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Use of Representative Climate Futures in impact and adaptation assessment

机译：在影响和适应性评估中使用代表性气候期货

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A key challenge for climate projection science is to serve the rapidly growing needs of impact and adaptation assessments (hereafter risk assessments) in an environment where there are substantial differences in the regional projections of climate models, an expanding number of potentially relevant climate model results, and a desire amongst many users to limit the number of future climate scenarios in their assessments. While it may be attractive to select a small number of climate models based on their ability to replicate current climate, there is no robust method for doing this. We outline and illustrate a method that addresses this challenge in a different way. The range of plausible future climates simulated by climate models is classified into a small set of Representative Climate Futures (RCFs) and the relative likelihood of these estimated. For each region, the RCFs are then used as a framework in which to classify more detailed information, such as available climate model and downscaled data sets. Researchers wishing to apply the RCFs in risk assessments can then choose to use a subset of RCFs, such as the “most likely”, “high risk” and “least change” cases for their impact system. Preparation and analysis of future climate data sets can therefore be confined to those models whose simulations best represent the selected RCFs. This significantly reduces the number of models involved, and potentially the effort required to undertake the risk assessment. Consistently applied within a region, RCFs, rather than individual climate models, can become the boundary objects which anchor discussion between the climate science and risk assessment communities, simplifying communication. Since the RCF descriptions need not change as new climate model results emerge, they can also provide a stable framework for assimilating risk assessments undertaken at different times with different sets of climate models. Systematic application of this approach requires various challenges to be addressed, such as robustly classifying future regional climates into a small set and estimating likelihoods.
机译：气候预测科学面临的主要挑战是，在气候模型的区域预测存在实质性差异，潜在相关气候模型结果数量不断增加的环境中，如何满足影响力和适应性评估（以下称风险评估）的快速增长的需求，并且许多用户希望在评估中限制未来气候方案的数量。虽然根据其复制当前气候的能力来选择少量气候模型可能很有吸引力，但尚无健壮的方法可以做到这一点。我们概述并说明了以不同方式应对这一挑战的方法。由气候模型模拟的可能的未来气候范围分为一小类代表性气候期货（RCF），并对其进行估计的相对可能性。然后，对于每个区域，将RCF用作框架，在其中可以对更详细的信息进行分类，例如可用的气候模型和缩减的数据集。希望将RCF应用于风险评估的研究人员可以选择使用RCF的子集，例如影响系统的“最可能”，“高风险”和“最小变化”案例。因此，未来气候数据集的准备和分析可以局限于那些模拟最能代表所选RCF的模型。这显着减少了所涉及模型的数量，并可能减少了进行风险评估所需的工作量。在一个区域内始终使用RCF，而不是单个气候模型，它们可以成为边界对象，成为气候科学和风险评估社区之间讨论的基础，从而简化了沟通。由于RCF的描述不需要随着新的气候模型结果的出现而改变，因此它们还可以提供一个稳定的框架，以吸收在不同时间使用不同的气候模型集进行的风险评估。这种方法的系统应用需要解决各种挑战，例如将未来的区域气候可靠地分类为一小组并估计可能性。

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  • 来源
    《Climatic Change》 |2012年第4期|p.433-442|共10页
  • 作者

    Penny Whetton; Kevin Hennessy; John Clarke; Kathleen McInnes; David Kent;

  • 作者单位

    CSIRO Marine and Atmospheric Research, Aspendale, Australia;

    CSIRO Marine and Atmospheric Research, Aspendale, Australia;

    CSIRO Marine and Atmospheric Research, Aspendale, Australia;

    CSIRO Marine and Atmospheric Research, Aspendale, Australia;

    CSIRO Marine and Atmospheric Research, Aspendale, Australia;

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