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| Collaborative Research: Dynamics of Seasonal Forecast Uncertainty: Cross-Basin Ocean-Atmosphere Interactions | |
| 项目编号 | 2105641 |
| Yuko Okumura | |
| 项目主持机构 | University of Texas at Austin |
| 开始日期 | 2021-07-15 |
| 结束日期 | 06/30/2024 |
| 英文摘要 | Every few years the Pacific ocean warms dramatically in a narrow strip along the equator extending roughly from the South American coast to the dateline. This warming is called an El Nino event, and El Ninos and their cold La Nina opposites are referred to collectively as El Nino/Southern Oscillation (ENSO) events. While ENSO events are broadly similar there are important differences, in particular they differ in whether the sea surface temperature (SST) change is greatest in the eastern or central Pacific. They also differ in their magnitude, and El Nino events are often stronger than La Nina events. The consequences of ENSO are felt worldwide, from changes in the Indian monsoon to the severity of winters in Canada, and these impacts vary according to the pattern and magnitude of the event. The dynamical mechanisms that cause diversity in behavior among ENSO events and their global impacts are thus an important practical problem as well as a topic of scientific interest. This project examines the idea that much of the diversity of ENSO events occurs because of interactions between the developing ENSO event and a variety of less prominent climate variability modes occurring over the global oceans. For example the Pacific meridional mode (PMM) is a variability pattern in which fluctuations of the Aleutian Low over the North Pacific generate warm SSTs (or cold, in the opposite phase) which propagate slowly toward the equatorial Pacific through air-sea interactions. The overlap between the domains of the PMM and ENSO makes the PMM a likely suspect in diversifying ENSO events. Likewise, the episodic warming of the Indian Ocean in the Indian Ocean Dipole (IOD) mode affects the trade winds in the western Pacific, with potential consequences for ENSO given the central role of trade wind fluctuations in ENSO evolution. To examine the effect of secondary modes on ENSO diversity the Principal Investigators (PIs) of this award take advantage of the ensemble method used to predict ENSO events. Ensemble prediction means using a climate model to predict ENSO based on observed initial conditions (the state of the atmosphere and ocean at a given time) but performing several forecast simulations instead of one, and starting each forecast simulation with slightly different initial conditions. The resulting Perturbed Initial Condition Ensemble (PICE) gives a best estimate of the evolution of ENSO and also estimates the uncertainty in the prediction. The idea of this project is that the individual forecast simulations in a PICE contain different secondary modes, and the interactions between secondary modes and ENSO can be assessed by examining differences in ENSO evolution among the simulations. An advantage of this method is that a large database of PICE simulations has been created by the Earth System Prediction (ESP) Working Group of the Communtiy Earth System Model (CESM). The PICE dataset provides a much larger sample size than the observational record, thus statistically robust results can be obtained. The work is of societal importance given its direct connection to ENSO prediction. In addition to its examination of ENSO evolution in prediction simulations the research uses the PICE simulations to understand how differences among ENSO events lead to differences in the impacts of ENSO in populous parts of the world. The project also supports two graduate students and provides internship opportunities for undergraduates. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. |
| 资助机构 | US-NSF |
| 项目经费 | $299,130.00 |
| 项目类型 | Standard Grant |
| 国家 | US |
| 语种 | 英语 |
| 文献类型 | 项目 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/213263 |
| 推荐引用方式 GB/T 7714 | Yuko Okumura.Collaborative Research: Dynamics of Seasonal Forecast Uncertainty: Cross-Basin Ocean-Atmosphere Interactions.2021. |
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