Sandia Labs FY22 Laboratory Directed Research & Development Annual Report

FY22 ANNUAL REPORT

QUANTIFYING AEROSOL INJECTION BEHAVIOR FROM LARGE-SCALE SATELLITE IMAGERY USING STATISTICAL MODELING AND MACHINE LEARNING. Solar climate intervention (SCI) strategies using aerosol injections exploit absorption and scattering properties to calibrate temperatures. However, true trajectorial dispersion of aerosols remains the largest source of uncertainty in climate models. To improve modeling accuracy, the Sandia LDRD team, in collaboration with Sandia National/Regional partner University of Washington, developed novel aerosol dispersion parametrizations at different spatio-temporal scales, one of which was highlighted in the cross-disciplinary journal Environmental Data Science . Machine and

statistical learning algorithms created by the team have allowed for more understanding of dispersion fields from imagery and are to be exploited with complex climate models, leading to opportunities to improve aerosol SCI strategies for Sandia’s climate intervention roadmap. In the project’s second year, Sandia’s large fog chamber will extend the experiments and analysis done using the table-top mini chamber built by the team by upscaling the domain of consideration from a single point to a 3D chamber on the order of tens of meters. The chambers will show the impact of scale on different parametrizations and what can be learned about atmospheric aerosols. (PI: Lekha Patel)

Satellite imagery can detect ship tracks, temporary cloud trails created via cloud seeding by the emitted aerosols of large ships traversing the world’s oceans.

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