First global carbon dynamics from an observational and process-informed hybrid perspective : Oversimplified respiration representation likely drives divergence in terrestrial carbon sequestration across models
Zhu, Songyan ; Dong, Wenquan LU
; Myrgiotis, Vasileios
; Xu, Jian
; Reyes-Muñoz, Pablo
; Gharun, Mana
; Ma, Rui
; Chen, Man
and Dash, Jadu
(2026)
In Agricultural and Forest Meteorology
385.
- Abstract
Process-based biosphere models, which formulate biophysical ecosystem processes, provide a mechanistic framework for understanding terrestrial carbon dynamics. In contrast, data-driven approaches, e.g., upscaling eddy covariance (EC) fluxes using satellite observations and machine learning, offer empirical estimates. These complementary methods often diverge significantly, particularly in estimating global photosynthetic uptake and ecosystem respiration, with discrepancies exceeding 50 PgCyr−1and highlighting persistent uncertainties. To bridge this gap, we adopt a hybrid strategy that embeds physiological understanding via semi-empirical models, refines it with EC fluxes constrained by machine learning, and integrates... (More)
Process-based biosphere models, which formulate biophysical ecosystem processes, provide a mechanistic framework for understanding terrestrial carbon dynamics. In contrast, data-driven approaches, e.g., upscaling eddy covariance (EC) fluxes using satellite observations and machine learning, offer empirical estimates. These complementary methods often diverge significantly, particularly in estimating global photosynthetic uptake and ecosystem respiration, with discrepancies exceeding 50 PgCyr−1and highlighting persistent uncertainties. To bridge this gap, we adopt a hybrid strategy that embeds physiological understanding via semi-empirical models, refines it with EC fluxes constrained by machine learning, and integrates process-based allocation to resolve component fluxes. This process-informed hybrid approach links ecological knowledge with predictive models, enabling generalisation beyond flux tower sites and supporting the development of new insights. We assess global carbon dynamics over the past two decades, applying Bayesian inference to evaluate climate impacts on land carbon processes. Our study delivers the first observational and process-informed hybrid assessment of global carbon flux and stock changes. Notably, while gross carbon uptake is consistent across methods (≈ 130 PgCyr−1in 2022, increasing by 0.4 annually), net carbon uptake estimates diverge, from 6 PgCyr−1in process-based models to 26 in conventional upscaling, and 16 in our hybrid model, reflecting structural differences in respiration parameterisation. Improved representation of respiratory processes is essential to capture the competing roles of photosynthesis and respiration under climate change. Despite rising global carbon fluxes and biomass stocks, tipping point risks remain: in tropical regions, increased photosynthesis (0.1 PgCyr−1) is offset by rising respiration (0.05 PgCyr−1).
(Less)
- author
- Zhu, Songyan
; Dong, Wenquan
LU
; Myrgiotis, Vasileios
; Xu, Jian
; Reyes-Muñoz, Pablo
; Gharun, Mana
; Ma, Rui
; Chen, Man
and Dash, Jadu
- organization
- publishing date
- 2026-06
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Carbon cycles, Climate change, Eddy covariance, Machine learning, Process-based model, Remote sensing, Terrestrial carbon dynamics
- in
- Agricultural and Forest Meteorology
- volume
- 385
- article number
- 111197
- publisher
- Elsevier
- external identifiers
-
- scopus:105037048317
- ISSN
- 0168-1923
- DOI
- 10.1016/j.agrformet.2026.111197
- language
- English
- LU publication?
- yes
- id
- 7e565709-2e57-493b-8ef7-c630807b8894
- date added to LUP
- 2026-05-29 11:07:50
- date last changed
- 2026-06-01 09:27:26
@article{7e565709-2e57-493b-8ef7-c630807b8894,
abstract = {{<p>Process-based biosphere models, which formulate biophysical ecosystem processes, provide a mechanistic framework for understanding terrestrial carbon dynamics. In contrast, data-driven approaches, e.g., upscaling eddy covariance (EC) fluxes using satellite observations and machine learning, offer empirical estimates. These complementary methods often diverge significantly, particularly in estimating global photosynthetic uptake and ecosystem respiration, with discrepancies exceeding 50 PgCyr<sup>−1</sup>and highlighting persistent uncertainties. To bridge this gap, we adopt a hybrid strategy that embeds physiological understanding via semi-empirical models, refines it with EC fluxes constrained by machine learning, and integrates process-based allocation to resolve component fluxes. This process-informed hybrid approach links ecological knowledge with predictive models, enabling generalisation beyond flux tower sites and supporting the development of new insights. We assess global carbon dynamics over the past two decades, applying Bayesian inference to evaluate climate impacts on land carbon processes. Our study delivers the first observational and process-informed hybrid assessment of global carbon flux and stock changes. Notably, while gross carbon uptake is consistent across methods (≈ 130 PgCyr<sup>−1</sup>in 2022, increasing by 0.4 annually), net carbon uptake estimates diverge, from 6 PgCyr<sup>−1</sup>in process-based models to 26 in conventional upscaling, and 16 in our hybrid model, reflecting structural differences in respiration parameterisation. Improved representation of respiratory processes is essential to capture the competing roles of photosynthesis and respiration under climate change. Despite rising global carbon fluxes and biomass stocks, tipping point risks remain: in tropical regions, increased photosynthesis (0.1 PgCyr<sup>−1</sup>) is offset by rising respiration (0.05 PgCyr<sup>−1</sup>).</p>}},
author = {{Zhu, Songyan and Dong, Wenquan and Myrgiotis, Vasileios and Xu, Jian and Reyes-Muñoz, Pablo and Gharun, Mana and Ma, Rui and Chen, Man and Dash, Jadu}},
issn = {{0168-1923}},
keywords = {{Carbon cycles; Climate change; Eddy covariance; Machine learning; Process-based model; Remote sensing; Terrestrial carbon dynamics}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Agricultural and Forest Meteorology}},
title = {{First global carbon dynamics from an observational and process-informed hybrid perspective : Oversimplified respiration representation likely drives divergence in terrestrial carbon sequestration across models}},
url = {{http://dx.doi.org/10.1016/j.agrformet.2026.111197}},
doi = {{10.1016/j.agrformet.2026.111197}},
volume = {{385}},
year = {{2026}},
}