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2019~2020年北极野火极端事件的气象成因解析

彭道福 乐旭 朱君 田晨光 宫成 马一勉 雷亚栋 周浩

彭道福, 乐旭, 朱君, 等. 2023. 2019~2020年北极野火极端事件的气象成因解析[J]. 气候与环境研究, 28(2): 195−206 doi: 10.3878/j.issn.1006-9585.2022.22011
引用本文: 彭道福, 乐旭, 朱君, 等. 2023. 2019~2020年北极野火极端事件的气象成因解析[J]. 气候与环境研究, 28(2): 195−206 doi: 10.3878/j.issn.1006-9585.2022.22011
PENG Daofu, YUE Xu, ZHU Jun, et al. 2023. Exploration of the Meteorological Drivers of Extreme Wildfire Events in the Arctic during 2019–2020 [J]. Climatic and Environmental Research (in Chinese), 28 (2): 195−206 doi: 10.3878/j.issn.1006-9585.2022.22011
Citation: PENG Daofu, YUE Xu, ZHU Jun, et al. 2023. Exploration of the Meteorological Drivers of Extreme Wildfire Events in the Arctic during 2019–2020 [J]. Climatic and Environmental Research (in Chinese), 28 (2): 195−206 doi: 10.3878/j.issn.1006-9585.2022.22011

2019~2020年北极野火极端事件的气象成因解析

doi: 10.3878/j.issn.1006-9585.2022.22011
基金项目: 国家重点研发计划项目2019YFA0606802
详细信息
    作者简介:

    彭道福,男,1997年出生,硕士研究生,主要从事环境变化及其影响研究。E-mail: yxpuff@163.com

    通讯作者:

    乐旭,E-mail: yuexu@nuist.edu.cn

  • 中图分类号: P467

Exploration of the Meteorological Drivers of Extreme Wildfire Events in the Arctic during 2019–2020

Funds: National Key Research and Development Program of China (Grant 2019YFA0606802)
  • 摘要: 利用MERRA-2再分析气象变量(降雪量、降水量、地表风速、相对湿度、2 m气温、日最高气温)和加拿大火险天气指数,通过逐步回归方法构建1997~2020年间北极不同关键区内气象因子与野火碳排放量之间的关系,在此基础上解析北极地区2019~2020年野火极端事件的主导气象因子。结果显示在北极的3个关键区中,野火排放的主导因子都是火险指数之一的粗腐殖质湿度码(Duff Moisture Code, DMC)。2019~2020年北极地区850 hPa位势高度异常偏高,带来极端升高的日最高温和显著减少的降水,共同导致异常偏高的DMC值并促进了野火极端事件的爆发。这一结果表明高温和干旱等气候异常对于目前频发的北极野火有较强的促进作用。
  • 图  1  北极地区(a)1997~2020年6~8月野火燃烧碳排放量的逐年变化以及(b)2019~2020年相比1997~2018年平均的碳排放量变化。红色方框为排放量显著增加的区域(3个关键区)

    Figure  1.  (a) Summertime (Jun–Aug) carbon emissions from wildfires in the Arctic during 1997–2020 and (b) anomalous carbon emissions during 2019–2020 relative to the long-term mean of 1997–2018. Red boxes represent areas where emissions increased significantly during 2019–2020 (three key Arctic regions)

    图  2  北极关键区(a)1、(b)2、(c)3逐步回归模型得到的野火燃烧碳排放量的年际变化

    Figure  2.  Interannual variations in wildfire carbon emissions from observations and predictions with the stepwise regression models in the three key Arctic region (a) 1, (b) 2, and (c) 3

    图  3  同图2,但为留一法验证回归模型

    Figure  3.  Same as Fig. 2, but for the Leave-One-Out method

    图  4  北极地区(a)2019年和(b)2020年粗腐殖质湿度码(Duff Moisture Code, DMC)指数相对历史平均态(1997~2018年)的变化

    Figure  4.  Changes of the Arctic Duff Moisture Code (DMC) index in (a) 2019 and (b) 2020 relative to the historical mean state (1997–2018)

    图  5  北极地区夏季(6~8月)(a、b)日最高温(Tmax)、(c、d)相对湿度(RH)和(e、f)降水量(Prec)对2019年(左列)和2020年(右列)DMC变化的贡献

    Figure  5.  Contributions of (a, b) daily maximum temperature (Tmax), (c, d) relative humidity (RH), and (e, f) precipitation (Prec) to the changes of summer (Jun–Aug) DMC in the Arctic in 2019 (left panel) and 2020 (right panel)

    图  6  北极夏季1997~2020年850 hPa位势高度相比气候平均态(1997~2020年)的异常

    Figure  6.  Changes in summer geopotential height at 850 hPa in the Arctic relative to climatology (1997–2020)

    图  7  北极地区(a)2019年和(b)2020年夏季日最高温相对历史平均态(1997~2018年)的变化

    Figure  7.  Changes in the Arctic daily maximum temperature in the summer of (a) 2019 and (b) 2020 relative to the historical mean state (1997–2018)

    图  8  同图7,但为降水量

    Figure  8.  Same as Fig. 7, but for precipitation

    表  1  北极2019~2020年3个关键区内逐步回归模型及其决定系数

    Table  1.   Stepwise regression models and their coefficients of determination to variance in the three key Arctic regions during 2019–2020

    关键区逐步回归模型R2
    1Y = −26.84+2.32 DMC_fir_0+2314416.46 Prec_spr_1−41.6 Wind_aut_20.90
    2Y = −664.25+3.16 DMC_fir_0+2.7 T_win_00.83
    3Y = 23.5+1.44 DMC_max_1 - 6283427.64 Prec_spr_0+18.42 DSR_fir_00.80
    注:DMC_fir_0为当年火季(5~9月)的粗腐殖质湿度码的平均值,Prec_spr_1为前一年春季(3~5月)的降水量均值,Wind_aut_2为前两年的秋季(9~11月)的风速均值,T_win_0为当年冬季2 m的空气温度均值,DMC_max_1为前一年的粗腐殖质湿度码的日最大值,Prec_spr_0为当年春季的降水量均值,DSR_fir_0为当年火季的每日火灾严重等级均值。
    下载: 导出CSV

    表  2  气象因子对2019年DMC的贡献

    Table  2.   Contributions of meteorological factors to DMC in 2019

    贡献
    区域 日最高气温降水相对湿度
    163.55%17.85%18.60%
    221.34%65.40%13.26%
    318.38%46.45%35.17%
    下载: 导出CSV

    表  3  气象因子对2020年DMC的贡献

    Table  3.   Contributions of meteorological factors to DMC in 2020

    贡献
    区域 日最高气温降水相对湿度
    153.56%25.74%20.70%
    222.05%58.98%18.97%
    315.31%49.25%35.44%
    下载: 导出CSV
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