Collaborative Research: Investigating the Effect of Internal Climate Variability on Sea Level in the Indian Ocean

合作研究：调查内部气候变率对印度洋海平面的影响

• 批准号: 1558736
• 负责人: Weiqing Han
• 金额: $ 8.62万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1558736&HistoricalAwards=false
• 关键词: Collaborative; Research; Investigating; Effect; Internal

Some of the most densely populated and vulnerable coastal regions in the world are located in the Indian Ocean basin, and there is considerable evidence that the impact of Indian Ocean variability on climate extends beyond the basin to global scales. Resulting largely from a short and sparse historical record, the understanding of sea level in the Indian Ocean is lacking when compared to other ocean basins. Studying the effect of internal climate variability on sea level in the Indo-Pacific has therefore relied largely on model-based approaches that have been unable to resolve several open questions about climate and sea level variability in the region. In this project, an observation-driven approach is employed to examine these questions and provide an improved understanding of the relationship between internal climate variability and sea level in the Indo-Pacific region. With a longer observational record and through the simultaneous analysis of multiple climate variables and modeled data, this project will improve the understanding of how internal climate variability affects Indian Ocean sea level trends and variability. Established and tested statistical will be used to extract the sea level variability associated with several different climate signals in the Indo-Pacific region. By removing this internal variability, the anthropogenic portion of the sea level trend can be uncovered, reading to improved estimates and interpretation of both past and future sea level rise in the region. Understanding the sea level contribution of internal variability on inter-annual to decadal timescales can also lead to improved prediction of future sea level, again aiding in adaptation and mitigation efforts. Finally, providing an improved assessment of the effect of climate signals on precipitation patterns could have significant impact to populations across the globe. This project is led by two early career investigators and will train two graduate students, thus enhancing the scientific and professional development of young scientists in an area of study (sea level rise) that is of growing concern.This project has the potential to substantially improve sea level rise estimates in the region and contribute to improved planning efforts for the Indian Ocean coastlines most vulnerable to the effects of higher sea levels. By isolating the anthropogenic portion of the sea level trends in the region, assessments can be made regarding how human-activity is affecting sea level and may continue to do so in the future. New and innovative statistical methods will be employed that can better isolate internal oscillatory modes to better quantify Indian Ocean sea level change and advance the current understanding of the Indian Ocean?s role in regional and global climate variability. Using a multivariate reconstruction approach based on cyclo-stationary empirical orthogonal functions (CSEOFs), challenges provided by poor historical sampling can be overcome to investigate sea level in the Indo-Pacific region. The CSEOF multivariate reconstruction algorithm represents a significant advancement in the reconstruction of climate variability. With an improved representation of internal variability and by comparing the current state of sea level to past states, valuable insight will be gained into both how sea level has changed as a result of anthropogenic influences, and how it may change or continue to change in the future. Only with a long, consistent sea level record (in addition to other climate variables) is it possible to gain such an understanding. Furthermore, by combining models with the long observational record, many open questions regarding Indo-Pacific sea level can be investigated and subsequently answered. An assessment of the quality of these models and historical data will also be made, which will provide guidance to those seeking to study the region in the future.

世界上人口最稠密和最脆弱的一些沿海地区位于印度洋盆地，有相当多的证据表明，印度洋的多变性对气候的影响超出了该盆地的范围，延伸到全球范围。很大程度上是由于历史记录短而稀少，与其他海洋盆地相比，人们对印度洋的海平面缺乏了解。因此，研究内部气候变化对印度洋-太平洋海平面的影响在很大程度上依赖于基于模型的方法，这些方法一直无法解决该区域气候和海平面变化的若干悬而未决的问题。在该项目中，采用了观测驱动的方法来审查这些问题，并更好地了解了印度-太平洋区域内部气候变化和海平面之间的关系。利用更长的观测记录，通过同时分析多个气候变量和模拟数据，该项目将增进对内部气候变化如何影响印度洋海平面趋势和变化的了解。建立和检验的统计数据将用于提取与印度-太平洋区域的几个不同气候信号有关的海平面变化。通过消除这种内部变异性，可以揭示海平面趋势的人为部分，从而改进对该区域过去和未来海平面上升的估计和解释。了解海平面在年际到年代际时间尺度上的内部变化对海平面的贡献，还可以改进对未来海平面的预测，再次有助于适应和缓解努力。最后，改进对气候信号对降水模式的影响的评估可能会对全球人口产生重大影响。该项目由两名早期职业研究人员领导，将培训两名研究生，从而促进青年科学家在日益令人关注的研究领域(海平面上升)的科学和专业发展。该项目有可能大大改善该地区的海平面上升估计，并有助于改进最容易受到海平面上升影响的印度洋海岸线的规划工作。通过隔离该区域海平面趋势的人为部分，可以评估人类活动如何影响海平面，并可能在未来继续这样做。将采用新的和创新的统计方法，可以更好地隔离内部振荡模式，更好地量化印度洋海平面变化，并促进对印度洋-S在区域和全球气候变化中的作用的当前理解。利用基于循环平稳经验正交函数(CSEOFs)的多变量重建方法，可以克服历史采样差带来的挑战，以调查印度洋-太平洋地区的海平面。CSEOF多变量重建算法代表了气候变率重建方面的重大进步。通过改进对内部变异性的表示，并通过将海平面的当前状态与过去的状态进行比较，将获得关于海平面如何由于人为影响而变化以及未来可能如何变化或将如何变化的宝贵见解。只有拥有长期、一致的海平面记录(以及其他气候变量)，才有可能获得这样的理解。此外，通过将模型与长期观测记录相结合，可以调查并随后回答许多关于印度洋-太平洋海平面的未决问题。还将对这些模型和历史数据的质量进行评估，这将为今后寻求研究该地区的人提供指导。