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

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.