Preprints
https://doi.org/10.5194/bg-2023-33
https://doi.org/10.5194/bg-2023-33
06 Mar 2023
 | 06 Mar 2023
Status: this discussion paper is a preprint. It has been under review for the journal Biogeosciences (BG). The manuscript was not accepted for further review after discussion.

Nitrogen limitation information retrieved from data assimilation

Song Wang, Carlos Sierra, Yiqi Luo, Jinsong Wang, Weinan Chen, Yahai Zhang, Aizhong Ye, and Shuli Niu

Abstract. Nitrogen (N) limitation greatly constrains terrestrial ecosystem carbon (C) uptake and its response to climate change and elevated carbon dioxide. Hence, accurate assessments of ecosystem N limitation are crucial for predicting C-N feedbacks, and vital for providing guidance for policy making or ecosystem management as well. This study aims to retrieve N limitation information by data model fusion from one field N addition experiment so that we can better understand N controls on the terrestrial C cycle. We estimated two sets of parameters with one C-only model and one coupled C-N model. Our results showed that the estimated leaf photosynthetic efficiency (LPE) and process rates (e.g., senescence and decomposition rates) of organic C from almost all pools were higher with the coupled C-N model than those with the C-only model at the ambient treatment. However, the differences in the LPE and the C exit rates between the coupled C-N model and the C-only model decreased with the increasing N addition rates. Both the C-only and coupled C-N models simulated similar C pool sizes as observed at every N addition treatment with their respective parameter estimates. However, simulated ecosystem C storage and gross primary productivity (GPP) decreased if we ran the coupled C-N model with the parameters estimated by the C-only model. This decrease was larger at the ambient treatment and became smaller with the increase of N addition. In general, we put forward a new method to retrieve N limitation information from observations by data model fusion. This method will make it possible to estimate the global nutrient limitation and benefit ecosystem management and policy making.

Song Wang, Carlos Sierra, Yiqi Luo, Jinsong Wang, Weinan Chen, Yahai Zhang, Aizhong Ye, and Shuli Niu

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2023-33', Anonymous Referee #1, 31 Mar 2023
  • RC2: 'Comment on bg-2023-33', Anonymous Referee #2, 08 Apr 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2023-33', Anonymous Referee #1, 31 Mar 2023
  • RC2: 'Comment on bg-2023-33', Anonymous Referee #2, 08 Apr 2023
Song Wang, Carlos Sierra, Yiqi Luo, Jinsong Wang, Weinan Chen, Yahai Zhang, Aizhong Ye, and Shuli Niu
Song Wang, Carlos Sierra, Yiqi Luo, Jinsong Wang, Weinan Chen, Yahai Zhang, Aizhong Ye, and Shuli Niu

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Short summary
Nitrogen is important for plant growth and carbon uptake, which is uaually limited in nature and can constrain carbon storage and impact efforts to combat climate change. We developed a new method of combining data and models to determine if and how much an ecosystem is nitrogen limited. This new method can help determine if and to what extent an ecosystem is nitrogen-limited, providing insight into nutrient limitations on a global scale and guiding ecosystem management decisions.
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