Valler Veronika, Franke Jörg, Brugnara Yuri, Brönnimann Stefan
Oeschger Centre for Climate Change Research University of Bern Bern Switzerland.
Institute of Geography University of Bern Bern Switzerland.
Geosci Data J. 2022 Jun;9(1):89-107. doi: 10.1002/gdj3.121. Epub 2021 May 4.
Data assimilation techniques are becoming increasingly popular for climate reconstruction. They benefit from estimating past climate states from both observation information and from model simulations. The first monthly global paleo-reanalysis (EKF400) was generated over the 1600 and 2005 time period, and it provides estimates of several atmospheric fields. Here we present a new, considerably improved version of EKF400 (EKF400v2). EKF400v2 uses atmospheric-only general circulation model simulations with a greatly extended observational network of early instrumental temperature and pressure data, documentary evidences and tree-ring width and density proxy records. Furthermore, new observation types such as monthly precipitation amounts, number of wet days and coral proxy records were also included in the assimilation. In the version 2 system, the assimilation process has undergone methodological improvements such as the background-error covariance matrix is estimated with a blending technique of a time-dependent and a climatological covariance matrices. In general, the applied modifications resulted in enhanced reconstruction skill compared to version 1, especially in precipitation, sea-level pressure and other variables beside the mostly assimilated temperature data, which already had high quality in the previous version. Additionally, two case studies are presented to demonstrate the applicability of EKF400v2 to analyse past climate variations and extreme events, as well as to investigate large-scale climate dynamics.
数据同化技术在气候重建中越来越受欢迎。它们得益于从观测信息和模型模拟中估计过去的气候状态。首个月度全球古再分析(EKF400)是在1600年至2005年期间生成的,它提供了几个大气场的估计值。在此,我们展示了一个全新的、大幅改进的EKF400版本(EKF400v2)。EKF400v2使用仅包含大气的通用环流模型模拟,并结合了早期仪器温度和压力数据、文献证据以及树木年轮宽度和密度代用记录的大幅扩展的观测网络。此外,新的观测类型,如月度降水量、降水日数和珊瑚代用记录也被纳入了同化过程。在版本2系统中,同化过程在方法上有所改进,例如背景误差协方差矩阵是通过时间相关协方差矩阵和气候协方差矩阵的混合技术来估计的。总体而言,与版本1相比,所应用的改进提高了重建技能,特别是在降水量、海平面气压以及除了大多已被同化且在先前版本中质量就很高的温度数据之外的其他变量方面。此外,还给出了两个案例研究,以证明EKF400v2在分析过去气候变化和极端事件以及研究大规模气候动力学方面的适用性。