Also, NDVI data have limited information for vegetation phenology in tropical areas. For example, many observational studies have detected lags between phenology of NDVI, LAI and GPP. One potential issue of the model is that NDVI has some limitations for constraining parameters in phenology models. It would be better to check if their findings were affected by the initial states. I’m not sure if 500 years is long enough for a dynamic vegetation model. In section 2.6, the authors mentioned that the model was spun up by 500 years to avoid the differences in vegetation and soil states. Investigating the sensitivity of the model to different input parameters and potential uncertainties can help understand the reliability and limitations of the proposed approach. The study does not conduct a sensitivity analysis to evaluate the robustness of the new phenology modules. The authors can map some key parameters in their phenology modules to show the spatial variation of phenological parameters. However, it would be beneficial to know if these parameterizations can be generalized to different regions and ecosystems with varying characteristics. The study mentions that the new phenology modules were parameterized using remote sensing-based observations and reanalysis data. In other words, if they used the particle swarm algorithm to calibrate the original phenology module, will the major findings be different from the current simulations? Then the authors need to check whether the model improvements were achieved due to the replacement of phenology module or optimization of parameters. The parameterization of DROMPHOT and DM models, which is described in the section 2.5, was done with the particle swarm algorithm. 2, the phenology module in LPJ-GUESS was replaced by DROMPHOT and DM model, which is driven by Gimms NVDI data. While the paper on improving vegetation phenology simulation in DGVMs presents promising results, there are a few major concerns that could be addressed:Īs shown in Fig. The integration of the new phenology modules into LPJ-GUESS represents a significant step towards improving the simulation of vegetation phenology in DGVMs. Overall, this study emphasizes the importance of accurate phenology estimation to reduce uncertainties in plant distribution and terrestrial carbon and water cycling. An interesting finding of this study is that variations in the simulated start and end of the growing season can have a substantial impact on the ecological niches and competitive relationships among different plant functional types (PFTs). The results demonstrate that the implementation of the new phenology modules in LPJ-GUESS significantly improved the accuracy of capturing the start and end dates of growing seasons. The models were parameterized using remote sensing-based phenological observations and ERA5 land reanalysis dataset. The authors developed and integrated spring and autumn phenology models into the LPJ-GUESS DGVM, driven by temperature and photoperiod. The paper focuses on enhancing the accuracy of vegetation phenology simulation in Dynamic Global Vegetation Models (DGVMs). Hence, our study highlights the importance getting accurate of phenology estimation to reduce the uncertainties in plant distribution and terrestrial carbon and water cycling. Interestingly, we have also found that differences in simulated start and end of growing season can largely alter the ecological niches and competitive relationships among different plant functional types (PFTs), and subsequentially impact the community structure and in turn influence the terrestrial carbon and water cycles. For the autumn phenology, the simulated RMSE for deciduous tree and shrubs decreased by 22.61 and 17.60, respectively. For the start of growing season, the simulated RMSE for deciduous tree and shrubs decreased by 8.04 and 17.34, respectively. The results show that the developed LPJ-GUESS with new phenology modules substantially improved the accuracy in capturing start and end dates of growing seasons. These process-based phenology models driven by temperature and photoperiod, and are parameterized for deciduous trees and shrubs using remote sensing-based phenological observations and reanalysis dataset ERA5 land. Here, we developed and coupled the spring and autumn phenology models into one of the DGVMs, LPJ-GUESS. Nevertheless, it is still a challenge to achieve accurate simulation of vegetation phenology in the DGVMs. Dynamic Global Vegetation Models (DGVMs), serving as pivotal simulation tools for investigating terrestrial ecosystem carbon and water cycles, typically incorporate representations of vegetation phenological processes. Vegetation phenological shifts impact the terrestrial carbon and water cycle, and affects local climate system through biophysical and biochemical processes between biosphere and atmosphere.
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