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An updated global atmospheric paleo‐reanalysis covering the last 400 years

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Valler Veronika, Franke Jörg, Brugnara Yuri, Brönnimann Stefan,
Project Reconstructing Climate Using Ensemble Kalman Fitting (REUSE)
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Original article (peer-reviewed)

Journal Geoscience Data Journal
Page(s) gdj3.121 - gdj3.121
Title of proceedings Geoscience Data Journal
DOI 10.1002/gdj3.121

Open Access

URL http://doi.org/10.1002/gdj3.121
Type of Open Access Publisher (Gold Open Access)

Abstract

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.
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