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

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.