Heather Auld

Possible impacts of climate change on extreme weather events at local scale in south-central Canada

Synoptic weather typing and regression-based downscaling approaches have become popular in evaluating the impacts of climate change on a variety of environmental problems, particularly those involving extreme impacts. One of the reasons for the popularity of these approaches is their ability to categorize a complex set of meteorological variables into a coherent index, facilitating the projection of changes in frequency and intensity of future daily extreme weather events and/or their impacts. This paper illustrated the capability of the synoptic weather typing and regression methods to analyze climatic change impacts on a number of extreme weather events and environmental problems for south–central Canada, such as freezing rain, heavy rainfall, high-/low-streamflow events, air pollution, and human health. These statistical approaches are helpful in analyzing extreme events and projecting their impacts into the future through three major steps or analysis procedures: (1) historical simulation modeling to identify extreme weather events or their impacts, (2) statistical downscaling to provide station-scale future hourly/daily climate data, and (3) projecting changes in the frequency and intensity of future extreme weather events and their impacts under a changing climate. To realize these steps, it is first necessary to conceptualize the modeling of the meteorology, hydrology and impacts model variables of significance and to apply a number of linear/nonlinear regression techniques. Because the climate/weather validation process is critical, a formal model result verification process has been built into each of these three steps. With carefully chosen physically consistent and relevant variables, the results of the verification, based on historical observations of the outcome variables simulated by the models, show a very good agreement in all applications and extremes tested to date. Overall, the modeled results from climate change studies indicate that the frequency and intensity of future extreme weather events and their impacts are generally projected to significantly increase late this century over south–central Canada under a changing climate. The implications of these increases need be taken into consideration and integrated into policies and planning for adaptation strategies, including measures to incorporate climate change into engineering infrastructure design standards and disaster risk reduction measures. This paper briefly summarized these climate change research projects, focusing on the modeling methodologies and results, and attempted to use plain language to make the results more accessible and interesting to the broader informed audience. These research projects have been used to support decision-makers in south–central Canada when dealing with future extreme weather events under climate change.

An Automated Synoptic Typing Procedure to Predict Freezing Rain: An Application to Ottawa, Ontario, Canada

Abstract Freezing rain is a major weather hazard that can compromise human safety, significantly disrupt transportation, and damage and disrupt built infrastructure such as telecommunication towers and electrical transmission and distribution lines. In this study, an automated synoptic typing and logistic regression analysis were applied together to predict freezing rain events. The synoptic typing was developed using principal components analysis, an average linkage clustering procedure, and discriminant function analysis to classify the weather types most likely to be associated with freezing rain events for the city of Ottawa, Ontario, Canada. Meteorological data used in the analysis included hourly surface observations from the Ottawa International Airport and six atmospheric levels of 6-hourly NCEP–NCAR upper-air reanalysis weather variables for the winter months (Nov– Apr) of 1958/59–2000/01. The data were divided into two parts: a developmental dataset (1958/59–1990/91) for construction (developmen...

Possible Impacts of Climate Change on Freezing Rain Using Downscaled Future Climate Scenarios: Updated for Eastern Canada

The methods used in an earlier study focusing on the province of Ontario, Canada, were adapted for this current study to expand the study area over eastern Canada where the infrastructure is at risk of being impacted by freezing rain. To estimate possible impacts of climate change on future freezing rain events, a three-step process was used in the study: (1) statistical downscaling, (2) synoptic weather typing, and (3) future projections. A regression-based downscaling approach, constructed using different regression methods for different meteorological variables, was used to downscale the outputs of eight general circulation models to each of 42 hourly observing stations over eastern Canada. Using synoptic weather typing (principal components analysis, a clustering procedure, discriminant function analysis), the freezing rain-related weather types under historical climate (1958–2007) and future downscaled climate conditions (2016–2035, 2046–2065, 2081–2100) were identified for all selected stations. The potential changes in the frequency of future daily freezing rain events can be projected quantitatively by comparing future and historical frequencies of freezing rain-related weather types. The modelled results show that eastern Canada could experience more freezing rain events late this century during the coldest months (i.e., December to February) than the averaged historical conditions. Conversely, during the warmest months of the study season (i.e., November and April in the southern regions, October in the northern regions), eastern Canada could experience less freezing rain events late this century. The increase in the number of daily freezing rain events in the future for the coldest months is projected to be progressively greater from south to north or from southwest to northeast across eastern Canada. The relative decrease in magnitude of future daily freezing rain events in the warmest months is projected to be much less than the relative increase in magnitude in the coldest months.

A Synoptic Weather Typing Approach to Simulate Daily Rainfall and Extremes in Ontario, Canada: Potential for Climate Change Projections

Abstract An automated synoptic weather typing and stepwise cumulative logit/nonlinear regression analyses were employed to simulate the occurrence and quantity of daily rainfall events. The synoptic weather typing was developed using principal component analysis, an average linkage clustering procedure, and discriminant function analysis to identify the weather types most likely to be associated with daily rainfall events for the four selected river basins in Ontario. Within-weather-type daily rainfall simulation models comprise a two-step process: (i) cumulative logit regression to predict the occurrence of daily rainfall events, and (ii) using probability of the logit regression, a nonlinear regression procedure to simulate daily rainfall quantities. The rainfall simulation models were validated using an independent dataset, and the results showed that the models were successful at replicating the occurrence and quantity of daily rainfall events. For example, the relative operating characteristics score...

A Synoptic Weather-Typing Approach to Project Future Daily Rainfall and Extremes at Local Scale in Ontario, Canada

AbstractThis paper attempts to project possible changes in the frequency of daily rainfall events late in this century for four selected river basins (i.e., Grand, Humber, Rideau, and Upper Thames) in Ontario, Canada. To achieve this goal, automated synoptic weather typing as well as cumulative logit and nonlinear regression methods was employed to develop within-weather-type daily rainfall simulation models. In addition, regression-based downscaling was applied to downscale four general circulation model (GCM) simulations to three meteorological stations (i.e., London, Ottawa, and Toronto) within the river basins for all meteorological variables (except rainfall) used in the study. Using downscaled GCM hourly climate data, discriminant function analysis was employed to allocate each future day for two windows of time (2046–65, 2081–2100) into one of the weather types. Future daily rainfall and its extremes were projected by applying within-weather-type rainfall simulation models together with downscaled ...

Changing Weather Patterns, Uncertainty and Infrastructure Risks: Emerging Adaptation Requirements

As the climate changes, it is likely that risks for infrastructure failure will increase worldwide due to shifting weather patterns and extreme weather conditions becoming more variable and regionally more intense. Existing studies indicate that small increases in weather and climate extremes have the potential to bring large increases in damages to existing infrastructure. Almost all of todays infrastructure has been designed using climatic design values calculated from historical climate data on the assumption that past extremes will represent future conditions. Changes in climate will require changes to these climatic design values, as well as larger societal changes. Uncertainties in the climate change models and in the projections on the magnitudes and directions of future changes limit abilities to design infrastructure for future conditions. Until these uncertainties in the climate change projections are reduced, it will become critically important that climatic design values be calculated as accurately as possible and that values are regularly updated to reflect the changing climate. Since uncertainty is accepted as a part of construction codes and standards, it should be possible to deal with the growing uncertainty of future climate design values through measures such as increasing safety factors, forensic analyses of extreme events and use of climate trends and climate model projections based on surrogate climate variables.

Possible Impacts of Climate Change on Wind Gusts under Downscaled Future Climate Conditions over Ontario, Canada

AbstractHourly/daily wind gust simulation models and regression-based downscaling methods were developed to assess possible impacts of climate change on future hourly/daily wind gust events over the province of Ontario, Canada. Since the climate/weather validation process is critical, a formal model result verification process has been built into the analysis to ascertain whether the methods are suitable for future projections. The percentage of excellent and good simulations among all studied seven wind gust categories ranges from 94% to 100% and from 69% to 95%, respectively, for hourly and daily wind gusts, for both model development and validation.The modeled results indicate that frequencies of future hourly/daily wind gust events are projected to increase late this century over the study area under a changing climate. For example, across the study area, the annual mean frequency of future hourly wind gust events ≥28, ≥40, and ≥70 km h−1 for the period 2081–2100 derived from the ensemble of downscale...

Regionalization of heavy rainfall to improve climatic design values for infrastructure: case study in Southern Ontario, Canada

Abstract Southern Ontario, Canada, has been impacted in recent years by many heavy rainfall and flooding events that have exceeded existing historical estimates of infrastructure design rainfall intensity–duration–frequency (IDF) values. These recent events and the limited number of short-duration recording raingauges have prompted the need to research the climatology of heavy rainfall events within the study area, review the existing design IDF methodologies, and evaluate alternative approaches to traditional point-based heavy rainfall IDF curves, such as regional IDF design values. The use of additional data and the regional frequency analysis methodology were explored for the study area, with the objective of validating identified clusters or homogeneous regions of extreme rainfall amounts through Wards method. As the results illustrate, nine homogeneous regions were identified in Southern Ontario using the annual maximum series (AMS) for daily and 24-h rainfall data from climate and rate-of-rainfall or tipping bucket raingauge (TBRG) stations, respectively. In most cases, the generalized extreme value and logistic distributions were identified as the statistical distributions that provide the best fit for the 24-h and sub-daily rainfall data in the study area. A connection was observed between extreme rainfall variability, temporal scale of heavy rainfall events and location of each homogeneous region. Moreover, the analysis indicated that scaling factors cannot be used reliably to estimate sub-daily and sub-hourly values from 24- and 1-h data in Southern Ontario. Citation Paixao, E., Auld, H., Mirza, M.M.Q., Klaassen, J. & Shephard, M.W. (2011) Regionalization of heavy rainfall to improve climatic design values for infrastructure: case study in Southern Ontario, Canada. Hydrol. Sci. J. 56(7), 1067–1089.

Probability of Tornado Occurrence across Canada

The number of tornado observations in Canada is believed to be significantly lower than the actual occurrences. To account for this bias, the authors propose a Bayesian modeling approach founded upon the explicit consideration of the population sampling bias in tornado observations and the predictive relationship between cloud-to-ground (CG) lightning flash climatology and tornado occurrence. The latter variable was used as an indicator for quantifying convective storm activity, which is generally a precursor to tornado occurrence. The CG lightning data were generated from an 11-yr lightning climatology survey (1999‐2009) from the Canadian Lightning Detection Network. The results suggest that the predictions of tornado occurrence in populated areas are fairly reliable with no profound underestimation bias. In sparsely populated areas, the analysis shows that the probability of tornado occurrence is significantly higher than what is represented in the 30-yr data record. Areas with low population density but high lightning flash density demonstrate the greatest discrepancy between predicted and observed tornado occurrence. A sensitivity analysis with variousgridsizes was alsoconducted. It wasfound that thepredictivestatementssupportedby themodel are fairlyrobusttothegrid configuration,but thepopulationdensity pergrid cellis more representativetothe actual population density at smaller resolution and therefore more accurately depicts the probability of tornado occurrence. Finally, a tornado probability map is calculated for Canada based on the frequency of tornado occurrence derived from the model and the estimated damage area of individual tornado events.

Planning for Atmospheric Hazards and Disaster Management Under Changing Climate Conditions

Reducing societal vulnerability to weather related disasters under current and changing climate conditions will require a diverse and interconnected range of adaptive actions. Included among these actions are hazard identification and risk assessment, comprehensive emergency and disaster management, improved predictions of high impact weather, better land use planning, strategic environmental and ecosystem protection, continuously updated and improved climatic design values and changes to infrastructure codes and standards to support disaster resistant infrastructure. These actions will need to be undertaken by all levels of government, by individuals, planners, professional associations and investors. One critical disaster reduction response is that of emergency and disaster preparedness, which involves the development of an emergency response and management capability long before a disaster occurs. The provinces of Ontario and Quebec, in central Canada, have both passed provincial legislation requiring that all municipal and regional governments adopt emergency management planning. In support of these legislated measures in Ontario, Environment Canada along with its partner Emergency Management Ontario, have developed an atmospheric hazards publication and web site that supports municipalities in accessing climatological, extreme weather and air quality information, customizing atmospheric hazards maps for their localities and in linking hazards maps. Maps can be functionally linked through cumulative co-recognition software that allows the user to select specific thresholds per hazard map and to display the cumulative result of regional combinations of hazards. Information on climate trends for the hazards variables is presently available on the site, and future plans for the site include climate change trend projections, where appropriate.