A33J-0306
Dynamic carbon allocation significantly changed land carbon sink and carbon pool sizes

Wednesday, 16 December 2015
Poster Hall (Moscone South)
Jiangzhou Xia and Wenping Yuan, Beijing Normal University, Beijing, China
Abstract:
The allocation of photosynthate among the plant components (e.g., leaves, stems, and roots) plays an important role in regulating plant growth, competition, and terrestrial carbon cycle. However, the carbon allocation process is still a weak part in the earth system models (ESMs). In this study, the Integrated BIosphere Simulator (IBIS) model coupled with a dynamic carbon allocation model (IBISAL) is used to explore the impact of carbon allocation on the terrestrial carbon cycle. This dynamic carbon allocation model suggests that plants should allocate the largest part of carbon to the plant components which need to capture the most limiting resources, such as light, water and nitrogen. In comparison to the results of original IBIS model using fixed allocation ratios, the net ecosystem productivity, global biomass and soil organic carbon simulated by IBISAL model decreased by13.4% , 9.9% and 20.8%, respectively . The dynamic allocation scheme tends to benefit roots allocation. Because roots had short turnover times, high roots allocation led to the decreases of global carbon sink and carbon pool sizes. The observations showed that the carbon allocation ratios changed with temperature and precipitation. The dynamic carbon allocation model could reproduce this phenomenon correctly. The results show that the dynamic carbon allocation ratios of boreal evergreen forests and C3 grasses are consistent well with the observations. However, the IBISAL, and another three ESMs (i.e., CESM1-BGC, IPSL-CM5A-MR and NorESM1-ME models) adopting dynamic allocation scheme overestimated the stems allocation of tropical forests. This study shows the substantial influences of carbon allocation on the carbon sink and carbon pool sizes. Therefore, improving estimations of carbon allocation by ESMs are an important and effective path to reduce uncertainties in the global carbon cycle simulation and climate change prediction.