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

机译:使用NESM3.0改进东亚夏季降水的季节性预测:初步结果

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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.
机译:对于模拟和预测东亚夏季风(EASM)降雨的全球气候模型而言,这一直是一项艰巨的挑战。本研究使用新开发的南京信息工程大学地球系统模型版本3.0(NESM3.0)评估了动态后播技能。为了改善对NESM3.0早期版本的不良预测,我们修改了对流参数化方案,以抑制过度的深对流并增强不足的浅层和层状云。带有修改后的参数化功能的NESM3.0新版本(以下简称MOD)可改善中国北部和南部的降雨预报,但不会超过长江流域。改进的预报主要归因于预报的夏季气候平均降雨量和环流,Nino 3.4 SST异常以及与El Nino事件的发展和衰减相关的降雨异常的改进。但是,MOD在降水年际变化的预测主导模式方面仍存在偏差。领先模式捕获了南中国海(北东亚)上的干(湿)异常,但错位了长江流域上的降水异常。该模型可以很好地捕获循环指数的年际变化。此处的结果表明,在东亚陆地地区,熟练的降雨预测不仅依赖于模型具有更好的夏季均值预报能力以及与EASM的ENSO遥相关的能力,还依赖于对年际变化主导模式的准确预测。

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