The importance of input data quality and quantity in climate field reconstructions – results from the assimilation of various tree-ring collections
2020
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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