Quantifying contributions of errors in model structure and parameters to biases in a land surface model (LSM) is critical for model improvement. This paper investigated the uncertainties in parameterizations and parameters in the Noah with multiparameterization (Noah‐MP) LSM with dynamic vegetation using eddy flux data. First, we conducted full factorial experiments of eight land subprocesses, followed by sensitivity analysis (SA) to identify the subprocesses for which possible parameterizations made significant difference. Then, based on the full factorial experiments and SA results, we selected the statistically optimal parameterizations combination and the most biased parameterizations combination. Lastly, we calibrated the parameters in two selected parameterizations combinations. The results showed that five subprocesses—surface exchange coefficient, soil moisture β threshold, radiation transfer, runoff and groundwater, and surface resistance to evaporation—had significant influence on the model performances, and the interactions were generally low but contributed up to 80% of the variation in the performance at some sites. In the optimization period, following the criterion by Moriasi et al. (2007, https://doi.org/10.13031/2013.23153), parameter optimization improved the performance of both parameterizations combinations at most sites to be satisfactory, and the superiority between two parameterizations combinations was preserved; in the validation period, adjusting the parameterizations was more robust than parameter optimization in improving LSMs. Finally, we found that uncertainty in soil parameters was much higher than that in vegetation parameters because the optimal soil parameters were significantly different among sites with the same soil types and recommended that spatially calibrating the soil parameters was a major issue for Noah‐MP dynamic vegetation modeling.

中文翻译：

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量化Noah‐MP动态植被建模中物理参数化方案和模型参数不确定性对总体误差的贡献

量化模型结构和参数中的误差对陆面模型（LSM）中的偏差的贡献对于模型改进至关重要。本文利用涡流数据研究了动态植被多参数化（Noah-MP）LSM的Noah参数化和参数的不确定性。首先，我们对8个陆地子过程进行了全因子试验，然后进行了敏感性分析（SA），以识别可能进行参数化处理的子过程。然后，基于充分的阶乘实验和SA结果，我们选择了统计上最优的参数化组合和偏差最大的参数化组合。最后，我们以两个选定的参数化组合来校准参数。β 阈值，辐射传输，径流和地下水以及表面的抗蒸发性对模型性能产生了重大影响，并且相互作用通常较低，但在某些站点上造成了性能变化的80％。在优化阶段，遵循Moriasi等人的标准。（2007，https://doi.org/10.13031/2013.23153），参数优化使大多数站点的两个参数组合的性能令人满意，并且保留了两个参数组合之间的优势；在验证期间，在改进LSM方面，调整参数设置比参数优化更健壮。最后，