Simple Uncertainty Frameworks for Selecting Weighting Schemes and Interpreting Multimodel Ensemble Climate Change Experiments
Simple Uncertainty Frameworks for Selecting Weighting Schemes and Interpreting Multimodel Ensemble Climate Change Experiments
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DOI：
10.1175/jcli-d-12-00462.1
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发表时间：
2013-06
影响因子：
4.9
通讯作者：
Philip G. Sansom;D. Stephenson;C. Ferro;G. Zappa;L. Shaffrey
中科院分区：
文献类型：
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作者：
Philip G. Sansom;D. Stephenson;C. Ferro;G. Zappa;L. Shaffrey
未来的气候变化预测通常是使用启发式加权方案从多个全球环流模型的模拟集合中得出的。本研究通过引入由三个简单方差分析框架组成的嵌套系列，为此提供了更严格的论证。为了量化与平均气候变化响应估计相关的不确定性，统计框架至关重要。最通用的框架产生了气候预测中经常使用的“一个模型，一票”加权方案。然而，当气候变化响应不强烈依赖于模型时，更简单的加性框架被发现是更可取的。在这种情况下，即使存在共享模型偏差，加权多模型平均值也可以解释为对实际气候响应的估计。推导统计显着性检验，为特定的多模型集成数据选择最合适的框架。框架假设是明确的，可以使用简单的测试和图形技术进行检查。该框架可用于测试非零气候响应的证据并构建响应大小的置信区间。该方法通过应用耦合模型比对项目第 5 阶段 (CMIP5) 多模型集合的北大西洋风暴轨迹数据来说明。尽管历史风暴路径变化很大，但在大部分地区，气旋频率气候变化响应并不依赖于模型。这使得响应估计具有很高的可信度。从统计上看，北大西洋风暴路径的侧翼和地中海盆地的气旋频率显着下降。
Future climate change projections are often derived from ensembles of simulations from multiple global circulationmodelsusingheuristicweightingschemes.Thisstudyprovidesamorerigorousjustificationforthisby introducinga nested family of threesimple analysisofvariance frameworks. Statistical frameworksareessential in order to quantify the uncertainty associated with the estimate of the mean climate change response. The most general framework yields the ‘‘one model, one vote’’ weighting scheme often used in climate projection. However, a simpler additive framework is found to be preferable when the climate change responseisnotstronglymodeldependent.Insuchsituations,theweightedmultimodelmeanmaybeinterpreted as an estimate of the actual climate response, even in the presence of shared model biases. Statistical significance tests are derived to choose the most appropriate framework for specific multimodel ensemble data. The framework assumptions are explicit and can be checked using simple tests and graphical techniques. The frameworks can be used to test for evidence of nonzero climate response and to construct confidence intervals for the size of the response. The methodology is illustrated by application to North Atlantic storm track data from the Coupled Model Intercomparison Project phase 5 (CMIP5) multimodel ensemble. Despite large variations in the historical storm tracks, the cyclone frequencyclimate change response is not found to be model dependentover most of the region. This gives high confidence in the response estimates. Statistically significant decreases in cyclone frequency are found on the flanks of the North Atlantic storm track and in the Mediterranean basin.