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Abstract Tropical Pacific seawater and precipitation stable oxygen isotope data aid in understanding modern oceanic and atmospheric interactions, and these data are particularly valuable as they are archived in isotope‐based paleoclimate records. However, the absence of modern seawater isotope time series limits the ability to identify the atmospheric influences on these data, precluding robust paleoclimate interpretations. We present a new 10 year sub‐monthly record of seawater and precipitation stable oxygen isotope values (δ¹⁸O_swand δ¹⁸O_p) from Koror, Palau. Our dataset indicates that temporally, δ¹⁸O_swis strongly influenced by local δ¹⁸O_p.Both monthly δ¹⁸O_swand δ¹⁸O_pare highly correlated with outgoing longwave radiation across the tropical Pacific, reflecting a Walker Circulation imprint on the surface ocean. Changes in the Palau δ¹⁸O_sw—salinity relationship correspond to NINO3.4 variability, indicating a difference in how these variables record El Niño Southern Oscillation (ENSO) information, but demonstrating the utility of δ¹⁸O_swto reconstruct ENSO variability in the western tropical Pacific. 

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 (δ¹⁸O_sw) and salinity in the surface ocean. An increasing number of δ¹⁸O_swobservations 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 δ¹⁸O_swand 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 δ¹⁸O_sw‐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 δ¹⁸O_sw‐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.