Leaf area index (LAI) is a vegetation structural parameter that modulates the interaction between the land surface and the atmosphere and therefore is used in many terrestrial biosphere models. However, there are still large uncertainties in simulating the global LAI in Earth system models. In this study, we used climate and soil variables and the Farquhar's biochemical model to reconstruct global LAI, explore the mechanisms controlling global LAI seasonality, and analyze the feasibility of Farquhar's biochemical model in estimating the effect of CO₂ fertilization on global LAI. The results show that the reconstructed LAI (RLAI) based on climate and soil variables can explain 93% of the seasonal dynamics of global LAI. However, RLAI only explained 27.3% of the global LAI trend and captured 29.8% of the land area with a significant trend. RLAI after incorporating the CO₂ fertilization effect, which is estimated by Farquhar's biochemical model, can explain 68.8% (41.5% improvement from RLAI) of the global LAI trend and capture 63.3% (33.5% improvement from RLAI) of the area with a significant LAI trend. These results suggest that it is feasible to use Farquhar's biochemical model to estimate the effect of CO₂ fertilization on the global trend in LAI. The statistical model for reconstructing the seasonal dynamics of LAI and the Farquhar model‐based method for estimating the LAI temporal trend developed in this study would be useful for improving or evaluating the performance of prognostic models for future global carbon cycle research. Furthermore, this study may provide a new way to simulate global LAI for prognostic modeling.

中文翻译：

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重建2001-2017年全球叶面积指数的季节性和趋势以进行预测模型

叶面积指数（LAI）是一种植被结构参数，可调节土地表面与大气之间的相互作用，因此可用于许多陆地生物圈模型中。但是，在地球系统模型中模拟全局LAI仍然存在很大的不确定性。在这项研究中，我们使用气候和土壤变量以及Farquhar的生化模型来重建全球LAI，探索控制全球LAI季节性的机制，并分析Farquhar的生化模型在估算CO ₂影响方面的可行性。施肥对全球LAI。结果表明，基于气候和土壤变量的重建LAI（RLAI）可以解释全球LAI的93％的季节动态。但是，RLAI仅解释了全球LAI趋势的27.3％，并以显着趋势占领了29.8％的土地面积。Farquhar的生化模型估计，结合CO ₂施肥效应后的RLAI可以解释全球LAI趋势的68.8％（比RLAI改善41.5％），并占该地区的63.3％（比RLAI改善33.5％），具有显着性莱趋势。这些结果表明，使用Farquhar的生化模型估算CO ₂的影响是可行的。施肥对LAI的全球趋势。本研究中开发的用于重建LAI的季节动态的统计模型和基于Farquhar模型的估计LAI时间趋势的方法将有助于改善或评估未来全球碳循环研究的预测模型的性能。此外，这项研究可能提供一种新的方法来模拟用于预测模型的全局LAI。