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Feedback From Vegetation to Interannual Variations of Indian Summer Monsoon Rainfall

机译:从反馈到植被到续年变化的印度夏季季风降雨

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摘要

Interannual variations of Indian summer monsoon rainfall (ISMR) are modulated by external forcings such as El Nino Southern Oscillation, Indian Ocean Dipole, and the Atlantic Nino. Vegetation over land responds to variations in ISMR, but the feedback from vegetation to ISMR variability has not been fully explored yet. To address this gap, we perform two simulations with the regional Weather Research and Forecasting model coupled to the Community Land Surface Model (WRF-CLM) for the period of 2004-2018. We use the same boundary forcing from ERA-interim reanalysis for the two experiments, but with two different vegetation prescriptions, (1) observed, inter-annually varying Leaf Area Index (LAI), obtained from satellite images/data (VAR-LAI); and (2) climatological Leaf Area Index from the same product, to suppress interannual LAI variations (CLIM-LAI). We find that the correlation coefficient of simulated total seasonal rainfall with the observed data is higher for VAR-LAI simulation as compared to CLIM-LAI. To elicit causality among eco-hydro-climatological variables, we develop a network based on information theory, i.e., a process network. We find that LAI plays a major role in influencing precipitation in the network through evapotranspiration. The number of links originating from LAI and evapotranspiration increases during drought years, making the eco-hydro-climatological network denser. Our findings indicate that the ISMR predictions and projections need to represent the time-varying LAI to fully capture the varying feedbacks from evolving vegetation to the atmosphere especially during drought years.
机译:印度夏季季风降雨(ISMR)的续集变体由El Nino Southern振荡,印度洋偶极和大西洋Nino等外部强制调制。植被在土地上应对ismr的变化,但植被到ismr变异的反馈尚未完全探索。为了解决这一差距,我们与2004 - 2018年期间的区域天气研究和预测模型进行了两种模拟,耦合到社区陆地面模型(WRF-CLM)。我们使用相同的边界强迫从时期的两次实验中的ERA-临时再分析,但是从卫星图像/数据(VAR-LAI)中获得了两种不同的植被处方,(1),每年的每年间变化的叶子区域指数(LAI) ; (2)来自同一产品的气候叶面积指数,抑制依际莱变型(攀爬)。我们发现,与观察到的数据相比,与升降液相比,VAR-LAI模拟的模拟总季节降雨的相关系数更高。引发生态水力 - 气候变量中的因果关系,我们基于信息理论,即流程网络开发网络。我们发现Lai在通过蒸散蒸腾来影响网络中的降水作用。源于赖莱和蒸散蒸腾的链接数量在干旱期间增加,使得生态水力气候网络密集。我们的调查结果表明,ISMR预测和预测需要代表时变赖,以完全捕捉不同的反馈,特别是在干旱期间在大气中从不断发展植被。

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  • 来源
    《Oceanographic Literature Review》 |2021年第7期|1447-1447|共1页
  • 作者单位

    Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai India;

    Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai India;

    Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai India;

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