Preprints
https://doi.org/10.5194/egusphere-2025-1243
https://doi.org/10.5194/egusphere-2025-1243
07 Apr 2025
 | 07 Apr 2025
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

TECO-CNP Sv1.0: A coupled carbon-nitrogen-phosphorus model with data assimilation for subtropical forests

Fangxiu Wan, Chenyu Bian, Ensheng Weng, Yiqi Luo, and Jianyang Xia

Abstract. Subtropical forests play a crucial role in global cycle, yet their carbon sink capacity is significantly constrained by phosphorus availability. Models that omit phosphorus dynamics risk overestimating carbon sinks, potentially undermining the scientific basis for carbon neutrality strategies. In this study, we developed TECO-CNP Sv1.0, a coupled carbon-nitrogen-phosphorus model based on the Terrestrial ECOsystem (TECO) model, explicitly capturing key biogeochemical interactions and nutrient-regulated carbon cycling. The model simulates how plant growth and carbon partitioning respond to both external soil nutrient availability and internal physiological constraints, enabling plant acclimation to varying nutrient conditions. Using observations from a phosphorus-limited subtropical forest in East China, we first evaluated model performance on estimating state variables with empirically calibrated parameters. Compared to the C-only and coupled C-N configurations, the CNP model better reproduced observed plant and soil C, N, and P pools. To systematically optimize model parameters and reduce uncertainties in predictions, we further incorporated a built-in data assimilation framework for parameter optimization. The CNP model with optimized parameters significantly improved carbon flux estimates, reducing root mean square errors and enhancing concordance correlation coefficients for gross primary productivity, ecosystem respiration, and net ecosystem exchange. By explicitly incorporating phosphorus dynamics and data assimilation, this study provides a more accurate and robust framework for predicting carbon sequestration in phosphorus-limited subtropical forests.

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Fangxiu Wan, Chenyu Bian, Ensheng Weng, Yiqi Luo, and Jianyang Xia

Status: open (until 02 Jun 2025)

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Fangxiu Wan, Chenyu Bian, Ensheng Weng, Yiqi Luo, and Jianyang Xia
Fangxiu Wan, Chenyu Bian, Ensheng Weng, Yiqi Luo, and Jianyang Xia

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Short summary
We developed an improved model that captures how nutrients, especially phosphorus, influence carbon cycle in subtropical forest. By combining biogeochemical cycling with advanced data analysis techniques, our model creates a powerful tool for parameter optimization and reliable predictions. Using field observations from a phosphorus-limited forest, we validated that this integrated approach provides more accurate estimates, offering better support for climate-related decision making.
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