Welcome to clim-recal, a specialised resource which provides a data-processing pipeline for extracting parts of the UK Climate Projections 2018 Convection Permitting model (UKCP18-CPM) in order to apply and assess bias correction methods via adjustment to and comparison with the HadUK-Grid.
clim-recal:
- Is a software library for pre-processing climate data to ready it for bias-correction
- 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)
Regional climate models (RCMs) 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.
However, in order to apply and assess these methods, the climate model of interest needs to be overlaid to corresponding observation data. This can be a time-consuming and laborious process where data is spatially and temporally very granular.
The clim-recal pipeline addresses this by providing preprocessed data, including the innovative UKCP18-CPM datasets, to facilitate the assessment of these methods on aligned, reprojected data, without requiring the whole (very large) dataset.
clim-recal is a data-processing pipeline, with the following steps:
- Set-up & data download We provide custom scripts to facilitate download of data
- Preprocessing This includes reprojecting, resampling & splitting the data prior to bias correction
For a quick start on installing and running the pipeline, refer to our pipeline guide.
Our work is however, just like climate data, intended to be dynamic, and we welcome collaboration from researchers who wish to further our aims!
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:
- See setup instructions
- See
pythonREADMEfor an overview of the pipeline - Once installed, using the
clim-recal --helpoption for details - See the reproducibility page for information on how we used
clim-recal
- There are extensive
API Referencewithin the python code.
- See the Contributing section below
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.
We make use of the Convection Permitting Model (CPM). This dataset represents a much finer spatial resolution of climate model (2.2km grid) than typical climate-models, representing a step forward in the ability to simulate small scale behavior (in particular 'atmospheric convection'), and the influence of mountains, coastlines and urban areas. As a result, the CPM provides access to credible climate information important for small-scale weather features and also on local (kilometre) scale; which is particularly important for improving our understanding of climate change in cities.
The UKCP18-CPM represents a high-emission scenario (RCP 8.5).
The UKCP18-CPM is comprised of 12 ensemble members (or runs), driven by the same 12km Regional Climate Model (Strand 3 12km RCM ensemble). In addition to run 1, we selected the following runs:
- Run 05: Represents the ensemble member with the second lowest mean annual tasmax of all ensembles members
- Run 06: Represents the ensemble member with the second highest mean annual tasmax of all ensembles members
- Run 07 & Run 08: Represent the ensemble members with the average mean annual tasmax of all ensemble members
We believe that this combination will provide users with enough uncertainty in their estimates to appropriately assess bias correction methods.
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.
If you have suggestions on the repository, please:
- raise an issue
- get in touch
- see our contributing section, which includes details on contributing to the documentation.
All are welcome and appreciated.
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.mdfile. - Added the citation below.
python-cmethods: Benjamin T. Schwertfeger. (2024). btschwertfeger/python-cmethods: v2.3.0 (v2.3.0). Zenodo. https://doi.org/10.5281/zenodo.12168002
Footnotes
-
Senatore et al., 2022, https://doi.org/10.1016/j.ejrh.2022.101120 ↩
-
Ayar et al., 2021, https://doi.org/10.1038/s41598-021-82715-1 ↩