A Dynamical Reconstruction of the Global Monthly Mean Oxygen Isotopic Composition of Seawater

Abstract

AbstractWe present a dynamically consistent gridded data set of the global, monthly mean oxygen isotope ratio of seawater ( ). The data set was created from an optimized simulation of an ocean general circulation model constrained by global monthly data collected from 1950 to 2011 and climatological salinity and temperature data collected from 1951 to 1980. The optimization was obtained using the adjoint method for variational data assimilation, which yields a simulation that is consistent with the observational data and the physical laws embedded in the model. Our data set performs equally well as a previous data set in terms of model‐data misfit but brings an improvement in terms of the seasonal cycle and physical consistency. As a result the data set does not show any sharp transitions between water masses or in areas where the data coverage is low. The data assimilation method shows high potential for interpolating sparse data sets in a physically meaningful way. Comparatively big errors, however, are found in our data set in the surface levels in the Arctic Ocean mainly because the influence of isotopically highly depleted precipitation is not preserved in the sea ice model, and the low model resolution of about 285 km horizontally. The data set is publicly available, and it is anticipated to be useful for a large range of applications in (paleo‐) oceanographic studies.