By Steven Matthew Huryn 2016

Analysis of Thunderstorm Trends in Southern Ontario, Canada: Past and Future

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

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 42-46

Figure 4.1. Probability of observing a thunderstorm as a function of maximum daily CAPE at the nine weather stations. ……………………………………………………………..page 70

Figure 4.2. Observed, modeled and projected Values of annual mean CAPE, days

vii with 50% and 80% probability of observing a thunderstorm and summer mean CAPE at the nine weather stations. ……………………………………………………...page 90-107

List of Tables

Table 2.1. Results from the regression analysis on an hourly scale, over the 43,824 hours from 2006-2010. Thunderstorm reporting changes significantly with distance. Differences in day/night results are significantly at Wiarton and Windsor only. Hamilton, Trenton and Wiarton are the only sites with significant year-to-year variability.……………………….…………………………………………………………………………page 26

Table 2.2. Results from the regression analysis on a daily scale, over the 1826 days from 2006-2010. Thunderstorm reporting changes significantly with distance. Hamilton, London and Wiarton are the only sites with significant year-to-year variability.………………………………………………………………………………………………….page 29

Table 2.3. Distance at which thunderstorm hours(days) = lightning hours(days) on a daily scale (a), hourly scale (b) and comparison between day dn night (c). Beyond the indicated distance the number of lightning hours (days) is greater than number of thunderstorm hours (days). ………………………………………………………………page 30-32

Table 3.1. Data availability for hourly thunderstorm reports and daily total precipitation and maximum wind gust speed. …………………………………………….page 37

Table 3.2 Results of the Mann-Kendall test for thunderstorm trends over the period of data availability at the nine weather stations. …………………………………….page 41-42

Table 3.3. Results of the Mann-Kendall test for Early (pre-1980) and Recent (1980 onwards) periods. ……………………………………………………………………………………..page 47

Table 3.4. Results of the Mann-Kendall test for daily total precipitation on days with thunderstorms, days without thunderstorms, and all days. …………..……………..page 48

Table 3.5. Results of the Mann-Kendall test for daily maximum wind gust speed for days with thunderstorms, days without thunderstorms, and all days. ………………………………………………………………………………………………………………….page 49

Table 3.6. Results for the Mann-Kendall test and Theil Sen approach with data separated by seasons. …………………………………………………………………..……….page 50-51

Table 3.7. Correlation of number of thunderstorm hours per year and the Multivariate ENSO Index (MEI) and North Atlantic Oscillation (NAO). …....page 51-53

Table 4.1. Median daily maximum CAPE values for days with and days without thunderstorms at the nine weather stations over the reference period and p value from Mood’s median test. ……………………………………………………………………...page 68-69

viii

Table 4.2. Maximum daily CAPE associated with a 50% probability and 80% probability of observing a thunderstorm at each of the nine weather stations.……………………………………………………………………………………………………..page 70

Table 4.3. Correlation between number of thunderstorm days per year and annual mean and number of summer thunderstorm days per year and summer mean CAPE. ………………………………………………………………………………………………………………….page 71

Table 4.4. Results of Mann-Kendall test for trends in CAPE over the reference period. …………………………………………………………………………………………………………….page 72-73

Table 4.5. Predictor Variables used at the nine weather stations……………..page 74-75

Table 4.6. Comparison of observed vs. modeled data for reference period and observed reference period data to future modeled data. ………………………...page 77-90

ix 1. Introduction

1.1 Importance of Thunderstorms

The thunderstorm is without a doubt one of the atmosphere’s most spectacular phenomena. With the requisite amount of moisture, instability and a lifting mechanism a seemingly benign cumulus cloud can grow into a fully developed towering cumulonimbus in less than half an hour, stretching from near the surface to the tropopause, and producing dangerous weather including intense lightning, torrential rain, damaging wind, hail and tornadoes.

Although more common in warmer climates, Canada is no stranger to thunderstorms. The nation also experiences the second highest number of tornadoes in the world after the United States (NOAA, 2013a). Southern Ontario is one of Canada’s most active thunderstorm areas, and it also happens to be Canada’s most industrialized region and home to over a third of the country’s population

(Statistics Canada, 2012). Thunderstorms have been responsible for many of this region’s catastrophic and costliest natural disasters, as demonstrated by the following examples.

On the last day of May 1985, a warm spring day turned deadly as a severe thunderstorm outbreak spawned several tornadoes that wrecked havoc in central

Southern Ontario, including an F4 tornado that tore through Barrie. Twelve people were killed and as many as 300 injured. Close to 200 homes and large industrial complexes were completely destroyed while several hundred more homes were extensively damaged, making it one of Canada’s costliest natural disasters (Etkin et al., 2001). In August 2011, large portions of Goderich were demolished as a result of

1 an F3 tornado that unexpectedly formed in a supercell thunderstorm while over

Lake Huron, a situation that baffled meteorologists (Sills & Ashton, n.d.) and in

August 2009, Toronto residents were reminded that tornadoes do not respect urban boundaries when an F2 tornado touched town within Greater Toronto Area (GTA) limits and heavily damaged over 600 homes in the communities of Woodbridge and

Maple (Environment Canada, 2013a).

Tornadoes are not the only dangerous part of thunderstorms. While thunderstorms provide a large proportion of the summer precipitation to Southern

Ontario, convective storm rainfall can come at such high intensity that it overwhelms infrastructure and causes flooding. An intense thunderstorm dropped over 200mm of rain in Peterborough on July 15, 2004, causing severe flooding and over $100 million in damage (Gough and Mohsin, 2006; Environment Canada,

2013b). On August 19, 2005 large sections of major roadways were washed out in

Toronto when a high precipitation supercell moved through and more recently, on

July 8, 2013 a slow moving heavy thunderstorm developed virtually over the GTA and dropped 126mm of rain in two hours – the most intense rainfall the city has ever experienced (The Weather Network, 2013). Homes, transit systems and major roadways, including the Don Valley Parkway, one of the city’s main North/South highways, were completely flooded. This event is at present Ontario’s costliest natural disaster with property damage claims close to $1 Billion (Insurance Bureau of Canada, 2014; Environment Canada, 2014a).

Finally, a defining feature of a thunderstorm itself, lightning, kills on average

10 Canadians per year and results in $600 million to $1 billion in economic loss per

2 year across various sectors (Mills et al., 2008; Mills et al., 2010). Not surprisingly, over half of insurance claims in Canada for lightning related damage are in Ontario.

Aside from the obvious, thunderstorms may also increase emergency room visits for asthma and allergies as shown in Eastern Ontario by Dales et al. (2003). It is believed that heavy rain and wind can rupture pollen grains and spores and carry them into the atmosphere where they are small enough to remain suspended for long periods of time. The downdrafts in a thunderstorm could then concentrate these particles near the ground where they are more likely to be inhaled (D’Amato et al., 2007).

1.2 Thunderstorm Dynamics

The development of thunderstorms requires instability in the atmosphere, water vapour and a lifting mechanism. Instability results when the temperature in the atmosphere decreases with height. The actual measure of this decrease is known as the lapse rate, and its value in a given air mass is defined as the environmental lapse rate. The air mass itself is made of many individual air parcels. An individual air parcel is a small volume of air with constant characteristics throughout, the exact size of which is arbitrary. When lifted, each individual air parcel expands and cools adiabatically, that is without the transfer of heat to its surrounding parcels.

Unsaturated air cools at the dry adiabatic lapse rate (DALR), equivalent to 10°C/km, while saturated air cools at the moist adiabatic lapse rate (MALR). The reduced lapse rate is a result of water vapour condensing in the air and releaseing latent heat. The exact value of the moist adiabatic lapse rate will vary with temperature and moisture content, although it is usually approximated at 6°C/km (Ahrens, 2007;

3 Kundu et al., 2016). If the environmental lapse rate is less than the MALR, the environment will be absolutely stable, because individual air parcels will never be warmer than their surroundings. If an air parcel is lifted to a level where it will always be warmer than its surroundings, it is said to have reached the level of free convection (LFC). It will therefore rise from this level until it reaches an equilibrium level (EL). If instability is deep, the EL will not occur until the stratosphere. A cumulonimbus cloud pushing against the stable stratospheric air is what gives fully developed thunderstorms the characteristic flattened top.

To initiate the lifting process, air parcels require a lifting mechanism. The three categories of lifting mechanisms are terrain, frontal boundaries and surface heating.

As air parcels flow over elevated terrain they are forced upwards. Frontal boundaries can force air parcels upwards as incoming warmer air is forced above cooler air along a warm front or incoming cooler air forces itself under the warmer air ahead of it along a cold front. Finally surface heating can cause air parcels to become warmer than their surrounding environment and begin to rise. In all situations, once the lifting process has been initiated, air parcels will continue to rise as long as they remain warmer than the surrounding environment. If they cool to their specific dew point temperature, water vapour will begin to condense and clouds will form. In deep instability the air parcels will reach the LFC and ascend to the EL at the tropopause, producing fully developed thunderstorms. Because these storms extend above the freezing level, the upper portions of the clouds are ice crystals while the lower portions are water vapour, which is what leads to charge separation and lightning formation (Ahrens, 2007).

4 The best measure of atmospheric stability and the energy available for thunderstorm formation is Convective Available Potential Energy (CAPE). Measured in J/kg, CAPE is a function of the vertical temperature profile and moisture content of the troposphere, and indicates the energy available for convection (NOAA, 2009;

Holley et al., 2014). CAPE is normally calculated through software, using measurements of the vertical temperature profile and moisture content. The equation used is

(�� − ��) �� = !"Σ � Δ� !"# �� where CAPE is the sum of all relative differences of the temperature a given air parcel would have (Tp) compared to the surrounding environment (Te) multiplied by gravity (g) for all levels between the level of free convection (LFC) and equilibrium level (EL), multiplied by the height difference between the LFC and EL

(Δz). While technically there are an infinite number of levels between the LFC and

EL, CAPE is often calculated through the use of measurements at set intervals.

A greater lapse rate and/or moisture content would result in greater instability and a higher CAPE value, increasing the ability of air parcels within the air mass to ascend, leading to condensation of water vapour and release latent heat and, if the instability is strong enough, thunderstorms to develop. Other factors unchanged, the risk of thunderstorm development and severity increases as CAPE increases

(Kirkpatrick et al., 2011).

Once developed ordinary cell thunderstorms usually last 30 minutes. This short- lived lifespan is a result of cool precipitation and downdrafts undercutting the

5 updraft. A factor that can affect thunderstorm life expectancy and severity is the presence of vertical wind shear in the environment. Vertical wind shear is a change of wind speed and/or direction with height and its presence increases the risk that a thunderstorm will become severe. This results because the shear helps push precipitation and cold downdrafts away from the thunderstorm’s updraft region, increasing the storm’s longevity and probability of reaching severe limits. Vertical wind shear is the primary factor involved in the formation of rotation in thunderstorms resulting in supercells and tornadoes, because it can cause air to rotate along a horizontal axis which can then be tilted vertical by the updraft of a developing thunderstorm (Weisman & Klemp, 1982; Davies-Jones, 1984; Holton,

2004).

1.3 Thunderstorm Climatology in Ontario

Notable research on thunderstorms in Southern Ontario began in the 1990s. The role of the Great Lakes and lake breeze convergence in the thunderstorm climatology of the region has been established by King (1996), Sills & King (1998) and King et al. (2003). In 1996 King reported that the occurrence of severe convective storms in Southern Ontario may be related to lake breeze boundaries, as all of Ontario’s strongest tornadoes have occurred in lake breeze convergence zones.

Sills & King (1998) used field observations and models to locate the position of lake breeze fronts. They found that convective storms frequently developed along lake breeze fronts, even when synoptic conditions would otherwise not be favourable to thunderstorm formation.

6 King (2003) examined the relationship between lake breezes and severe weather with respect to shoreline geometry, synoptic flow and interactions with cold fronts. It was found that different patterns of convection and potential for severe weather occurred with synoptic winds of varying intensities and directions as this affected the positions of lake breeze fronts and where or whether or not they will interact. The greatest threat for a widespread severe weather outbreak in

Southern Ontario was found to be on a day with lake breezes, a southwest synoptic flow and an approaching cold front from west. It was also noted that a lower number of tornadoes occurs along shorelines and in the areas behind lake breeze fronts. It is believed that either storms are suppressed by cooler lake modified air or in some areas storms dissipate faster as they move over descending terrain. Another interesting example occurred on a particular day of the study when an area of the neighboring state of Michigan was not experiencing lake breezes while areas of

Southern Ontario were. On that day Michigan required a higher surface temperature for thunderstorm formation compared to Southern Ontario, suggesting that the lake breezes provide additional moisture, lift or both that allows an air parcel to reach the LFC with lower surface heating. Overall these studies have shown that the lake breeze fronts, combined with topography, have a significant role in determining the patterns of severe convective weather in Southern Ontario. As with the drylines in the “Tornado Alley” region of the central United States, these are low-level boundaries that can enhance convection and create the vertical wind shear necessary for strong thunderstorm and tornado development.

7 There has not, to our knowledge, been any analysis of past thunderstorm trends in Southern Ontario, or in in any region for that matter. Cao & Cai (2011) used records of tornadic events over Ontario as a whole and found a small but statistically significant increasing trend in the number of tornadoes per year from

1950-2007, an approximate increase of 1.6 tornadoes per decade. They also found tornado frequency was related to ENSO events as did Etkin et al (2001). Cao & Ma

(2009) used Ontario Storm Prediction Centre records of severe summer rainfall events in Ontario from 1979-2002 and found a significant increasing trend of about

11 events per decade and Cao (2008) found an increasing trend in severe hail events over the period 1979-2002. These studies did not consider thunderstorm events as a whole, rather they relied on records of tornadoes, summer severe rainfall events and hail events from across the entire province that were reported to the Ontario

Storm Prediction Centre. It is possible that some of the observed trends in these events may be related to an increasing population in this region, considering that a statistically significant correlation between possible tornadoes and population density was found in Southwestern Ontario by King (1997). There have been no studies to this point examining thunderstorms as a whole at fixed-point weather observation stations, or any studies in this region examining how thunderstorm frequency might change into the future.

1.4 Thunderstorms and Climate Change

Considering that it is these types of weather events that usually are responsible for the greatest damage and danger to society, research on

8 thunderstorm climatology has been disproportionately low. With an expected continued increase in global mean temperatures over the current century (IPCC

AR5), any assessment of potential impacts of climate change would be incomplete without an analysis of thunderstorm climatology. However one of the main reasons for the lack of studies on thunderstorm climatology is likely the nature of thunderstorms themselves – particularly their small spatial and temporal scales – makes them difficult to research. They are too small, both spatially and temporally, to be accounted for in General Circulation Models (GCMs) and their observation has traditionally relied on manual weather stations at point locations.

Nevertheless, climatologists have attempted to project future changes in severe thunderstorm environments based on changes in parameters associated with such environments. The variables most commonly associated with thunderstorm favourable environments are CAPE and vertical wind shear.

Rasmussen & Blanchard (1998), Brooks et al. (2003) and Craven & Brooks

(2004) showed that the product of CAPE and vertical wind shear could be used to distinguish severe thunderstorm favourable environments. These variables were then used by Trapp et al. (2007), Van Klooster & Roebber (2009) and Diffenbaugh et al. (2013) to predict future changes in severe thunderstorm environments in the

United States. Through the projection of these proxies, all of these studies found an expected increase in number of severe thunderstorm days across the continental

United States through the current century. Allen et al. (2014a and 2014b) also found large increases in these variables in Australia by the end of the century leading to an increase in severe thunderstorm environments there. These studies are further

9 discussed in Chapter 4. To date we are not aware of any studies on future thunderstorm projections in Canada. The unique characteristics of Southern

Ontario’s thunderstorm climatology in described in Section 1.2 suggest that one cannot simply extrapolate the results of the American studies into this region.

Thunderstorm climatology studies for Southern Ontario will have to use GCMs that have been downscaled to account for the local features such as the Great Lakes.

1.5 Climate Change Impact Assessment

The goal of this project is to complete a Climate Change Impact Assessment

(CCIA) on thunderstorm occurrence in Southern Ontario. The ultimate objective of a

CCIA is to project changes for a variable of interest, or exposure unit – in this case number of thunderstorms. The first stage of a CCIA is to establish a baseline of trends in the exposure unit to date. As is the case with thunderstorms, often an exposure unit will not be directly represented in climate models. This will require finding variables that are resolved for in models and can be related to thunderstorm occurrence through statistical relationships. In our case, the variable that we will relate to thunderstorm occurrence is CAPE. If the baseline of the related variable is successfully reproduced through climate models, future projections of the variable can be made through GCMs. GCMs take into account the physics of the climate system and typically include between 10 and 30 vertical levels of the atmosphere and have a horizontal grid resolution of 250 to 600km. There are over 30 GCMs worldwide. Each one can determine how the climate system will respond to alterations in radiative forcing caused by variations in greenhouse gas

10 concentrations. Because of their coarse horizontal resolution GCMs may not account for local features that could have a significant impact on the local climate – an example in Southern Ontario being the Great Lakes. One way to overcome this is through the use of a regional climate model (RCM), if available for the region of interest. A RCM is a GCM that has been dynamically downscaled to produce higher resolution data for a local area. If there are no RCMs available for the study area that have pre-calculated the variable of interest, resolution of a GCM can only be increased through statistical downscaling, which is described in Chapter 4.

Various emissions scenarios can be selected for the future projections. The A2 scenario assumes a “business as usual” future climate. In its fifth assessment report, the Intergovernmental Panel on Climate Change (IPCC AR5) introduced

Representative Concentration Pathways (RCPs) to replace the previous emissions scenarios. RCPs take into account future greenhouse gas concentrations, with RCP

8.5 being the closest to the former A2 designation, assuming greenhouse gas concentrations will continue to increase over the remainder of the 21st century.

The final stage of a CCIA is to determine how the exposure unit might change based on the projected changes in the related climate variable. It can then be determined what impact the changes in the exposure unit might have on society.

1.6 Research Objectives

With the primary objective of determining how thunderstorm frequency might change over the remainder of the current century in Southern Ontario, this project will follow the stages of a CCIA. Prior to this it is important to determine if

11 there have been trends in the baseline thunderstorm record. Historical thunderstorm observations are available through Environment Canada’s climate data archive. The observations are based on a weather observer seeing lightning or hearing thunder at a weather station (Environment Canada, 2013c). As a result of potential observer bias and the small-scale nature of thunderstorms, we would like to confirm the accuracy of this data before we use it for establishing thunderstorm trends to date. We will do this by comparing the manual observations to data from the Canadian Lightning Detection Network (CLDN). Chapter 2 evaluates the manual thunderstorm observations by comparing them to the automated lightning data. If the data is considered to be sufficiently reliable, we will use it in Chapter 3 to establish a baseline of thunderstorm trends to data across Southern Ontario, including both number of thunderstorms per year and, if possible, thunderstorm intensity. In Chapter 4 we link thunderstorm occurrence to related variables such as

CAPE that can be accounted for and projected in climate models. Finally we will use these variables to project future thunderstorm frequency by downscaling GCM output to point locations across Southern Ontario. The results of this project provide

Southern Ontario with its first analysis of thunderstorm trends, both past and future. This will help determine if there will be changes in severe weather patterns as a response to future climate change and will be able to assist with taking precautions including land use planning, infrastructure design and development of warning and emergency response strategies. The results from Chapter 2 will also benefit any other researchers wishing to use the archived Environment Canada thunderstorm data.

12 2. Evaluating thunderstorm Observations in Southern Ontario using Automated Lightning Detection Data

Publication Huryn, S., Gough, W., Butler, K. & Mohsin, T. 2015: An evaluation of thunderstorm observations in Southern Ontario using automated lightning detection data. Journal of Applied Meteorology and Climatology, 54(9): 1837-1846.

2.1 Objective

To determine if historical thunderstorm observations at Environment Canada manned weather stations are reliable and, if so, for what distance they are valid.

2.2 Background

Prior to establishing a baseline of thunderstorm trends to date or relating thunderstorm occurrence to other variables, it is important to determine if the historical thunderstorm record is reliable. In total there are nine 24-hour weather stations across Southern Ontario that have historical thunderstorm data (Figure

2.1). These stations are all located at major airports. While there are a few other weather stations that have recorded thunderstorm data, such as Kingston and

Toronto Island, these stations are either not 24-hour stations or have significant amounts of missing data that would compromise analysis. The thunderstorm data begins in 1953 at most of these stations, except for Buttonville and Hamilton, where the data begins in 1986 and 1970 respectively. At the time of completion of this chapter, the data was available up to the end of 2010 at all stations, with uneven extensions beyond that point. As we explain below, we resolved to limit this analysis to the end of 2010. The Gore Bay weather station was closed in 1993, therefore we omit it from this chapter.

At these stations, an on-duty weather observer is required to make specific observations at regular time intervals. One of the elements that is required to be recorded is the presence of thunderstorm activity. According to the Environment

Canada Manual of Surface Weather Observations (MANOBS):

“Thunderstorm activity at the station shall be reported when: 1) Thunder is heard within the past 15 min; or 2) Overhead lightning is observed within the

14 past 15 min and the local noise level is such as might prevent hearing thunder. In this case, hail may also be an indicator of a thunderstorm in progress.” (Environment Canada 2013c)

The records are then transcribed into the hourly climate data, with each hour being marked with either the presence or absence of a thunderstorm. Because it is dependent on the observer hearing thunder or seeing lighting, this system could be subject to bias. Visibility issues and noise from within or around the weather station could affect the accuracy of thunderstorm reports. In addition, due to the small-scale nature of thunderstorms it is unclear what distance these data are valid for, with a possibility they only account for the area within a small radius around the weather station. It would be beneficial if we could test the manual observations, using an automated system for thunderstorm detection. Fortunately such a system exists in the form of the Canadian Lightning Detection Network (CLDN).

The CLDN was established in 1998 as an extension of the American National

Lightning Detection Network (NLDN) with the two collectively being known as the

North American Lightning Detection Network (NALDN). The CLDN consists of 83 sensors across Canada that detect sferics (Environment Canada 2013d). Sferics are electromagnetic energy caused by a lightning strike at various frequencies and are detected by the sensors. The location of the strike can be determined based on the time of arrival at different sensors. A complete description of the operation of the

NALDN, as well as references to studies assessing its performance, can be found in

Cummins and Murphy (2009). Up to 95% of cloud to ground (CG) lightning is detected to a location accuracy of within 500 m. Only 10-20% of intracloud (IC) lightning is detected, although recent upgrades have increased this to 50% for the

15 NLDN (see Holle et al. 2014). IC lightning typically outnumbers CG lightning, although to what extent depends on several factors, with a typical ratio of IC to CG lightning in the region of our study of 2:1 (Boccippio et al. 2001). We will take into account the low detection efficiency of IC lightning when interpreting our results.

The CLDN is owned by Environment Canada and operated by Vaisala. The latitude, longitude, charge and polarity, as well as number of return strokes per flash are recorded and archived at Vaisala offices in Tucson, Arizona. A further description and preliminary observations from the CLDN are presented by Burrows et al. (2002), Burrows and Kochtibajda (2010), & Kochtibajda and Burrows (2010).

Recently, the CLDN has also been used to help estimate tornado occurrence in sparsely populated regions (Cheng et al. 2013).

Work presented by Corbosiero and Lazear (2012) compared METAR thunderstorm reports to NLDN data within 5, 10, 15 and 20km at 20 weather stations across the United States. They found that with respect to number of thunderstorm days the correlation between the two datasets varied between the stations, with METAR data at some stations being valid for greater distances than at others. Worldwide, lightning detection networks have also been used in this manner. Most recently, Enno (2015) compared data from the Nordic Lightning

Information System to manual thunderstorm observations in Estonia from 2006-

2011, and found the number of thunderstorm days reported by the two types of observations corresponded for a radius between 9 and 14.7km from the meteorological stations.

16 This analysis will determine if a thunderstorm was manually recorded at each weather station when lightning was detected within a given radius, and confirm that lightning was detected when a thunderstorm was reported. Because thunderstorms may be more easily observed between sunset and sunrise, we also compared the error rates between day and night. Although we will only be using a few years of recent CLDN data, the results will provide an indication if the manual observations are reliably identifying thunderstorm events and the radius of reliable observation around a weather station.

2.3 Data

The archived thunderstorm data was obtained by contacting Environment

Canada climate services. For this chapter, all stations other than Gore Bay were used. The data at Gore Bay ends in 1993, which is prior to the establishment of the

CLDN.

Data from the CLDN was obtained for the five year period, 2006-2010 for a

100 km radius around each weather station. Equipment upgrades over the early years of the network’s operation ensure that data during this period is reliable with consistent detection efficiencies of CG and IC lightning, and not subject to year-to- year variability in performance of the network (Holle, personal communication

2014). Upgrades to the network prior to 2006 may have resulted in year-to-year variability in detection efficiency, whereas NLDN upgrades following 2010 to increase cloud pulse detection may have affected our study area since Southern

Ontario borders the CLDN and NLDN. We were assured that any year-to-year

17 variability during our study period is not a result of the network’s performance

(Holle, personal communication 2014).

It should be noted that while we would have preferred using a longer time period of data for this chapter, we wanted to ensure the data used is of good quality, and this limited us to a coincident period of the CLDN and manual observations from 2006 to 2010. Many renowned studies using lightning data have used even shorter time periods. Early studies evaluating CLDN performance used as little as two years of data (Burrows et al., 2002). Holle et al. (2014) which examined lightning warnings after 2013 NLDN sensor upgrades used only one month of data whereas the time period in the Boccippio et al. (2001) study examining CG:IC lightning ratios across North America was four years. Abreu et al. (2010) evaluated the performance of the World Wide Lightning Location Network relative to the

CLDN using only a four month period in 2008, and the Enno (2015) study which we mention above, that compared lightning data to manual thunderstorm observations in Estonia, used six years of data, 2006-2011. Certain studies using NLDN data, including Corbosiero & Lazar (2012) were able to use longer periods of data, however the NLDN was established ten years earlier than the CLDN. As more data becomes available it will be possible to extend the time periods of these types of analyses.

2.4 Methodology

Consistent with other studies using the lightning detection network data

(Orville et al. 2011; Burrows et al. 2002; Burrows and Kochtibajda 2010;

Kochtibajda and Burrows 2010; Cheng et al. 2013) we will focus on lightning flashes

18 rather than individual return strokes. The distance of each flash to the weather station was calculated as the great circle distance, d (km), using the Haversine

Formula: d = arccos(cos(90o-LFL)) * cos(90o-WSL) +sin(90o-LFL) * sin(90o-WSL) * cos(LFG-WSG) * 6371 (km) where

LFL – lightning flash latitude

WSL – weather station latitude

LFG – lightning flash longitude

WSG – weather station longitude

A database was created for each weather station containing each of the

43,824 hours of data over the five-year period, 2006-2010. Each hour was marked as having a reported thunderstorm or not based on the manual observations. Using the sunrise and sunset time of each location for every day of the year, each hour was also marked as day or night. Assuming that the data from the CLDN represent the true state, false positive and false negative error rates were calculated across an increasing radius away from each weather station. A false positive occurs when there is a manual observation of a thunderstorm but no corresponding lightning strike recorded by the CLDN within a given radius (as noted above, we assume the

CLDN represents the true state). Conversely, a false negative occurs when a manual observation of thunderstorm is not made but the CLDN reports a lightning strike within a given radius. Thus, the false positive metric was calculated as the percentage of hours with no lightning within a given radius that were identified as having a thunderstorm by manual observation. The false negative metric was

19 calculated as the percentage of hours with lightning within a given radius that did not have a thunderstorm reported.

A logistic regression analysis (see Hosmer and Lemeshow 1989; Appendix

A1) was used to determine if the reporting of thunderstorms differed significantly by distance from each station, by day-night at each station, between years at each station, and between stations. In this case, whether or not a thunderstorm was manually observed was considered the binary response variable. This procedure, other than day-night separation, was repeated on a time scale of days instead of hours, with each day spanning from 00:00 to 23:59 LST.

The “radius of equality” around each weather station, that is the distance where number of thunderstorm days and number of days with lightning were equal was also calculated. This was determined by subtracting the number of reported thunderstorm days from the number of days with lightning within each radius. Close to the weather station the number of thunderstorm days will be higher than days with lightning. At a certain radius the two values will balance and the difference will equal zero, after which the number of days with lightning will be higher than the number of days with thunderstorms reported at the weather station. This was compared by year. It was repeated for thunderstorm hours and separated into day and night.

2.5 Results and Discussion

According to the regression analysis, on both an hourly and daily time scale, the reporting of thunderstorms was significantly different among the stations

(p<0.05).

2.5.1 Hourly Data – false positives

The false positive (manual thunderstorm observation but no corresponding lightning reported) error rates were low. Within 1 km of each weather station, no site other than Wiarton was greater than 0.6% (Figure 1.2). Wiarton’s rate was considerably higher at over 8%. We will discuss some reasons why Wiarton may be anomalous below. At a 22 km radius away from each weather station, all except

Wiarton had declined to 0.05% for false positives, in other words almost every manual thunderstorm observation had a corresponding lightning event detected by the CLDN. Although below 0.05%, the rate did not reach absolute 0 other than at three sites – Ottawa at 48km, Trenton at 82km and Windsor at 68km (Figure 2.2).

The reason for this, and reason a false positive error existed at all, could be related to the CLDN’s low detection efficiency of IC lightning. It is possible that there were hours with only IC lightning that was reported by the observers, however not recorded by the CLDN. Overall, the low false positive error confirms that when observers are reporting a thunderstorm there is almost always lightning activity within a reasonably small radius around each weather station, and therefore thunderstorm reports are not being fabricated or falsely recorded.

21 (a) 0.7

0.6 Buttonville 0.5 Hamilton 0.4 London 0.3 Ottawa 0.2 Pearson 0.1 Trenton False Positive Error Rate (%) 0 Windsor 0 10 20 30 40 50 60 70 80 90 Distance from Airport (km)

(b) 10 9 8 7 6 5 4 Wiarton 3 2

False Positive Error Rate (%) 1 0 0 10 20 30 40 50 60 70 80 90 Distance from Airport (km)

22 2.5.2 Hourly Data – false negatives

False negative (lightning detected but no corresponding manual thunderstorm observation) error rates are relatively low around each weather station and quickly increase with distance (Figure 2.3). Note that there is some fluctuation within the first km from each weather station due to a small sample size of lightning strikes that actually occur within that radius. It is not necessarily expected that this should reach 100% as distance reaches 100 km away from the weather station because the percent error is based on all lightning within the given radius.

Using the false negative (lightning detected but no thunderstorm reported) error rate, it is possible to determine the percentage of missed thunderstorm hours at given distances from each weather station. For example, at 5 km from each weather station, the average false negative error is 34%, with a range from 15%

(Wiarton) to 48% (Windsor). At 10 km it increases to 43% with a range from 22%

(Wiarton) to 60% (Windsor). It is also possible to determine the distances where the rate reaches certain thresholds. For example, the average distance where the error rate reaches 50% is 16 km, although this ranges from 6 km (Windsor) to 33 km (Wiarton).

23 100 90 80 Buttonville 70 Hamilton 60 London 50 Ottawa 40 30 Pearson 20 Trenton False Negetive Error Rate 10 Wiarton 0 0 10 20 30 40 50 60 70 80 90 Windsor Distance from Airport (km)

Figure 2.3. False negative error rates for manual thunderstorm observations compared to CLDN data as a function of distance.

2.5.3 Hourly Data – Day vs. Night

The daytime/nighttime analysis showed there was very little difference in the already low false positive (thunderstorm reported but no lightning detected) error rates (Figure 2.4). The highest difference was Hamilton, which was just under

0.4% higher during the day within the first kilometer around the weather station.

The differences for all sites, except Wiarton, reached 0.0% beyond 30 km from each weather station. As expected, false negative (lightning detected but no thunderstorm reported) error rates were lower at all sites during the night (Figure

2.5). This is likely because the observer would be better able to see lightning at night and thus make a manual report of thunderstorm activity. Although all sites overall had a lower false negative error curve at night, according to the regression analysis the difference in day and night reporting of thunderstorms was statistically significant only at Wiarton and Windsor (Table 2.1).

24 0.5

0.4 Buttonville 0.3 Hamilton 0.2 London

0.1 Ottawa (%) 0 Pearson 0 10 20 30 40 50 60 70 80 90 Trenton -0.1 Wiarton -0.2

Difference in False Positive Error Windsor -0.3 Distance from Airport (km)

Figure 2.4. Day-Night Difference in false positive error rate.

15 Buttonville Hamilton 10 London

5 Ottawa Pearson 0 5 15 25 35 45 55 65 75 85 95 Trenton -5 Wiarton

FN Day-Night Difference (%Error) Windsor -10 Distance from Airport (km)

Figure 2.5. Day-Night difference in false negative error rate.

25 Site Significance (p values) Distance Day/Night Year - ANOVA Buttonville 0.337 0.5247 <0.001 Hamilton 0.42 <0.001 0.009 London 0.141 0.4727 <0.001 Ottawa <0.001 0.224 0.2972 Pearson 0.114 0.4857 <0.001 Trenton 0.257 <0.001 0.01542 Wiarton <0.001 <0.001 <0.001 Windsor 0.2121 <0.001 <0.001

Red – Statistically Significant

2.5.4 Hourly Data - year-to-year variability

There was year-to-year variability in false negative (lightning detected; thunderstorm not reported) error at each site however, according to the regression analysis, this was only significant at Hamilton, Trenton and Wiarton (Table 2.1). The range, represented as the lowest annual false negative error subtracted from the highest annual false negative error is shown for all sites across all distances in

Figure 2.6. Again there is variability around the 1 km mark likely due to the low sample size. At 5 km from each weather station the average year-to year range at each site is 20% with a range from 9% (London) to 30% (Trenton).

26 80 70 Buttonville 60 Hamilton 50 London 40 Ottawa 30 Pearson 20 Trenton 10 Wiarton Percent Error Range 2006-2010 0 0 10 20 30 40 50 60 70 80 90 Windsor Distance From Airport

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.

2.5.5 Daily Data

When reducing the time-scale resolution to daily instead of hourly, the false positive (thunderstorm reported, no lightning detected) error rate is slightly higher immediately around each weather station however it drops to 0.0% for all sites except Wiarton at just over 30 km from each weather station. It also reaches absolute 0 for all sites except Wiarton (Figure 2.7). The false negative (lightning detected, thunderstorm not reported) rate is lower for all sites, with a 5 km average of 14%, ranging from 7% (Hamilton) to 25% (Windsor) and a 10 km average of 22% ranging from 12% (Wiarton) to 34% (Windsor). The average distance at which 50% is crossed is 32 km with a range from 21 km (Windsor) to 51 km (Wiarton) (Figure

2.8). Year-to-year variability remains, with an average at 5 km of 17% ranging from

11% (Hamilton) to 27% (Windsor) (Figure 2.9). This difference is only significant at

Hamilton, London and Wiarton (Table 2.2).

27 8 7 Buttonville 6 Hamilton 5 London 4 Ottawa 3 Pearson 2 Trenton 1 False Positive Error Rate (%) Wiarton 0 0 10 20 30 40 50 60 70 80 90 Windsor Distance from Airport (km)

Figure 2.7. False positive error rate of manual thunderstorm observations on a daily scale.

90 80 Buttonville 70 Hamilton 60 50 London 40 Ottawa 30 Pearson 20 Trenton 10 False Negative Error Rate (%) Wiarton 0 0 10 20 30 40 50 60 70 80 90 Windsor Distance from Airport (km)

Figure 2.8. False negative error rate of manual thunderstorm observations on a daily scale.

28 45 40 Buttonville 35 Hamilton 30 25 London 20 Ottawa

Range (%) 15 Pearson 10 Trenton 5 Wiarton 2006-2010 Flase Negative Error 0 0 10 20 30 40 50 60 70 80 90 Windsor Distance form Airport (km)

Figure 2.9. Year-to-year range of false negative error rate on a daily scale.

Site Significance (p values) Distance Year - ANOVA Buttonville 0.6205 <0.001 Hamilton <0.001 0.0425 London <0.001 0.03662 Ottawa 0.3502 <0.001 Pearson 0.1556 <0.001 Trenton 0.5109 <0.001 Wiarton <0.001 <0.001 Windsor 0.4384 <0.001

Red – Statistically Significant

2.5.6 Threshold distances – Radius of Equality

While the false negative error may seem high, and the false positive error rate low, the number of thunderstorms hours (days) per year at one of these weather stations is relatively low, while the number of hours (days) without thunderstorms is relatively high. It is therefore relevant to place these error rates in context of absolute numbers. While looking at individual missed thunderstorms and false alarms may be beneficial for improving warnings, especially at the airports that correspond to each of these weather stations, the purpose of our research is to determine how accurate the manual observations are in determining the number of thunderstorm days per year. Therefore, we are more interested in how the number of lightning days and number of thunderstorm days balance out. By subtracting the number of reported thunderstorm days from number of days with lightning within a given radius, we were able to determine the distance at which the two balance after which the number of lightning days increase beyond number of reported thunderstorm days. The average of this distance, where lightning days minus thunderstorm days equals zero was found to be 8.8 km with a range from 4.7 km

(Windsor) to 14.7 km (Wiarton). The average year-to-year range of this distance was 4.9 km, although it varied from 2 km (Windsor) to 9.5 km (London) (Table

2.3a). For hourly reports, the average distance dropped to 8.3 km, with a range from

5.4 km (Windsor) to 15 km (Wiarton) (Table 2.3b). The average year-to-year range dropped to 3.6 km with a range from 1.7 km (London & Windsor) to 5.8 km

(Wiarton) (Table 2.3b). On average, the distance was 2.4 km greater at night, with a range from 0.3 km greater (Buttonville) to 8.1 km greater (Wiarton) (Table 2.3c).

(a) Daily:

Station Min (km) Average Max (km) Max-Min(km) (km) Buttonville 6 6.5 10 (2007) 4 (2006,2008,2009) Hamilton 8 (2008) 9 12(2009) 4 London 6(2010) 9 15.5(2007) 9.5 Ottawa 5(2010) 8.5 11.5(2008) 6.5 Pearson 6.3(2010) 7 9.2(2006) 2.9 Trenton 10(2006, 2010) 11.1 12.5(2008) 2.5 Wiarton 9.5(2007) 14.7 17(2010) 7.5 Windsor 4(2007) 4.7 6(2010) 2

Overall Average: 8.8 km Average Year-to-Year Range: 4.9 km

(b) Hourly: Station Min (km) Average Max (km) Max-Min(km) (km) Buttonville 4.8 5.8 7.2(2007) 2.4 (2008,2010 ) Hamilton 7.5(2007) 9 11.7(2009) 4.2 London 5.3(2006) 6 7(2009) 1.7 Ottawa 5(2010) 7.5 9.8(2009) 4.8 Pearson 5.8(2007) 6.4 7(2008) 1.2 Trenton 6.8(2010) 11 14(2008) 7.2 Wiarton 12.2(2006) 15 18(2008) 5.8 Windsor 5(2008,200 5.4 6.7(2007) 1.7 9,2010)

Overall Average: 8.3 km Average Year-to-Year Range: 3.6 km

(c)Hourly: Station Day (km) Night (km) Night-Day (km) Buttonville 5.7 6 0.3 Hamilton 8.8 9.6 0.8 London 5.9 6.4 0.5 Ottawa 7.3 8 0.7 Pearson 5.9 8.2 2.3 Trenton 9.7 12.4 2.7 Wiarton 11.9 20 8.1 Windsor 4 7.7 3.7

Average Day: 7.4 km Average Night: 9.8 km Average Difference (Night-Day): 2.4 km

Table 2.3. Distance at which thunderstorm hours(days) = lightning hours(days) on a daily scale (a), hourly scale (b) and comparison between day and night (c). Beyond the indicated distance the number of lightning hours (days) is greater than number of thunderstorm hours (days).

2.5.7 Discussion

The results indicate that the manual thunderstorm data is reasonably accurate for a climatological study when limited to small distances, usually less than

10 km, around each weather station. This is reasonable given the small-scale nature of thunderstorms and requirements that the human observer is able to see the lightning or hear the thunder in order to report a thunderstorm. It is also supports the results presented by Corbosiero and Lazear (2012) for US cities, and Enno

(2015) for Estonia. The observations are somewhat better at night and contain some year-to-year variability although it is not statistically significant except for at

Hamilton, Trenton and Wiarton (hourly) and Hamilton, London and Wiarton (daily).

These results provide us with some confidence in using the thunderstorm data set extending from 1953. The manual observations appear credible for small areas near the observation station and any temporal trends, if they exist, in thunderstorm frequency should be detectable.

Likewise, it may be important to complete a directional analysis on the manual observations to determine how these error rates differ by direction. That may provide indication of obstacles to viewing thunderstorms around the weather stations. The surrounding area likely has a large role in the distance for siting

32 thunderstorms. As is apparent in our in our results, Wiarton is an outlier. This site has much lower false negative and higher false positive errors, suggesting observers at Wiarton Airport are “seeing” more thunderstorms. It is possible that the airport’s location on the Bruce Peninsula (Figure 2.10) results in this. It is possible that observers at this station are able to see lightning across Lake Huron into Michigan

State, especially at night, and record a thunderstorm even if there is no lightning activity within the immediate vicinity. Further research could determine what exactly makes Wiarton different from the other weather stations when reporting thunderstorms.

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.

33 3. A Review of Thunderstorm Trends from the 1950s to Present

Publication Huryn, S., Gough W. & Butler, K. 2016: A review of thunderstorm trends across Southern Ontario from the 1950s to present (under revision).

3.1 Objective

To use the data that was validated in Chapter 2 to determine if there have been any trends in thunderstorm occurrence at the weather stations over the period of the historical record.

3.2 Background

At present we are not aware of any studies analyzing past thunderstorm trends in Southern Ontario, or any region for that matter. One of the reasons for this may be the lack of usable data. In Chapter 2 we found that the archived thunderstorm data from Environment Canada weather stations should be reliable for detecting consistent trends over time in thunderstorm activity at the weather stations. In this chapter we will use that data to determine if there is a trend in the number of thunderstorms per year at the nine weather stations. We will also divide the data into seasons to determine if there is evidence that thunderstorm distribution throughout the year is changing.

Studies have shown an increase in mean surface temperatures across Ontario over the past several decades (Nalley et al., 2013; Mohsin & Gough, 2010). While it may be intuitive to assume that higher temperatures would lead to more thunderstorms, this is not necessarily the case because thunderstorms do not depend on temperature alone but rather the entire atmospheric profile and stability,

34 hence the reason CAPE is used as an indicator of thunderstorm favourable environments, not surface temperature. While trends in thunderstorms themselves have not received scrutiny, other types of severe events have been investigated. Cao

& Cai (2011) used records of tornadic events over Ontario as a whole and found a small but statistically significant increasing trend in the number of tornadoes per year from 1950-2007, an approximate increase of 1.6 tornadoes per decade. They also found a positive significant relationship between tornado frequency and the

Multivariate Enso Index (MEI). This is similar to the findings of Etkin et al (2001), whose more detailed findings suggest there are fewer tornadoes in Southern

Ontario during La Nina years compared to neutral years while during El Nino years tornado frequency was slightly higher in the spring and then lower in the summer compared to neutral years. Cao & Ma (2009) used Ontario Storm Prediction Centre records of severe summer rainfall events in Ontario from 1979-2002 and found a significant increasing trend of about 11 events per decade and Cao (2008) found a trend of approximately 6 additional severe hail events per decade over that same time period. We will determine if thunderstorm trends at the weather stations are similar to those of these other reported severe events over the whole province, however our methods will involve fixed point weather stations, therefore eliminating any trends that are detected as a result of increased severe weather reports due to the increasing population in the region. Considering that there may be an ENSO signature, we will also compare number of thunderstorms per year to the Multivariate Enso Index (MEI) of that year (season). We will do the same for the

North Atlantic Oscillation (NAO) index of that year (season).

35 Another issue with the thunderstorm record is that the MANOBS does not ascribe intensity to thunderstorms. For warning purposes however Environment

Canada defines a severe thunderstorm as having at least one of (1) wind gusts of

90km/h or greater, (2) hail of 2cm diameter or larger and/or (3) rainfall of 50mm or more in one hour (Environment Canada, 2013e). We thus will use precipitation and wind gust data from the corresponding weather stations to determine if there have been any trends in thunderstorm intensity over this time period.

3.3 Data

The thunderstorm data is that used in Chapter 2. Data for all nine stations, including Gore Bay will be used from the earliest point it is available to the most recent. Although the Gore Bay data was not validated in Chapter 2, we will assume it is of similar quality to the other eight stations. If the results from Gore Bay are anomalous compared to the others, we will consider data quality as a possible explanation. The thunderstorm data is based on the manual observations as defined in Chapter 2, and is available for the time periods outlined in Chapter 2, which are again shown in Table 3.1. As we explained, the stations are reasonably spread across

Southern Ontario (Figure 2.1), including along shorelines of the Great Lakes and between the Lower and Upper Great Lakes where lake breeze convergence would be a factor in thunderstorm development and therefore we should be able to determine any spatial difference in thunderstorm trends.

In addition to thunderstorm data these weather stations record hourly and daily precipitation amounts and maximum wind gust speed. Unfortunately there is a considerable amount of missing data across all stations in the hourly dataset for

36 both precipitation and wind therefore requiring us to use daily precipitation and wind gust data. Although not ideal, we are limited to what is available and therefore will use daily total precipitation and maximum wind gust data. This daily precipitation and wind gust data is available for most of the period of available thunderstorm data at each station (Table 3.1). As with the thunderstorm data for our analysis of intensity of thunderstorms we will use precipitation and wind data from the earliest it is available to the most recent at each station.

Station Thunderstorm Precipitation Wind Gust Buttonville 1986-2014 1986-2013 1986-2013 Gore Bay 1953-1993 1953-1992 1963-1992 Hamilton 1970-2011 1959-2010 1971-2010 London 1953-2011 1953-2002 1962-2002 Ottawa 1953-2011 1953-2010 1955-2010 Pearson 1953-2012 1953-2012 1955-2012 Trenton 1953-2014 1953-2012 1955-2012 Wiarton 1953-2014 1953-2013 1959-2012 Windsor 1953-2014 1953-2013 1955-2013

Table 3.1. Data availability for hourly thunderstorm reports and daily total precipitation and maximum wind gust speed.

Daily precipitation data refers to the total amount of precipitation that fell within a given day at the weather station. Rainfall is measured using a rain gauge and reported in mm. Snowfall is measured as depth in cm. While lightning may occur during winter snowstorms, “thundersnow” is rare – using Toronto Pearson as an example of 23 hours with winter thunderstorms in our data from 1953-2012, only 6 events had precipitation falling as snow. Total precipitation is the combined value of total rainfall and total snowfall recorded during a given day and converted

37 to mm. According to MANOBS wind gusts “are sudden, rapid, and brief changes in the wind speed. They are characterized by the more or less continual fluctuations between the high (peak) and low (lull) speed.” and “Gusts shall be reported when: 1) The highest peak speed is at least 5 kts higher than the current two-minute average; and

2) The highest peak is at least 15 kts.” (Environment Canada, 2015). The archived maximum daily wind gusts are presented in km/h. If there was no wind gust on a given day the value is recorded as <31. We used a baseline of 30km/h for these values.

Bimonthly MEI values were obtained from NOAA, from the following link: http://www.esrl.noaa.gov/psd/enso/mei/table.html. The data is available from

1950 to present. Positive values of the MEI indicate EL Nino conditions while negative values indicate La Nina Conditions (NOAA, 2015). Annual MEI values were calculated as the mean of all the bimonthly values over one year. Spring values were the mean of the bimonthly periods February/March, March/April and April/May.

Summer were the values of May/June, June/July and July/August. Fall included

August/September, September/October and October/November, and winter included November/December, December/January and January/February. Monthly

NAO index values were obtained from NOAA through http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/nao.shtml. As with the MEI the data is available from 1950 to present. Positive values of the NAO index indicate the positive phase of NAO, while negative values indicate the negative phase of NAO. The mean of all months in a year was calculated for annual NAO index

38 values, while seasonal NAO values were calculated as the mean of NAO index values for the months in the specific season as defined above (NOAA, 2005).

3.4 Methodology

Hourly thunderstorm data was used from its earliest to most recent full year of availability at each station (Table 3.1). A Mann-Kendall test was used to determine if there were any significant trends in the number of thunderstorms per year. The Mann-Kendall test (Appendix A2) is a non-parametric test that determines if an observed variable, in this case number of thunderstorm hours, increases or decreases with time. As explained in Mohsin & Gough (2010) the null hypothesis assumes that all values are independent, whereas the alternative hypothesis is that a monotonic increasing or decreasing trend exists over time. A p value below the desired significance level, in this case 0.05 (95%), would lead to rejection of the null hypothesis and indication of a statistically significant trend. The tau coefficient measures the strength of the correlation between the observed variable and time, as well as whether the correlation (trend) is positive (increasing) or negative

(decreasing), similar to the r coefficient in a Pearson correlation. Whether the trend at each station was significant or not we determined the magnitude of the trend by applying the Theil-Sen approach. The Theil-Sen approach (Appendix A2) determines the slope of the trend and is less affected by outliers than the least squares method, with the final slope being the median of all possible pair wise slopes in the dataset

(Sen, 1968; Hirsch et al., 1982; Mohsin & Gough, 2010).

For determining seasonal trends the year was split into four seasons based on the meteorological definition of seasons (NOAA, 2013b). Winter consisted of

39 December, January and February, spring of March, April and May, summer of June,

July and August and fall of September, October and November. It is known that most thunderstorms in this region will occur from spring through fall, with the peak occurring during the summer season (Environment Canada, 2014b).

To determine if the intensity of storms has changed the daily total precipitation amount in mm and daily maximum wind gust speed in km/h were extracted for days with at least one hour with a reported thunderstorm. A Mann-

Kendall test was applied to these values. The p values and Kendall’s tau coefficient were then compared to their counterparts from Mann-Kendall tests on the entire record of precipitation and wind gust data for days without thunderstorms and all days, with the goal of determining whether the direction and/or magnitude of the trends in these variables largely differed between days with and without thunderstorms.

Our preliminary results indicated there may be changes in trends following

1980, which would be consistent with an increased rate of climate warming from approximately that point onward (IPCC, 2007). To determine if trends before and after 1980 differ the annual hourly thunderstorm data was divided into “Early” and

“Recent” periods. “Early” included the start of data at each station to the end of 1979 and “Recent” included data from the beginning of 1980 to the end year of data at each station.

A regression analysis was used to compare the number of thunderstorm hours per year(season) to MEI and NAO index values, from the earliest the thunderstorm data was available at each station to the most recent (Table 3.1).

40 3.5 Results and Discussion

3.5.1 Annual trends

There was year to year-to-year variability in the number of thunderstorm hours at all sites. There was not, however, a consistent pattern in long-term trends among all the sites (Table 3.2; Figure 3.1). Most sites did not have any significant trends over the time period of available data (p <0.05, Table 3.2). Buttonville and

Windsor had a significant decreasing trend with Theil-Sen slopes of -0.76 and -0.22 thunderstorms per year repectively, whereas Hamilton and Gore Bay had a significant increasing trend with Theil-Sen slopes of 0.63 and 0.78 thunderstorms per year respectively (Table 3.2). The strongest trends – in terms of lowest p value and greatest Theil-Sen slope were at the three sites with thunderstorm data that did not span the entire time period – Buttonville and Hamilton, where the dataset started late and Gore Bay where the dataset ended early. This, along with the larger scale observation that climate change has been more intense since the late 1970s to early 1980s (IPCC, 2007) led us to examine the trends in thunderstorm occurrence prior to and after approximately 1980.

Station Period p-value tau Theil-Sen Slope Buttonville 1986-2014 0.0001 -0.51 -0.76 Gore Bay 1953-1993 0.0057 0.30 0.63 Hamilton 1970-2011 <0.001 0.46 0.78 London 1953-2011 0.23 0.12 0.21 Ottawa 1953-2011 0.30 -0.09 -0.05 Pearson 1953-2012 0.52 -0.06 -0.05 Trenton 1953-2014 0.45 -0.07 0 Wiarton 1953-2014 0.40 0.07 0.06 Windsor 1953-2014 0.03 -0.19 -0.22 Red – Significant Increasing Blue – Significant Decreasing

41 Table 3.2 Results of the Mann-Kendall test for thunderstorm trends over the period of data availability at the nine weather stations.

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.

Separating the data into Early (pre 1980) and Recent (1980 onwards) for all stations however only added significance to the trends at Trenton, where in the period prior to 1980 the trend in thunderstorm hours was significantly increasing with a Theil-Sen slope of 0.65 thunderstorms per year and after 1980 the trend was significantly decreasing with a Theil-Sen slope of -0.61 thunderstorms per year

(Table 3.3). While Windsor was the only site with a significant trend over a complete dataset period of 1953-2014, with a decreasing trend of -0.22

46 thunderstorms per year, neither the early or recent periods alone were significant at

Windsor (Table 3.3).

Station p-value tau Theil-Sen Slope Early Recent Early Recent Early Recent Buttonville N/A 0.0001 N/A -0.51 N/A -0.76 Gore Bay 0.001977 0.07 0.43 -0.37 1.63 -1.18 Hamilton 0.86 0.00029 -0.04 0.45 0 1.0 London 0.40 0.41 0.11 -0.10 0.77 -0.225 Ottawa 0.14 0.39 -0.21 0.11 -0.15 0.18 Pearson 0.56 0.465 0.08 -0.09 0.33 -0.245 Trenton 0.008 0.002 0.36 -0.37 0.65 -0.61 Wiarton 0.92 0.08 -0.01 0.21 0.45 0.42 Windsor 0.07 0.096 -0.25 -0.2 -0.32 -0.65

Table 3.3. Results of the Mann-Kendall test for Early (pre-1980) and Recent (1980 onwards) periods.

3.5.2 Intensity

All weather stations showed a significantly increasing trend of daily precipitation totals for all days and days without thunderstorms, with positive albeit low tau values, and significance at all sites other than Buttonville (p<0.05, Table

3.4). All sites other than Hamilton showed an increasing trend in daily precipitation totals on days with thunderstorms, although the strength of the correlation was low at all sites with the highest tau value being 0.094 at Windsor. The trend on days with thunderstorms is only significant at London, Ottawa and Windsor and none of the tau values stand out as being largely higher than those for the general trend in daily total precipitation at the weather stations on all days (Table 3.4). Therefore while there may be slight increases in the amount of precipitation that falls on days with

47 thunderstorms, according to available daily precipitation data the trend is not

particularly large.

Station Daily p- tau p-value tau p-value all tau all precip. value Days Days Days (with (with + period Days with t without without +without) without) with t storm t storm t storm storm Buttonville 1986- 0.5522 0.0167 0.0533 0.0154 0.5487 0.0045 2013 Gore Bay 1953- 0.9466 0.00154 <0.001 0.0259 <0.001 0.0302 1992 Hamilton 1959- 0.7551 -0.007 <0.001 0.0246 <0.001 0.0428 2010 London 1953- 0.003 0.054 0.04 0.114 0.044 0.011 2002 Ottawa 1953- 0.011 0.052 0.004 0.149 0.001 0.0165 2010 Pearson 1953- 0.06 0.036 0.002 0.016 0.0004 0.017 2012 Trenton 1953- 0.2713 0.020 0.0005 0.018 <0.001 0.020 2012 Wiarton 1953- 0.1334 0.026 <0.001 0.0253 <0.001 0.0278 2013 Windsor 1953- <0.001 0.094 <0.001 0.031 <0.001 0.026 2013 Red – Significant Increasing Blue – Significant Decreasing

Table 3.4. Results of the Mann-Kendall test for daily total precipitation on days with thunderstorms, days without thunderstorms, and all days.

Similar to daily precipitation totals, there is no clear signature for

thunderstorm days in trends of daily maximum wind gust speed over time. Most

sites have a small decreasing trend in daily maximum wind gust speed, with no

major differences of days with or without thunderstorms (Table 3.5). Only London

showed a significant increasing trend on days with thunderstorms and a smaller

although not statistically significant increasing trend on days without

48 thunderstorms (Table 3.5). The tau values in both instances however remain low, at

0.063 on days with thunderstorms and 0.009 on days without thunderstorms (Table

3.5).

Station Daily p- tau p-value tau p-value all tau all wind value Days Days Days (with (with + gust Days with t without without +without) without) period with t storm t storm t storm storm Buttonville 1986- 0.2621 0.034 0.2168 -0.01 0.1775 -0.0105 2013 Gore Bay 1963- 0.3138 -0.027 0.053 -0.014 0.04 -0.014 1992 Hamilton 1971- <0.001 -0.112 <0.001 -0.09 <0.001 -0.089 2010 London 1962- 0.004 0.063 0.1655 0.009 0.0397 0.013 2002 Ottawa 1955- <0.001 -0.14 <0.001 -0.116 <0.001 -0.117 2010 Pearson 1955- 0.41 -0.017 <0.001 -0.056 <0.001 -0.053 2012 Trenton 1955- <0.001 -0.10 <0.001 -0.09 <0.001 -0.09 2012 Wiarton 1959- 0.855 -0.004 0.002 -0.017 0.006 -0.01 2012 Table 3.5. Results of the Mann-Kendall test for daily maximum wind gust speed for days with thunderstorms, days without thunderstorms, and all days.

3.5.3 Seasonal Trends

The annual trends in thunderstorm occurrence are most closely matched to

the summer season (Table 3.2; Table 3.6; Figure 3.2). The significance for

Buttonville and Gore Bay extends into the fall. While most sites did have a positive

trend of number of thunderstorms in winter and spring, they all fell below the

threshold for statistical significance (Table 3.6). Part of this may be due to the

relatively low number of thunderstorm hours during these seasons.

Spring: Station Period p-value tau Theil-Sen slope Buttonville 1986-2014 0.1283 -0.2264 -0.2321 Gore Bay 1953-1993 0.8107 0.0276 0.055 Hamilton 1970-2011 0.077 0.205 0.134 London 1953-2011 0.6277 0.0469 0 Ottawa 1953-2011 0.223 0.117 0 Pearson 1953-2012 0.7504 0.0298 0.02 Trenton 1953-2014 0.8574 0.01680 0 Wiarton 1953-2014 0.4505 0.0683 0.0096 Windsor 1953-2014 0.8378 0.0185 0 Summer: Station Period p-value tau Theil-Sen slope Buttonville 1986-2014 0.00099 -0.4657 -0.5 Gore Bay 1953-1993 0.0051 0.310 0.61 Hamilton 1970-2011 0.000657 0.3729 0.415 London 1953-2011 0.9327 -0.0081 -0.0357 Ottawa 1953-2011 0.4047 -0.0763 0 Pearson 1953-2012 0.3991 -0.0757 -0.091 Trenton 1953-2014 0.767 0.0272 -0.040 Wiarton 1953-2014 0.5873 0.0486 0 Windsor 1953-2014 0.03511 -0.1887 -0.167 Fall: Station Period p-value tau Theil-Sen slope Buttonville 1986-2014 0.0337 -0.3128 -0.199 Gore Bay 1953-1993 0.0073 0.303 0.2 Hamilton 1970-2011 0.6033 0.0609 0.024 London 1953-2011 0.1412 0.1429 0.1024 Ottawa 1953-2011 0.255 -0.1173 0 Pearson 1953-2012 0.7553 -0.0274 0 Trenton 1953-2014 0.387 -0.081 -0.025 Wiarton 1953-2014 0.1733 0.123 0.086 Windsor 1953-2014 0.2875 -0.098 0 Winter Station Period p-value tau Theil-Sen slope Buttonville 1986-2014 0.7963 0.079 0 Gore Bay 1953-1993 1 0 0 Hamilton 1970-2011 0.7237 0.126 0 London 1953-2011 0.4721 0.139 0 Ottawa 1953-2011 0.7697 -0.1155 0 Pearson 1953-2012 0.1262 0.305 0 Trenton 1953-2014 0.6831 0.1101 0 Wiarton 1953-2014 0.09361 0.3817 0.0122

50 Windsor 1953-2014 0.0855 -0.2931 0 Red – Significant Increasing Blue – Significant Decreasing

Table 3.6. Results for the Mann-Kendall test and Theil Sen approach with data separated by seasons.

3.5.4 ENSO/NAO

Annually, most sites had a negative correlation between the MEI and number of thunderstorms per year, although this was only significant at Pearson, Wiarton and Windsor (Table 3.7). Correlation between number of thunderstorm hours per year and NAO were more varied, with only one site, Buttonville, showing a significant (increasing) trend. Seasonally the results were varied with no consistent trends across all sites and little significance (Table 3.7).

Full Year: Station MEI p MEI R MEI R NAO p NAO r NAO R value squared value squared Buttonville 0.683 0.006 0.079 0.0296 0.1634 0.404 Gore Bay 0.49 0.01228 0.1108 0.201 0.0415 0.204 Hamilton 0.689 0.004 -0.06 0.4349 0.0153 -0.124 London 0.254 0.023 -0.151 0.266 0.0216 0.147 Ottawa 0.25 0.023 -0.152 0.122 0.0415 -0.204 Pearson 0.0088 0.1125 -0.335 0.639 0.0038 0.0619 Trenton 0.817 0.0009 -0.03 0.8 0.001 0.0328 Wiarton 0.0075 0.1132 -0.337 0.277 0.0197 -0.140 Windsor 0.0364 0.07 -0.267 0.383 0.013 0.1127

Spring: Station MEI p MEI R MEI R NAO p NAO r NAO R value squared value squared Buttonville 0.683 0.01 0.1189 0.187 0.064 0.252 Gore Bay 0.49 0.01 -0.119 0.37 0.021 -0.146 Hamilton 0.689 0.013 -0.113 0.095 0.0698 0.264 London 0.254 0.023 -0.164 0.446 0.0112 0.106 Ottawa 0.717 0.0023 -0.049 0.941 9.985e-5 -0.01 Pearson 0.136 0.03787 -0.19 0.27 0.021 0.145

51 Trenton 0.07 0.054 -0.231 0.0595 0.058 0.241 Wiarton 0.0075 0.06 -0.25 0.735 0.0019 0.044 Windsor 0.0098 0.1061 -0.32 0.0165 0.09 0.303

Summer: Station MEI p MEI R MEI R NAO p NAO r NAO R value squared value squared Buttonville 0.567 0.0123 0.1109 0.631 0.009 -0.09 Gore Bay 0.47 0.0138 0.117 0.135 0.058 0.241 Hamilton 0.865 0.0007 0.027 0.013 0.1444 -0.38 London 0.356 0.016 -0.128 0.0197 0.1003 0.317 Ottawa 0.339 0.016 -0.128 0.122 0.042 -0.205 Pearson 0.112 0.043 -0.207 0.658 0.003 -0.06 Trenton 0.858 0.0005 0.023 0.955 5.357e-5 -0.007 Wiarton 0.12 0.04 -0.199 0.451 0.0096 -0.098 Windsor 0.118 0.04 -0.201 0.526 0.007 0.082

Fall: Station MEI p MEI R MEI R NAO p NAO r NAO R value squared value squared Buttonville 0.589 0.011 0.105 0.609 0.0098 0.099 Gore Bay 0.659 0.005 0.07 0.597 0.007 0.086 Hamilton 0.519 0.0114 0.107 0.835 0.0012 0.034 London 0.686 0.0032 -0.06 0.955 6.425e-5 0.008 Ottawa 0.0141 0.1029 -0.321 0.132 0.04 -0.200 Pearson 0.603 0.005 0.07 0.506 0.008 0.09 Trenton 0.629 0.004 -0.06 0.068 0.054 -0.23 Wiarton 0.292 0.018 -0.136 0.788 0.0012 0.035 Windsor 0.628 0.0039 0.0628 0.41 0.0114 0.11

Winter: Station MEI p MEI R MEI R NAO p NAO r NAO R value squared value squared Buttonville 0.99 3.58e-6 0.0019 0.267 0.05 -0.2218 Gore Bay 0.826 0.0013 -0.036 0.052 0.096 0.309 Hamilton 0.70 0.004 -0.064 0.48 0.014 -0.118 London 0.0138 0.111 -0.333 0.355 0.016 -0.128 Ottawa 0.47 0.009 0.097 0.70 0.003 -0.051 Pearson 0.205 0.028 -0.167 0.603 0.005 0.069 Trenton 0.55 0.006 -0.077 0.59 0.005 -0.07 Wiarton 0.28 0.019 -0.138 0.604 0.004 -0.06 Windsor 0.093 0.046 -0.215 0.607 0.004 0.07

Blue – Significant negative correlation Red – Significant positive correlation

Table 3.7. Correlation of number of thunderstorm hours per year and the Multivariate ENSO Index (MEI) and North Atlantic Oscillation (NAO).

3.5.5 Discussion

While one might expect a warming climate to lead to more frequent and more intense thunderstorms, this signal using available data has yet to be detected.

There were not consistent and robust increases in the number of thunderstorm hours per year across the nine weather stations. There was also no evidence for large increases in the intensity of thunderstorms using daily precipitation and maximum wind gust speed as proxies, nor is there any significant evidence that the thunderstorm season is expanding at any of these weather stations. Likewise, there is an absence of significant correlation between number of thunderstorm hours per year or season at the nine weather stations and ENSO and NAO.

It may appear that our results are contradictory to those of other studies that looked at severe weather events in Ontario. It is important to note, however, the difference in our methods. Cao & Cai (2011), Cao & Ma (2009) and Cao (2008) found an increasing trend in tornadic events, summer severe rainfall events and severe hail events respectively across Ontario. All of these studies used the reported number of these events across the whole province. The study on tornadic events used two databases: one from 1950 -1979 and one from 1979-2007. The summer severe rainfall and hail studies used data from 1979-2002. These datasets are based on reports to the Ontario Storm Prediction Centre (OSPC) and subsequent validation of the reports by the OSPC. It is possible that over time population increases could

53 affect the reporting of weaker tornadoes and non-tornadic severe events (King,

1997). It is also possible that greater public awareness could lead to an increase in reporting of severe weather events – Canada’s main weather broadcasting station,

The weather Network, was not established until 1988. The three Cao studies referenced above also do not involve counts of thunderstorms, but rather types of severe weather that can be associated with thunderstorms. It is also entirely possible that there is in fact an increase in severe weather associated with thunderstorms as a whole across Ontario, it was just not captured in the thunderstorm trends at the fixed observation points used in our study. As per our results from Chapter 2, we emphasize again that thunderstorm reports at these stations are valid for approximately 10km around each station, and just because a thunderstorm was reported does not indicate that every element that can be associated with a thunderstorm will be present. We do have confidence that our results will not be affected by increases in population density, and the method used for observing thunderstorms at the weather stations has remained consistent over the period of data availability.

It is also important to note that four of the stations did in fact show significant trends in thunderstorm occurrence (Table 3.2). Two of these stations –

Gore Bay and Hamilton – had significant increasing trends, with the slope at

Hamilton at almost 0.8 thunderstorms per year. It is unclear at this point what would make these sites different from the others. We must, however, consider a few potential issues with these stations. Three of the stations that showed significance – Buttonville, Gore Bay and Hamilton – had data that did not span the

54 entire length of the study. Although our early-recent analysis suggests there has not been a large scale change in thunderstorm trends since 1980 compared to before

1980, the data availability for Buttonville was for an exceptionally short period, spanning only 18 years from 1986 to 2014 (Table 3.1). Gore Bay was the northernmost site used in this study, however it is unfortunate that it does not have data available beyond 1993 so we cannot determine if this trend has continued into more recent years, or if it is a result of data quality as this site was not used in our

Chapter 2 validation. Windsor, which also showed a significant trend, had the lowest radius of equality as found in Chapter 2. Although not starting until 1970, the data from Hamilton should be from a sufficient duration (1970- 2011; Table 3.1) to accurately determine any trends, and the results from Chapter 2 do not suggest anything unusual about the observations at this station. It is possible that

Hamilton’s location along the Niagara Escarpment between Lakes Ontario and Erie has an effect on the thunderstorm trends at this site. Interestingly, in an earlier study linking the Weekday/Weekend Effect on ground level ozone concentrations to population size Hamilton was found to have a smaller weekday/weekend effect magnitude than its population would suggest, and it was speculated that it may be due to its unique geographical location (Huryn & Gough, 2014). While we again emphasize the importance of lake breezes in the distribution of thunderstorms in

Southern Ontario (King et al., 2003, Sills & King, 1998), if they were the dominant factor behind changes in thunderstorm trends then one might expect differences between shoreline and inland stations, with London, in the middle of the lake breeze convergence zone (Figure 2.1) being the most differentiated from the rest.

55 While Cao & Ma (2009) were able to link the 10 years with the highest and lowest number of severe summer rainfall events to surface temperature, they also found differences in precipitable water, geopotential height at 1000hPa and 1000-

500hPa thickness between high event and low event years. Cao & Ma (2009) also noted no significant trends in CAPE and wind shear over Ontario over the course of their study period, from 1979-2002, which could be related to the lack of widespread thunderstorm trends that we observed in this study. In Chapter 4 we will link thunderstorm occurrence to annual and seasonal mean CAPE, and will determine if trends in CAPE can explain trends in thunderstorm occurrence. If this relationship can be established, we will attempt to project future changes in predictor variables, because although there have not been widespread trends in thunderstorm occurrence to date does not mean this will hold true into the future.

56 4. Determining future thunderstorm trends in Southern Ontario by using statistical downscaling to project changes in CAPE

Publication Huryn, S., Gough, W., Butler, K. & Mohsin, T. (2016?).Determining future thunderstorm trends in Southern Ontario by using statistical downscaling to project changes in convective available potential energy (CAPE) (to be submitted)

4.1 Objective

To determine the relationship between thunderstorm occurrence in

Southern Ontario and convective available potential energy, and use this relationship to project future trends in thunderstorm occurrence through the use of climate models and statistical downscaling.

4.2 Background

In Chapter 2 we determined that the manual thunderstorm observations at the Environment Canada weather stations are reasonable for small distances around the weather stations. In Chapter 3 we found that if there have been temporal trends in thunderstorm occurrence in Southern Ontario over the past several decades, they have not been detected in the manual observations in a statistically significant fashion. However even if there have not been significant trends to date, it does not imply there will not be changes in thunderstorm frequency in the future, and therefore this topic requires careful consideration. In this final research chapter we will determine the relationship between thunderstorm occurrence and convective available potential energy (CAPE). We will then use this relationship with a statistical downscaling model in combination with three general circulation models (GCMs) to project future changes in CAPE, and therefore expected changes

57 in thunderstorm frequency. Because the finest temporal resolution of GCMs is the daily scale, we will focus on thunderstorm days for this chapter, rather than thunderstorm hours.

In the Introduction, we noted several studies that were able to project future thunderstorm favourable environments based on changes in predictor variables.

These included Trapp et al. (2007) and Van Klooster & Roebber (2009) who used

CAPE and vertical wind shear as proxies for severe thunderstorm environments across the United States. The choice of these parameters was based on previous research that was useful for defining severe thunderstorm environments

(Rasmussen & Blanchard, 1998; Brooks et al., 2003; Craven & Brooks, 2004).

Through projections from the Parallel Climate Model General Circulation Model

(Van Klooster & Roebber, 2009) and United States Regional Climate Model in combination with the National Aeronautics and Space Administration Finite Volume

General Circulation Model (Trapp et al., 2007) widespread increases in number of severe thunderstorm days over the contiguous United States are expected by the middle (Van Klooster & Roebber, 2009) and latter (Trapp et al., 2007) parts of the current century. Diffenbaugh et al. (2013) found an increase in severe thunderstorm days across the United States using the CMIP5 GCM and through the use of two

GCMs and Allen et al. (2014a and 2014b) also projected widespread increases in

CAPE and vertical wind shear in Australia by the end of the century, leading to increased severe thunderstorm favorable environments.

Because in Chapter 3 we discovered that the hourly wind and precipitation data was insufficient to determine the intensity of individual thunderstorms, and we

58 did not evaluate the intensity of individual storms using the daily wind and precipitation data, in this chapter we will focus on all thunderstorms, and disregard any measure of intensity, rather than limiting our analysis to severe thunderstorms.

Therefore, unlike the previous studies that used the product of CAPE and vertical wind shear to isolate for severe thunderstorm days, we will focus our attention on

CAPE alone and determine its relationship to all thunderstorm occurrence. As explained in the Introduction, CAPE, reported as J/kg, is a measure of the energy available for convection, and the probability of thunderstorm formation increases as

CAPE increases, with more severe thunderstorms typically occurring with CAPE values of at least 1000J/kg (Kirkpatrick et al., 2011). The maximum updraft speed in a thunderstorm is also directly related to CAPE through the equation Wmax =

√2CAPE where Wmax is maximum updraft speed in m/s. Rasmussen & Blanchard

(1998) found the average CAPE associated with ordinary, severe and tornado producing thunderstorms to increase with each of these category of storm respectively, based on radiosonde soundings within a 400km radius of the event suggesting that although not as robust as when combined with vertical wind shear,

CAPE itself is useful for predicting supercell formation. Therefore although our analysis will only involve CAPE, higher CAPE values could also indicate a greater risk for more severe storms as well.

The previous analyses on CAPE projections have depended on General

Circulation Models (GCMs). GCMs typically have a horizontal resolution of 250-

600km (IPCC, 2013). While previous analyses have used GCMs for CAPE projection over large areas to determine severe thunderstorm favourable environments, the

59 lack of a finer resolution would not take into account local features that may affect thunderstorm occurrence at a particular location. This would be especially important in our study area because the Great Lakes are known to have a large effect on Southern Ontario’s thunderstorm distribution, with differences between shorelines and inland locations where lake breeze convergence is a significant factor in severe weather patterns (King, 1996; Sills & King, 1998; King et al., 2003; Sills et al., 2011). Given our objective of relating actual thunderstorm occurrence at the nine weather observation stations to local CAPE, the ability to project CAPE at a local level is crucial. While some of the discussed studies have employed the use of higher resolution regional climate models (for example the RegCM3 model used by

Trapp et al. (2007) operates on a 25km grid), no such model exists for our area with a CAPE dataset. Considering that historical CAPE values from the National Centre for

Environmental Prediction’s North American Regional Reanalysis (NCEP NARR) are available for a 32km grid, it would be beneficial to take advantage of that, although it is not possible with the currently available climate models. One way to resolve this issue is to build a statistical downscaling model (SDSM) to project the local CAPE in the 32 km resolution NCEP grid box for each station from the coarser GCM output.

Statistical downscaling involves determining the statistical relationship between the variable of interest, in this case NCEP CAPE values associated with each weather station, and larger scale predictor variables that are represented in GCMs.

Projections in the predictor variables are then obtained through the GCMs and projections in the variable of interest then calculated based on the determined relationship. A detailed description of SDSM can be found in Wilby et al. (2002).

60 The goal of this chapter therefore is twofold: (1) to determine if a relationship exists at these nine weather stations between thunderstorm occurrence and CAPE and, if so, (2) determine how CAPE at each of the nine locations might change between now and the end of the current century in a “business as usual” future climate by linking CAPE to predictor variables and building a high resolution statistical downscaling model (SDSM). We believe that this is the first study in

Canada projecting future thunderstorm climatology, the first study focused on predicting future thunderstorm occurrence as a whole, and the first time the

Statistical Downscaling Modelling approach will be used for projecting CAPE.

4.3 Data

4.3.1 Thunderstorm Data

The thunderstorm data is that from the nine weather stations used in the preceding two chapters (Figure 2.1). Data from all nine stations will be used for this chapter.

4.3.2 CAPE Data

CAPE data was extracted from NOAA’s National Centre for Environmental

Prediction North American Regional Reanalysis (NCEP NARR). The data is available through http://www.esrl.noaa.gov/psd/data/gridded/data.narr.monolevel.html.

NARR uses the NCEP Eta model and runs on a resolution of 3 hours, 45 vertical layers and horizontal grid of approximately 32 km (Mesinger et al., 2004). All nine weather stations used in this study are in separate NARR grid cells. The data is available for each grid cell from 1979 to 2014.

4.4 Methodology

4.4.1 Determining the relationship between thunderstorm days and CAPE

After being extracted from the NARR dataset, the 3-hourly CAPE values were converted to local standard time to match the thunderstorm data. Because SDSM operates on a daily scale, we extracted the maximum value for every 24-hour day, to yield maximum daily CAPE. Other studies have limited their analyses to one particular time of day, usually that expected to have the maximum CAPE value. For example, Van Klooster & Roebber (2009) limited analysis to CAPE from 0000UTC radiosonde soundings, which would corresponded to late afternoon or evening across the continental United States, the most common time severe thunderstorm occur. We however used daily maximum CAPE, regardless of which 3-hour period it occurred in. We did not take into account the hour at which a thunderstorm occurred, or any other measure of intensity, rather marking each day as having a thunderstorm or not. For the comparison of thunderstorm occurrence and daily maximum CAPE values, we used a 30-year reference period from 1981-2010 for each station, except for Buttonville where the thunderstorm data begins in 1986, and Gore Bay where the thunderstorm data ends in 1993. For these stations we used reference periods of 1986-2010 and 1981-1993 respectively.

Summary statistics were compared for daily maximum CAPE on days with and days without thunderstorms. Mood’s median test (Appendix A3; Desu and

Raghavarao, 2004) was used to determine if the median values of daily maximum

CAPE for days with and days without thunderstorms are significantly different.

Mood’s median test, although not as powerful as a t test, is more reliable for skewed

62 distributions, as is the case here, where most days will have 0 or very low maximum

CAPE and days with high maximum CAPE values into the 1000s J/kg are relatively rare. The test uses the median of the entire sample and compares the number of values exceeding the median in each sub-sample. The null hypothesis suggests that both subsets have the same distribution around the overall median. As with a t test, a p value below a determined significance threshold allows us to reject the null hypotheses that there is no difference between the medians of the two datasets.

To determine how the probability of observing a thunderstorm on a given day changes relative to daily maximum CAPE a generalized linear model (GLM) was used (Appendix A1; Hosmer & Lemeshow, 1989). In this case the GLM determines what the chance of having a reported thunderstorm is on any day, given the maximum CAPE value for that day. This was completed using all possible daily maximum CAPE values from 0 to 5000J/kg at each weather station and will determine the CAPE values associated with threshold probabilities that a certain day will be a thunderstorm day. The effect of station on the probability was also determined through an analysis of variance (ANOVA) (Appendix A1) to determine if the threshold CAPE values differ significantly among the stations.

Number of thunderstorm days each year was compared to annual mean

CAPE using a regression analysis. Annual mean CAPE was calculated as the mean of the maximum daily CAPE for every given day in each year. The R value for each station determines the strength of the correlation between number of thunderstorm days and annual mean CAPE, while the R squared value determines what percentage of the variation in number of thunderstorm days per year can be explained by

63 annual mean CAPE alone. The p value of the regression determines whether this relationship is statistically significant. As with a t test and Mood’s median test, if the p value is below a selected threshold (in this case 0.05) the null hypothesis is rejected and the relationship is considered statistically significant. Because it is the season in which the study area experiences the most thunderstorms per year, this test was repeated using number of thunderstorm days each summer and summer mean CAPE, calculated as the average of all daily maximum CAPE values over the

June through August period of every year.

4.4.2 CAPE trends to date

A Mann-Kendall test and Theil-Sen approach (see Chapter 3.4, Appendix A2) was used to determine trends in maximum daily CAPE values over the reference period. The test was performed for annual mean CAPE, summer mean CAPE number of days per year with CAPE exceeding the 50% chance of observing a thunderstorm at each station and number of days per year with CAPE exceeding the 80% chance of observing a thunderstorm as calculated in Section 4.4.1. Annual mean CAPE was calculated as the mean of all maximum daily CAPE values for each year and summer mean CAPE was calculated as the mean of all the daily maximum CAPE values for

June, July and August of each year.

4.4.3 Future CAPE Projections

SDSM software version 4.2.9 was used to develop our models for future CAPE projections. The observed maximum daily CAPE values from 1981-2010 were used as a baseline for all nine weather stations, including Buttonville and Gore Bay. A fourth root transformation and conditional model was used. This normalized the

64 data and managed the large number of zero values. These settings are normally used with precipitation studies with SDSM, where the majority of days will have a precipitation amount of zero, and the larger the value the less common it will be

(Wilby & Dawson, 2007; Chen et al., 2012).

NCEP predictor variables were obtained from http://www.sdsm.org.uk and extracted for each weather station. A model was then built using the Screen

Variables function, correlating each variable to the maximum daily CAPE value.

Building the model is a process of elimination. Each of the 25 compatible predictor variables and combination of the variables is screened for correlation and significance to maximum daily CAPE. The final model for each station used the four significant predictor variables that yielded the highest overall R squared value. The number of predictor variables used is at the discretion of the researcher and will depend on the particular situation. In general, increasing the number of predictors will reduce the partial r squared variable of each individual predictor, to a point where adding more predictors will no longer improve the overall model. A correlation matrix was used to check the effects of variables on each other. The predictors were chosen in way that they are physically meaningful for the formation of CAPE and also have a strong statistical correlation with it. In general it is suggested to keep the number of predictors within a maximum of six, and this is the usual procedure for studies involving SDSM (Wilby & Dawson, 2007; Wilby et al.,

2002; Leung, 2015). In the case of CAPE, for each location the 5th predictor was added to check if the model performed better than the model with four predictors.

In all cases the addition of the fifth predictor did not increase the overall R squared

65 value. In our case we chose four predictors per station because we found that the overall fit of the models was not improved by retaining additional predictors. Once the four predictors for each site were chosen the weather generator was then utilized to reproduce maximum daily CAPE values using the model and values of the predictor variables at each station.

Annual mean CAPE, summer mean CAPE, the number of days with CAPE values meeting or exceeding those that correspond to a 50% chance of observing a thunderstorm on a given day and number of days with CAPE values meeting or exceeding those that correspond to a 80% chance of observing a thunderstorm on a given day were calculated for each year from both the “observed” NCEP NAAR maximum daily CAPE values and the “modeled” maximum daily CAPE values reproduced through the weather generator. Summary statistics were taken for each calculated value of both datasets and a two tailed paired t test (Appendix A4) was used to determine if the “observed” and “modeled” datasets were significantly different. A p value greater than the selected level of significance leading to an acceptance the null hypothesis - that there is no difference between the two datasets

– is preferred. The correlation R and R squared values between the two datasets were also calculated. A higher correlation is preferred as it suggests the modeled values are closer to the actual observed values.

Daily maximum CAPE values were projected at each weather station to the end of the current century through the Scenario Generator based on changes in the predictor variables for each station according to three separate GCMs. The GCMs used were the AR4 Canadian Centre for Climate Modelling and Analysis (CCCma)

66 Third Generation Coupled Global Climate Model (CGCM3) and Hadley Centre

Coupled Model Version 3 (HAD3) and the AR5 CCCma second generation Canadian

Earth System Model (CanESM2). CGCM3 runs utilize 31 vertical layers and a horizontal grid resolution of approximately 400km in our area of study

(Environment Canada, 2014c). HAD3 consists of 19 vertical layers and a horizontal grid resolution close to 300km in our study region (UK MetOffice, 2013). CanESM2 includes 35 vertical layers and operates on a T63 projection horizontal grid (IPCC

AR5). Each model was run based on “business as usual” emission scenarios: A2 for

CGCM3 and HAD 3 and Representative Concentration Pathway (RCP) 8.5 for

CanESM2. Twenty ensembles were run for each model at each station, the average being used for final calculations. The final thirty years of each model (2071-2100 for CGCM3 & CanESM2 and 2070-2099 for HAD3) was compared to the reference period using summary statistics and a two-tailed independent t test (Appendix A4).

A Mann-Kendall test and Theil-Sen approach was also used to determine if there are any trends in the projections from 2011-2100.

4.5 Results and Discussion

4.5.1 Relationship between Number of Thunderstorm Days and CAPE

Days with thunderstorms generally had much higher maximum daily CAPE values than days without thunderstorms (Table 4.1). According to Mood’s median test, the medians of daily maximum CAPE for days with and days without thunderstorms were significantly different at all nine weather stations (Table 4.1).

For days without thunderstorms, the 75th percentile (3rd quarter) daily maximum

CAPE values were 90J/kg or lower for all weather stations (Table 4.1). In other

67 words at all the weather stations, 75% of days that did not have thunderstorms reported, also did not have maximum CAPE values that reached 90/kg. There was a small number of days at each station that had high maximum CAPE values and no reported thunderstorms and the maximum values for daily maximum CAPE were similar for both days with and without thunderstorms. This illustrates the fact that while the chance of observing a thunderstorm dramatically increases as CAPE increases, high CAPE does not guarantee that a thunderstorm will occur.

Interestingly the lowest maximum daily CAPE value for both days with and without thunderstorms was 0 J/kg. It is unclear why a day with a reported thunderstorm would have a CAPE value of 0. Further analysis shows only a small number of days over the reference period at each weather station had such conditions and this could be related to the spatial scale of the reanalysis data, or the temporal scale of the reanalysis data (3 hours), or may be explained by the small number of “false positive” thunderstorm reports as observed in Chapter 2. While the exact reason remains unclear, the numbers are small, ranging between 15 and 45 such days over the entire 30-year period, and there are evidently much higher daily maximum

CAPE values on days with thunderstorms.

Station Median Daily Median Daily Mood’s p value Maximum CAPE Maximum CAPE (J/kg) Days (J/kg) Days With Without Thunderstorms Thunderstorms Buttonville 20 840 <0.001 Gore Bay 10 410 <0.001 Hamilton 20 960 <0.001 London 20 880 <0.001 Ottawa 10 875 <0.001

68 Pearson 20 770 <0.001 Trenton 10 710 <0.001 Wiarton 20 650 <0.001 Windsor 10 1100 <0.001 Table 4.1. Median daily maximum CAPE values for days with and days without thunderstorms at the nine weather stations over the reference period and p value from Mood’s median test.

The results of the GLM confirm that the probability of observing a thunderstorm at each of the nine weather stations increases as maximum daily

CAPE increases (Figure 4.1). According to the ANOVA the threshold CAPE values associated with a certain probability of observing a thunderstorm are significantly different between the stations. The 50% and 80% values, as will be used in for the future projections vary between the stations and are shown in Table 4.2. It is noticeable that Gore Bay and Windsor show different curves compared to the other locations, Gore Bay being on the lower end of the max CAPE while Windsor being on the higher end of the of the max CAPE. Further analysis, by only using 1981-1993 data at all stations confirmed that for Gore Bay the shortened data period was not the issue, as the curve remained shifted to the left of the others. Regarding Windsor, the location of the station is between two Great Lakes, Huron and Erie and this may have contributed to more convective events and therefore, reflected as higher maximum CAPE for this location. It is also possible that the differences observed with these two stations are a result of the thunderstorm observations. The observations at Gore Bay were never validated in Chapter 2 due to the early closure of the station, and the results of the Chapter 2 validation showed Windsor to have the smallest radius of equality, suggesting thunderstorm observations are valid for a

69 smaller distance around the Windsor weather station compared to the others, as well as the highest false negative error, suggesting observers are seeing fewer thunderstorms than they should be.

Station 50% CAPE (J/kg) 80% CAPE (J/kg) Buttonville 1728 2409 Gore Bay 1022 1445 Hamilton 1808 2487 London 1636 2301 Ottawa 1628 2206 Pearson 1782 2464 Trenton 1511 2121 Wiarton 1461 2062 Windsor 2168 3020

Table 4.2. Maximum daily CAPE associated with a 50% probability and 80% probability of observing a thunderstorm at each of the nine weather stations.

Figure 4.1. Probability of observing a thunderstorm as a function of maximum daily CAPE at the nine weather stations.

The correlation between number of thunderstorm days per year and annual mean CAPE and number of summer thunderstorm days per year and summer mean

70 CAPE is generally positive (Table 4.3). The only negative correlation that exists is for thunderstorm days per year and annual mean CAPE at Buttonville, although it is not statistically significant. The significance and magnitude of correlation is stronger with summer mean CAPE compared to annual mean CAPE (Table 4.3).

Full Year: Station p value R squared R Buttonville 0.382 0.03342 -0.1828 Gore Bay 0.239 0.1236 0.3515 Hamilton 0.143 0.075 0.2739 London 0.251 0.047 0.2164 Ottawa 0.267 0.043 0.2093 Pearson 0.448 0.021 0.1440 Trenton 0.168 0.067 0.2582 Wiarton 0.138 0.077 0.2774 Windsor 0.962 8.2e-5 0.009 Summer: Station p value R squared R Buttonville 0.719 0.0057 0.0757 Gore Bay 0.0623 0.2812 0.5303 Hamilton 0.0486 0.1318 0.3631 London 0.0223 0.1728 0.4157 Ottawa 0.1985 0.058 0.2415 Pearson 0.0799 0.1055 0.3248 Trenton 0.00149 0.3069 0.5540 Wiarton 0.0345 0.15 0.3873 Windsor 0.3058 0.037 0.1934 Red – Significant Positive Correlation

Table 4.3. Correlation between number of thunderstorm days per year and annual mean and number of summer thunderstorm days per year and summer mean CAPE.

4.5.2 CAPE Trends to Date

While there was year-to-year variability in CAPE, the Mann-Kendall test for

CAPE trends to date showed most of the weather stations did not have significant

71 trends in annual mean CAPE, summer mean CAPE, or count of days with CAPE above the 50% and 80% thresholds of observing a thunderstorm (Table 4.4). This could be related to the lack of widespread trends in thunderstorm occurrence to date observed in Chapter 3.

Annual Mean CAPE: Station P value Tau Theil Sen Slope Buttonville 0.004 0.366 1.83 Gore Bay 0.058 0.246 1.65 Hamilton 0.2413 0.154 1.44 London 0.3384 0.126 0.87 Ottawa 0.018 0.306 0.98 Pearson 0.014 0.315 1.68 Trenton 0.214 0.163 0.51 Wiarton 0.058 0.246 1.13 Windsor 0.094 0.218 1.16

Summer Mean CAPE: Station P value Tau Theil Sen Slope Buttonville 0.004 0.368 5.212 Gore Bay 0.053 0.250 5.14 Hamilton 0.44 0.103 2.629 London 0.50 0.09 -0.136 Ottawa 0.008 0.338 6.08 Pearson 0.053 0.251 3.62 Trenton 0.27 0.145 1.94 Wiarton 0.18 0.177 3.30 Windsor 0.09 0.218 6.07

50% Threshold Days: Station P value Tau Theil Sen Slope Buttonville 0.002 0.41 0.2 Gore Bay 0.628 0.064 0.025 Hamilton 0.052 0.26 0.125 London 0.2437 0.154 0.154 Ottawa 0.072 0.2436 0 Pearson 0.025 0.301 0.133 Trenton 0.1727 0.1804 -0.128 Wiarton 0.315 0.133 0

72 Windsor 0.03 0.29 0.211

80% Threshold Days: Station P value Tau Theil Sen Slope Buttonville 0.0045 0.399 0.057 Gore Bay 0.744 0.044 0 Hamilton 0.2895 0.1907 0.02 London 0.1617 0.175 0.059 Ottawa 0.1913 0.184 0 Pearson 0.1376 0.210 0 Trenton 0.1657 0.19 -0.128 Wiarton 0.2367 0.1618 0 Windsor 0.07 0.2505 0.053 Red – Significant Increasing Trend

Table 4.4. Results of Mann-Kendall test for trends in CAPE over the reference period.

4.5.3 Future CAPE Projections

The predictor variables used for each site varied (Table 4.5), although they tended to include variables related to pressure, humidity and wind velocity. A few of the sites had surface temperature as a significant predictor variable (Table 4.5) suggesting that while alone it is not a predictor of CAPE, it can be significant when used in combination with other variables. While we did not examine the magnitude or direction of these variables with respect to their relationship to CAPE, their presence as the most significant predictors is reasonable. A higher water vapour content is likely to increase CAPE, because it makes air parcels more likely to reach saturation. Once a rising air parcel reaches saturation the water vapour condenses and provides latent heat which in turn reduces the adiabatic lapse rate of air parcels

(see Section 1.2) increasing the probability that rising air parcels will remain warmer relative to their surroundings. Therefore it is not surprising that specific humidity is a significant predictor for CAPE at each of the stations. At least one

73 measure of pressure – either mean sea level pressure or upper level geopotential height is also present at each station. Again, this is intuitive considering air pressure is associated with whether or not air parcels are ascending and an indicator of stability (Downing, 2013). Wind velocity in either the zonal or meridional component was also present at all but one weather station (Hamilton). This suggests that the magnitiude or direction of the wind has a role in determining the magnitude of daily maximum CAPE at these sites. This is likely a result of wind direction being related to the type of airmass over the region, and whether or not it would result in high or low CAPE values. For example as King (2003) found, all of Southern

Ontario’s major tornado outbreaks occurred on days with a Southwest flow, which is likely to bring warm humid air into the region therefore increasing CAPE.

Station Predictors Buttonville Mean sea level pressure Surface zonal velocity 850 geopotential height surface specific humidity Gore Bay Mean sea level pressure 500geopotential height 800zonal velocity surface specific humidity Hamilton Mean sea level pressure 500geopotential height surface specific humidity surface temperature London Mean sea level pressure Surface zonal velocity 500 geopotential height surface specific humidity Ottawa Surface zonal velocity 500 geopotential height surface specific humidity surface temperature Pearson Mean sea level pressure

74 Surface meridional velocity 500 geopotential height surface specific humidity Trenton 500 geopotential height 800 zonal velocity surface specific humidity surface temperature Wiarton Surface specific humidity Surface temperature 850 geopotential height surface zonal velocity Windsor Mean sea level pressure Surface zonal velocity Surface specific humidity Surface temperature

Table 4.5. Predictor Variables used at the nine weather stations

For annual mean CAPE, summer mean CAPE and 50% threshold days, all observed values by year were not significantly different than those modeled through the weather generator over the reference period (t test, p<0.05; Table 4.6).

This suggests that the SDSM model built for each of the stations reasonably reproduced historical CAPE values. For annual mean CAPE, the average (mean) correlation between modeled and observed values was 0.61, and average R squared value was 0.4. The correlation and R squared values were 0.57 and 0.34 respectively for 50% threshold days, and 0.72 and 0.53 for summer mean CAPE. 80% threshold days were not as well represented by the models, with only 4 having t test p values below 0.05. The correlation and R squared values were 0.5 and 0.27 respectively for

80% threshold days.

While the weather generator is in line with the results from Section 4.5.2 above, showing little overall trends in any of these values to date, all models at all stations show significant (t test, p<0.05) increases in all of these values by the end of

75 the current century, when comparing the final 30 years of each model to the reference period. The Mann-Kendall test and Theil-Sen approach for the projected

CAPE values also show significant increasing trends in all four values, at all stations and with all GCMs from 2011-2100. As shown in Figure 4.2, output from the scenario generator appears to show the increasing trend to intensify starting around the middle of the century. When compared to the reference period, the final

30 years (2071-2100 for CGCM3 and CanESM2 and 2070-2099 for HAD3) have dramatic increases in all four measures for all weather stations (Table 4.6).

Although it varies by station and GCM, the average projections across all three GCMS show a doubling of annual mean CAPE from 183 to 368 across all stations, ranging from a 45% increase from 113 to 163J/kg at Gore Bay to a 140% increase from 260 to 625J/kg at Windsor. Summer mean CAPE between the reference and future time periods also on average doubles with an increase from 522 to 1010J/kg across all stations, and a range from a 53% increase from 303 to 466J/kg and 515 to 788J/kg at Gore Bay and Pearson respectively to a 154% increase from 136 to 1869J/kg at

Windsor. The number of days with a 50% probability of observing a thunderstorm increases on average 236% across all stations, from 7.05 to 23.68 days per year ranging from a 83% increase from 8.5 days per year to 15.6 days per year at Gore

Bay to a 368% increase from 7.3 days per year to 34.1 days per year at Windsor and

80% threshold days increase on average 618% from and average of 2.2 to 15.79 days per year across all weather stations ranging from a 161% increase from 3.5 to

9.2 days per year at Gore Bay to a 1173 % increase from 1.8 to 22.9 days per year at

Windsor. The difference between the reference period and final 30 years of each

76 model is statistically significant for all measures at all sites under all models (t test,

P<0.05).

Annual Mean Reference CGCM3 A2 HAD3 A2 CanESM2 RCP 8.5 Buttonville Observed Mean 184.48 t Test Observed- 0.635 Modelled R Squared 0.426 Observed-Modelled Projected Mean 357.86 344.40 372.79 2071-2100/2070- 2099 % Change +94% +87% +102% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.720 0.520 0.655 2011-2100 tau Theil-Sen 2011- 2.417 1.990 2.528 2100 Gore Bay Observed Mean 113.76 t Test Observed- 0.449 Modelled R Squared 0.184 Observed-Modelled Projected Mean 178.20 160.27 152.92 2071-2100/2070- 2099 % Change +57% +41% +34% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.665 0.497 0.546 2011-2100 tau Theil-Sen 2011- 0.857 0.593 0.678 2100 Hamilton Observed Mean 205.35 t Test Observed- 0.376 Modelled R Squared 0.356 Observed-Modelled Projected Mean 404.59 368.36 503.56

77 2071-2100/2070- 2099 % Change +97% +79% +145% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.657 0.490 0.685 2011-2100 tau Theil-Sen 2011- 2.440 2.253 2.723 2100 London Observed Mean 206.44 t Test Observed- 0.492 Modelled R Squared 0.254 Observed-Modelled Projected Mean 424.98 320.15 370.82 2071-2100/2070- 2099 % Change +106% +55% +80% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.685 0.369 0.536 2011-2100 tau Theil-Sen 2011- 2.723 1.638 2.781 2100 Ottawa Observed Mean 172.54 t Test Observed- 0.532 Modelled R Squared 0.480 Observed-Modelled Projected Mean 357.08 402.29 459.21 2071-2100/2070- 2099 % Change +107% +133% +166% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.710 0.611 0.628 2011-2100 tau Theil-Sen 2011- 2.373 2.909 3.935 2100 Pearson Observed Mean 178.12 t Test Observed- 0.948 Modelled

78 R Squared 0.301 Observed-Modelled Projected Mean 281.43 228.75 259.35 2071-2100/2070- 2099 % Change +58% +28% +46% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.671 0.326 0.457 2011-2100 tau Theil-Sen 2011- 1.370 1.388 1.225 2100 Trenton Observed Mean 165.10 t Test Observed- 0.440 Modelled R Squared 0.529 Observed-Modelled Projected Mean 347.00 318.90 488.98 2071-2100/2070- 2099 % Change +110% +93% +196% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.701 0.490 0.615 2011-2100 tau Theil-Sen 2011- 2.268 1.992 4.036 2100 Wiarton Observed Mean 162.38 t Test Observed- 0.386 Modelled R Squared 0.406 Observed-Modelled Projected Mean 316.00 376.99 292.21 2071-2100/2070- 2099 % Change +95% +132% +80% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.715 0.614 0.641 2011-2100 tau Theil-Sen 2011- 2.069 3.013 2.023 2100 Windsor

79 Observed Mean 259.64 t Test Observed- 0.592 Modelled R Squared 0.473 Observed-Modelled Projected Mean 726.05 488.78 660.13 2071-2100/2070- 2099 % Change +179% +88% +154% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.744 0.534 0.696 2011-2100 tau Theil-Sen 2011- 6.001 2.940 5.301 2100

Summer Mean Reference CGCM3 A2 HAD3 A2 CanESM2 RCP 8.5 Buttonville Observed Mean 528.34 t Test Observed- 0.884 Modelled R Squared 0.519 Observed-Modelled Projected Mean 1126.15 1120.39 1154.80 2071-2100/2070- 2099 % Change +113% +112% +119% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.681 0.557 0.596 2011-2100 tau Theil-Sen 2011- 7.926 7.405 8.367 2100 Gore Bay Observed Mean 302.91 t Test Observed- 0.581 Modelled R Squared 0.244 Observed-Modelled Projected Mean 524.36 497.22 378.81 2071-2100/2070- 2099 % Change +73% +64% +25% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value

80 Mann-Kendall 0.609 0.528 0.425 2011-2100 tau Theil-Sen 2011- 3.079 2.316 0.425 2100 Hamilton Observed Mean 594.17 t Test Observed- 0.642 Modelled R Squared 0.528 Observed-Modelled Projected Mean 1128.07 950.15 1575.29 2071-2100/2070- 2099 % Change +90% +60% +165% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.626 0.384 0.513 2011-2100 tau Theil-Sen 2011- 7.586 5.694 13.47 2100 London Observed Mean 583.80 t Test Observed- 0.772 Modelled R Squared 0.488 Observed-Modelled Projected Mean 1270.74 966.35 988.78 2071-2100/2070- 2099 % Change +118% +66% +69% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.645 0.342 0.441 2011-2100 tau Theil-Sen 2011- 8.296 5.716 7.210 2100 Ottawa Observed Mean 514.36 t Test Observed- 0.713 Modelled R Squared 0.536 Observed-Modelled Projected Mean 1138.32 1354.71 1505.49 2071-2100/2070- 2099

81 % Change +121% +163% +193% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.681 0.618 0.582 2011-2100 tau Theil-Sen 2011- 7.476 10.064 13.645 2100 Pearson Observed Mean 515.41 t Test Observed- 0.753 Modelled R Squared 0.491 Observed-Modelled Projected Mean 886.02 725.02 753.97 2071-2100/2070- 2099 % Change +72% +41% +46% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.649 0.345 0.347 2011-2100 tau Theil-Sen 2011- 4.576 2.882 3.336 2100 Trenton Observed Mean 484.53 t Test Observed- 0.691 Modelled R Squared 0.632 Observed-Modelled Projected Mean 1129.75 1037.55 1587.58 2071-2100/2070- 2099 % Change +133% +114% +228% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.659 0.493 0.564 2011-2100 tau Theil-Sen 2011- 7.450 7.113 13.995 2100 Wiarton Observed Mean 444.11 t Test Observed- 0.649 Modelled R Squared 0.522 Observed-Modelled

82 Projected Mean 1010.62 1237.34 869.84 2071-2100/2070- 2099 % Change +128% +179% +96% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.685 0.615 0.586 2011-2100 tau Theil-Sen 2011- 6.920 11.20 6.719 2100 Windsor Observed Mean 735.92 t Test Observed- 0.924 Modelled R Squared 0.755 Observed-Modelled Projected Mean 2325.54 1474.42 1810.02 2071-2100/2070- 2099 % Change +216% +100% +146% Mann-Kendall 2.2e-16 7.9e-12 2.2e-16 2011-2100 p value Mann-Kendall 0.737 0.493 0.623 2011-2100 tau Theil-Sen 2011- 18.854 8.689 14.564 2100

50% Days Reference CGCM3 A2 HAD3 A2 CanESM2 RCP 8.5 Buttonville Observed Mean 5.77 t Test Observed- 0.370 Modelled R Squared 0.456 Observed-Modelled Projected Mean 22.75 21.60 23.04 2071-2100/2070- 2099 % Change +294% +274% +299% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.687 0.567 0.634 2011-2100 tau Theil-Sen 2011- 0.199 0.186 0.203 2100 Gore Bay

83 Observed Mean 8.5 t Test Observed- 0.228 Modelled R Squared 0.111 Observed-Modelled Projected Mean 17.29 15.45 13.96 2071-2100/2070- 2099 % Change +103% +82% +64% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.621 0.558 0.556 2011-2100 tau Theil-Sen 2011- 0.124 0.107 0.104 2100 Hamilton Observed Mean 6.67 t Test Observed- 0.335 Modelled R Squared 0.349 Observed-Modelled Projected Mean 25.53 22.50 30.05 2071-2100/2070- 2099 % Change +283% +237% +350% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.650 0.524 0.569 2011-2100 tau Theil-Sen 2011- 0.198 0.199 0.270 2100 London Observed Mean 9.23 t Test Observed- 0.595 Modelled R Squared 0.236 Observed-Modelled Projected Mean 29.51 20.31 25.64 2071-2100/2070- 2099 % Change +220% +120% +178% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.667 0.422 0.556 2011-2100 tau

84 Theil-Sen 2011- 0.236 0.162 0.252 2100 Ottawa Observed Mean 6.27 t Test Observed- 0.646 Modelled R Squared 0.359 Observed-Modelled Projected Mean 23.86 26.32 29.53 2071-2100/2070- 2099 % Change +280% +320% +371% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.667 0.660 0.600 2011-2100 tau Theil-Sen 2011- 0.194 0.241 0.276 2100 Pearson Observed Mean 4.83 t Test Observed- 0.065 Modelled R Squared 0.315 Observed-Modelled Projected Mean 15.20 11.38 14.23 2071-2100/2070- 2099 % Change +215% +136% +195% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.653 0.431 0.489 2011-2100 tau Theil-Sen 2011- 0.115 0.079 0.098 2100 Trenton Observed Mean 7.3 t Test Observed- 0.995 Modelled R Squared 0.514 Observed-Modelled Projected Mean 25.99 23.17 30.86 2071-2100/2070- 2099 % Change +256% +217% +323% Mann-Kendall <0.001 <0.001 <0.001

85 2011-2100 p value Mann-Kendall 0.678 0.520 0.592 2011-2100 tau Theil-Sen 2011- 0.207 0.202 0.266 2100 Wiarton Observed Mean 7.63 t Test Observed- 0.156 Modelled R Squared 0.344 Observed-Modelled Projected Mean 23.10 29.86 19.94 2071-2100/2070- 2099 % Change +203% +291% +161% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.676 0.614 0.625 2011-2100 tau Theil-Sen 2011- 0.198 0.326 0.194 2100 Windsor Observed Mean 7.3 t Test Observed- 0.066 Modelled R Squared 0.391 Observed-Modelled Projected Mean 41.52 25.57 35.34 2071-2100/2070- 2099 % Change +469% +250% +384% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.730 0520 0.672 2011-2100 tau Theil-Sen 2011- 0.418 0.200 0.354 2100

80% Days Reference CGCM3 A2 HAD3 A2 CanESM2 RCP 8.5 Buttonville Observed Mean 1.57 t Test Observed- 0.005 Modelled

86 R Squared 0.259 Observed-Modelled Projected Mean 13.75 13.13 15.30 2071-2100/2070- 2099 % Change +776% +736% +875% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.701 0.579 0.612 2011-2100 tau Theil-Sen 2011- 0.129 0.132 0.147 2100 Gore Bay Observed Mean 3.53 t Test Observed- 0.809 Modelled R Squared 0.145 Observed-Modelled Projected Mean 10.08 9.17 8.43 2071-2100/2070- 2099 % Change +186% +160% +139% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.628 0.538 0.529 2011-2100 tau Theil-Sen 2011- 0.087 0.073 0.066 2100 Hamilton Observed Mean 2.3 t Test Observed- 0.020 Modelled R Squared 0.144 Observed-Modelled Projected Mean 17.46 14.94 22.34 2071-2100/2070- 2099 % Change +659% +550% +871% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.632 0.531 0.558 2011-2100 tau Theil-Sen 2011- 0.141 0.146 0.210 2100 London

87 Observed Mean 3 t Test Observed- 0.0007 Modelled R Squared 0.299 Observed-Modelled Projected Mean 19.53 12.51 17.84 2071-2100/2070- 2099 % Change +551% +317% +495% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.660 0.423 0.552 2011-2100 tau Theil-Sen 2011- 0.162 0.113 0.182 2100 Ottawa Observed Mean 1.73 t Test Observed- 0.012 Modelled R Squared 0.339 Observed-Modelled Projected Mean 16.51 19.00 23.03 2071-2100/2070- 2099 % Change 854% 998% 123% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.668 0.639 0.604 2011-2100 tau Theil-Sen 2011- 0.140 0.194 0.238 2100 Pearson Observed Mean 1.23 t Test Observed- 2e-5 Modelled R Squared 0.051 Observed-Modelled Projected Mean 8.84 6.08 9.11 2071-2100/2070- 2099 % Change +619% +394% +641% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.647 0.418 0.471 2011-2100 tau

88 Theil-Sen 2011- 0.074 0.048 0.068 2100 Trenton Observed Mean 2.27 t Test Observed- 0.051 Modelled R Squared 0.454 Observed-Modelled Projected Mean 17.48 15.20 23.22 2071-2100/2070- 2099 % Change +670% +570% +923% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.660 0.512 0.582 2011-2100 tau Theil-Sen 2011- 0.151 0.146 0.225 2100 Wiarton Observed Mean 2.37 t Test Observed- 0.229 Modelled R Squared 0.402 Observed-Modelled Projected Mean 14.56 18.43 12.80 2071-2100/2070- 2099 % Change +514% +678% +440% Mann-Kendall <0.001 <0.001 <0.001 2011-2100 p value Mann-Kendall 0.664 0.603 0.600 2011-2100 tau Theil-Sen 2011- 0.132 0.197 0.125 2100 Windsor Observed Mean 1.8 t Test Observed- 2e-7 Modelled R Squared 0.299 Observed-Modelled Projected Mean 28.30 15.92 24.50 2071-2100/2070- 2099 % Change +147% +784% +126% Mann-Kendall <0.001 <0.001 <0.001

89 2011-2100 p value Mann-Kendall 0.734 0.502 0.674 2011-2100 tau Theil-Sen 2011- 0.308 0.137 0.262 2100

Table 4.6. Comparison of observed vs modeled data for reference period and observed reference period data to future modeled data.

Buttonville:

450 400 350 300 250 200 150 100

Annual Mean CAPE (J/kg) 50 0

1400

1200

1000

800

600

400

200 Summer Mean CAPE (J/kg) 0

90 30

0 Days per year with CAPE > 1728J/kg

18 16 14 12 10 8 6 4 2 0 Days per year with CAPE > 2409J/kg

Gore Bay:

91 450 400 350 300 250 200 150 100

Annual Mean CAPE (J/kg) 50 0

1400

1200

1000

800

600

400

200 Summer Mean CAPE (J/kg) 0

92 30

0 Days per year with CAPE > 1022J/kg

18 16 14 12 10 8 6 4 2 0 Days per year with CAPE > 1445J/kg

Hamilton:

93 450 400 350 300 250 200 150 100

Annual Mean CAPE (J/kg) 50 0

1400

1200

1000

800

600

400

200 Summer Mean CAPE (J/kg) 0

94 30

0 Days per year with CAPE > 1808J/kg

18 16 14 12 10 8 6 4 2 0 Days per year with CAPE > 2487J/kg

London:

95 600

500

400

300

200

100 Annual Mean CAPE (J/kg)

1600 1400 1200 1000 800 600 400

Summer Mean CAPE (J/kg) 200 0

96 40 35 30 25 20 15 10 5 0 Days per year with CAPE >1636J/kg

0 Days per year with CAPE >2301J/kg

Ottawa:

97 600

500

400

300

200

100 Annual Mean CAPE (J/kg)

1800 1600 1400 1200 1000 800 600 400

Summer Mean CAPE (J/kg) 200 0

98 40 35 30 25 20 15 10 5 0 Days per Year with CAPE >1629J/kg

0 Days per Year with CAPE >2206J/kg

Pearson:

99 350

300

250

200

150

100

Annual Mean CAPE (J/kg) 50

1200

1000

800

600

400

200 Summer Mean CAPE (J/kg) 0

100 18 16 14 12 10 8 6 4 2 0 Days per Year with CAPE >1782J/kg

0 Days per Year with CAPE >2464J/kg

Trenton:

101 450 400 350 300 250 200 150 100

Annual Mean CAPE (J/kg) 50 0

1400

1200

1000

800

600

400

200 Summer Mean CAPE (J/kg) 0

102 30

0 Days per year with CAPE > 1511J/kg

18 16 14 12 10 8 6 4 2 0 Days per year with CAPE > 2121J/kg

Wiarton:

103 450 400 350 300 250 200 150 100

Annual Mean CAPE (J/kg) 50 0

1400

1200

1000

800

600

400

200 Summer Mean CAPE (J/kg) 0

104 30

0 Days per year with CAPE > 1461J/kg

18 16 14 12 10 8 6 4 2 0 Days per year with CAPE > 2062J/kg

Windsor:

105 450 400 350 300 250 200 150 100

Annual Mean CAPE (J/kg) 50 0

1400

1200

1000

800

600

400

200 Summer Mean CAPE (J/kg) 0

106 30

0 Days per year with CAPE > 2168J/kg

18 16 14 12 10 8 6 4 2 0 Days per year with CAPE > 3020J/kg

Figure 4.2. Observed, modeled and projected Values of annual mean CAPE, days with 50% and 80% probability of observing a thunderstorm and summer mean CAPE at the nine weather stations.

4.5.4 Discussion

The past thunderstorm trends observed in Chapter 3 were small if they existed at all, however it does not imply that larger trends will not develop into the

107 future. In this chapter our goal was to project how thunderstorm frequency might change across Southern Ontario over the coming decades. We chose CAPE as the variable to project thunderstorm activity, and found correlation between annual mean and summer mean CAPE and number of thunderstorms per year and per summer at nine weather stations across the region. Understandably the relationship was not perfect, because average CAPE over a year or season does not take into account how many days had CAPE above certain values. However, using a GLM we found a significant increasing probability of observing a thunderstorm at each of the nine weather stations as daily maximum CAPE increased. Interestingly there were differences in the threshold values among the weather stations, and it is not completely clear why this might be the case.

Rather than calculating CAPE directly from a coarse GCM to determine how it might change into the future we estimated for the first time future maximum daily

CAPE at the nine weather stations using statistical downscaling modeling approach at point location using the corresponding GCM outputs. By linking CAPE to four predictor variables per weather station, we were able to reasonably reproduce annual mean and summer mean CAPE, as well as days that have maximum CAPE values associated with at least a 50% chance of observing a thunderstorm at each weather station. By downscaling three GCMs, we found large increases in annual mean, summer mean, and number of days exceeding a 50% probability and 80% probability of observing a thunderstorm at all weather stations in a business as usual and RCP 8.5 scenarios for future climate. On average the annual and summer mean CAPE was twice as high by the end of the century as compared to the

108 reference period, and days with a 50% chance and 80% chance of observing a thunderstorm more than triple at all weather stations. Considering there have been little trends in CAPE over the reference period, which may explain why we did not observe widespread thunderstorm trends at these nine weather stations in Chapter

3, the expected future changes would suggest, if all else holds true, an increase in thunderstorm activity across the region. Interestingly, the final few years of the observed record did appear to have higher CAPE values at many of the stations

(Figure 4.3) Although this did not show up in the Mann-Kendall test at most of the stations, it further adds credibility to our results that CAPE may only now be starting to increase. This implies a potential increase in thunderstorm frequency across this region.

Our results agree with those of Trapp et al (2007), Van Klooster & Roebber

(2009) and Diffenbaugh et al (2013), who expect large scale increases in CAPE across the continental United States over the current century. Unlike these studies, we directly related daily maximum CAPE to all reported thunderstorm occurrence at the nine weather stations, focusing on how the total number of thunderstorm days might change rather than determine the frequency of days with convection that have favourable conditions for severe thunderstorms. Our study benefits from the high spatial resolution achieved from SDSM, which is particularly important in

Southern Ontario where terrain and lake breezes can result in differences in severe weather occurrence over small distances (King, 1996; Sills & King, 1998; King et al.,

2003; Sills et al., 2011). The use of high resolution NAAR data and SDSM give us a better idea of local CAPE values around the precise location where thunderstorm

109 occurrence was reported. While we did not notice any detectable differences as a result of lake breezes (again, if there were any one might expect London, the station in the middle of the lake breeze convergence zone to be the most different from the rest), there may by a latitudinal gradient as Windsor, our southernmost site has the largest increase in all of our calculated values and Gore Bay, our northernmost site the lowest. None of the other sites appear to fit this pattern, so we cannot conclude that this is the case.

Despite the advances we have made, there are many aspects of this topic that can still benefit from future research. We have not projected how mean CAPE might change at times of year other than summer, or if there is a diurnal effect. We also have not taken into account storm severity. Rasmussen & Blanchard (1998) found that although not as effective as when combined with vertical wind shear,

CAPE itself could be a predictor of storm severity, with more severe and tornadic storms occurring at higher CAPE values. This is potentially significant considering all our 50% days have CAPE over 1000J/kg, which suggests these days also have

CAPE values large enough to produce severe storms. Tripling the number of days with CAPE exceeding these values could mean that not only there would be the potential of more thunderstorms but also more severe thunderstorms. Either way, future research should also address storm severity in this region more closely.

It is worth noting that we assume all other variables except the predictor variables, including convective inhibition, to remain constant while projecting the future. As we explained while high CAPE will increases the risk of a thunderstorm, it will not guarantee a thunderstorm, and there are days that had high maximum daily

110 CAPE values and no observed thunderstorms at all of our weather stations over the reference period. Future analysis should determine a way to include the effects of

Convective Inhibition (CIN). Also measured in J/kg, CIN is essentially the inverse of

CAPE, and is the energy that must be overcome for thunderstorm development. The relationship between CIN and thunderstorm development is somewhat complex. A layer with high CIN is often referred to as a cap, that must be overcome with high

CAPE values due to surface heating or other factors in order to result in thunderstorm development. In areas with low CAPE, the presence of CIN would likely suppress any possible convection. In areas with high CAPE, however, the presence of a layer with low to moderate CIN could enhance storm severity because the cap separates levels of warm and cool air, allowing energy to build in air parcels below the cap, such that only those that attain the most energy are able to break through the cap into the unstable air aloft (Lock & Houston, 2014; Moller, 2001).

Recently, studies in the United States have observed that the number of tornado days may be decreasing although the number of tornadoes that occur on tornado days increasing (Elsner et al., 2014; Brooks et al., 2014; Tippett & Cohen, 2016)

Elsner et al. (2014) suggest that one explanation for this increasing efficiency of tornado days may be the result of increases in both CAPE and CIN. Although it would have challenges, a more comprehensive approach would be to classify every day in terms of both CAPE and CIN, and possibly vertical wind shear as well. Given the potential risks and costs associated with thunderstorms, these topics certainly warrant future research.

111 5. Summary and Conclusions

5.1. Research Summary

Despite the potential dangers and economic impact associated with thunderstorms, they have been relatively underrepresented in climate change studies. One of the main reasons for this is the lack of data availability and the difficulties associated with accounting for thunderstorms in climate models.

Motivated by the potential implications of changes in thunderstorm frequency, we completed a climate change impact assessment on thunderstorm occurrence for the

Southern Ontario region of Canada, the country’s most populated and heavily industrialized region and most active thunderstorm region.

In Chapter 2 we compared manual thunderstorm observations from nine

Environment Canada 24-hour weather stations to data from the lightning detection network, and determined that the manual observations are valid for small distances, up to approximately 10 km around the weather stations, which is consistent with the small-scale nature of thunderstorms. In Chapter 3 we used the data from these weather stations to analyze thunderstorm temporal trends in this region over the past several decades. While there was year-to-year variability in number of reported thunderstorm hours at the weather stations, there were no consistent, statistically significant, overall trends in the observed data. The lack of trends extended across all seasons. Based on daily precipitation totals and maximum wind gust speed data, there was no evidence for widespread changes in thunderstorm intensity over the historical record. We also did not find a link between thunderstorm frequency and the larger-scale phenomena ENSO and NAO. In Chapter

112 4 we related thunderstorm occurrence to convective available potential energy, and, as expected, found the probability of observing a thunderstorm at all the weather stations increases as maximum daily CAPE increases. There are also positive correlations between annual mean CAPE and summer mean CAPE and thunderstorm frequency in a given year. In order to provide a high-resolution future projection of daily maximum CAPE, we downscaled three GCMs through the use of

SDSM software, using four predictor variables per weather station. The models acceptably reproduced our calculated metrics over a 30-year reference period from

1981-2010. In line with our results from Chapter 3, there were no widespread consistent trends in CAPE over the reference period. However, all three models at all nine stations showed consistent and robust increases in all our metrics of daily maximum CAPE over the remainder of the current century. This result is of great significance as it suggests, all else being equal, a large increase in thunderstorm favourable environments across this region over the coming decades, including a large increase in days with CAPE in excess of 1000J/kg at all sites, which is normally considered sufficient for the development of severe thunderstorms.

5.2 Limitation of the Research

We have made a significant contribution to the thunderstorm climatology of

Southern Ontario. This was the first study to evaluate Environment Canada thunderstorm observations and the first to examine trends in observed thunderstorm records from fixed-point weather stations. This was also the first

Canadian study aimed at projecting future thunderstorm occurrence, and the first use of statistical downscaling to project CAPE. Despite these achievements, it is

113 important to place our results in context and discuss some of the limitations of our work.

As we discussed in Chapter 3, our results on past thunderstorm trends should not be affected by changes in population density, because they are based on reports by weather observers at fixed locations. The limitation of our data, however, is that its validity is restricted to the area around each weather station, as we determined in Chapter 2. Although we did not observe widespread CAPE trends over our reference period in Chapter 4, which appears to be consistent with our lack of thunderstorm trends in Chapter 3, these CAPE trends were also only valid for the

32km grid cell according to the NCEP data. Therefore we would not have captured the trend in the absolute number of thunderstorms across the entire region, rather only what was observed at each weather station. As more data from the CLDN becomes available, and we can filter out year-to-year variability based on equipment modifications, it will be possible to determine thunderstorm trends across the region more accurately using lightning flash density. Another important note is our analysis for trends in thunderstorm intensity relied on daily precipitation and wind gust data. It would have been ideal to use hourly data to determine the intensity of each individual thunderstorm, however we were confined to using what was available and of sufficient quality.

Our results from Chapter 4 suggest large increases in CAPE at all weather stations. This would translate into an increase in thunderstorm favourable environments over the coming decades. That is, there is expected to be a large increase in the number of days that have the sufficient instability and available

114 energy for thunderstorm formation. As we have demonstrated, while the presence of large CAPE significantly increases the risk of thunderstorm development, it does not guarantee it. We also did not take into account how CIN might change, and as we discussed, depending on the magnitude of both CIN and CAPE, a small layer of CIN can serve to either suppress or enhance thunderstorm development. While large

CAPE values generally result in more severe storms due to more available energy, we also did not directly account for thunderstorm intensity in our analysis, which would involve the use of other proxies such as vertical wind shear. Further, due to the nature of our methods, due to a low sample size of thunderstorms outside the traditional thunderstorm season, we only compared thunderstorm occurrence to

CAPE for the entire year and did not investigate any seasonal differences or determine any possible changes in the seasonal distribution of thunderstorm favourable days.

5.3 Significance of the Research

The results of this project should be taken into account for future land use and infrastructure planning, as well as for warning and emergency preparedness programs. The results also may be of interest to the insurance industry. The potential of more thunderstorms could result in more intense precipitation events such as those on July 8, 2013 in Toronto, and issues with flooding and drainage. It is important to make sure municipal codes and design standards account for a potential increase in thunderstorm frequency. More frequent thunderstorms would also inherently increase lightning related risks and damage, and this should be taken into account in sectors most affected by lightning, including the utilities,

115 transportation and recreational sectors. It is important that citizens are educated about the risks associated with lightning and that reliable warning systems are in place. Given the growing attention and recognition that thunderstorms exacerbate allergies and asthma, the healthcare sector should be prepared for increases in thunderstorm frequency. Finally, although we did not assess changes in vertical wind shear, the dramatic and consistent increases in CAPE could suggest the possibilities of more wind damage and tornadoes in this area over the coming decades. It is important that building codes account for this. It also may be worthwhile to investigate the expansion of tornado warning systems including the addition of sirens, as has already been done in some Southwestern Ontario communities.

We have provided Ontario with its first analysis of thunderstorm trends, both past and future. Our results from Chapter 2 are also of great benefit to any other researcher wishing to work with the manual thunderstorm observations.

Nevertheless, there are unanswered questions that we suggest as directions for future research. This particularly centers on how thunderstorm intensity might change, and how variables that interact with CAPE, such as CIN might change

Given the potential implications of our results, exploring these issues would be constructive.

5.4 Future Directions

Considering the importance of determining how thunderstorm frequency may change, my primary recommendation for future research is to determine how other variables related to thunderstorm occurrence may change as well. While this

116 study, and those in other areas have consistently shown an expected increasing trend in CAPE over the coming decades, leading to an increase in available energy for thunderstorms, it is important to consider other factors that can enhance or suppress thunderstorm initiation. The most beneficial of these to consider would be

CIN. It would be constructive to establish an index of both CAPE and CIN, and determine and determine how the number of days with ideal values of both may change. It also would be highly valuable to consider other phenomena that enhance thunderstorm development might change, especially the occurrence of frontal boundaries. A changing climate may alter midlatitude cyclone strength and frequency, and therefore the occurrence of warm and cold fronts passing through this region. Equally important would be to scrutinize how the prevalence of lake breezes may change, given their role in severe weather patterns in Southern

Ontario. Although the data available to us was insufficient in establishing any trends in thunderstorm intensity, how the severity of storms that do develop might change is also a question essential to this topic. One way of approaching this question is to take a sample of several days that had known severe weather outbreaks and determine what large scale conditions were present on those days, including values of CAPE, CIN and vertical wind shear. It then may be possible to determine how the frequency of such ideal conditions for a severe weather outbreak might change.

Finally, it is also important to monitor how thunderstorm trends actually respond to the predictions made in this study. One way of doing this is through the manual observations, however with the continued operation of the CLDN, it will be possible to eventually establish a thunderstorm trend for the entire region, using

117 lighting flash density, and not having to rely on fixed point weather stations that may only be valid for small areas or observations that could be subject to population bias. It is my hope that this project will encourage further exploration of these topics over the coming years.

118 Appendix – Statistical Methods

A1. Logistic Regression and ANOVA

A logistic regression determines the probability of a response being true based on one or more explanatory variables. We used a logistic regression in Chapter 2 to determine the probability of observing a thunderstorm at a weather station based on the closest lightning flash and whether it was day or night. We used a logistic regression in Chapter 4 to determine the probability of observing a thunderstorm at each weather station based on the daily maximum CAPE. In both cases we factored the effect of the station into the analysis.

For Chapter 1, on an hourly scale, the equation for determining the probability of observing a thunderstorm was determined to be p = 1/(1+exp(-(1.442-0.197km-0.431d+s)) where p is the probability of reporting a thunderstorm, km is the distance of the closest lightning strike that hour, d is an indicator of day or night (d=1 if day, d=0 if night) and s is the station factor, with some stations performing better than others. The values for s are:

Station s Buttonville 0 Hamilton 0.643 London 0.211 Ottawa 0.197 Pearson 0.051 Trenton 0.873 Wiarton 1.456 Windsor -0.027

On a daily scale the equation was determined to be: p = 1/(1+exp(-(1.442-0.197km+s))

The values for s in this case were as follows:

Station s Buttonville 0 Hamilton 0.631 London 0.824 Ottawa 0.446 Pearson 0.192

119 Trenton 0.999 Wiarton 1.752 Windsor -0.154

For Chapter 4, when determining the probability of reporting a thunderstorm at each weather station based on daily maximum CAPE, the equation is p = 1/(1+exp(-(-3.542+0.00208CAPE+s)) where CAPE is the value of daily maximum CAPE and the value of s is as shown below

Station s Buttonville 0 Gore Bay 0.514 Hamilton -0.16 London 0.144 Ottawa -0.164 Pearson -0.966 Trenton 0.199 Wiarton 0.258 Windsor -0.347

The analysis of variance (ANOVA) in this case performs a likelihood ratio test. This involves completing the analysis with various explanatory variables removed and determining whether or not each individual explanatory variable has a significant effect on the overall model. In Chapter 2, when working on the hourly time scale we tested whether or not daytime and station were significant in the overall model and when working on the daily time scale we tested whether station had a significant effect on the overall model. In Chapter 4, when working with daily maximum CAPE, we determined whether or not the station had a significant effect on the overall model.

If p1 is the maximum likelihood or probability in one model and p2 is the maximum probability in another model, the ratio of the two is calculated as

λ = p2/ p1

The chi-squared (χ2) value can then be calculated as

χ2 = -2lnλ

Based on the χ2 value a p value can be determined. If the p value is <0.05 we reject the null hypothesis that the two models are not different and accept the alternative hypothesis that the additional variable does have a significant effect.

120

Additional References:

Chambers, J. M. and Hastie, T. J. (1992) Statistical Models in S, Wadsworth & Brooks/Cole.

Hosmer, D.W. & Lemeshow, S. (1989). Applied Logistic Regression. John Wiley & Sons Ltd.

A2. Mann-Kendall Test and Theil-Sen Approach

The Mann-Kendall test determines whether or not a significant monotonic trend is present over time. The null hypothesis assumes that there is no monotonic trend, while the alternative hypothesis is that a trend exists. The Kendall Score (S) is calculated through:

S = ∑(sign(Xb – Xa) ∗ sign(Yb− Ya)) where sign = 1 if a>b = 0 if a=b = -1 if a

In our case X represents the time variable and Y represents the observed variable.

Kendall’s Correlation Coefficient, tau, is calculated as tau = S/D where D = (n(n-1)/2) n being the number of observations.

The p value of tau is calculated using the algorithm presented by Best & Gipps (1974). If the p value is greater than 0.05, the null hypothesis, that the trend is not significant is accepted. If the p value is less than 0.05, the null hypothesis is rejected and the alternative hypothesis, that the trend is significant, is accepted.

The Theil-Sen slope is calculated as the median of all pairwise slopes in the dataset:

Theil-Sen Slope = Median((Yb− Ya)/( Xb – Xa)), where 1<=a

Additional References:

Best, D.J. & Gipps, P.G. (1974). Algorithm AS 71: The Upper Tail Probabilities of Kendall’s Tau. Applied Statistics 23(1), 98-100.

121 Komsta, L. (2013). mblm: Median-Based Linear Models. R package version 0.12. retrieved from: http://CRAN.R-project.org/package=mblm

McLeod, A.I. (2011). Kendall: Kendall rank correlation and Mann-Kendall trend test. Rpackage version 2.2. Retrieved from: http://CRAN.R-project.org/package=Kendall

Mohsin, T. & Gough, W. A. (2010). Trend analysis of long-term temperature time series in the Greater Toronto Area (GTA). Theoretical and Applied Climatology 101, 311-327.

A3. Mood’s Median Test

Mood’s median test is used to determine if the medians of two different samples differ significantly. The null hypothesis is that the medians do not differ significantly while the alternative is that there is a difference. The median of the two samples combined is determined, and then the number of observations above and below the median is counted for each subsample. We used Mood’s median test in Chapter 4 to compare test if the median daily maximum CAPE differed significantly on days with versus days without thunderstorms. The median of the entire sample is found and then frequency tables are constructed with the count of observations above and below the overall median for each subsample. After determining the counts above and below the median in each sub-sample, the null hypothesis can be tested using the chi-squared test.

For each station, a table was created as follows:

Above Overall Mean Non-Thunderstorm Thunderstorm Days Days No Number of days Number of days Yes Number of days Number of days

The chi-squared statistic (χ2 ) was calculated as

χ2 = ∑(observed-expected)2/expected where the expected frequencies are calculated as: expected = (row total * column total)/overall number of days

Based on the χ2 statistic a p value can be determined. If the p value is less than 0.05 the null hypothesis, that there is no significant difference in the medians is rejected and the alternative hypothesis, that the medians of the two datasets differ significantly is accepted.

122 Additional References:

Desu, M.M. & D. Raghavarao (2004). Nonparametric Statistical Methods for Compelte and Censored Data. Chapman & Hall.

Neave, H.R. & Worthington, P.L. (1988). Distribution-Free Tests. Unwin Hyman Limited. London, UK.

A4. T Test

A two-tailed paired t test was used to compare observed and modelled output over the reference period in Chapter 4. The null hypothesis for this test is that the means of the two samples do not differ, while the alternative hypothesis is that they are significantly different.

The difference of each of the pairs is calculated. The mean (x̄ ) and variance (s) of the differences are then calculated. The t statistic is then calculated as

� � = � √�

A p value is then found based on the t statistic and sample size. If the p value is above 0.05 we accept the null hypothesis, that there is no significant difference between the means of the two samples. If the p value is less than 0.05 we reject the null hypothesis and accept the alternative hypothesis.

A two tailed independent t test was used to compare the observed values over the reference period to the final 30 years of modelled input at the end of the current century. This test operates the same way as the paired t test except rather than calculate the difference between matched pairs the t statistic is calculated directly from the means and variances of the two samples, as follows:

(� − � ) � = ! ! �! �! √ ! + ! �! �!

where x̄ 1 and x̄ 2 are the means of samples 1 & 2 respectively, and s1 & s2 are the variances of samples 1 & 2 respectively.

Additional Reference:

Moore, D.S. & McCabe, G.P. (2006). Introduction to the Practice of Statistics (Fifth Edition). W.H. Freeman and Company, New York.

123 References

Ahrens, C.D. (2007). Meteorology Today: An Introduction to Weather, Climate and the Environment. 8thed. Thomson Brooks/Cole, Belmont, CA.

Allen, J.T., Karoly, D.J. & Walsh, K.J. (2014a). Future Australian Severe Thunderstorm Environments: Part I: A Novel Evaluation and Climatology of Convective Parameters from Two Climate Models for the Late Twentieth Century. Journal of Climate, 27(10), 3827-3847.

Allen, J.T., Karoly, D.J. & Walsh, K.J. (2014b). Future Australian Severe Thunderstorm Environments: Part II: The Influence of a Strongly Warming Climate on Convective Environments. Journal of Climate, 27(10), 3848-3868.

Best, D.J. & Gipps, P.G. (1974). Algorithm AS 71: The Upper Tail Probabilities of Kendall’s Tau. Applied Statistics 23(1), 98-100.

Boccippio, D.J., Cummins, K.L., Christian, H.J. & Goodman, S.J. (2001). Combined satellite and surface-based estimation of the intracloud-cloud-to-ground lightning ratio over the continental United States. Monthly Weather Review 129, 108-129.

Brooks, H.E., Lee, J.W. & Craven, J. P. (2003). The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data. Atmospheric Research 67-68, 73-94.

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