Reparameterization Required After Model Structure Changes From Carbon Only to Carbon-Nitrogen Coupling

Song Wang, Yiqi Luo, Shuli Niu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Prediction of carbon (C) sequestration in terrestrial ecosystems under climate change depends on C-nitrogen (N) interactions. While many newly developed C-N coupling models predict lower C storage than their C-only counterpart models, it has not been carefully examined whether or not such model predictions would be supported by real-world observations. This study is to retrieve knowledge on N cycle from one field warming experiment in an alpine meadow of China so that we can better predict land C sequestration. We estimated two sets of parameters with one C-only model and one C-N coupling model, respectively, with the data assimilation technique. Our results showed that estimated process rates (e.g., senescence and decomposition rates) of organic C from almost all pools except standing litter and leaf are higher with the C-N coupling model than those with the C-only model. The allocation coefficient of C to root estimated with the C-only model was 68.0% and 56.6% smaller than that with the C-N coupling model under the ambient and warming treatments, respectively. Both the C-only and C-N coupling models simulate similar C pool sizes as observed at either the ambient or warming treatment with their respective parameter estimates. Meanwhile, the warming treatment increased the slow soil organic matter (SOM) pool due to decrease in estimated decomposition rate of the slow SOM via parameter fitting and increase in the C input from surface litter. In general, our results suggest that reparameterization is required when we add N processes to a C cycle model to realistically predict the ecosystem dynamics in response to future changes in N availability.

Original languageEnglish (US)
Article numbere2021MS002798
JournalJournal of Advances in Modeling Earth Systems
Volume14
Issue number4
DOIs
StatePublished - Apr 2022

Keywords

  • Bayesian probabilistic inversion
  • MCMC
  • carbon and nitrogen cycles
  • model parameterization
  • model structure
  • warming

ASJC Scopus subject areas

  • Global and Planetary Change
  • Environmental Chemistry
  • General Earth and Planetary Sciences

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