ABSTRACT

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.

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