Characterizing non-stationary compound extreme events in a changing climate based on large-ensemble climate simulations

The dependence structure of temperature-precipitation compound events is analyzed across Canada using three datasets derived from Canadian Regional Climate Model Large Ensemble simulations, including raw model outputs (CanRCM4-LE) and two sets of multivariate bias-corrected model outputs (Canadian Large Ensembles Adjusted Datasets, CanLEAD-EWEMBI/S14FD). The performance of the ensembles to represent tail dependencies corresponding to warm-wet and warm-dry events is evaluated against NRCANmet observations for 1951-2000 using the copula goodness of fit test. The parameters of the copula model are estimated using a Bayesian framework to characterize the corresponding uncertainties. The non-stationarity of compound extreme climate events is analyzed for 1951-2100 using an ensemble pooling approach and the results are compared with the ones based on the independence assumption. Results show that multivariate bias-corrected climate simulations (i.e. CanLEAD) can better represent the correlated temperature-precipitation extremes compared to raw CanRCM4-LE outputs. The estimated joint return periods reduce significantly when the dependence structure is considered, compared to the independence assumption, for most regions especially in winter and summer. Therefore, analysis of extreme temperature and precipitation in isolation can result in dramatic underestimations of compound warm-wet and warm-dry events. Further, there is strong non-stationarity in the dependence structure of temperature and precipitation under climate change that can play a significant role in future compound extremes.

Automated summary

The study analyzed the dependence structure of temperature-precipitation compound events in Canada using different datasets and copula models. It found that bias-corrected climate simulations better represent extreme values compared to raw model outputs. Considering the dependence structure reduced joint return periods significantly, especially in winter and summer, highlighting the importance of analyzing compound extremes rather than individual temperature and precipitation extremes. The non-stationarity of the dependence structure under climate change could play a significant role in future compound extreme events.