Matrix approach to land carbon cycle modeling : A case study with the Community Land Model
© 2017 John Wiley & Sons Ltd.
| Veröffentlicht in: | Global change biology. - 1999. - 24(2018), 3 vom: 30. März, Seite 1394-1404 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2018
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| Zugriff auf das übergeordnete Werk: | Global change biology |
| Schlagworte: | Journal Article Research Support, U.S. Gov't, Non-P.H.S. CO2 fertilization carbon storage data assimilation residence time soil organic matter Soil Carbon 7440-44-0 mehr... |
| Zusammenfassung: | © 2017 John Wiley & Sons Ltd. The terrestrial carbon (C) cycle has been commonly represented by a series of C balance equations to track C influxes into and effluxes out of individual pools in earth system models (ESMs). This representation matches our understanding of C cycle processes well but makes it difficult to track model behaviors. It is also computationally expensive, limiting the ability to conduct comprehensive parametric sensitivity analyses. To overcome these challenges, we have developed a matrix approach, which reorganizes the C balance equations in the original ESM into one matrix equation without changing any modeled C cycle processes and mechanisms. We applied the matrix approach to the Community Land Model (CLM4.5) with vertically-resolved biogeochemistry. The matrix equation exactly reproduces litter and soil organic carbon (SOC) dynamics of the standard CLM4.5 across different spatial-temporal scales. The matrix approach enables effective diagnosis of system properties such as C residence time and attribution of global change impacts to relevant processes. We illustrated, for example, the impacts of CO2 fertilization on litter and SOC dynamics can be easily decomposed into the relative contributions from C input, allocation of external C into different C pools, nitrogen regulation, altered soil environmental conditions, and vertical mixing along the soil profile. In addition, the matrix tool can accelerate model spin-up, permit thorough parametric sensitivity tests, enable pool-based data assimilation, and facilitate tracking and benchmarking of model behaviors. Overall, the matrix approach can make a broad range of future modeling activities more efficient and effective |
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| Beschreibung: | Date Completed 05.11.2018 Date Revised 05.11.2018 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1365-2486 |
| DOI: | 10.1111/gcb.13948 |