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

沙尘是地球陆地-大气-海洋-生物圈系统的重要组成部分，影响气候、海洋肥力、陆地植物群落和人类健康。风能够将大量的灰尘从地球表面吹到大气中。仅北非每年就排放5 -10亿吨灰尘。为了预测未来的天气和气候，至关重要的是，数值模型是我们预测这种预测的关键工具，它代表了与尘埃排放、运输和沉积的重要关系。从模型中排除灰尘会导致很大的局部和全局误差。精确的粉尘建模始于对排放的正确模拟。这是至关重要的，因为源区域模拟误差会导致局部气候动力学误差和不正确的粉尘输送。然而，世界上许多主要粉尘来源区域都在极其偏远的地方，没有关于粉尘排放或其控制的地面数据。虽然最近在确定主要粉尘来源方面取得了进展，例如从卫星数据，但许多粉尘排放模型仍然非常简单，不受实际观测数据的限制。为了在有利于规划决策的空间尺度上预测天气和气候，数值模式提高了它们的分辨率，因此一些全球模式的分辨率接近1度，许多区域模式的分辨率优于0.2度。少数观测到的数据集描述了确实存在的粉尘源区域和行为，但根本不支持这些模型正在运行的规模。因此，评估或改进模型的粉尘排放成分是极其困难的。因此，对粉尘源区域的模拟是非常不准确的，并将继续如此。为了解决这一问题，我们建议开发第一个模型沙尘排放方案，该方案基于专门建立的观测数据集，这些数据集是故意构建的，以精确匹配区域气候模式的尺度。为此，我们建议首先使用高分辨率卫星数据来确定野外地区的主要粉尘来源，这些地区是世界许多地方发现的粉尘来源地区的特征。然后，我们将使用最先进的现场设备，系统地调查控制模型网格箱尺度上的粉尘排放的真实过程，测量长时间的背景条件以及沙尘暴期间发生的重要过程。因此，我们将测量和监测控制地面上灰尘对风的可用性的因素（可蚀性），以及风移动沉积物的能力（侵蚀性），以创建源区域的确定数据集，这些数据集可以用于未来几年的模型开发。我们将能够首次确定哪种尘源数据（如表面粗糙度、土壤湿度、风力）对观测约束模式排放方案的改善最大。如果没有收集现场数据，只使用遥感数据作为模型的输入，我们还能够说明模拟的错误。这将为今后如何以及在何处花费时间和金钱改进气候模型提供重要指导，并为收集哪种野外数据最重要提供方向。气象局没有能力承担广泛的实地调查工作，以提供对模型开发至关重要的观测数据。我们的建议跨越了实地工作、地球观测和数值模拟之间的传统障碍。只有这样，才能实现粉尘数值模拟的突破。我们为满足数值模式需要而进行的前所未有的实地观测将是迈向新一代模式方案的重要一步。

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.