Spatial and temporal variability in the stable isotope systematics of modern precipitation in China: implications for paleoclimate reconstructions
Spatial and temporal variability in the stable isotope systematics of modern precipitation in China: implications for paleoclimate reconstructions
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DOI：
10.1016/s0012-821x(04)00036-6
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发表时间：
2001-12
影响因子：
5.3
通讯作者：
Kathleen R. Johnson;B.Lynn Ingram
中科院分区：
文献类型：
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作者：
Kathleen R. Johnson;B.Lynn Ingram
中低纬度地区的冰川、树木年轮、湖泊沉积物和洞穴沉积物等物质的稳定同位素组成包含了过去温度（T）和降水量（P）变化的信息。然而，将δ 18 Op与T或P的变化联系起来的传递函数dδ 18 Op/dT和dδ 18 Op/dP在这些区域中可以表现出显著的时间和空间变异性。在受东南亚季风影响的地区，过去降水的δ 18 O和δD的变化归因于季风强度、风暴路径和/或温度变化的变化。要正确解释过去的δ 18 Opvariations，需要理解这些复杂的稳定同位素系统学。由于中国气温和降水正相关，而对δ 18 Op的影响相反，因此有必要确定这些影响中的哪一种对特定地区起主导作用，以便进行更定性的古气候重建。在这里，我们评估现代降水的传递函数的值，以更准确地解释古记录。利用全球降水同位素网络（GNIP）10个站点的数据进行多元回归分析，研究了这些转换函数在中国的强度。δ 18 Opis建模为温度和降水量的函数。在任何给定的地点，转换函数的大小和符号是密切相关的夏季风的影响程度。在夏季风强降水区，δ 18 Ops值更多地依赖于降水量而不是温度，因此在夏季表现出更多的负值。与此相反，在不受夏季风降水影响的站点，δ 18 Ops值显示出δ 18 Ops与温度之间的强烈关系。在夏季风北方边界附近的站点表现出对温度和降水量的依赖性。与简单的线性模型（δ 18 Ops作为T或P的函数）和地理模型（δ 18 Ops作为纬度和高度的函数）的比较表明，多元回归模型在重现受夏季风强烈影响的地点的δ 18 Ops值方面更为成功。传递函数值在空间上变化很大，并且与夏季季风影响程度密切相关，这一事实表明这些值也可能随时间变化。由于东南亚季风强度是众所周知的，表现出很大的变化在一些时间尺度（每年的冰川间冰期），和大小和符号的转换函数是季风强度有关，我们建议，作为季风强度的变化，大小，甚至可能是符号的转换函数可能会有所不同。因此，基于δ 18 Opvariations的定量古气候重建可能是无效的。
The stable isotopic composition of materials such as glacial ice, tree rings, lake sediments, and speleothems from low-to-mid latitudes contains information about past changes in temperature (T) and precipitation amount (P). However, the transfer functions which link δ18Opto changes in T or P, dδ18Op/dT and dδ18Op/dP, can exhibit significant temporal and spatial variability in these regions. In areas affected by the Southeast Asian monsoon, past variations in δ18O and δD of precipitation have been attributed to variations in monsoon intensity, storm tracks, and/or variations in temperature. Proper interpretation of past δ18Opvariations here requires an understanding of these complicated stable isotope systematics. Since temperature and precipitation are positively correlated in China and have opposite effects on δ18Op, it is necessary to determine which of these effects is dominant for a specific region in order to perform even qualitative paleoclimate reconstructions. Here, we evaluate the value of the transfer functions in modern precipitation to more accurately interpret the paleorecord. The strength of these transfer functions in China is investigated using multiple regression analysis of data from 10 sites within the Global Network for Isotopes in Precipitation (GNIP). δ18Opis modeled as a function of both temperature and precipitation. The magnitude and signs of the transfer functions at any given site are closely related to the degree of summer monsoon influence. δ18Opvalues at sites with intense summer monsoon precipitation are more dependent on the amount of precipitation than on temperature, and therefore exhibit more negative values in the summer. In contrast, δ18Opvalues at sites that are unaffected by summer monsoon precipitation exhibit strong relationships between δ18Opand temperature. The sites that are near the northern limit of the summer monsoon exhibit dependence on both temperature and amount of precipitation. Comparison with simple linear models (δ18Opas a function of T or P) and a geographic model (δ18Opas a function of latitude and altitude) shows that the multiple regression model is more successful at reproducing δ18Opvalues at sites that are strongly influenced by the summer monsoon. The fact that the transfer function values are highly spatially variable and closely related to the degree of summer monsoon influence suggests that these values may also vary temporally. Since the Southeast Asian monsoon intensity is known to exhibit large variations on a number of timescales (annual to glacial–interglacial), and the magnitude and sign of the transfer functions is related to monsoon intensity, we suggest that as monsoon intensity changes, the magnitude and possibly even the sign of the transfer functions may vary. Therefore, quantitative paleoclimate reconstructions based on δ18Opvariations may not be valid.