概率逻辑
气候模式
多样性(控制论)
计算机科学
集合(抽象数据类型)
气候变化
统计模型
集合预报
机器学习
人工智能
生态学
生物
程序设计语言
作者
Claudia Tebaldi,Reto Knutti
标识
DOI:10.1098/rsta.2007.2076
摘要
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.
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