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Title: Machine Learning Solutions to Regional Surface Ocean δ 18 O‐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 δ18Oswobservations 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 δ18Oswand 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.  more » « less
Award ID(s):
1847791
PAR ID:
10501860
Author(s) / Creator(s):
; ;
Publisher / Repository:
AGU
Date Published:
Journal Name:
Paleoceanography and Paleoclimatology
Volume:
38
Issue:
9
ISSN:
2572-4517
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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