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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2019-08-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2018MS001577 |
| _version_ | 1819041907841433600 |
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| 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 |
| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-21T09:32:27Z |
| format | Article |
| id | doaj.art-3206d6c76cd840aa874739c91964ef40 |
| institution | Directory Open Access Journal |
| issn | 1942-2466 |
| language | English |
| last_indexed | 2024-12-21T09:32:27Z |
| publishDate | 2019-08-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj.art-3206d6c76cd840aa874739c91964ef402022-12-21T19:08:42ZengAmerican 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 |
| 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 |
| 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_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_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_sort | parametric sensitivity of vegetation dynamics in the triffid model and the associated uncertainty in projected climate change impacts on western u s forests |
| 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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