Collaborative Research: The Relationship between El Niño-Southern Oscillation (ENSO) Diversity and Tropical Cyclones in a Hierarchy of Models

飓风是美国和世界各地许多沿海社区面临的最具破坏性的自然灾害。 由于这些风暴，也被称为热带气旋（TC），在温暖的海面温度（SST）上的暖空气中发展，因此很自然地会问，随着世界变暖，它们是否会构成更大的威胁。基本热力学意味着，在较冷的气候中，最强的飓风比在较温暖的气候中的飓风要弱，而这种随着变暖而增强的现象现在已经在观测记录中被发现。 但是，最强风暴的加强可能只是变暖导致的TC活动变化之一。 这里的一个问题是TC活动将如何受到海洋表面变暖模式的影响，因为观测和模拟都显示热带海洋变暖伴随温室气体增加的不均匀模式。在厄尔尼诺事件（也称为厄尔尼诺/南方涛动事件，或ENSO）中可以找到一些关于SST增加的区域分布可能如何影响TC活动的线索，其中南美洲西海岸和日界线之间的赤道太平洋变暖。 首席研究员（PI）和其他人先前的工作表明，厄尔尼诺事件对TC的影响是不同的，这取决于厄尔尼诺引起的变暖是在赤道太平洋中部还是东部最大。 特别是，他们发现所有厄尔尼诺事件都可以抑制大西洋的TC，但厄尔尼诺的中太平洋“风味”在大西洋TC抑制方面更为有效。因此，厄尔尼诺-热带气旋的联系可以指导人们对未来SST变暖的区域模式如何影响热带气旋活动的预期。 如果未来的变暖影响厄尔尼诺事件的频率和强度，或者影响中太平洋和东太平洋的相对发生率，这也可能产生直接的影响。该奖项下的工作使用气候模型模拟、机器学习技术和混合动力统计模型的组合来研究厄尔尼诺-TC关系及其对气候变化的影响。 该项目的一个目标是开发“紧急约束”，将气候模型中发现的厄尔尼诺-热带气旋关系与观测记录中发现的关系联系起来。 如果模型显示当前气候中的厄尔尼诺-TC关系与未来TC变化之间有很强的联系，那么正确模拟当前厄尔尼诺-TC关系的模型对未来TC变化的预测就更可信。厄尔尼诺事件影响热带气旋活动的物理机制也进行了探讨，这项工作具有社会价值，因为热带气旋对沿海社区构成的风险和指导的价值，如何在未来的风险可能会发生变化。 该项目还建立了一个研究网络，涵盖从东部海鸟到夏威夷的三个机构。 除了其科学价值外，该网络还有助于克服夏威夷大学这一为少数群体服务的机构在地理上的孤立。 该项目还支持一名研究生参加哥伦比亚大学的博士课程，该课程帮助来自代表性不足群体的大学毕业生过渡到研究生院。 最后，PI正在开发一个名为“天气和气候中的机器学习应用”的在线课程，该课程将利用该项目的工具和成果。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。

Hurricanes are the most devastating natural disasters facing many coastal communities in the US and around the world. Since these storms, also called tropical cyclones (TCs), develop in warm air over warm sea surface temperatures (SSTs), it is natural to ask wether they will pose a greater threat as the world warms. Basic thermodynamics implies that the strongest hurricanes in a cooler climate are weaker than their counterparts in warmer climates, and this strengthening with warming has now been detected in the observed record. But the strengthening of the strongest storms may be just one of a number of changes in TC activity that result from warming. One question here is how TC activity will be influenced by the pattern of ocean surface warming, as observations and simulations both show an uneven pattern of tropical ocean warming accompanying greenhouse gas increases. Some clues to how the regional distribution of SST increases is likely to affect TC activity can be found in El Nino events(also called El Nino/Southern Oscillation events, or ENSOs), in which the equatorial Pacific warms between the west coast of South America and the dateline. Prior work by the Principal Investigators (PIs) and others shows that the influence of El Nino events on TCs is different depending on whether the El Nino-induced warming is greatest in the central or eastern equatorial Pacific. In particular they found that all El Nino events can suppress TCs in the Atlantic, but the central Pacific "flavor" of El Nino is substantially more effective at Atlantic TC suppression. The El Nino-TC connection can thus serve to guide expectations as to how the regional pattern of future SST warming might affect TC activity. It may also have direct implications if future warming affects either the frequency and intensity of El Nino events or the relative occurrence of the central and eastern Pacific flavors.Work performed under this award examines the El Nino-TC relationship and its implications for climate change using a combination of climate model simulations, machine learning techniques, and hybrid dynamical-statistical models. One goal of the project is to develop "emergent constraints" that connect the El Nino-TC relationship found in climate models to the relationship found in the observed record. If models show a strong connection between the El Nino-TC relationship in present-day climate and future TC change, then future TC change projections from models which correctly simulate the present-day El Nino-TC relationship are more credible. The physical mechanisms through which El Nino events influence TC activity are also explored.The work has societal value given the risks posed by TCs for coastal communities and the value of guidance as to how that risk might change in the future. The project also builds a research network spanning three institutions from the eastern seabord to Hawaii. In addition to its scientific value the network helps to overcome the geographical isolation of the University of Hawaii, a minority-serving institution. The project also supports a graduate student in Columbia University's Bridge to the PhD program, which helps college graduates from underrepresented groups with the transition to graduate school. Finally, the PIs are developing an online class titled "Machine learning applications across weather and climate" which will take advantage of tools and results from the project.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.