Methodology

Method

1. Define a list of priority areas of uncertainty. Uncertainties are high priority if they strongly impact the feasibility, design or expected consequences of potential near-term SAI deployment. There is a trade-off between the level of granularity and maintaining a manageable list; we've tried to keep it sufficiently granular to be actionable without having too many uncertainties to keep track of.
2. For each uncertainty, define a 'metric'; this is a quantitative statement related to this uncertainty, about which it is possible (at least in principle) to estimate a probability the event under discussion will occur and the decision relevance of it occurring. Note that how "tight" or "loose" one makes the metric affects both the likelihood and the consequences; there is no intrinsic right answer, but we try to aim for a quantification so that where possible either the probability or decision relevance is "medium" (see below). In some cases we choose one metric as representative of a class (e.g., choosing a metric related to AMOC strength to capture uncertainties in ocean circulation).
3. Estimate the degree of uncertainty by assigning a probability to this metric: Low (0-10%), Medium (10-50%), and High (>50%).
4. Assign a "decision relevance" level: Low (changes efficacy of SAI by less than 20% from the central estimate and there is no plausible case where this uncertainty changes the overall cost-benefit enough to materially impact its importance to informed decision-making). Medium (could cause a greater than 20% change in efficacy or could materially impact side effect(s), but it is very unlikely this uncertainty changes the overall cost-benefit enough to materially impact its importance to informed decision-making). High (there is a plausible case in which the outcome of this uncertainty alone substantially changes the overall cost-benefit of SAI so as to materially impact its importance to informed decision-making, or the existence of the uncertainty would make it difficult to make informed decisions).
5. For the engineering uncertainties, decision relevance levels are defined as: Low (no impact on when one could feasibly deploy), Medium (an impact on when one could deploy of 5 years or less, as it relates to the scenario assumption below), High (an impact on when one could deploy of over 5 years, as it relates to the scenario assumption below).
6. Assign a scale at which these uncertainties will be resolved: In silico, Small-scale testing (10-100t SO2), Large-scale testing (100Kt SO2 in a season, still too small to affect climate), Discernible surface climate impact (>1 Mt/year of SO2, ~0.1°C global cooling), Long-term sustained deployment (≥0.5°C for ≥20 years).
7. Assign research activities which are needed to resolve the uncertainty, and whether these rely on deployment of SAI at a scale with a discernible (regional) climate impact, which we define as greater than 1 Mt/year SO2 (~0.1°C global cooling). We define "long-term sustained deployment" as at least 0.5°C (~5Mt/year) for at least 20 years.

Scenario

The importance of some of the uncertainties may depend somewhat on the scenario envisioned. While we understand that this is an important uncertainty in itself, we use the assumptions below to constrain our initial effort by focusing on a “well-managed,” “moderate” scenario. Where specification depends on the scenario, we assume the following:

• Deployment would be roughly hemispherically balanced, with injection in the subtropics at approximately 21km.
• Deployment would be gradually ramped up, and that 0.5°C of cooling would be at least a decade into deployment.
• Where relevant, we anchor our assessment of uncertainty and consequences on a deployment that cools by 0.5°C (by then we will know much more than now, and can re-evaluate uncertainties and whether to continue to increase cooling in light of what we know then).
• Injection of a gaseous precursor to sulfate (i.e. SO2 or H2S).

For many of the "engineering" related uncertainties, the degree of uncertainty depends on the assumed timeline of deployment. For the purposes of thinking through the uncertainties associated with the deployment engineering, we assume a scenario with:

• A start date of 2035 for deployment sufficient for discernable surface climate impact (as defined above), corresponding to ~0.1°C of cooling (or roughly 1 Tg SO2/yr) as a threshold.
• Initial deployment would be at higher latitudes with modified existing aircraft, so deployment at 13-15km, at ~50-70°N and 50-70°S, in respective spring and early summer (e.g., MAMJ in the Northern Hemisphere, though the details here don't matter).
• While one could start at higher latitudes with existing aircraft, within 5-10 years the deployment would transition to using new aircraft at higher altitudes, pursuant to scenario above.That is, there would never be sustained global cooling of 0.5°C or more using high-latitude only deployment, and hence for the purposes of this assessment, uncertainty in impacts iscan be based solely on the subtropical case; the high-latitude/low-altitude case is only included herein to capture the relevant engineering uncertainties).

Other notes and assumptions

• In principle, uncertainties could be both negative and positive (that is, beneficial impacts from SAI could be even better than expected rather than worse). Here, we choose metrics which emphasize the potential downsides in each case, such that our 'high' decision relevance would always reduce the likelihood of deploying SAI. This is a choice that could be revisited in the future.
• We explicitly do not consider uncertainties and risks arising from the societal and geopolitical dimensions (e.g. "moral hazard"). Some of these could be added in the future, though it is likely that there would be substantial disagreement on the probabilities of these. This is an important task, and we encourage groups with more expertise in these areas to create similar databases analyzing these non-technical uncertainties.
• Our approach is global in scale. For example, the response of the West African Monsoon to SAI is not explicitly included as its uncertainty, but instead is classed under uncertainty in tropical circulation. To compliment this aspect, we are finalizing a climate impacts tab that will allow for assessment of more regional impacts.
• When assessing impacts of SAI, it is common to compare either against the background warming scenario at the same time period, which we might call a 'direct' effect, or against a climate state at the same global mean temperature as is achieved under SAI but with lower GHG levels (an earlier climate state). The latter can be referred to as giving 'residual' changes relative to a baseline or target. Both of these approaches can be valuable, depending on the context. Here, we use a mixture of the two, but generally give precedence to the 'direct' effect assessments, since we are more interested in linking uncertainties to novel risks or harms caused by SAI than to any incomplete offsets of harms caused by GHG-forced warming.
• In various places we refer to the multi-model mean or range of some quantity (e.g. aerosol size distribution). This should be understood to refer to the set of modern global climate models with adequate performance to be useful in assessing the process in question - the assessment of which models contribute is therefore part of the subjective assessment of the degree of uncertainty. The value in question (e.g. a mean across these models) may not (yet) be available in the literature but we plan to have this available in the future.

Impacts view

In the future, we aim to link our uncertainties to a set of impacts. In some cases, the uncertainty directly maps onto a relevant impact. For example, uncertainty in the monsoon response to SAI directly maps onto the potential impact of "monsoon disruption". In other cases, a physical uncertainty may affect multiple impacts, or a given impact (e.g. sea-level rise) may be influenced by multiple uncertainties.

Our motivation for including both the "uncertainties" framing and the "impacts" framing is that they are aimed at different audiences:

• The uncertainties view is primarily aimed at scientists and aims to prioritise between the near-term uncertainties, understand the causes of uncertainty and how these can be resolved.
• In contrast, the impacts view is primarily aimed at policymakers and the public, and aims to communicate: An acknowledgment of the impacts and risks about which policymakers and the public are concerned. How our scientific uncertainties relate to our understanding of these risks – i.e. do we need to learn more before we can confidently state that each item is or is not a risk? Whether these risks are relevant at a given scale of deployment, and particularly, whether they are meaningful risks at the small-to-large-scale testing stages.

Research activities

Also in the future, we aim to link uncertainties to research activities that are necessary to reduce or resolve them. In addition, we plan on including estimated timelines and budgets for these activities with the goal of making the required research more digestible to policymakers and funders, and informing our internal research roadmap.