权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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.

通过过去50年来改进的模式物理和初始条件，现在可以合理地预测长达5天的天气。另一方面，由于过去20-30年对厄尔尼诺动态的监测和了解有所改善，月度或季度展望技能也取得了良好进展。长期（5-30天）预测是目前最困难的。在扩展范围内，主要的可预报性来源是一种名为“Madden-Julian振荡（MJO）”的大气低频模态。然而，目前的操作模型很难预测这种振荡。该项目将研究26个当前最先进的气候模型中基本动力和物理过程的表示和耦合，以了解为什么只有大约四分之一的这些模型能够捕捉到观测到的MJO时空演变特征，而其他模型则不能。这项研究结合了观测数据分析，26个气候模式输出的诊断，和理想化的数值模型实验，其中关键的动力/物理过程是孤立的。这个项目的成功完成不仅将推进马登-朱利安振荡动力学的知识，而且还有助于确定目前国家的问题，艺术业务天气预报模型，并帮助改进大气低频振荡及其相关遥相关模式的模型表示。扩大范围预测能力的提高将为弥合短期天气和长期气候预测之间的差距提供一条途径，有利于经济和社会预防自然灾害和政府规划。该项目支持热带大尺度动力学和气候建模领域的研究生。