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

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.

Download Full-text

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

Quarterly Journal of the Royal Meteorological Society ◽

10.1002/qj.2448 ◽

2014 ◽

Vol 141 (689) ◽

pp. 1389-1403 ◽

Cited By ~ 40

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

Download Full-text

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

Atmosphere ◽

10.3390/atmos9120487 ◽

2018 ◽

Vol 9 (12) ◽

pp. 487 ◽

Cited By ~ 6

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.

Download Full-text

Interdecadal Change in the Intensity of Interannual Variation of Spring Precipitation over Southern China and Possible Reasons

Journal of Climate ◽

10.1175/jcli-d-18-0351.1 ◽

2019 ◽

Vol 32 (18) ◽

pp. 5865-5881 ◽

Cited By ~ 3

Author(s):

Chao Xu ◽

Yunting Qiao ◽

Maoqiu Jian

Keyword(s):

South China ◽

Yangtze River ◽

Interannual Variation ◽

Southern China ◽

Yangtze River Valley ◽

River Valley ◽

Interdecadal Change ◽

High Latitudes ◽

The Yangtze River ◽

Spring Precipitation

AbstractThe intensity of interannual variation of spring precipitation over southern China during 1979–2014 and possible reasons for it are investigated in this paper. There is a significant interdecadal change in the intensity of interannual variation of spring precipitation over southern China around 1995/96. The intensity of interannual variation of spring rainfall over South China is stronger during 1979–95 than that during 1996–2014. The possible reason may be the larger amplitude of the sea surface temperature anomaly (SSTA) in the western Pacific Ocean (WP) before 1995/96. The cooler (warmer) SSTA in WP may trigger an abnormal local anticyclone (cyclone) at lower levels. The anomalous southwesterly (northeasterly) flow at the northwestern flank of the WP anticyclone (cyclone) covers South China, transporting more (less) moisture to South China. Meanwhile, the anomalous winds converge (diverge) in South China at lower levels and diverge (converge) at upper levels, which causes the anomalous ascent (descent) to enhance (reduce) the precipitation over there. However, during 1996–2014, the intensity of interannual variation of spring rainfall over the middle and lower reaches of the Yangtze River valley becomes much stronger than that during 1979–95, which is related to the intensified interannual variation of the atmospheric circulation in the middle and high latitudes over Eurasia. The weak (strong) Siberian high and East Asian trough may reduce (enhance) the northerly wind from the middle and high latitudes. As a result, the middle and lower reaches of the Yangtze River valley are subjected to the anomalous southerly wind, favoring more (less) precipitation over there.

Download Full-text

Improving the regional model forecasting of persistent severe rainfall over the Yangtze River Valley using the spectral nudging and update cycle methods: a case study

Atmospheric Science Letters ◽

10.1002/asl.731 ◽

2017 ◽

Vol 18 (2) ◽

pp. 96-102 ◽

Cited By ~ 4

Author(s):

Yanfeng Zhao ◽

Donghai Wang ◽

Jianjun Xu

Keyword(s):

Yangtze River ◽

Regional Model ◽

Yangtze River Valley ◽

River Valley ◽

The Yangtze River ◽

Spectral Nudging

Download Full-text

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 ◽

10.3390/w13223294 ◽

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.

Download Full-text