A new method to identify water masses -- a network-based analysis of oceanographic point measurement time series

TL;DR

This paper introduces a network-based method for identifying water masses in oceanographic time series data, avoiding interpolation and revealing detailed flow regimes and topographical features.

Contribution

A novel network-based analysis technique for water mass identification that bypasses interpolation and enhances detection of flow patterns and topography.

Findings

Reproduces known flow patterns accurately

Reveals topographical features not seen in traditional methods

Eliminates need for data interpolation

Abstract

This is a statistical analysis of the oceanographic time series measured across Fram Strait at a latitude of 78{\deg}50'N. Fram Strait is the deepest passage between the Arctic Ocean and the North Atlantic. There are up to 16 mooring lines with instruments at different depths measuring water temperature and velocity. These variables vary on different time scales and the challenge is to distinguish different spatial flow regimes. For Fram Strait, a temperature criterion is traditionally applied to identify water-masses, i.e. water volumes of similar origin. Interpolation leads to a vertical latitudinal 2D cross-section from which a scalar - the hypothetical area of waters within a certain temperature interval - can be extracted. The scalar is combined with a similar interpolation of the velocities to approximate the volume flows through the gateway. This approach is not only numerically expensive but also incorporates many assumptions. The present study suggest a new network-based approach to discriminate between flow regimes without the need to introduce artificial data through interpolation. The new approach not only reproduces the known flow patterns, but also reveals topographical features which are not captured by a standard water-masses analysis.