You et al. (2025) Predicting Ecosystem Respiration Under Climate Extremes Requires Varying Parameters

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Advances in Modeling Earth Systems
• Year: 2025
• Date: 2025-12-01
• Authors: Cuihai You, Shiping Chen, Jian Zhou, Chenyu Bian, Fangxiu Wan, Ning Wei, Xingli Xia, Liuting Chen, Liming Yan, Jianyang Xia
• DOI: 10.1029/2025ms005220

Research Groups

Not available in the provided abstract.

Short Summary

This study investigated the predictability of conventional ecosystem respiration (ER) models in a semi-arid grassland under climatic extremes. It found that models calibrated with fixed parameters from normal years performed poorly during extreme drought and wet years due to significant and asymmetric parameter divergence, highlighting the need for varying parameters to accurately predict ER under climate change.

Objective

• To investigate whether parameters calibrated under normal climates can reliably predict ecosystem respiration (ER) under extreme drought and wet events using long-term eddy covariance data from a semi-arid grassland.

Study Configuration

• Spatial Scale: Ecosystem/Plot scale (semi-arid grassland)
• Temporal Scale: Daily resolution over multiple years (long-term data)

Methodology and Data

• Models used: Conventional linear and non-linear microbial models. Parameters were assimilated using a Monte Carlo Markov Chain approach.
• Data sources: Long-term eddy covariance data from a semi-arid grassland.

Main Results

• Both conventional linear and non-linear microbial models, parameterized under normal climatic conditions, exhibited poor performance in simulating daily ER during extreme drought and wet years.
• This poor performance was attributed to significant parameter divergence between normal and extreme years.
• All parameters in both models varied significantly between normal and extreme years, with approximately 29% displaying high variability (coefficient of variation >0.3).
• Principal component analysis revealed substantial parameter divergence among different hydrological regimes.
• Sensitivity analysis indicated that 93% of parameters exhibited asymmetric responses in extreme drought and wet years.
• Fixed parameters calibrated under normal climatic conditions cannot represent the emergent properties of ecosystems during extreme events.

Contributions

• Demonstrates that conventional ecosystem models with fixed parameters fail to capture ER variability under climatic extremes due to fundamental parameter divergence and asymmetric responses.
• Highlights that varying parameters are not merely a technical adjustment but a fundamental requirement for improving the predictability of ER under climate extremes.
• Provides quantitative evidence of parameter divergence and asymmetric responses across different hydrological regimes and extreme events.

Funding

Not available in the provided abstract.

Citation

@article{You2025Predicting,
  author = {You, Cuihai and Chen, Shiping and Zhou, Jian and Bian, Chenyu and Wan, Fangxiu and Wei, Ning and Xia, Xingli and Chen, Liuting and Yan, Liming and Xia, Jianyang},
  title = {Predicting Ecosystem Respiration Under Climate Extremes Requires Varying Parameters},
  journal = {Journal of Advances in Modeling Earth Systems},
  year = {2025},
  doi = {10.1029/2025ms005220},
  url = {https://doi.org/10.1029/2025ms005220}
}