On the interpretation of constrained climate model ensembles
On the interpretation of constrained climate model ensembles
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DOI：
10.1029/2012gl052665
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发表时间：
2012-08
影响因子：
5.2
通讯作者：
B. Sanderson;R. Knutti
中科院分区：
文献类型：
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作者：
B. Sanderson;R. Knutti
模型的集合可以用两种方式来解释。第一种方法把每个模型都看作是真实系统的近似，带有一些随机误差。或者，真实系统可以被解释为从模型分布中抽取的样本，使得模型和真实在统计上无法区分。这两种解释都是普遍存在的，对模型预测的不确定性有不同的后果，但很少有人为之辩护。在这里，我们认为，这两个看似矛盾的观点实际上是互补的，和合奏的解释可能会无缝地从前者演变到后者。我们显示了一些“真理加错误”的属性存在的历史和现今的气候模拟CMIP档案，他们可以解释的合奏设计和调整观测，虽然模型和调整是不完美的。对于未来的预测，模型响应的结构差异出现，这是独立于目前的状态，因此“不可区分”的解释越来越受到青睐。我们无法定义识别“好”和“坏”模型的性能指标，这可以解释为模型在很大程度上利用了可用的观察结果。剩余的模型误差主要是结构性的，观测结果往往没有提供信息，以进一步减少模型偏差或减少集合覆盖的预测范围。这里的讨论是出于在气候预测中使用多模式集成的动机，但参数是通用的任何情况下，多个不同的模型约束的观测被用来描述同一个系统。
An ensemble of models can be interpreted in two ways. The first treats each model as an approximation of the true system with some random error. Alternatively, the true system can be interpreted as a sample drawn from a distribution of models, such that model and truth are statistically indistinguishable. Both interpretations are ubiquitous and have different consequences for the uncertainty of model projections, but are rarely defended. Here we argue that the two seemingly conflicting views are in fact complementary, and the interpretation of the ensemble may evolve seamlessly from the former to the latter. We show some ‘truth plus error’ like properties exist for historical and present day climate simulations in the CMIP archive, and that they can be explained by the ensemble design and tuning to observations, although both models and tuning are imperfect. For future projections, structural differences in model response arise which are independent of the present day state and thus the ‘indistinguishable’ interpretation is increasingly favored. Our inability to define performance metrics that identify ‘good’ and ‘bad’ models can be explained by the models having largely exploited the available observations. The remaining model error is largely structural and the observations are often uninformative to further reduce model biases or reduce the range of projections covered by the ensemble. The discussion here is motivated by the use of multi model ensembles in climate projections, but the arguments are generic to any situation where multiple different models constrained by observations are used to describe the same system.