Welcome to the clim-recal repository!

Welcome to clim-recal, a specialized resource designed to tackle systematic errors or biases in Regional Climate Models (RCMs). As researchers, policy-makers, and various stakeholders explore publicly available RCMs, they need to consider the challenge of biases that can affect the accurate representation of climate change signals.

clim-recal provides both a broad review of available bias correction methods as well as software, practical tutorials and guidance that helps users apply these methods methods to various datasets.

clim-recal is an extensive software library and guide to application of Bias Correction (BC) methods:

• Contains accessible information about the why and how of bias correction for climate data
• Is a software library for for the application of BC methods (see our full pipeline for bias-correction of the ground-breaking local-scale (2.2km) Convection Permitting Model (CPM). clim-recal brings together different software packages in python and R that implement a variety of bias correction methods, making it easy to apply them to data and compare their outputs.
• Was developed in partnership with the MetOffice to ensure the propriety, quality, and usability of our work
• Provides a framework for open additions of new software libraries/bias correction methods (in planning)

Overview: Bias Correction Pipeline

clim-recal is a debiasing pipeline, with the following steps:

1. Set-up & data download We provide custom scripts to facilitate download of data
2. Preprocessing This includes reprojecting, resampling & splitting the data prior to bias correction
3. Apply bias correction Our pipeline embeds two distinct methods of bias correction
4. Assess the debiased data We have developed a way to assess the quality of the debiasing step across multiple alternative methods

For a quick start on bias correction, refer to our pipeline guide.

Documentation

We are in the process of developing comprehensive documentation for our code base to supplement the guidance provided in this and other README.md files. In the interim, there is documentation available in the following forms:

User documentation

• See setup instructions
• See python README for an overview of the pipeline
• Once installed, using the clim-recal --help option for details
• See the reproducibility page for information on how we used clim-recal

To use clim-recal programmatically

• There are extensive API Reference within the python code.
• Comments within R scripts

To contribute to clim-recal

• See the Contributing section below

The Datasets

UKCP18

The UK Climate Projections 2018 (UKCP18) dataset offers insights into the potential climate changes in the UK. UKCP18 is an advancement of the UKCP09 projections and delivers the latest evaluations of the UK's possible climate alterations in land and marine regions throughout the 21st century. This crucial information aids in future Climate Change Risk Assessments and supports the UK’s adaptation to climate change challenges and opportunities as per the National Adaptation Programme.

HADS

HadUK-Grid is a comprehensive collection of climate data for the UK, compiled from various land surface observations across the country. This data is organized into a uniform grid to ensure consistent coverage throughout the UK at up to 1km x 1km resolution. The dataset, spanning from 1836 to the present, includes a variety of climate variables such as air temperature, precipitation, sunshine, and wind speed, available on daily, monthly, seasonal, and annual timescales.

Why Bias Correction?

Regional climate models contain systematic errors, or biases in their output ¹. Biases arise in RCMs for a number of reasons, such as the assumptions in the general circulation models (GCMs), and in the downscaling process from GCM to RCM.

Researchers, policy-makers and other stakeholders wishing to use publicly available RCMs need to consider a range of "bias correction” methods (sometimes referred to as "bias adjustment" or "recalibration"). Bias correction methods offer a means of adjusting the outputs of RCM in a manner that might better reflect future climate change signals whilst preserving the natural and internal variability of climate ².

Part of the clim-recal project is to review several bias correction methods. This work is ongoing and you can find our initial taxonomy here. When we've completed our literature review, it will be submitted for publication in an open peer-reviewed journal.

Our work is however, just like climate data, intended to be dynamic, and we are in the process of setting up a pipeline for researchers creating new methods of bias correction to be able to submit their methods for inclusion on in the clim-recal repository.

Contributing

If you have suggestions on the repository, or would like to include a new method (see below) or library, please

• raise an issue
• get in touch
• see our contributing section, which includes details on contriubting to the documentation.

All are welcome and appreciated.

Future plans

• Finish refactor for BC: The infrastructure for testing bias correction methods needs some reworking and documentation.
• Release BC results: Provide results from example BC runs.
• More BC Methods: Further bias correction of UKCP18 products. This is planned for a future release and is not available yet.
• Pipeline for adding new methods: This is planned for a future release and is not available yet.

Acknowledgements

Prior to 12th September 2024 we included a reference to the python-cmethods library, written by Benjamin Thomas Schwertfeger.

This was via a git submodule which targeted https://github.com/alan-turing-institute/python-cmethods, itself a fork of the original library.

Inadvertently, we did not identify that the license for the python-cmethods library (GPL3) is not compatible with the license for this package (MIT). We apologise for this mistake and have taken the following actions to resolve it:

• We have now removed the relevant submodule from this repository.
• Added this note to the README.
• Added a note to the python/README.md file.
• Added the citation below.

Citation

python-cmethods: Benjamin T. Schwertfeger. (2024). btschwertfeger/python-cmethods: v2.3.0 (v2.3.0). Zenodo. https://doi.org/10.5281/zenodo.12168002

Footnotes

1. Senatore et al., 2022, https://doi.org/10.1016/j.ejrh.2022.101120 ↩

2. Ayar et al., 2021, https://doi.org/10.1038/s41598-021-82715-1 ↩