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Abstract. Freezing rain is a major atmospheric hazard in mid-latitude nations of the globe. Among all Canadian hydrometeorological hazards, freezing rain is associated with the highest damage costs per event. Using synoptic weather typing to identify the occurrence of freezing rain events, this study estimates changes in future freezing rain events under future climate scenarios for south-central Canada. Synoptic weather typing consists of principal components analysis, an average linkage clustering procedure (i.e., a hierarchical agglomerative cluster method), and discriminant function analysis (a nonhierarchical method). Meteorological data used in the analysis included hourly surface observations from 15 selected weather stations and six atmospheric levels of six-hourly National Centers for Environmental Prediction (NCEP) upper-air reanalysis weather variables for the winter months (November-April) of 1958/59-2000/01. A statistical downscaling method was used to downscale four general circulation model (GCM) scenarios to the selected weather stations. Using downscaled scenarios, discriminant function analysis was used to project the occurrence of future weather types. The within-type frequency of future freezing rain events is assumed to be directly proportional to the change in frequency of future freezing rain-related weather typesThe results showed that with warming temperatures in a future climate, percentage increases in the occurrence of freezing rain events in the north of the study area are likely to be greater than those in the south. By the 2050s, freezing rain events for the three colder months (December-February) could increase by about 85% (95% confidence interval -CI: ±13%), 60% (95% CI: ±9%), and 40% (95% CI: ±6%) in northern Ontario, eastern Ontario (including Montreal, Quebec), and southern Ontario, respectively. The increase by the 2080s could be even greater: about 135% (95% CI: ±20%),Correspondence to: C. S. Cheng (shouquan.cheng@ec.gc.ca) 95% (95% CI: ±13%), and 45% (95% CI: ±9%). For the three warmer months (November, March, April), the percentage increases in future freezing rain events are projected to be much smaller with some areas showing either a decrease or little change in frequency of freezing rain. On average, northern Ontario could experience about 10% (95% CI: ±2%) and 20% (95% CI: ±4%) more freezing rain events by the 2050s and 2080s, respectively. However, future freezing rain events in southern Ontario could decrease about 10% (95% CI: ±3%) and 15% (95% CI: ±5%) by the 2050s and 2080s, respectively. In eastern Ontario (including Montreal, Quebec), the frequency of future freezing rain events is projected to remain the same as it is currently.

This paper forms the second part of an introduction to a synoptic weather typing approach to assess differential and combined impacts of extreme temperatures and air pollution on human mortality, focusing on future estimates. A statistical downscaling approach was used to downscale daily five general circulation model (GCM) outputs (three Canadian and two US GCMs) and to derive six-hourly future climate information for the selected cities (Montreal, Ottawa, Toronto, and Windsor) in south-central Canada. Discriminant function analysis was then used to project the future weather types, based on historical analysis defined in a companion paper (Part I). Future air pollution concentrations were estimated using the within-weather-type historical simulation models applied to the downscaled future GCM climate data. Two independent approaches, based on (1) comparing future and historical frequencies of the weather groups and (2) applying within-weather-group elevated mortality prediction models, were used to assess climate change impacts on elevated mortality for two time windows (2040-2059 and 2070-2089). Averaging the five GCM scenarios, across the study area, heat-related mortality is projected to be more than double by the 2050s and triple by the 2080s from the current condition. Cold-related mortality could decrease by about 45-60% and 60-70% by the 2050s and the 2080s, respectively. Air pollution-related mortality could increase about 20-30% by the 2050s and 30-45% by the 2080s, due to increased air pollution levels projected with climate change. The increase in air pollution-related mortality would be largely driven by increases in ozone effects. The population acclimatization to increased heat was also assessed in this paper, which could reduce future heat-related mortality by 40%. It is most likely that the estimate of future extreme temperature-and air pollutionrelated mortality from this study could represent a bottomline figure since many of the factors (e.g., population growth, age structure changes, and adaptation measures) were not directly taken into account in the analyses.

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An automated synoptic weather typing and stepwise cumulative logit/nonlinear regression analyses were employed to simulate the occurrence and quantity of daily rainfall events. The synoptic weather typing was developed using principal component analysis, an average linkage clustering procedure, and discriminant function analysis to identify the weather types most likely to be associated with daily rainfall events for the four selected river basins in Ontario. Within-weather-type daily rainfall simulation models comprise a twostep process: (i) cumulative logit regression to predict the occurrence of daily rainfall events, and (ii) using probability of the logit regression, a nonlinear regression procedure to simulate daily rainfall quantities. The rainfall simulation models were validated using an independent dataset, and the results showed that the models were successful at replicating the occurrence and quantity of daily rainfall events. For example, the relative operating characteristics score is greater than 0.97 for rainfall events with daily rainfall $10 or $25 mm, for both model development and validation. For evaluation of daily rainfall quantity simulation models, four correctness classifications of excellent, good, fair, and poor were defined, based on the difference between daily rainfall observations and model simulations. Across four selected river basins, the percentage of excellent and good simulations for model development ranged from 62% to 84% (of 20 individuals, 16 cases $ 70%, 7 cases $ 80%); the corresponding percentage for model validation ranged from 50% to 76%