Tebaldi Claudia, Knutti Reto
Institute for the Study of Society and Environment, National Center for Atmospheric Research, Boulder, CO 80304, USA.
Philos Trans A Math Phys Eng Sci. 2007 Aug 15;365(1857):2053-75. doi: 10.1098/rsta.2007.2076.
Recent coordinated efforts, in which numerous climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multi-model ensembles sample initial condition, parameter as well as structural uncertainties in the model design, and they have prompted a variety of approaches to quantify uncertainty in future climate in a probabilistic way. This paper outlines the motivation for using multi-model ensembles, reviews the methodologies published so far and compares their results for regional temperature projections. The challenges in interpreting multi-model results, caused by the lack of verification of climate projections, the problem of model dependence, bias and tuning as well as the difficulty in making sense of an 'ensemble of opportunity', are discussed in detail.
近期开展了协同工作,针对一组共同的实验运行了众多气候模型,从而生成了大量关于各种情景下未来气候预测的数据集。那些多模型集合对模型设计中的初始条件、参数以及结构不确定性进行了采样,并且促使人们采用多种方法以概率方式量化未来气候的不确定性。本文概述了使用多模型集合的动机,回顾了迄今已发表的方法,并比较了它们对区域温度预测的结果。详细讨论了在解释多模型结果时所面临的挑战,这些挑战包括气候预测缺乏验证、模型依赖性问题、偏差和调整问题以及理解“机会集合”的困难。