Abstract

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 Pathways (RCPs) 4.5 and 8.5, with and without assumed fire suppression, for three different time periods. We produced maps of mean, minimum, and maximum biomass loss risk and uncertainty for each RCP / +/- fire suppression / time period. We characterized the uncertainty due to RCP, fire suppression, and climate projection choice. Finally, we evaluated whether fire or climate maladaptation mortality was the dominant driver of risk for each model run. The risk of biomass loss generally increases in current high biomass areas within the study region through time. The pattern of increased risk is generally south to north and upslope into the Coast and Cascade mountain ranges and along the coast. Uncertainty from climate future choice is greater than that attributable to RCP or +/- fire suppression. Fire dominates as the driving factor for biomass loss risk in more model runs than mortality. This method of interpreting DGVM results and the associated uncertainty provides managers with data in a form directly applicable to their concerns and should prove helpful in adaptive management planning.

Highlights

  • Anthropogenic emissions have caused oceanic and atmospheric warming, diminished snow and ice, and rising sea level [1]

  • We present detailed results for the Representative Concentration Pathways (RCPs) 8.5 / NFS / 2071–2099 time period ensemble and summary results from other ensembles

  • For RCP 8.5 with or without fire suppression, mean and minimum values of Biomass Loss Risk are highest during the mid 21st c. in the southern portion of the study area (Fig 6 and S3A, S3B, S3E, S3F, S3I, S3J Fig)

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Summary

Introduction

Anthropogenic emissions have caused oceanic and atmospheric warming, diminished snow and ice, and rising sea level [1]. The effects of climate change vary regionally [1] and have already affected crop yields [2,3,4,5]), biodiversity [6,7], and wildfire risk [8,9,10]. Study design, data collection and analysis, decision to publish, or preparation of the manuscript

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