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基于慢特征分析方法研究陆地表面气温变率的驱动力

朱丽飞 孙诚 李建平 张静 刘雨森 宫湛秋

朱丽飞, 孙诚, 李建平, 等. 2022. 基于慢特征分析方法研究陆地表面气温变率的驱动力[J]. 大气科学, 46(3): 520−540 doi: 10.3878/j.issn.1006-9895.2106.20213
引用本文: 朱丽飞, 孙诚, 李建平, 等. 2022. 基于慢特征分析方法研究陆地表面气温变率的驱动力[J]. 大气科学, 46(3): 520−540 doi: 10.3878/j.issn.1006-9895.2106.20213
ZHU Lifei, SUN Cheng, LI Jianping, et al. 2022. Driving Force of Land Surface Air Temperature Variability Based on the Slow Feature Analysis Method [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(3): 520−540 doi: 10.3878/j.issn.1006-9895.2106.20213
Citation: ZHU Lifei, SUN Cheng, LI Jianping, et al. 2022. Driving Force of Land Surface Air Temperature Variability Based on the Slow Feature Analysis Method [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(3): 520−540 doi: 10.3878/j.issn.1006-9895.2106.20213

基于慢特征分析方法研究陆地表面气温变率的驱动力

doi: 10.3878/j.issn.1006-9895.2106.20213
基金项目: 国家自然科学基金项目41975082
详细信息
    作者简介:

    朱丽飞,女,1997年出生,硕士研究生,从事海—气相互作用研究。E-mail: 201921490019@mail.bnu.edu.cn

    通讯作者:

    孙诚,E-mail: scheng@bnu.edu.cn

  • 中图分类号: P467

Driving Force of Land Surface Air Temperature Variability Based on the Slow Feature Analysis Method

Funds: National Natural Science Foundation of China (Grant 41975082)
  • 摘要: 慢特征分析(SFA)方法可以从非平稳时间序列中提取出慢变的外强迫信息。近年来,SFA方法被应用于气候变化研究领域,用于探究气候变化的潜在驱动力及相关的动力学机制。本文基于SFA方法,提取全球陆地表面气温(LSAT)的慢变外强迫信息,研究全球LSAT慢变驱动力的空间结构特征及低频变率的主要驱动因子。SFA方法提取的LSAT慢变驱动力与历史时期全球辐射强迫(GRF)和全球海表温度(SST)的主模态(大西洋多年代际振荡AMO、热带太平洋ENSO变率和太平洋年代际振荡PDO)有显著的相关关系,表明全球大部分地区LSAT的变率受到GRF和三个SST模态的显著影响。GRF对LSAT变率的影响有全球一致性的特征,而三个SST模态对LSAT变率的影响则呈现出明显的区域特点。此外,由于SFA方法可以有效降低原始LSAT序列中随机噪声的干扰,GRF和SST模态对LSAT变率的解释方差显著提高,进一步表明GRF和SST模态是全球LSAT低频变率主要的驱动因子。最后,利用历史海温驱动AGCM试验(即AMIP试验)的结果,验证了三个SST模态对区域LSAT变率的显著影响。
  • 图  1  1901~2009年(a)观测LSAT、(b)SFA方法提取的LSAT慢变驱动力与逐年GRF的相关系数的空间分布。蓝色方框内为相关系数显著增加的区域,分别为北非地区(0°~30°N,0°~30°E)、欧亚大陆中部地区(30°~60°N,30°~60°E)。黑色打点区域代表通过95%的信度水平检验

    Figure  1.  Correlation of (a) the original observed LSAT(Land Surface Air Temperature) and (b) the LSAT slow driving force extracted by the SFA(Slow Feature Analysis) method with the annual GRF(Global Radiation Forcing ) for 1901–2009. In the blue boxes, the regions with significantly increased correlation coefficients are Central Eurasia (30°–60°N, 30°–60°E) and North Africa (0°–30°N, 0°–30°E). Regions above 95% confidence level are black spotted

    图  2  1901~2009年(a)欧亚大陆中部地区(30°~60°N,30°~60°E)、(b)北非地区(0°~30°N,0°~30°E)区域年平均LSAT与逐年GRF的标准化时间序列。红线表示GRF,灰线表示观测LSAT,黑线表示SFA方法提取的LSAT慢变驱动力

    Figure  2.  Normalized time series of the regional mean annual LSAT in (a) Central Eurasia (30°–60°N, 30°–60°E) and (b) North Africa (0°–30°N, 0°–30°E) and annual GRF for 1901–2009. The red lines represent the annual GRF. The gray line represents the original observed LSAT. The black line represents the LSAT slow driving force extracted by the SFA method

    图  3  图1,但为1901~2009年(a)观测LSAT、(b)SFA方法提取的LSAT慢变驱动力与逐月AMO指数的相关系数的空间分布。蓝色方框区域分别为东亚地区(17°~37°N,90°~110°E)、北美洲东部(43°~63°N,49°~69°W)和格陵兰岛地区(58°~78°N,37°~57°W)

    Figure  3.  As in Fig. 1, but for correlation of (a) the original observed LSAT and (b) the LSAT slow driving force extracted by the SFA method with the monthly AMO (Atlantic Multidecadal Oscillation) index. The blue boxes represent East Asia (17°–37°N, 90°–110°E), eastern North America (43°–63°N, 49°–69°W), and Greenland (58°–78°N, 37°–57°W)

    图  4  图2,但为(a)东亚地区(17°~37°N,90°~110°E)、(b)北美洲东部(43°~63°N,49°~69°W)、(c)格陵兰岛地区(58°~78°N,37°~57°W)区域月平均LSAT和逐月AMO指数。红线表示逐月AMO指数,灰线表示观测LSAT,黑线表示SFA方法提取的LSAT慢变驱动力

    Figure  4.  As in Fig. 2, but for the regional mean monthly LSAT in East Asia (17°–37°N, 90°–110°E), eastern North America (43°–63°N, 49°–69°W), and Greenland (58°–78°N, 37°–57°W) and the monthly AMO index(AMOI). The red lines represent the monthly AMOI. The gray line represents the original observed LSAT. The black line represents the LSAT slow driving force extracted by the SFA method

    图  5  图1,但为1901~2009年(a)观测LSAT、(b)SFA方法提取的LSAT慢变驱动力与逐月Niño3.4指数的相关系数的空间分布。方框区域分别为北美洲南部(18°~38°N,88°~108°W)、印度地区(15°~20°N,70°~80°E)、中南半岛(7°~27°N,91°~111°E)

    Figure  5.  As in Fig. 1, but for correlation of (a) the original observed LSAT and (b) the LSAT slow driving force extracted by the SFA method with the monthly Niño3.4 index. The boxes represent southern North America (18°–38°N, 88°–108°W), India (15°–20°N, 70°–80°E), and the Indo-China Peninsula (7°–27°N, 91°–111°E)

    图  6  图2,但为(a)北美洲南部(18°~38°N,88°~108°W)、(b)印度地区(15°~20°N,70°~80°E)、(c)中南半岛(7°~27°N,91°~111°E)区域月平均LSAT和逐月Niño3.4指数。红线为逐月Nino3.4指数经过12个月滑动平均的标准化时间序列,灰线表示观测LSAT,黑线表示SFA方法提取的LSAT慢变驱动力

    Figure  6.  As in Fig. 2, but for the regional mean monthly LSAT in southern North America (18°–38°N, 88°–108°W), India (15°–20°N, 70°–80°E), and Indo-China Peninsula (7°–27°N, 91°–111°E) and the monthly Niño3.4 index (NI). The red lines run through the 12-month moving average The gray line represents the original observed LSAT. The black line represents the LSAT slow driving force extracted by the SFA method

    图  7  图1,但为1901~2009年(a)观测LSAT、(b)SFA方法提取的LSAT慢变驱动力与逐月PDO指数的相关系数的空间分布。方框区域分别为北美洲西北部(50°~70°N,100°~160°W)、南美洲中部(10°~30°S,55°~65°W)、澳洲东部(20°~30°S,140°~150°E)、北美洲南部(25°~35°N,100°~105°W)

    Figure  7.  As in Fig. 1, but for correlation of (a) the original observed LSAT and (b) the LSAT slow driving force extracted by the SFA method with the monthly PDO index. The boxes represent northwestern North America (50°–70°N, 100°–160°W), central South America (10°–30°S, 55°–65°W), Australia (20°–30°S, 140°–150°E), and southern North America (25°–35°N, 100°–105°W)

    图  8  图2,但为(a)北美洲西北部(50°~70°N,100°~160°W)、(b)南美洲中部(10°~30°S,55°~65°W)、(c)澳洲(20°~30°S,140°~150°E)、(d)北美洲南部(25°~35°N,100°~105°W)区域月平均LSAT和逐月PDO指数。红线表示逐月PDO指数,灰线表示观测LSAT,黑线表示SFA方法提取的LSAT慢变驱动力

    Figure  8.  As in Fig. 2, but for the regional mean monthly LSAT in (a) northwestern North America (50°–70°N, 100–160°W), (b) central South America (10°–30°S, 55°–65°W), (c) Australia (20°–30°S, 140°–150°E), and southern North America (25°–35°N, 100°–105°W) and the monthly PDO index (PDOI)The red lines represent the monthly PDOI. The gray line represents the original observed LSAT. The black line represents the LSAT slow driving force extracted by the SFA method

    图  9  AMIP试验10个集合平均1901~2009年的LSAT与逐月AMO指数同期相关系数的空间分布。方框内为据观测结果选择的关键区,黑色打点区域代表通过95%的信度水平检验

    Figure  9.  Correlation of the ten sets averaging LSAT simulated by the AMIP experience with the monthly AMO index for the same period from1901–2009. The boxes represent the key areas selected according to the observation results, regions above 95% confidence level are black spotted

    图  10  图9,但为AMIP试验10个集合平均1901~2009年的LSAT与逐月Niño3.4指数同期相关系数的空间分布

    Figure  10.  As in Fig. 9, but for correlation of the ten sets averaging LSAT simulated by the AMIP experience with the monthly Niño3.4 index for the same period from 1901~2009

    图  11  图9, 但为AMIP试验10个集合平均1901~2009年的LSAT与逐月PDO指数同期相关系数的空间分布

    Figure  11.  As in Fig. 9, but for correlation of the ten sets averaging LSAT simulated by the AMIP experience with the monthly PDO index for the same period from 1901~2009

    表  1  区域平均的LSAT与逐年GRF的相关系数和解释方差

    Table  1.   Correlation coefficient and explanatory variance of the regional mean LSAT and the annual GRF

    区域相关系数增长量(绝对值)解释方差增长量
    原始序列SFA方法提取的慢变驱动力原始序列SFA方法提取的慢变驱动力
    欧洲中部0.580.710.1334%50%16%
    北非地区0.640.700.0641%49%8%
    下载: 导出CSV

    表  2  表1,但为逐月AMO指数

    Table  2.   As in Table 1, but for the monthly AMO index

    区域相关系数增长量(绝对值)解释方差增长量
    原始序列SFA方法提取的慢变驱动力原始序列SFA方法提取的慢变驱动力
    东亚地区0.130.330.202%11% 9%
    北美洲东部0.250.440.196%19%13%
    格陵兰岛0.180.390.213%15%12%
    下载: 导出CSV

    表  3  表1,但为逐月Niño3.4指数

    Table  3.   As in Table 1, but for the monthly NI

    区域相关系数增长量(绝对值)解释方差增长量
    原始序列SFA方法提取的慢变驱动力原始序列SFA方法提取的慢变驱动力
    北美洲南部−0.16−0.320.163%10%8%
    印度0.260.450.197%20%13%
    中南半岛0.190.320.134%10%7%
    下载: 导出CSV

    表  4  表1,但为逐月PDO指数

    Table  4.   As in Table 1, but for the monthly PDOI

    区域相关系数增长量(绝对值)解释方差增长量
    原始序列SFA方法提取的慢变驱动力原始序列SFA方法提取的慢变驱动力
    北美洲西北部0.180.350.173%12%9%
    南美洲中部0.160.320.163%10%8%
    澳洲0.220.370.155%14%9%
    北美洲南部−0.21−0.380.174%14%10%
    下载: 导出CSV
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  • 收稿日期:  2020-10-16
  • 录用日期:  2021-09-06
  • 网络出版日期:  2021-09-09
  • 刊出日期:  2022-05-19

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