Machine Learning Solutions to Regional Surface Ocean δ18O-Salinity Relationships for Paleoclimatic Reconstruction

Abstract

Stable isotope-based reconstructions of past ocean salinity and hydroclimate depend on accurate, regionally constrained relationships between the stable oxygen isotopic composition of seawater (δ18Osw) and salinity in the surface ocean. An increasing number of δ18Osw observations suggest greater spatial variability in this relationship than previously considered, highlighting the need to reassess these relationships on a global scale. Here, we use available, paired δ18Osw and salinity data (N = 11,119) to create global interpolations of each variable. We then use a self-organizing map, a specialized form of machine learning, to define 19 regions with unique δ18Osw-salinity relationships in the surface (<50 m) ocean. Inclusion of atmospheric moisture-related variables and oceanic tracer data in additional self-organizing map experiments indicates global surface δ18Osw-salinity spatial patterns are strongly forced by the atmosphere, as the SOM spatial output is highly similar despite no overlapping input data. Our approach is a useful update to the previously delimited regions, and highlights the utility of neural network pattern extraction in spatiotemporally sparse data sets.