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大规模植被恢复对东亚气候的可能影响

马丹萌 俞淼 刘伟光

马丹萌, 俞淼, 刘伟光. 2022. 大规模植被恢复对东亚气候的可能影响[J]. 气候与环境研究, 27(3): 351−367 doi: 10.3878/j.issn.1006-9585.2021.20147
引用本文: 马丹萌, 俞淼, 刘伟光. 2022. 大规模植被恢复对东亚气候的可能影响[J]. 气候与环境研究, 27(3): 351−367 doi: 10.3878/j.issn.1006-9585.2021.20147
MA Danmeng, YU Miao, LIU Weiguang. 2022. Possible Impact of Large-Scale Vegetation Restoration on the Climate in East Asia [J]. Climatic and Environmental Research (in Chinese), 27 (3): 351−367 doi: 10.3878/j.issn.1006-9585.2021.20147
Citation: MA Danmeng, YU Miao, LIU Weiguang. 2022. Possible Impact of Large-Scale Vegetation Restoration on the Climate in East Asia [J]. Climatic and Environmental Research (in Chinese), 27 (3): 351−367 doi: 10.3878/j.issn.1006-9585.2021.20147

大规模植被恢复对东亚气候的可能影响

doi: 10.3878/j.issn.1006-9585.2021.20147
基金项目: 国家自然科学基金面上项目41575084、42075115,国家自然科学基金重大项目41991285
详细信息
    作者简介:

    马丹萌,女,1996年出生,硕士研究生,主要从事区域气候变化研究。E-mail: 438405721@qq.com

    通讯作者:

    俞淼,E-mail: yum@nuist.edu.cn

  • 中图分类号: P435

Possible Impact of Large-Scale Vegetation Restoration on the Climate in East Asia

Funds: National Natural Science Foundation of China (General Program) (Grants 41575084 and 42075115), National Natural Science Foundation of China (Major Program) (Grant 41991285)
  • 摘要: 利用动态植被模型CLM4-CNDV、区域气候模式RegCM4.6-CLM3.5和全球气候模式CAM4探究了当前气候状态下东亚区域可能的自然植被分布以及自然植被恢复对东亚区域气候产生的可能影响。结果表明,当前气候条件下,农作物区可能分布的自然植被为:蒙古高原以北、东北、华北平原和四川盆地的部分地区为裸土;东亚东南部及蒙古高原以北地区主要为林地;四川盆地及山东半岛主要为灌木;东北地区、东南沿海和长江中下游地区主要为草地。将农作物区恢复为自然植被后将对区域气候产生显著影响。其中,东亚东部大部分地区由于植被叶面积指数增加引起的蒸散发增强,使得夏季降水增加且温度降低显著;华北、四川盆地和广东中部平原地区植被叶面积指数减小,伴随区域内夏季降水显著减少且温度升高。而蒙古高原地区的气候变化不仅受区域内植被覆盖变化影响,还可能与印度地区和我国东南部植被变化引起的大气环流调整有关,使得蒙古高原西部冬季温度降低,而其东部夏季温度升高,同时夏季降水减少显著。研究所采用的试验方案是在相对理想的情况下进行的,但其结果为进一步区分不同地区植被覆盖变化的影响提供一定的参考。
  • 图  1  (a)CN05.1、(b)UDelaware观测的和(c)RCM (CAM)模拟的1983~2012年平均2 m气温及(d)CAM模拟、(e)RCM(R2)模拟、(f)RCM (CAM)模拟与UDelaware观测的偏差

    Figure  1.  (a) CN05.1 and (b) UDelaware observed and (c) RCM(CAM) simulated average 2-m temperature during 1983–2012 and biases between UDelaware observation and (d) CAM simulation, (e) RCM(R2) simulation, and (f) RCM(CAM) simulation, respectively

    图  2  图1,但为平均降水量

    Figure  2.  Same as Fig. 1, but for average precipitation

    图  3  当前气候状态下东亚区域自然植被功能型(Plant Function Types, PFTs)的可能分布:(a)温带常绿针叶林;(b)寒带常绿针叶林;(c)热带常绿阔叶林;(d)温带常绿阔叶林;(e)热带落叶阔叶林;(f)温带落叶阔叶林;(g)寒带落叶阔叶林;(h)温带落叶阔叶灌木;(i)寒带落叶阔叶灌木;(j)北极C3草;(k)C3草;(l)C4草

    Figure  3.  Possible distributions of natural vegetation Plant Function Types (PFTs) in East Asia under current climatic conditions: (a) Temperate evergreen coniferous forest; (b) frigid zone evergreen coniferous forest; (c) tropical evergreen broad-leaved forest; (d) temperate evergreen broad-leaved forest; (e) tropical deciduous broad-leaved forest; (f) temperate deciduous broad-leaved forest; (g) frigid zone deciduous broad-leaved forest; (h) temperate deciduous broad-leaved shrub; (i) frigid zone deciduous broad-leaved shrub; (j) Arctic C3 grass; (k) C3 grass; (l) C4 grass

    图  4  MODIS观测(左列)、LCC模拟(中列)和LCC模拟减去MODIS观测(右列)的(a–c)裸土、(d–f)林地、(g–i)灌木和(j–l)草地的分布百分比

    Figure  4.  Coverage percentages in (a–c) bare soil, (d–f) woodland, (g–i) shrub, and (j–l) grassland derived from MODIS data (first panel), LCC experiments (middle panel) and the differences between LCC experiments and MODIS data (third panel)

    图  5  RCM_LC(LC)减去RCM(CAM)模拟的(a)冬季和(b)夏季叶面积指数的差值分布。打点区域表示通过95%的信度检验

    Figure  5.  Difference of LAI in (a) winter and (b) summer derived by RCM_LC(LC) simulations minus RCM(CAM) simulations. The dotted area passes the 95% confidence level

    图  6  CAM_LCC减去CAM试验的冬季(左列)和夏季(右列)(a、b)气温和(c、d)降水量的差值分布。打点区域表示通过95%的信度检验

    Figure  6.  Difference of (a, b) temperature and (c, d) precipitation in winter (left panel) and summer (right panel) derived by CAM_LCC simulations minus CAM simulations. The dotted area passes the 95% confidence level

    图  7  同图6,但为RCM_LC(LC)减去RCM(CAM)试验

    Figure  7.  Same as Fig. 6, but for RCM_LC(LC) simulations minus RCM(CAM) simulations

    图  8  (a)RCM(CAM)、(b)RCM_LC(LC)模拟的东亚东部105°E~120°E平均降水量的纬度—时间剖面以及(c)二者的差值(滤去10日以下变率)

    Figure  8.  (a) RCM(CAM) and (b) RCM_LC(LC) simulated longitude−time profile of the mean precipitation in East Asia at 105°E–120°E, and (c) the their difference (the variability below 10 days is filtered out)

    图  9  RCM_LC(LC)减去RCM(CAM)模拟的每年(a)第95百分位和(b)第10百分位的日平均2 m温度以及每年日降水的(c)第95百分位的差值。打点区域表示通过95%的信度检验

    Figure  9.  Differences of the daily average 2-m temperature at (a) the 95th percentile every year, (b) the 10th percentile every year, and (c) the daily average precipitation at the 95th percentile every year derived by RCM_LC(LC) simulations minus RCM(CAM) simulations. The dotted area passes the 95% confidence level

    图  10  RCM_LC(LC)减去RCM(CAM)模拟的冬季(左列)和夏季(右列)(a、b)净辐射、(c、d)反照率)、(e、f)雪盖的差值分布。打点区域表示通过95%的信度检验

    Figure  10.  Differences of (a, b) radiation, (c, d) albedo, and (e, f) snow cover in winter (left panel) and summer (right panel) derived by RCM_LC(LC) simulations minus RCM(CAM) simulations. The dotted area passes the 95% confidence level

    图  11  RCM_LC(LC)减去RCM(CAM)模拟的冬季(左列)和夏季(右列)(a、b)蒸散发、(c、d)感热通量、(e、f)10 m风速的差值分布。打点区域表示通过95%的信度检验

    Figure  11.  Differences of (a, b) evapotranspiration, (c, d) sensible heat flux, and (e, f) 10-m wind speed in winter (left panel) and summer (right panel) derived by RCM_LC(LC) simulations minus RCM(CAM) simulations. The dotted area passes the 95% confidence level

    图  12  RCM_LC(LC)减去RCM(CAM)模拟的冬季(左列)和夏季(右列)(a、b)500 hPa和(c、d)850 hPa位势高度场(阴影)与风场的差值分布。实线表示RCM(CAM)模拟的位势高度场分布,斜线阴影区域表示位势高度场通过95%的信度检验

    Figure  12.  Difference of (a, b) 500-hPa and (c, d) 850-hPa potential height fields (shaded area) and the wind field in winter (left panel) and summer (right panel) derived by RCM_LC(LC) simulations minus RCM(CAM) simulations. The solid line indicates the potential height field distribution simulated by RCM(CAM) and the scribed area indicates that the potential height fields pass the 95% confidence level

    表  1  试验设计

    Table  1.   Experimental design

    试验名称侧边界条件陆面覆盖资料
    RCM(R2)NNRP2MODIS
    RCM(CAM)CAMMODIS
    RCM_LC(LC)CAM-LCCLCC
    下载: 导出CSV

    表  2  模拟和观测的1983~2012年东亚年、冬季、夏季平均气温和降水量及其均方根误差和空间相关系数

    Table  2.   Simulated and observed annual, winter, and summer average temperature and precipitation in East Asia during 1983−2012 and their root-mean-square errors and spatial correlation coefficients

    气温降水量
    试验时段平均值/°C均方根误差/°C相关系数*平均值/mm d−1均方根误差/mm d−1相关系数*
    UDel6.941.69
    冬季−7.290.53
    夏季19.683.46
    RCM(R2)11.324.670.962.161.680.61
    冬季−2.824.890.970.830.850.56
    夏季24.085.350.894.283.520.63
    RCM(CAM)12.035.640.952.142.030.45
    冬季−2.186.250.960.670.870.26
    夏季25.346.690.844.324.640.5
    CAM11.143.480.952.691.410.7
    冬季−1.954.180.961.140.640.57
    夏季24.025.020.854.853.280.66
    *相关系数全部通过99%的信度检验。
    下载: 导出CSV
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  • 收稿日期:  2020-11-26
  • 网络出版日期:  2021-09-20
  • 刊出日期:  2022-06-02

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