[1] In the biomass, soils, and peatlands of Siberia, boreal Russia holds one of the largest pools of terrestrial carbon. Because Siberia is located where some of the largest temperature increases are expected to occur under current climate change scenarios, stored carbon has the potential to be released with associated changes in fire regimes. Our concentration is on estimating a wide range of current and potential emissions from Siberia on the basis of three modeled scenarios. An area burned product of Siberia is introduced, which spans from 1998 through 2002. Emissions models are spatially explicit; therefore area burned is extracted from associated ecoregions for each year. Carbon consumption estimates are presented for 23 unique ecoregions across Siberia, which range from 3.4 to 75.4 t C ha À1 for three classes of severity. Total direct carbon emissions range from the traditional scenario estimate of 116 Tg C in 1999 (6.9 M ha burned) to the extreme scenario estimate of 520 Tg C in 2002 (11.2 M ha burned), which are equivalent to 5 and 20%, respectively, of total global carbon emissions from forest and grassland burning. Our results suggest that disparities in the amount of carbon stored in unique ecosystems and the severity of fire events can affect total direct carbon emissions by as much as 50%. Additionally, in extreme fire years, total direct carbon emissions can be 37-41% greater than in normal fire years, owing to increased soil organic matter consumption. Mean standard scenario estimates of CO 2 (555-1031 Tg), CO (43-80 Tg), CH 4 (2.4-4.5 Tg), TNMHC (2.2-4.1 Tg), and carbonaceous aerosols (4.6-8.6 Tg) represent 10, 15, 19, 12 and 26%, respectively, of the global estimates from forest and grassland burning. Accounting for smoldering combustion in soils and peatlands results in increases in CO, CH 4 , and TNMHC and decreases in CO 2 emitted from fire events.

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We modeled tree mortality occurring two years following wildfire in Pinus ponderosa forests using data from 1275 trees in 25 stands burned during the 1987 Stanislaus Complex fires. We used logistic regression analysis to develop models relating the probability of wildfire-induced mortality with tree size and fire severity for Pinus ponderosa, Calocedrus decurrens, Quercus chrysolepis, and Q. kelloggii. One set of models predicts mortality probability as a function of DBH and height of stem-bark char, a second set of models uses relative char height (height of stem-bark char as a proportion of tree height) as the predictor. Probability of mortality increased with increasing height of stem-bark char and decreased with increasing tree DBH and height. Analysis of receiver operating characteristic (ROC) curves indicated that both sets of models perform well for all species, with 83 to 96 percent concordance between predicted probabilities and observed outcomes. The models can be used to predict die probability of post-wildfire mortality of four tree species common in Pinus ponderosa forests in the central Sierra Nevada of California.

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