Details

Autor(en) / Beteiligte

• Luo, Yiqi
• Huang, Yuanyuan
• Sierra, Carlos A.
• Xia, Jianyang
• Ahlström, Anders
• Chen, Yizhao
• Hararuk, Oleksandra
• Hou, Enqing
• Jiang, Lifen
• Liao, Cuijuan
• Lu, Xingjie
• Shi, Zheng
• Smith, Benjamin
• Tao, Feng
• Wang, Ying‐Ping

Titel

Matrix Approach to Land Carbon Cycle Modeling

Ist Teil von

• Journal of advances in modeling earth systems, 2022-07, Vol.14 (7), p.n/a

Ort / Verlag

Washington: John Wiley & Sons, Inc

Erscheinungsjahr

2022

Quelle

Access via Wiley Online Library

Beschreibungen/Notizen

• Land ecosystems contribute to climate change mitigation by taking up approximately 30% of anthropogenically emitted carbon. However, estimates of the amount and distribution of carbon uptake across the world's ecosystems or biomes display great uncertainty. The latter hinders a full understanding of the mechanisms and drivers of land carbon uptake, and predictions of the future fate of the land carbon sink. The latter is needed as evidence to inform climate mitigation strategies such as afforestation schemes. To advance land carbon cycle modeling, we have developed a matrix approach. Land carbon cycle models use carbon balance equations to represent carbon exchanges among pools. Our approach organizes this set of equations into a single matrix equation without altering any processes of the original model. The matrix equation enables the development of a theoretical framework for understanding the general, transient behavior of the land carbon cycle. While carbon input and residence time are used to quantify carbon storage capacity at steady state, a third quantity, carbon storage potential, integrates fluxes with time to define dynamic disequilibrium of the carbon cycle under global change. The matrix approach can help address critical contemporary issues in modeling, including pinpointing sources of model uncertainty and accelerating spin‐up of land carbon cycle models by tens of times. The accelerated spin‐up liberates models from the computational burden that hinders comprehensive parameter sensitivity analysis and assimilation of observational data to improve model accuracy. Such computational efficiency offered by the matrix approach enables substantial improvement of model predictions using ever‐increasing data availability. Overall, the matrix approach offers a step change forward for understanding and modeling the land carbon cycle. Plain Language Summary Earth system models (ESMs) are the tools we have to predict future states of climate and ecosystems. However, land carbon cycle models, a critical component of ESMs, are highly diverse in both structures and predictions, hindering our ability to obtain consistent future projections. The latter is needed as part of the evidence base to inform climate change mitigation strategies. This paper describes a matrix approach that unifies land carbon cycle models in one matrix form. The matrix models offer consistency and simplicity in structure that make the models analytically tractable. In addition, the matrix approach provides a theoretical framework for understanding the general behavior of the land carbon cycle. More importantly, the matrix approach solves some key contemporary issues in land carbon cycle modeling, such as pinning down sources of model uncertainty and accelerating spin‐up. The accelerated spin‐up speeds up land carbon cycle simulations by tens of times, making it feasible to perform parameter sensitivity analysis and data assimilation to constrain models with big data. Overall, the matrix approach represents a step change forward for understanding and modeling the land carbon cycle. Key Points The matrix approach unifies land carbon cycle models in a matrix form and thus helps gain simplicity in model structure The matrix approach provides a theoretical framework to understand the general behavior of land carbon cycle It helps address contemporary issues in land carbon cycle modeling, including pinning down model uncertainty and accelerating spin‐up