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青藏高原未来气候变化预估的不确定性来源及其降低途径

郭悦 张文青 刘浏 周雪婷

郭悦, 张文青, 刘浏, 周雪婷. 青藏高原未来气候变化预估的不确定性来源及其降低途径[J]. 北京师范大学学报(自然科学版). doi: 10.12202/j.0476-0301.2023133
引用本文: 郭悦, 张文青, 刘浏, 周雪婷. 青藏高原未来气候变化预估的不确定性来源及其降低途径[J]. 北京师范大学学报(自然科学版). doi: 10.12202/j.0476-0301.2023133
GUO Yue, ZHANG Wenqing, LIU Liu, ZHOU Xueting. Quantifying the Uncertainty Sources of Future Climate Projections and Narrowing Uncertainties with Bias Correction Techniques in Tibetan Plateau[J]. Journal of Beijing Normal University(Natural Science). doi: 10.12202/j.0476-0301.2023133
Citation: GUO Yue, ZHANG Wenqing, LIU Liu, ZHOU Xueting. Quantifying the Uncertainty Sources of Future Climate Projections and Narrowing Uncertainties with Bias Correction Techniques in Tibetan Plateau[J]. Journal of Beijing Normal University(Natural Science). doi: 10.12202/j.0476-0301.2023133

青藏高原未来气候变化预估的不确定性来源及其降低途径

doi: 10.12202/j.0476-0301.2023133
基金项目: 国家自然科学基金资助项目(52079138,51961145104)
详细信息
    通讯作者:

    刘浏(1986—),男,教授. 研究方向:水文学及水资源. E‒mail:liuliu@cau.edu.cn

  • 中图分类号: P339

Quantifying the Uncertainty Sources of Future Climate Projections and Narrowing Uncertainties with Bias Correction Techniques in Tibetan Plateau

  • 摘要: 在全球变暖加剧的背景下,准确预估未来时期气候变化至关重要.本文基于最新发布的CMIP6(Coupled Model Intercomparison Project 6)中的15个GCM模式(Global Climate Model),在未来4种共享社会经济路径和典型浓度路径组合情景下,量化青藏高原地区气温和降水预估的不确定性.然后使用DT方法(Daily Translation),对CMIP6模式输出数据进行偏差校正.通过对比DT前后不确定性的大小,讨论偏差校正方法在降低气温和降水预估不确定性的潜力.结果显示,DT方法校正结果与实测值更为接近;气候预估不确定性从长期来看,模式不确定性占据主导地位,而情景不确定性和内部变异性的贡献相对较低;DT方法对于降低降水预估不确定性的效果较好,其中模式不确定性降低最多,对情景不确定性的大小影响不大,但影响了不确定性的相对贡献大小.综合来看,利用DT方法进行偏差校正能在一定程度上降低预估不确定性.研究结果可为揭示气候变化对高寒区水循环的影响机制提供科学依据.

     

  • 图  1  偏差校正前不同情景下年降水量变化

    图  2  偏差校正后不同情景下年降水量变化

    图  3  偏差校正前不同情景下年平均气温变化

    图  4  偏差校正后不同情景下年平均气温变化

    图  5  未来不同情景不同时期的降水量变化

    图  6  未来不同情景不同时期的降水量变化

    图  7  DT前后气候预估不确定性

    图  8  DT前后气候预估不确定性方差比例

    图  9  DT前后降水预估不确定性大小

    图  10  DT前后降水预估不确定性方差比例

    图  11  DT前后平均气温预估不确定性大小

    图  12  DT前后平均气温预估不确定性方差比例

    表  1  CMIP6模式具体信息

    编号 模式名称 所属机构及国家 分辨率
    1 ACCESS‒CM2 Commonwealth Scientific and Industrial Research Organization, Australia 1.25° × 1.675°
    2 CanESM5 Canadian Centre for Climate Modelling and Analysis, Canada 2.7673° × 2.8125°
    3 CESM2‒WACCM National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, USA 0.9424° × 1.25°
    4 CMCC‒CM2‒SR5 Fondazione Centro Euro‒Mediterraneo sui Cambiamenti Climatici, Italy 0.9424° × 1.25°
    5 GFDL‒ESM4 Geophysical Fluid Dynamics Laboratory, Princeton, USA 1°×1.25°
    6 INM‒CM4‒8 Institute for Numerical Mathematics, Russian Academy of Science, Russia 1.5°×2°
    7 IPSL‒CM6A‒LR Institut Pierre Simon Laplace, France National Institute of Meteorological 1.2676°×2.5°
    8 KACE‒1‒0‒G Sciences/Korea Meteorological Administration, Republic of Korea 1.25°×1.875°
    9 MIROC6 Agency for Marine‒Earth Science and Technology, Atmosphere and Ocean Research Institute, and National Institute for Environmental Studies, Ibaraki, Japan 1.389°×1.406°
    10 MPI‒ESM1‒2‒HR Max Planck Institute for Meteorology , Germany 1.865°×1.875°
    11 MPI‒ESM1‒2‒LR Max Planck Institute for Meteorology , Germany 1.865°×1.875°
    12 MRI‒ESM2‒0 Meteorological Research Institute, Tsukuba, Japan 1.124°×1.125°
    13 NESM3 Nanjing University of Information Science and Technology, China 1.865°×1.875°
    14 NorESM2‒LM Norwegian Climate Centre, Norway 1.895°×2.5°
    15 NorESM2‒MM Norwegian Climate Centre, Norway 0.942°×1.25°
    下载: 导出CSV

    表  2  未来情景变化具体信息

    编号 组合形式 具体含义
    SSP126 SSP1(低强迫情景)+RCP2.6 到2100年,辐射强迫水平为2.6W·m−2的绿色发展路径
    SSP245 SSP2(中强迫情景)+RCP4.5 到2100年,辐射强迫水平为4.5W·m−2的中等发展路径
    SSP370 SSP3(中高强迫情景)+RCP7.0 到2100年,辐射强迫水平为7.0W·m−2的中高等发展路径
    SSP585 SSP5(高强迫情景)+RCP8.5 到2100年,辐射强迫水平为8.5W·m−2的高等发展路径
    下载: 导出CSV

    表  3  验证期模式输出和偏差校正降水量各统计指标评价结果

    Ibias(%)FREQINTQ90
    月份GCMsDTGCMsDTGCMsDT
    1409.66‒18.441229.0540.66397.77‒25.48
    2408.14‒20.4428.85‒3.34366.44‒31.02
    3416.45‒14.8531.63‒0.69353.22‒14.55
    4344.20‒13.3134.49‒0.55394.83‒11.95
    572.49‒17.3513.43‒7.59105.39‒18.36
    622.15‒8.787.40‒4.8231.70‒11.12
    7‒10.03‒6.341.50‒7.025.97‒11.77
    8‒16.91‒1.324.39‒3.784.49‒1.61
    9‒22.58‒11.486.58‒2.01‒13.02‒11.23
    10171.57‒18.5215.040.29200.53‒16.90
    11342.56‒20.6952.64‒7.56755.68‒22.62
    12519.97‒4.82613.2444.68713.22‒13.37
    CFREQINTQ90
    月份GCMsDTGCMsDTGCMsDT
    10.430.960.420.960.480.98
    20.520.970.650.980.600.99
    30.370.980.560.970.640.99
    40.290.990.480.960.600.99
    50.510.980.670.960.710.98
    60.620.970.660.960.680.96
    70.430.930.680.960.680.96
    80.530.920.680.960.700.97
    90.720.940.720.950.760.97
    100.630.990.670.950.720.98
    110.430.980.580.930.560.98
    120.360.910.530.900.270.91
    EnrFREQINTQ90
    月份GCMsDTGCMsDTGCMsDT
    10.210.000.010.020.460.04
    20.190.030.030.010.260.06
    30.080.020.030.010.060.03
    40.060.010.030.010.060.00
    50.120.010.070.070.000.05
    60.070.010.070.020.040.02
    70.040.000.320.010.280.03
    80.040.010.160.000.150.01
    90.040.000.020.040.030.00
    100.010.020.020.150.030.04
    110.140.010.090.000.190.01
    120.220.020.020.060.510.06
    下载: 导出CSV

    表  4  验证期模式输出和偏差校正平均气温各统计指标评价结果

    Ibias/%Q90Q10
    月份GCMsDTGCMsDT
    1‒12.020.80‒163.82146.00
    2‒9.284.46‒190.76‒0.63
    310.610.62‒31.56‒0.05
    4‒25.01‒12.01‒94.30‒6.08
    5‒4.067.52‒21.58‒0.51
    6‒16.342.98‒66.84‒20.55
    7‒9.130.62‒33.63‒4.84
    8‒4.15‒0.35‒9.61‒4.77
    9‒5.90‒3.59‒74.83‒22.02
    1013.9215.36‒40.94‒0.76
    11‒5.6530.27‒125.46‒13.52
    12‒7.797.46‒11.683.96
    CQ90Q10
    月份GCMsDTGCMsDT
    10.74260.99720.83460.9967
    20.77360.99750.82560.9947
    30.79100.99810.80280.9974
    40.80900.99790.78590.9971
    50.80410.99710.79980.9979
    60.79030.99830.80710.9961
    70.80210.99720.78000.9923
    80.80620.99730.77930.9961
    90.77200.99730.77500.9940
    100.77550.99750.80420.9967
    110.76950.99640.79440.9953
    120.70290.99620.80490.9962
    EnrQ90Q10
    月份GCMsDTGCMsDT
    10.03870.00320.03600.0001
    20.02930.00360.04120.0011
    30.02980.00290.05500.0014
    40.03550.00230.06040.0057
    50.03660.00460.06650.0026
    60.04880.00350.06040.0005
    70.05640.00150.07500.0015
    80.05860.00100.07640.0040
    90.06160.00210.07010.0026
    100.05050.00200.04960.0019
    110.04080.00050.04290.0002
    120.03930.00110.03980.0028
    下载: 导出CSV

    表  5  DT前后未来不同时期不同情景的年降水量变化范围

    年份 SSP DT前降水量范围/mm DT后降水量范围/mm
    2030—2039年 126 684~1218 376~474
    245 673~1236 365~684
    370 691~1203 368~670
    585 696~1242 354~589
    2060—2069年 126 728~1361 387~534
    245 738~1299 402~678
    370 738~1295 391~741
    585 742~1351 394~704
    2090—2099年 126 738~1365 395~572
    245 765~1388 401~664
    370 814~1396 409~860
    585 860~1568 449~862
    下载: 导出CSV

    表  6  DT前后未来不同时期不同情景的年平均气温变化范围

    年份 SSP水平 DT前年平均
    气温范围/°C
    DT后年平均
    气温范围/°C
    2030—2039年 126 −6.36~3.18 −0.16~1.42
    245 −6.57~3.08 −0.09~2.55
    370 −6.39~3.35 −0.14~3.15
    585 −6.62~3.56 −0.15~3.39
    2060—2069年 126 −5.81~3.48 0.23~3.15
    245 −5.12~4.25 0.99~3.90
    370 −4.55~4.54 1.28~5.63
    585 −3.52~5.38 1.82~5.89
    2090—2099年 126 −6.27~3.61 0.21~6.18
    245 −3.89~4.74 1.34~4.50
    370 −1.64~6.40 1.72~7.73
    585 0.53~7.98 3.15~9.35
    下载: 导出CSV
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  • 收稿日期:  2023-07-10
  • 网络出版日期:  2023-09-04

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