Are SRM scientists "boosters" or "blockers"?
An analysis of prominent papers and press releases found a bias towards exaggerating the risks and downplaying the benefits of SRM, but the bias against SRM also works in more subtle ways.
Are the scientists who study Solar Radiation Modification “boosters” as some critics suggest? Do they exaggerate its benefits while downplaying its risks, limitations and the challenges it presents?
Do they instead display “an unusual self-reflexivity, as they are well aware of and seriously consider all the technology’s risks” as one analysis of the literature found?
Or would it be better to describe them as “blockers”, exaggerating the risks, limitations and challenges, while downplaying its potential benefits?
There have now been hundreds of studies into the climate effects of Stratospheric Aerosol Injection (SAI), authored by many tens of individual scientists, and the picture is mixed. But taken together, it seems clear that while most scientists and studies are in the self-reflexive middle, there is some bias towards blocking and against boosting.
Cases of exaggeration in SRM studies and press release
Jesse Reynolds, my co-host on the Challenging Climate Podcast, published an article last year analyzing 9 cases where studies or their press releases distorted their findings to paint an exaggerated picture of the risks and limitations of SRM. He attempted to find similar cases where the benefits had been exaggerated, but could not1.
Let’s take a look at some of his examples, which all focus on Stratospheric Aerosol Injection (SAI).
SAI “disrupts” the monsoons and the food supply for billions
Robock et al. (2008) is a landmark study in the field. It was the first climate model simulation of SAI that actually injected sulphate into the model’s stratosphere, and it made an enormous scientific contribution to the field. It also has had an enormous impact on the public discussion of SAI because of this line in the abstract:
"Both tropical and Arctic SO2 injection would disrupt the Asian and African summer monsoons, reducing precipitation to the food supply for billions of people."
This statement is not wrong, but it is rather leading. The use of evocative language (“disrupt”), rather than a quantitative description of the findings, leaves it up to the reader’s imagination how large this reduction is. The important context that climate change is expected to intensify monsoon precipitation in these regions is also not mentioned. And the reference to the "food suply for billions of people” insinuates that there might be famine, though no analysis of crop productivity is conducted.
SAI (greatly reduces, but) fails to elimate risk of stratocumulus collapse
Schneider et al. (2019) was the first study to identify a potential atmospheric tipping point that could accelerate warming in marine stratocumulus clouds. Marine stratocumulus clouds are low, bright clouds that occur in large patches off of the continents in the sub-tropics and have a significant cooling effect.
In a small-scale, cloud resolving model Schneider et al. found that very high CO2 concentrations (>1200 ppmv or >4x preindustrial, compared to today’s ~1.5x) could lead to a collapse of marine strato-cumulus clouds. They extrapolated from their findings to the global level and suggested this collapse could add substantially to global-mean temperatures2.
Schneider et al. (2020) repeated this study but with idealized SRM and found that CO2 concentrations would need to reach even more extreme levels (>1700 ppmv or >6x preindustrial) to lead to a collapse of the clouds and in this case would lead to a smaller rise in temperatures. However, their study didn't make this comparison, focusing on the fact that SRM fails to prevent this tipping point. Their plain language summary then misleadingly states that SRM “does not mitigate risks to the climate system that arise from direct effects of greenhouse gases on cloud cover”
(Sudden changes in) SAI deployment is bad for biodiversity
Trisos et al. (2018) report on the biodiversity impacts of the GeoMIP G4 simulations, an idealized case where SAI is suddenly turned on and then later turned off. They apply an environmental niche approach, which assumes that species and ecosystems thrive within a certain limited range of climate conditions and that if those conditions change they will be stressed.
They finds that if SAI were deployed continually it would greatly reduce the magnitude of climate change and its rate of change, reducing the stress that ecosystems face. While their abstract states that they “assess the effects of the rapid implementation, continuation and sudden termination of geoengineering…,” their abstract only reports on the sudden initiation and sudden termination, highlighting the risks this could pose. Their press release amplifies and generalizes their claim: "Researchers from the US say deliberately changing Earth’s climate is more dangerous to ecosystems than global warming is likely to be..."
Figure 1. Shows the global temperature response to the RCP4.5 and G4 scenarios. Figure 1a from Trisos et al. (2018).
(Assymetric) SAI could have devastating hydrological impacts
Haywood et al. (2013) evaluated the impact of adding sulphate aerosols into a single hemisphere (whether from a volcano or as a deployment strategy for SAI) and found substantial shifts in tropical rainfall towards the uncooled hemisphere. Every year the Inter-Tropical Convergence Zone (the intense band of rain in the tropics) tracks the “thermal equator” of the planet, shifting into the warmer hemisphere in its respective summer. Haywood et al. found that a Northern Hemisphere-biased aerosol layer would cause a substantial reduction in rainfall in the Sahel, which could have substantial societal and ecosystem impacts.
Jones et al. (2017) followed up on this study to evaluate the impact of single-hemisphere SAI on tropical cyclones. They found that northern-hemispheric SAI would suppress northern hemisphere tropical cyclones, but lead to those substantial shifts in rainfall patterns noted above. The measured statements in the paper are not matched in the press release which generalizes from assymetric SAI to SAI in general and makes strong policy demands:
"(SAI) could have a devastating effect on global regions prone to either tumultuous storms or prolonged drought... the team of researchers have called on policymakers worldwide to strictly regulate any large scale unilateral geoengineering programmes ..."
Some other forms of bias in SRM science
Jesse’s study does a great job of exploring the biases in particular SRM publications and their press releases, and if you want to see more case studies or to read his discussion of the potential drivers for this bias, its well worth checking out.
Like Jesse, I couldn’t identify any examples from memory where studies of SAI had exaggerated their benefits, though it is important to note that there have been many media articles exaggerating its benefits. However, unlike the press releases prepared for their studies, the framing adopted in media coverage is beyond the direct control of researchers.
Jesse’s analysis is important but it doesn’t tell the whole story. I think it’s fair to say that the bias against SRM in climate science runs much deeper than a skew towards blocking and away from boosting in studies on SRM and in their press releases. However, many of these forms of bias are harder to spot.
Burying the good news on SRM
Jesse focused on the exaggerations in published studies and press releases that had a notable impact on the public discussion of SRM. However, its practically impossible to spot when researchers choose not to publicize findings that could be media worthy.
In the early 2010s, I remember most of us working on the climate response to SAI were surprised (and unsettled) by how well it seemed to work. While it was clear early on that it had a modest impact on stratospheric ozone, and imperfectly offset regional rainfall changes, it still reduced the overall magnitude of rainfall changes. We were all poring over our results trying to find out if a deal-breaking problem existed.
I remember reading a dry, technical study on the climate effects of SAI in the mid-2010s that focused on a key potential area where a deal-breaker might be lurking. They found that SAI was again highly effective at offsetting the effects of climate change. I thought this was a really big deal and was surprised that they weren’t making a bigger deal of it.
When a co-author of that paper visited the research institute that I worked at, they admitted that they were hoping to find negative results and would have promoted those if they did. They could have potentially presented their results in a higher impact journal and promoted it with a press release, but the fact they had broader concerns about SAI led them to bury their findings where only technically-minded colleagues would find them.
It’s impossible to know how widespread this kind of bias against promoting good news on SRM is, but I suspect there are several other such examples.
Withholding expertise from SRM
The scientific models, tools, and expertise needed to assess SRM are the same as those needed to assess climate change. As such, the SRM scientific community is a sub-set of the broader climate science community. While the publications on SRM taken together exhibit some bias towards blocking and away from boosting, it is much harder to see the bias against publishing or contributing in the first place.
Around the same time as the previous anecdote, I was preparing for an expert elicitation project on sea-level rise (that never materialized) and I was arranging meetings with sea-level rise experts. I remember meeting with one expert and he was clear: he didn’t want to participate as it was obvious to him that SAI would be highly effective at offsetting sea level rise. Like the other researcher mentioned before, he had other concerns about SAI and didn’t want to be involved in work that could make it look good.
Blocking funding, publication and hiring?
Publication, funding, and hiring decisions are made or strongly influenced by senior scientists. As it would be very hard to demonstrate biases in such decision-making, I will let the reader make their own speculations about the extent to which senior researchers in a field work to promote ideas they favour and suppress those they dislike.
I’ll also leave it to the reader to guess which way such a bias, if it exists, leans in this case. However, I will note that several colleagues I’ve spoken to were warned in no uncertain terms that if they were to focus their research on SRM it would be career suicide. Several of them took this advice and relayed it on to me.
Is SRM science being boosted or blocked?
Most of the scientific work on SRM is carried out carefully and even-handedly, and the researchers involved are well aware of the broader risks and challenges it raises. This consciousness of the broader challenges of SRM means that no studies I’m aware of exaggerate SRM’s benefits or downplay its risks3. However, as Jesse demonstrates some studies do the opposite, exaggerating its risks and downplaying its benefits.
As I’ve suggested, the bias against SRM operates not only in the published literature where it can be seen, but it is likely also working behind the scenes where it is much less obvious. Researchers who are biased against SRM can bury good news, withold their expertise and tools, advise junior colleagues against studying the topic, and can influence publication, hiring and funding decisions.
As a result of all this, the picture of SRM’s risks and benefits that a casual observer gets from following the media coverage is distorted. Far from SRM scientists acting as boosters for this idea, the reluctance of some and the opposition of others, is blocking the public and policymakers from forming a clear picture.
I have hope that in the coming years an objective assessment of SRM’s risks and benefits will be made, but in the mean-time I’ll leave you with a few questions to ask yourself when reading the next piece of bad news on SRM:
Have they reported a seemingly dangerous trend under SRM, without comparing this against the trend under climate change?
Have they reported that SRM fails to achieve perfection, downplaying the fact that it greatly reduced risks?
Have they focused on the risks of an extreme scenario, where a more sensible one would avoid this risk?
FIN
If you have some examples, please reply letting me know.
It seems unlikely that we’ll ever reach atmospheric CO2 concentrations so high, unless future generations make a strong commitment to rapidly expanding coal use, as is assumed in the unrealistically high emissions scenarios RCP8.5 and SSP5-8.5.
The fact that scientists studying SAI and most other forms of SRM have no commercial interest in the idea is another important reason.
For what it is worth, my article faced peer review that was, in my opinion, hostile. One reviewer was one of the authors of the paper(s) whose communication I critique. After self-identifying by stating his name in the generally anonymous review, he strawmanned the paper and made ad hominem criticism of me. After nine months of back and forth, that journal rejected my paper.
-Jesse Reynolds
why do you think this bias exists?
possibilities I can imagine
1: scientists believe in policy goals which SRM disagrees with
2: scientists believe it will be widely enacted and if they don't predict bad outcomes they'll be blamed for side effects
3: if scientists find positive outcomes in models, they might be pushed to do or suggest field experiments, which will then get them attacked by fringe political groups and possibly fired (and also sound like quite a bit of work)