Dynamic global vegetation models may not capture the dynamics of the leaf area index in the tropical rainforests: A data-model intercomparison

Improving our understanding of the mechanism of the carbon cycle in the Tropical Rainforests (TRFs) requires systematic testing of vegetation and ecosystem response to environmental drivers in Dynamic Global Vegetation Models (DGVMs) over different time scales. Here, we use reprocessed monthly MODIS LAI products from 2003 to 2019, combined with in-situ data, to determine how well 14 state-of-the-art models (CABLE, CLASSIC, CLM5, DLEM, IBIS, ISAM, ISBA, JULES, LPJ, LPX, OCN, ORCHIDEE, and SDGVM) simulate the long-term trend, interannual variability, seasonality, and El Nin & SIM;o impacts in terms of the greenness in the global TRFs. We find that most DGVMs highly overestimate the long-term trend in the TRFs because of the overestimation of the impact of CO2 fertilization. The multi-model ensemble mean (MME) successfully represents the interannual variability of the TRF LAI in the regional scales; however, failed in the pixeled scales. This inconsistency may be due to DGVMs using Plant Function Type (PFT) to parameterize vegetation dynamics. Finally, most DGVMs poorly modelled both the seasonality and the vegetation conditions during El Nin & SIM;o, likely due to the lack of lagged effects for climate drivers and poor acclimation during drought conditions.

Automated summary

Systematic testing of vegetation and ecosystem response to environmental drivers in Dynamic Global Vegetation Models (DGVMs) is necessary to improve our understanding of the carbon cycle in Tropical Rainforests (TRFs). This study uses reprocessed monthly MODIS LAI products and in-situ data to evaluate the performance of 14 state-of-the-art models in simulating long-term trends, interannual variability, seasonality, and El Nin & SIM;o impacts on greenness in global TRFs. The results show that most DGVMs overestimate the long-term trend, fail to capture the fine-scale variability, and poorly represent the vegetation conditions during El Nin & SIM;o.