8.2 Development of Regional Abnormality Climate Indices for Southern Quebec, Canada

Thursday, 13 January 2000: 9:30 AM
Alain Bourque, Environment Canada, Montreal, PQ, Canada

During the past five to ten years, many climate impacts and adaptation studies produced in Quebec have recommended the development of scientifically sound climate indices which could help scientists from various fields to make better links between climate and its impacts on society. Additionally, decision makers have shown interest in this development since it may be able to provide them with easy to understand and easy to use information on the evolution of impacts of climate change on their sector of interest, providing them with a tool for better risk assessments and adaptation strategies.

Since 1) most impact scientists and decision makers are especially interested in regional to local influences of climate on their sector of interest and; 2) since society as a whole usually works at an optimal level when weather is not abnormal and; 3) since a possible increase in the occurrence of extreme weather events (like floods, droughts, heat waves...) is one of the most apprehended feature of climate change and; 4) since almost 80% of the total population of the Quebec province lives within 150km of the Upper St-Larence and Ottawa river, it was decided to develop regional climate indices which would be useful on the regional to local scale, and would put emphasis on periods of persisting abnormal weather occuring over the most densely populated area of Quebec.

In this work, we used the Quebec Regional Climate Databank, a 500+ climate stations subset of the Canadian National Climate Acrhives, to develop a regional abnormality temperature index for a region approximately delimited by latitudes 45N and 47N and longitudes 71W and 76,5W. The index, which can be computed on a daily/monthly/seasonnal/annual perspective, is based on the average temperature over the defined area for a fixed period of time and is scaled according to climatological statistics like averages and percentiles. Before reconstructing the history of the index and correlating it with impacts, the effects of the varying density of the observation climate network over the defined area had to be evaluated and taken into account. This helped to identify reference stations to be used to reconstruct an index consistent with time. Additionally, by correlating past values of the index with operational weather stations, it is also possible to rapidly provide users with an estimated value of the index which is updated daily, that it be on a daily, monthly, seasonnal or annual perspective. Effects of adjustments/homogeneisation on some station data were also looked at. The evolution of the index for the past 50+ years is presented and links with specific climatological events of impact over the defined area are discussed.

Although this presentation will focuss on the development of the temperature index, the development of a precipitation index, links with climate model data, and the identification of more correlations with climate impacts data is also under way.

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