| Qingdao Institute of marine geology, China Geological Survey | Host |
| Citation: |
LI Jinlan, TIAN Jun. Effects of Sunda Shelf exposure and vegetation changes on land-atmosphere carbon exchange during the Last Glacial Maximum[J]. Marine Geology & Quaternary Geology, 2022, 42(2): 110-118. doi: 10.16562/j.cnki.0256-1492.2022021101
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Effects of Sunda Shelf exposure and vegetation changes on land-atmosphere carbon exchange during the Last Glacial Maximum
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Abstract
The land exposure area of the Sunda Shelf in the southern South China Sea during the last glacial maximum (LGM) was nearly twice that in modern times. Was the Sunda Shelf a stronger carbon sink at that time? Though the study of the LGM carbon cycle depends on reliable vegetation reconstruction, both GOSAT satellite data and measured carbon density data utilized show that the role of different ecosystems in the carbon cycle could be very different. Whether there were tropical forests or savanna grassland on the Sunda Shelf during the LGM is a controversy. The land-atmosphere carbon exchange of the forest ecosystem is much greater than that of the grassland ecosystem. We used the Community Land Model (CLM4) to carry out two groups of sensitivity cases, aiming at quantifying the impacts of land area increase and vegetation distribution on land-atmosphere carbon exchange. Combining the pollen fossil evidence, our results showed that the exposed Sunda Shelf covered by the forest ecosystem in the LGM absorbed more carbon from the atmosphere at a rate of 0.16 PgC/a than in modern times. It indicated that the Sunda Shelf in LGM was a carbon sink, which was opposite to the role of other terrestrial carbon sources and was worthy of further study.
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References
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LI Jinlan, TIAN Jun. Effects of Sunda Shelf exposure and vegetation changes on land-atmosphere carbon exchange during the Last Glacial Maximum[J]. Marine Geology & Quaternary Geology, 2022, 42(2): 110-118. doi: 10.16562/j.cnki.0256-1492.2022021101
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Figure 1.
Global carbon cycle in the 1990s [1]
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Figure 2.
Topography and rivers in Southeast Asia
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Figure 3.
Simulated global terrestrial gross primary production by MPI-ESM-P, IPSL-CM5A-LR, and MIROC-ESM, respectively
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Figure 4.
The differences of (a) GPP and (b) NPP between 1850_tree and 1850_grass, the differences of (c) GPP and (d) NPP between lgm_tree and lgm_grass
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Figure 5.
(a) Simulated NEP in the 1850_tree experiment, (b) the differences of NEP between 1850_tree and 1850_grass