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Dominique Bachelet

Dynamic global vegetation model (DGVM) projections are often put forth to aid resource managers in climate change-related decision making. However, interpreting model results and understanding their uncertainty can be difficult. Sources of uncertainty include embedded assumptions about atmospheric CO2 levels, uncertain climate projections driving DGVMs, and DGVM algorithm selection. For western Oregon and Washington, we implemented an Environmental Evaluation Modeling System (EEMS) decision support model using MC2 DGVM results to characterize biomass loss risk. MC2 results were driven by climate projections from 20 General Circulation Models (GCMs) and Earth System Models (ESMs), under Representative Concentration...
Categories: Publication; Types: Citation
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In order to assess and understand the potential impacts of climate change on important natural resources, managers, planners, and decision-makers need climate information at a local or regional scale. In general, Global Climate Models (GCMs) provide data at coarser scales than most natural resource managers need but Regional Climate Models (RCMs) are starting to deliver finer scale results. The project team will explore both dynamic downscaling products such as results from RCMs and statistical downscaling products generated at scales finer than the original projections. The Northwest CSC has supported a series of projects that have either generated or tested downscaled climate data for the Pacific Northwest...
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