Discerning Changes in High‐Frequency Climate Variability Using Geochemical Populations of Individual Foraminifera
Discerning Changes in High‐Frequency Climate Variability Using Geochemical Populations of Individual Foraminifera
复制标题
DOI：
10.1029/2020pa004065
复制
发表时间：
2021-02
影响因子：
3.5
通讯作者：
R. Glaubke;K. Thirumalai;M. Schmidt;J. Hertzberg
中科院分区：
文献类型：
--
作者：
R. Glaubke;K. Thirumalai;M. Schmidt;J. Hertzberg
个体有孔虫分析（IFA）已被证明是重建高频气候信号幅度的有用工具，例如年周期和厄尔尼诺 - 南方涛动（ENSO）。然而，使用IFA评估过去气候变率的变化因许多因素而变得复杂，这些因素包括地理位置、有孔虫生态学、样本处理方法以及多个叠加的高频气候信号的影响。因此，需要强大的统计工具和严格的不确定性分析，以确保基于IFA对古气候变化解释的可靠性。在此，我们提出了一种新的替代系统模型——称为利用个体有孔虫分析的温度分位数分析（QUANTIFA），它将评估IFA检测灵敏度的方法与处理和解释IFA数据的分析工具相结合，以标准化和简化使用IFA - Mg/Ca测量的重建工作。利用模拟和真实IFA数据进行的模型实验表明，IFA种群所保留的主要信号在很大程度上取决于给定位置和深度的气候变率的年际与年比，并且可能受到有孔虫生产力的季节性偏差的影响。此外，我们的实验还表明，极端分位数可以是过去气候变率变化的可靠指标，通常比分布内部的分位数对气候变化更敏感，并且可用于区分像ENSO这样的年际现象与季节性的变化。总之，QUANTIFA为模拟IFA不确定性和处理IFA数据提供了一个有用的工具，可用于建立过去气候变率的历史。
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.