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

深对流云（DCC）是由热驱动的系统，它将潮湿的暖空气从对流层低层垂直输送到对流层高层。它们可以在热带辐合带（ITCZ）和大陆上的中纬度地区找到，由于对流可用位能（CAPE）的释放，强烈的上升气流开始形成积雨云。根据其大小，DCC可以分为几个亚类，从一个单一的塔通过一个云团到中尺度的细胞蔓延数百公里宽。然而，无论其大小，DCC在气候系统中都非常重要;一方面，它们负责通过释放潜热来加热大气，因为它们生长并吸收长波辐射，但另一方面，DCC负责通过反向散射短波太阳辐射来冷却。此外，地区合作中心在当地和全球都非常重要。在局部，它们控制着水的收支，在全球范围内，它们驱动着大规模的循环，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模拟中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。气溶胶来源于天然来源（例如，火山、海洋、森林和沙漠）和人为来源（例如，化石燃料），并在云微物理学中发挥重要作用。气溶胶直接影响气候，因为它们散射和吸收（和重新发射）太阳辐射（和地球辐射）。气溶胶通过改变云的微物理性质间接影响气候，通过充当云凝结核（CCN）使其更明亮或更持久，其性质影响水凝物的类型和大小分布;因此气溶胶在云微物理学中至关重要。作为云凝结核，它们是通过降低所需的相对湿度来形成云滴的关键因素，而作为IN，它们可以引发过冷水滴的结晶。此外，气溶胶与降水和其他气溶胶在云中的相互作用，可以控制在不同的情况下的微物理过程的速率。为了解决与DCC微物理过程的复杂性，理论，观测和模式相结合。因此，为了模拟云，需要参数化微物理过程。如上所述，微物理过程是高度非线性的，因此，它们需要局部参数化。由于微物理过程的复杂性，必须进行各种简化，从而在模拟的云结构和降水中产生不确定性。最后，如前所述，云微物理的表现，特别是模式中的DCC微物理，是模式不确定性的主要来源之一，正如许多最近的研究所描述的那样。这有两个主要的贡献者;与微物理过程本身有关的基本不确定性，以及真实的过程和它们在模型中的表示之间在时间和长度尺度上的差异。微物理过程的长度尺度在纳米到厘米之间，时间尺度在微秒到分钟之间，而在模型中，长度尺度在公里到几百公里之间，时间尺度通常是几个小时。在云对变暖和气溶胶-云相互作用的反馈的高分辨率模型中，特别是对于混合相和冰相云，与过度表示相关的不确定性是明显的。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球模型中表示它们的最佳方式至关重要。

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.