基于FLUXNET的CLM模型总初级生产力模拟评价与误差分析

Evaluation and error analysis of gross primary productivity using land surface model CLM over FLUXNET

总初级生产力(gross primary productivity,GPP)的准确估计是陆地生态系统碳循环研究以及未来气候变化预测的基础。本文利用全球通量网的通量观测数据评估了CLM4.5的GPP模拟效果,结合叶面积指数(leaf area index,LAI)观测数据分析了CLM4.5模型GPP模拟误差的主要原因,并对CLM4.5模型主要的光合参数进行敏感性分析,探讨改善光合作用模拟精度的可能途径。结果表明:CLM4.5对GPP的模拟效果优于CLM4,月尺度和年尺度GPP模拟值的平均绝对偏差分别降低15%和29%。但与观测值相比,CLM4.5模拟的GPP年总量仍然具有较大偏差,平均偏差为366.06 g C·m-2·a-1。不同植被功能型GPP模拟误差具有不同的季节变化特征,误差主要发生在春季和夏季。冠层顶部比叶面积、叶片碳氮比和叶片氮含量中Rubisco氮含量所占比例是GPP模拟的3个敏感参数。未来应主要从物候期及LAI模拟的改进、磷循环过程的影响、生态系统水平光合参数集的构建等方面实现对GPP模拟精度的提高。

Accurate estimate of gross primary productivity(GPP)is the basis for the modeling of terrestrial ecosystem carbon cycle and climate change projection.We evaluated the performance of CLM4.5 simulated GPP and analyzed the causes of errors using observed GPP data from FLUXNET,combined with leaf area index(LAI)data.We conducted a sensitivity analysis to examine the key parameters in simulating GPP in CLM4.5 and discussed potential ways to improve the accuracy of photosynthesis simulation.Our results showed that CLM4.5 was better than CLM4 in simulating monthly and annual GPP.The mean absolute error(MAE)for the monthly and annual simulated GPP in CLM4.5 across plant functional types was reduced by 15%and 29%,respectively.However,CLM4.5 still presented a large bias in annual GPP with a MAE of 366.06 g C·m-2·a-1.The annual GPP bias had different seasonal variation for different plant functional types,with the bias mainly occurring in spring and summer.The GPP simulated by CLM4.5 was most sensitive to three parameters,i.e.specific leaf area,leaf carbon-to-nitrogen ratio,and the fraction of nitrogen in Rubisco.The improvement in GPP simulation requires better modeling in phenology and leaf area index,P dynamics and C-N-P interactions,and a ecosystem-level photosynthetic parameter dataset.