Future seasonal surface temperature predictability with and without ARISE-stratospheric aerosol injection-1.5
To help reduce anthropogenic climate change impacts, various forms of solar radiation modification have been proposed to reduce the rate of warming. One method to intentionally reflect sunlight into space is through the introduction of reflective particles into the stratosphere, known as stratospher...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | Environmental Research: Climate |
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| Online Access: | https://doi.org/10.1088/2752-5295/ad9b43 |
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| author | Kirsten J Mayer Elizabeth A Barnes James W Hurrell |
| author_facet | Kirsten J Mayer Elizabeth A Barnes James W Hurrell |
| author_sort | Kirsten J Mayer |
| collection | DOAJ |
| description | To help reduce anthropogenic climate change impacts, various forms of solar radiation modification have been proposed to reduce the rate of warming. One method to intentionally reflect sunlight into space is through the introduction of reflective particles into the stratosphere, known as stratospheric aerosol injection (SAI). Previous research has shown that SAI implementation could lead to future climate impacts beyond surface temperature, including changes in El Niño Southern Oscillation (ENSO) variability. This response has the potential to modulate midlatitude variability and predictability through atmospheric teleconnections. Here, we explore possible differences in seasonal surface temperature predictability under a future with and without SAI implementation, using neural networks and the ARISE-SAI-1.5 simulations. We find significant future predictability changes in both boreal summer and winter under SSP2-4.5 with and without SAI. However, during boreal winter when SAI is implemented, seasonal predictability is more similar to the base climate than when SAI is not implemented, particularly in regions impacted by ENSO teleconnections. |
| format | Article |
| id | doaj-art-bef493b4750e4e688a308a38e521c217 |
| institution | DOAJ |
| issn | 2752-5295 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research: Climate |
| spelling | doaj-art-bef493b4750e4e688a308a38e521c2172025-08-20T02:39:47ZengIOP PublishingEnvironmental Research: Climate2752-52952024-01-013404502610.1088/2752-5295/ad9b43Future seasonal surface temperature predictability with and without ARISE-stratospheric aerosol injection-1.5Kirsten J Mayer0https://orcid.org/0000-0003-3582-8337Elizabeth A Barnes1https://orcid.org/0000-0003-4284-9320James W Hurrell2U.S. National Science Foundation National Center for Atmospheric Research , Boulder, CO, United States of AmericaDepartment of Atmospheric Science, Colorado State University , Fort Collins, CO, United States of AmericaDepartment of Atmospheric Science, Colorado State University , Fort Collins, CO, United States of AmericaTo help reduce anthropogenic climate change impacts, various forms of solar radiation modification have been proposed to reduce the rate of warming. One method to intentionally reflect sunlight into space is through the introduction of reflective particles into the stratosphere, known as stratospheric aerosol injection (SAI). Previous research has shown that SAI implementation could lead to future climate impacts beyond surface temperature, including changes in El Niño Southern Oscillation (ENSO) variability. This response has the potential to modulate midlatitude variability and predictability through atmospheric teleconnections. Here, we explore possible differences in seasonal surface temperature predictability under a future with and without SAI implementation, using neural networks and the ARISE-SAI-1.5 simulations. We find significant future predictability changes in both boreal summer and winter under SSP2-4.5 with and without SAI. However, during boreal winter when SAI is implemented, seasonal predictability is more similar to the base climate than when SAI is not implemented, particularly in regions impacted by ENSO teleconnections.https://doi.org/10.1088/2752-5295/ad9b43stratospheric aerosol injectionanthropogenic climate changeseasonal temperature predictabilitymachine learningENSO |
| spellingShingle | Kirsten J Mayer Elizabeth A Barnes James W Hurrell Future seasonal surface temperature predictability with and without ARISE-stratospheric aerosol injection-1.5 Environmental Research: Climate stratospheric aerosol injection anthropogenic climate change seasonal temperature predictability machine learning ENSO |
| title | Future seasonal surface temperature predictability with and without ARISE-stratospheric aerosol injection-1.5 |
| title_full | Future seasonal surface temperature predictability with and without ARISE-stratospheric aerosol injection-1.5 |
| title_fullStr | Future seasonal surface temperature predictability with and without ARISE-stratospheric aerosol injection-1.5 |
| title_full_unstemmed | Future seasonal surface temperature predictability with and without ARISE-stratospheric aerosol injection-1.5 |
| title_short | Future seasonal surface temperature predictability with and without ARISE-stratospheric aerosol injection-1.5 |
| title_sort | future seasonal surface temperature predictability with and without arise stratospheric aerosol injection 1 5 |
| topic | stratospheric aerosol injection anthropogenic climate change seasonal temperature predictability machine learning ENSO |
| url | https://doi.org/10.1088/2752-5295/ad9b43 |
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