Potential for perceived failure of stratospheric aerosol injection deployment

Keys, Patrick; Barnes, Elizabeth A.; Diffenbaugh, Noah S.; Hurrell, James W.; Bell, Curtis

doi:10.1073/pnas.2210036119

Cited by 24 publications

(39 citation statements)

References 63 publications

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“…Richter Jadwiga et al, 2018), but has seen little exploration with respect to climate impacts. Policymakers and planning practitioners often assess climate information on time horizons of 10 years or fewer (e.g., Bolson et al, 2013;DePolt, 2021;Pearman & Cravens, 2022;Keys et al, 2022). Thus, we portray our results consistently with how information could hypothetically be used for decisions about SAI deployment, governance, and evaluation.…”

Section: Analysis Metricssupporting

confidence: 68%

Assessing Outcomes in Stratospheric Aerosol Injection Scenarios Shortly After Deployment

Hueholt

Barnes

Hurrell

et al. 2023

Preprint

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Current global actions to reduce greenhouse gas emissions are very likely to be insufficient to meet the climate targets outlined under the Paris Agreement. This motivates research on possible methods for intervening in the Earth system to minimize climate risk while decarbonization efforts continue. One such hypothetical climate intervention is stratospheric aerosol injection (SAI), where reflective particles would be released into the stratosphere to cool the planet by reducing solar insolation. The climate response to SAI is not well understood, particularly on short-term time horizons frequently used by decision makers and planning practitioners to assess climate information. This knowledge gap limits informed discussion of SAI outside the scientific community. We demonstrate two framings to explore the climate response in the decade after SAI deployment in modeling experiments with parallel SAI and no-SAI simulations. The first framing, which we call a snapshot around deployment, displays change over time within the SAI scenarios and applies to the question “What happens before and after SAI is deployed in the model?” The second framing, the intervention impact, displays the difference between the SAI and no-SAI simulations, corresponding to the question “What is the impact of a given intervention relative to climate change with no intervention?” We apply these framings to annual mean 2-meter temperature, precipitation, and a precipitation extreme in the first two experiments to use large ensembles of Earth system models that comprehensively represent both the SAI injection process and climate response, and connect these results to implications for other climate variables.

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Section: Analysis Metricssupporting

confidence: 68%

Assessing Outcomes in Stratospheric Aerosol Injection Scenarios Shortly After Deployment

Hueholt

Barnes

Hurrell

et al. 2023

Preprint

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“…Deser et al (2012,2020), for instance, have used large ensembles of simulations with climate models to show that natural variability can dominate regional changes in seasonal-mean temperature and precipitation over the coming decades. Similarly, Keys et al (2022) show that the signal of SAI forcing can be strongly masked by natural variability over large regions of the globe. The presence of natural climate variability has thus been a challenge for studies attempting to detect regional climate changes due to external forcing.…”

Section: Discussionmentioning

confidence: 86%

Detecting Changes in Global Extremes Under the GLENS‐SAI Climate Intervention Strategy

Barnes

Hurrell

Sun

2022

Geophysical Research Letters

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Significant advances in the scientific understanding of climate change over the past several decades have made it clear that there has been a change in climate that goes beyond the range of natural variability (e.g., Santer, Painter, Mears, et al., 2013, Santer, Painter, Bonfils et al., 2013Bonfils et al., 2020). The culprit is the astonishing rate at which greenhouse gas concentrations have increased in the atmosphere, mostly through the burning of fossil fuels and changes in land use, such as those associated with agriculture and deforestation (IPCC, 2021). Greenhouse gasses are relatively transparent to incoming solar radiation while they absorb and reemit outgoing infrared radiation. The result is that more energy stays in the global climate system, raising not only temperature but also producing many other direct and indirect changes in the climate system, including changes in the frequency and intensity of extreme events (NASEM, 2016).Heat waves, for instance, are exceedingly important to human systems and infrastructure, as well as natural systems. People and ecosystems are adapted to a range of natural weather variations, but it is the extremes of weather and climate that exceed tolerances (e.g., Curtis et al., 2017;Ummenhofer & Meehl, 2017). Widespread changes in temperature extremes have been observed over the last 50 years. In particular, the number of heat waves globally has increased, and there have been widespread increases in the numbers of warm nights.Days with frost as well as cold days and cold nights have become rarer (IPCC, 2021). Changes are also occurring in the amount, intensity, frequency, and type of precipitation in ways that are also consistent with a warming

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“…This result is not surprising given the challenges in disentangling the influences of anthropogenic aerosols, greenhouse gases, and internal variability on the forced response of regional precipitation (Lin et al, 2016;Deser et al, 2020;Ha et al, 2020). As shown in Keys et al (2022), internal variability can modulate or altogether mask the influences of SCI in the ARISE-SAI-1.5 simulation. Nonetheless, we cannot rule out higher prediction skill with more available training data.…”

Section: Discussionmentioning

confidence: 87%

“…This includes warming in parts of the extratropical Northern Hemisphere. Rather than suggesting that this is a robust, forced response to SCI, it is more likely that this is simply a reflection of internal variability, which is discussed in more detail in Keys et al (2022) and is also demonstrated by the large spread of ensemble member trends in figure s14. Furthermore, there is large variability in precipitation trends in the first decade since SCI initiation (figure s13(a)), but weaker trends in the longer 2045 to 2069 period (figure s13(d)).…”

Section: Time-evolving Climate Signals From Saimentioning

confidence: 95%

“…Although there are di↵erences in the ensemble mean trends of temperature between SAI and SSP2-4.5, the ensemble member spread overlaps in all regions for at least the first 10 years after the start of SCI. This suggests that internal variability alone could inhibit determining whether a region is observing a SAI or SSP2-4.5 world (Keys et al, 2022;NASEM, 2021). To investigate this question, we utilize our first machine learning method.…”

Section: Detecting the Regional Emergence Of Saimentioning

confidence: 99%

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Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations

Labe¹,

Barnes²,

Hurrell³

2023

Preprint

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Stratospheric aerosol injection is a proposed form of solar climate invention (SCI) that could potentially reduce the amount of future warming from externally-forced climate change. However, more research is needed, as there are significant uncertainties surrounding the possible impacts of SCI, including unforeseen effects on regional climate patterns. In this study, we consider a climate model simulation of the deployment of stratospheric aerosols to maintain the global mean surface temperature at 1.5°C above pre-industrial levels. Leveraging two different machine learning methods, we evaluate when the effects of SCI would be detectable at regional scales. Specifically, we train a logistic regression model to classify whether an annual mean map of near-surface temperature or total precipitation is from a future climate under the influence of SCI or not. We then design an artificial neural network to predict how many years it has been since the deployment of SCI by inputting the regional maps from the climate intervention scenario. In both detection methods, we use feature attribution methods to spatially understand the forced climate patterns that are important for the machine learning model predictions. The effect of SCI on regional temperature patterns is detectable in under a decade for most regions. However, the effect of SCI on regional precipitation patterns is more difficult to distinguish due to the presence of internal climate variability.

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Potential for perceived failure of stratospheric aerosol injection deployment

Cited by 24 publications

References 63 publications

Assessing Outcomes in Stratospheric Aerosol Injection Scenarios Shortly After Deployment

Assessing Outcomes in Stratospheric Aerosol Injection Scenarios Shortly After Deployment

Detecting Changes in Global Extremes Under the GLENS‐SAI Climate Intervention Strategy

Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations

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