Assessing climate change impacts on extreme weather events: the case for an alternative (Bayesian) approach
Assessing climate change impacts on extreme weather events: the case for an alternative (Bayesian) approach
复制标题
DOI：
10.1007/s10584-017-2048-3
复制
发表时间：
2017-09-01
期刊：
影响因子：
4.8
通讯作者：
Oreskes, Naomi
中科院分区：
文献类型：
--
作者：
Mann, Michael E.;Lloyd, Elisabeth A.;Oreskes, Naomi
检测气候变化对极端天气事件的影响并将其归因于气候变化的传统方法通常基于频率统计推断，其中假设没有影响的零假设，并且只有当零假设可以在足够高的概率下被拒绝时，才接受影响的备择假设（例如，95%或“p = 0.05”）的置信水平。使用一个简单的概念模型的极端天气事件的发生，我们表明，如果目标是尽量减少预测误差，一种替代方法，其中的影响的可能性不断更新的数据变得更可取。使用一个简单的“概念验证”，我们表明，这种方法将在相当一般的假设下，产生更准确的预测。我们还认为，这种方法将更好地服务于社会，提供一种更有效的手段，提醒决策者注意潜在的和正在发生的危害，并避免机会成本。简而言之，贝叶斯方法无论从经验上还是从道德上都是可取的。
The conventional approach to detecting and attributing climate change impacts on extreme weather events is generally based on frequentist statistical inference wherein a null hypothesis of no influence is assumed, and the alternative hypothesis of an influence is accepted only when the null hypothesis can be rejected at a sufficiently high (e.g., 95% or "p = 0.05") level of confidence. Using a simple conceptual model for the occurrence of extreme weather events, we show that if the objective is to minimize forecast error, an alternative approach wherein likelihoods of impact are continually updated as data become available is preferable. Using a simple "proof-of-concept," we show that such an approach will, under rather general assumptions, yield more accurate forecasts. We also argue that such an approach will better serve society, in providing a more effective means to alert decision-makers to potential and unfolding harms and avoid opportunity costs. In short, a Bayesian approach is preferable, both empirically and ethically.