| Summary: | Part I Climate Change An equilibrium model driven by climatic parameters, the Siberian Vegetation Model, was used to estimate changes in the phytomass of Siberian vegetation under climate change scenarios (CO2 doubling) from four general circulation models (GCM’s) of the atmosphere. Ecosystems were classified using a three-dimensional climatic ordination of growing degree days (above a 5 °C threshold), Budyko’s dryness index (based on radiation balance and annual precipitation), and Conrad’s continentality index. Phytomass density was estimated using published data of Bazilevich covering all vegetation zones in Siberia. Under current climate, total phytomass of Siberia is estimated to be 74.1 ± 2.0 Pg. Note that this estimate is based on the current forested percentage in each vegetation class compiled from forest inventory data. Moderate warming associated with the GISS (Goddard Institute for Space Studies) and OSU (Oregon State Univ.) projections resulted in a 23–26% increase in phytomass (to 91.3 ± 2.1 Pg and 93.6 ± 2.4 Pg, respectively), primarily due to an increase in the productive Southern Taiga and Subtaiga classes. Greater warming associated with the GFDL (General Fluid Dynamics Laboratory) and UKMO (United Kingdom Meteorological Office) projections resulted in a small 3–7% increase in phytomass (to 76.6 ± 1.3 Pg and 79.6 ± 1.2 Pg, respectively). A major component of predicted changes using GFDL and UKMO is the introduction of a vast Temperate Forest-Steppe class covering nearly 40% of the area of Siberia, at the expense of Taiga; with current climate, this vegetation class is nearly non-existent in Siberia. In addition, Subboreal Forest-Steppe phytomass doubles with all GCM predictions. In all four climate change scenarios, the predicted phytomass stock of all colder, northern classes is reduced considerably (viz., Tundra, Forest-Tundra, Northern Taiga, and Middle Taiga). Phytomass in Subtaiga increases greatly with all scenarios, from a doubling with GFDL to quadrupling with OSU and GISS. Overall, ...
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