Graduate Thesis Or Dissertation

 

Modeling Forest Response to Changing Climate Conditions in Western North America Public Deposited

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/cc08hm928

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  • Forests play an integral role in the global carbon cycle, regulating the atmospheric CO₂ concentration by sequestering nearly one third of anthropogenic carbon emissions and storing carbon for centuries. Forest ecosystems are integrated into the culture, ecology, and economy of western North America, supporting wildlife habitat, local economies, recreation, and tribal uses. Anthropogenic warming has increased the likelihood that precipitation deficits occur during warmer temperatures. Hotter temperatures during drought exacerbate tree stress by increasing the evaporative demand and enhancing water limitations. In recent years summer temperatures have had an increasing contribution to drought conditions, which have been connected to an increase in tree mortality rates in some regions. There is growing concern that tree stress will be amplified by projected future climate conditions, leading to enhanced vulnerability to fire, insects, and disease. Episodic mortality events can accelerate climate driven forest transitions, because as plant types better suited for the new climate begin to thrive they can out-compete the historical vegetation. Forest transitions can have impacts on local hydrology, regional climate, and the global carbon cycle. However, much uncertainty still exists around representing plant mortality, competition, and physiological response to soil water deficits and vapor pressure deficits in Earth System Models. To investigate the impacts of changing climate conditions on forests in western North America, we used a Regional Earth System Model with dynamic vegetation. We leveraged the vast capacity of a volunteer computational platform to perform large ensembles of simulations and evaluate sources of uncertainty. We quantified uncertainty due to the mathematical parameterization used to simulate vegetation competition and identified processes that introduced the largest uncertainties into modeled vegetation distributions. While multiple unique model parameterizations improved the simulated historical vegetation reducing the error in modeled forest cover by 31±9%, each projected different future forest transitions. Simulations generally agree on the direction of future vegetation transitions regardless of parameterization, but the magnitude varies considerably: for example, along the northwest coast the expansion of broadleaf trees and corresponding decline of needleleaf trees ranged from 4% to 28% across parameterizations. This study demonstrated that model parameterization contributes considerably to uncertainty in vegetation transition and carbon cycle feedbacks under non-stationary climate conditions, which has important implications for carbon stocks, ecosystem services, and climate feedbacks. To understand the climatic drivers of forest transitions, we evaluated the impacts of changing climate conditions on forest productivity. During periods of low forest productivity trees are more vulnerable to biotic and abiotic driven mortality. Using large ensembles of coupled land-atmosphere model simulations we assessed the climatic drivers of low forest productivity, and the feedbacks on summer water availability. In an average year, increased atmospheric CO₂ concentrations enhance forest productivity in western North America, altering summer soil moisture availability. However, in regions where snowpack is projected to decline, the combination of water limitation and temperature stress counteract the CO₂ fertilization effect. Periods of extremely low forest productivity are projected to persist under future climate conditions, leaving these regions vulnerable to vegetation transformations. This body of research assessed feedbacks among climate, forest productivity, and summer water availability. Furthermore it illustrates key uncertainties in model projections of future forest transformations and identifies areas for further improvement to model representation of vegetation physiology.
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