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

Abstract. Subtropical forests play a crucial role in the global carbon 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, which explicitly captures 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 the model's performance in estimating state variables with empirically calibrated parameters. Compared to the C-only and coupled C-N configurations, the CNP model more accurately reproduced the observed pools of plant and soil C, N, and P. 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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Publication Details

Journal
Geoscientific model development
Published
2025-10-22
DOI
https://doi.org/10.5194/gmd-18-7545-2025
Citations
1
Primary Topic
Plant Water Relations and Carbon Dynamics
Type
article
Field-Weighted Citation Impact
1.11

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article

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

Chenyu Bian, Kun Huang, Ensheng Weng, Jianyang Xia et al.
1 citations
Geoscientific model development
Plant Water Relations and Carbon Dynamics
1.11
article

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

Chenyu Bian, Kun Huang, Ensheng Weng, Jianyang Xia, Yiqi Luo, Fayu Wan
article en
1 citations

Abstract

Abstract. Subtropical forests play a crucial role in the global carbon 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, which explicitly captures 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 the model's performance in estimating state variables with empirically calibrated parameters. Compared to the C-only and coupled C-N configurations, the CNP model more accurately reproduced the observed pools of plant and soil C, N, and P. 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.

Geoscientific model developmentVol. 18(20)
Cornell University (US), New York State College of Agriculture & Life Sciences (US), East China Normal University (CN), Columbia University (US)
National Natural Science Foundation of China
Openalex Percentile: Top 18%
Plant Water Relations and Carbon Dynamics
1.11
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