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

深层对流云（DCCs）是热驱动的系统，它将潮湿的暖空气从对流层的下层垂直输送到对流层的上层。在热带辐合带（ITCZ）和大陆上空的中纬度地区，由于对流有效势能（CAPE）的释放，形成了强烈的上升气流，形成积雨云。根据它们的大小，可以将dcs分为几个子类，从单个塔穿过一簇云到散布数百公里宽的中尺度细胞。然而，无论其大小如何，dcs在气候系统中都非常重要；一方面，它们在生长过程中通过释放潜热和吸收长波辐射来加热大气，但另一方面，dcs通过后向散射短波太阳辐射来冷却大气。此外，dcs在当地和全球都非常重要。在当地，它们控制着水的收支，而在全球，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，在模拟气候系统辐射强迫时，云和气溶胶的辐射效应和气候影响是最重要的不确定因素之一。气溶胶是可以在大气中找到的微小颗粒或液滴。气溶胶来自自然来源（如火山、海洋、森林和沙漠）和人为来源（如化石燃料），并在云微物理中发挥重要作用。气溶胶直接影响气候，因为它们散射和吸收（并重新释放）太阳辐射（和地面辐射）。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）使其更亮或持续时间更长，其性质影响水流星的类型和大小分布；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，它们可以启动过冷水滴的结晶。此外，气溶胶与云中的水成物和其他气溶胶相互作用，可以控制不同情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，采用了理论、观测和模型相结合的方法。因此，为了模拟云，微物理过程需要参数化。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，如上所述，云微物理，特别是模型中DCC微物理的表示，是模型中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。这有两个主要贡献者；基本的不确定性与微观物理过程本身以及实际过程与模型中的表示在时间和长度尺度上的差异有关。微物理过程的长度尺度为纳米到厘米，时间尺度为微秒到分钟，而在模型中，长度尺度为公里到数百公里，时间尺度通常为几个小时。对于混合相云和冰相云，高分辨率云对变暖和气溶胶-云相互作用的反馈模式中，与表示相关的过度不确定性尤为明显。因此，这对于充分了解dcs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.