Goal
Our aim is to publish an initial assessment of the key technical uncertainties in SAI in the form of a dynamic, public, scientifically-grounded, database. This is the first step toward a transparent, prioritized research roadmap that helps the field plan, sequence, and fund the most decision-relevant work.
It addresses the uncertainties that affect the ability to make informed decisions on SAI in a timely manner — namely around engineering feasibility and SAI-induced changes to climate and Earth system processes. Each uncertainty is ranked by (1) how likely research is to be wrong and (2) how much it would matter for decision-making if it was. Different people will of course reach different conclusions regarding both of these aspects. However, an initial assessment and an evidence-based rationale for our choices is essential to enable those conversations.
In order to bound scope, we exclude scenario-based uncertainty by selecting a single deployment scenario, and also exclude other societal or geopolitical uncertainties, restricting ourselves only to physical science and engineering.
It is important to acknowledge that questions around scenarios and governance issues will be critical in future decision making. We have chosen to exclude them here not because we think they are of lesser importance, rather we have done so to constrain the task so we can release a first version in a timely manner. We are considering ways to add more comprehensive analyses with future iterations. It is also important to note that while we aimed to be as comprehensive as we could be in our list of uncertainties, there may still be important technical uncertainties that we did not capture here. We welcome feedback from the scientific community on all parts of the database here.
How we did this
For each uncertainty, we define a quantitative metric — a statement about the engineering or physical system for which one can, in principle, estimate both (a) the probability that metric will be met or exceeded and (b) the impact to informed decision making if that is the case. We refer to these two fields as “level of uncertainty” and “decision relevance.” These assessments reflect extensive desk research along with feedback from trusted members of the SAI field. This is just a snapshot of the process — there are many more categories and details we included that are described in the methodology linked below.
Read the full methodology →Scenario assumptions
To bound the technical scope, we adopt some scenario assumptions. These assumptions have uncertainty themselves, and we have described why we chose specific parts of the scenario in the link above. The purpose is not to imply that the chosen scenario is preferable or more “likely,” but to ensure comparability across uncertainties and to ensure the release of this first version in a timely manner. Future iterations may introduce multiple scenarios or explicitly represent scenario-driven uncertainty.
Learn more →Frequently asked questions
Stratospheric Aerosol Injection (SAI) is a proposed solar geoengineering approach that would reflect a small amount of sunlight back to space to reduce global warming. It mimics the cooling effect observed after large volcanic eruptions.
This project serves multiple audiences:
- Researchers looking to understand priority areas for investigation
- Policymakers seeking to understand the state of scientific knowledge
- Funders evaluating where research investments could have the most impact
- The public interested in understanding SAI research
Uncertainties are organized by type (e.g., Aerosol processes, Climate response, Engineering) and assessed along two key dimensions: the degree of uncertainty (Low, Medium, High) and the potential consequence if the uncertainty resolves unfavorably.
The database is designed as a living resource and will be updated as new research emerges and our understanding evolves. Major updates will be documented in the changelog.