Defining Events

Set up

The following text is the result of many conversations between the listed authors and many other individuals about ‘heat waves’. In situations where limited resources or rapidly shifting social and/or environmental conditions make exacting analyses impossible - what are some ways to use existing tools and publicly available data to be more proactive about responding to the dangers of extreme temperatures? The question seems very worthwhile and straight forward, but it raises another tricky question: When all you have is large scale indicators, what are you really ‘on the look out for’? Experiences and circumstances are varied, it’s not uncommon for people get sun stroke on ski slopes and hypothermia while out walking in mild weather. What is extreme and/or dangerous can vary from place to place and person to person. Typically such factors are dissected as part of the research and decision making processes. Such nuanced analyses however, require nuanced records of stationary environments. So what can you hope to say when you don’t have that luxury? Often there’s not much that can be said definitively, or with general applicability. The questions become less focused on “what do we need to know, in order to be sure?” and more on “are we able to understand how much we actually do/don’t know?” and “what are we prepared to do with that information, given that what we have is more than nothing, but still opaque at best?” We wanted to provide a work through of some of our efforts and thought process(es), both to illustrate the complications of this sort of analysis, and to provide some starting points for people interested in expanding on these ideas.

What do you mean by “heat wave”?

Extreme high temperature events, or “heat waves”, have always presented a danger to people’s well being. Discussions of climate change have brought about an increasing global concern regarding the dangers of these phenomena (Kovats 2008), which are expected to become more common and severe over the coming years (Meehl 2004, Weisheimer 2005, Akhtar 2007, Mishra 2015, Roldán 2015). Heat waves are responsible for large numbers of deaths in vulnerable regions; the Indian heat wave in May 2015 was reported to have caused 2,500 deaths. Since most heat related deaths can be prevented with adequate preparation (Hutter 2007), there is much emphasis and ongoing work in forecasting extreme heat hazards. To identify these hazards, climatologists typically focus on regional weather patterns and base their definitions around long term averages and the probability of exceeding a set metric; e.g., (Hunt 2007) and (Kent 2013).

However, vulnerability and exposure to ‘extreme’ heat varies between individuals and regions, and it is often noted that the definition of a heat wave should vary accordingly; e.g., (Robinson 2001) and (Perkins 2013). Local climates can vary a great deal; \(20^{\circ}\mathrm{C}\) is considered a balmy summer temperature on Fogo Island, NL, but would probably be considered quite ‘disappointing’ by residents of Cape Town, RSA. Different populations, though, can experience heat waves very differently even within the same city; e.g., (Harlan 2012). Vulnerability to extreme heat events can be affected by local population demographics, including health and economic status, weather variations, and ability to adapt (Knowlton 2008). ‘Vulnerable subpopulations’ often include the elderly, chronically ill, those with limited mobility or social contact, individuals living alone or those with place-based risk factors including limited access to public transportation or appropriate shelter (Klinenberg 2002, Bittner Martin-Immanuel; Stößel 2012, Rosenthal 2014). It has also been noted that not just levels of heat tolerance, but notions of comfort and risk acceptance may differ between different societies. What someone would imply from, or describe as, “high” or “low” risk will be influenced by a personal/cultural approach to risk overall (Douglas 1982). As such, it is pretty well accepted there is no globally applicable definition of a “heat wave” (Tong 2010).

Due to these complexities, it can be a very involved and local specific task to identify what levels of extreme temperatures (and other factors), when combined with local vulnerability and exposure, will have negative impacts (Nogueira 2008). This becomes even more complicated since adverse effects from extreme heat are often prevented through taking adequate precautions. How do you identify a meteorological event where something bad would have happened if someone hadn’t prevented it, when, because it was prevented, there’s no event to draw your