Using Optimization Tools to Explore Stratospheric Aerosol Injection Strategies Article Swipe
Ezra Brody
,
Yan Zhang
,
Douglas G. MacMartin
,
Daniele Visioni
,
Ben Kravitz
,
Ewa Bednarz
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2024-3974
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2024-3974
Stratospheric aerosol injection (SAI), as a possible supplement to emission reduction, has the potential to reduce some of the impacts associated with climate change. However, the outcomes will depend on how it is deployed: not just how much, but the latitudes of injection and the distribution of injection rates across those latitudes. Different such strategies have been proposed, managing up to three climate metrics simultaneously by injecting at multiple latitudes. Nonetheless, these strategies still do not fully compensate for the pattern of climate changes caused by increased greenhouse gas concentrations, creating a novel climate state. To date there has not been a systematic assessment of whether there are strategies that could do a better job of managing some specific climate goals, nor an assessment of any underlying trade-offs between managing different sets of climate goals. Herein we use existing climate model simulations of the response to injection at 7 different latitudes, and apply optimization tools to explore the limitations and trade-offs when designing strategies that combine injection across these latitudes. This relies on linearity being a sufficiently good assumption, which we first validate. The resulting "best"' strategy of course depends on what goals are being optimized for. For example, at 1 degree Celsius of cooling, we predict that there exist strategies that do a better job than those simulated to date at simultaneously balancing regional temperature and precipitation responses, but the differences may be too small to detect at lower levels of cooling.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-2024-3974
- OA Status
- gold
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406479571
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406479571Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/egusphere-2024-3974Digital Object Identifier
- Title
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Using Optimization Tools to Explore Stratospheric Aerosol Injection StrategiesWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-01-16Full publication date if available
- Authors
-
Ezra Brody, Yan Zhang, Douglas G. MacMartin, Daniele Visioni, Ben Kravitz, Ewa BednarzList of authors in order
- Landing page
-
https://doi.org/10.5194/egusphere-2024-3974Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5194/egusphere-2024-3974Direct OA link when available
- Concepts
-
Aerosol, Environmental science, Computer science, Meteorology, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.climate | 23, 63, 83, 94, 120, 134, 140 |
| abstract_inverted_index.combine | 166 |
| abstract_inverted_index.depends | 190 |
| abstract_inverted_index.explore | 157 |
| abstract_inverted_index.impacts | 20 |
| abstract_inverted_index.metrics | 64 |
| abstract_inverted_index.pattern | 81 |
| abstract_inverted_index.predict | 207 |
| abstract_inverted_index.whether | 106 |
| abstract_inverted_index.However, | 25 |
| abstract_inverted_index.cooling, | 205 |
| abstract_inverted_index.cooling. | 243 |
| abstract_inverted_index.creating | 91 |
| abstract_inverted_index.emission | 10 |
| abstract_inverted_index.example, | 199 |
| abstract_inverted_index.existing | 139 |
| abstract_inverted_index.managing | 59, 117, 130 |
| abstract_inverted_index.multiple | 69 |
| abstract_inverted_index.outcomes | 27 |
| abstract_inverted_index.possible | 7 |
| abstract_inverted_index.regional | 225 |
| abstract_inverted_index.response | 145 |
| abstract_inverted_index.specific | 119 |
| abstract_inverted_index.strategy | 187 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.Different | 53 |
| abstract_inverted_index.balancing | 224 |
| abstract_inverted_index.deployed: | 34 |
| abstract_inverted_index.designing | 163 |
| abstract_inverted_index.different | 131, 150 |
| abstract_inverted_index.increased | 87 |
| abstract_inverted_index.injecting | 67 |
| abstract_inverted_index.injection | 3, 43, 48, 147, 167 |
| abstract_inverted_index.latitudes | 41 |
| abstract_inverted_index.linearity | 174 |
| abstract_inverted_index.optimized | 196 |
| abstract_inverted_index.potential | 14 |
| abstract_inverted_index.proposed, | 58 |
| abstract_inverted_index.resulting | 185 |
| abstract_inverted_index.simulated | 219 |
| abstract_inverted_index.validate. | 183 |
| abstract_inverted_index.assessment | 104, 124 |
| abstract_inverted_index.associated | 21 |
| abstract_inverted_index.compensate | 78 |
| abstract_inverted_index.greenhouse | 88 |
| abstract_inverted_index.latitudes, | 151 |
| abstract_inverted_index.latitudes. | 52, 70, 170 |
| abstract_inverted_index.reduction, | 11 |
| abstract_inverted_index.responses, | 229 |
| abstract_inverted_index.strategies | 55, 73, 109, 164, 211 |
| abstract_inverted_index.supplement | 8 |
| abstract_inverted_index.systematic | 103 |
| abstract_inverted_index.trade-offs | 128, 161 |
| abstract_inverted_index.underlying | 127 |
| abstract_inverted_index.assumption, | 179 |
| abstract_inverted_index.differences | 232 |
| abstract_inverted_index.limitations | 159 |
| abstract_inverted_index.simulations | 142 |
| abstract_inverted_index.temperature | 226 |
| abstract_inverted_index.Nonetheless, | 71 |
| abstract_inverted_index.distribution | 46 |
| abstract_inverted_index.optimization | 154 |
| abstract_inverted_index.sufficiently | 177 |
| abstract_inverted_index.Stratospheric | 1 |
| abstract_inverted_index.precipitation | 228 |
| abstract_inverted_index.simultaneously | 65, 223 |
| abstract_inverted_index.concentrations, | 90 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5042335835, https://openalex.org/A5043688506, https://openalex.org/A5041474717, https://openalex.org/A5018026394, https://openalex.org/A5011148802, https://openalex.org/A5100456430 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I142606810, https://openalex.org/I161675122, https://openalex.org/I188538660, https://openalex.org/I205783295, https://openalex.org/I4210119109, https://openalex.org/I4210132178 |
| citation_normalized_percentile.value | 0.82312293 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |