论文标题
贝叶斯对协方差矩阵估计估计不确定性的定量最佳指纹识别
Bayesian Quantification of Covariance Matrix Estimation Uncertainty in Optimal Fingerprinting
论文作者
论文摘要
用于气候变化检测和归因的基于回归的最佳指纹技术需要估计强制信号以及内部可变性协方差矩阵，以区分它们在观测记录中的影响。尽管先前开发的方法已经考虑到与强制信号估计有关的不确定性，但尽管已知该协方差矩阵的规范对结果有意义地影响结果，但对描述自然变异性的协方差矩阵的不确定性的关注较少。在这里，我们建议使用协方差矩阵的laplacian基函数参数化参数化贝叶斯最佳指纹框架。与基于主组件的传统方法不同，这种参数化不需要从气候模型数据中估算基础向量本身，这使得在估计协方差结构的不确定性传播到最佳指纹回归参数。我们通过一项CMIP6验证研究表明，这种提出的方法比基于主要成分的方法实现了真实回归参数的覆盖率更好。与基于主要组件的方法相比，当应用于HADCRUT观察数据时，所提出的方法可检测具有更高置信度的人为变暖，并且在气候模型选择中的变异性较低。
Regression-based optimal fingerprinting techniques for climate change detection and attribution require the estimation of the forced signal as well as the internal variability covariance matrix in order to distinguish between their influences in the observational record. While previously developed approaches have taken into account the uncertainty linked to the estimation of the forced signal, there has been less focus on uncertainty in the covariance matrix describing natural variability, despite the fact that the specification of this covariance matrix is known to meaningfully impact the results. Here we propose a Bayesian optimal fingerprinting framework using a Laplacian basis function parameterization of the covariance matrix. This parameterization, unlike traditional methods based on principal components, does not require the basis vectors themselves to be estimated from climate model data, which allows for the uncertainty in estimating the covariance structure to be propagated to the optimal fingerprinting regression parameter. We show through a CMIP6 validation study that this proposed approach achieves better-calibrated coverage rates of the true regression parameter than principal component-based approaches. When applied to HadCRUT observational data, the proposed approach detects anthropogenic warming with higher confidence levels, and with lower variability over the choice of climate models, than principal-component-based approaches.
