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Spatio-temporal variability of Arctic summer temperatures over the past 2 millennia

Authors: J. P. Werner; D. V. Divine; D. V. Divine; F. Charpentier Ljungqvist; F. Charpentier Ljungqvist; T. Nilsen; P. Francus; +1 Authors

Spatio-temporal variability of Arctic summer temperatures over the past 2 millennia

Abstract

Abstract. In this article, the first spatially resolved and millennium-length summer (June–August) temperature reconstruction over the Arctic and sub-Arctic domain (north of 60° N) is presented. It is based on a set of 44 annually dated temperature-sensitive proxy archives of various types from the revised PAGES2k database supplemented with six new recently updated proxy records. As a major advance, an extension of the Bayesian BARCAST climate field (CF) reconstruction technique provides a means to treat climate archives with dating uncertainties. This results not only in a more precise reconstruction but additionally enables joint probabilistic constraints to be imposed on the chronologies of the used archives. The new seasonal CF reconstruction for the Arctic region can be shown to be skilful for the majority of the terrestrial nodes. The decrease in the proxy data density back in time, however, limits the analyses in the spatial domain to the period after 750 CE, while the spatially averaged reconstruction covers the entire time interval of 1–2002 CE.The centennial to millennial evolution of the reconstructed temperature is in good agreement with a general pattern that was inferred in recent studies for the Arctic and its subregions. In particular, the reconstruction shows a pronounced Medieval Climate Anomaly (MCA; here ca. 920–1060 CE), which was characterised by a sequence of extremely warm decades over the whole domain. The medieval warming was followed by a gradual cooling into the Little Ice Age (LIA), with 1766–1865 CE as the longest centennial-scale cold period, culminating around 1811–1820 CE for most of the target region.In total over 600 independent realisations of the temperature CF were generated. As showcased for local and regional trends and temperature anomalies, operating in a probabilistic framework directly results in comprehensive uncertainty estimates, even for complex analyses. For the presented multi-scale trend analysis, for example, the spread in different paths across the reconstruction ensemble prevents a robust analysis of features at timescales shorter than ca. 30 years. For the spatial reconstruction, the benefit of using the spatially resolved reconstruction ensemble is demonstrated by focusing on the regional expression of the recent warming and the MCA. While our analysis shows that the peak MCA summer temperatures were as high as in the late 20th and early 21st centuries, the spatial coherence of extreme years over the last decades of the reconstruction (1980s onwards) seems unprecedented at least back until 750 CE. However, statistical testing could not provide conclusive support of the contemporary warming to exceed the peak of the MCA in terms of the pan-Arctic mean summer temperatures: the reconstruction cannot be extended reliably past 2002 CE due to lack of proxy data and thus the most recent warming is not captured.

Keywords

reconstruction, VDP::Mathematics and natural science: 400::Geosciences: 450::Meteorology: 453, 550, probability, PALEOTEMPERATURE RECORD, Environmental protection, Environmental pollution, RECONSTRUCTING CLIMATE ANOMALIES, ENVIRONMENTAL-CHANGE, BAYESIAN ALGORITHM, SURFACE CONDITIONS, TD169-171.8, spatiotemporal analysis, GE1-350, uncertainty analysis, database, MAXIMUM LATEWOOD, Medieval Warm Period, proxy climate record, chronology, VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Meteorologi: 453, NORTHERN GREENLAND, Environmental sciences, ELLESMERE-ISLAND, TD172-193.5, Earth and Environmental Sciences, DENSITY, Little Ice Age, TREE-RING-WIDTH, trend analysis, SEA-ICE

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
33
Top 10%
Top 10%
Top 10%
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