Article
Will Scientists Know if SRM Caused Extreme Weather?
If sunlight reflection methods, also known as solar radiation modification (SRM), were deployed to lower temperatures, and even if it worked well, extreme weather events would continue to happen. How would scientists know if SRM helped alleviate weather extremes or made them worse?
Key takeaways
- Climate change does not cause specific extreme weather events, but it does make some extremes more likely or more intense.
- Determining what contribution global warming or SRM made to a particular observed extreme would depend on comparing climate model simulations with and without these factors.
- While the effects of global warming or SRM on some extremes, e.g., heatwaves, could be determined confidently, for other extremes, e.g., droughts, it would be much more uncertain.
Imagine the world in the not-too-distant future. It is five years into a deployment of stratospheric aerosol injection (SAI) that has already stalled global warming and is on track to drive global temperature down in the following years. To meet this goal, the scale of SAI deployment will continue growing to offset the warming effect of continued, albeit declining, greenhouse gas emissions.
Suppose that despite the success of SAI in decreasing global average temperatures, local and regional weather records continue to be broken. When a record-breaking heat wave strikes Northern Europe and the monsoon in Southeast Asia brings far less rain than usual, policymakers question whether SAI made these extremes worse and whether they should continue supporting SAI deployment.
While this scenario is hypothetical, it is inevitable that any deployment of SRM would be followed by extreme weather events, some of which could be unprecedented. Lower temperatures after SRM implementation could reduce the intensity of heatwaves and extreme rainfall,1 though shifts in rainfall patterns could mean some regions experience less drought and others more under SRM.2
What kinds of answers could scientists provide to policymakers concerned about the role SRM played in such extreme events?
Detecting and attributing climate changes
Determining whether the deployment of SRM reduced or worsened some observed extreme weather event would rely on detection and attribution methods. These methods identify trends in average climate conditions and changes in extremes, along with their potential drivers.
Determining that global temperatures have changed is an example of detection. Scientists are confident they have detected this trend as the change in global temperature is clearly distinct from the “noise” of natural year-to-year variability.
Once a climate trend is confidently detected, the change in climate can be attributed to some cause.3 Methods for attribution rely on climate models rather than observations alone.
By comparing climate model simulations with the factor of interest included and excluded, it is possible to determine the contribution of that factor to the observed trend. Scenarios that exclude human influence fail to reproduce the observed global warming trend. Using such methods, the Intergovernmental Panel on Climate Change (IPCC) has attributed global warming to human influence and stated that this is unequivocal.4
Global Temperature Change with and without Human Influence
Climate models show that observed warming can only be reproduced in climate model simulations if human actions, including greenhouse gas emissions, are included.
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Determining the role of climate change in weather extremes
Detection and attribution techniques can be applied to extreme weather in a process known as event attribution. By comparing the statistics of some weather event in modelled scenarios with and without human influence, it is possible to determine how much human influence affected the intensity of the event. It is then possible to calculate the change in the likelihood of seeing an extreme of the same magnitude as one that was observed.
Attribution is most straightforward when there is a clear connection to rising temperatures and a relatively large trend compared to year-to-year variability. For example, scientists determined that climate change increased the likelihood of the soaring temperatures like those seen across Mexico, Guatemala, Honduras, and the Southwestern United States in early summer 2024.
Attributing rainfall-related extremes to climate change is more difficult due to the large year-to-year variability in rainfall and its more complicated relationship to climate change. In 2022, devastating floods submerged about one third of Pakistan, affecting about 33 million people. Studies found that climate change increased the likelihood of the heavy rainfall, but there were compounding factors5 and large uncertainties across different models,6 highlighting complications in attributing extreme rainfall to climate change.
If SRM were deployed, there would still be extreme weather events
This article started with a hypothetical scenario in which future policymakers are faced with a heatwave in Northern Europe and a drought in Southeast Asia following the deployment of SRM. Something like this will be inevitable in the initial years of a potential SRM deployment as weather extremes are likely to continue breaking records regardless of how SRM is used.7
While it would be natural to think SRM could be responsible, it would be important for policymakers to know whether such extremes would have been even worse without SRM. Luckily, scientists have the tools to evaluate this question and will be able to provide estimates of whether and to what extent SRM reduced or worsened any particular extreme weather event.
However, while some weather extremes such as heatwaves will be easier to assess, other extremes such as droughts and storms would be much more challenging. So, while scientists may be confident that the Northern European heatwave in the scenario would have been even worse without SRM, they may be much less certain about the role of SRM in the Southeast Asian drought.
While researchers may make confident predictions about the general effects of SRM, it will never be possible to know all the effects of SRM with full confidence, either in advance or after the fact.8 However, the same is true of climate change and this has not prevented policymakers taking action. With SRM, though, the question of liability for harms may mean that these uncertainties pose a much greater challenge.
Open questions
- Which weather extremes could different SRM ideas help to reduce, and which could they worsen, and where?
- How could uncertainties regarding the effects of SRM on weather extremes affect whether and how countries cooperate on SRM?
- Would there be sufficient evidence to justify compensation or should alternative frameworks for liability, like insurance, be explored?
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Endnotes
- Irvine PJ, Keith DW. (2020). Halving warming with stratospheric aerosol geoengineering moderates policy-relevant climate hazards. Environmental Research Letters. 15(4):044011. https://doi.org/10.1088/1748-9326/ab76de
- Ricke K, Wan JS, Saenger M, et al. (2023) Hydrological consequences of solar geoengineering. Annual review of earth and planetary sciences. 51(1):447-70. https://doi.org/10.1146/annurev-earth-031920-083456
- The IPCC defines attribution as “the process of evaluating the relative contributions of multiple causal factors to a change or event with an assessment of confidence”.
- IPCC. (2023). Technical Summary. In Climate Change 2021 – The Physical Science Basis (pp. 35–144). Cambridge University Press. https://doi.org/10.1017/9781009157896.002
- Hong CC, Huang AY, Hsu HH, et al. (2023). Causes of 2022 Pakistan flooding and its linkage with China and Europe heatwaves. Npj Climate and Atmospheric Science, 6(1). https://doi.org/10.1038/s41612-023-00492-2
- Otto FEL, Zachariah M, Saeed F, et al. (2023). Climate change increased extreme monsoon rainfall, flooding highly vulnerable communities in Pakistan. Environmental Research: Climate, 2(2), 025001. https://doi.org/10.1088/2752-5295/acbfd5
- Climate change and SRM can be thought of as loading the dice of weather, changing the likelihood of extreme outcomes. Even if some extreme is made less likely by SRM, there will still be unlucky rolls of the dice in some places.
- Kravitz B, MacMartin DG. (2020). Uncertainty and the basis for confidence in solar geoengineering research. Nature Reviews Earth & Environment. 1(1):64-75. https://doi.org/10.1038/s43017-019-0004-7
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