Connection between Spring Conditions and Peak Summer Monsoon Rainfall in South America: Role of Soil Moisture, Surface Temperature, and Topography in Eastern Brazil

2007 ◽  
Vol 20 (24) ◽  
pp. 5929-5945 ◽  
Author(s):  
Alice M. Grimm ◽  
Jeremy S. Pal ◽  
Filippo Giorgi

Abstract A link between peak summer monsoon rainfall in central-east Brazil, composing part of the South American monsoon core region, and antecedent conditions in spring is disclosed. Rainfall in this region during part of spring holds a significant inverse correlation with rainfall in peak summer, especially during ENSO years. A surface–atmosphere feedback hypothesis is proposed to explain this relationship: low spring precipitation leads to low spring soil moisture and high late spring surface temperature; this induces a topographically enhanced low-level anomalous convergence and cyclonic circulation over southeast Brazil that enhances the moisture flux from northern and central South America into central-east Brazil, setting up favorable conditions for excess rainfall. Antecedent wet conditions in spring lead to opposite anomalies. The main links in this hypothesis are confirmed through correlation analysis of observed data: spring precipitation is negatively correlated to late spring surface temperature in central-east Brazil, and surface temperature in southeast Brazil is positively correlated with peak summer monsoon precipitation in central-east Brazil. The intermediary links of the surface–atmosphere feedback are tested in sensitivity experiments with the regional climate model version 3 (RegCM3). These experiments confirm that the proposed links are possible: the reduced soil moisture in central-east Brazil is shown to increase the surface temperature and produce a cyclonic anomaly over southeast Brazil, as well as increased precipitation in central-east Brazil. A crucial role of the mountains of southeast Brazil in anchoring the patterns of intraseasonal variability, and sustaining the “dipolelike” precipitation mode observed over South America, is suggested. The low predictability of monsoon rainfall anomalies in central-east Brazil during the austral summer might be partially ascribed to the fact that the models do not well reproduce the topographical features and the land–atmosphere interactions that are important for the variability in that region.

MAUSAM ◽  
2021 ◽  
Vol 49 (2) ◽  
pp. 229-234
Author(s):  
V. THAPLIYAL ◽  
M. RAJEEVAN ◽  
S. R. PATIL

Sea surface temperature (SST) variations over the three key regions over equatorial Pacific, viz., Nino (1+2), Nino 3 and Nino 4 and their relationships with Indian summer monsoon rainfall have been examined in this study. On monthly scale, SST anomalies over the three key regions show an oscillatory type of lagged correlations with Indian monsoon rainfall, positive correlations almost one year before the monsoon season (CC's are of the order of 0.3) which gradually change to significant negative correlation peaking in September/October during/after the monsoon season. The variations on seasonal scale also exhibit the same pattern of monthly variations but more smooth in nature. Composites of similar monsoon years show that during deficient (excess) monsoon years SST anomalies over all the three regions have warmer (cooler) trend which starts about 6 months prior to monsoon season. Tendencies of SST anomalies from previous winter (DJF) to summer (MAM) seasons over Nino 3 and Nino 4 regions are better predictors than EI-Nino categories currently being used in IMD's operational LRF model. By using tendency of SST over EI- Nino -4 region, in place of the category of EI-Nino, the 16 parameter operational Power Regression Model of IMD has been modified. The new forecast model shows better reduction in the forecast error.


2013 ◽  
Vol 26 (15) ◽  
pp. 5689-5697 ◽  
Author(s):  
Jieshun Zhu ◽  
Jagadish Shukla

Abstract This study examines the role of the air–sea coupled process in the seasonal predictability of Asia–Pacific summer monsoon rainfall by comparing seasonal predictions from two carefully designed model experiments: tier 1 (fully coupled model) and tier 2 (AGCM with prescribed SSTs). In these experiments, an identical AGCM is used in both tier 1 and tier 2 predictions; the daily mean SSTs from tier 1 coupled predictions are prescribed as a boundary condition in tier 2 predictions. Both predictions start in April from 1982 to 2009, with four ensemble members for each case. The model used is the Climate Forecast System, version 2 (CFSv2), the current operational climate prediction model for seasonal-to-interannual prediction at the National Centers for Environmental Prediction (NCEP). Comparisons indicate that tier 2 predictions produce not only higher rainfall biases but also unrealistically high rainfall variations in the tropical western North Pacific (TWNP) and some coastal regions as well. While the prediction skill in terms of anomaly correlations does not present a significant difference between the two types of predictions, the root-mean-square errors (RMSEs) are clearly larger over the above-mentioned regions in the tier 2 prediction. The reduced RMSE skills in the tier 2 predictions are due to the lack of a coupling process in AGCM-alone simulations, which, particularly, results in an unrealistic SST–rainfall relationship over the TWNP region. It is suggested that for a prediction of summer monsoon rainfall over the Asia–Pacific region, it is necessary to use a coupled atmosphere–ocean (tier 1) prediction system.


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