Modeling Atmospheric Turbulence and Its Impacts on Plume Dispersion for Stratospheric Aerosol Injection

  • Awardees
  • Robert Wood, Ph.D. University of Washington
  • Lekha Patel, Ph.D. Sandia National Laboratories
Year Awarded


The concept behind stratospheric aerosol injection (SAI) is to inject aerosols or their precursors into the stratosphere, likely using aircraft, to increase the reflection of incoming sunlight back to space, thereby cooling the Earth’s climate and mitigating some of the impacts of global warming. Previous studies have shown that injected aircraft or rocket plumes (containing aerosols) can keep their quasi-linear structure for several days or weeks with plumes as long as thousands of kilometers and cross-sections as narrow as a few kilometers. The evolution (including physical, chemical and aerosol processes) of these line-shape plumes highly depends on near-field (less than 100 km) atmospheric turbulence, which is poorly represented in global climate models (GCMs) due to the controlling processes occurring at the subgrid-scale for these models, and due to limited observations of both plumes and turbulence in the stratosphere with which to constrain the models. Better understanding of stratospheric near-field turbulence and its impacts on the evolution of injected plumes is needed to improve representations of stratospheric turbulence and plume evolution for SAI in global-scale modeling (e.g., GCMs), which will affect the evaluation of both the cooling magnitude and side effects (e.g., stratospheric warming, ozone depletion, changes in precipitation) of SAI.

Two main goals of this study include: (1) using large eddy simulations (LES, with a sufficiently high vertical and horizontal resolution to explicitly simulate much of the energy-containing atmospheric turbulence) to accurately assess the near-field (less than 100 km) stratospheric turbulence and its impacts on the dispersion of SAI’s aerosol plumes, and (2) atmospheric turbulence results from LES will be used to optimize the turbulence parameterizations (e.g., diffusion coefficient) of the Lagrangian plume model that we have developed to better represent the evolution of line-shape plumes (e.g., aircraft plumes containing injected aerosols for SAI). We can couple the optimized Lagrangian plume model (with improved representations of aerosols and turbulence) into a GCM to build a multiscale plume-in-grid model. This study will be carried out as a collaboration between the University of Washington and Sandia National Laboratories.

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