Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests
Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests
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DOI：
10.1029/2018ms001577
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发表时间：
2019-08
影响因子：
6.8
通讯作者：
L. Hawkins;D. Rupp;D. McNeall;Sihan Li;R. Betts;P. Mote;S. Sparrow;D. Wallom
中科院分区：
文献类型：
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作者：
L. Hawkins;D. Rupp;D. McNeall;Sihan Li;R. Betts;P. Mote;S. Sparrow;D. Wallom
不断变化的气候条件影响生态系统动态，并对水和碳循环产生局部乃至全球的影响。动态植被模型（DVM）中的许多过程是参数化的，未知/不可知的参数值引入了在强迫变化预测中很少量化的不确定性。在这项研究中，我们确定的过程和参数，引入最大的不确定性，在植被状态模拟的DVM自上而下的表示交互式树叶和植物群包括动态（TRIFFID）耦合到一个区域气候模式。我们同时调整参数的平衡植被模拟的合奏，并使用统计仿真，探索敏感性，参数之间的相互作用。我们发现，植被分布是最敏感的参数有关的碳分配和竞争。使用一套统计模拟器，我们确定了参数空间的区域，使建模森林覆盖率的误差减少了31± 9%。然后，我们生成大的初始大气条件合奏与10个改进的DVM参数化下的工业化前，当代和未来的气候条件下，以评估由于参数化的强制响应的不确定性。我们发现，虽然大多数参数化同意在美国西部的未来植被过渡的方向，幅度变化很大：例如，在西北海岸的阔叶树的扩张和相应的针叶树的下降范围从4%到28%之间的10 DVM参数化预测未来的气候条件。我们表明，模型参数化有助于在非平稳气候条件下的植被过渡和碳循环反馈的不确定性，这对碳储量，生态系统服务和气候反馈具有重要意义。
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.