Synoptic-scale precursors of the East Asia/Pacific teleconnection pattern responsible for persistent extreme precipitation in the Yangtze River Valley

2014 ◽  
Vol 141 (689) ◽  
pp. 1389-1403 ◽  
Author(s):  
Yang Chen ◽  
Panmao Zhai
Keyword(s):  
East Asia ◽  
Extreme Precipitation ◽  
Yangtze River ◽  
Teleconnection Pattern ◽  
Yangtze River Valley ◽  
River Valley ◽  
Asia Pacific ◽  
The Yangtze River ◽  
Synoptic Scale

scholarly journals Potential Influence of the East Asia–Pacific Teleconnection Pattern on Persistent Precipitation in South China: Implications of Atypical Yangtze River Valley Cases

2018 ◽  
Vol 33 (1) ◽  
pp. 267-282 ◽  
Author(s):  
Hui Li ◽  
Panmao Zhai ◽  
Yang Chen ◽  
Er Lu
Keyword(s):  
East Asia ◽  
South China ◽  
Yangtze River ◽  
The Philippines ◽  
Teleconnection Pattern ◽  
Yangtze River Valley ◽  
River Valley ◽  
Asia Pacific ◽  
Low Latitudes ◽  
Precipitation Processes

Abstract In this study, cases of the East Asia–Pacific (EAP) teleconnection pattern not responsible for persistent precipitation processes in the Yangtze River valley (YRV) have been investigated. The results suggest that such a type of EAP pattern has some linkage with persistent precipitation processes in south China (SC) with the following properties: 1) in response to the negative SSTAs and anticyclone near the Philippines, the meridional energy propagates from the low latitudes over the north of the Philippines; 2) the western Pacific subtropical high (WPSH) then intensifies and extends westward; 3) a meridional triple structure of the EAP teleconnection pattern is established; 4) at the same time, the cyclonic circulation over northeastern China introduces cold and dry air to the lower latitudes, merging with the water vapor into SC and leading to heavy precipitation from the fringe of the WPSH, the South China Sea, and the Bay of Bengal and the combination of systems persists for at least 3 days, leading to the persistent precipitation processes in SC; and 5) compared with the EAP teleconnection responsible for the precipitation in YRV, the positions of the three centers in the mid- and low latitudes are more southerly located than the YRV EAP centers. Further study indicates that the ocean surface heat conditions in the areas near the Philippines seem to be important in affecting the EAP teleconnection pattern for persistent precipitation processes in SC. Finally, all of the cases with persistent precipitation in SC during 1961–2010 linked with the EAP pattern have been investigated; the results are consistent with the above conclusions.

Seasonality and Three-Dimensional Structure of Interdecadal Change in the East Asian Monsoon

2007 ◽  
Vol 20 (21) ◽  
pp. 5344-5355 ◽  
Author(s):  
Rucong Yu ◽  
Tianjun Zhou
Keyword(s):  
East Asia ◽  
Yangtze River ◽  
Lower Stratosphere ◽  
Northeast Asia ◽  
Three Dimensional ◽  
Yangtze River Valley ◽  
River Valley ◽  
Interdecadal Change ◽  
The Yangtze River ◽  
Surface Cooling

Abstract A significant interdecadal cooling with vivid seasonality and three-dimensional (3D) structure is first revealed in the upper troposphere and lower stratosphere over East Asia. A robust upper-tropospheric cooling appears in March and has two peaks in April and August, but in June, a moderate upper-tropospheric warming interrupts the cooling, while strong cooling occurs in the lower stratosphere. The seasonally dependent upper-tropospheric cooling leads to a clear seasonality of interdecadal changes in the atmospheric general circulation and precipitation against their normal seasonal cycle over East Asia. In March, precipitation over southern China (south of 26°N) has increased in accordance with the strong upper-tropospheric cooling occurring in northeast Asia. In April and May, following the southward extension and intensification of the upper-tropospheric cooling, the normal seasonal march of the monsoon rainband has been interrupted, resulting in a drying band to the south of the Yangtze River valley in late spring. In June, the moderate upper-tropospheric warming and strong lower-stratospheric cooling over northeast Asia has suddenly enhanced the northward migration of the rainband and resulted in an increase of precipitation in the mid–lower reaches of the Yangtze River and farther north. During July and August, the return of upper-tropospheric cooling has weakened the northward progression of southerly monsoon winds, resulting in a mid–lower Yellow River valley (34°–40°N) drought and excessive rain in the Yangtze River valley. The change of surface temperature is well correlated with the change in precipitation, especially in the spring. The surface cooling is generally collocated with excessive rain, while the warming is generally collocated with droughts. Possible causes for the robust interdecadal change are discussed, and stratosphere–troposphere interaction is suggested to play a crucial role in seasonally dependent 3D atmospheric cooling over East Asia.

scholarly journals Improving Seasonal Prediction of East Asian Summer Rainfall Using NESM3.0: Preliminary Results

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 487 ◽  
Author(s):  
Young-Min Yang ◽  
Bin Wang ◽  
Juan Li
Keyword(s):  
East Asia ◽  
Interannual Variability ◽  
Yangtze River ◽  
Southern China ◽  
Yangtze River Valley ◽  
River Valley ◽  
Global Climate Models ◽  
The Yangtze River ◽  
Rainfall Prediction ◽  
Leading Mode

It has been an outstanding challenge for global climate models to simulate and predict East Asia summer monsoon (EASM) rainfall. This study evaluated the dynamical hindcast skills with the newly developed Nanjing University of Information Science and Technology Earth System Model version 3.0 (NESM3.0). To improve the poor prediction of an earlier version of NESM3.0, we modified convective parameterization schemes to suppress excessive deep convection and enhance insufficient shallow and stratiform clouds. The new version of NESM3.0 with modified parameterizations (MOD hereafter) yields improved rainfall prediction in the northern and southern China but not over the Yangtze River Valley. The improved prediction is primarily attributed to the improvements in the predicted climatological summer mean rainfall and circulations, Nino 3.4 SST anomaly, and the rainfall anomalies associated with the development and decay of El Nino events. However, the MOD still has biases in the predicted leading mode of interannual variability of precipitation. The leading mode captures the dry (wet) anomalies over the South China Sea (northern East Asia) but misplaces precipitation anomalies over the Yangtze River Valley. The model can capture the interannual variation of the circulation indices very well. The results here suggest that, over East Asia land regions, the skillful rainfall prediction relies on not only model’s capability in predicting better summer mean and ENSO teleconnection with EASM, but also accurate prediction of the leading modes of interannual variability.

scholarly journals Potential Predictability of Seasonal Extreme Precipitation Accumulation in China

2017 ◽  
Vol 18 (4) ◽  
pp. 1071-1080 ◽  
Author(s):  
Wenguang Wei ◽  
Zhongwei Yan ◽  
P. D. Jones
Keyword(s):  
Extreme Precipitation ◽  
Yangtze River ◽  
Large Scale ◽  
Southern China ◽  
Yangtze River Valley ◽  
River Valley ◽  
Northwestern China ◽  
Chain Model ◽  
The Yangtze River ◽  
Potential Predictability

Abstract The potential predictability of seasonal extreme precipitation accumulation (SEPA) across mainland China is evaluated, based on daily precipitation observations during 1960–2013 at 675 stations. The potential predictability value (PPV) of SEPA is calculated for each station by decomposing the observed SEPA variance into a part associated with stochastic daily rainfall variability and another part associated with longer-time-scale climate processes. A Markov chain model is constructed for each station and a Monte Carlo simulation is applied to estimate the stochastic part of the variance. The results suggest that there are more potentially predictable regions for summer than for the other seasons, especially over southern China, the Yangtze River valley, the north China plain, and northwestern China. There are also regions of large PPVs in southern China for autumn and winter and in northwestern China for spring. The SEPA series for the regions of large PPVs are deemed not entirely stochastic, either with long-term trends (e.g., increasing trends in inland northwestern China) or significant correlation with well-known large-scale climate processes (e.g., East Asian winter monsoon for southern China in winter and El Niño for the Yangtze River valley in summer). This fact not only verifies the claim that the regions have potential predictability but also facilitates predictive studies of the regional extreme precipitation associated with large-scale climate processes.

scholarly journals Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3294
Author(s):  
Chentao He ◽  
Jiangfeng Wei ◽  
Yuanyuan Song ◽  
Jing-Jia Luo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Yangtze River ◽  
Climate Models ◽  
Summer Precipitation ◽  
Yangtze River Valley ◽  
River Valley ◽  
Training Data ◽  
The Yangtze River ◽  
Learning Methods

The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely populated regions in China, are subject to frequent flooding. In this study, the predictor importance analysis model was used to sort and select predictors, and five methods (multiple linear regression (MLR), decision tree (DT), random forest (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)) were used to predict the interannual variation of summer precipitation over the middle and lower reaches of the YRV. Predictions from eight climate models were used for comparison. Of the five tested methods, RF demonstrated the best predictive skill. Starting the RF prediction in December, when its prediction skill was highest, the 70-year correlation coefficient from cross validation of average predictions was 0.473. Using the same five predictors in December 2019, the RF model successfully predicted the YRV wet anomaly in summer 2020, although it had weaker amplitude. It was found that the enhanced warm pool area in the Indian Ocean was the most important causal factor. The BPNN and CNN methods demonstrated the poorest performance. The RF, DT, and climate models all showed higher prediction skills when the predictions start in winter than in early spring, and the RF, DT, and MLR methods all showed better prediction skills than the numerical climate models. Lack of training data was a factor that limited the performance of the machine learning methods. Future studies should use deep learning methods to take full advantage of the potential of ocean, land, sea ice, and other factors for more accurate climate predictions.

Contribution of Water Vapor to the Record-Breaking Extreme Meiyu Rainfall along the Yangtze River Valley in 2020

2021 ◽  
Vol 35 (4) ◽  
pp. 557-570
Author(s):  
Licheng Wang ◽  
Xuguang Sun ◽  
Xiuqun Yang ◽  
Lingfeng Tao ◽  
Zhiqi Zhang
Keyword(s):  
Water Vapor ◽  
Yangtze River ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yangtze River
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