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The importance of input data quality and quantity in climate field reconstructions – results from the assimilation of various tree-ring collections

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

Journal Climate of the Past
Volume (Issue) 16(3)
Page(s) 1061 - 1074
Title of proceedings Climate of the Past
DOI 10.5194/cp-16-1061-2020

Open Access

URL http://doi.org/10.5194/cp-16-1061-2020
Type of Open Access Publisher (Gold Open Access)

Abstract

Abstract. Differences between paleoclimatic reconstructions are caused by two factors: the method and the input data. While many studies compare methods, we will focus in this study on the consequences of the input data choice in a state-of-the-art Kalman-filter paleoclimate data assimilation approach. We evaluate reconstruction quality in the 20th century based on three collections of tree-ring records: (1) 54 of the best temperature-sensitive tree-ring chronologies chosen by experts; (2) 415 temperature-sensitive tree-ring records chosen less strictly by regional working groups and statistical screening; (3) 2287 tree-ring series that are not screened for climate sensitivity. The three data sets cover the range from small sample size, small spatial coverage and strict screening for temperature sensitivity to large sample size and spatial coverage but no screening. Additionally, we explore a combination of these data sets plus screening methods to improve the reconstruction quality. A large, unscreened collection generally leads to a poor reconstruction skill. A small expert selection of extratropical Northern Hemisphere records allows for a skillful high-latitude temperature reconstruction but cannot be expected to provide information for other regions and other variables. We achieve the best reconstruction skill across all variables and regions by combining all available input data but rejecting records with insignificant climatic information (p value of regression model >0.05) and removing duplicate records. It is important to use a tree-ring proxy system model that includes both major growth limitations, temperature and moisture.
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