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.

中文翻译：

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利用单个有孔虫的地球化学种群识别高频气候变异

事实证明，单个有孔虫分析（IFA）是重建高频气候信号振幅的有用工具，例如年周期和厄尔尼诺-南方涛动（ENSO）。但是，使用IFA评估过去气候变化的变化会受到许多因素的影响，其中包括地理位置，有孔虫生态，样品处理方法以及多个叠加的高频气候信号的影响。因此，需要可靠的统计工具和严格的不确定性分析来确保基于IFA的古气候变化解释的可靠性。这里，我们提出了一种新的代理系统模型，即使用有孔虫分析（QUANTIFA）的温度分位数分析方法，该模型将评估IFA检测灵敏度的方法与用于处理和解释IFA数据的分析工具相结合，以标准化和简化使用IFA-Mg / Ca的重建测量。使用模拟和实际IFA数据进行的模型演算表明，IFA种群保留的主导信号很大程度上取决于给定位置和深度的年际气候变异率年际比率，并且可能受到有孔虫生产力的季节性偏差的影响。此外，我们的练习还表明，极端分位数可以作为过去气候变化的可靠指标，通常比分布内部的分位数对气候变化更为敏感，并且可以用于区分ENSO等年际现象的变化与季节变化。总之，QUANTIFA提供了一个有用的工具，可用于为IFA不确定性建模和处理IFA数据，这些数据可用于建立过去气候变化的历史。