A quantitative analysis of short-term 18O variability with a Rayleigh-type isotope circulation model
A quantitative analysis of short-term 18O variability with a Rayleigh-type isotope circulation model
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DOI：
10.1029/2003jd003477
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发表时间：
2003-10
影响因子：
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通讯作者：
K. Yoshimura;T. Oki;N. Ohte;S. Kanae
中科院分区：
文献类型：
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作者：
K. Yoshimura;T. Oki;N. Ohte;S. Kanae
[1]降水中的稳定水同位素(D和18O)具有很大的时空变异性，被广泛用于追踪全球水文循环。过去用来研究降水同位素变异性的两个模型是瑞利型模型和同位素-大气环流模型。然而，降水同位素短期(1-10天)变化的原因尚不清楚。这项研究试图在这样的尺度上定量解释同位素的可变性。发展了一种新的全球尺度水同位素环流模式，该模式包括瑞利方程和外部气象强迫的使用。新模型考虑了早期瑞利模型中忽略的水团和同位素的输运和混合过程。利用全球能量和水循环实验(Gewex)和亚洲季风实验(GAME)再分析的资料，对1998年的18O进行了模拟。全球降水同位素网(GNIP)月观测值的相关系数R=0.76，显着水平为99%，泰国三个站点的逐日观测的相关性和显著性相似，验证了上述结果。对结果的定量分析表明，在引起同位素变化的三个因素中，水汽通量的贡献最大，在清迈占37%，全球占46%。这突出了具有不同同位素浓度的气团的输送和混合的重要性。还对每个变量所需的时间和空间分辨率进行了敏感性分析，并将该模型应用于另外两个数据集。更准确的全球降水气候学项目(GPCP)降水数据集在泰国的所有三个观测点产生了更好的模型结果。国家环境预测中心/国家大气研究再分析中心允许模拟覆盖两年，再现合理的年际同位素变化。
[1] Stable water isotopes (D and 18O) in precipitation have large spatial and temporal variability and are used widely to trace the global hydrologic cycle. The two models that have been used in the past to examine the variability of precipitation isotopes are Rayleigh-type models and isotope-atmospheric general circulation models. The causes of short-term (1–10 day) variability in precipitation isotopes, however, remain unclear. This study seeks to explain isotope variability quantitatively at such scale. A new water isotope circulation model on a global scale that includes a Rayleigh equation and the use of external meteorological forcings is developed. Transport and mixing processes of water masses and isotopes that have been neglected in earlier Rayleigh models are included in the new model. A simulation of 18O for 1998 is forced with data from the Global Energy and Water Cycle Experiment (GEWEX) Asian Monsoon Experiments (GAME) reanalysis. The results are validated by Global Network of Isotopes in Precipitation (GNIP) monthly observations with correlation R = 0.76 and a significance level >99% and by daily observations at three sites in Thailand with similar correlation and significance. A quantitative analysis of the results shows that among three factors that cause isotopic variability, the contribution of moisture flux is the largest, accounting for 37% at Chiangmai, and 46% globally. This highlights the importance of transport and mixing of air masses with different isotopic concentrations. A sensitivity analysis of the temporal and spatial resolution required for each variable is also made, and the model is applied to two additional data sets. The more accurate Global Precipitation Climatology Project (GPCP) precipitation data set yields improved model results at all three observation sites in Thailand. The National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis allows the simulation to cover 2 years, reproducing reasonable interannual isotopic variability.