Individual foraminiferal analysis (IFA) has proven to be a useful tool in reconstructing the amplitude of high-frequency climate signals such as the annual cycle and the El Niño-Southern Oscillation (ENSO). However, using IFA to evaluate past changes in climate variability is complicated by many factors including geographic location, foraminiferal ecology, methods of sample processing, and the influence of multiple, superimposed high-frequency climate signals. Robust statistical tools and rigorous uncertainty analysis are therefore required to ensure the reliability of IFA-based interpretations of paleoclimatic change. Here, we present a new proxy system model—called the Quantile Analysis of Temperature using Individual Foraminiferal Analyses (QUANTIFA)—that combines methods for assessing IFA detection sensitivity with analytical tools for processing and interpreting IFA data to standardize and streamline reconstructions employing IFA-Mg/Ca measurements. Model exercises with simulated and real IFA data demonstrate that the dominant signal retained by IFA populations is largely determined by the annual-to-interannual ratio of climate variability at a given location and depth and can be impacted by seasonal biases in foraminiferal productivity. In addition, our exercises reveal that extreme quantiles can be reliable indicators of past changes in climate variability, are often more sensitive to climate change than quantiles within the distributional interior, and can be used to distinguish changes in interannual phenomena like ENSO from seasonality. Altogether, QUANTIFA provides a useful tool for modeling IFA uncertainties and processing IFA data that can be leveraged to establish a history of past climate variability.
ASJC Scopus subject areas
- Atmospheric Science