On Constraining Estimates of Climate Sensitivity with Present-Day Observations through Model Weighting
On Constraining Estimates of Climate Sensitivity with Present-Day Observations through Model Weighting
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DOI：
10.1175/2011jcli4193.1
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发表时间：
2011-12
影响因子：
4.9
通讯作者：
D. Klocke;R. Pincus;J. Quaas
中科院分区：
文献类型：
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作者：
D. Klocke;R. Pincus;J. Quaas
30多年来，基于模型的平衡气候敏感性估计数的分布没有实质性变化。通过根据模型保真度的衡量标准对预测进行加权来缩小这一分布的努力迄今为止都失败了，这主要是因为气候敏感性与当前整套模型中的当前技能衡量标准无关。这项工作提出了一个警示性的例子，表明在缩小未来预测分布方面有效的模型保真度措施（因为它们与模型集合中的气候敏感性系统相关）可能是模型提供准确估计气候敏感性的可能性的不良措施（因此，如果它们被用作权重，则会降低预测分布）。此外，单靠统计检验似乎不太可能确定可靠的可能性度量。结论是从两个集合中得出的：一个集合是通过扰动单一气候模型中的参数获得的，另一个集合包含了世界上大多数气候模型。简单的集合再现了多模式集合的许多方面，包括再现当今云和辐射气候学的技能分布，气候敏感性的分布，以及气候敏感性对某些云态的依赖性。针对这些制度的误差措施的加权允许发展更紧密的气候敏感性和模式误差之间的关系，因此，在简单的合奏气候敏感性分布较窄。然而，这些关系并不适用于多模式集成。这表明，仅仅基于统计关系的模型加权是没有根据的，也许气候模型误差仍然很大，模型加权是不明智的。
The distribution of model-based estimates of equilibrium climate sensitivity has not changed substantially in more than 30 years. Efforts to narrow this distribution by weighting projections according to measures of modelfidelity have so far failed, largely because climate sensitivity is independent of current measures of skill in current ensembles of models. This work presents a cautionary example showing that measures of model fidelity that are effective at narrowing the distribution of future projections (because they are systematically related to climate sensitivity in an ensemble of models) may be poor measures of the likelihood that a model will provideanaccurateestimateofclimatesensitivity(andthus degradedistributions ofprojectionsiftheyare used as weights). Furthermore, it appears unlikely that statistical tests alone can identify robust measures of likelihood.The conclusions are drawn fromtwo ensembles: one obtainedby perturbingparameters in a single climate model and a second containing the majority of the world’s climate models. The simple ensemble reproduces many aspects of the multimodel ensemble, including the distributions of skill in reproducing the present-day climatology of clouds and radiation, the distribution of climate sensitivity, and the dependence of climate sensitivity on certain cloud regimes. Weighting by error measures targeted on those regimes permits the development of tighter relationships between climate sensitivity and model error and, hence, narrower distributions of climate sensitivity in the simple ensemble. These relationships, however, do not carry into the multimodel ensemble. This suggests that model weighting based on statistical relationships alone is unfounded and perhaps that climate model errors are still large enough that model weighting is not sensible.