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 <thomas.muench@awi.de> 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.
Please consider citing proxysnr when using it in a publication.
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.
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.
If you use proxysnr for analyses in a publication, please consider citing the
package in the publication as follows:
Münch, T.: proxysnr: An R package to separate the common signal from local noise in climate proxy records using spectral analyses, Zenodo, https://doi.org/10.5281/zenodo.2027638, 2025.
For the spectral signal and noise separation method, please cite the journal
publication of Münch and Laepple (2018). If you also used
the DML and/or WAIS isotope data included in proxysnr, please also cite the
respective data and journal publications; see ?dml and ?wais for citation
details on these.
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.