SAM-Melt_EA v5.0 (R Code)
Overview
The notebooks in this repository contain R code that has been used to investigate the influence of the Southern Annular Mode (SAM) on surface melt in East Antarctica.
This work has been undertaken as part of my PhD with MISI and SAEF at Monash University, where I am supervised by Prof. Andrew Mackintosh, Dr. Felicity McCormack, and Dr. Richard Jones. The work has also been supported by co-author Dr. Christiaan van Dalum (IMAU, Utrecht University).
This project uses output from the RACMO2.3p3 regional climate model to identify spatiotemporal variability in the SAM-Melt relationship, and to understand the physical mechanisms responsible for the relationship. An accompanying manuscript is currently under review for publication.
Code & Instructions
This project uses the R programming language and was written using RStudio, on both Windows 10 and MacOS 14.1. It uses the renv package to help with portability / replicability.
The code here can also be found on GitHub in the SAM-Melt_EA repository, and is tagged as Version v5.0. See the "README.md" file on GitHub for more information about the project and the repository set-up, plus where to download the necessary raw data. In particular, it is necessary to download the RACMO2.3p3 data, available online at: https://doi.org/10.5281/zenodo.7639053.
NOTE: This version of the code (v5.0) makes some minor changes to the formatting of the v4.1 figures following the first stage of the peer review process, and includes more comments and notes in the code.
Note: In addition to standard R packages from CRAN, it will be necessary to install 5 packages from my GitHub page. These packages (and the version used in this project) are:
- polarcm (v0.1.3) eases use of output from the polar regional climate models RACMO and MAR
- terrapin (v0.1.1) spinoff from terra that eases handling dates for spatial data
- figuR (v0.1.2) for easy-to-customise figures
- kulaR (v0.1.5) a wrapper around khroma to ease colour management in plots
- domR (v0.1.5) functions for easing how I approach a project with R