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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 method allows the correction of the estimated spectra for the effects of time uncertainty, proxy smoothing processes (e.g. diffusion), and measurement noise.

The method is introduced and in detail explained in Münch and Laepple (2018), and it has been applied there to oxygen isotope records from Antarctic firn and ice cores.

proxysnr has been implemented by Dr. Thomas Münch with contributions by Dr. Thomas Laepple, Dr. Andrew Dolman, Dr. Torben Kunz, and Dr. Mara McPartland. Please contact Dr. Thomas Münch <> 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

The current version ofproxysnr can be installed directly from GitHub:

# install.packages("remotes")
remotes::install_github("EarthSystemDiagnostics/proxysnr")

Released versions of proxysnr can be downloaded in source form from its Zenodo repository under the DOI 10.5281/zenodo.2027638. The package can then be installed using

# install.packages("devtools")
devtools::install(.)

where . is your source package root directory.

Documentation and vignettes

The full package documentation is available from the proxysnr homepage.

Among this documentation, proxysnr includes three vignettes to highlight the main aspects of the package:

  • The vignette("proxysnr") introduces the main signal and noise decomposition method with a simple surrogate data example.
  • The vignette("plot-muench-laepple-figures") applies this method on two real-world proxy datasets from Antarctic firn and ice cores, thereby showing how to reproduce the results of Münch and Laepple (2018).
  • The vignette("calculate-transfer-functions") demonstrates how to obtain spectral transfer functions describing the loss in spectral power for two special cases: time-uncertainty in layer-counted chronologies and isotope diffusion in polar firn.

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.