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Evaluation of clouds in climate and forecasting models using CloudSat and Calipso data.

使用 CloudSat 和 Calipso 数据评估气候和预测模型中的云。

基本信息

批准号：
NE/C519697/1
负责人：
Robin Hogan
金额：
$ 25.3万
依托单位：
University of Reading
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2006
资助国家：
英国
起止时间：
2006 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FC519697%2F1
关键词：
Evaluation clouds climate forecasting models

项目摘要

Climate change is one of the great challenges facing the world today. There is compelling evidence that the inexorable increase in 'greenhouse gases' (particularly carbon dioxide) due to human activity is having a warming effect on climate. However, it is very difficult to determine just how much average global surface temperature will rise in future in response to a particular increase in greenhouse gas concentration, and in fact the forecasts vary by a factor of three. One of the major problems stems from clouds. Everyday experience tells us the profound effect the presence of a cloud has on the amount of the sun's energy that reaches the ground, and similarly the fact that cloudy nights tend to be warmer than clear is because the infrared energy emitted by the surface is then nearly balanced by the energy emitted back towards it by the cloud. Exactly the same processes act on large scales, and to calculate surface temperature with any skill we need to know accurately how cloud is distributed around the globe, including its detailed properties such as average droplet size. There are two main difficulties. Firstly, the computer simulations used to predict climate split the atmosphere into large grid-boxes (typically 200 km across and up to 1 km deep), with often only two numbers being used to describe the cloud in each box (e.g. the amount of cloud and the mass of cloud water). This is clearly very crude. Secondly, the information available to test the simulated clouds is from imagers carried on satellites, which cannot see very far into a cloud to determine how it is arranged vertically or to detect one cloud layer beneath another. In this project we will make use of exciting new data from two satellites to be launched by NASA in May 2005. 'CloudSat' carries a cloud radar that works by continually sending short pulses of radio waves downwards. Clouds scatter some of these waves back to the radar, and by timing how long the echo takes to be returned and by measuring its strength, we can calculate the height of the cloud and how much water it contains. As CloudSat orbits the earth it will for the first time be able to infer cloud properties at each height in the atmosphere. The second satellite 'Calipso' carries a lidar and works on the same principle but using light rather than radio waves. The key aspect to this project is to combine the radar and lidar to learn more about the nature of the clouds. The fact that radar is more sensitive to the large cloud particles while lidar is more sensitive to the small means that we can estimate important cloud properties such as the size of ice crystals in cirrus and the rate of drizzle falling from low altitude water clouds (which is important for determining how long the cloud will persist). Between 0 and -40°C, clouds can contain both small liquid droplets and large ice crystals; our results from ground-based lidar show that these clouds are particularly badly simulated by computer models, but with radar and lidar the two components can be easily distinguished and their properties estimated. We will then use all the cloud properties extracted from around a year of global observations to test the clouds in two of the world's best computer models, the Met Office climate model and the ECMWF forecast model. This will be used to highlight problems with the models, and address them by developing new ways to simulate clouds and testing them again against the new observations. Of particular interest will be schemes shortly to be introduced in both models to represent the horizontal cloud structure in a large model grid-box which we will test around the globe for the first time. The resulting improvements in simulated clouds should give us more confidence in predictions of climate change.

气候变化是当今世界面临的重大挑战之一。有令人信服的证据表明，由于人类活动造成的“温室气体”（特别是二氧化碳）的无情增加正在对气候产生变暖效应。然而，很难确定未来全球平均地表温度将因温室气体浓度的特定增加而上升多少，事实上，预测值相差三倍。其中一个主要问题来自云。日常经验告诉我们，云的存在对到达地面的太阳能量有着深远的影响，同样，多云的夜晚往往比晴朗的夜晚更温暖，这是因为表面发射的红外线能量几乎被云向它发射的能量所平衡。在大尺度上，同样的过程也会发生作用，为了计算地表温度，我们需要准确地知道云在地球仪上的分布情况，包括其详细的性质，如平均液滴大小。有两个主要的困难。首先，用于预测气候的计算机模拟将大气层分成大的网格盒（通常为200公里宽，最深1公里），通常只有两个数字用于描述每个盒子中的云（例如云的数量和云水的质量）。这显然非常粗糙。其次，可用于测试模拟云层的信息来自卫星上携带的成像仪，这些成像仪无法看到云层很远的地方，无法确定云层的垂直排列方式，也无法检测到云层下的云层。在这个项目中，我们将利用美国航天局将于2005年5月发射的两颗卫星提供的令人兴奋的新数据。“CloudSat”携带一个云雷达，通过不断向下发送短脉冲无线电波来工作。云会将其中一些波散射回雷达，通过计算回波返回的时间和测量其强度，我们可以计算出云的高度和含水量。当CloudSat绕地球运行时，它将首次能够推断出大气层中每个高度的云的特性。第二颗卫星“卡利普索”携带一个激光雷达，工作原理相同，但使用的是光而不是无线电波。该项目的关键方面是将雷达和激光雷达联合收割机结合起来，以了解更多关于云的性质。雷达对大的云粒子更敏感，而激光雷达对小的云粒子更敏感，这意味着我们可以估计重要的云属性，例如卷云中冰晶的大小和从低空水云中落下的毛毛雨的速度（这对于确定云将持续多久很重要）。在0到-40 °C之间，云可以包含小的液滴和大的冰晶;我们的地面激光雷达结果表明，这些云用计算机模型模拟得特别糟糕，但是用雷达和激光雷达可以很容易地区分这两种成分，并估计它们的属性。然后，我们将使用从大约一年的全球观测中提取的所有云特性，在世界上最好的两个计算机模型中测试云，即气象局气候模型和ECMWF预测模型。这将用于突出模型的问题，并通过开发新的方法来模拟云并根据新的观测结果再次测试它们来解决这些问题。特别令人感兴趣的是，不久将在这两种模式中引入的方案，以在一个大型模型网格框中表示水平云结构，我们将首次在地球仪周围进行测试。由此产生的模拟云的改进应该让我们对气候变化的预测更有信心。