During the month of February, parts of Europe were affected by intense dust storms from North Africa. Earlier in the month, a huge column of dust originating in northeastern Algeria caused reddish skies over much of Europe. As it settled, the dust turned the snow that covered the Pyrenees and the Alps brown. At the end of the month, another intense Saharan dust event affected the Canary Islands before heading towards the European continent, and reaching Scandinavia.
Although such intense and persistent dust activity is unusual, it is not so strange that Europe experiences a few strong events each year.
Why is dust a problem?
Desert dust is produced by wind erosion of arid and semi-arid surfaces. It affects weather, climate and atmospheric chemistry, contributes iron and phosphorus to terrestrial and ocean ecosystems, and increases photosynthesis and biological productivity.
Although there are some positive effects, dust storms generally have negative impacts, particularly in countries downwind of major sources in North Africa, the Middle East, and Central and East Asia. Unusually high dust levels reached in Europe this February are common in these regions.
Dust storms increase eye infections and the incidence of respiratory and cardiovascular morbidity and mortality, and are associated with the incidence of meningitis in the African Sahel. Intense episodes can disrupt communications, force road and airport closures due to poor visibility, and can damage crops and livestock.
Dust affects solar energy production by reducing solar radiation reaching the surface and by accumulating on the surface of solar panels. The dust deposited on the snow drastically reduces its reflectivity and increases the absorption of solar radiation, which causes the snow cover to melt faster.
Mitigate emissions or mitigate impacts?
Mitigating dust emissions is possible in regions where wind erosion is exacerbated by human activities that disturb the soil, such as crops, grazing, recreational activities and suburbanization, and diversion of water for irrigation . A classic example is the “Dust Bowl” that occurred in the 1930s on the Great Plains of the United States. Poor land management practices coupled with the duration of the drought led to heavy wind erosion and dust storms on an unprecedented scale.
When dust sources are naturally occurring (for example, in a desert area), locally mitigating emissions when sand and dust significantly disrupt human activities is possible through surface stabilization and the deployment of fences. However, the implementation of these measures is neither feasible nor desirable on a large scale. Globally, the mitigation potential for emissions is quite limited, making mitigating impacts even more crucial.
Mitigating the negative impacts of dust storms requires monitoring, modeling, forecasting and early warning systems. Tactical mitigation applications focus on actions that can be taken in the short term when forecasts call for a dust storm at a specific time and place. For example, dust forecasts can help hospitals anticipate spikes in emergency room visits for respiratory problems, optimize the timing of planting and harvesting of crops, safeguard livestock, manage schedules of solar energy generation and cleaning of solar panels, and to minimize the time in which low visibility procedures are applied at airports.
Strategic mitigation applications are those related to long-term planning and investments, such as deciding where to place a solar power plant. Another application is assisting post-storm assessments, as governments and international institutions need to know the precise causes of air quality degradation, outbreaks or damage to crops. Finally, scientific communities, such as the public health community, need high spatial and temporal resolution dust data to study and evaluate the effects of dust on a range of ailments.
Modeling and forecasting capabilities
A great effort is being made to develop reliable global and regional dust models and forecasts for impact mitigation, for example at the Regional Dust Storm Warning and Assessment Center for North Africa, the Middle East and Europe of the World Meteorological Organization.
Dust models use mathematical and numerical techniques to simulate the cycle of atmospheric dust, from its emission, transport and deposition, to its interaction with solar radiation and clouds.
To calculate dust emissions, the models use input parameters from surface, soil, and meteorological conditions. However, the success of emission models is limited by the uncertainties of these parameters, including those that are related to spatial and temporal heterogeneities that are not resolved at the current resolution of the models. Due to the non-linear relationship between wind and dust emission, small errors in wind speed in models can lead to large errors in estimated dust emissions. Also, the processes that control the deposition of dust particles, especially coarse ones, are subject to significant uncertainties.
In general, dust models perform relatively well when dust events are caused by synoptic-scale meteorological systems, that is, with features of about 1,000 kilometers in diameter or more. A good example is the predictions of the February events in Europe that hit the arrival time and the geographical extent of the dust plume.
Instead, the representation of haboobs – immense walls of sand and dust produced by strong downdrafts that occur regularly in arid and semi-arid regions – requires resolving wet convection explicitly, which represents a formidable challenge.
Dust forecasts that use actual satellite aerosol data perform better than forecasts that rely solely on modeling to define initial conditions. Further improvements in global observing systems and assimilation techniques show promising prospects.
In addition to model and forecast improvements, effective mitigation of the negative effects of dust storms requires further development. The poor integration of information and quantitative dust forecasts in practice is often due to a lack of understanding of the precise impact of storms in particular sectors. Other factors include the lack of products or services tailored to specific applications; lack of awareness, understanding, capacity or structures to use the information; and the general challenge of incorporating uncertain information or forecasts into management practices.
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