Abstract
AbstractChanging 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.
Highlights
Global climate change is increasing the vulnerability of some forested ecosystems to tree mortality (Allen et al, 2010; Anderegg et al, 2013)
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
Dynamic vegetation models (DVMs) are often parameterized to match observed vegetation distributions; this study demonstrates that many model parameterizations are plausible given available benchmark data
Summary
Global climate change is increasing the vulnerability of some forested ecosystems to tree mortality (Allen et al, 2010; Anderegg et al, 2013). Dynamic vegetation models (DVMs) in particular simulate establishment, growth, competition, and mortality, making them a useful tool for understanding how vegetation species distributions and associated carbon stocks respond to climate forcings. DVMs, being process‐based, can be applied at global (Friedlingstein et al, 2006; Sitch et al, 2008) to local scales (Kim et al, 2018) and have been used to project changes in vegetation distributions and associated carbon consequences in the western United States (Bachelet et al, 2015; Jiang et al, 2012; Kerns et al, 2018; Rehfeldt et al, 2012; Shafer et al, 2015)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
