Synergistic contribution of precipitation anomalies over northwestern India and the South China Sea to high temperature over the Yangtze River Valley

Abstract Yearly variations in the observed initial and final dates of heavy, persistent monsoon rainband precipitation across China are quantified. The development of a semiobjective analysis that identifies these values also makes it possible to calculate annual rainband duration and total rainfall. Relationships between total rainband precipitation and the Eurasian circulation are then determined. This research is designed such that observed rainband characteristics can be used in future investigations to evaluate GCM simulations. Normalized daily precipitation time series are analyzed between 1951 and 1990 for 85 observation stations to develop criteria that describe general rainband characteristics throughout China. Rainfall is defined to be “heavy” if the daily value at a given location is greater than 1.5% of the annual mean total. Heavy precipitation is then shown to be “persistent” and is thus identified with the rainband when the 1.5% threshold is exceeded at least 6 times in a 25-day period. Finally, rainband initial (final) dates are defined to immediately follow (precede) a minimum period of 5 consecutive days with no measurable precipitation. A semiobjective analysis based on the above definitions and rainband climatology is then applied to the time series to determine annual initial and final dates. Analysis application produces results that closely correspond to the systematic pattern observed across China, where the rainband arrives in the south during May, advances to the Yangtze River valley in June, and then to the north in July. Rainband duration (i.e., final − initial + 1) is approximately 30–40 days while total rainfall decreases from south to north. A significant positive correlation is found between total rainfall and duration interannual variability, where increased rainband precipitation corresponds to initial (final) dates that are anomalously early (late). No clear trends are identified except over north China, where both duration and total rainfall decrease substantially after 1967. The Eurasian sea level pressure and 500-hPa height fields are then correlated with total rainfall over south China, the Yangtze River valley, and north China to identify statistically significant relationships. Results indicate that precipitation amount is influenced by the interaction of several circulation features. Total rainfall increases over south China when the surface Siberian high ridges to the south and is overrun by warm moist air aloft. Yangtze River valley precipitation intensifies when westward expansion of the subtropical high along with strengthening of the Siberian high and monsoon low cause moisture advection, upward motion, and the thermal gradient along the Mei-Yu front to increase. North China total rainfall increases in response to intense heating over the landmass, westward ridging of the subtropical high, and greater moisture transport over the region.

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AMIP GCM Simulations of Precipitation Variability over the Yangtze River Valley

Journal of Climate ◽

10.1175/2011jcli3631.1 ◽

2011 ◽

Vol 24 (8) ◽

pp. 2116-2133 ◽

Cited By ~ 15

Author(s):

Chenghai Wang ◽

Xin-Zhong Liang ◽

Arthur N. Samel

Keyword(s):

Interannual Variability ◽

South China ◽

Annual Cycle ◽

Yangtze River ◽

Yangtze River Valley ◽

River Valley ◽

The Yangtze River ◽

The South ◽

Low Level ◽

Sst Anomalies

Abstract Analysis of 26 simulations from 11 general circulation models (GCMs) of the Atmospheric Model Intercomparison Project (AMIP) II reveals a basic inability to simultaneously predict the Yangtze River Valley (YRV) precipitation (PrYRV) annual cycle and summer interannual variability in response to observed global SST distributions. Only the Community Climate System Model (CCSM) and L’Institut Pierre-Simon Laplace (IPSL) models reproduce the observed annual cycle, but both fail to capture the interannual variability. Conversely, only Max Planck Institute (MPI) simulates interannual variability reasonably well, but its annual cycle leads observations by 2 months. The interannual variability of PrYRV reveals two distinct signals in observations, which are identified with opposite subtropical Pacific SST anomalies in the east (SSTe) and west (SSTw). First, negative SSTe anomalies are associated with equatorward displacement of the upper-level East Asian jet (ULJ) over China. The resulting transverse circulation enhances low-level southerly flow over the South China Sea and south China while convergent flow and upward motion increase over the YRV. Second, positive SSTw anomalies are linked with westward movement of the subtropical high over the west-central Pacific. This strengthens the low-level jet (LLJ) to the south of the YRV. These two signals act together to enhance PrYRV. The AMIP II suite, however, generally fails to reproduce these features. Only the MPI.3 realization is able to simulate both signals and, consequently, realistic PrYRV interannual variations. It appears that PrYRV is governed primarily by coherent ULJ and LLJ variations that act as the atmospheric bridges to remote SSTe and SSTw forcings, respectively. The PrYRV response to global SST anomalies may then be realistically depicted only when both bridges are correctly simulated. The above hypothesis does not exclude other signals that may play important roles linking PrYRV with remote SST forcings through certain atmospheric bridges, which deserve further investigation.

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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.

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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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