Surface salinity fields in the Arctic Ocean and statistical approaches to predicting anomalies and patterns

TL;DR

This study analyzes Arctic Ocean surface salinity variability, identifies patterns and anomalies, and develops a statistical model to predict salinity changes based on environmental factors, successfully forecasting recent freshening trends.

Contribution

It introduces a novel statistical model linking principal components to environmental factors for predicting Arctic surface salinity anomalies.

Findings

Identified significant inter-annual salinity variability and anomalies.

Developed a predictive model accurately forecasting surface salinity for 2013-2014.

Detected a dramatic shift in salinity behavior during 2007-2012.

Abstract

Significant salinity anomalies have been observed in the Arctic Ocean surface layer during the last decade. Using gridded data of winter salinity in the upper 50 m layer of the Arctic Ocean for the period 1950-1993 and 2007-2012, we investigated the inter-annual variability of the salinity fields, attempted to identify patterns and anomalies, and developed a statistical model for the prediction of surface layer salinity. The statistical model is based on linear regression equations linking the principal components with environmental factors, such as atmospheric circulation, river runoff, ice processes, and water exchange with neighboring oceans. Using this model, we obtained prognostic fields of the surface layer salinity for the winter period 2013-2014. The prognostic fields demonstrated the same tendencies of surface layer freshening that were observed previously. A phase portrait analysis involving the first two principal components exhibits a dramatic shift in behavior of the 2007-2012 data in comparison to earlier observations.