Optimizing sampling strategies in high-resolution paleoclimate records

The aim of paleoclimate studies resolving climate variability from noisy proxy records can in essence be reduced to a statistical problem. The challenge is to extract meaningful information about climate variability from these records by reducing measurement uncertainty through combining measurements for proxies while retaining the temporal resolution needed to assess the timing and duration of variations in climate parameters. 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 the hydrological cycle as well as changes in the accretion rate of the archive and parameters such as sampling resolution and age model uncertainty in 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 compared to conventional stable oxygen isotope analyses, especially in settings in which the isotopic composition of the water is poorly constrained. Smoothing data using a moving average often leads to an apparent 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 in which growth rate or water composition cycles obscure the seasonal temperature cycle. Our analysis of a wide range of natural situations reveals that the effect of temperature seasonality on oxygen isotope records almost invariably exceeds that of changes in water composition. Thus, in most cases, oxygen isotope records allow reliable identification of growth seasonality as a basis for age modeling in the 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 briefly discuss the implications of our results for solving common problems in paleoclimatology and stratigraphy.</p

Cenozoic evolution of deep ocean temperature from clumped isotope thermometry

Characterizing past climate states is crucial for understanding the future consequences of ongoing greenhouse gas emissions. Here, we revisit the benchmark time series for deep ocean temperature across the past 65 million years using clumped isotope thermometry. Our temperature estimates from the deep Atlantic Ocean are overall much warmer compared with oxygen isotope–based reconstructions, highlighting the likely influence of changes in deep ocean pH and/or seawater oxygen isotope composition on classical oxygen isotope records of the Cenozoic. In addition, our data reveal previously unrecognized large swings in deep ocean temperature during early Eocene acute greenhouse warmth. Our results call for a reassessment of the Cenozoic history of ocean temperatures to achieve a more accurate understanding of the nature of climatic responses to tectonic events and variable greenhouse forcing

Cenozoic evolution of deep ocean temperature from clumped isotope thermometry

Characterizing past climate states is crucial for understanding the future consequences of ongoing greenhouse gas emissions. Here, we revisit the benchmark time series for deep ocean temperature across the past 65 million years using clumped isotope thermometry. Our temperature estimates from the deep Atlantic Ocean are overall much warmer compared with oxygen isotope–based reconstructions, highlighting the likely influence of changes in deep ocean pH and/or seawater oxygen isotope composition on classical oxygen isotope records of the Cenozoic. In addition, our data reveal previously unrecognized large swings in deep ocean temperature during early Eocene acute greenhouse warmth. Our results call for a reassessment of the Cenozoic history of ocean temperatures to achieve a more accurate understanding of the nature of climatic responses to tectonic events and variable greenhouse forcing

Cenozoic evolution of deep ocean temperature from clumped isotope thermometry

Characterizing past climate states is crucial for understanding the future consequences of ongoing greenhouse gas emissions. Here, we revisit the benchmark time series for deep ocean temperature across the past 65 million years using clumped isotope thermometry. Our temperature estimates from the deep Atlantic Ocean are overall much warmer compared with oxygen isotope–based reconstructions, highlighting the likely influence of changes in deep ocean pH and/or seawater oxygen isotope composition on classical oxygen isotope records of the Cenozoic. In addition, our data reveal previously unrecognized large swings in deep ocean temperature during early Eocene acute greenhouse warmth. Our results call for a reassessment of the Cenozoic history of ocean temperatures to achieve a more accurate understanding of the nature of climatic responses to tectonic events and variable greenhouse forcing

Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion that relies on the recognition of patterns and the clinical context for detection of specific diseases. In this study, we aimed to explore the accuracy and interrater variability of pulmonologists when interpreting PFTs compared with artificial intelligence (AI)-based software that was developed and validated in more than 1500 historical patient cases. 120 pulmonologists from 16 European hospitals evaluated 50 cases with PFT and clinical information, resulting in 6000 independent interpretations. The AI software examined the same data. American Thoracic Society/European Respiratory Society guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests. The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4 +/- 5.9% of the cases (range 56-88%). The interrater variability of kappa=0.67 pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6 +/- 8.7% of the cases (range 24-62%) with a large interrater variability (kappa=0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p The interpretation of PFTs by pulmonologists leads to marked variations and errors. AI-based software provides more accurate interpretations and may serve as a powerful decision support tool to improve clinical practice