The strengthened relationship between the Yangtze River Valley summer rainfall and the Southern Hemisphere annular mode in recent decades

2020 ◽  
Vol 54 (3-4) ◽  
pp. 1607-1624 ◽  
Author(s):  
Juan Dou ◽  
Zhiwei Wu ◽  
Jianping Li
Keyword(s):  
Southern Hemisphere ◽  
Yangtze River ◽  
Summer Rainfall ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yangtze River ◽  
Annular Mode ◽  
Southern Hemisphere Annular Mode

Effect of the East Asian Westerly Jet’s Intensity on Summer Rainfall in the Yangtze River Valley and Its Mechanism

2016 ◽  
Vol 29 (7) ◽  
pp. 2395-2406 ◽  
Author(s):  
Shixin Wang ◽  
Hongchao Zuo
Keyword(s):  
Yangtze River ◽  
Interannual Variation ◽  
East Asian ◽  
Summer Rainfall ◽  
Early Summer ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yellow Sea ◽  
The Yangtze River ◽  
Warm Advection

Abstract Many studies have shown that the northward (southward) displacement of the East Asian westerly jet (EAWJ) drastically reduces (increases) summer rainfall in the Yangtze River valley (YRV). However, the effect of the jet’s intensity on interannual variation in summer rainfall has not been systematically studied. The present study investigates the effect of the EAWJ’s intensity on this interannual variation and analyzes the mechanism by which this process occurs. In early summer, the EAWJ consists of two branches: one located over northern continental East Asia [western branch (EAWJWB)] and one extending from southern China to the northern Pacific [eastern branch (EAWJEB)]. The former merges into the latter over the Yellow Sea. A stronger EAWJEB leads to increased rainfall in the YRV, while the EAWJWB does not significantly affect rainfall in the YRV. The faster EAWJEB directly strengthens midtropospheric warm advection over the YRV because the corresponding changes in the meridional wind and horizontal temperature gradient are insignificant. The strengthened warm advection increases rainfall in the YRV by accelerating both adiabatic ascent and the ascent associated with diabatic heating primarily generated by convection. In midsummer, the EAWJ has no branches and is located over the midlatitudes of Asia. The strengthening of the EAWJ reduces rainfall in the YRV in midsummer through the Pacific–Japan (PJ) pattern. As the EAWJ strengthens, the PJ pattern turns to its positive phase. This results in the deceleration of the midtropospheric westerly wind and a reduction in the meridional temperature contrast, which weakens midtropospheric warm advection. The weakened warm advection in turn reduces rainfall in the YRV, following the process outlined for early summer.

scholarly journals Forecasting Summer Rainfall and Streamflow over the Yangtze River Valley Using Western Pacific Subtropical High Feature

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2580
Author(s):  
Ranran He ◽  
Yuanfang Chen ◽  
Qin Huang ◽  
Wenpeng Wang ◽  
Guofang Li
Keyword(s):  
Yangtze River ◽  
Western Pacific Subtropical High ◽  
Summer Rainfall ◽  
Yangtze River Valley ◽  
River Valley ◽  
Western Pacific ◽  
Subtropical High ◽  
Exceedance Probability ◽  
The Yangtze River ◽  
Extreme Flood

The western Pacific subtropical high (WPSH) is one of the key systems affecting the summer rainfall over the Yangtze River Valley in China. In this study, the forecasting capacity of the WPSH for summer rainfall and streamflow is evaluated based on the WPSH index (WPSHI) derived from the NCEP/NCAR reanalysis dataset. It has been found that WPSHI can identify extreme flood years with a higher skill than normal wet years. Specifically, exceedance probability forecasting based on WPSHI has higher skills for higher thresholds of rainfall. For streamflow, adding WPSHI as a predictor only enhances the skill for higher thresholds of streamflow relative to models based on antecedent streamflow. Under the same framework, performances of two postprocessing approaches for dynamical forecasts, i.e., the model output statistics (MOS) approach and the reanalysis-based (RAN) approach are compared. Hindcasts from Climate Forecast System version 2 from the National Center for Environmental Prediction (CFSv2) are used to calculate WPSHI, which is used as the predictor for rainfall and streamflow. The result shows that the RAN approach performs better than the MOS approach. This study emphasizes the fact that the forecasting skill of exceedance probability would largely depend on the selected threshold of the predictand, and this fact should be noticed in future studies in the long-term forecasting field.

LASSO as a tool for downscaling summer rainfall over the Yangtze River Valley

2019 ◽  
Vol 64 (1) ◽  
pp. 92-104
Author(s):  
Ran-Ran He ◽  
Yuanfang Chen ◽  
Qin Huang ◽  
You Kang
Keyword(s):  
Yangtze River ◽  
Summer Rainfall ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yangtze River

scholarly journals A Moving Updated Statistical Prediction Model for Summer Rainfall in the Middle-Lower Reaches of the Yangtze River Valley

2017 ◽  
Vol 56 (8) ◽  
pp. 2275-2287 ◽  
Author(s):  
Yan Guo ◽  
Jianping Li ◽  
Jiangshan Zhu
Keyword(s):  
Correlation Coefficient ◽  
Yangtze River ◽  
Decadal Variability ◽  
North Atlantic Ocean ◽  
Summer Rainfall ◽  
Yangtze River Valley ◽  
River Valley ◽  
Statistical Prediction ◽  
The Yangtze River ◽  
Rainfall Model

AbstractBecause summer rainfall in the middle-lower reaches of the Yangtze River valley has remarkable interannual and decadal variability and because the precursors that modulate the interannual rainfall change with the decadal variation of the background state, a new model that employs a novel statistical idea is needed to yield an accurate prediction. In this study, the interannual rainfall model (IAM) and the decadal rainfall model (DM) were constructed. Moving updating of the IAM with the latest data within an optimal length of training period (20 yr) can partially offset the effect of decadal change of precursors in IAM. To predict the interannual rainfall of 2001–13 for validation, 13 regression models were fitted with precursors that change every 4–5 yr, from the preceding winter North Atlantic Ocean sea surface temperature anomaly (SSTA) dipole to the Mascarene high, followed by the East Asia sea level pressure anomaly (SLPA) dipole and the preceding autumn North Pacific SSTA dipole. The moving updated model demonstrated high skill in predicting interannual rainfall, with a correlation coefficient of 0.76 and a hit rate of 76.9%. The DM was linked to the April SLPA in the central tropical Pacific Ocean, and it maintained good performance in the testing period, with a correlation coefficient of 0.77 and a root-mean-square error (RMSE) of 7.7%. The statistical model exhibited superior capability even when compared with the best forecast by the Climate Forecast System, version 2 (CFSv2), initiated in early June, as indicated by increased correlation coefficient from 0.62 to 0.75 and reduced RMSE from 12.3% to 10.7%.

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