Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests

• Published in: Journal of advances in modeling earth systems 2019-08, Vol.11 (8), p.2787-2813
• Main Authors: Hawkins, Linnia R., Rupp, David E., McNeall, Doug J., Li, Sihan, Betts, Richard A., Mote, Philip W., Sparrow, Sarah N., Wallom, David C. H.
• Format: Article
• Language: English
• Subjects: Agriculture; Carbon; carbon allocation; Carbon cycle; Climate change; Climate models; Climatic conditions; dynamic global vegetation models; Dynamics; Ecosystem dynamics; Ecosystem services; Environmental impact; Feedback; Flora; Foliage; Forest ecosystems; Forests; Future climates; parameter uncertainty; Parameterization; Parameters; Plants; Regional climate models; Regional climates; sensitivity analysis; Simulation; statistical emulator; Stocks; Uncertainty; Vegetation; Vegetation distribution; vegetation transitions
• ISSN: 1942-2466; 1942-2466
• DOI: 10.1029/2018MS001577

Changing climate conditions impact ecosystem dynamics and have local to global impacts on water and carbon cycles. Many processes in dynamic vegetation models (DVMs) are parameterized, and the unknown/unknowable parameter values introduce uncertainty that has rarely been quantified in projections of forced changes. In this study, we identify processes and parameters that introduce the largest uncertainties in the vegetation state simulated by the DVM Top‐down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID) coupled to a regional climate model. We adjust parameters simultaneously in an ensemble of equilibrium vegetation simulations and use statistical emulation to explore sensitivities to, and interactions among, parameters. We find that vegetation distribution is most sensitive to parameters related to carbon allocation and competition. Using a suite of statistical emulators, we identify regions of parameter space that reduce the error in modeled forest cover by 31±9%. We then generate large initial atmospheric condition ensembles with 10 improved DVM parameterizations under preindustrial, contemporary, and future climate conditions to assess uncertainty in the forced response due to parameterization. We find that while most parameterizations agree on the direction of future vegetation transitions in the western United States, the magnitude varies considerably: for example, in the northwest coast the expansion of broadleaf trees and corresponding decline of needleleaf trees ranges from 4 to 28% across 10 DVM parameterizations under projected future climate conditions. We demonstrate that model parameterization contributes to uncertainty in vegetation transition and carbon cycle feedback under nonstationary climate conditions, which has important implications for carbon stocks, ecosystem services, and climate feedback. Plain Language Summary Changing climate conditions can impact the composition of an ecosystem. As plant types better suited for the new climate conditions begin to thrive, they can out‐compete the historical vegetation. This transformation can have impacts on the local water cycle and, if changes are large enough, on the global carbon cycle. Dynamic vegetation models (DVMs) are used to analyze the impacts of climate on vegetation and project future changes. DVMs are used across scales ranging from watersheds, informing management decisions, to the globe as an important component of the global carbon cycle. The math