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    Dr. John Haneisak

    University of Manitoba

      Research areas

      Hanesiak’s CCRN research has focused on weather and climate extremes and elevated nocturnal deep convection processes that contribute to the hydrometeorological cycle and regional climate of the Canadian Prairies.

      Years 1-2 of CCRN focused on two journal articles1,2 about Prairie adjacent regions that concurrently experienced droughts and floods, whereas other areas transitioned rapidly from pluvial to drought conditions and vice versa between 2009-2011. Such events led to major impacts such as floods in the Assiniboine River Basin (ARB) and forest fires in the town of Slave Lake, Alberta. Results suggest that subtle differences in large-scale atmospheric flow had marked impact on precipitation. Additionally, multiple events — rather than individual extremes— were responsible for the flooding over Saskatchewan River Basin and the ARB. Heavy rainfall events (≥ 25 mm per day) accounted for up to 55% of warm season rain at some locations, and the frequency of heavy rainfall events was critical for determining whether a region experienced drought or pluvial conditions. These papers have added to our knowledge concerning characteristics, impacts and mechanisms associated with rapidly transitioning disparate precipitation states on the Canadian Prairies and will aid in better understanding both past and projected future hydro-climatic extremes in the region.

      Years 3-5 involved (1) the participation in a major U.S. field project called the Plains Elevated Convection At Night (PECAN) focusing on nocturnal elevated convection initiation processes, and (2) assessments of how convective rainfall, hail and severe weather environments may change in a future climate. My group contributed to a summary PECAN article that provided a background to PECAN and preliminary results3. Our PECAN work (via two Masters Theses by Scott Kehler and Kyle Ziolkowski) identified several key meteorological processes that control nocturnal elevated convection initiation to contribute to improved prediction of severe nocturnal storms (journal articles to be submitted soon). Results from this work were reported in several media interviews and local weather offices; Canadian weather forecasters are now using our results.

      Our climate research (via collaborations with Environment and Climate Change Canada scientists, Drs. Julian Brimelow and Bill Burrows) produced a journal article on the future changing threat of hail across North America, based on hail model simulations driven by three NARCCAP regional climate models4. This work suggests that many regions will experience less hail days in the future, however, many of those regions (including many Canadian Prairie locations) may experience more damaging hail when hail does fall. Analysis of the same three NARCCAP models used in the hail work also suggests it is likely that the Prairies will experience more convective rainfall in the future (results to be published from a Masters Thesis by Jennifer Bruneau); our results are similar to several other studies using different climate model datasets. The potential reasons for the increased hail threat and more convective precipitation are due to increases in lower atmosphere moisture (i.e. storm energy) and no change to slight increases in upper level winds over the Prairies in the future. Preliminary results also suggest higher potential for stronger storms and tornadoes will exist in the future, over the southern portion of the Prairies. Although the environment for stronger storms and tornadoes may exist (when they do occur), greater convective inhibition in the future also takes place, potentially decreasing the frequency of storms. This work could not assess whether future frequency of storm “triggers” (e.g. warm and cold fronts) will change, which can also affect the frequency of severe weather. Since all of the hail, convective rain and severe weather threat results are based only on three regional climate models, more studies of this type are required using different models to provide greater confidence in the predictions.