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JOURNAL OF GEOPHYSICAL RESEARCH,
VOL. 108,
8171,
17 PP., 2003
doi:10.1029/2002JD002559
Climate change and Arctic ecosystems: 2. Modeling, paleodata-model comparisons, and future projections
Max Planck Institute for Biogeochemistry, Jena, Germany
Alaska Quaternary Center, University of Alaska, Fairbanks, Alaska, USA
Max Planck Institute for Biogeochemistry, Jena, Germany
Max Planck Institute for Biogeochemistry, Jena, Germany
Dynamic Paleoclimatology, Lund University, Lund, Sweden
Department of Geography, University of Oregon, Eugene, Oregon, USA
Climate Impacts Group, Department of Ecology, Lund University, Lund, Sweden
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Department of Vegetation of the Far North, Komarov Botanical Institute, St. Petersburg, Russia
U.S. Geological Survey, Alaska Cooperative Fish and Wildlife Research Unit, University of Alaska, Fairbanks, Alaska, USA
University of Alaska Museum, Fairbanks, Alaska, USA
Department of Vegetation of the Far North, Komarov Botanical Institute, St. Petersburg, Russia
Max Planck Institute for Biogeochemistry, Jena, Germany
Climate Impacts Group, Department of Ecology, Lund University, Lund, Sweden
Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado, USA
Institute of Arctic Biology, University of Alaska, Fairbanks, Alaska, USA
Quaternary Research Center, University of Washington, Seattle, Washington, USA
Alfred Wegner Institute for Polar and Marine Research, Forschungsstelle Potsdam, Potsdam, Germany
College of Forest Resources, University of Washington, Seattle, Washington, USA
Institute of Arctic Biology, University of Alaska, Fairbanks, Alaska, USA
Department of Geography, Norges Teknisk-Naturvitenskapelige Universitet, Trondheim, Norway
Northeast Interdisciplinary Scientific Research Institute, Russian Academy of Sciences, Magadan, Russia
Large variations in the composition, structure, and function of Arctic ecosystems are determined by climatic gradients, especially of growing-season warmth, soil moisture, and snow cover. A unified circumpolar classification recognizing five types of tundra was developed. The geographic distributions of vegetation types north of 55°N, including the position of the forest limit and the distributions of the tundra types, could be predicted from climatology using a small set of plant functional types embedded in the biogeochemistry-biogeography model BIOME4. Several palaeoclimate simulations for the last glacial maximum (LGM) and mid-Holocene were used to explore the possibility of simulating past vegetation patterns, which are independently known based on pollen data. The broad outlines of observed changes in vegetation were captured. LGM simulations showed the major reduction of forest, the great extension of graminoid and forb tundra, and the restriction of low- and high-shrub tundra (although not all models produced sufficiently dry conditions to mimic the full observed change). Mid-Holocene simulations reproduced the contrast between northward forest extension in western and central Siberia and stability of the forest limit in Beringia. Projection of the effect of a continued exponential increase in atmospheric CO2 concentration, based on a transient ocean-atmosphere simulation including sulfate aerosol effects, suggests a potential for larger changes in Arctic ecosystems during the 21st century than have occurred between mid-Holocene and present. Simulated physiological effects of the CO2 increase (to >700 ppm) at high latitudes were slight compared with the effects of the change in climate.
Received 23 May 2002; accepted 29 January 2003; published 8 October 2003.
Citation: (2003), Climate change and Arctic ecosystems: 2. Modeling, paleodata-model comparisons, and future projections, J. Geophys. Res., 108(D19), 8171, doi:10.1029/2002JD002559.
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