期刊
GLOBAL CHANGE BIOLOGY
卷 19, 期 7, 页码 2104-2116出版社
WILEY
DOI: 10.1111/gcb.12172
关键词
benchmarking analysis; biome; carbon and nitrogen coupling; model intercomparison; traceability; vegetation type
资金
- US National Science Foundation (NSF) [DBI 0850290, EPS 0919466, DEB 0743778, DEB 0840964, EF 1137293]
- Div Of Biological Infrastructure
- Direct For Biological Sciences [0850290] Funding Source: National Science Foundation
- EPSCoR
- Office Of The Director [0919466] Funding Source: National Science Foundation
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 (E). The latter E can be further traced to (ii) baseline carbon residence times (E), which are usually preset in a model according to vegetation characteristics and soil types, (iii) environmental scalars (), 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 (31.5kgcm-2). Deciduous needle leaf forest had the longest residence time (163.3years) and low NPP, leading to moderate carbon storage (18.3kgcm-2). The longest E in deciduous needle leaf forest was ascribed to its longest E (43.6years) and small (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 E or both. The decreases in E resulted from nitrogen-induced changes in 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.
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