Читать книгу Extreme Events and Climate Change - Группа авторов - Страница 18

If we want to synthesize across multiple regions and types of impacts, then we need to have a common metric that is applicable to all of those regions and types of impacts. In one of the tornado impact analyses just given we used the human fatality rate. Human fatality impacts are a standard and obvious metric, because under the ethical and judicial standards of most countries all human deaths are equivalent. The use of the injury metric in the other tornado analysis is less clear‐cut, however: some injuries may be more severe and consequential than others. And neither of those metrics is applicable for impacts outside of human health. A starting point might be money, considering that so much of our lives is spent using it as a universal currency. But can we put a monetary value on a species going extinct? Or on various aspects of livelihoods and culture?

A partial way around this challenge is to use a qualitative measure of relative change instead of a quantitative metric (Cramer et al., 2014; Oppenheimer et al., 2014; Smith et al., 2001, 2009). For instance, in their synthesis assessment of the detection and attribution of changes in risk associated with extreme weather, Cramer et al. (2014) synthesized only across like systems (e.g., bleaching/stress/mortality of warm water corals) when assigning a level of confidence to the evaluation of whether observed climate trends had played a major or minor role in an observed change. Hence, their summary statement highlighted “High‐temperature spells have impacted one system with high confidence (coral reefs), indicating Risks Associated with Extreme Weather Events. Elsewhere, extreme events have caused increasing impacts and economic losses, but there is only low confidence in attribution to climate change for these” (Cramer et al., 2014, p. 983) but included no cross‐system synthesis. However, these system‐specific conclusions were then aggregated into a past‐to‐future assessment of the qualitative change in risk by Oppenheimer et al. (2014). Synthesizing across qualitative, rather than quantitative, outputs of detection and attribution analyses means that the synthesis is more flexible in the types of detection and attribution analyses it can include. For instance, a multiple linear regression analysis may be appropriate for a system that behaves fairly linearly to external perturbations, but another type of analysis may be required for a system with a highly nonlinear response. In a quantitative synthesis it would be hard to include the output parameters of both analyses in a consistent way. Similarly, being able to include more disparate types of analyses of each component input (e.g., different studies of butterfly range shifts using different techniques) means that a qualitative synthesis can incorporate a more robust representation of uncertainty. However, the trade‐off is a lack of transparency over technical details that may be important.

An alternative approach is to convert results of individual studies into a binary metric, such as “predictions consistent with observations” versus “predictions inconsistent with observations” (Rosenzweig et al., 2007, 2008; Savo et al., 2016). For predictions of future risks, a possible binary metric might be based on a threshold for losses or damages or based on a threshold for relative importance in relation to predicted effects of other factors. With some loss of information about severity, this approach can in practice produce a single synthesis measure. However, it has several important assumptions (Stone et al., 2013). Most important, by assuming that each unit of study (for which a binary result is assigned) is equivalently important, it is still assigning value. Such an approach has yet to be applied specifically to impacts related to extreme weather.