| Summary: | In the northern high latitudes, vegetation distribution and carbon cycling have been continuously changed in the past and could change more rapidly as the climate warming. The purpose of my PhD dissertation is to quantify the uncertainty in modeling vegetatidynamics and to assess the effect of permafrost on vegetation dynamics and carbon cycling in the northern high latitudes under different levels of warming conditions. The uncertainty in current modeling of vegetation dynamics is considerably large. The first part of this study is to assess how high-latitude vegetation may respond under various climate scenarios during the 21st Century with a focus on analyzing model parameters induced uncertainty and how this uncertainty compares to the uncertainty induced by various climates. The analysis was based on a set of 10,000 Monte Carlo ensemble LPJ simulations for the 45°N polewards region from 1900 to 2100. LPJ-DGVM was run under contemporary and future climates from four Special Report Emission Scenarios (SRES), A1FI, A2, B1, and B2, based on the Hadley Centre General Circulation Model (GCM), and six climate scenarios, X901M, X902L, X903H, X904M, X905L, and X906H from the Integrated Global System Model (IGSM) at the Massachusetts Institute of Technology (MIT). In the current dynamic vegetation model, some parameters are more important than others in determining the vegetation distribution. Parameters that control plant carbon uptake and light-use efficiency have the predominant influence on the vegetation distribution of both woody and herbaceous plant functional types. The relative importance of different parameters varies temporally and spatially and is influenced by climate inputs. In addition to climate, these parameters play an important role in determining the vegetation distribution in the region. The parameter-based uncertainties contribute most to the total uncertainty. The current warming conditions lead to a complexity of vegetation responses in the region. Temperate trees will be more sensitive to ...
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