Application of Bayesian Model Averaging in the Reconstruction of Past Climate Change Using PMIP3/CMIP5 Multimodel Ensemble Simulations
Application of Bayesian Model Averaging in the Reconstruction of Past Climate Change Using PMIP3/CMIP5 Multimodel Ensemble Simulations
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DOI：
10.1175/jcli-d-14-00752.1
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发表时间：
2016
影响因子：
4.9
通讯作者：
M. Fang;X. Li
中科院分区：
文献类型：
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作者：
M. Fang;X. Li
摘要基于气候模型的气候变化模拟不可避免地存在不确定性。这种不确定性通常源于气候模型中的参数和结构不确定性以及气候强迫。然而，使用适当的统计方法将模型模拟与仪器观测相结合是描述这种不确定性的有效方法。在这项研究中，作者将贝叶斯模型平均(BMA)这一统计后处理方法应用于古气候模式比较项目第三阶段(PMIP3)和耦合模式比较项目(CMIP5)第五阶段的气候模式模拟集合。使用国家环境预测中心-国家大气研究中心(NCEP-NCAR)再分析数据集，从培训期间估计单个模型模拟的不确定性、权重和方差。结果表明，BMA方法是成功的，在本研究中取得了积极的效果。..。
AbstractClimate change simulations based on climate models are inevitably uncertain. This uncertainty typically stems from parametric and structural uncertainties in climate models as well as climate forcings. However, combining model simulations with instrumental observations using appropriate statistical methods is an effective approach for describing this uncertainty. In this study, the authors applied Bayesian model averaging (BMA), a statistical postprocessing method, to an ensemble of climate model simulations from the Paleoclimate Modelling Intercomparison Project phase 3 (PMIP3) and phase 5 of the Coupled Model Intercomparison Project (CMIP5). Uncertainties, weights, and variances of individual model simulations were estimated from a training period using the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset. The results presented here demonstrate that the BMA method is successful and attains a positive performance in this study. ...