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oai:mountainscholar.org:10217/2427282026-01-21T15:24:01Zcom_10217_100532com_10217_100000com_10217_100333com_10217_100303col_10217_182111col_10217_100335

Connolly, Charlotte J., author Barnes, Elizabeth A., advisor Hurrell, James W., committee member Randall, David, committee member Anderson, Chuck, committee member 2026-01-12T11:29:19Z 2025 Connolly_colostate_0053A_19248.pdf https://hdl.handle.net/10217/242728 https://doi.org/10.25675/3.025620 Stratospheric aerosol injection (SAI) is a proposed climate intervention strategy that may alleviate the most severe climate change impacts. The ways in which a possible SAI deployment may occur are vast and require social, environmental, and political consideration. To help scientists decide which of the many possible futures to study, scenario design methods are often used. SAI scenarios generally fall into one of two categories. The first, coordinated global deployment, is characterized by global cooperation among all or most of the world's population in deciding how to deploy SAI. This work explores a common tool used alongside coordinated global deployment climate model simulations to quantify the tool's sensitivity to internal variability. The second category, single actor deployment, is characterized by a single party or a small group independently deploying SAI to achieve their own climate goals. Under a range of these scenarios, atmospheric responses may motivate additional counter SAI deployments or retaliation from other parties. This work quantifies atmospheric responses shortly after a wide range of single actor deployments. Finally, this work concludes by introducing a novel machine learning emulator designed to explore SAI deployment scenarios for use alongside earth system models to inform future simulation design. This machine learning emulator is comprised of multiple interpretable machine learning models that are trained on simulations from the Community Earth System Model version 2 including a novel simulation titled, Climate Response After Stratospheric Sulfur dioxide injection (CRASSULA). Here the emulator design is described along with how this emulator can be used for scenario design. The work in this dissertation emphasizes the need to question how SAI scenarios are simulated, what assumptions are made, and what possible scenarios are being overlooked. born digital doctoral dissertations English eng Colorado State University. Libraries 2020- Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. climate change data science machine learning climate intervention atmosphere emulator Studying climate intervention scenarios with data science methods Text Image Doctor of Philosophy (Ph.D.) Doctoral Atmospheric Science Colorado State University This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). open.access