标题： 通过集成嵌套拉普拉斯近似法进行公元纪年气候的高效重建 显示英文标题

标题： Efficient Reconstructions of Common Era Climate via Integrated Nested Laplace Approximations

摘要： 对公元1年至2000年的古气候重建提供了近期变暖趋势的关键背景。 这项工作利用集成嵌套拉普拉斯近似（INLA）在贝叶斯分层模型下进行推断，使用了三个来源的数据：最先进的代理数据库（PAGES 2k）、地表温度观测数据（HadCRUT4）以及最新的外部强迫估计。 INLA的计算效率使得可以探索几种模型形式（包括或不包括强迫因素，是否明确建模内部变率），以及五种数据降维技术。 两种不同的验证实验发现数据降维选择的影响较小，但模型选择的影响较大，其中包含外部强迫的两个模型效果最好。 这些模型确认，人为温室气体排放是公元时期温度变化的最大贡献者，其次是火山强迫。 太阳效应无法与零区分开。 INLA提供了一种高效估计后验均值的方法，与成本更高的蒙特卡洛马尔可夫链过程相当，但不确定性范围更宽。 我们建议将其用于模型设计的探索，但对于正确的不确定性量化应使用完整的MCMC解决方案。 显示英文摘要

摘要： Paleoclimate reconstruction on the Common Era (1-2000AD) provide critical context for recent warming trends. This work leverages integrated nested Laplace approximations (INLA) to conduct inference under a Bayesian hierarchical model using data from three sources: a state-of-the-art prox database (PAGES 2k), surface temperature observations (HadCRUT4), and latest estimates of external forcings. INLA's computational efficiency allows to explore several model formulations (with or without forcings, explicitly modeling internal variability or not), as well as five data reduction techniques. Two different validation exercises find a small impact of data reduction choices, but a large impact for model choice, with best results for the two models that incorporate external forcings. These models confirm that man-made greenhouse gas emissions are the largest contributor to temperature variability over the Common Era, followed by volcanic forcing. Solar effects are indistinguishable from zero. INLA provide an efficient way to estimate the posterior mean, comparable with the much costlier Monte Carlo Markov Chain procedure, but with wider uncertainty bounds. We recommend using it for exploration of model designs, but full MCMC solutions should be used for proper uncertainty quantification.