Holocene treeline history and climate change across northern Eurasia

Radiocarbon-dated macrofossils are used to document Holocene treeline history across northern Russia (including Siberia), Boreal forest development in this region commenced by 10,000 yr B.P, Over most of Russia, forest advanced to or near the current arctic coastline between 9000 and 7000 yr B.P. and retreated to its present position by between 4000 and 3000 yr B.P. Forest establishment and retreat was roughly synchronous across most of northern Russia, Treeline advance on the Kola Peninsula, however, appears to have occurred later than in other regions. During the period of maximum forest extension, the mean July temperatures along the northern coastline of Russia may have been 2.5 degrees to 7.0 degrees C warmer than modern. The development of forest and expansion of treeline likely reflects a number of complimentary environmental conditions, including heightened summer insolation, the demise of Eurasian ice sheets, reduced sea-ice cover, greater continentality with eustatically lower sea level, and extreme Arctic penetration of warm North Atlantic waters. The late Holocene retreat of Eurasian treeline coincides with declining summer insolation, cooling arctic waters, and neoglaciation, (C) 2000 University of Washington

Climatic factors affecting the tree-ring width of Betula ermanii at the timberline on Mount Norikura, central Japan

The original publication is available at www.springerlink.comArticleECOLOGICAL RESEARCH. 20(4):445-451(2005)journal articl

Development of a Chironomid-based Air Temperature Inference Model for the Central Canadian Arctic

Subfossil midge remains were identified in surface sediment recovered from 88 lakes in the central Canadian Arctic. These lakes spanned five vegetation zones, with the southern-most lakes located in boreal forest and the northern-most lakes located in mid-Arctic tundra. The lakes in the calibration are characterized by ranges in depth, summer surface-water temperature (SSWT), average July air temperature (AJAT) and pH of 15.5 m, 10.60°C, 8.40°C and 3.69, respectively. Redundancy analysis (RDA) indicated that maximum depth, pH, AJAT, total nitrogen-unfiltered (TN-UF), Cl and Al capture a large and statistically significant fraction of the overall variance in the midge data. Inference models relating midge abundances and AJAT were developed using different approaches including: weighted averaging (WA), weighted averaging-partial least squares (WA-PLS) and partial least squares (PLS). A chironomid-based inference model, based on a two-component WA-PLS approach, provided robust performance statistics with a high coefficient of determination (r 2 = 0.77) and low root mean square error of prediction (RMSEP = 1.03°C) and low maximum bias. The use of a high-resolution gridded climate data set facilitated the development of the midge-based inference model for AJAT in a region with a paucity of meteorological stations and where previously only the development of a SSWT inference model was possible