Robustness of pattern scaled climate change scenarios for adaptation decision support
Robustness of pattern scaled climate change scenarios for adaptation decision support
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DOI：
10.1007/s10584-013-1022-y
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发表时间：
2014-02-01
期刊：
影响因子：
4.8
通讯作者：
Smith, Leonard A.
中科院分区：
文献类型：
--
作者：
Lopez, Ana;Suckling, Emma B.;Smith, Leonard A.
模式尺度提供了探索气候系统对人为气候强迫响应的空间细节的希望，而无需最先进的全球气候模式进行全面模拟。模式缩放方法能够实现这一承诺的情况下，探讨量化其性能在一个理想化的设置。考虑到一个大的集合，假设采样的全方位的变化，并提供定量的决策相关的信息，应用模式缩放方法，以产生决策相关的气候情景的合理性进行了探讨。当然，模式缩放并不能精确地再现其目标，而且自其首次提出以来，其一般性的局限性已经得到了很好的证明。在这项工作中，作为一个特定的例子，在南欧的热浪的风险量化，它表明，在模式缩放估计的误差幅度可以是显着的，足以取消使用这种方法在定量决策支持。这表明，未来在气候科学中应用模式尺度不仅应向决策者提供对所作假设的重述，而且还应提供证据，证明该方法在实践中适用于所考虑的情况。
Pattern scaling offers the promise of exploring spatial details of the climate system response to anthropogenic climate forcings without their full simulation by state-of-the-art Global Climate Models. The circumstances in which pattern scaling methods are capable of delivering on this promise are explored by quantifying its performance in an idealized setting. Given a large ensemble that is assumed to sample the full range of variability and provide quantitative decision-relevant information, the soundness of applying the pattern scaling methodology to generate decision relevant climate scenarios is explored. Pattern scaling is not expected to reproduce its target exactly, of course, and its generic limitations have been well documented since it was first proposed. In this work, using as a particular example the quantification of the risk of heat waves in Southern Europe, it is shown that the magnitude of the error in the pattern scaled estimates can be significant enough to disqualify the use of this approach in quantitative decision-support. This suggests that future application of pattern scaling in climate science should provide decision makers not just a restatement of the assumptions made, but also evidence that the methodology is adequate for purpose in practice for the case under consideration.