Abstract This study assessed the climate and trend of cyclone activity in Canada using mainly the occurrence frequency of cyclone deepening events and deepening rates, which were derived from hourly mean sea level pressure data observed at 83 Canadian stations for up to 50 years (1953–2002). Trends in the frequency of cyclone activity were estimated by logistic regression analysis, and trends of seasonal extreme cyclone intensity, by linear regression analysis. The results of trend analysis show that, among the four seasons, winter cyclone activity has shown the most significant trends. It has become significantly more frequent, more durable, and stronger in the lower Canadian Arctic, but less frequent and weaker in the south, especially along the southeast and southwest coasts. Winter cyclone deepening rates have increased in the zone around 60°N but decreased in the Great Lakes area and southern Prairies–British Columbia. However, extreme winter cyclone activity seems to have experienced a weaker increase in northwest-central Canada but a stronger decline in the Great Lakes area and in southern Prairies. The results also show more frequent summer cyclone activity with slower deepening rates on the east coast, as well as less frequent cyclone activity with faster deepening rates in the Great Lakes area in autumn. Cyclone activity in Canada was found to be closely related to the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), and El Niño–Southern Oscillation (ENSO). Overall, cyclone activity in Canada is most closely related to the NAO. The simultaneous NAO index explains about 44% (41%) of the winter (autumn) cyclone activity variance in the east coast, 31% of winter cyclone activity variance in the 60°–70°N zone, and 17% of autumn cyclone activity variance in the Great Lakes area. Also, in several regions (e.g., the east coast, the southwest, and the 60°–70°N zone) up to 15% of the seasonal cyclone activity variance can be explained by the NAO/PDO/ENSO index one–three seasons earlier, which is useful for seasonal forecasting.
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