GFDL - Geophysical Fluid Dynamics Laboratory

Projection of American dustiness in the late 21st century due to climate change

July 17th, 2017

Bing Pu and Paul Ginoux. Scientific Reports. DOI: 10.1038/s41598-017-05431-9.

Summary

Mineral dust is one of the most abundant atmospheric aerosols by mass. It is lifted to the atmosphere by strong wind from dry and bare surfaces. This study identifies key factors influencing dust activity in the U.S. and uses the projected changes of these influential factors to understand dust activity in the future.

Severe dust storms have far-reaching socioeconomic impacts, affecting public transportation and health by degrading visibility, causing breathing problems and lung diseases. Dust plumes can even be continental in scale, such as the one originating in October 2012 from Nebraska and settling in the Tennessee Valley. More impressive are walls of dust (haboobs) associated with thunderstorm clouds that regularly sweep over Arizona, affecting air quality and ground transportation, as well as air traffic.

Climate models project “unprecedented” dry conditions in the late 21st century over the southwestern and central U.S., regions that are co-located with major dust sources (such as the Mojave, Sonoran, and Chihuahuan deserts). However, whether dust events in the U.S. will increase in the future is not clear, as most current climate models have difficulty capturing the spatial pattern and magnitude of the dust loading in the U.S.

Using satellite observations, the authors identified the main factors influencing dust activity in the U.S. They then used projected changes of these influential factors from the Coupled Model Intercomparison Project Phase 5 (CMIP5) models to project dust activity in the future. The authors found a close connection between dust activity and drought. Dust event frequency peaks during severe drought, such as the 2011 drought in the southern Great Plains, the 2012 drought centered in the central U.S., and the California droughts during 2007-2009 and 2011-2016.

In addition to precipitation deficit, factors such as surface wind speeds and vegetation coverage are also related to dust emission and transport. This study shows how these factors influence dustiness in the U.S. About 49% to 88% of the variances of dust event frequency over the western U.S. and the Great Plains during 2004-2015 can be explained by these factors.

CMIP5 models simulate changes of the climate system in response to different greenhouse gas emission scenarios. Using the projected changes of precipitation, surface wind, and bareness, the authors projected that more dust events will occur in the southern Great Plains (Texas, Oklahoma, southern Kansas) from spring to autumn (up to ~5 more dusty days compared to historical conditions) and part of the southwestern U.S. (California, Arizona) in summer and fall in the late half of the 21st century. This increase in dust events is largely due to reduced precipitation, enhanced bareness, and increased surface wind speed. Over the northern Great Plains (North Dakota, South Dakota, Nebraska, northern Kansas), less dusty days are expected in spring due to increased precipitation and reduced bareness. These projections may provide early warning for erosion control, and could help guide future hazards prevention and water resource management.

There are some uncertainties associated with this work. The relationships between dust activity and the influential factors are established based on a relatively short time period. It is possible that the strength of these relationships would vary over a longer time period, and this adds to the uncertainty of the projections. How future anthropogenic activities will influence land use change and consequently dust emission is not included in the projection. The uncertainties associated with the variations of precipitation and surface bareness projected by the CMIP5 models also limit the accuracy of the projections on dust activities.

Figure: Projected changes of dust event frequency in the late half of the 21st century with reference to the historical condition (1861-2005) using a statistical model and CMIP5 model output.