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Novel climate and disturbance regimes in the 21st century threaten to increase the vulnerability of some western U.S. forests to loss of biomass and function. However, the timing and magnitude of forest vulnerabilities are uncertain and will be highly variable across the complex biophysical landscape of the region. Assessing future forest trajectories and potential management impacts under novel conditions requires place-specific and mechanistic model projections. Stakeholders in the high-carbon density forests of the northern U.S. Rocky Mountains (NRM) currently seek to understand and mitigate climate risks to these diverse conifer forests, which experienced profound 20th century disturbance from the 1910 “Big Burn” and timber harvest. Present forest management plan revisions consider approaches including increases in timber harvest that are intended to shift species compositions and increase forest stress tolerance. We utilize CLM-FATES, a dynamic vegetation model (DVM) coupled to an Earth Systems Model (ESM), to model shifting NRM forest carbon stocks and cover, production, and disturbance through 2100 under unprecedented climate and management. Across all 21st century scenarios, domain forest C-stocks and canopy cover face decline after 2090 due to the interaction of intermittent drought and fire mortality with declining Net Primary Production (NPP) and post-disturbance recovery. However, mid-century increases in forest vulnerability to fire and drought impacts are not consistently projected across climate models due to increases in precipitation that buffer warming impacts. Under all climate scenarios, increased harvest regimes diminish forest carbon stocks and increase period mortality over business-as-usual, despite some late-century reductions in forest stress. Results indicate that existing forest carbon stocks and functions are moderately persistent and that increased near-term removals may be mistimed for effectively increasing resilience.

Plant community composition influences carbon, water, and energy fluxes at regional to global scales. Vegetation demographic models (VDMs) allow investigation of the effects of changing climate and disturbance regimes on vegetation composition and fluxes. Such investigation requires that the models can accurately resolve these feedbacks to simulate realistic composition. Vegetation in VDMs is composed of plant functional types (PFTs), which are specified according to plant traits. Defining PFTs is challenging due to large variability in trait observations within and between plant types and a lack of understanding of model sensitivity to these traits. Here we present an approach for developing PFT parameterizations that are connected to the underlying ecological processes determining forest composition in the mixed-conifer forest of the Sierra Nevada of California, USA. We constrain multiple relative trait values between PFTs, as opposed to randomly sampling within the range of observations. An ensemble of PFT parameterizations are then filtered based on emergent forest properties meeting observation-based ecological criteria under alternate disturbance scenarios. A small ensemble of alternate PFT parameterizations is identified that produces plausible forest composition and demonstrates variability in response to disturbance frequency and regional environmental variation. Retaining multiple PFT parameterizations allows us to quantify the uncertainty in forest responses due to variability in trait observations. Vegetation composition is a key emergent outcome from VDMs and our methodology provides a foundation for robust PFT parameterization across ecosystems.