Dynamically-downscaled probabilistic projections of precipitation changes: A Canadian case study

Area of Science:

• Climate Science
• Environmental Science
• Atmospheric Science

Background:

• Global warming impacts precipitation patterns.
• Regional climate modeling is crucial for understanding localized climate change effects.
• Ontario, Canada faces potential shifts in its hydrological cycle.

Purpose of the Study:

• Investigate plausible changes in annual and seasonal precipitation over Ontario, Canada.
• Utilize a regional climate modeling approach to project future climate scenarios.
• Quantify uncertainties in climate model projections.

Main Methods:

• Employed a high-resolution regional climate model ensemble using the Providing REgional Climates for Impacts Studies (PRECIS) model.
• Applied a Bayesian hierarchical model to quantify uncertainties and generate probabilistic projections.
• Analyzed precipitation changes at grid point scales for various time periods (2030s, 2050s, 2080s).

Main Results:

• Projected median annual precipitation changes are between 0% and 20%, indicating a likely increase.
• Annual precipitation is expected to increase by approximately 7.5% by the 2030s/2050s and 12.5% by the 2080s.
• Significant spatial variability in seasonal precipitation changes observed, with notable variations in spring precipitation and a projected decrease in summer precipitation.

Conclusions:

• Annual precipitation over Ontario is likely to increase under global warming scenarios.
• Seasonal precipitation patterns will exhibit significant variability, with potential decreases in summer.
• Model biases in historical simulations correlate with increased uncertainty in future climate projections.