Impacts of Enhanced Cloud Condensation Nuclei (CCN) on the Organization of Convection for Monsoon Depressions

该项目旨在了解气溶胶污染对季风低气压的影响。季风低气压是天气尺度系统（2,000至3,000公里），具有斜压（或倾斜）垂直结构，向西传播，形成于孟加拉湾，在经过任何地方时产生多达150至200毫米的降雨量。它们发生在季风季节，此时纬向风的垂直切变足够强大，足以抑制飓风强度的热带气旋的形成，它们与被称为“季风低压”的较弱但相似的系统不同，后者具有正压（或垂直）垂直结构。首席研究员认为，在印度地区发现的严重气溶胶污染导致季风低气压发生的频率减少。这一说法基于以下几方面的证据：首先，季风性低气压的发生已经大幅减少，从历史上的年平均7到8次减少到2009年、2010年和2011年的1次、0次和2次。其次，近年来印度地区的气溶胶污染急剧增加。第三，全球预测模型可能没有考虑到气溶胶污染的变化，近年来，由于低气压很少，它们对季风低气压的预测过高。第四，气象研究与预报（WRF）模式的初步实验表明，通过将气溶胶负担从相对原始的（每立方米5 × 10^8个颗粒）增加到严重污染的（每立方米5 × 10^10个颗粒）值，可以显著降低季风低气压的强度，以及其中降水的强度和组织。这项研究是基于气溶胶抑制季风低气压的假设，因为它们减缓了降雨的形成，因为更多的气溶胶会导致更多的云凝结核（CCN），从而产生更多的小云滴，这些云滴需要更长的时间才能凝聚成雨滴。季风低气压是大尺度气流的斜压/正压不稳定与低气压内伴随降水的凝结潜热相互作用形成的。这种相互作用可以被过量的气溶胶所抑制，这些气溶胶将降水发展所需的时间增加了大约两倍，从而在水动力不稳定与降水组织和伴随的潜热释放的时间尺度之间产生不匹配。在该奖项下开展的工作，使用更复杂和更真实的模拟，验证了气溶胶污染对季风低气压的抑制假设，并根据卫星数据，在季风低气压形成和不存在的时期，对气溶胶强迫进行了分析。考虑到受季风洼地影响的人口众多，本研究中讨论的季风洼地的抑制既有科学意义，也有社会意义。如果成功，这项工作的结果可以直接应用于用于预测这些天气系统的业务预报系统。此外，该项目还为一名博士后和一名研究生提供支持和培训，从而为该研究领域的下一代科学家提供支持。

This project seeks to understand the effect of aerosol pollution on monsoon depressions. Monsoon depressions are synoptic-scale systems (2,000km to 3,000km) with baroclinic (or tilted) vertical structure which propagate westward, forming in the Bay of Bengal and producing as much as 150 to 200 mm of rainfall during their passage over any location. They occur during the monsoon season, when the vertical shear of the zonal wind is strong enough to inhibit the formation of hurricane-strength tropical cyclones, and they are distinct from weaker but similar systems called "monsoon lows", which have a barotropic (or upright) vertical structure. The Principal Investigator argues that the heavy aerosol pollution found over the Indian sector causes a reduction in the frequency of occurrence of monsoon depressions. This claim is based on several lines of evidence: first, there has been a dramatic reduction in the occurrence of monsoon depressions, from an annual average of 7 to 8 historically to 1, 0, and 2 in the years 2009, 2010, and 2011. Second, aerosol pollution has increased dramatically over the Indian sector in recent years. Third, global forecast models, which presumably do not account for the change in aerosol pollution, have overpredicted monsoon depressions in the recent years with few depressions. Fourth, preliminary experiments with the Weather Research and Forecasting (WRF) model show that the strength of monsoon depressions, and the intensity and organization of precipitation within them, can be dramatically reduced by increasing the aerosol burden from relatively pristine (5x10^8 particles per cubic meter) to heavily polluted (5x10^10 per cubic meter) values. The research is based on the hypothesis that aerosols suppress monsoon depressions because they slow the formation of rainfall, as more aerosols lead to more cloud condensation nuclei (CCN), which produce a greater number of smaller cloud droplets, which take longer to coalesce into raindrops. Monsoon depressions form through a mutual interaction between the baroclinic/barotropic instability of the large-scale flow and the latent heat of condensation accompanying precipitation within the depression. This mutual interaction can be suppressed by the excess aerosols, which increase time required for the development of precipitation by roughly a factor of two, and thus produce a mismatch between the timescales of the hydrodynamic instability and the organization of precipitation and accompanying latent heat release. Work performed under this award tests the hypothesized suppression of monsoon depressions by aerosol pollution using more sophisticated and realistic simulations, and also performs analysis of aerosol forcing, based on satellite data, during periods when monsoon depressions form and when they are absent.The suppression of monsoon depressions addressed in this research is of both scientific and societal interest, given the large population that is affected by them. If successful, the results of this work could be directly applied to operational forecasting systems used to predict these weather systems. In addition, the project provides support and training for a postdoc and a graduate student, thereby providing for the next generation of scientists in this research area.