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Tidal Records as Liquid Climate Archives for Large-Scale Interior Mediterranean Variability.


ABSTRACT: Characterization of interior ocean variability is necessary for understanding climate. Water mass evolution shapes ocean-atmosphere interactions and contributes to determine timescales for global and regional climate variability. However, a robust assessment of past state and variability of the ocean interior is prevented by sparseness/shortness of historical subsurface observations and uncertainties affecting proxy-based reconstructions. Here, we propose a novel approach to infer past large-scale interior ocean variability with unprecedented accuracy and temporal resolution. It exploits links between stratification determined by "large-scale" water mass distributions and local dynamics. We characterize interannual interior ocean variability in the Mediterranean Sea in the early 20th century contained in tidal measurements in the Strait of Messina, and demonstrate the general applicability of our method, paving the way to a new approach to analyze historical oceanographic records: Regions where different water masses are known to collide can thus act as magnifying glasses for basin-scale interior ocean variability, hence providing "liquid archives" for climatology.

SUBMITTER: Rubino A 

PROVIDER: S-EPMC6105714 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Tidal Records as Liquid Climate Archives for Large-Scale Interior Mediterranean Variability.

Rubino Angelo A   Zanchettin Davide D   Androsov Alexey A   Voltzinger Naum E NE  

Scientific reports 20180822 1


Characterization of interior ocean variability is necessary for understanding climate. Water mass evolution shapes ocean-atmosphere interactions and contributes to determine timescales for global and regional climate variability. However, a robust assessment of past state and variability of the ocean interior is prevented by sparseness/shortness of historical subsurface observations and uncertainties affecting proxy-based reconstructions. Here, we propose a novel approach to infer past large-sca  ...[more]

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