_phi_c = 2 * pi / 3, phi_a = pi / 2, sigma.cal = 0.25, sigma.meas = 0.25, sigma.ind = 1, clim.spec.fun = "ModelSpectrum", clim.spec.fun.args = list(latitude = example.lat, beta = 1) )
ProxyErrorSpectrum
with these parameters and plot it.proxy.err.spec <- do.call(ProxyErrorSpectrum, spec.pars.ex1) PlotSpecError(proxy.err.spec)
tsd.error.var <- IntegrateErrorSpectra(proxy.err.spec) PlotTSDVariance(tsd.error.var)
err.500 <- GetProxyError(tsd.error.var, timescale = 500) knitr::kable(err.500, digits = 2)
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