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.
Highlights
Climate variability can be examined over several temporal and spatial timescales
The aim of this paper is to describe the dataset, highlight the improvements over EKF400 version 1 (EKF400v1) and to inform the users about its limitations, so that it can be effectively used in future climate studies
In EKF400v2, tree-ring proxies from the Southern Hemisphere were added to the assimilated observations which can affect the growing season climate fields; we can see some increase in the correlation between the Auckland temperature series and EKF400v2 (Figure 6a)
Summary
Climate variability can be examined over several temporal and spatial timescales. To study climate variation at decadal to centennial timescales, long time series are required. Given that most of the new data come from coastal stations, we compared the independent time series with the analysis ensemble mean of the closest grid cell that we considered sufficiently representative of land surface temperature and that is present both in v1 and v2; for this reason, the selected grid cell can be up to 200 kilometres away from the location of the observations. In EKF400v2, tree-ring proxies from the Southern Hemisphere were added to the assimilated observations which can affect the growing season climate fields; we can see some increase in the correlation between the Auckland temperature series and EKF400v2 (Figure 6a). In version 2, numerous precipitation and temperature series are added in North America thanks to the assimilation of the GHCN- Monthly v2 precipitation and ISTI datasets (see Table 2) This brings a clear improvement in the reconstruction of the ENSO signal in the south-eastern United States, where La Niña conditions are related to strong negative (g). This signal was already well captured in version 1, negative precipitation anomalies over Southern and Western Europe are more prominent in version 2
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