末次盛冰期巽他陆架海平面和植被变化对陆表碳通量影响的数值模拟研究

李金澜, 田军

李金澜,田军. 末次盛冰期巽他陆架海平面和植被变化对陆表碳通量影响的数值模拟研究[J]. 海洋地质与第四纪地质,2022,42(2): 110-118. DOI: 10.16562/j.cnki.0256-1492.2022021101
引用本文: 李金澜,田军. 末次盛冰期巽他陆架海平面和植被变化对陆表碳通量影响的数值模拟研究[J]. 海洋地质与第四纪地质,2022,42(2): 110-118. DOI: 10.16562/j.cnki.0256-1492.2022021101
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
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

末次盛冰期巽他陆架海平面和植被变化对陆表碳通量影响的数值模拟研究

基金项目: 国家自然科学基金重点项目“探索晚新生代太平洋中深层经向翻转流与气候演变冰期旋回的关系”(42030403);国家自然科学基金面上项目“晚中新世大洋碳位移事件的成因机制及其古环境效应”(41776051)
详细信息
    作者简介:

    李金澜(1995—),女,博士研究生,大气物理与环境专业,E-mail:12031335@mail.sustech.edu.cn

    通讯作者:

    田军(1974—),男,教授,从事古海洋学与古环境变化研究,E-mail:tianjun@tongji.edu.cn

  • 中图分类号: P736.21

Effects of Sunda Shelf exposure and vegetation changes on land-atmosphere carbon exchange during the Last Glacial Maximum

More Information
    Corresponding author:

    TIAN Jun: tianjun@tongji.edu.cn

  • 摘要: 末次盛冰期巽他陆架陆地暴露面积比现代增加将近一倍,该时期东南亚的碳汇能力是否比现代更强?本文利用GOSAT现代卫星数据集、实测碳密度数据集,对现代森林和草原生态系统碳通量(陆表碳通量)进行分析,发现二者的固碳能力相差较大,与地球系统模式的结果一致。本文基于末次盛冰期巽他陆架上植被分布类型的争议,为量化末次盛冰期巽他陆架暴露对大气二氧化碳浓度变化(陆表碳通量)的影响,利用美国国家大气研究中心(NCAR)的通用陆地模型(CLM4),以巽他陆架的植被、陆地面积为敏感条件进行了两组试验,考察末次盛冰期巽他陆架植被变化对陆表碳通量的影响。通过敏感试验结果分析,结合已有的孢粉化石证据,认为当末次盛冰期巽他陆架暴露且被热带雨林覆盖时,仅通过陆表碳交换就会使得东南亚的碳汇能力增强约0.16 PgC/a,在全球大气二氧化碳浓度的冰期-间冰期旋回中扮演着重要角色, 表明植被重建的可靠性对模拟末次盛冰期巽他陆架的陆地碳循环过程及其对气候的反馈非常重要。模拟结果还表明,末次盛冰期暴露的巽他陆架应具有较强的储碳能力,与冰期陆地的碳源角色相反,值得进一步研究。
    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.
  • 图  1   1990s全球碳循环[1]

    单位:PgC或PgC/a。

    Figure  1.   Global carbon cycle in the 1990s [1]

    Unit: PgC or PgC/a.

    图  2   巽他陆架地形与河流

    蓝色虚线代表巽他陆架暴露后发育的河流[9,18]。地形数据来源:NOAA全球地形数据ETOPO1(doi:10.7289/V5C8276M)。原图来自李金澜等[19]

    Figure  2.   Topography and rivers in Southeast Asia

    The blue dotted line represents the rivers developed after the exposure of the Sunda Shelf[9,18]. Terrain data source: NOAA global terrain data etopo1 (DOI: 10.7289 / v5c8276m). The picture is from LI Jinlan et al[19].

    图  3   3个地球系统模式的LGM和PI试验对全球陆地总初级生产量GPP的模拟结果

    单位:PgC/a。

    Figure  3.   Simulated global terrestrial gross primary production by MPI-ESM-P, IPSL-CM5A-LR, and MIROC-ESM, respectively

    Unit: PgC/a.

    图  4   1850_tree与1850_grass的GPP(a)、NPP(b)之差,lgm_tree与lgm_grass的GPP(c)、NPP(d)之差

    单位:gC·m−2·s−1

    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

    Unit: gC·m−2·s−1.

    图  5   1850_tree的NEP(a),1850_tree与1850_grass的NEP之差(b)

    单位:gC·m−2·s−1

    Figure  5.   (a) Simulated NEP in the 1850_tree experiment, (b) the differences of NEP between 1850_tree and 1850_grass

    Unit: gC·m−2·s−1.

    表  1   敏感试验设计方案

    Table  1   Experimental design

    试验简称海陆分布植被覆盖
    1850_tree现代的海陆分布东南亚全区为热带常绿阔叶林
    1850_grass现代的海陆分布东南亚全区为C4草本植物
    lgm_tree东南亚全区海平面下降120 m东南亚全区为热带常绿阔叶林
    lgm_grass东南亚全区海平面下降120 m东南亚全区为C4草本植物
     注:东南亚所指的具体范围是东经95°至130°、南纬10°至北纬20°。
    下载: 导出CSV

    表  2   东南亚陆地GPP总和及其占全球的比例

    Table  2   Terrestrial GPP in Southeast Asia and its proportion in the world

    PgC/a  
    LGMPILGM-PI
    MPI-ESM-P7.17(9.40%)6.72(6.09%)0.45(3.31%)
    IPSL-CM5A-LR9.63(14.48%)0.17(0.14%)9.46(14.34%)
    MIROC-ESM13.52(15.67%)10.57(9.29%)2.95(6.38%)
    下载: 导出CSV

    表  3   东南亚森林面积总和 及其占全球的比例

    Table  3   Total forest area in Southeast Asia and its proportion in the world

    1014 m2  
    LGMPILGM-PI
    MPI-ESM-P4.67(13.40%)2.66(7.31%)2.01(6.09%)
    IPSL-CM5A-LR4.93(9.81%)2.35(4.52%)2.58(5.29%)
    MIROC-ESM6.24(7.12%)3.86(5.37%)2.37(1.75%)
    下载: 导出CSV

    表  4   东南亚草地面积总和及其占全球的比例

    Table  4   Total grassland area in Southeast Asia and its proportion in the world

    1013 m2  
    LGMPILGM-PI
    MPI-ESM-P1.81(0.84%)0.94(0.42%)0.87(0.42%)
    IPSL-CM5A-LR8.00(1.78%)3.05(0.66%)4.95(1.12%)
    MIROC-ESM2.22(0.93%)1.04(0.37%)1.18(0.55%)
    下载: 导出CSV

    表  5   4个敏感试验的GPP、NPP比较

    Table  5   Comparison of GPP and NPP in four sensitivity tests

    PgC/a  
    1850_tree1850_grasslgm_treelgm_grass
    全球GPP117.00112.53126.22118.72
    全球NPP39.5237.7242.3039.34
    东南亚GPP11.66
    (9.97%)
    7.78
    (6.91%)
    20.86
    (16.53%)
    13.65
    (11.50%)
    东南亚NPP3.66
    (9.25%)
    2.12
    (5.63%)
    6.48
    (15.32%)
    3.64
    (9.27%)
     注:括号内数值是东南亚占全球的比例。
    下载: 导出CSV

    表  6   4个敏感试验的NBP、NEP比较

    Table  6   Comparison of NBP and NEP in four sensitivity tests

    PgC/a  
    1850_tree1850_grasslgm_treelgm_grass
    全球NBP−0.0250.0106−0.00490.0093
    东南亚NBP−0.00132.08×10−4−0.0012.58×10−4
    全球NEP1.7111.59431.89161.6121
    东南亚NEP0.1705
    (9.96%)
    0.032
    (2.01%)
    0.33
    (17.45%)
    0.046
    (2.85%)
     注:括号内数值是东南亚占全球的比例。
    下载: 导出CSV
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  • 收稿日期:  2022-02-10
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