末次冰期-间冰期转换期间黄土高原亚洲季风强度和降水的解耦。

Decoupled Asian monsoon intensity and precipitation during glacial-interglacial transitions on the Chinese Loess Plateau.

机构信息

College of Urban and Environmental Sciences and MOE Laboratory for Earth Surface Processes, Peking University, Beijing, China.

Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, 430074, Wuhan, China.

出版信息

Nat Commun. 2022 Sep 14;13(1):5397. doi: 10.1038/s41467-022-33105-2.

DOI:10.1038/s41467-022-33105-2

PMID:36104341

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9474459/

Abstract

The discrepancies among the variations in global ice volume, cave stalagmite δO and rainfall reconstructed by cosmogenic Be tremendously restrain our understanding of the evolution of the East Asian summer monsoon (EASM). Here, we present a 430-ka EASM mean annual precipitation record on the Chinese Loess Plateau obtained using branched glycerol dialkyl glycerol tetraethers based on a deep learning neural network; this rainfall record corresponds well with cave-derived δO data from southern China but differs from precipitation reconstructed by Be. Both branched tetraether membrane lipids and cave δO may be affected by soil moisture and atmospheric temperature when glacial and interglacial conditions alternated and were thus decoupled from atmospheric precipitation; instead, they represent variations in the intensity of the EASM. Furthermore, we demonstrate that the brGDGT-DLNN method can significantly extend the temporal scale record of the EASM and is not restricted by geographic location compared with stalagmite records.

摘要

全球冰量变化、洞穴石笋δO 和宇宙成因 Be 重建的降雨量之间的差异极大地限制了我们对东亚夏季风（EASM）演化的理解。在这里，我们使用基于深度学习神经网络的分支甘油二烷基甘油四醚（brGDGT-DLNN）方法，提供了中国黄土高原上的 43 万年 EASM 年平均降水记录；该降水记录与来自中国南方的洞穴衍生δO 数据非常吻合，但与 Be 重建的降水记录不同。在冰期和间冰期交替时，分支四醚膜脂和洞穴δO 可能同时受到土壤湿度和大气温度的影响，因此与大气降水脱钩；相反，它们代表了 EASM 强度的变化。此外，我们证明 brGDGT-DLNN 方法可以显著扩展 EASM 的时间尺度记录，并且与石笋记录相比，不受地理位置的限制。