δ¹⁸O water isotope in the iLOVECLIM model (version 1.0) – Part 3: A palaeo-perspective based on present-day data–model comparison for oxygen stable isotopes in carbonates

Abstract. Oxygen stable isotopes (δ¹⁸O) are among the most useful tools in palaeoclimatology/palaeoceanography. Simulation of oxygen stable isotopes allows testing how the past variability of these isotopes in water can be interpreted. By modelling the proxy directly in the model, the results can also be directly compared with the data. Water isotopes have been implemented in the global three-dimensional model of intermediate complexity iLOVECLIM, allowing fully coupled atmosphere–ocean simulations. In this study, we present the validation of the model results for present-day climate against the global database for oxygen stable isotopes in carbonates. The limitation of the model together with the processes operating in the natural environment reveal the complexity of use the continental calcite-δ¹⁸O signal of speleothems for a global quantitative data–model comparison exercise. On the contrary, the reconstructed surface ocean calcite-δ¹⁸O signal in iLOVECLIM does show a very good agreement with the late Holocene database (foraminifers) at the global and regional scales. Our results indicate that temperature and the isotopic composition of the seawater are the main control on the fossil-δ¹⁸O signal recorded in foraminifer shells when all species are grouped together. Depth habitat, seasonality and other ecological effects play a more significant role when individual species are considered. We argue that a data–model comparison for surface ocean calcite δ¹⁸O in past climates, such as the Last Glacial Maximum (≈ 21 000 yr), could constitute an interesting tool for mapping the potential shifts of the frontal systems and circulation changes throughout time. Similarly, the potential changes in intermediate oceanic circulation systems in the past could be documented by a data (benthic foraminifers)-model comparison exercise whereas future investigations are necessary in order to quantitatively compare the results with data for the deep ocean.