2010
DOI: 10.1175/2009jcli3155.1
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Pollen-Based Quantitative Reconstruction of Holocene Climate Changes in the Daihai Lake Area, Inner Mongolia, China

Abstract: Vegetation around the Daihai Lake, northern China, is very sensitive to climate changes. In this paper, pollen-based quantitative climate reconstructions using three methods [weighted averaging partial least squares method (WAPLS), modern analog technique (MAT), and pollen response surface method (PRS)] were conducted to obtain robust reconstructions of Holocene climate changes in the Daihai Lake area. The result obtained by the three methods all consistently show the annual precipitation to have been 50-100 m… Show more

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Cited by 194 publications
(125 citation statements)
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References 36 publications
(52 reference statements)
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“…The biome change in the forest-steppe transition zone is reflected by the record from Lake Daihai in Inner Mongolia (China; 40.5 • N, 112.5 • E; 1225 m a.s.l. ; Xu et al, 2010) that is in line with other records in north-central China , and references therein). To better compare the simulated biome distribution, these records were biomised in accordance with the BIOME4 biome classification.…”
Section: Pollen-based Biome Reconstruction For Key Areas Of Vegetatiosupporting
confidence: 92%
See 1 more Smart Citation
“…The biome change in the forest-steppe transition zone is reflected by the record from Lake Daihai in Inner Mongolia (China; 40.5 • N, 112.5 • E; 1225 m a.s.l. ; Xu et al, 2010) that is in line with other records in north-central China , and references therein). To better compare the simulated biome distribution, these records were biomised in accordance with the BIOME4 biome classification.…”
Section: Pollen-based Biome Reconstruction For Key Areas Of Vegetatiosupporting
confidence: 92%
“…Shown is also the present-day extent of the Asian summer monsoon region (light blue) based on the observed mean 2 mm/day summer isohyet (taken from GPCP data; Adler et al, 2003). Red numbers mark the location of the pollen sites: 1 is Lake Daihai (40.5 • N, 112.5 • E; Xu et al, 2010) and 2 is small lake on southern Taymyr Peninsula 72.4 • N, 102.29 • E; Klemm et al, 2016). tribution but also for the analysis of Holocene vegetation changes.…”
Section: Introductionmentioning
confidence: 99%
“…However, such data are available for other regions across the globe at similar latitudes, mainly based on pollen records (e.g. Xu et al 2009). These data show rapid warming and increased precipitation around 10k years ago and subsequently some variability in mean climate parameters, with certain periods of significant change (e.g.…”
Section: Holocene Climate and Potential Evapotranspirationmentioning
confidence: 99%
“…We used (1) a combination of 2 North American (NA) datasets (Gajewski et al 2004, Bunbury et al 2006), but adjusted the onset of major change to 10k years ago to reflect UK conditions; (2) a reconstruction based on Oak Ridge National Laboratory (ORNL) information (www.esd. ornl.gov/projects/qen/nercEUROPE.html) using records for China (Xu et al 2009) as an indication of the magnitude of changes; and (3) a constant present climate (4.75°C and 1922 mm) based on elevation-corrected MetOffice (gap free) records from 1931 to 2005 (Clark et al 2010, this Special). Crucially, such data include MAT and MAP, which can be used to derive AET using a simple equation (Turc 1954); although more sophisticated equations exist, the necessary input data are currently not available for the Holocene, although future projects might deliver much better Holocene climate reconstructions (e.g.…”
Section: Holocene Climate and Potential Evapotranspirationmentioning
confidence: 99%
“…During the past few decades, various studies have reconstructed temper-ature change on different timescales in northern China, using for example pollen (e.g. Wen et al, 2010;Xu et al, 2010), glycerol dialkyl glycerol tetraethers (GDGTs; e.g. Gao et al, 2012;Jia et al, 2013;Peterse et al, 2014), stalagmites (Tan et al, 2003) and historical archives (Ge et al, 2003).…”
Section: Introductionmentioning
confidence: 99%