Multi-decadal surface temperature changes may be forced by natural as well as anthropogenic factors, or arise unforced from the climate system. Distinguishing these factors is essential for estimating sensitivity to multiple climatic forcings and the amplitude of the unforced variability. Here we present 2,000-year-long global mean temperature reconstructions using seven different statistical methods that draw from a global collection of temperature-sensitive paleoclimate records. Our reconstructions display synchronous multi-decadal temperature fluctuations, which are coherent with one another and with fully forced CMIP5 millennial model simulations across the Common Era. The most significant attribution of pre-industrial (1300-1800 CE) variability at multi-decadal timescales is to volcanic aerosol forcing. Reconstructions and simulations qualitatively agree on the amplitude of the unforced global mean multi-decadal temperature variability, thereby increasing
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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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