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Evaluation and improvement of the E3SM land model for simulating energy and carbon fluxes in an Amazonian peatland

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Tropical peatlands are one of the largest natural sources of atmospheric methane (CH4) and play a significant role in regional and global carbon budgets. However, large uncertainties persist regarding their feedbacks to climate variations. The Energy Exascale Earth System Model (E3SM) Land Model (ELM) is an ongoing state-of-the-science model, which has developed new representations of soil hydrology and biogeochemistry and includes a new microbial-functional-group-based CH4 module. This model has been tested in boreal forest peatlands, but has not yet been evaluated for simulating energy and carbon exchange for tropical peatlands. Here, we evaluated the ELM performance in simulating energy, carbon dioxide (CO2) and CH4 fluxes of an Amazonian palm swamp peatland in Iquitos, Peru. ELM simulations using default parameter values resulted in poor performance of seasonal carbon dynamics. Several algorithms were improved according to site-specific characteristics and key parameters were optimized using an objective surrogate-assisted Bayesian approach. The modified algorithms included the soil water retention curve, a water coverage scalar function for CH4 processes, and a seasonally varying leaf carbon-to-nitrogen ratio function. The revised tropics-specific model better simulated the diel and seasonal patterns of energy and carbon fluxes of the palm swamp peatland. Global sensitivity analyses indicated that the strong controls on energy and carbon fluxes were mainly attributed to the parameters associated with vegetation activities, such as plant carbon distribution, stomatal regulation, photosynthetic capacity, and leaf phenology. Parameter relative importance depended on biogeochemical processes and shifted significantly between wet and dry seasons. This modeling study advanced the understanding of biotic controls on the energy and carbon exchange in Amazonian palm swamp peatlands and identified knowledge gaps that need to be addressed for better prediction of carbon cycle processes and budgets for tropical peatlands.

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