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proxysnr: Separate Signal and Noise in Climate Proxy Records

Introduction

proxysnr implements a method working in the spectral domain to separate the common signal from the local noise as recorded by a spatial network of climate proxy records, which yields an estimate of the timescale dependence of the proxy signal-to-noise ratio (SNR). The implemented method includes the correction of relevant estimated power spectral densities for the loss in spectral power by two effects: (1) time uncertainty in the case of layer-counted record chronologies and (2) water vapour diffusion through the open-porous firn in the case of stable isotope records from firn and ice cores.

The method is in detail explained in Münch and Laepple (2018) and has been applied there to Antarctic firn-core oxygen isotope records.

The R code has been implemented by Dr. Thomas Münch with contributions by Dr. Thomas Laepple and Dr. Andrew Dolman. Please contact Dr. Thomas Münch <[email protected]> at the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Germany, for further information.

This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 716092) and Helmholtz funding through the Polar Regions and Coasts in the Changing Earth System (PACES) programme of the Alfred Wegener Institute. It further contributes to the German BMBF project PalMod.

Installation

proxysnr can be installed directly from GitHub:

if (!require("remotes")) {
  install.packages("remotes")
}

remotes::install_github("EarthSystemDiagnostics/proxysnr")

Examples

Two example vignettes are provided along with the package source as rendered .html files to demonstrate the main functions of the package. These vignettes can be found in the directory ./doc/, where . stands for the root of the package source directory, while their creating R markdown source codes are located under ./vignettes/.

  • The vignette plot-muench-laepple-figures shows the basic way of applying the package to obtain estimates of the signal, noise and SNR spectra. For this, the oxygen isotope data from Münch and Laepple (2018) are used, which are provided with this package. The vignette further demonstrates the plotting of all main figures shown in Münch and Laepple (2018).

  • The vignette calculate-transfer-functions demonstrates the application of package functions for obtaining the spectral transfer functions describing the loss in spectral power by the effects of time uncertainty and diffusion. These transfer functions can be used to correct the estimated signal, noise and SNR spectra as explained in Münch and Laepple (2018).

After installing the package with remotes::install_github or after cloning the git repository and installing the package using devtools::install, setting build_vignettes = TRUE in both cases, the vignettes belonging to the version of the installed package are also available directly from the R command line by typing

vignette("plot-muench-laepple-figures", package = "proxysnr")

and

vignette("calculate-transfer-functions", package = "proxysnr")

Please note that for rebuilding the vignettes, e.g., in order to test the package functionality, you will need to install the R package simproxyage used for modelling the time uncertainty of the layer-counted isotope records in order to calculate respective time uncertainty transfer functions. simproxyage is available from GitHub and can be installed directly using remotes::install_github("EarthSystemDiagnostics/simproxyage"). Also note that the package vignettes are based on R Markdown v2 and require Pandoc.

Literature cited

Münch, T. and Laepple, T.: What climate signal is contained in decadal- to centennial-scale isotope variations from Antarctic ice cores?, Clim. Past, 14, 2053-2070, doi: 10.5194/cp-14-2053-2018, 2018.