Globe Temperature from Regional Climate Models

Calculating Globe Temperature from Regional Climate Model Data This repository contains the Python code for calculating Wet Bulb Globe Temperature (WBGT), modified from the Cython code under the pyWBGT package (Kong & Huber, 2022; Qinkong, 2022; Liljegren et al., 2008). From this code, individual components of WBGT such as Globe Temperature can be calculated. A notebook showing the same is included. These scripts support the analysis done in Puri et al. (2026). Citation: If you reuse the code, please cite Puri, R., Nam, C. C. W., & Bouwer, L. M. (2026). Rethinking infrastructure thermal exposure beyond air temperature: globe temperature from regional climate models. Environmental Research Letters, 21(12), 124006. https://doi.org/10.1088/1748-9326/ae7390 and Riddhima Puri. (2025). Globe Temperature from Regional Climate Models. Zenodo. https://doi.org/10.5281/zenodo.17800946 Details of the files included calculate_GT_example.ipynbA Jupyter notebook demonstrating how to calculate Globe Temperature (GT) using the `_wbgt.py` script and EURO-CORDEX data. It shows data loading, pre-processing, and the use of xclim. _wbgt.py A Python module implementing the calculation of Wet Bulb Globe Temperature (WBGT), Globe Temperature (Tg), and Natural Wet Bulb Temperature (Tnwb), integrated with the xclim open-source library (Bourgault et al., 2023). It is modified from the open-source pyWBGT package (Kong and Huber, 2022; Qinkong, 2022), based on the original Liljegren et al. (2008) code. Used as a backend for the notebook and other scripts. environment.txt A text file listing the main Python packages required and their versions to reproduce the computational environment needed to run the two files above. You can install these using `pip install -r environment.txt`. Usage See `calculate_GT_example.ipynb` for a step-by-step example. Data These scripts are designed for use with EURO-CORDEX NetCDF data, but given that you have all the variables needed to calculate GT, you can use this notebook to calculate GT from other climate model/reanalysis data. Contact: For questions, please contact Riddhima Puri or open an issue on this repository. References Bourgault, P., Huard, D., Smith, T. J., Logan, T., Aoun, A., Lavoie, J., ... & Whelan, C. (2023). xclim: Xarray-based climate data analytics. Journal of Open Source Software, 8(85), 5415. doi: 10.21105/joss.05415 Kong, Q., & Huber, M. (2022). Explicit calculations of wet-bulb globe temperature compared with approximations and why it matters for labor productivity. Earth’s Future, 10 (3). doi: 10.1029/2021EF002334 Liljegren, J. C., Carhart, R. A., Lawday, P., Tschopp, S., & Sharp, R. (2008). Modeling the wet bulb globe temperature using standard meteorological measurement. Journal of Occupational and Environmental Hygiene, 5 (10), 645–655. doi: 10.1080/15459620802310770 Puri, R., Nam, C. C. W., & Bouwer, L. M. (2026). Rethinking infrastructure thermal exposure beyond air temperature: globe temperature from regional climate models. Environmental Research Letters, 21(12), 124006. doi: 10.1088/1748-9326/ae7390 Qinkong. (2022). Explicit calculations of wet bulb globe temperature compared with approximations and why it matters for labor productivity (v1.0.0). Zenodo. doi: 10.5281/zenodo.5980536 Note: The repository license has been updated to CC BY-NC-SA 4.0 in v1.0.1. No scientific functionality or calculations were changed between v1.0.0 and v1.0.1. License: This repository contains code adapted from the pyWBGT package (Kong & Huber, 2022; Qinkong, 2022). To align with the licensing of the original project, this repository is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. Main modifications from the original pyWBGT package include conversion from Cython to Python, integration with xclim and xarray workflows for increased efficiency, additional numerical error handling, and adaptations for regional climate model applications.