IMPROVING CIRRUS ESTIMATES OF RADIATIVE FORCING: BACKSCATTERING FOR MODELS AND OBSERVATIONS (ICE-RF)

改进辐射强迫的 CIRUS 估计：模型和观测的后向散射 (ICE-RF)

• 批准号: NE/T00147X/1
• 负责人: Ann Webb
• 金额: $ 79.76万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT00147X%2F1
• 关键词: IMPROVING; CIRRUS; ESTIMATES; RADIATIVE; FORCING

One of the big uncertainties in both weather forecasting and prediction of future climate change is cloud. In both cases the models that estimate the future weather and climate must represent cloud in a simplified way. If we can improve the way that clouds are represented in the models, then we can improve the model outputs.Cirrus are the high, often thin and wispy-looking clouds, consisting entirely of ice particles. Their influence on weather and climate change is very hard to determine because of the different ways they interact with radiation. They reflect solar radiation (a cooling effect) but also absorb and re-radiate longwave radiation from the Earth (resulting in a warming effect). Improving the way that cirrus are represented in models will bring advances in both weather forecasting and climate change prediction. This project aims to both develop and test a new scheme for representing cirrus that will be used in the Met Office UKV forecast model and in the Met Office Earth System Model used for climate change prediction. One of the problems with understanding cirrus is the difficulty in knowing the microphysical form of the cloud - that is the size, shape and roughness of the ice particles in the cloud - the properties that determine how the cloud interacts with radiation. Research aircraft can fly through the cloud and sample the ice particles, but that is not a practical method for widespread use or routine monitoring. Alternatively the clouds can be systematically interrogated from below by ground-based lidar, or from above by satellite. Both systems rely on radiation back-scattered from the ice particles, using two wavelengths, one strongly and one weakly absorbed by ice particles. However, to make sense of the lidar data we need to know how different habits (sizes and shapes) of ice crystal backscatter radiation at the lidar wavelengths. This can be determined using the Manchester Ice Cloud Chamber (MICC).Ice clouds of different habits can be made and characterized in the MICC. As the ice particles fall they pass through a scattering chamber. We will shine lasers into the scattering chamber, at paired lidar wavelengths (for ground-based or satellite systems), and measure the exact backscatter from the different ice clouds, as well as polarization and scattering in other directions. The two wavelengths of a pair interact with the particles in different ways, depending on their size, shape and roughness, so we can determine a colour ratio (the ratio of the two backscattered signals) that tells us something about the ice particles in the cloud.Having developed a catalogue of laboratory ice cloud colour ratios and scattering functions we will model these known conditions using the discrete dipole approximation method to ensure that this method can reproduce the laboratory results, and provide for a parameterisation of colour ratio according to particle size distribution (PSD). Further colour ratios will be modeled and parameterised for ice clouds with PSDs that we have not reproduced in the MICC, but are possible in nature.Now, if we take the colour ratio measured from real cirrus by satellite lidars we can use our parameterisation of the backscatter signal to obtain the mean mass weighted ice crystal size (Dmmw). The ice microphysics scheme in the UKV model also predicts the Dmmw, thus we can directly validate the UKV microphysics scheme by comparing the predicted and actual Dmmw for cirrus. The same microphysics scheme is part of the Met Office Earth System Model used for climate change prediction, so its improvement also increases confidence in estimates of future climate change that inform Government policy. The backscatter parameterisations will be made across a range of wavelengths that can be applied to ground-based, current and future satellite missions, to tell us whether particular cirrus are having a net warming or cooling effect on the atmosphere.

云层是天气预报和未来气候变化预报中的一大不确定因素。在这两种情况下，估计未来天气和气候的模型必须以一种简化的方式表示云。如果我们能够改进云在模型中的表示方式，那么我们就可以改进模型的输出。卷云是一种高度很高的云，通常很薄，看起来很小，完全由冰粒组成。它们对天气和气候变化的影响很难确定，因为它们与辐射相互作用的方式不同。它们反射太阳辐射(一种冷却效应)，但也吸收并重新辐射来自地球的长波辐射(导致变暖效应)。改进卷云在模型中的表示方式将带来天气预报和气候变化预测的进步。该项目旨在开发和测试一种表示卷云的新方案，该方案将用于气象局UKV预报模式和用于气候变化预测的气象局地球系统模式。理解卷云的一个问题是，很难知道云的微物理形态--即云中冰粒的大小、形状和粗糙度--这些特性决定了云与辐射的相互作用。研究飞机可以飞越云层并采集冰粒样本，但对于广泛使用或常规监测来说，这并不是一种实用的方法。或者，可以通过地面激光雷达从下面系统地探测云层，或者通过卫星从上面系统地探测云层。这两个系统都依赖于冰粒后向散射的辐射，使用两个波长，一个被冰粒强烈吸收，另一个被冰粒弱吸收。然而，为了理解激光雷达数据，我们需要知道不同习惯(大小和形状)的冰晶是如何在激光雷达波长上进行后向散射的。这可以使用曼彻斯特冰云室(MICC)来确定。在MICC中可以制造不同习惯的冰云并对其进行表征。当冰粒落下时，它们会穿过一个散射室。我们将以成对的激光雷达波长(用于地面或卫星系统)将激光照射到散射室，并测量不同冰云的准确后向散射，以及其他方向的偏振和散射。根据粒子的大小、形状和粗糙度，两个波长对以不同的方式与粒子相互作用，因此我们可以确定颜色比率(两个反向散射信号的比率)，它告诉我们关于云中冰粒的一些信息。开发了实验室冰云颜色比率和散射函数的目录后，我们将使用离散偶极子近似方法对这些已知条件进行建模，以确保该方法可以重现实验室结果，并根据粒子尺寸分布(PSD)提供颜色比率的参数化。我们将对带有PSD的冰云进行进一步的颜色比例建模和参数化，这是我们在MICC中没有复制的，但在自然界中是可能的。现在，如果我们用卫星激光雷达从真实的卷云测量颜色比例，我们可以使用后向散射信号的参数来获得平均质量加权冰晶尺寸(DMmw)。UKV模式中的冰微物理方案也预测了DMMW，因此我们可以通过比较预测的卷云DMMW和实际的DMMW来直接验证UKV微物理方案。同样的微物理计划是气象局用于气候变化预测的地球系统模型的一部分，因此它的改进也增加了对未来气候变化估计的信心，这些估计将为政府政策提供信息。后向散射参数将在一系列波长上进行，这些波长可以应用于地面、当前和未来的卫星任务，以告诉我们特定的卷云是否对大气产生了净变暖或降温效应。