Data-driven climate indices as a climate translation service
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Weather and climate have a powerful influence on humans and society. The ways in which individuals, organizations, and communities are sensitive to weather and climate varies considerably due to social, economic, institutional, and technological factors (Kirchhoff et al. 2013). The complexity and variability across space and time of the human-environment interface motivates the demand for tools and techniques that are able to effectively translate climatic information into usable products and services for decision-making. Furthermore, notwithstanding the extensive availability of weather and climate information, its use in informing both weather risk-management decisions and climate-change adaptation initiatives remains limited. One factor in the underutilization of weather and climate information stems from the difficulty of translating weather and climate data into useable information for decision-makers (Rayner et al. 2005, Lemos 2008, Weaver et al. 2013, Fellman 2012, Kirchhoff et al. 2013, Soares & Dessai 2015). Organizations have been increasingly seeking tools that can inform decision-making for both short-term weather risk management and long-term climate change adaptation measures (WMO 2016). Regardless of the temporal scope of a decision, there is a need to identify and quantify the climatic sensitivity and associated risks and opportunities of climatic stimuli (Damm et al. 2019). The non-linearity of climate-society interactions combined with the highly context-dependent nature of societal sensitivities to climatic stimuli poses a number of practical challenges. This gap in research, and in practice, provides a novel research opportunity to investigate the prospect of developing techniques that can quantify weather sensitivity in a variety of applications. These context-specific and user-driven climatic information products and services are often referred to as climate translation products and services (Damm et al. 2019). A core impediment to the development of climate translation services is an incomplete understanding of how individuals, organizations, and sectors are sensitive to climatic stimuli. A number of methods has been used to define this sensitivity but to date and there has been a dominant focus on stated-preference methods to ascertain user needs and sectoral climatic sensitivities. Expert consultations, user interviews, and participant surveys have been used extensively to define context-specific weather and climate sensitivities. However, a growing literature explores the use of data-driven techniques to explore societal sensitivity to weather and climate. Focusing on the highly climate-sensitive transportation and tourism sectors, this dissertation proposes a conceptualization of climatic sensitivity that is premised on the need for multiple climatic thresholds. This dissertation proposes a framework for data-driven techniques that can be used to develop climatic indices based on the underlying relationships between weather and society and presents the first data-driven approach to define multiple climatic thresholds for the climate-society nexus in two climate-sensitive sectors. The overarching purpose of this dissertation is to further the development of climate services and increase the scholarly understanding of context-specific climatic thresholds that communicate a societal response and can be applied to weather forecasts and climate projections at different temporal scales. The first manuscript uses expert knowledge in combination with mathematical optimization to develop a data-driven winter severity index that works well in predicting winter maintenance activity across 20 road maintenance jurisdictions in Ontario. The second manuscript builds on the first paper through an extension to include climate change projections, and provides greater focus on role of co-production in climate services development. This second manuscript explores the frequency, and intensity of past and future winter weather as it relates to winter road maintenance of provincial highways in Ontario, Canada. The climate change analysis reveals that winter severity, as it relates to snow and ice control, is projected to decrease through to the end of the century. The third manuscript of this dissertation explores the feasibility of transferring the methods developed in the first two manuscripts to develop a data-driven tourism climate index for Ontario Provincial Parks. This third study advances our understanding of beach park-visitor’s climatic sensitivity and provides tourism planners, managers, and decision-makers with enhanced information to inform decision-making. The final manuscript of the dissertation examines the intra-annual effect of weather on tourism demand to three Caribbean destinations (Barbados, Antigua and Barbuda, and Saint Lucia) from Ontario, Canada. This study refines the Holiday Climate Index: Beach through optimization to develop two new indices which estimate the climatic pull-factor of the destination, and the climatic push-factor from the source market. Findings reveal that the data-driven indices have greater predictive accuracy than the extant climate indices for tourism. In conclusion, this dissertation demonstrates the feasibility of developing data-driven indices in the transportation and tourism sectors that can form the foundation of climate service translation tools.
Cite this version of the work
Lindsay Matthews (2020). Data-driven climate indices as a climate translation service. UWSpace. http://hdl.handle.net/10012/15835
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