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An updated global atmospheric paleo‐reanalysis covering the last 400 years
Geoscience Data Journal ( IF 2.714 ) Pub Date : 2021-05-04 , DOI: 10.1002/gdj3.121
Veronika Valler, Jörg Franke, Yuri Brugnara, Stefan Brönnimann

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.

中文翻译:

最近400年来最新的全球大气古再分析

数据同化技术在气候重建中正变得越来越流行。他们可以从观测信息和模型模拟中估算过去的气候状态,从而受益。在1600年和2005年这段时间里,首次产生了每月一次的全球古重新分析(EKF400),它提供了几个大气场的估计值。在这里,我们介绍了EKF400(EKF400v2)的一个新的,经过重大改进的版本。EKF400v2使用仅限大气的一般循环模型模拟,并大大扩展了早期仪器温度和压力数据,文献证据以及树轮宽度和密度代理记录的观测网络。此外,同化还包括了新的观测类型,例如月降水量,湿天数和珊瑚代理记录。在版本2系统中,同化过程得到了方法上的改进,例如使用时间相关矩阵和气候协方差矩阵的混合技术来估算背景误差协方差矩阵。通常,与版本1相比,所应用的修改导致增强的重建技能,尤其是在降水,海平面压力和除大部分被同化的温度数据以外的其他变量方面,这些数据在先前版本中已经具有很高的质量。此外,还提供了两个案例研究,以证明EKF400v2在分析过去的气候变化和极端事件以及调查大规模气候动态方面的适用性。
更新日期:2021-05-04
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