By Steven Matthew Huryn 2016
Total Page：16
File Type：pdf, Size：1020Kb
Analysis of Thunderstorm Trends in Southern Ontario, Canada: Past and Future by Steven Matthew Huryn A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Physical and Environmental Sciences University of Toronto Scarborough © Copyright by Steven Matthew Huryn 2016 Analysis of Thunderstorm Trends in Southern Ontario, Canada: Past and Future Steven Matthew Huryn Doctor of Philosophy Department of Physical and Environmental Sciences University of Toronto Scarborough 2016 Despite the potential hazards associated with thunderstorms, they have been underrepresented in climatology studies. Southern Ontario is Canada’s most active thunderstorm region, and the country’s most populous and industrialized region. To date there has been no analysis of past trends of thunderstorms in Southern Ontario, or any analysis of how thunderstorm frequency might change over the current century. This thesis consists of three research chapters flanked by an introduction (Chapter 1) and discussion (Chapter 5). In Chapter 2 manual thunderstorm observations from eight Environment Canada weather stations are evaluated for accuracy by comparing them to data from the Canadian Lightning Detection Network. The results indicate the manual observations are reliable for small distances around each weather station, as is expected given the normally localized nature of thunderstorms. In Chapter 3 the historical manual hourly thunderstorm observations are evaluated for trends over the past several decades. Daily precipitation and wind gust data are used as proxies to determine if there have been changes in thunderstorm intensity, and yearly thunderstorm occurrence is compared to the larger scale phenomena ENSO and NAO. No consistent significant ii trends were observed over this period in either thunderstorm occurrence or intensity and a correlation between thunderstorm frequency and ENSO and NAO was also not detected. In Chapter 4 thunderstorm occurrence was successfully related to convective available potential energy (CAPE), with the probability of observing a thunderstorm on a given day at each of the weather stations increasing with daily maximum CAPE. While there were no consistent significant trends in CAPE observed over the reference period, by statistically downscaling three general circulation models it was found that large and robust increases in CAPE are expected over the coming decades across all weather stations, which consequently will have the potential to result in an increase in thunderstorm frequency. iii Acknowledgements First and foremost I would like to thank my research supervisor, Dr. William Gough, and doctoral committee members, Dr. Ken Butler and Dr. Tanzina Mohsin, for their guidance and support over the years. Without their assistance and dedication this project would not be what it is. I also thank my friends and colleagues in the UTSC Climate Lab who have been a pleasure to work with this entire time. I appreciate the assistance of Andrew Leung, Shannon Allen and Julian Morales of Environment Canada in obtaining all of the observed weather data used in this project. I wish to thank Environment Canada for their generous permission to use Canadian Lightning Detection Network (CLDN) data for Chapter 2 of this project, and am grateful to Ron Holle, meteorologist at Vaisala, for his consultation and advice on using the CLDN data. A very special heartfelt thanks goes out to my family, especially my parents, whose encouragement to follow my interests led me to pursue this endeavour in the first place, and whose never-ending patience and support have brought me to where I am today. iv Table of Contents List of Figures………………………………………………………………………….…………………………vii List of Tables……………………………………………………………………………..………………………viii Chapter 1 – Introduction………………..………………………………………………………………..1 1.1 Importance of Thunderstorms………………………………………………………………………...1 1.2 Thunderstorm Dynamics………………………………………………………………………………...3 1.3 Thunderstorm Climatology in Ontario……………………………………………………………..6 1.4 Thunderstorms and Climate Change………………………………………………………………..8 1.5 Climate Change Impact Assessment……………………………………………………………….10 1.6 Research Objectives………………………………………………………………………………………11 Chapter 2 – Evaluating thunderstorm Observations in Southern Ontario using Automated Lightning Detection Data………………………….………………………13 2.1 Objective……………………………………………………………..……………………………………….13 2.2 Background……………………………………………………………..…………………………………...13 2.3 Data……………………………………………………………………………….…………………………….17 2.4 Methodology………………………………………………………………………..……………………….18 2.5 Results and Discussion………………………………………………………………………………….20 2.5.1 Hourly Data – false positives…………………………………………………...………...21 2.5.2 Hourly Data – false negatives…………………………………………………………….23 2.5.3 Hourly Data – Day vs. Night………………………………………………………………24 2.5.4 Hourly Data - year-to-year variability………………………………………….…….26 2.5.5 Daily Data…………………………………………..……………………………………………27 2.5.6 Threshold distances – Radius of Equality………………………………………...….29 2.5.7 Discussion………………………………………………………………………..………...…….32 Chapter 3 – A Review of Thunderstorm Trends from the 1950s to Present…..34 3.1 Objective…………………………………………………………………………………………..………….34 3.2 Background………………………………………………………………………………………………….34 3.3 Data………………………………………………………………………………………………………….….36 3.4 Methodology……………………………………………………………………………..………………….39 3.5 Results and Discussion………………………………………………………………………………….41 3.5.1 Annual trends……………………………………………………..……………………………41 3.5.2 Intensity………………………………………………………………………………….…...….47 3.5.3 Seasonal Trends…………………………………………..…………………………………...49 3.5.4 ENSO/NAO……………………………………………………………………………..………..51 3.5.5 Discussion………………………………..………………………………………………………53 v Chapter 4 - Determining future thunderstorm trends in Southern Ontario by using statistical downscaling to project changes in CAPE…………….57 4.1 Objective………………………………………………………………………………………………………57 4.2 Background………………………………………………………………………………………………….57 4.3 Data……………………………………………………………………………………………………………..61 4.3.1 Thunderstorm Data…………………………………………………………………...…......61 4.3.2 CAPE Data…………………………………………………..………...…………………………61 4.4 Methodology……………………………………………………………………………………………..….62 4.4.1 Determining the relationship between thunderstorm days and CAPE…...62 4.4.2 CAPE trends to date……...……………………………………………………………..……64 4.4.3 Future CAPE Projections…………………………………...………………………………64 4.5 Results and Discussion………………………………………………………………………………….67 4.5.1 Relationship between Number of Thunderstorm Days and CAPE……….....67 4.5.2 CAPE Trends to Date……………………………………………………………………..…71 4.5.3 Future CAPE Projections……………………………………………...……………………73 4.5.4 Discussion………………………………………………………………………………..…….107 Chapter 5 – Summary and Conclusions……………………………………….....………...…112 5.1 Research Summary……………………………………………………………………………………..112 5.2 Limitation of the Research…………………………………………………………………………..113 5.3 Significance of the Research………………………………………………………………………..115 5.4 Future Directions………………………………………………………………………………………..116 Appendix – Statistical Methods……………………………………………………………………119 A.1 Logistic Regression and ANOVA…………………………………………………………………..119 A.2 Mann-Kendall Test and Theil-Sen Approach………………………………………………...121 A.3 Mood’s Median Test……………………………………………………………………………………122 A.4 T Test………………………………………………………………………………………………………...123 References…..……………………………………………………………………….…………………………124 vi List of Figures Figure 2.1. Nine 24-hour Environment Canada weather stations in Southern Ontario have archived thunderstorm data. They are (1) Buttonville – Toronto Buttonville Airport, (2) Gore Bay – Gore Bay-Manitoulin Airport, (3) Hamilton – John C. Munro Hamilton International Airport, (4) London – London International Airport, (5) Ottawa – Ottawa Macdonald-Cartier International Airport, (6) Pearson – Toronto Pearson International Airport, (7) Trenton – Canadian Forces Base Trenton Airport, (8) Wiarton – Wiarton-Keppel International Airport and (9) Windsor – Windsor International Airport………………………………………………………………………………….page 14 Figure 2.2. (a) False positive error rates for manual thunderstorm observations compared to CLDN data as a function of distance. (b) False positive error rates for manual thunderstorm observations compared to CLDN data as a function of distance for Wiarton. …………………………………………………………………………………page 22 Figure 2.3. False negative error rates for manual thunderstorm observations compared to CLDN data as a function of distance. ……………………………………….page 24 Figure 2.4. Day-Night Difference in false positive error rate. ………………………..page 25 Figure 2.5. Day-Night difference in false negative error rate. ……………………….page 25 Figure 2.6. Year-to-year range in false negative error rate. Difference of highest annual error rate - lowest annual error rate at each site over the five years…page 27 Figure 2.7. False positive error rate of manual thunderstorm observations on a daily scale. …………………………………………………………………………………………………………page 28 Figure 2.8. False negative error rate of manual thunderstorm observations on a daily scale. …………………………………………………………………………………………………………page 28 Figure 2.9. Year-to-year range of false negative error rate on a daily scale.…...page 29 Figure 2.10. Location of Wiarton Airport on the Bruce Peninsula. The location of this weather station between Lake Huron and Georgian Bay may allow observers to see more lightning. ………………………………………………………………………………………….page 33 Figure 3.1 Time Series of annual and seasonal thunderstorm trends at the nine weather stations. Slope of the overall annual trend is shown according to the Theil Sen Approach. ………………………………………………………………………………………page