A Tracing Study on Influence Factors of East Asian Stable Isotopes in Atmospheric Water Vapor
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摘要: 大气水汽稳定同位素是现代水循环的重要示踪剂，可以有效地追踪水汽来源及其输送过程。在中低纬度季风区，局地“降水量效应”是大气水汽稳定同位素的主要特征，但是近期研究表明，水汽来源及其输送过程等非局地因素也有重要影响。因此，本文基于拉格朗日粒子扩散模式和卫星遥感观测的大气水汽稳定氘同位素数据（数值表示为千分差，δD），针对前人研究较少的中国东部石笋氧同位素区域，进行水汽源地追踪，并在季节和年际尺度上分析水汽δD的主要影响因素。结果表明，在季节尺度上，水汽δD在夏末秋初较低，冬春季较高，这种特征与局地气象因子、水汽源地贡献的关系较弱，水汽输送路径上的累积降水是影响水汽δD季节变化的主要因素，两者为显著的负相关关系。在年际尺度上，厄尔尼诺（El Niño）年夏季中国东部水汽δD较高，拉尼娜（La Niña）年夏季水汽δD较低。水汽源地贡献在ENSO（厄尔尼诺—南方涛动）不同位相的变化较小，而水汽输送路径上的累积降水在La Niña年较之El Niño年偏多，表明La Niña年热带对流活动和水汽输送过程的贫化作用更强，导致目标区域的水汽δD更低。因此，代表热带对流活动的累积降水是水汽δD季节和年际变化的主要影响因素，热带对流活动增强（减弱）将降低（增加）目标区域的水汽δD。Abstract: Stable isotopes in atmospheric water vapor, which can track moisture sources and water vapor transport, are extensively used as a crucial tracer of the present-day water cycle. To interpret water vapor stable isotopes in the mid-low latitude monsoon region, the “amount effect” is invoked. However, recent studies have demonstrated that nonlocal factors, such as moisture sources and water vapor transport, have a significant effect on stable isotopes. Thus, the Lagrangian Particle Dispersion Model and Satellite remote sensing deuterium isotope data (expressed by parts per thousand of their deviation, δD) in water vapor are used to investigate the primary factors affecting water vapor δD in the region with abundant Chinese stalagmite δ18O records. On the seasonal scale, water vapor δD is more depleted in late summer and early autumn and enriched in winter and spring. This characteristic is difficult to interpret in terms of “temperature effect” or “amount effect.” However, accumulated rainfall over water vapor transport paths is the dominant factor of water vapor δD, and there is a significant negative correlation between them. On an interannual scale, water vapor δD is enhanced in the summer of the El Niño year and depleted in the summer of La Niña year. The contribution of moisture sources to water vapor δD is small; however, the accumulated rainfall over water vapor transport paths increased substantially in the La Niña year compared with the El Niño year. This shows that in the La Niña year, tropical convection and depletion in water vapor transport paths are significant, resulting in depleted water vapor δD in the study area. Finally, on a seasonal to interannual scale, upstream convection, as measured by accumulated rainfall, is the primary driver of water vapor δD variations. In the study area, enhanced convection will deplete δD, whereas the weakened convection will enrich δD.
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图 1 （a）1953～2015年的全球大气降水同位素检测网（GNIP）GNIP站点降水氘同位素数据（数值表示为千分差，δD）和（b）2004～2011年卫星对流层放射光谱仪（TES）观测水汽δD年平均空间分布
Figure 1. Spatial pattern of annual mean (a) deuterium isotope data (expressed by parts per thousand of their deviation, δD) in precipitation for GNIP (Globe Network of Isotopes Precipitation) stations for the period of 1953−2015 and (b) δD in water vapor for TES (Tropospheric Emission Spectrometer) observation for the period of 2004−2011
图 2 （a）2004～2011年的中国TES水汽δD年平均空间分布（黑色三角表示我国石笋位置）；（b）2004～2011年的夏季整层积分（1000～300 hPa）的水汽通量散度（填色，单位：10−5 kg m−2 s−1）和水汽通量（箭头，单位：300 kg m−1 s−1）分布。黑色方框为研究区域（24°N～34°N，102°E～120°E）
Figure 2. (a) Spatial pattern of annual mean δD in water vapor for TES observations in China for the period of 2004−2011. The triangles indicate Chinese stalagmite δ18O records. (b) Vertically integrated moisture flux divergence (shaded, units: 10−5 kg m−2 s−1) and moisture flux (vectors, units: 300 kg m−1 s−1) distributions from 1000 hPa to 300 hPa in summer for the period of 2004−2011. The black boxes indicate the study area (24°N–34°N, 102°E–120°E)
图 3 2004～2011年，研究区域各月聚类平均的空气块的轨迹（聚类数：200）。填色是空气块的比湿（单位：g kg−1），星号为空气块的起始位置
Figure 3. Monthly cluster mean trajectories (number: 200) of air particles reaching the study area for the period of 2004−2011. Specific humidity (units: g kg−1) of air particles is indicated by colors. The asterisks are the initial positions of air particles
图 4 2004～2011年，研究区域空气块各月E−P（单位：kg）空间分布，E−P>0表示水汽源地，E−P<0表示水汽汇
Figure 4. Monthly E−P (units: kg) of air particles reaching the study area for the period of 2004−2011. E−P > 0 represent moisture sources and E−P < 0 represents moisture sink
图 5 （a）2004～2011年，研究区域TES水汽δD（黑色实线）、温度（红色虚线，单位：°C）、降水量（蓝色虚线，单位：mm d−1）和上游区域（10°N～20°N，60°E～180°）OLR（棕色虚线，单位：W m−2）的季节变化；（b）水汽源地划分：局地、欧亚大陆、孟加拉湾、南海、西太平洋、阿拉伯海、北印度洋、非洲和北大西洋
Figure 5. (a) Seasonal patterns in long-term averaged monthly δD (black solid line) in water vapor for TES observation, temperature (red dotted line, units: °C), precipitation (blue dotted line, units: mm d−1) in the study area, and OLR (brown dotted line, units: W m−2) in the upstream region (10°N–20°N，60°E–180°E). (b) Moisture sources, include Local, Eurasia, Bay of Bengal, South China Sea, West Pacific Ocean, Arabian Sea, North Indian Ocean, Africa, and North Atlantic
图 6 2004～2011年，（a）水汽源地贡献和（b）水汽输送路径上的累积降水量（单位：1016 kg）的季节变化。Local、EA、BOB、SCS、WP、AS、NIO、AF、NAO和Other分别表示目标区域、东亚、孟加拉湾、南海、西太平洋、阿拉伯海、北印度洋、非洲、北大西洋和其他区域
Figure 6. Seasonal patterns of (a) contribution of moisture sources and accumulated rainfall (units: 1016 kg) over water vapor transport paths. Local, EA, BOB, SCS, WP, AS, NIO, AF. NAO and Other represent study area, East Asia, Bay of Bengal, South China Sea, Western Pacific, Arabian Sea, Northern India Ocean, Africa, North Atlantic Ocean and other areas
图 7 （a、c）El Niño年和（b、d）La Niña年夏季（6月到9月，简称JJAS）（a、b）水汽δD异常（填色）叠加整层积分水汽通量异常（箭头，单位：300 kg m−1 s−1）和（c、d）降水量异常（填色，单位：mm d−1）叠加850 hPa风场异常（箭头，单位：m s−1）分布
Figure 7. (a, b) Distributions of δD anomalies (shaded) and vertically integrated moisture flux anomalies (vectors, units: 300 kg m−1 s−1), (c, d) distributions of precipitation anomalies (shaded, units: mm d−1) and 850 hPa wind anomalies (vectors, units: m s−1) in JJAS (June–July–August–September) of (a, c) El Niño year and (b, d) La Niña year
图 8 El Niño年和La Niña年（a）水汽源地贡献和（b）水汽源地累积降水量（单位：1016 kg）；（c）El Niño年和（d）La Niña年JJAS向外长波辐射异常（单位：W m−2）分布
Figure 8. (a) Contribution of moisture sources and (b) accumulated rainfall over water vapor transport paths (units: 1016 kg) in El Niño year and La Niña year). JJAS OLR (Outgoing Longwave Radiation) anomalies (units: W m−2) in (c) El Niño year and (d) La Niña year
表 1 研究区域水汽
$\delta {\rm{D}} $ 、水汽源地贡献和过程累积降水逐月异常数据的相关系数，加粗为通过95%信度的显著性检验Table 1. Correlation coefficients between monthly
$\delta {\rm{D}} $ anomalies in water vapor, contribution of moisture source anomalies, and accumulated rainfall anomalies. Boldface values indicate correlation coefficients exceeding the 0.05 significance level水汽δD、水汽源地贡献和累积降水逐月异常数据的
相关系数Local EA BOB SCS WP AS 水汽源地贡献 0.12 0.04 0.58 0.02 −0.35 0.05 过程累积降水 −0.53 −0.25 −0.36 -0.55 −0.48 0.13 
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