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

Abstract 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 proje...

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Main Authors: Linnia R. Hawkins, David E. Rupp, Doug J. McNeall, Sihan Li, Richard A. Betts, Philip W. Mote, Sarah N. Sparrow, David C. H. Wallom
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2019-08-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
Online Access:https://doi.org/10.1029/2018MS001577
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spelling doaj-3206d6c76cd840aa874739c91964ef402020-11-24T21:17:42ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662019-08-011182787281310.1029/2018MS001577Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. ForestsLinnia R. Hawkins0David E. Rupp1Doug J. McNeall2Sihan Li3Richard A. Betts4Philip W. Mote5Sarah N. Sparrow6David C. H. Wallom7Oregon Climate Change Research Institute, College of Earth, Ocean, and Atmospheric Sciences Oregon State University Corvallis OR USAOregon Climate Change Research Institute, College of Earth, Ocean, and Atmospheric Sciences Oregon State University Corvallis OR USAMet Office Hadley Centre Exeter UKEnvironmental Change Institute, School of Geography and the Environment University of Oxford Oxford UKMet Office Hadley Centre Exeter UKOregon Climate Change Research Institute, College of Earth, Ocean, and Atmospheric Sciences Oregon State University Corvallis OR USAOxford e‐Research Centre University of Oxford Oxford UKOxford e‐Research Centre University of Oxford Oxford UKAbstract 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.https://doi.org/10.1029/2018MS001577vegetation transitionsparameter uncertaintysensitivity analysisdynamic global vegetation modelsstatistical emulatorcarbon allocation
collection DOAJ
language English
format Article
sources DOAJ
author Linnia R. Hawkins
David E. Rupp
Doug J. McNeall
Sihan Li
Richard A. Betts
Philip W. Mote
Sarah N. Sparrow
David C. H. Wallom
spellingShingle Linnia R. Hawkins
David E. Rupp
Doug J. McNeall
Sihan Li
Richard A. Betts
Philip W. Mote
Sarah N. Sparrow
David C. H. Wallom
Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests
Journal of Advances in Modeling Earth Systems
vegetation transitions
parameter uncertainty
sensitivity analysis
dynamic global vegetation models
statistical emulator
carbon allocation
author_facet Linnia R. Hawkins
David E. Rupp
Doug J. McNeall
Sihan Li
Richard A. Betts
Philip W. Mote
Sarah N. Sparrow
David C. H. Wallom
author_sort Linnia R. Hawkins
title Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests
title_short Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests
title_full Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests
title_fullStr Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests
title_full_unstemmed Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests
title_sort parametric sensitivity of vegetation dynamics in the triffid model and the associated uncertainty in projected climate change impacts on western u.s. forests
publisher American Geophysical Union (AGU)
series Journal of Advances in Modeling Earth Systems
issn 1942-2466
publishDate 2019-08-01
description Abstract 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.
topic vegetation transitions
parameter uncertainty
sensitivity analysis
dynamic global vegetation models
statistical emulator
carbon allocation
url https://doi.org/10.1029/2018MS001577
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