Spatial Patterns of Sea Level Variability Associated with Natural Internal Climate Modes

• Published in: Surveys in Geophysics
• Main Authors: Han, Weiqing, Meehl, Gerald A., Stammer, Detlef, Hu, Aixue, Hamlington, Benjamin, Kenigson, Jessica, Palanisamy, Hindumathi, Thompson, Philip
• Format: Text
• Language: English
• Published: Springer Netherlands 2016
• Subjects: Article; Arctic; Indian; Pacific
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115065/; https://doi.org/10.1007/s10712-016-9386-y

Sea level rise (SLR) can exert significant stress on highly populated coastal societies and low-lying island countries around the world. Because of this, there is huge societal demand for improved decadal predictions and future projections of SLR, particularly on a local scale along coastlines. Regionally, sea level variations can deviate considerably from the global mean due to various geophysical processes. These include changes of ocean circulations, which partially can be attributed to natural, internal modes of variability in the complex Earth’s climate system. Anthropogenic influence may also contribute to regional sea level variations. Separating the effects of natural climate modes and anthropogenic forcing, however, remains a challenge and requires identification of the imprint of specific climate modes in observed sea level change patterns. In this paper, we review our current state of knowledge about spatial patterns of sea level variability associated with natural climate modes on interannual-to-multidecadal timescales, with particular focus on decadal-to-multidecadal variability. Relevant climate modes and our current state of understanding their associated sea level patterns and driving mechanisms are elaborated separately for the Pacific, the Indian, the Atlantic, and the Arctic and Southern Oceans. We also discuss the issues, challenges and future outlooks for understanding the regional sea level patterns associated with climate modes. Effects of these internal modes have to be taken into account in order to achieve more reliable near-term predictions and future projections of regional SLR.