Confidence, uncertainty and decision-support relevance in climate predictions
Confidence, uncertainty and decision-support relevance in climate predictions
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DOI：
10.1098/rsta.2007.2074
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发表时间：
2007-08-15
期刊：
影响因子：
5
通讯作者：
Smith, L. A.
中科院分区：
文献类型：
--
作者：
Stainforth, D. A.;Allen, M. R.;Smith, L. A.
在过去 20 年里，气候模型已经发展到令人印象深刻的复杂程度。它们是研究许多气候过程相互作用的核心工具，并合理地为人为气候变化是一个关键的全球问题的论点提供了一条额外的线索。在同一时期，人们对计算机模型输出的解释和概率分析越来越感兴趣。特别是自然系统模型。这些领域的研究成果正在被寻求并用于政策制定、其他学科以及更广泛的社会决策。在这里，我们的重点仅放在复杂的气候模型上，作为十年和更长时间尺度的预测工具。我们主张重新评估此类模型在用于此目的时的作用，并重新考虑模型开发和实验设计的策略。在更通用的工作的基础上，我们对与这个特定问题相关的不确定性来源进行了分类，并讨论了可用于其定量的实验策略。阳离子。复杂的气候模型作为许多变量和尺度的预测工具，无法进行有意义的校准，因为它们正在模拟以前从未经历过的系统状态；问题是外推法之一。因此，应用任何当前可用的通用技术（利用观测来校准或加权模型来生成现实世界的预测概率）都是不合适的。这样做会误导更广泛社会的气候科学用户。在此背景下，我们讨论了我们从何处获得对气候预测的信心，并提出了一些概念来帮助讨论和交流最新技术。预测不确定性的基本假设和来源的有效沟通对于气候科学、影响社区和整个社会之间的相互作用至关重要。
Over the last 20 years, climate models have been developed to an impressive level of complexity. They are core tools in the study of the interactions of many climatic processes and justifiably provide an additional strand in the argument that anthropogenic climate change is a critical global problem. Over a similar period, there has been growing interest in the interpretation and probabilistic analysis of the output of computer models; particularly, models of natural systems. The results of these areas of research are being sought and utilized in the development of policy, in other academic disciplines, and more generally in societal decision making. Here, our focus is solely on complex climate models as predictive tools on decadal and longer time scales. We argue for a reassessment of the role of such models when used for this purpose and a reconsideration of strategies for model development and experimental design. Building on more generic work, we categorize sources of uncertainty as they relate to this specific problem and discuss experimental strategies available for their quanti. cation. Complex climate models, as predictive tools for many variables and scales, cannot be meaningfully calibrated because they are simulating a never before experienced state of the system; the problem is one of extrapolation. It is therefore inappropriate to apply any of the currently available generic techniques which utilize observations to calibrate or weight models to produce forecast probabilities for the real world. To do so is misleading to the users of climate science in wider society. In this context, we discuss where we derive confidence in climate forecasts and present some concepts to aid discussion and communicate the state-of-the-art. Effective communication of the underlying assumptions and sources of forecast uncertainty is critical in the interaction between climate science, the impacts communities and society in general.