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 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).
| Original language | English |
|---|---|
| Article number | 111197 |
| Journal | Agricultural and Forest Meteorology |
| Volume | 385 |
| DOIs | |
| Publication status | Published - 2026 Jun |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 13 Climate Action
-
SDG 15 Life on Land
Subject classification (UKÄ)
- Physical Geography
Free keywords
- Carbon cycles
- Climate change
- Eddy covariance
- Machine learning
- Process-based model
- Remote sensing
- Terrestrial carbon dynamics
Fingerprint
Dive into the research topics of 'First global carbon dynamics from an observational and process-informed hybrid perspective: Oversimplified respiration representation likely drives divergence in terrestrial carbon sequestration across models'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver