Zonal Asymmetry of Moist Static Energy Tendency and Madden-Julian Oscillation (MJO) Eastward Propagation in Climate Models

气候模型中潮湿静能趋势的地带性不对称性和马登-朱利安振荡（MJO）东向传播

基本信息

批准号：
1643297
负责人：
Tim Li
金额：
$ 40.76万
依托单位：
University of Hawaii
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2021-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1643297&HistoricalAwards=false
关键词：
Zonal Asymmetry Moist Static Energy

项目摘要

Through improved model physics and initial conditions in past 50 years, weather can now be predicted reasonably well for up to 5 days. On the other hand, due to improved monitoring and understanding of El Nino dynamics in past 20-30 years, monthly or seasonal outlook skill is also in good progress. The extended-range (5-30-day) prediction is currently most difficult. The major predictability source in extended range is an atmospheric low-frequency mode named "Madden-Julian Oscillation (MJO)". However, current operational models have difficulty to predict this oscillation. This project will investigate the representations and couplings of fundamental dynamic and physical processes in 26 current state-of-the-art climate models to gain a understanding of why only about a quarter of these models are able to capture the observed spatiotemporal evolution characteristics of MJO while others cannot. This study combines observational data analysis, diagnosis of the 26 climate model outputs, and idealized numerical model experiments in which key dynamic/physical processes are isolated.The successful completion of this project would not only advance the knowledge of dynamics of the Madden-Julian Oscillation but also help identify problems in the current state-of-the-art operational weather prediction models and help improve the model representations of the atmospheric low-frequency oscillation and its associated teleconnection patterns. The improved capability of extended-range predictions would offer an avenue for bridging the gap between the short-range weather and long-term climate predictions and benefit economy and society for prevention of natural disasters and for government planning. This project supports a graduate student in the area of tropical large-scale dynamics and climate modeling.

通过改进的模型物理和过去50年的初始条件，现在可以很好地预测天气长达5天。另一方面，由于过去20至30年中对El Nino动态的监测和理解，每月或季节性的前景技能也取得了良好的进步。目前，扩展范围（5-30天）预测是最困难的。扩展范围内的主要可预测源是一种名为“ Madden-Julian振荡（MJO）”的大气低频模式。但是，当前的操作模型很难预测这种振荡。该项目将研究26个最新的气候模型中基本动态和物理过程的表示形式和耦合，以了解为什么只有四分之一的模型只能捕获观察到的MJO的时空演化特征，而其他人则不能捕获其他模型。这项研究结合了观察数据分析，26个气候模型输出的诊断以及理想化的数值模型实验，其中关键的动态/物理过程是孤立的。该项目的成功完成不仅会推动Madden-Julian振荡动态的知识，而且还可以帮助确定当前现状的经营模型模型和相关模型模型的问题，并有助于识别出相关的模型模型的氛围和FRE的相关性模型。远程连接模式。扩展范围预测的提高能力将为弥合短期天气和长期气候预测与预防自然灾害和政府计划的社会之间的差距提供途径。该项目支持热带大规模动态和气候建模领域的研究生。