CAREER: Narrowing the Spread: Process Understanding for Reduced Uncertainty in Future El Nino/Southern Oscillation Projections

厄尔尼诺/南方涛动（ENSO）主导着全球气候变率，但其对气候变化的总体敏感性仍然未知。气候模式对未来厄尔尼诺/南方涛动驱动的海洋变率预测出明显不同的变化，这在一定程度上与模拟的热带太平洋平均气候变化的差异有关。该项目将增进对厄尔尼诺/南方涛动对气候变化的反应的了解。该项目将把观测数据与现有的模型集合和新的、有针对性的敏感性试验综合起来，以便从物理上了解厄尔尼诺/南方涛动气候变化的反应，并改进对未来厄尔尼诺/南方涛动预测的观测限制。该项目将培养本科生、博士生和博士后研究人员。研究成果将纳入一系列气候和海洋建模教学单元（“气候数据实验室”）。介绍性模块将使环境科学专业人员熟悉气候模型的基本原理，更高级的模块将描述海洋模型参数化和动态。Climate DataLab还将通过Bren/Environmental Studies Leadership Program（布伦/环境研究领导力计划）用于培训代表性不足的少数民族本科生，提供数据科学技能并促进STEM职业的保留。这项工作的目标是改善对未来ENSO预测的限制。该项目将探讨厄尔尼诺/南方涛动的强迫变化如何与平均气候以及中尺度和次中尺度海洋动力学相关联。它还将研究中尺度如何影响ENSO和平均状态之间的相互作用。将使用的主要工具将是一系列前所未有的CMIP级合奏。该项目还将调查厄尔尼诺/南方涛动与平均和中尺度动力学之间的关系在CMIP级模式中是否一致。一套理想化的海洋唯一的实验和新的诊断热带不稳定波（TIW）活动将允许隔离个别过程对中尺度涡动行为的影响，以及它们与平均气候的相互作用。此外，将建立第一套完全耦合的扰动物理学集合，并与现有耦合模式集合的行为进行对比，这些集合是为了阐明海洋对厄尔尼诺/南方涛动预测的物理控制而优化的。当与新的，更详细的紧急约束诊断的拟议发展相结合，其结果将是更强大的洞察力如何气候变化可能影响21世纪世纪ENSO。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The El Nino/Southern Oscillation (ENSO) dominates worldwide climate variability, yet its overall sensitivity to climate change remains unknown. Climate models project distinctly different changes to future ENSO-driven oceanic variability, which are related in part to differences in simulated tropical Pacific mean climate changes. This project will improve understanding of ENSO response to climate change. The project will synthesize observational data with existing model ensembles and novel, targeted sensitivity experiments to provide both physical understanding of the ENSO climate change response and improved observational constraints on future ENSO projections. The project will train undergraduate and PhD students and postdoctoral researchers. Research results will be incorporated into a series of instructional modules on climate and ocean modeling (the “Climate DataLab”). Introductory modules will familiarize environmental science professionals with climate model fundamentals, and more advanced modules will describe ocean model parameterizations and dynamics. Climate DataLab will also be used to train underrepresented minority undergraduates through the Bren/Environmental Studies Leadership Program, providing data science skills and facilitating retention in STEM careers.The objective of this work is to improve constraints on future ENSO projections. The project will address how forced changes in ENSO are related to mean climate and mesoscale and submesoscale ocean dynamics. It will also examine how the mesoscale influences the interactions between ENSO and the mean state. The primary tool to be used will be an unprecedented array of CMIP-class ensembles. The project will also investigate whether the relationships between ENSO and mean and mesoscale dynamics are consistent across CMIP-class models. A suite of idealized ocean-only experiments and novel diagnostics for Tropical Instability Wave (TIW) activity will allow isolation of the influences of individual processes on mesoscale eddy behavior, and their interactions with mean climate. Additionally, the first set of fully coupled perturbed physics ensembles optimized for elucidating ocean physical controls on ENSO projections will be created and contrasted with the behavior of existing coupled model ensembles. When combined with the proposed development of new, more detailed emergent constraint diagnostics, the result will be more robust insights into how climate change may affect 21st century ENSO.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.