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.

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.

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 Changes in Characteristics of Late-Summer Precipitation over Eastern China in the Past 40 Years Revealed by Hourly Precipitation Data

2010 ◽  
Vol 23 (12) ◽  
pp. 3390-3396 ◽  
Author(s):  
Rucong Yu ◽  
Jian Li ◽  
Weihua Yuan ◽  
Haoming Chen
Keyword(s):  
Yangtze River ◽  
Rainfall Intensity ◽  
Eastern China ◽  
Late Summer ◽  
Rain Gauge ◽  
Rainfall Amount ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yangtze River ◽  
Hourly Rainfall

Abstract Using hourly station rain gauge data during 1966–2005, the authors studied changes in the characteristics of the late-summer (July–August) rainfall, which has exhibited a so-called southern flooding and northern drought (SFND) pattern over eastern China in recent decades. Although the rainfall amount and frequency have significantly increased (decreased) in the mid–lower reaches of the Yangtze River valley (North China) during this period, the rainfall intensity has decreased (increased). This finding differs from previous results based on daily data, which showed that the rainfall intensity has increased in the mid–lower reaches of the Yangtze River valley. In this region, the mean rainfall hours on rainy days have increased because of the prolonged rainfall duration, which has led to an increased daily rainfall amount and to a decreased hourly rainfall intensity. Results also show that the SFND pattern is mostly attributed to changes in precipitation with moderate and low intensity (≤10 mm h−1), which contributes 65% (96%) of rainfall amount to the “flooding” (“drought”) in the mid–lower reaches of the Yangtze River valley. Neither frequency nor amount of strong intensity (>20 mm h−1) rainfall exhibits the SFND pattern.

scholarly journals New Predictors and a Statistical Forecast Model for Mei-Yu Onset Date in the Middle and Lower Reaches of the Yangtze River Valley

2014 ◽  
Vol 29 (3) ◽  
pp. 654-665 ◽  
Author(s):  
Yijia Hu ◽  
Yimin Zhu ◽  
Zhong Zhong ◽  
Yao Ha
Keyword(s):  
Yangtze River ◽  
Asian Summer Monsoon ◽  
East Asian ◽  
Forecast Model ◽  
Yangtze River Valley ◽  
River Valley ◽  
Onset Date ◽  
The Yangtze River ◽  
Tropical Ocean ◽  
Preceding Winter

Abstract The prediction of mei-yu onset date (MOD) in the middle and lower reaches of the Yangtze River valley (MLYRV) is an important and challenging task for those making seasonal climate predictions in China. In this paper, the atmospheric and oceanic conditions in the preceding winter and spring related to MOD are analyzed. It is found that the MOD is associated with the intensity of the Ural high and the East Asian trough in high latitudes, with the intensity of the upper-level westerly jet in middle latitudes, and with the contrast of land–sea temperature and pressure in the preceding winter and spring, which are proxies for the intensity of the East Asian winter monsoon (EAWM). It is suggested that the intensity of the EAWM is the most crucial factor affecting the MOD. Years with an early MOD usually correspond to strong EAWMs in the preceding winter, and vice versa. The EAWM can affect the MOD by influencing the East Asian summer monsoon (EASM) through tropical ocean–atmosphere and tropical–extratropical interactions. Based on the above analysis, a physics-based statistical forecast model is established using multivariable linear regression techniques. The hindcast of MOD during the 13 yr from 1998 to 2010 is carried out to evaluate the performance of this forecast model. The MOD can be predicted successfully in 8 out of the 13 yr. The forecast model predicts the MOD in the years with strong mei-yu intensity more accurately than in those with weak mei-yu intensity, especially for cases of extreme flooding. This is useful in the prevention of flooding disasters.

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