Year: 2025
Type: article
Abstract: Aim: To analyze long-term mean monthly values for sea surface height (SSH) and nutrient concentrations (ammonia,nitrate, phosphate, and chlorophyll) in a part of the Mediterranean Sea, with a focus on the influence of spatial-temporal SSH patterns on nutrient concentrations.Methods: We downloaded monthly mean reanalysis data covering the period from 1998–2020 from the EuropeanUnion Copernicus Marine Service. We applied a neural gas machine learning method to cluster SSH data and usedMATLAB for data processing, anomaly calculations, spatial mapping, and exploring relationships with biological parameters.Results: The neural gas method identified distinct interannual signals in SSH anomaly data, linking them to changesin surface and subsurface circulations in the Ionian Sea. Classification of SSH allowed for the association of SSHanomalies with varying nutrient concentrations. The analysis highlighted the role of the Adriatic-Ionian BimodalOscillating System in regulating SSH and nutrient patterns.Conclusions: The neural gas method was established as a robust classification tool for detecting low-variability signalsin the spatio-temporal dynamics of SSH, enabling connections with nutrient concentrations. This underscores theimportance of employing nonlinear statistical methods to understand the relationship between biological and physicalparameters in the Mediterranean Sea.
Source: St open
DOI: 10.48188/so.6.13
Authors: Ivona Siber, Frano Matić
Institution: University of Split
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4415954174
FWCI:
Cites:
Cited by:
Related to: