Using science to create a better place: applying probabilistic climate change information to strategic resource assessment.
Using science to create a better place: applying probabilistic climate change information to strategic resource assessment.
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利用科学创造更美好的地方：将概率气候变化信息应用于战略资源评估。
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
M. New
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文献类型：
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作者：
C. Fung;A. López;M. New
到目前为止，大多数气候变化影响和适应研究都是基于对未来气候的最多几个确定性认识，通常代表不同的排放情景。目前有大量的气候模型集合，或者是机会集合，或者是扰动物理集合，提供了大量额外的数据，可能有助于改进气候变化适应战略。随着英国21世纪世纪气候情景（2008年）UKCIP 08的发布，来自不同部门的用户将能够获得英国气候变化的概率预测。由于这类综合气候变化信息的新奇，以往很少有实际应用的经验，也很少有这类信息对影响和适应决策的附加值。在这里，我们描述了一种方法来执行一个自上而下的方法，使用大量的气候变化信息的影响评估。我们使用作为案例研究的水资源系统在英格兰西南部。气候数据来自迄今为止最大的扰动物理集合，climateprediction.net。使用降雨径流模型模拟河流流量，并将其输入水资源系统模型。该模型旨在分析有关供水区的水供应和需求之间的相互作用，以便根据现有的气候变化信息探索各种适应途径。我们分析了在气候模型集合数据驱动下以及在不同的需求和供应管理情景下运行时水资源系统的响应。我们的研究表明，与使用单一模式情景相比，气候模式集合中包含的额外信息可以更好地了解未来条件的可能范围。此外，通过仔细的介绍，决策者将发现更容易获得大量模型的结果，并能够更容易地比较不同管理备选方案的优点和不同适应的时机。决策过程质量的提高将证明进行影响分析所需的额外时间和专门知识的开销是合理的。我们注意到，即使我们把我们的研究集中在英国的水资源系统，我们的结论的附加值的气候模型集成在指导适应决策可以推广到其他部门和地理区域。
The majority of climate change impacts and adaptation studies so far have been based on at most a few deterministic realisations of future climate, usually representing different emissions scenarios. Large ensembles of climate models are currently available either as ensembles of opportunity, or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. With the release of the UK 21st Century Climate Scenarios (2008), UKCIP08, users from different sectors will have access to probabilistic projections of climate change for the UK. Due to the novelty of this ensemble-like climate change information, there is little previous experience of practical applications or of the added value of this information for impacts and adaptation decision-making. Here we describe a methodology to perform a top-down approach to impacts assessment using large ensembles of climate change information. We use as a case study a water resource system in the South West of England. The climate data are obtained from the largest perturbed physics ensemble publicly available to date, climateprediction.net. River flows are simulated using a rainfall runoff model and feed into the water resource system model. This model is designed to analyse the interactions between water supply and demand for the water supply zone of interest, allowing for the exploration of various adaptation paths given the climate change information available. We analyse the response of the water resource system when driven by the climate model ensemble data, and operating under different scenarios of demand and supply management. Our research shows that the additional information contained in the climate model ensemble provides a better understanding of the possible ranges of future conditions, compared to the use of single model scenarios. Furthermore, with careful presentation, decisionmakers will find the results from large ensembles of models more accessible and be able to more easily compare the merits of different management options and the timing of different adaptation. The overhead in additional time and expertise for carrying out the impacts analysis will be justified by the increased quality of the decision making process. We remark that even though we have focused our study in a water resource system in the UK, our conclusions about the added-value of climate model ensembles in guiding adaptation decisions can be generalized to other sectors and geographical regions.