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Climate, vegetation phenology and forest fires in Siberia

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8 Author(s)
Balzter, H. ; Univ. of Leicester, Leicester ; Weedon, G. ; Grey, W. ; Los, S.
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A time series of 18 years of fAPAR (fraction of photosynthetically active radiation absorbed by the green parts of vegetation) data from the NOAA AVHRR instrument series was analyzed for interannual variations in the start, peak, end and length of the season of vegetation photosynthetic activity in Central and East Siberia. Variations in these indicators of seasonality can give important information on interactions between the biosphere and atmosphere. A second order local moving window regression model called the "camel back method" was developed to determine the dates of phenological events at subcontinental scale. The algorithm was validated by comparing the estimated dates to phenological field observations. Using spatial correlations with temperature and precipitation data and climatic oscillation indices, we postulate two geographically distinct mechanisms in the system of climatic controls of the biosphere in Siberia: Central Siberia is controlled by an "Arctic oscillation/temperature mechanism" while East Siberia is controlled by an "El Nino/precipitation mechanism". While the analysis of data from 1982 to 1991 indicates a slight increase in the length of the growing season for some land cover types due to an earlier beginning of the growing season, the overall trend from 1982 to 1999 is towards a slightly shorter season for some land cover types caused by an earlier end of season. The Arctic Oscillation tended towards a more positive phase in the Eighties leading to enhanced high pressure system prevalence but towards a less positive phase in the Nineties. We suggest that the two mechanisms also control the fire regimes in Central and East Siberia. Several extreme fire years in Central Siberia were associated with a highly positive Arctic Oscillation phase, while several years with high fire damage in East Siberia occurred in El Nino years. An analysis of remote sensing data of forest fire partially supports this hypothesis.

Published in:

Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International

Date of Conference:

23-28 July 2007