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.

世界上一些人口最密集和最脆弱的沿海地区位于印度洋盆地，有大量证据表明，印度洋气候变化对气候的影响超出了盆地范围，达到了全球范围。与其他海洋盆地相比，对印度洋海平面的了解主要是由于历史记录短而少。因此，研究印度洋-太平洋地区内部气候变化对海平面的影响主要依赖于基于模型的方法，这些方法无法解决有关该地区气候和海平面变化的几个悬而未决的问题。在这个项目中，一个观测驱动的方法来研究这些问题，并提供了一个更好的了解印度-太平洋地区的内部气候变率和海平面之间的关系。该项目有较长的观测记录，并通过同时分析多个气候变量和模拟数据，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。建立和测试的统计将被用来提取与印度洋-太平洋地区几个不同的气候信号相关的海平面变化。通过消除这种内部变化，可以揭示海平面趋势的人为部分，阅读以改进对该区域过去和未来海平面上升的估计和解释。了解海平面内部变化对年际到十年时间尺度的贡献也可以改善对未来海平面的预测，这也有助于适应和减缓努力。最后，对气候信号对降水模式的影响进行更好的评估可能会对地球仪的人口产生重大影响。该项目由两名早期职业调查人员领导，将培训两名研究生，从而促进青年科学家在某一研究领域的科学和专业发展（海平面上升）该项目有可能大大改善该区域海平面上升的估计，并有助于改善最容易受到海平面上升影响的印度洋海岸线的规划工作。程度.通过分离该区域海平面趋势中的人为部分，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，以更好地量化印度洋海平面变化，并推进目前对印度洋的了解？在区域和全球气候变化中的作用。使用基于循环平稳经验正交函数（CSEOFs）的多变量重建方法，可以克服历史采样不佳所带来的挑战，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的一个重大进展。通过改进内部变化的表示，并将海平面的当前状态与过去的状态进行比较，将获得有价值的见解，了解海平面如何由于人为影响而变化，以及它如何在未来变化或继续变化。只有有长期、一致的海平面记录（加上其他气候变量），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有关印度洋-太平洋海平面的许多悬而未决的问题。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区域的人提供指导。