DO4models- Dust Observations for models: Linking a new dust source-area data set to improved physically-based dust emission schemes in climate models

DO4models-模型的粉尘观测：将新的粉尘源区域数据集与改进的气候模型中基于物理的粉尘排放方案联系起来

基本信息

批准号：
NE/H021450/1
负责人：
Helen Brindley
金额：
$ 11.41万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2011
资助国家：
英国
起止时间：
2011 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FH021450%2F1
关键词：
DO4models Dust Observations models Linking

项目摘要

Dust is an important part of the Earth's land-atmosphere-ocean-biosphere system affecting climate, the fertility of oceans, plant communities on land, and human health. Wind is able to move vast amounts of dust over the Earth's surface and into the atmosphere. North Africa alone emits 500-1000 million tons of dust a year. To predict future weather and climate it is crucial that numerical models, our key tool for such prediction, represent the relationships important to the emission, transport and deposition of dust. Excluding dust from models leads to large local and global errors. Accurate modelling of dust begins with the correct simulation of emission. This is vital because source area simulation errors lead to errors in local climate dynamics and incorrect dust transport. However, many of the major dust source regions of the world are in extremely remote places for which there is no ground-based data on dust emission or its controls. Although recent advances have been made in identifying major dust sources, for example from satellite data, many models of dust emission are still very simple and are not constrained by real observed data. In the drive to predict weather and climate at spatial scales useful for planning decisions, numerical models have increased their resolution so that some global models run at near 1 degree and many regional models at better than 0.2 degree resolution. The few observed data sets characterising dust source areas and behaviour that do exist simply do not support the scale at which these models are being run. It is therefore extremely difficult to either evaluate or improve the dust emission component of models as things stand. Simulation of dust source areas is consequently very inaccurate and is set to remain so. We propose to address this problem by developing the first model dust emission scheme which is based on purpose built observed data sets that have been deliberately constructed to exactly match the scale of regional climate models. We propose to do this by first using high-resolution satellite data to identify key sources of dust within field areas that are characteristic of dust source areas found in many parts of the world. We will then use state-of-the-art field equipment to systematically investigate the real processes that control dust emission at the model grid box scale, measuring background conditions over a long period as well as the important processes that occur during dust storms. We will therefore measure and monitor both the factors that control the availability of dust to the wind on the ground (erodibility), and the ability of the wind to move that sediment (erosivity) to create this definitive data set on source regions that can be used in model development for years to come. We will be able to determine for the first time what kind of dust source data (e.g. surface roughness, soil moisture, wind gustiness) lead to the largest improvement in the observationally-constrained model emission scheme. We will also be able to say what errors result in simulations if no field data is collected and only remotely sensed data are used as inputs to the models. This will provide important guidance on how and where to spend time and money in the improvement of climate models in the future and also to provide direction on what kind of field data are most important to collect. The Met Office does not have the capacity to undertake the extensive fieldwork required to deliver the observational data that are critical to model development. Our proposal cuts across the traditional barriers between field work, Earth Observation and numerical modelling. It is only by doing so that breakthroughs in dust numerical modelling will be achieved. Our unprecedented field observations which are tailored to numerical model needs will be a significant step towards a new generation of model schemes.

尘埃是影响气候，海洋的生育，陆地上的植物群落和人类健康的地球陆地 - 大气 - 海界系统的重要组成部分。风能在地球表面上移动大量的灰尘并进入大气中。仅北非，每年就会发出50亿吨灰尘。为了预测未来的天气和气候，至关重要的是，我们预测的关键工具数值模型代表了对灰尘的发射，运输和沉积重要的关系。从模型中排除灰尘会导致巨大的本地和全球错误。灰尘的准确建模始于正确模拟发射。这是至关重要的，因为源区域模拟错误导致局部气候动态和不正确的尘埃运输错误。但是，世界上许多主要的灰尘源区域都在极端偏远的地方，没有关于灰尘排放或其控制的地面数据。尽管在识别主要的灰尘源方面已经取得了最新进展，例如从卫星数据中，许多尘埃排放模型仍然非常简单，并且不受实际观察到的数据的限制。在预测有助于计划决策的空间量表的天气和气候的动力中，数值模型增加了分辨率，以使某些全球模型在接近1度和许多区域模型中以优于0.2度分辨率的分辨率运行。表征确实存在的尘埃源区域和行为的几个观察到的数据集根本不支持运行这些模型的规模。因此，在事物的存在时，很难评估或改善模型的灰尘排放成分。因此，尘埃源区域的模拟非常不准确，并且将保持这种状态。我们建议通过开发第一个模型灰尘排放方案来解决此问题，该方案基于目的构建的观察到的数据集，这些数据集是故意构建的，以与区域气候模型的规模完全匹配。我们建议通过首先使用高分辨率卫星数据来识别在世界许多地方发现的尘埃源区域的特征的尘土区域内的关键灰尘来源。然后，我们将使用最先进的现场设备来系统地研究在模型网格箱中控制灰尘发射的实际过程，并在长期内测量背景条件以及在尘埃雨期间发生的重要过程。因此，我们将测量和监视控制尘埃在地面上可用的尘埃的因素（可易折射性），以及风能移动沉积物（侵蚀率）的能力，以在未来几年中在模型开发中使用该源区域创建此确定的数据集。我们将能够首次确定哪种尘埃源数据（例如表面粗糙度，土壤水分，风味）导致观察受到约束的模型发射方案的最大改善。如果未收集字段数据，并且仅将远程感知的数据用作模型的输入，我们还将能够说出哪些错误导致模拟导致模拟。这将为未来的气候模型的改善，并为收集最重要的哪种现场数据提供指导，以提供有关如何和何处花费时间和金钱的重要指导。大都会办公室没有能力进行提供对模型开发至关重要的观察数据所需的广泛野外工作。我们的建议在现场工作，地球观察和数值建模之间跨越了传统障碍。只有这样做才能实现尘埃数值建模中的突破。根据数值模型需求量身定制的前所未有的现场观察将是迈向新一代模型方案的重要一步。