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Evaluation of Indian Summer Monsoon Rainfall Using the NCEP Global Model: An SST Impact Study

机译:利用NCEP全球模型评估印度夏季季风降雨:SST影响研究

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The study evaluates the Indian summer monsoon prediction skill of the Atmospheric General Circulation Model (AGCM) and the impact of sea surface temperature (SST) boundary forcing on the model performance. The National Center for Environmental Prediction's (NCEP's) T170/L42 AGCM model configured with a horizontal resolution of 75 x 75 km, with 42 vertical levels is used for the study. The SST-rainfall relationship is examined in the coupled Climate Forecast System version 2 (CFSv2) model, as CFSv2-predicted SST is used as input for the T170 model. The NCEP Global Forecast System-T170 (GFS-T170) simulations are carried out with boundary forcing of observed SST, CFSv2-predicted SST and the bias-corrected CFSv2 SST. An ensemble of seasonal runs was made using the initial conditions of May to September, and integrated up to September 30th. The significance of discontinuity in the initial conditions due to climate forecast system reanalysis (CFSR) is assessed based on the two-period approach of climatology for the two time scales of 1985-1998 and 1998-2009. CFSv2 predicted climatological summer monsoon rainfall with a significant dry bias over the three convection zones; Western Ghats, Central India and North-east India, and cold bias over the Indian ocean basin and central equatorial Pacific, with strong cold bias over a narrow region of equatorial Pacific. The model could capture 64% (16 out of 25) of the year's rainfall anomaly signal. The skill of the model is improved in the recent period (1999-2009). The model could simulate the negative Nino 3 and excess rainfall and the La Nina event realistically for the year 1988. The model shows a large difference in Nino indices for the years 1987 and 1998, which led to the unrealistic rainfall simulation. The model has a low skill for indicating the relationship between the Indian Ocean Dipole (IOD) and Indian summer monsoon rainfall (ISMR). The CFSv2 model could not capture the strong positive correlation of the IOD and str
机译:该研究评估了大气通用循环模型(AGCM)的印度夏季季风预测技能,以及海面温度(SST)边界强迫模型性能的影响。国家环境预测中心(NCEP)T170 / L42 AGCM模型配置,水平分辨率为75 x 75 km,用于研究42个垂直电平。在耦合的气候预测系统版本2(CFSv2)模型中检查了SST降雨关系,因为CFSv2预测的SST用作T170模型的输入。 NCEP全局预测系统-T170(GFS-T170)模拟,使用观察到的SST,CFSV2预测的SST和偏置CFSV2 SST的边界强制执行。使用5月至9月的初始条件进行了季节性运行的集合,并在9月30日融入了9月30日。基于1985-1998和1998-2009的两次尺度的两期气候方法评估了由于气候预测系统再分析(CFSR)而导致的初始条件下不连续性的重要性。 CFSv2预测气候夏季季风降雨,在三个对流区域上具有显着的干燥偏见;西仓,印度中部和印度东北部,以及印度洋盆地和中央赤道太平洋的冷偏见,在赤道太平洋狭窄的区域上具有强烈的冷偏见。该模型可以捕获今年降雨异常信号的64%(16分中为25分)。近期模型的技能得到改善(1999-2009)。该模型可以在1988年度模拟负极Nino 3和多余的降雨和La Nina活动。该模型在1987年和1998年的Nino Indices中显示出很大差异,这导致了不切实际的降雨模拟。该模型具有指示印度海洋偶极(IOD)与印度夏季季风降雨(ISMR)之间关系的技能。 CFSv2模型无法捕获IOD和STR的强正相关

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