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    Most models of the potential effects of climate change on forest growth have produced deterministic predictions. However, there are large uncertainties in data on regional forest condition, estimates of future climate, and quantitative relationships between environmental conditions and forest growth rate. We constructed a new model to analyze these uncertainties along with available experimental results to make probabilistic estimates of climate change effects on the growth of loblolly pine (Pinus taeda L.) throughout its range in the USA. Complete regional data sets were created by means of spatial interpolation, and uncertainties in these data were estimated. A geographic information system (GIS) was created to integrate current and predicted climate data with regional data including forest distribution, growth rate, and stand characteristics derived from USDA Forest Service data. A probabilistic climate change scenario was derived from the results of four different general circulation models (GCM). Probabilistic estimates of forest growth were produced by linking the GIS to a Latin Hypercube carbon (C) budget model of forest growth. The model estimated a greater than 50% chance of a decrease in loblolly pine growth throughout most of its range. The model also estimated a 10% chance that the total regional basal area growth will decrease by more than 24 X 106 m2 yr-1 (a 92% decrease), and a 10% chance that basal area growth will increase by more than 62 X l06 m2 yr-1 (a 142% increase above current rates). The most influential factor at all locations was the relative change in C assimilation. Of climatic factors, CO, concentration was found to be the most influential factor at all locations. Substantial regional variation in estimated growth was observed, and probably was due primarily to variation in historical growth rates and to the importance of historical growth in the model structure.

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    Woodbury, Peter B.; Smith, James E.; Weinstein, David A.; Laurence, John A. 1998. Assessing Potential Climate Change Effects on Loblolly Pine Growth: A Probabilistic Regional Modeling Approach. Forest Ecology and Management 107 (1998) 99-116


    Monte Carlo, uncertainty analysis, risk assessement, geographical information system

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