CMG: Improved Bayesian Estimators for Uncertainty in Climate System Properties

研究人员正在开发贝叶斯统计模型，以研究气候系统特性的分布。这项研究是基于麻省理工学院2DLO气候模式的输出以及从大气-海洋环流模式(AOGCM)对自然气候变异性的估计。统计模型侧重于对自然变化的主要模式的估计，从而考虑了所有不确定性。这是通过从AOGCM的系综运行建立协方差矩阵的先验分布来实现的。特别注意协方差矩阵的谱分解。此外，统计模型是分层次的，以便全面考虑所有误差来源。这些误差包括由于不可能在足够短的时间内评估气候模型而导致的内插误差，以便将其嵌入蒙特卡罗迭代估计方法。这项拟议的研究显然符合美国国家科学基金会数学地球科学合作计划的“表现地球系统中的不确定性”主题。主要的重点是改进对控制气候系统大规模行为的参数的估计。由此产生的分析将包括对这些估计数的不确定性的评估。这项研究是气候科学家和统计学家之间的合作努力，因为它需要使用气候系统模型以及分析气候观测数据集。该项目更广泛的方面是，气候系统行为中估计的不确定性可用于气候变化预测的不确定性分析。通过提高分析气候变化对社会的风险的能力，这项研究将为政策制定者提供宝贵的意见。

The investigators are developing Bayesian statistical models for the study of the distribution of climate system properties. The study is based on output from the MIT 2DLO climate model as well as an estimation of the natural climate variability from atmosphere-ocean general circulation models (AOGCM). The statistical models account for all uncertainties by focusing on the estimation of the main patterns of natural variability. This is achieved by building prior distributions for the covariance matrix from ensemble runs of AOGCMs. Particular attention is paid to the spectral decomposition of the covariance matrix. In addition the statistical models are hierarchical in order to consider all sources of errors in a comprehensive way. These errors include the interpolation error due to the impossibility of evaluating climate models in a time short enough to embed it within a Monte Carlo iterative estimation method. The proposed research falls clearly into the ``Representing uncertainty in geosystems'' theme of the NSF Program for Collaborations in Mathematical Geosciences. The main focus is to improve the estimates of parameters that govern the large-scale behavior of the climate system. The resulting analysis will include an assessment of the uncertainty of those estimates. The research is a collaborative effort between climate scientists and statisticians as it requires the use of climate system models as well as analyzing climate observational datasets. The broader aspect of this project is that the estimated uncertainties in climate system behavior can be used for uncertainty analysis of climate change projections. By enhancing the ability to analyze the risks of climate change on society, this research will provide valuable input to policymakers.