An empirical model calibration approach is presented that aims to approximate missing biosphere processes in a global land surface model without the need for substantial model structural changes. The strategy is implemented here using the NASA Catchment‐CN land surface model and Moderate Resolution Imaging Spectroradiometer (MODIS) observations of the fraction of absorbed photosynthetically active radiation (FPAR). Existing plant functional types (PFTs) of the Catchment‐CN model are divided into three subtypes, based on the bias between the model‐simulated and MODIS‐observed FPAR. Separate sets of vegetation parameters for each subtype are then calibrated at a small number of grid cells with homogeneous, single‐PFT land cover, using MODIS FPAR reference observations from 2003 to 2009. The effectiveness of the empirical approach at improving the realism of modeled vegetation dynamics is investigated with two global model simulations for the period 2010–2016, one using the newly calibrated parameter values and the other using the original values. Globally, the calibrated parameters reduce the root mean square error (RMSE) of the modeled FPAR with respect to MODIS by 0.029 (∼10%) on average. In some regions, substantially larger RMSE reductions are achieved. RMSE reductions are primarily driven by model bias reductions, with neutral effects on the temporal correlation skill. While the empirical approach is suitable for achieving consistent model improvements, it is shown to be sensitive to the characteristics of the model error, specifically a dominance of the bias component in the case of Catchment‐CN. Ultimately, more fundamental model structural changes may be required to achieve better improvements.

中文翻译：

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基于观测驱动的全球陆地表面模型中改善植被物候的方法

提出了一种经验模型校准方法，该方法旨在在不进行实质性模型结构变化的情况下，对全球陆地表面模型中缺少的生物圈过程进行近似估算。该策略在这里使用NASA Catchment-CN地表模型和中等分辨率成像光谱仪（MODIS）对吸收的光合有效辐射（FPAR）分数的观察来实施。基于模型仿真的FPAR与MODIS观测的FPAR之间的偏差，Catchment-CN模型的现有工厂功能类型（PFT）分为三个子类型。然后，使用2003年至2009年的MODIS FPAR参考观测值，在少数具有均质，单PFT土地覆盖的网格单元上，校准每种亚型的单独植被参数集。在2010-2016年期间，通过两种全局模型仿真研究了经验方法在改善建模的植被动力学的真实性方面的有效性，一种使用新校准的参数值，另一种使用原始值。总体而言，校准参数使建模FPAR相对于MODIS的均方根误差（RMSE）平均降低0.029（〜10％）。在某些地区，RMSE的降低幅度更大。RMSE减少主要由模型偏差减少驱动，对时间相关技能产生中性影响。尽管经验方法适合于实现一致的模型改进，但它显示出对模型误差的特征敏感，特别是在Catchment-CN的情况下，偏见分量的优势。最终，