Climate Services (Apr 2018)

Simulating vegetation response to climate change in the Blue Mountains with MC2 dynamic global vegetation model

  • John B. Kim,
  • Becky K. Kerns,
  • Raymond J. Drapek,
  • G. Stephen Pitts,
  • Jessica E. Halofsky

Journal volume & issue
Vol. 10
pp. 20 – 32

Abstract

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Warming temperatures are projected to greatly alter many forests in the Pacific Northwest. MC2 is a dynamic global vegetation model, a climate-aware, process-based, and gridded vegetation model. We calibrated and ran MC2 simulations for the Blue Mountains Ecoregion, Oregon, USA, at 30 arc-second spatial resolution. We calibrated MC2 using the best available spatial datasets from land managers. We ran future simulations using climate projections from four global circulation models (GCM) under representative concentration pathway 8.5. Under this scenario, forest productivity is projected to increase as the growing season lengthens, and fire occurrence is projected to increase steeply throughout the century, with burned area peaking early- to mid-century. Subalpine forests are projected to disappear, and the coniferous forests to contract by 32.8%. Large portions of the dry and mesic forests are projected to convert to woodlands, unless precipitation were to increase. Low levels of change are projected for the Umatilla National Forest consistently across the four GCM’s. For the Wallowa-Whitman and the Malheur National Forest, forest conversions are projected to vary more across the four GCM-based simulations, reflecting high levels of uncertainty arising from climate. For simulations based on three of the four GCMs, sharply increased fire activity results in decreases in forest carbon stocks by the mid-century, and the fire activity catalyzes widespread biome shift across the study area. We document the full cycle of a structured approach to calibrating and running MC2 for transparency and to serve as a template for applications of MC2. Keywords: Climate change, Regional change, Simulation, Calibration, Forests, Fire, Dynamic global vegetation model