Optimizing sampling strategies in high-resolution paleoclimate records

Abstract. The aim of paleoclimate studies to resolve climate variability from noisy proxy records can in essence be reduced to a statistical problem. The challenge is to isolate meaningful information on climate events from these records by reducing measurement uncertainty through a combination of proxy data while retaining the temporal resolution needed to assess the timing and duration of the event. In this study, we explore the limits of this compromise by testing different methods for combining proxy data (smoothing, binning and sample size optimization) on a particularly challenging paleoclimate problem: resolving seasonal variability in stable isotope records. We test and evaluate the effects of changes in the seasonal temperature and hydrology cycle as well as changes in accretion rate of the archive and parameters such as sampling resolution and age model uncertainty on the reliability of seasonality reconstructions based on clumped and oxygen isotope analyses in 33 real and virtual datasets. Our results show that strategic combinations of clumped isotope analyses can significantly improve the accuracy of seasonality reconstructions if compared with conventional stable oxygen isotope analyses, especially in settings where the isotopic composition of the water is poorly constrained. Smoothing data using a moving average often leads to a dampening of the seasonal cycle, significantly reducing the accuracy of reconstructions. A statistical sample size optimization protocol yields more precise results than smoothing. However, the most accurate results are obtained through monthly binning of proxy data, especially in cases where growth rate or water composition cycles dampen the seasonal temperature cycle. Our analysis of a wide range of natural situations reveals that the effect of temperature seasonality on isotope records almost invariably exceeds that of changes in water composition. Thus, in most cases, isotope records allow reliable identification of growth seasonality as a basis for age modelling and seasonality reconstructions in absence of independent chronological markers in the record. These specific findings allow us to formulate general recommendations for sampling and combining data in paleoclimate research and have implications beyond the reconstruction of seasonality. We discuss the implications of our results for solving common problems in paleoclimatology and stratigraphy, including cyclostratigraphy, strontium isotope dating and event stratigraphy.