This is the repository of the ClimBayes package (simple CLIMate models from a BAYESian perspective) in R. It provides data and code to perform Bayesian inference of climate parameters using multi-box energy balance models (EBMs). Please see the manuscript M. Schillinger et al. "Separating internal and externally-forced contributions to global temperature variability using a Bayesian stochastic energy balance framework" (2022), accepted in Chaos and available as preprint http://arxiv.org/abs/2206.14573, for example application and further documentation of the ClimBayes package.
The primary goal of ClimBayes is to provide a versatile and powerful tool for climate parameter estimation from global mean temperature data. To this end, it combines a Bayesian approach with an N-box simple climate model. To estimate the best fit to the observations, the climate drivers (i.e. radiative forcing) and the temperature response are required as inputs. By combining these inputs with prior information on the model's parameters via Bayes theorem, reliable posterior distribution of the model's parameter can be inferred and used to study the forced temperature response across temporal scales.
Please find the tutorial-vignette which explains how to perform the model fit using the HadCRUT5 oberservations and PMIP3 forcing data sets as an example. The vignette also describes how the package can be used to generate synthetic temperature timeseries from the N-box model given arbitrary forcing. The config_file-vignette explains how to include and document own data sets using *config.yml. For more details on vignettes, see below.
Authors: Maybritt Schillinger, Beatrice Ellerhoff, Robert Scheichl, Kira Rehfeld
Responsibility for this repository: Maybritt Schillinger (@m-schillinger) and Beatrice Ellerhoff (@bellerhoff)
Please see the ./license.md for terms of use.
The package can be easily installed using devtools in R:
require(devtools)
devtools::install_github("paleovar/ClimBayes")
library(ClimBayes)
Please also find the package requirements in the DESCRITPION file of this repository.
Please also check for new releases of the package and the latest version at Zenodo (upon publication of the main manuscript).
If you'd like to contribute to the package, you might want to clone it (type git clone https://github.com/paleovar/ClimBayes.git in the terminal) and open the .Rproj in RStudio. Please don't hesitate to report issues, bugs, etc. to the authors (maybritt.schillinger(at)stat.math.ethz.ch, beatrice.ellerhoff(at)student.uni-tuebingen.de).
Our repository contains all relevant code and data of the ClimBayes-package. The structure is very similar to that of many R packages, with a DESCRIPTION, NAMESPACE, README and license file. The directories ./R, /man, ./src and ./inst contain internal and external functions, their documentation, imported C++ code as well as the internal data of the package. Tutorial can be found in ./vignettes. We provide ./tests with relevant test scripts to check the functionality and speed of the package. ./data-raw contains example script to load your own data sets. Processed data is stored in ./data.
We include the following vignettes:
- Tutorial: Introduction and first examples Link to HTML / Link to PDF
- Config file: Explanation on how to work with the config file Link to HTML / Link to PDF
- Sensitivity of Bayesian estimates to noise: Example application of the framework to characterize sensitivity of estimators to data uncertainties Link to HTML / Link to PDF
To build vignettes locally, you can also specify
devtools::install_github("paleovar/ClimBayes", build_vignettes = TRUE),
which will also allow to you to browse vignettes via browseVignettes().
As exemplary data sets, the ClimBayes package loads data from:
- C.P. Morice et al., An updated assessment of near-surface temperature change from 1850: the HadCRUT5 dataset, Journal of Geophysical Research (Atmospheres) (in press)
- G. A. Schmidt et al., Climate forcing reconstructions for use in PMIP simulations of the Last Millennium (v1.1), Geoscientific Model Development (2012)
We thank the MET Office and the Paleoclimate Modelling Intercomparison Project Phase III for making available their data from surface air temperature observations, and for compiling and providing reconstructions of radiative forcing.
We also thank the R Core team and the developers of all packages that ClimBayes buids on. Please see citation() for details on the R Core Team and citation("packagename") for details on the developers of individual packages.
The development of this package has been supported by funds of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project No. 395588486, by the PalMod project (subproject no. 01LP1926C), the Heinrich-Böll-Stiftung, and the Studienstiftung des deutschen Volkes. The study benefited from discussions within the CVAS working group, a working group of the Past Global Changes (PAGES) project. We thank the members of the Earth's climate and environmental dynamics and the SPACY groups for discussion at different stages of the package development.
The authors,
Maybritt Schillinger & Beatrice Ellerhoff, June 2022