20th Century Multivariate Indian Ocean Regional Sea Level Reconstruction. Issue 10 (15th October 2020)

• Record Type: Journal Article
• Authors: Kumar, Praveen; Hamlington, Benjamin; Cheon, Se‐Hyeon; Han, Weiqing; Thompson, Philip
• Is Part Of: Journal of geophysical research. Volume 125:Issue 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-15
• Subjects: sea level -- multivariate reconstruction -- Indian ocean -- 20th century sea level -- interannual‐to‐decadal; Oceanography -- Periodicals; 551.4605
• DOI: 10.1029/2020JC016270
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: Despite having some of the world's most densely populated and vulnerable coastlines, Indian Ocean sea level variability over the past century is poorly understood relative to other ocean basins primarily, due to the short and sparse observational records. In an attempt to overcome the limitations imposed by the lack of adequate observations, we have produced a 20th century Indian Ocean sea level reconstruction product using a new multivariate reconstruction technique. This technique uses sea level pressure and sea surface temperature in addition to sea level data to help constrain basin‐wide sea level variability by (1) the removal of large spurious signals caused as a result of insufficient tide gauge data specifically during the first half of the 20th century and (2) through its information on large‐scale climate modes such as El Niño‐Southern Oscillation and Indian Ocean Dipole. Basis functions generated by Cyclostationary Empirical Orthogonal Functions are used for the reconstruction. This new multivariate technique provides improved regional sea level variability estimates along with a longer record length in comparison to existing globally reconstructed sea level data. The biggest advantage of using this multivariate reconstruction technique lies in its ability to reconstruct Indian Ocean sea level for the first half of the 20th century, providing a long sea level record for the study of Indian Ocean internal climate variability. This will enable future studies to help improve the understanding of how sea level trends and variability can be modulated by internal climate variability in the Indian Ocean. Plain Language Summary: Densely populated coastal regions of the countries surrounding the Indian Ocean are becoming increasingly vulnerable to the effects of sea level rise. Accurate predictions of future sea level change will help minimize the social and economic damage posed to these coastal communities. The key to predicting future sea level lies in how well we understand past and present sea level change. However, due to the presence of only a few short and scattered sea level observational records, change in sea level over the Indian Ocean is not well understood. In this paper we have created a new sea level data set by developing new methodology which is particularly well suited for filling data gaps left by the observational record in the Indian Ocean. Additionally, this new technique allows us to reliably extend the observational record to now span the entire 20th century and has been shown to have improved sea level estimates when compared to an older existing sea level data product. Using this new longer sea level data set, we hope that future studies will help shed more light and improve the understanding of Indian Ocean sea level change over long time scales. Key Points: We have developed a new multivariate sea level reconstruction technique designed to overcome sparse spatio‐temporal tide gauge sampling Created a new sea level data set with improved 20th century interannual to decadal sea level variability estimates for the Indian Ocean This sea level reconstruction is used to give context to satellite measured sea level in the Indian Ocean