Variation of gross primary productivity dominated by leaf area index in significantly greening area

The leaf area index (LAI) shows a significant increasing trend from global to regional scales, which is known as greening. Greening will further enhance photosynthesis, but it is unclear whether the contribution of greening has exceeded the CO2 fertilization effect and become the dominant factor in the gross primary productivity (GPP) variation. We took the Yangtze River Delta (YRD) of China, where cropland and natural vegetation are significantly greening, as an example. Based on the boreal ecosystem productivity simulator (BEPS) and Revised-EC-LUE models, the GPP in the YRD from 2001 to 2020 was simulated, and attribution analysis of the interannual variation in GPP was performed. In addition, the reliability of the GPP simulated by the dynamic global vegetation model (DGVM) in the area was further investigated. The research results showed that GPP in the YRD had three significant characteristics consistent with LAI: (1) GPP showed a significant increasing trend; (2) the multiyear mean and trend of natural vegetation GPP were higher than those of cropland GPP; and (3) cropland GPP showed double-high peak characteristics. The BEPS and Revised-EC-LUE models agreed that the effect of LAI variation (4.29 Tg C yr(-1) for BEPS and 2.73 Tg C yr(-1) for the Revised-EC-LUE model) determined the interannual variation in GPP, which was much higher than the CO2 fertilization effect (2.29 Tg C yr(-1) for BEPS and 0.67 Tg C yr(-1) for the Revised-EC-LUE model). The GPP simulated by the 7 DGVMs showed a huge inconsistency with the GPP estimated by remote sensing models. The deviation of LAI simulated by DGVM might be a potential cause for this phenomenon. Our study highlights that in significant greening areas, LAI has dominated GPP variation, both spatially and temporally, and DGVM can correctly simulate GPP only if it accurately simulates LAI variation.