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Bias-corrected high-resolution temperature and precipitation projections for Canada.

Hebatallah M Abdelmoaty^1,2, Chandra Rupa Rajulapati³, Sofia D Nerantzaki⁴

¹Department of Civil Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada.

Scientific Data

|February 1, 2025

Summary

A new database provides high-resolution climate projections for Canada until 2100. This resource uses Semi-Parametric Quantile Mapping (SPQM) to bias-correct climate model data for better risk assessment and planning.

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Area of Science:

Climate Science
Environmental Science
Data Science

Background:

High-resolution climate projections are crucial for effective decision-making, risk assessment, and planning in Canada.
Existing climate model data often requires bias correction to accurately represent observed conditions.

Purpose of the Study:

To develop an extensive database of high-resolution (0.1°) daily precipitation and temperature projections for Canada until 2100.
To bias-correct Coupled Model Intercomparison Project, Phase-6 (CMIP6) climate model outputs using a novel methodology.

Main Methods:

Developed and applied the Semi-Parametric Quantile Mapping (SPQM) methodology for bias correction.
Utilized four Shared Socio-economic Pathways (SSPs) to analyze future climate scenarios.
Compiled a comprehensive database (SPQM-CMIP6-CAN) encompassing numerous climate model simulations.

Main Results:

The SPQM methodology effectively reproduces observed marginal properties, trends, and variability.
The database includes 693 precipitation simulations from 34 climate models and 581 temperature simulations from 27 climate models.
Projections extend daily to the year 2100 at a 0.1° resolution for Canada.

Conclusions:

The SPQM-CMIP6-CAN database offers a valuable, high-resolution resource for hydrological, environmental, and ecological studies in Canada.
This database aids in quantifying uncertainties associated with different climate models, configurations, and future scenarios.
The findings support improved climate change adaptation and mitigation strategies through enhanced data accessibility and reliability.