scholarly journals AMIP GCM Simulations of Precipitation Variability over the Yangtze River Valley

2011 ◽  
Vol 24 (8) ◽  
pp. 2116-2133 ◽  
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.

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 The Monsoon Rainband over China and Relationships with the Eurasian Circulation

1999 ◽  
Vol 12 (1) ◽  
pp. 115-131 ◽  
Author(s):  
Arthur N. Samel ◽  
Wei-Chyung Wang ◽  
Xin-Zhong Liang
Keyword(s):  
Time Series ◽  
South China ◽  
Yangtze River ◽  
North China ◽  
Yangtze River Valley ◽  
River Valley ◽  
Subtropical High ◽  
The Yangtze River ◽  
Total Rainfall ◽  
Siberian High

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.

scholarly journals Improving Seasonal Prediction of East Asian Summer Rainfall: Experiments with NESM3.0

2018 ◽  
Author(s):  
Young Min Yang ◽  
Bin Wang ◽  
Juan Li
Keyword(s):  
Interannual Variability ◽  
Yangtze River ◽  
Southern China ◽  
Yangtze River Valley ◽  
River Valley ◽  
Global Climate Models ◽  
The Yangtze River ◽  
Rainfall Prediction ◽  
Leading Mode ◽  
Seasonal March

It has been an outstanding challenge for global climate models to simulate and predict East Asia (EA) summer monsoon (EASM) rainfall. This study evaluates the dynamical hindcast skills with the newly developed Nanjing University of Information Science and Technology Earth System Model version 3.0 (NESM3.0). To improve the poor prediction of an earlier version of NESM3.0, we have modified convective parameterization schemes to suppress excessive deep convection and enhance insufficient shallow and stratiform clouds. The new version of NESM3.0 with modified parameterizations (MOD hereafter) yields significantly improved rainfall prediction in the northern and southern China but not over the Yangtze River Valley. The improved prediction is primarily attributed to the improvements in the predicted climatological summer mean rainfall and circulations, seasonal march of the subtropical rain belt, Nino 3.4 SST anomaly, and the rainfall anomalies associated with the development and decay of El Nino events. However, the MOD still has notable biases in the predicted leading mode of interannual variability of precipitation. The leading mode captures the dry (wet) anomalies over the South China Sea (northern EA) but misplaced precipitation anomalies over the Yangtze River Valley. The model can capture the interannual variation of the circulation indices very well, but the bias in the circulation-rainfall connection caused predicted rainfall errors. The results here suggest that over EA land regions, the skillful rainfall prediction relies on not only model’s capability in predicting better summer mean and seasonal march of rainfall and ENSO teleconnection with EASM, but also accurate prediction of the leading modes of interannual variability.

scholarly journals Improving Seasonal Prediction of East Asian Summer Rainfall Using NESM3.0: Preliminary Results

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 487 ◽  
Author(s):  
Young-Min Yang ◽  
Bin Wang ◽  
Juan Li
Keyword(s):  
East Asia ◽  
Interannual Variability ◽  
Yangtze River ◽  
Southern China ◽  
Yangtze River Valley ◽  
River Valley ◽  
Global Climate Models ◽  
The Yangtze River ◽  
Rainfall Prediction ◽  
Leading Mode

It has been an outstanding challenge for global climate models to simulate and predict East Asia summer monsoon (EASM) rainfall. This study evaluated the dynamical hindcast skills with the newly developed Nanjing University of Information Science and Technology Earth System Model version 3.0 (NESM3.0). To improve the poor prediction of an earlier version of NESM3.0, we modified convective parameterization schemes to suppress excessive deep convection and enhance insufficient shallow and stratiform clouds. The new version of NESM3.0 with modified parameterizations (MOD hereafter) yields improved rainfall prediction in the northern and southern China but not over the Yangtze River Valley. The improved prediction is primarily attributed to the improvements in the predicted climatological summer mean rainfall and circulations, Nino 3.4 SST anomaly, and the rainfall anomalies associated with the development and decay of El Nino events. However, the MOD still has biases in the predicted leading mode of interannual variability of precipitation. The leading mode captures the dry (wet) anomalies over the South China Sea (northern East Asia) but misplaces precipitation anomalies over the Yangtze River Valley. The model can capture the interannual variation of the circulation indices very well. The results here suggest that, over East Asia land regions, the skillful rainfall prediction relies on not only model’s capability in predicting better summer mean and ENSO teleconnection with EASM, but also accurate prediction of the leading modes of interannual variability.

scholarly journals Interdecadal Change in the Intensity of Interannual Variation of Spring Precipitation over Southern China and Possible Reasons

2019 ◽  
Vol 32 (18) ◽  
pp. 5865-5881 ◽  
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.

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.

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