Twenty-first century probabilistic projections of precipitation over Ontario, Canada through a regional climate model ensemble

Abstract

In this study, probabilistic projections of precipitation for the Province of Ontario are developed through a regional climate model ensemble to help investigate how global warming would affect its local climate. The PRECIS regional climate modeling system is employed to perform ensemble simulations, driven by a set of boundary conditions from a HadCM3-based perturbed-physics ensemble. The PRECIS ensemble simulations are fed into a Bayesian hierarchical model to quantify uncertain factors affecting the resulting projections of precipitation and thus generate probabilistic precipitation changes at grid point scales. Following that, reliable precipitation projections throughout the twenty-first century are developed for the entire province by applying the probabilistic changes to the observed precipitation. The results show that the vast majority of cities in Ontario are likely to suffer positive changes in annual precipitation in 2030, 2050, and 2080 s in comparison to the baseline observations. This may suggest that the whole province is likely to gain more precipitation throughout the twenty-first century in response to global warming. The analyses on the projections of seasonal precipitation further demonstrate that the entire province is likely to receive more precipitation in winter, spring, and autumn throughout this century while summer precipitation is only likely to increase slightly in 2030 s and would decrease gradually afterwards. However, because the magnitude of projected decrease in summer precipitation is relatively small in comparison with the anticipated increases in other three seasons, the annual precipitation over Ontario is likely to suffer a progressive increase throughout the twenty-first century (by 7.0 % in 2030 s, 9.5 % in 2050 s, and 12.6 % in 2080 s). Besides, the degree of uncertainty for precipitation projections is analyzed. The results suggest that future changes in spring precipitation show higher degree of uncertainty than other seasons, resulting in more uncertainties in annual precipitation projections.