A Quantitative Holocene Climatic Record from Diatoms in Northern Fennoscandia

Abstract

A diatom-based calibration model for predicting summer temperatures was developed using climatically sensitive subarctic lakes in northern Fennoscandia. The model was applied to a sediment core from a treeline lake to infer trends in Holocene climate. The record exhibits long-term variations, as well as a series of shorter-term fluctuations on a time scale of centuries. Summers were warmest in the area about 6200 cal yr B.P. and featured distinct cooling episodes around 8300, 7200, 4200, 3000, and 400 cal yr B.P., most of these coinciding with some known climate events (e.g., the 8200 cal yr B.P. event and the Little Ice Age). The similarity of the observed shifts with the pacings of climate events from marine and ice-core records represents evidence for coupled ocean–atmosphere forcing of the regional climate.

The temperature of Europe during the Holocene reconstructed from pollen data

“…These include dates of between 7900 and 6700 BP from pollen data (Sepp. a and Birks, 2001), 6200 BP from chironomids (Korhola et al, 2000) and maximum tree-line altitudes at 6300 BP (Barnekow, 2000) and between 6300 and 4500 BP (Barnekow and Sandgren 2001;. Land ice cover was also at a minimum at 6200 BP (Nesje et al, 2001), whilst glaciers were mainly absent from a catchment in Western Norway between 9800 and 6700 BP (Seierstad et al, 2002), and between 7300 and 6100 BP at least the northern part of the Jostedalsbreen ice cap melted away (Nesje et al, 2000).…”

Strengths and Weaknesses of Quantitative Climate Reconstructions Based on Late-Quaternary Biological Proxies

“…different transfer-function models, different data-sets, different records, different proxies) (Birks 1998). More sophisticated smoothers such as SiZer (significance of zero crossing of the derivative) (Chaudhuri & Marron 1999;Holmström & Erästö 2002) and the related BSiZer (Erästö & Holmström 2005) have considerable potential in palaeoclimatology because they can assess which features seen in a range of smoothed data are statistically significant and thus may be environmentally significant (see Korhola et al 2000;Erästö & Holmström 2005Weckström et al 2006 for palaeoecological applications). Given the random walk simulations of pollen data presented by Blaauw et al (2010), there is an increasing need to use techniques such as SiZer or BSiZer to distinguish signal from random red noise in stratigraphical time-series.…”

Multi-proxy studies in palaeolimnology

“…The SiZer smoothing procedure of Chaudhuri and Marron (1999) helps to assess which features in a smoothed time-series are statistically significant and hence which features may represent 'signal'. Korhola et al (2000) provide a palaeoecological application of SiZer. The approach could be extended to consider several stratigraphical records from a multi-proxy study to help distinguish 'signal' from 'noise'.…”