scholarly journals Linkage between winter sea surface temperature east of Australia and summer precipitation in the Yangtze River valley and a possible physical mechanism

2011 ◽  
Vol 56 (17) ◽  
pp. 1821-1827 ◽  
Author(s):  
BoTao Zhou
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Yangtze River ◽  
Physical Mechanism ◽  
Summer Precipitation ◽  
Yangtze River Valley ◽  
River Valley ◽  
Sea Surface ◽  
The Yangtze River

scholarly journals Forecast of summer precipitation in the Yangtze River Valley based on South China Sea springtime sea surface salinity

2019 ◽  
Vol 53 (9-10) ◽  
pp. 5495-5509 ◽  
Author(s):  
Lili Zeng ◽  
Raymond W. Schmitt ◽  
Laifang Li ◽  
Qiang Wang ◽  
Dongxiao Wang
Keyword(s):  
South China Sea ◽  
South China ◽  
Yangtze River ◽  
Summer Precipitation ◽  
Yangtze River Valley ◽  
River Valley ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Yangtze River ◽  
Surface Salinity

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.

scholarly journals Impact of Spring Soil Moisture on Surface Energy Balance and Summer Monsoon Circulation over East Asia and Precipitation in East China

2011 ◽  
Vol 24 (13) ◽  
pp. 3309-3322 ◽  
Author(s):  
Renhe Zhang ◽  
Zhiyan Zuo
Keyword(s):  
Soil Moisture ◽  
Yangtze River ◽  
Surface Energy Balance ◽  
Summer Precipitation ◽  
Western Pacific Subtropical High ◽  
Yangtze River Valley ◽  
River Valley ◽  
East China ◽  
Monsoon Circulation ◽  
The Yangtze River

Abstract Numerous studies have been conducted on the impact of soil moisture on the climate, but few studies have attempted to diagnose the linkage between soil moisture and climate variability using observational data. Here, using both observed and reanalysis data, the spring (April–May) soil moisture is found to have a significant impact on the summer (June–August) monsoon circulation over East Asia and precipitation in east China by changing surface thermal conditions. In particular, the spring soil moisture over a vast region from the lower and middle reaches of the Yangtze River valley to north China (the YRNC region) is significantly correlated to the summer precipitation in east China. When the YRNC region has a wetter soil in spring, northeast China and the lower and middle reaches of the Yangtze River valley would have abnormally higher precipitation in summer, while the region south of the Yangtze River valley would have abnormally lower precipitation. An analysis of the physical processes linking the spring soil moisture to the summer precipitation indicates that the soil moisture anomaly across the YRNC region has a major impact on the surface energy balance. Abnormally wet soil would increase surface evaporation and hence decrease surface air temperature (Ta). The reduced Ta in late spring would narrow the land–sea temperature difference, resulting in the weakened East Asian monsoon in an abnormally strengthened western Pacific subtropical high that is also located farther south than its normal position. This would then enhance precipitation in the Yangtze River valley. Conversely, the abnormally weakened East Asian summer monsoon allows the western Pacific subtropical high to wander to south of the Yangtze River Valley, resulting in an abnormally reduced precipitation in the southern part of the country in east China.

Summer precipitation changes over the Yangtze River Valley and North China: Simulations from CMIP3 models

2014 ◽  
Vol 50 (3) ◽  
pp. 355-364 ◽  
Author(s):  
Gang Zeng ◽  
Wei-Chyung Wang ◽  
Caiming Shen ◽  
Zhixin Hao
Keyword(s):  
Yangtze River ◽  
North China ◽  
Summer Precipitation ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yangtze River ◽  
Precipitation Changes

scholarly journals Effect of Spring Precipitation on Summer Precipitation in Eastern China: Role of Soil Moisture

2017 ◽  
Vol 30 (22) ◽  
pp. 9183-9194 ◽  
Author(s):  
Li Liu ◽  
Renhe Zhang ◽  
Zhiyan Zuo
Keyword(s):  
Soil Moisture ◽  
Yangtze River ◽  
Asian Summer Monsoon ◽  
Eastern China ◽  
Summer Precipitation ◽  
Northeastern China ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yangtze River ◽  
Spring Precipitation

The relation of spring (March–May) to summer (July–August) precipitation in eastern China is examined using observed data. It is found that when spring precipitation from the lower and middle reaches of the Yangtze River valley to northern China (the YRNC region) is higher (lower), more (less) summer precipitation occurs in northeastern China and the lower and middle reaches of the Yangtze River valley, and less (more) in southeastern China. The analysis of physical mechanism showed that higher (lower) spring precipitation in the YRNC region is closely related to wet (dry) spring soil moisture, which decreases (increases) the surface temperature and sensible heat flux in late spring. Because the memory of spring soil moisture in the YRNC region reaches about 2.4 months, the surface thermal anomaly lasts into the subsequent summer, resulting in a weak (strong) East Asian summer monsoon. A weak East Asian summer monsoon corresponds to an anomalous anticyclone and a cyclone over southeastern and northeastern China, respectively, in the lower troposphere. The anomalous anticyclone depresses the summer precipitation in southeastern China, and the anomalous cyclone promotes precipitation over northeastern China. The abnormal northerly and southerly winds associated with the anomalous cyclone and anticyclone, respectively, converge in the lower and middle reaches of the Yangtze River valley, inducing more summer precipitation there.

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