optimalcores: Analyse optimal ice core locations in a climate model simulation

Overview

optimalcores is an R software project to analyse the temperature and isotope time series in the isotope-enabled ECHAM5/MPI-OM-wiso past1000 climate model simulation and, specifically, to determine optimal spatial sampling configurations for Antarctic ice cores which maximize the correlation with a target site temperature time series.

The project is subdivided into three main components:

• the data/ folder provides the climate model data as an R data file,
• the lib/ folder contains R library functions which provide the main functionality, and
• the analysis/ folder contains R code for the actual analyses.

The optimalcores software is the basis for the manuscript Münch, Werner and Laepple, How precipitation intermittency sets an optimal sampling distance for temperature reconstructions from Antarctic ice cores, Clim. Past, 17, 1587-1605, 2021.

All code has been written by Dr. Thomas Münch at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research. For further information, code enhancements or potential bugs, please write an email or open an issue here. This work was supported by Helmholtz funding through the Polar Regions and Coasts in the Changing Earth System (PACES) programme of the Alfred Wegener Institute.

The original climate model data used here is archived under doi: 10.5281/zenodo.4001565.

Getting started

You can start with the analyses in optimalcores after just a few steps:

• Download or clone the repository to your machine into a directory of your choice.
• Run the dependencies.R to install all packages required for working with optimalcores.
• Update the setup.R:
  • Set the SRCPATH variable to the direcory into which you copied the optimalcores project;
  • Set the SAVEPATH variable to a directory where you want to save analysis plots.

Starting an analysis

Each new analysis using optimalcores starts with running the code in setup.R and init.R by calling source("setup.R") followed by source("init.R") from within R. The latter step provides all the project's functionality by loading the relevant R packages and the optimalcores' function library in lib/.

Extending optimalcores

To extend optimalcores by new climate model data you want to analyse, add the data as an R data file to the data/ folder and update the library function selectData() so that the data can be easily loaded. Note that the data need to be pField objects to comply with the optimalcores command syntax.