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) 是热驱动系统，可将潮湿的暖空气从对流层下层垂直输送到对流层上层。它们可以在热带辐合带（ITCZ）和大陆上空的中纬度地区找到，那里由于对流可用势能（CAPE）的释放，引发强烈的上升气流，形成积雨云。 DCC可以根据其大小分为几个子类别，从穿过云团的单个塔到分布数百公里宽的中尺度单元。然而，无论 DCC 大小如何，它在气候​​系统中都非常重要。一方面，它们在生长时通过释放潜热并吸收长波辐射来加热大气，但另一方面，DCC 则通过反向散射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，它们推动大规模循环，输送热量、动量、水分和气溶胶。此外，云和气溶胶的辐射效应和气候影响是气候系统辐射强迫建模中最重要的不确定性之一。气溶胶是大气中存在的微小颗粒或液体液滴。气溶胶源自天然来源（例如火山、海洋、森林和沙漠）和人造来源（例如化石燃料），在云微物理中发挥着重要作用。气溶胶直接影响气候，因为它们会散射和吸收（并重新发射）太阳辐射（和地面辐射）。气溶胶通过改变云的微物理特性来间接影响气候，通过充当云凝结核（CCN）使它们变得更亮或更持久，并且它们的特性影响水凝物类型和尺寸分布；因此，气溶胶在云微物理中至关重要。作为 CCN，它们是通过降低所需的相对湿度来形成云滴的关键因素，作为 IN，它们可以引发过冷水滴的结晶。此外，气溶胶与云内的水凝物和其他气溶胶相互作用，可以控制不同场景下微物理过程的速率。为了解决与DCC微物理过程相关的复杂性，需要结合理论、观测和模型。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，因此需要对其进行局部参数化。由于微物理过程的复杂性，必须进行各种简化，从而使模拟的云结构和降水产生不确定性。最后，如上所述，云微物理，特别是 DCC 微物理在模型中的表示，是模型中不确定性的主要根源之一，正如许多最近的研究所描述的那样。这有两个主要贡献者；与微物理过程本身相关的基本不确定性以及真实过程及其在模型中的表示之间的时间和长度尺度的差异。微观物理过程的长度尺度在纳米到厘米范围内，时间尺度在微秒到分钟之间，而在模型中，长度尺度在公里到数百公里范围内，时间尺度通常是几公里。小时。云对变暖和气溶胶-云相互作用的反馈的高分辨率模型中与表征相关的过多不确定性尤其对于混合相云和冰相云而言尤为明显。因此，这对于充分理解 DCC 中发生的微物理过程、它们之间的关系以及在全局模型中表示它们的最佳方式至关重要。