The Evolution Characteristics of Daily-Scale Silk Road Pattern and Its Relationship with Summer Temperature in the Yangtze River Valley

By employing multi-reanalysis daily datasets and station data, this study focuses on the evolution characteristics of the daily-scale Silk Road pattern (SRP) and its effect on summer temperatures in the Yangtze River Valley (YRV). The results manifest that the evolution characteristics of positive- and negative-phase SRP (referred to SRP+ and SRP−) exhibit marked distinctions. The anomaly centers of SRP+ over West Central Asia (WCA) and Mongolia emerge firstly, vanishing simultaneously one week after peak date; however, the Far East (FE) anomaly centers can persist for a longer period. The SRP− starts with the WCA and FE centers, with a rapid decline in the strength of the WCA center and preservation of other anomaly centers after its peak. In the vertical direction, daily-scale SRP mainly concentrates in the mid-to-upper troposphere. Baroclinicity accounts for its early development and barotropic instability process favors the maintenance. Moreover, the SRP+ (SRP−) is inextricably linked to heat wave (cool summer) processes in the YRV. Concretely, before the onset of SRP+ events, an anomalous anticyclone and significant negative vorticities over East Asia related to SRP+ favor the zonal advance between the South Asia high (SAH) and western Pacific subtropical high (WPSH), inducing local descents over YRV area. The sinking adiabatic warming and clear-sky radiation warming can be considered as the possible causes for the YRV heat waves. The adiabatic cooling with the local ascents leads to more total cloud cover (positive precipitation anomalies) and less solar radiation incident to surface of the YRV, inducing the cool summer process during SRP−.

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Relative contributions of external SST forcing and internal atmospheric variability to July–August heat waves over the Yangtze River valley

Climate Dynamics ◽

10.1007/s00382-017-3871-y ◽

2017 ◽

Vol 51 (11-12) ◽

pp. 4403-4419 ◽

Cited By ~ 16

Author(s):

Xiaolong Chen ◽

Tianjun Zhou

Keyword(s):

Yangtze River ◽

Heat Waves ◽

Yangtze River Valley ◽

River Valley ◽

The Yangtze River ◽

Atmospheric Variability ◽

Sst Forcing

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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

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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.

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