Thursday 26 November, 4 PM CET
Dr. Niels de Winter
ABSTRACT: Seasonal variability in sea surface temperature plays a fundamental role in climate and projecting seasonal extremes in a warmer future climate forms an integral part of climate mitigation strategies. Since instrumental records do not cover periods with climate states representative of those predicted for the near future, accurate seasonality reconstructions from greenhouse climates in the past are in high demand. Here, we present the first successful monthly sea surface temperature reconstructions from sequentially sampled clumped isotope analyses in fossil shells. We apply a novel data reduction routine that allows clumped isotope measurements on small carbonate aliquots to be combined strategically to reconstruct temperature and seawater oxygen isotope composition in different seasons.
We test our novel approach against a range of 32 virtual and natural carbonate isotope datasets containing variability in calcification temperature, water composition and growth rate of the archive to identify the sources of error on seasonality reconstructions. Next, we apply our novel sample size optimization protocol on well-preserved fossil bivalve shells from the European higher mid-latitudes during the Late Cretaceous greenhouse (~78 Ma). The results shed light on latitudinal trends in paleotemperature and seasonality and highlight significant biases in previous reconstructions resulting from assumptions about seawater oxygen isotope composition and seasonal variability. Our seasonality reconstructions agree remarkably well with projections from a fully coupled climate simulation of the same time period. Both results point towards significantly higher temperatures and seasonal ranges in the Late Cretaceous high latitudes compared to previous reconstructions. Our results highlight the advantage of high-resolution paleoclimate records for eliminating reconstruction bias and improving data-model comparison. They set a precedent for more accurate reconstructions of seasonality in greenhouse climates and help improve models for future climate projections.