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Humidex (Masterton and Richardson 1979) is an index developed by the Meteorological Service of Canada to describe how hot and humid the weather feels to the average person. In Canada, it is recommended that outdoor activities be moderated when the humidex exceeds 30, and that all unnecessary activities cease when it passes 40 (Mekis et al., 2015). With the increase in temperature projected by climate models over the coming decades over Canada, increases are also expected in the number of days with high-value Humidex across the country, which will have important consequences for human health.
This dataset consists of a multi-model ensemble of statistically downscaled climate model projections for three humidex threshold indices (annual number of days when humidex exceeds 30, 35 and 40, noted HXmax30, HXmax35 and HXmax40 respectively) on a 0.1-degree latitude-longitude grid over Canada. The three indices (HXmax30, HXmax35 and HXmax40) are available for download at annual time step and 30-year averages from 1950 to 2100, for each of the 19 individual models and for the 10th, 50th, and 90th ensemble percentiles.
The multi-model ensemble is using output from 19 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCM) that are available at the Earth System Grid Federation (ESGF) Data Nodes, for three emission scenarios called “Shared Socioeconomic Pathways” (SSPs) (Riahi et al. 2017): SSP126, SSP245 and SSP585.
The GCM outputs were statistically downscaled and bias corrected using the N-dimensional probability density function transform multivariate quantile mapping method (Cannon, 2018) against ERA5-Land data (Muñoz, 2019), using a method described in Diaconescu et al. (2022). This method is based on the observation that the time when Humidex reaches its daily maximum coincide statistically with the time when temperature reaches its daily maximum and relative humidity reaches its daily minimum. In order to eliminate model biases and the errors in the adjustment method, the daily maximum temperature and daily minimum relative humidity from GCMs are statistically downscaled and bias corrected against the hourly temperature and relative humidity at the time of daily maximum humidex from ERA5-Land. The bias-corrected values are used to compute the daily maximum humidex and next the three threshold annual indices.
These ensembles of indices are intended to enable users to better identify and reduce the susceptibility of vulnerable populations to illness and mortality due to increase in the frequency and intensity of extreme heat events due to climate change.
References:
Cannon, A. J. (2018). 'Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables', Climate Dynamics, 50(1-2), 31-49. Available at https://doi.org/10.1007/s00382-017-3580-6
Diaconescu, E. P. et al. (2022) ' A short note on the use of daily climate data to calculate Humidex heat-stress indices', International Journal of Climatology, 1– 13. https://doi.org/10.1002/joc.7833
Masterton, J. M., and Richardson, F. (1979) 'Humidex: a method of quantifying human discomfort due to excessive heat and humidity', Environment Canada, Atmospheric Environment, 45.
Mekis, É., et al. (2015) 'Observed trends in severe weather conditions based on humidex, wind chill, and heavy rainfall events in Canada for 1953–2012', Atmosphere-Ocean, 53, 383-397. Available at https://doi.org/10.1080/07055900.2015.1086970, (Accessed: 19 April 2022).
Muñoz Sabater, J., 2019: ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on < 25-Jun-2021 >), https://doi.org/10.24381/cds.e2161bac
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Crespo Cuaresma, J., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Aleluia Da Silva, L., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., & Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An Overview. Global Environmental Change, 42, 153-168. https://doi.org/10.1016/j.gloenvcha.2016.05.009
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| Champ | Valeur |
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| Dernière modification de la donnée | 16 janvier 2026 |
| Dernière modification de la métadonnée | 16 janvier 2026 |
| Créé le | 16 janvier 2026 |
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