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

Franke, Jörg; Valler, Veronika; Brönnimann, Stefan; Neukom, Raphael; Jaume-Santero, Fernando

doi:10.5194/cp-16-1061-2020

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Journal article

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

Franke, Jörg Institute of Geography, University of Bern, Bern, Switzerland - Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Valler, Veronika Institute of Geography, University of Bern, Bern, Switzerland - Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Brönnimann, Stefan Institute of Geography, University of Bern, Bern, Switzerland - Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Neukom, Raphael Institute of Geography, University of Bern, Bern, Switzerland - Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland - Department of Geography, University of Zurich, Zurich, Switzerland - Department of Geosciences, University of Fribourg, Fribourg, Switzerland
Jaume-Santero, Fernando Department of Earth Physics and Astrophysics, Universidad Complutense de Madrid, Madrid, Spain

18.06.2020

Published in:

Climate of the Past. - 2020, vol. 16, no. 3, p. 1061–1074

English 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.

Faculty

Faculté des sciences et de médecine

Department

Département de Géosciences

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