New tools for the reconstruction of Pleistocene Antarctic sea ice
New tools for the reconstruction of Pleistocene Antarctic sea ice
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重建更新世南极海冰的新工具
DOI：
10.1016/j.palaeo.2014.01.019
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
R. Gersonde
中科院分区：
文献类型：
--
作者：
O. Esper;R. Gersonde
通过对南大洋太平洋、大西洋和西印度洋274个表层沉积物样品中保存的硅藻组合的定量分析，定义了一个新的参考数据库，用于利用传递函数技术定量估算中-晚更新世南极海冰场。硅藻数据集的非趋势化正则分析(DCA)表明硅藻组合呈单峰分布。典型对应分析(CCA)表明，不仅冬季海冰(WSI)和夏季海温(Ssst)是控制物种空间分布的最显著环境变量。为了检验传递函数在海冰浓度和发生概率方面的适用性，我们使用了四种不同的方法，分别是Imbrie和Kipp方法(IKM)、现代模拟技术(MAT)、加权平均(WA)和加权平均偏最小二乘(WAPLS)，并以对数变换的硅藻数据和卫星(1981-2010年)的海冰数据为参考。对海冰浓度和夏季海冰浓度的预测均方根误差(RMSEP)分别为7.27%和11.4%，使用包含28个硅藻分类群/分类群的172个样本子集、二次回归和三因素模型(IKM-D172/28/3Q)获得了最佳的预测结果。利用274个样本/28个类群参考数据集(MAT-D274/28/4AN，-6AN)，用不同数量的类似物(4，6)计算MAT估计，WSI的RMSEP从5.52%(4AN)到5.91%(6AN)，SSI的RMSEP从8.93%(4AN)到9.05%(6AN)。与MAT相比，WA和WAPLS在D274数据集上的表现较差，WSI浓度RMSEP在WA和WAPLS分别达到9.91%和11.29%，建议使用IKM和MAT。对表层沉积物数据的应用表明，IKM和MAT与卫星衍生的冬季和夏季海冰场有很强的关系。在大西洋和太平洋南大洋沉积物岩芯上进行的海冰重建都记录了过去150,000年来海冰的多变性(信息系统1-信息系统6)，结果是冰川/间冰期趋势与基于冰盖的冰川/间冰期趋势相似。然而，与MAT相比，平均而言，与MAT相比，IKM估计显示出较小的WSI，在较低变异性下SSI浓度和概率略高。这种模式是不同估计技术的结果，通过应用IKM和选择特定的单一样本，将WSI和SSI信号整合到一个单因素组合中，从而通过MAT保持与原始硅藻数据库的接近和包含的可变性。与WSI的估计相比，对过去SSI变异性的重建仍然较弱。结合基于硅藻的估计，生物源蛋白石的丰度和通量模式是WSI和SSI范围的另一个指示。
Based on the quantitative analysis of diatom assemblages preserved in 274 surface sediment samples recovered in the Pacific, Atlantic and western Indian sectors of the Southern Ocean we have defined a new reference database for quantitative estimation of late-middle Pleistocene Antarctic sea ice fields using the transfer function technique. The Detrended Canonical Analysis (DCA) of the diatom data set points to a unimodal distribution of the diatom assemblages. Canonical Correspondence Analysis (CCA) indicates that not only winter sea ice (WSI) but also summer sea surface temperature (SSST) represent the most prominent environmental variables that control the spatial species distribution. To test the applicability of transfer functions for sea ice reconstruction in terms of concentration and occurrence probability we applied four different methods, the Imbrie and Kipp Method (IKM), the Modern Analog Technique (MAT), Weighted Averaging (WA), and Weighted Averaging Partial Least Squares (WAPLS), using logarithm-transformed diatom data and satellite-derived (1981–2010) sea ice data as a reference. The best performance for IKM results was obtained using a subset of 172 samples with 28 diatom taxa/taxa groups, quadratic regression and a three-factor model (IKM-D172/28/3q) resulting in root mean square errors of prediction (RMSEP) of 7.27% and 11.4% for WSI and summer sea ice (SSI) concentration, respectively. MAT estimates were calculated with different numbers of analogs (4, 6) using a 274-sample/28-taxa reference data set (MAT-D274/28/4an, -6an) resulting in RMSEP ranging from 5.52% (4an) to 5.91% (6an) for WSI as well as 8.93% (4an) to 9.05% (6an) for SSI. WA and WAPLS performed less well with the D274 data set, compared to MAT, achieving WSI concentration RMSEP of 9.91% with WA and 11.29% with WAPLS, recommending the use of IKM and MAT. The application of IKM and MAT to surface sediment data revealed strong relations to the satellite-derived winter and summer sea ice field. Sea ice reconstructions performed on an Atlantic- and a Pacific Southern Ocean sediment core, both documenting sea ice variability over the past 150,000 yr (MIS 1–MIS 6), resulted in similar glacial/interglacial trends of IKM and MAT-based sea-ice estimates. On the average, however, IKM estimates display smaller WSI and slightly higher SSI concentration and probability at lower variability in comparison with MAT. This pattern is a result of different estimation techniques with integration of WSI and SSI signals in one single factor assemblage by applying IKM and selecting specific single samples, thus keeping close to the original diatom database and included variability, by MAT. In contrast to the estimation of WSI, reconstructions of past SSI variability remain weaker. Combined with diatom-based estimates, the abundance and flux pattern of biogenic opal represents an additional indication for the WSI and SSI extent.
影响因子：
4
作者：
Gersonde, R;Crosta, X;Armand, L
通讯作者：
Armand, L