Classification of Interannual Surface Layer Salinity Variability. Issue 8 (23rd April 2023)

• Record Type: Journal Article
• Authors: Katsura, S.; Sprintall, J.; Kido, S.; Tanimoto, Y.; Nonaka, M.
• Is Part Of: Geophysical research letters. Volume 50:Issue 8(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-04-23
• Subjects: surface layer salinity -- interannual variability -- global ocean; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2022GL102261
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: Since surface layer salinity (SLS) mainly reflects sea surface freshwater flux and affects ocean circulation and upper ocean stratification by modifying seawater density, knowledge of SLS variability is important to better understand the global hydrological cycle and ocean and climate variability. The global pattern of SLS interannual variability has not received as much attention compared to the seasonal variability. The SLS interannual variability in the global ocean based on Argo profiling float data over 2003–2020 was classified into seven individual clusters. SLS interannual variability was found to be regionally different even within each ocean basin and primarily corresponded to large‐scale climate mode variability. The spatial distribution of the classified SLS clusters provides the first map of SLS interannual variability in the global ocean that largely reflects the variability of the global hydrological cycle. Plain Language Summary: Variability of salinity in the upper ocean mainly reflects the variability of evaporation and rainfall occurring at the sea surface. Thus, knowledge of surface layer salinity (SLS) variability is important for better understanding of the global water cycle and also climate variability. While the seasonal variability of SLS has been well described both regionally and globally, the global pattern of SLS interannual variability is still unclear. We classified the global SLS interannual variability into seven separate modes by applying a cluster analysis method to the time series of SLS based on data from Argo profiling floats from 2003 to 2020. Each cluster is found to be related to regional climate modes such as El Niño. As such, this global map of SLS interannual variability will be useful for understanding the variability of the global water cycle in each of these regimes. Key Points: A global map of the spatio‐temporal surface layer salinity interannual variability was classified into seven separate clusters Four clusters in the Pacific Ocean showed regionally different phases of salinity variability related to the Pacific Decadal Oscillation One cluster showed salinification in western boundary currents while other clusters showed little relationship to climate mode variability