The dynamics and multi-year predictability of La Nina

拉尼娜的动态和多年可预测性

• 批准号: 1756883
• 负责人: Yuko Okumura
• 金额: $ 62.5万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1756883&HistoricalAwards=false
• 关键词: dynamics; multi; year; predictability; La

La Niña, a recurrent cooling pattern over the tropical Pacific Ocean, has been linked to reduced wintertime precipitation across the southern tier of the United States. Historical observations show that La Niña events can last two years or longer, a feature that could make their associated drought impacts more persistent. Forecast systems are generally able to predict the onset of La Niña, as they virtually always follow El Niño. However, little is known about the predictability of multi-year La Niña events because current operational ENSO forecasts are limited to 8 months. This project addresses a series of questions that are critical to improve our ability to predict these events. The project will determine the processes, initial ocean states, and models that can produce skillful multi-year predictions of tropical Pacific. Predicting whether La Niña will return for a second year is critical for predicting the duration of associated droughts throughout the world. Results from this project can potentially improve our ability to predict both the strength and duration of US droughts caused by La Niña.Observations show that La Niña events tend to last for an additional year, causing persistent drought and flooding impacts in regions throughout the world. Forecasts of these 2-year La Niña events are not routinely generated by operational prediction systems because they focus on shorter lead times, typically up to eight months. This project will demonstrate the feasibility of skillful predictions of 2-year La Niña. Multi-year forecasts will be performed using the Community Earth System Model Version 1 (CESM1), a model that simulates realistic and highly predictable 2-year La Niña. Model dependence of 2-year predictability will be explored using decadal climate predictions performed by four climate models that participated in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). These retrospective forecasts (hindcasts) performed with CESM1 and CMIP5 will be used to explore the physical processes responsible for the predictability of historical 2-year La Niña. Advanced diagnostics will be used to attribute the processes causing forecast spread. Discrepancies in the hindcasts of historical 2-year La Niña events relative to their observed trajectory will be analyzed to identify their predictable and unpredictable drivers. An existing suite of seasonal predictions performed with CESM1 will be extended to produce 2-year hindcasts initialized in March, June, and September. Together with the existing suite of decadal predictions initialized in November, the proposed extension will be used to explore the impact of different lead times, initial state, and seasonality.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.

拉尼娜现象是热带太平洋上空的一种周期性冷却模式，与美国南部冬季降水量减少有关。历史观测表明，拉尼娜现象可持续两年或更长时间，这一特点可能使其相关的干旱影响更加持久。预报系统通常能够预测拉尼娜现象的发生，因为它们几乎总是跟随厄尔尼诺现象。然而，人们对多年拉尼娜现象的可预测性知之甚少，因为目前的厄尔尼诺/南方涛动业务预测仅限于8个月。该项目解决了一系列问题，这些问题对于提高我们预测这些事件的能力至关重要。该项目将确定过程，初始海洋状态和模型，可以产生熟练的热带太平洋多年预测。预测拉尼娜现象是否会在第二年再次出现，对于预测世界各地相关干旱的持续时间至关重要。从这个项目的结果可以潜在地提高我们的能力来预测由拉尼娜引起的美国干旱的强度和持续时间。观测表明，拉尼娜事件往往会持续一年，在世界各地的地区造成持续的干旱和洪水的影响。这些两年一次的拉尼娜现象的预报并不是由业务预报系统定期生成的，因为它们侧重于较短的提前期，通常长达8个月。该项目将证明熟练预测两年拉尼娜现象的可行性。将使用共同体地球系统模型第1版进行多年预测，该模型模拟现实和高度可预测的两年期拉尼娜现象。将利用参加耦合模式相互比较项目（CMIP 5）第5阶段的四个气候模式进行的十年气候预测来探讨模式对2年可预测性的依赖性。CESM 1和CMIP 5进行的这些回顾性预报（后报）将用于探索负责历史2年拉尼娜的可预测性的物理过程。高级诊断将用于确定导致预测扩散的过程的属性。将分析历史2年拉尼娜事件相对于其观测轨迹的后报差异，以确定其可预测和不可预测的驱动因素。CESM 1执行的现有季节性预测套件将扩展到生成在3月，6月和9月初始化的2年后报。与11月启动的现有十年期预测套件一起，拟议的延期将用于探索不同的提前时间，初始状态和季节性的影响。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Two-Year Dynamical Predictions of ENSO Event Duration during 1954–2015

• DOI: 10.1175/jcli-d-20-0619.1
• 发表时间: 2021-02
• 期刊: Journal of Climate
• 影响因子: 4.9
• 作者: Xiandu Wu;Y. Okumura;C. Deser;P. DiNezio

ENSO Diversity from an Atmospheric Perspective

• DOI: 10.1007/s40641-019-00138-7
• 发表时间: 2019-06
• 期刊: Current Climate Change Reports
• 影响因子: 9.5
• 作者: Y. Okumura

ENSO and Pacific Decadal Variability in the Community Earth System Model Version 2

• DOI: 10.1029/2019ms002022
• 发表时间: 2020-12-01
• 期刊: JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
• 影响因子: 6.8
• 作者: Capotondi, A.;Deser, C.;Larson, S. M.
• 通讯作者: Larson, S. M.

The equatorial Pacific cold tongue bias in CESM1 and its influence on ENSO forecasts

• DOI: 10.1175/jcli-d-21-0470.1
• 发表时间: 2022-02
• 期刊: Journal of Climate
• 影响因子: 4.9
• 作者: Xiandu Wu;Y. Okumura;S. Yeager;C. Deser;P. DiNezio