A Tree-Ring Based Late Summer Temperature Reconstruction (AD 1675–1980) for the Northeastern Mediterranean

This article presents a late summer temperature reconstruction (AD 1675–1980) for the northeastern Mediterranean (NEMED) that is based on a compilation of maximum latewood density tree-ring data from 21 high-elevation sites. This study applied a novel approach by combining individual series from all...

Full description

Bibliographic Details
Published in:Radiocarbon
Main Author: Trouet, Valerie
Format: Article in Journal/Newspaper
Language:English
Published: Cambridge University Press (CUP) 2014
Subjects:
Online Access:http://dx.doi.org/10.2458/azu_rc.56.18323
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0033822200050372
Description
Summary:This article presents a late summer temperature reconstruction (AD 1675–1980) for the northeastern Mediterranean (NEMED) that is based on a compilation of maximum latewood density tree-ring data from 21 high-elevation sites. This study applied a novel approach by combining individual series from all sites into one NEMED master chronology. This approach retains only the series with a strong and temporally robust common signal and it improves reconstruction length. It further improved the regional character of the reconstruction by using as a target averaged gridded instrumental temperature data from a broad NEMED region (38–45°N, 15–25°E). Cold (e.g. 1740) and warm (e.g. 1945) extreme years and decades in the reconstruction correspond to regional instrumental and reconstructed temperature records. Some extreme periods (e.g. cold 1810s) reflect European-wide or global-scale climate conditions and can be explained by volcanic and solar forcing. Other extremes are strictly regional in scope. For example, 1976 was the coldest NEMED summer over the last 350 years, but was anomalously dry and hot in northwestern Europe and is a strong manifestation of the summer North Atlantic Oscillation (sNAO). The regional NEMED summer reconstruction thus contributes to an improved understanding of regional (e.g. sNAO) vs. global-scale (i.e. external) drivers of past climate variability.