冬季北大西洋涛动与中国北方极端低温相关性的年代际变化

韩方红; 陈海山; 马鹤翟

doi:10.3878/j.issn.1006-9895.1705.17101

冬季北大西洋涛动与中国北方极端低温相关性的年代际变化

doi: 10.3878/j.issn.1006-9895.1705.17101

韩方红^{1, 2,},
陈海山^{1, 2, ,},
马鹤翟^{1, 2}

1.
南京信息工程大学气象灾害预报预警与评估协同创新中心/气象灾害教育部重点实验室, 南京 210044
2.
南京信息工程大学大气科学学院, 南京 210044

基金项目:

国家重点研发计划重点专项 2016YFA0600702

详细信息

作者简介:
韩方红, 女, 1991年出生, 硕士研究生, 主要从事短期气候预测与气候变化研究。E-mail:fangh0919@126.com

通讯作者:
陈海山, E-mail:haishan@nuist.edu.cn

中图分类号: P434
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出版历程
- 收稿日期: 2017-01-03
- 网络出版日期: 2017-06-01
- 刊出日期: 2018-03-15

Interdecadal Variation in the Relationship between North Atlantic Oscillation and Extreme Low Temperature over Northern China in Winter

Fanghong HAN^{1, 2
,},
Haishan CHEN^{1, 2
, ,},
Hedi MA^{1, 2}

1.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044
2.
School of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044

Funds:

National Key Research and Development Program of China 2016YFA0600702

摘要

摘要: 本文基于中国地面气温日值网格数据集（V2.0），采用滑动相关和相关分析等方法，揭示了冬季北大西洋涛动（NAO）对中国北方极端低温影响的事实，进一步证实了东北后冬（1、2月）冷日（夜）与同期NAO相关性的年代际变化。研究发现：在20世纪80年代中期前，东北后冬冷日（夜）频发，与NAO的相关性较好，而在80年代中期后东北后冬冷日（夜）少发，与NAO的相关性减弱。其中，1月在1969~1988阶段，东北冷日（夜）与NAO的相关性最好，相关区域显著，相关系数可达-0.68（-0.66），而在1989~2009阶段二者相关性最弱，相关区域不显著。进一步分析发现，在不同年代际背景下，NAO引起的大气环流异常是导致东北1月冷日（夜）与1月NAO相关性年代际变化的重要原因。相关性较好的年代，NAO引起的环流异常有利于冷涡等天气系统维持在贝加尔湖到东北一带，使东北地区气温偏低，冷日（夜）频发；相关性较弱的年代，不利于冷空气南下，使东北地区气温偏高，冷日（夜）少发。
- 极端温度 /
- 北大西洋涛动 /
- 相关分析 /
- 年代际变化
Abstract: Based on the gridded daily surface air temperature database (V2.0) of China, the influences of wintertime North Atlantic Oscillation (NAO) on extreme low temperature over northern China are investigated. The interdecadal variation in the relationship between NAO and cold days (nights) during the same period over Northeast China (NEC) in late winter is revealed based on sliding correlation analysis and correlation analysis. The results show that cold days (nights) are more frequent in late winter over NEC and have a better correlation with NAO before the middle 1980s compared to that after the middle 1980s. Additionally, the correlation was most significant over NEC in January during the period of 1969-1988, which could reach −0.68 (−0.66), but became weak during the period of 1989-2009. Furthermore, it is found that the interdecadal variation in the relationship between NAO and cold days (nights) over NEC in January is largely attributed to the NAO-related atmospheric circulation anomalies under different interdecadal backgrounds. The weather systems such as cold vortex caused by the NAO-related atmospheric circulation anomalies can maintain over a large area from Lake Baikal to NEC, leading to lower than normal temperature and more frequent cold days (nights) over NEC in the years when significant correlation exists between NAO and cold days (nights). The opposite is true when there is no significant correlation between NAO and cold days (nights) in NEC.
- Extreme temperature /
- NAO (North Atlantic Oscillation) /
- Correlation analysis /
- Interdecadal variation

HTML全文

图 1 中国北方极端低温指数与北大西洋涛动指数（NAOI）相关系数空间分布：（a、e）12~2月；（b、f）12月；（c、g）1月；（d、h）2月。其中（a–d）为冷日；（e–h）为冷夜。打点区域表示通过0.05显著性检验，下同

Figure 1. Spatial distributions of correlation coefficients between North Atlantic Oscillation Index (NAOI) and extreme low temperature index TX10P (cold days) over northern China in (a) winter (DJF, December–February), (b) December (Dec), (c) January (Jan), (d) February (Feb). (e–h) are the same as (a–d) but for correlation coefficients between NAOI and TN10P (cold nights). Black dots denote significance at the 95% confidence level, the same below

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图 2 中国北方平均气温与NAOI相关系数空间分布：（a）12~2月；（b）12月；（c）1月；（d）2月

Figure 2. Spatial distributions of correlation coefficients between NAOI and mean temperature over northern China in (a) DJF, (b) December, (c) January, (d) February

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图 3 东北极端低温指数EOF1时间序列：（a、d）12月；（b、e）1月；（c、f）2月。其中（a–c）为冷日；（d–f）为冷夜。曲线代表 11点高斯滤波，直线代表趋势线

Figure 3. Time series of EOF1 of extreme low temperature index TX10P over Northeast China (NEC) in (a) December, (b) January, (c) February. (d–f) are the same as (a–c), but for TN10P. Curves indicate results of 11-point Gaussian filtering, straight lines indicate the trend line

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图 4 东北后冬（1、2月）极端低温指数EOF1时间序列与NAOI滑动相关曲线（乘以−1.0）：（a）冷日；（b）冷夜。滑动窗口取21年，虚线为0.05显著性水平线

Figure 4. Sliding correlations (multiplied by −1.0) in a 21-year window between NAOI and time series of EOF1 of extreme low temperature indices over NEC in late winter: (a) TX10P; (b) TN10P. Dotted line indicates significance at the 95% confidence level

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图 5 东北1月极端低温指数与1月NAOI相关系数空间分布：（a、c）1969~1988；（b、d）1989~2009。其中（a、b）冷日，（c、d）为冷夜

Figure 5. Spatial distributions of correlation coefficients between NAOI and extreme low temperature index TX10P over NEC in January for the periods (a) 1969–1988 and (b) 1989–2009. (c, d) are the same as (a, b), but for correlation coefficients between NAOI and TN10P

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图 6 东北1月极端低温指数EOF1时间序列与500 hPa位势高度异常场回归系数（单位：gpm）：（a、c）1969~1988；（b、d）1989~2009。其中（a、b）为冷日，（c、d）为冷夜

Figure 6. Regressions of geopotential height anomalies at 500 hPa (units: gpm) against time series of EOF1 of extreme low temperature index TX10P over NEC in January for the periods (a)1969–1988 and (b)1989–2009. (c, d) are the same as (a, b), but for TN10P

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图 7 1月NAOI与500 hPa位势高度异常场回归系数（单位：gpm）：（a）1969~1988；（b）1989~2009

Figure 7. Regressions of geopotential height anomalies at 500 hPa (units: gpm) against NAOI in January for the periods (a) 1969–1988 and (b) 1989–2009

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表 1 极端温度指数定义

Table 1. Definition of extreme temperature indices

指数	名称	定义	单位
TX90P TX10P TN90P TN10P	暖日冷日暖夜冷夜	日最高温度＞90%分位值的天数日最高温度＜10%分位值的天数日最低温度＞90%分位值的天数日最低温度＜10%分位值的天数	d d d d

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表 2 东北极端低温指数EOF1时间序列与北大西洋涛动指数（NAOI）相关系数

Table 2. Correlation between North Atlantic Oscillation Index (NAOI) and the time series of EOF1 (the first EOF mode) of extreme low temperature indices over Northeast China (NEC)

	极端低温指数与NAOI的相关系数
时间尺度/月	TX10P	TN10P
12	−0.04	−0.03
1	−0.39^**	−0.34^*
2	−0.49^**	−0.50^**
^代表通过0.05显著性水平检验。 ^*代表通过0.01显著性水平检验。

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