Observational constraints on microphysics processes in deep-convective clouds in dependence of aerosol conditions combining cloud-resolving models and

结合云解析模型和气溶胶条件对深对流云中微物理过程的观测约束

• 批准号: 2598738
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2598738
• 关键词: Observational; constraints; microphysics; processes; deep

Deep convective clouds (DCCs) are thermally-driven systems that transport moist warm air vertically from the lower to the upper troposphere. They can be found in the inter-tropical convergence zone (ITCZ) and in the mid-latitudes over continents, where due to the release of convective available potential energy (CAPE), strong updrafts are initiated, forming cumulonimbus clouds. DCCs can be divided into subcategories depending on their size, from a single tower through a cluster of clouds to mesoscale cells spreading hundreds of kilometres wide. However, DCCs are highly important in the climate system regardless of their size; on the one hand, they are responsible for heating the atmosphere by releasing latent heat as they grow and absorbing longwave radiation, but on the other hand, DCCs are responsible for cooling by backscattering shortwave solar radiation. Furthermore, DCCs are highly important both locally and globally. Locally, they govern the water budget, and globally, they drive large-scale circulation, transporting heat, momentum, moisture, and aerosols. In addition, the radiative effect and climate impact of clouds and aerosols are among the most considerable uncertainties in modelling the radiative forcing of the climate system. Aerosols are tiny particles or droplets of liquid which can be found in the atmosphere. Aerosols originate from natural sources (e.g., volcanoes, ocean, forests, and deserts) and human-made sources (e.g., fossil fuels) and play a major role in cloud microphysics. Aerosols directly affect climate since they scatter and absorb (and re-emit) solar radiation (and terrestrial radiation). Indirectly, aerosols affect climate by altering cloud microphysical properties, making them brighter or longer-lasting by acting as cloud condensation nuclei (CCN), and their properties influence hydrometeor type and size distribution; hence aerosols are crucial in cloud microphysics. As CCN, they are the key factor in allowing the formation of cloud droplets by decreasing the relative humidity needed, and as IN, they can initiate crystallisation of supercooled water droplets. In addition, aerosols interact with hydrometeors and other aerosols within clouds and can control rates of the microphysical processes in different scenarios.In order to resolve the complexity associated with DCCs microphysical processes, a combination of theory, observations and models is used. Hence, to simulate clouds, microphysical processes need to be parameterised. As implied above, microphysical processes are highly nonlinear, and as a result, they need to be parametrised locally. Due to the complex nature of the microphysical processes, various simplifications must be made, and as a result, it produces uncertainties in the simulated cloud structures and precipitation. Lastly, as mentioned above, the representation of cloud microphysics, and especially DCC microphysics in models, is one of the major origins of uncertainty in models, as described in many recent studies. This has two major contributors; the fundamental uncertainty related to the microphysical processes themselves and differences in time and length scales between real processes and their representation in models. The length scale of microphysical processes is in the range of nanometers to centimetres, and the time scale is microseconds to minutes, while in models, the length scale is in the range of kilometres to hundreds of kilometres, and the time scale is usually a few hours. The excess representation-associated uncertainty in high-resolution models of cloud feedbacks to warming and aerosol-cloud interaction is pronounced especially for mixed-phase and ice-phase clouds. Hence, this is vital to fully understand the microphysical processes taking place in DCCs, the relationship between them, and the best way to represent them in global models.

深对流云（DCC）是热驱动的系统，可将潮湿的温暖空气从下部到对流层上部运输。它们可以在热带间收敛区（ITCZ）和大陆的中纬度地区找到，在那里由于对流可用的势能（CAPE）释放，启动了强大的上下流，形成了累积的云云。 DCC可以根据其尺寸分为子类别，从单个塔到一群云层到宽度数百公里宽的中尺度细胞。但是，DCC在气候系统中都非常重要，无论其规模如何。一方面，它们负责通过在生长和吸收长波辐射时释放潜热来加热大气，但另一方面，DCC负责通过反向散射的短波太阳辐射来冷却。此外，DCC在本地和全球都非常重要。在当地，它们控制着水预算，在全球范围内，它们可以驱动大规模循环，运输热量，动量，水分和气溶胶。此外，在对气候系统的辐射强迫建模时，云和气溶胶的辐射效应和气候影响是最大的不确定性之一。气溶胶是可以在大气中发现的微小颗粒或液滴。气雾剂来自天然来源（例如火山，海洋，森林和沙漠）和人造的来源（例如化石燃料），并在云微物理学中起着重要作用。气溶胶直接影响气候，因为它们会散射并吸收（和重新发射）太阳辐射（以及陆地辐射）。间接地，气溶胶通过改变云微物理特性来影响气候，从而使它们通过充当云凝结核（CCN）而更明亮或更长，并且它们的特性会影响Hydrometeor的类型和尺寸分布；因此，气溶胶在云微物理学中至关重要。作为CCN，它们是通过降低所需的相对湿度允许形成云滴的关键因素，并且它们可以启动超冷水滴的结晶。此外，气溶胶与云中的水晶和其他气溶胶相互作用，并且可以在不同情况下控制微物理过程的速率。为了解决与DCCS微物理过程相关的复杂性，理论，观察值和模型的组合被使用。因此，为了模拟云，需要对微物理过程进行参数化。如上所述，微物理过程是高度非线性的，因此，它们需要在本地进行参数化。由于微物理过程的复杂性质，必须进行各种简化，因此，它在模拟的云结构和降水中产生了不确定性。最后，如上所述，如许多最近的研究所述，云微物理学的表示，尤其是模型中的DCC微物理学，是模型中不确定性的主要起源之一。这有两个主要贡献者。与微物理过程本身以及实际过程之间的时间和长度差异有关的基本不确定性及其在模型中的表示。微物理过程的长度尺度在纳米到厘米的范围内，时间尺度为微秒至分钟，而在模型中，长度尺度在公里至数百公里的范围内，时间尺度通常为几个小时。高分辨率的云反馈对变暖和气溶胶云相互作用的高分辨率模型中与代表性相关的不确定性特别是对于混合相和冰相云而言。因此，这对于完全了解DCC中发生的微物理过程，它们之间的关系以及在全球模型中代表它们的最佳方法至关重要。