Matilda v1.0: An R package for probabilistic climate projections using a reduced complexity climate model

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1371/journal.pclm.0000295. This is version 1 of this Preprint.

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Authors

Joseph K Brown , Leeya Pressburger, Abigail Snyder, Kalyn Dorheim, Steven J Smith, Claudia Tebaldi , Ben Bond-Lamberty

Abstract

A primary advantage to using reduced complexity climate models (RCMs) has been their ability to quickly conduct probabilistic climate projections, a key component of uncertainty quantification in many impact studies and multisector systems. Providing frameworks for such analyses has been a target of several RCMs used in studies of the future co-evolution of the human and Earth systems. In this paper, we present Matilda, an open-science R software package that facilitates probabilistic climate projection analysis, implemented here using the Hector simple climate model in a seamless and easily applied framework. The primary goal of Matilda is to provide the user with a turn-key method to build parameter sets from literature-based prior distributions, run Hector iteratively to produce perturbed parameter ensembles (PPEs), weight ensembles for realism against observed historical climate data, and compute probabilistic projections for different climate variables. This workflow gives the user the ability to explore viable parameter space and propagate uncertainty to model ensembles with just a few lines of code. The package provides significant freedom to select different scoring criteria and algorithms to weight ensemble members, as well as the flexibility to implement custom criteria. Additionally, the architecture of the package simplifies the process of building and analyzing PPEs without requiring significant programming expertise, to accommodate diverse use cases. We present a case study that provides illustrative results of a probabilistic analysis of mean global surface temperature as an example of the software application.

DOI

https://doi.org/10.31223/X5N38C

Subjects

Oceanography and Atmospheric Sciences and Meteorology

Keywords

climate model uncertainty, probabilistic climate projections, probabilistic analysis, R package software, uncertainty propagation

Dates

Published: 2023-09-15 08:59

License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
All code to run analysis and recreate figures in this manuscript is available at https://github.com/jk-brown/Matilda-manuscript or https://doi.org/10.5281/zenodo.8326722

Conflict of interest statement:
None