Literature DB >> 23505019

Traceable components of terrestrial carbon storage capacity in biogeochemical models.

Jianyang Xia1, Yiqi Luo, Ying-Ping Wang, Oleksandra Hararuk.   

Abstract

Biogeochemical models have been developed to account for more and more processes, making their complex structures difficult to be understood and evaluated. Here, we introduce a framework to decompose a complex land model into traceable components based on mutually independent properties of modeled biogeochemical processes. The framework traces modeled ecosystem carbon storage capacity (Xss ) to (i) a product of net primary productivity (NPP) and ecosystem residence time (τE ). The latter τE can be further traced to (ii) baseline carbon residence times (τ'E ), which are usually preset in a model according to vegetation characteristics and soil types, (iii) environmental scalars (ξ), including temperature and water scalars, and (iv) environmental forcings. We applied the framework to the Australian Community Atmosphere Biosphere Land Exchange (CABLE) model to help understand differences in modeled carbon processes among biomes and as influenced by nitrogen processes. With the climate forcings of 1990, modeled evergreen broadleaf forest had the highest NPP among the nine biomes and moderate residence times, leading to a relatively high carbon storage capacity (31.5 kg cm(-2) ). Deciduous needle leaf forest had the longest residence time (163.3 years) and low NPP, leading to moderate carbon storage (18.3 kg cm(-2) ). The longest τE in deciduous needle leaf forest was ascribed to its longest τ'E (43.6 years) and small ξ (0.14 on litter/soil carbon decay rates). Incorporation of nitrogen processes into the CABLE model decreased Xss in all biomes via reduced NPP (e.g., -12.1% in shrub land) or decreased τE or both. The decreases in τE resulted from nitrogen-induced changes in τ'E (e.g., -26.7% in C3 grassland) through carbon allocation among plant pools and transfers from plant to litter and soil pools. Our framework can be used to facilitate data model comparisons and model intercomparisons via tracking a few traceable components for all terrestrial carbon cycle models. Nevertheless, more research is needed to develop tools to decompose NPP and transient dynamics of the modeled carbon cycle into traceable components for structural analysis of land models.
© 2013 Blackwell Publishing Ltd.

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Year:  2013        PMID: 23505019     DOI: 10.1111/gcb.12172

Source DB:  PubMed          Journal:  Glob Chang Biol        ISSN: 1354-1013            Impact factor:   10.863


  4 in total

1.  Global covariation of carbon turnover times with climate in terrestrial ecosystems.

Authors:  Nuno Carvalhais; Matthias Forkel; Myroslava Khomik; Jessica Bellarby; Martin Jung; Mirco Migliavacca; Mingquan Mu; Sassan Saatchi; Maurizio Santoro; Martin Thurner; Ulrich Weber; Bernhard Ahrens; Christian Beer; Alessandro Cescatti; James T Randerson; Markus Reichstein
Journal:  Nature       Date:  2014-09-24       Impact factor: 49.962

2.  Where does the carbon go? A model-data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites.

Authors:  Martin G De Kauwe; Belinda E Medlyn; Sönke Zaehle; Anthony P Walker; Michael C Dietze; Ying-Ping Wang; Yiqi Luo; Atul K Jain; Bassil El-Masri; Thomas Hickler; David Wårlind; Ensheng Weng; William J Parton; Peter E Thornton; Shusen Wang; I Colin Prentice; Shinichi Asao; Benjamin Smith; Heather R McCarthy; Colleen M Iversen; Paul J Hanson; Jeffrey M Warren; Ram Oren; Richard J Norby
Journal:  New Phytol       Date:  2014-05-21       Impact factor: 10.151

3.  The role of residence time in diagnostic models of global carbon storage capacity: model decomposition based on a traceable scheme.

Authors:  Chen Yizhao; Xia Jianyang; Sun Zhengguo; Li Jianlong; Luo Yiqi; Gang Chengcheng; Wang Zhaoqi
Journal:  Sci Rep       Date:  2015-11-06       Impact factor: 4.379

4.  Effects of substrate addition on soil respiratory carbon release under long-term warming and clipping in a tallgrass prairie.

Authors:  Xiaohong Jia; Xuhui Zhou; Yiqi Luo; Kai Xue; Xian Xue; Xia Xu; Yuanhe Yang; Liyou Wu; Jizhong Zhou
Journal:  PLoS One       Date:  2014-12-09       Impact factor: 3.240

  4 in total

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