Stable oxygen isotopic ratios in corals (δ18Ocoral) are commonly utilized to reconstruct climate variability beyond the limit of instrumental observations. These measurements provide constraints on past seawater temperature, due to the thermodynamics of isotopic fractionation, but also past salinity, as both salinity and seawater δ18O (δ18Osw) are similarly affected by precipitation/evaporation, advection, and other processes. We use historical observations, isotope‐enabled model simulations, and the PAGES Iso2k database to assess the potential of δ18Ocoralto provide information on past salinity. Using ‘‘pseudocorals’’ to represent δ18Ocoralas a function of observed or simulated temperature and salinity/δ18Osw, we find that δ18Oswcontributes up to 89% of δ18Ocoralvariability in the Western Pacific Warm Pool. Although uncertainty in the δ18Osw‐salinity relationship influences the inferred salinity variability, corals from these sites could provide valuable δ18Oswreconstructions. Coordinated in situ monitoring of salinity and δ18Oswis vital for improving estimates of hydroclimatic change.
- NSF-PAR ID:
- 10035700
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Paleoceanography
- Volume:
- 32
- Issue:
- 5
- ISSN:
- 0883-8305
- Page Range / eLocation ID:
- 484 to 497
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.
