The gross primary productivity (GPP) of terrestrial ecosystem is the largest carbon flux between atmosphere and land surface. However, accurately simulating ecosystem GPP remains a great challenge for most land surface models (LSMs) due to the biased leaf area index (LAI) simulated by the models. In this study, we use remotely sensed LAI dataset for the period 2003–2019 to drive the Farquhar model and simulate the spatial-temporal changes of GPP for the alpine ecosystem over Tibetan Plateau. The annual GPP over the Tibetan Plateau is estimated to be 540.8 ± 27.3 Tg C yr, which is consistent with the benchmark datasets derived from flux tower measurements and remote sensing estimations. The GPP for the past two decades has been increasing at a rate of 2.6 Tg C yr. The canopy greening featured by increasing LAI contributed to 27.2 % of the increasing trend in GPP, while the direct impacts from warming and wetting climate contributed to 65.2 % of GPP changes. The contribution from the direct impact of elevated atmosphere CO concentration is only 6.7 %. In this study we show that constraining Farquhar model with remotely sensed LAI datasets could provide reliable simulation of GPP. Given the great challenge in modeling LAI and the considerable overestimation of LAI in current LSMs, our results highlight the importance of improving LAI modeling in LSMs and the necessity of constraining LAI when tuning the parameters for photosynthesis module.

中文翻译：

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2003-2019年青藏高原陆地初级生产总值的数据约束模型


陆地生态系统的总初级生产力（GPP）是大气与地表之间最大的碳通量。然而，由于模型模拟的叶面积指数（LAI）有偏差，准确模拟生态系统 GPP 对于大多数地表模型（LSM）来说仍然是一个巨大的挑战。在本研究中，我们使用2003-2019年期间的遥感LAI数据集驱动Farquhar模型，模拟青藏高原高山生态系统GPP的时空变化。青藏高原年 GPP 估计为 540.8 ± 27.3 Tg C yr，这与通量塔测量和遥感估算得出的基准数据集一致。过去二十年来，GPP 一直以每年 2.6 Tg C 的速度增长。以LAI增加为特征的树冠绿化贡献了GPP增长趋势的27.2%，而气候变暖和湿润的直接影响贡献了65.2%的GPP变化。大气 CO 浓度升高的直接影响贡献仅为 6.7%。在这项研究中，我们表明用遥感 LAI 数据集约束 Farquhar 模型可以提供可靠的 GPP 模拟。鉴于 LAI 建模面临的巨大挑战以及当前 LSM 中 LAI 的严重高估，我们的结果强调了改进 LSM 中 LAI 建模的重要性以及在调整光合作用模块参数时约束 LAI 的必要性。