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Modeling the contribution of the microbial carbon pump to carbon sequestration in the South China Sea 被引量:4
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作者 Wenfang LU Yawei LUO +1 位作者 Xiaohai YAN Yuwu JIANG 《Science China Earth Sciences》 SCIE EI CAS CSCD 2018年第11期1594-1604,共11页
The two key mechanisms for biologically driven carbon sequestration in oceans are the biological pump(BP) and the microbial carbon pump(MCP); the latter is scarcely simulated and quantified in the China seas. In this ... The two key mechanisms for biologically driven carbon sequestration in oceans are the biological pump(BP) and the microbial carbon pump(MCP); the latter is scarcely simulated and quantified in the China seas. In this study, we developed a coupled physical-ecosystem model with major MCP processes in the South China Sea(SCS). The model estimated a SCSaveraged MCP rate of 1.55 mg C m^(-2) d^(-1), with an MCP-to-BP ratio of 1:6.08 when considering the BP at a depth of 1000 m.Moreover, the ecosystem responses were projected in two representative global warming scenarios where the sea surface temperature increased by 2 and 4°C. The projection suggested a declined productivity associated with the increased near-surface stratification and decreased nutrient supply, which leads to a reduction in diatom biomass and consequently the suppression of the BP. However, the relative ratio of picophytoplankton increased, inducing a higher microbial activity and a nonlinear response of MCP to the increase in temperature. On average, the ratio of MCP-to-BP at a 1000-m depth increased to 1:5.95 with surface warming of 4°C, indicating the higher impact of MCP in future ocean carbon sequestration. 展开更多
关键词 South China Sea Microbial Carbon Pump Global change Numerical model
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Spatiotemporal analysis of vegetation variability and its relationship with climate change in China 被引量:2
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作者 Bingwen QIU Weijiao LI +2 位作者 Ming ZHONG Zhenghong TANG Chongcheng CHEN 《Geo-Spatial Information Science》 SCIE EI 2014年第3期170-180,共11页
This paper investigated spatiotemporal dynamic pattern of vegetation,climate factor,and their complex relationships from seasonal to inter-annual scale in China during the period 1982–1998 through wavelet transform m... This paper investigated spatiotemporal dynamic pattern of vegetation,climate factor,and their complex relationships from seasonal to inter-annual scale in China during the period 1982–1998 through wavelet transform method based on GIMMS data-sets.First,most vegetation canopies demonstrated obvious seasonality,increasing with latitudinal gradient.Second,obvious dynamic trends were observed in both vegetation and climate change,especially the positive trends.Over 70%areas were observed with obvious vegetation greening up,with vegetation degradation principally in the Pearl River Delta,Yangtze River Delta,and desert.Overall warming trend was observed across the whole country(>98%area),stronger in Northern China.Although over half of area(58.2%)obtained increasing rainfall trend,around a quarter of area(24.5%),especially the Central China and most northern portion of China,exhibited significantly negative rainfall trend.Third,significantly positive normalized difference vegetation index(NDVI)–climate relationship was generally observed on the de-noised time series in most vegetated regions,corresponding to their synchronous stronger seasonal pattern.Finally,at inter-annual level,the NDVI–climate relationship differed with climatic regions and their long-term trends:in humid regions,positive coefficients were observed except in regions with vegetation degradation;in arid,semiarid,and semihumid regions,positive relationships would be examined on the condition that increasing rainfall could compensate the increasing water requirement along with increasing temperature.This study provided valuable insights into the long-term vegetation–climate relationship in China with consideration of their spatiotemporal variability and overall trend in the global change process. 展开更多
关键词 vegetation variability wavelet transform climate change NON-STATIONARY normalized difference vegetation index(NDVI)
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