For a broader overview of climate engineering, read our primer: www.srmprimer.org
Research focuses on better understanding Sunlight Reflection Methods (SRM; variously known as climate engineering, solar radiation modification, solar geoengineering, or solar climate intervention), with the aim of helping support future informed decisions. Research is interdisciplinary, but centered in the application of engineering-design ideas, dynamics, and feedback control as a tool to manage uncertainty; more generally this perspective is relevant in understanding climate dynamics and variability more broadly.
SRM or Climate Engineering refers to large-scale intentional intervention in the climate system as a possible additional tool to help manage some impacts of climate change; an example would be adding aerosols to the stratosphere to reflect some sunlight. This doesn’t reduce the need to cut greenhouse gas emissions, nonetheless deploying some amount of Climate Engineering might reduce climate damages, and more research is needed to evaluate it.
Our primary specific focus is GAUSS: Geoengineering Assessment across Uncertainty, Scenarios, and Strategies. Informed choices will require a holistic assessment of geoengineering, or climate engineering, that is not just our best estimate but captures uncertainty, for a range of future scenarios, and for different possible strategies. Simulations that span this space will be made available to the broader research community. Please check out our Q&A in the Cornell Chronicle for a deeper diver into our research philosophy.
Research is funded by NSF and by the Cornell University Atkinson Center through donations from Cornell alum.
Main research questions:
- System Design and Optimization: How can one “design” stratospheric-aerosol climate engineering, using available degrees of freedom (e.g., latitudes, times of year to inject material) to achieve desired objectives? What are the fundamental limits or trade-offs; that is, what can geoengineering do, and what can’t it do? How do outcomes depend on what the goals are?
- Systematic Uncertainty Assessment: Can we quantify how uncertain are our predictions? We need a risk-register: for any given uncertainty (e.g., aerosol microphysics), how uncertain is it, what are the consequences, what are the mitigation options? This is essential to prioritize research.
- Impacts: What would the effects be, both good and bad, on all the things we care about, and how does this depend on deployment choices?
- Policy and Governance: Research questions need to be informed by the societal context, and vice versa. E.g., what scenarios should we be simulating?
Specific ongoing research:
- How are results dependent on choice of scenario? This includes the amount of cooling, the effects of delaying the start date, the effects of interruptions in deployment or even multiple independent deployments. Can we use system identification to build an emulator for more general scenarios?
- How are results dependent on strategy, or choice of latitudes? Using optimization, what are the limits to how well stratospheric aerosol injection could compensate for greenhouse-gas driven warming? How does the answer depend on what metrics are used in optimization?
- How well can we design a strategy to simultaneously meet multiple climate metrics?
- What strategies could target specific climate objectives of concern, e.g. Arctic sea ice, or Antarctic ice-shelf stability?
- How different are different model representations? How sensitive are outcomes to parametric uncertainty in a climate model, and how well does feedback compensate for that uncertainty?
Other research ideas include (this is a very incomplete list but gives an idea for the flavour of questions):
- Current scenarios are somewhat idealized… what would happen if there were multiple actors with inconsistent goals, for example? How much worse would things be compared to idealized cases?
- Current simulations use feedback to manage climate goals directly. Design and demonstrate feedback control of aerosol optical depth instead, and then demonstrate an inner/outer loop structure to control surface temperature using desired AOD as the input.
- How sensitive are predictions to uncertainty? What happens if we change parameters influencing aerosol size distribution, for example?
- What would feedback look like in practice? What observations would be required, what would the first years of a deployment look like? What is the smallest useful global experiment to measure stratospheric aerosol properties? How detectable are different climate variables with different amounts of added aerosols?
- Validate an emulator (reduced-order dynamic model) on simulations of stratospheric aerosol injection, and predict the response for different scenarios, including for example predicting the rate-of-change of temperature and precipitation to understand stressors on ecosystems, or predict the multiple-actor scenario above.
- Validate whether taking aerosol fields from the “high-top” model CESM(WACCM) and applying them in the “low-top” version CESM(CAM) yields similar surface climate.
- Apply a design perspective to Marine Cloud Brightening; use system identification to estimate response patterns, develop feedback control to manage uncertainty and nonlinearity, and design and optimization to assess trade-offs and limitations. Further, integrate MCB with SAI.