This award provides funds for a 2-year Postdoctoral Research Fellowship for the PI, who will work under the mentorship of Professor Natalie Mahowald at Cornell University. During the Postdoctoral Research Fellowship the PI will develop and evaluate a physically-based parameterization of the emission of dust, primarily from deserts and semi-arid regions. The parameterization is based on a formal equivalence between the saltation process, through which dust aerosols are ejected from the surface as a result of impacts with bouncing sand grains, and the fragmentation of brittle materials such as glass. The project has three broad tasks: 1) Developing an improved dust emission parameterization that rigorously accounts for the dependence of the dust flux emitted by an eroding soil on wind speed, soil properties, and vegetation; 2) Implementing the dust emission parameterization into the NCAR Community Earth System Model (CESM), and using simulations of the present day dust cycle to further evaluate the parameterization against a wide range of ground-based and satellite measurements; and 3) Using the dust module to quantify the dust climate feedback in transitions from the preindustrial to the present day climate, and from the present day to a future climate in which carbon dioxide concentrations rise to twice their pre-industrial values.
The work will have broader impacts both practically and scientifically, as dust plays an important role in establishing the earth's climate, and the radiative effects of dust may constitute an important feedback for climate change. Thus, better representation of dust emission in climate and weather models may lead to better climate and weather predictions. In addition, dust emission plays an important role in cloud formation, drought physics, and atmospheric chemistry, and research in all these fields would be enhanced by better representation of dust emission in atmospheric models. Finally, the postdoctoral research fellowship, under the mentorship of an established scientist, will help to build the career of the PI, thereby providing support for the future workforce in this scientific discipline.
The main objective of this award was to more accurately quantify how the global cycle of atmospheric desert dust particulates (dust aerosols) responds to projected future changes in the climate, and how this change in atmospheric dust loading in turn feeds back onto the original change in climate. This goal was achieved by (i) the development of an improved physical model for the emission of dust aerosols, which reproduces dust emission measurements better than previous models; (ii) the implementation of this model into a global climate model (the Community Earth System Model, CESM), which results in better agreement of model simulations with dust optical depth measurements than results with previous dust emission models; and (iii) the use of this improved dust emission model in CESM to predict the feedback of changes in the global dust aerosol cycle onto the projected changes in climate. Specifically, we found that the emission of dust aerosols is more sensitive to the properties of desert soils than climate models currently account for. Since changes in climate affect these soil properties, the resulting response of the atmospheric loading of desert dust aerosols is probably larger than many climate models accounted for. Although the uncertainties in the magnitude of the dust climate feedback remain large, we find that this feedback is of the order of one to several percent of the total feedbacks in the climate system. The broader impacts of the performed research include the improvement of the community climate model CESM. Since CESM participates in reports by the Intergovernmental Panel on Climate Change (IPCC), which provides predictions of regional and global climate changes, this work contributed to informing U.S. and world climate policy. Furthermore, we anticipate that the dust emission model developed under this grant will be implemented in both operational weather forecasting and regional climate models. This should result in more accurate forecasts of weather and regional climate changes in dusty regions such as the southwestern U.S. and North Africa.
