时间尺度分离在华南夏季极端高温预测中的应用

陈思思; 张井勇; 黄刚

doi:10.3878/j.issn.1006-9585.2017.16220

时间尺度分离在华南夏季极端高温预测中的应用

doi: 10.3878/j.issn.1006-9585.2017.16220

陈思思^{1, 2,},
张井勇^{1, 2},
黄刚^{2, 3, 4, ,}

1.
中国科学院大气物理研究所季风系统研究中心, 北京 100190
2.
中国科学院大学, 北京 100049
3.
中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室, 北京 100029
4.
全球变化与中国绿色发展协同创新中心, 北京 100875

基金项目:

国家重点基础研究发展计划项目 2012CB955604

国家自然科学基金项目 51245089

国家自然科学基金项目 41305071

国家自然科学基金项目 41425019

国家自然科学基金项目 41661144016

海洋公益性行业科研专项 201505013

详细信息

作者简介:
陈思思, 女, 1989年出生, 博士研究生, 主要研究极端高温。E-mail: chensisi12@mails.ucas.ac.cn

通讯作者:
黄刚, E-mail: hg@mail.iap.ac.cn

中图分类号: P456
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- 被引次数: 0
出版历程
- 收稿日期: 2016-12-15
- 网络出版日期: 2017-03-31
- 刊出日期: 2018-03-20

Application of Time-Scale Decomposition Statistical Method in Climatic Prediction of Summer Extreme High-Temperature Events in South China

Sisi CHEN^{1, 2
,},
Jingyong ZHANG^{1, 2},
Gang HUANG^{2, 3, 4
, ,}

1.
Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100190
2.
University of Chinese Academy of Sciences, Beijing 100049
3.
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
4.
Joint Center for Global Change Studies, Beijing 100875

Funds:

National Basic Research Program of China 2012CB955604

National Natural Science Foundation of China 51245089

National Natural Science Foundation of China 41305071

National Natural Science Foundation of China 41425019

National Natural Science Foundation of China 41661144016

Public Science and Technology Research Funds Projects of Ocean 201505013

摘要

摘要: 基于高温日数存在受不同物理因子影响不同时间尺度变率的特征，应用滤波对华南夏季高温日数进行时间尺度分离，得到高温日数的年代际分量和年际分量。统计分析高温日数总量、年代际分量和年际分量在各自对应时间尺度上的影响因子，采用"向前"交叉检验逐步回归法，分别建立高温日数总量、年代际分量和年际分量的回归模型。高温日数总量的回归模型即为高温日数不区分时间尺度的直接回归模型，而两个分量回归模型拟合结果的叠加，即为高温日数时间尺度分离统计模型对总量的拟合。利用十折交叉检验法，对高温日数直接回归模型和时间尺度分离统计模型的拟合结果进行比较：相比高温日数直接回归模型，时间尺度分离统计模型的年代际分量均方根误差由2.6降低到2.3，与观测数据的相关系数由0.69提高到0.73（显著性水平α=0.01）；年际分量均方根误差由3.2降低到2.9，与观测数据的相关系数由0.4（α=0.1）提高到0.48（α=0.01）；高温日数总量均方根误差由4.1降低到3.7，与观测数据的相关系数由0.48提高到0.62（α=0.01）。1979~2010年拟合时段华南夏季高温日数的回报结果表明：两模型回报结果与观测数据均存在明显相关（α=0.01），直接回归模型的相关系数为0.57，时间尺度分离统计模型提高到0.72。2011~2013年独立检验时段的预测结果表明：直接回归模型预测结果的平均均方根误差为26.4%，时间尺度分离统计模型降低到12.3%。初步结果表明，两模型对华南夏季高温日数均有一定的预测能力，而时间尺度分离统计模型的预测结果有所改进。
- 华南 /
- 时间尺度分离 /
- 夏季高温日数 /
- 预测
Abstract: A time-scale decomposition (TSD) method to statistically downscale the predictand and predictors is used for seasonal forecast of summer extreme high temperature events (hot days) in South China. The hot days present a significant variability that is associated with distinct possible predictors. Both the hot days and the possible predictors are decomposed into inter-decadal and inter-annual components by fast flourier transformation filtering. Three downscaling regression models are then separately set up for the total hot days and the inter-decadal and inter-annual components of hot days. The downscaling regression model of the total hot days is named as direct regression model, while the downscaled inter-decadal and inter-annual regression models are combined together and named as TSD statistical regression model to obtain the total hot days. The fitting results of the direct regression model and TSD statistical regression model are tested by 10-fold cross-validation. The results show that compared to the direct regression model, the TSD statistical regression model decreases the root-mean-square error (RMSE) from 2.6 d to 2.3 d and increases the correlation coefficient with observations from 0.69 to 0.73 for the inter-decadal component; the TSD statistical regression model also decreases the RMSE from 3.2 d to 2.9 d and increases the correlation coefficient from 0.4 to 0.48 for the inter-annual component; for total hot days, the TSD statistical regression model decreases the RMSE from 4.1 d to 3.7 d and increases the correlation coefficient from 0.48 to 0.68. The hindcast results of hot days during 1979-2010 show that the correlation coefficient between observations and outputs of the direct regression model is 0.57, while the value is improved to 0.72 by the TSD statistical regression model. The forecast results of hot days during the independent validation period (2011-2013) show that the relative RMSE is 26.4% by the direct regression model, and it is 12.3% by the TSD statistical regression model. Compared with observations, both of the direct regression model and the TSD statistical regression model can predict the hot days to some extent in South China, and the TSD statistical regression model performs better for forecasts during 1979-2013.
- South China /
- Time-scale decomposition /
- Summer hot days /
- Prediction

HTML全文

图 1 华南地区(18°N~26°N，110°E~120°E)61个气象站点分布

Figure 1. Distribution of the 61 meteorological stations in South China (18°N-26°N, 110°E-120°E)

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图 2 1979~2010年华南夏季(6~8月)高温日数总量(黑实线)、年代际分量(红实线)、年际变率(蓝短线)、年际分量(年际变率减均值，蓝实线)时间序列

Figure 2. Time series of the total amount (black solid line), the inter-decadal component (red solid line), the inter-annual variability (blue short line), and the inter-annual component (the inter-annual variability minus average value; blue solid line) of summer (June–July–August) hot days over 1979–2010 in South China

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图 3 1979~2010年华南夏季高温日数(a)总量、(b)年代际分量和(c)年际分量与对应时间尺度春季土壤湿度相关空间分布图。图中打点区域表示通过相关系数显著性水平α=0.1的显著性检验

Figure 3. Correlation maps of (a) summer hot days, (b) its inter-decadal component, and (c) its inter-annual component with the spring soil moisture on corresponding time scales over 1979–2010 in South China. The dotted regions indicate correlation coefficients significant at α=0.1 level

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图 4 1979~2010年华南夏季高温日数(a)总量、(b)年代际分量和(c)年际分量与对应时间尺度春季印度洋海表温度相关空间分布。图中打点区域表示通过相关系数显著性水平α=0.1的显著性检验

Figure 4. Correlation maps of (a) summer hot days, (b) its inter-decadal component, and (c) its inter-annual component with the spring sea surface temperature in the Indian Ocean on corresponding time scales during 1979–2010. The dotted regions indicate correlation coefficients significant at α=0.1 level

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图 5 1979~2010年华南夏季高温日数(a)总量、(b)年代际分量和(c)年际分量与对应时间尺度气候指数的相关柱状图。五角星代表通过相关系数显著性水平α=0.1的显著性检验

Figure 5. Correlation coefficients of (a) summer hot days, (b) its inter-decadal component, and (c) its inter-annual component with seasonal climate indices on the corresponding time scales during 1979–2010. The stars represent correlation coefficients significant at α=0.1 level

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图 6 1979~2010年华南夏季高温日数逐步回归筛选过程中不同预报因子拟合回归方程在交叉检验中的均方根误差：(a)总量；(b)年代际分量；(c)年际分量

Figure 6. Root mean square errors between observed and cross-validation estimated summer hot days by different equations regressed by distinct predictors in stepwise regression screening for (a) the total summer hot days model, (b) the inter-decadal model, and (c) the inter-annual model

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图 7 1979~2010年华南夏季高温日数观测值(黑色实线)、直接回归模型(蓝色实心圆)和时间尺度分离统计模型(黄色实心圆)十折交叉检验拟合值的(a)年代际分量、(b)年际分量和(c)总量

Figure 7. (a) Inter-decadal component, (b) inter-annual component, and (c) total amount of the observed (black solid lines), 10-fold cross-validation estimated values (black solid lines) regressed by the regression model (blue dot), and the statistically downscaled mode (yellow dot) of the summer hot days in South China during 1979–2010

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图 8 华南夏季高温日数在1979~2013年的观测值(黑实线)、拟合时段(1979~2010年)统计模型的拟合值(蓝、黄实线)和独立检验时段(2011~2013年)统计模型的预测值(蓝、黄实心圆)：直接回归模型(蓝)；时间尺度分离统计模型(黄)

Figure 8. Time series of observed summer hot days during 1979-2013 (black solid line) and the estimated values during the training period of 1979-2010 (yellow and blue solid lines) and the validation period of 2011-2013 (yellow and blue dots) regressed by the regression model (blue) and statistically downscaled mode (yellow)

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表 1 1979~2010年华南夏季高温日数总量、年代际分量和年际分量与对应时间尺度华南春季土壤湿度区域平均的相关系数

Table 1. Correlation coefficients on different time scales between time series of summer hot days and spring soil moisture on corresponding time scales in South China during 1979–2010

高温日数总量与土壤湿度相关系数	高温日数年代际分量与土壤湿度相关系数	高温日数年际分量与土壤湿度相关系数
-0.26	-0.73^**	0.37^*
^、^*分别代表通过显著性水平α=0.1、α=0.01的显著性检验。

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表 2 1979~2010年华南夏季高温日数总量、年代际分量和年际分量与对应时间尺度春季印度洋关键区海温区域平均的相关系数

Table 2. Correlation coefficients on different time scales of time series of summer hot days and spring sea surface temperature over the Indian Ocean region marked by the box in Fig. 4 during 1979–2010

高温日数总量与关键区海温相关系数	高温日数年代际分量与关键区海温相关系数	高温日数年际分量与关键区海温相关系数
0.57^**	0.8^**	0.47^**
**代表通过显著性水平α=0.01的显著性检验。

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表 3 1979~2010年华南夏季高温日数总量及其年代际和年际分量的可能预报因子

Table 3. Potential predictors for summer hot days, its inter-decadal component, and inter-annual component in South China during 1979–2010

夏季高温日数总量可能的预报因子	夏季高温日数年代际分量可能的预报因子	夏季高温日数年际分量可能的预报因子
春季关键区海温(SST) 春季AMO 冬季Nino1+2指数	春季土壤湿度年代际分量(SM_d) 春季关键区海温年代际分量(SST_d) 秋季AMO年代际分量(AMO_d) 春季AO年代际分量(AO_d) 春季PDO年代际分量(PDO_d) 秋季Nino1+2指数年代际分量(Nino1+2_d)	春季土壤湿度年际分量(SM_a) 春季关键区海温年际分量(SST_a) 春季AMO年际分量(AMO_a) 春季PDO年际分量(PDO_a) 冬季Nino1+2指数年际分量(Nino1+2_a)

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表 4 1979~2010年华南夏季高温日数直接回归模型和时间尺度分离统计模型在各时间尺度十折交叉检验拟合值的均方根误差和与观测数据的相关系数

Table 4. Root mean square errors and correlation coefficients between observed and estimated values by 10-fold cross-validation for summer hot days, and the inter-decadal and inter-annual components in South China during 1979–2010

	均方根误差			与观测数据的相关系数
	年代际分量	年际分量	总量	年代际分量	年际分量	总量
直接回归模型	2.6	3.2	4.1	0.69^**	0.4^*	0.48^**
时间尺度分离统计模型	2.3	2.9	3.7	0.73^**	0.48^**	0.62^**
统计模型
^、^*分别代表通过显著性水平α=0.1、α=0.01的显著性检验。

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