Charman, D.J., Blundell, A., Chiverrell, R.C., Hendon, D. and Langdon, P.G.
Compilation of non-annually resolved Holocene proxy climate records: stacked Holocene peatland palaeo-water table reconstructions from northern Britain
Quaternary Science Reviews, 25, . (doi:10.1016/j.quascirev.2005.05.005).
Full text not available from this repository.
The number and range of Holocene palaeoclimate reconstructions from various regions of the world have increased dramatically
over the last decade. The data density for many regions and proxies now offers the potential of robust regional-scale reconstructions
that avoid the problems of records from individual sites, and improve communication between palaeoclimate subdisciplines and
climate modellers. However, there are problems with chronological uncertainties and quantification of proxies, which make
compilation of multiple records difficult. Here we explore a ‘stacking’ and ‘tuning’ approach to the derivation of regional records
from peatland climate proxies to test its applicability to non-annually resolved terrestrial records. Twelve individual records from
northern Britain based on water table reconstructions from testate amoebae analysis were divided into four regions. Records were
detrended, normalised and compared within regions to identify clear correlative events. The original chronologies of the records
were tuned using both these events and independent age markers. The stacked record for northern Britain indicates pronounced
changes to wet conditions at 3600, 2760 and 1600 cal yr BP with more minor changes at 3060, 2050, 1260, 860, 550 and 260 cal yr BP.
The main wet phases are highly correlated with mid-European lake highstands, wider North Atlantic climate change inferred from
ocean and ice core records, and solar variability. Tuning and stacking of non-annual terrestrial palaeoclimate records is a new
approach to the compilation and reconciliation of individual records within coherent climatic regions and provides a tool for
upscaling of palaeoclimate records for climate model-data comparisons.
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