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An overview of the Earth system science of solar geoengineering

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Solar geoengineering has been proposed as a means to cool the Earth by increasing the reflection of sunlight back to space, for example, by injecting reflective aerosol particles (or their precursors) into the lower stratosphere. Such proposed techniques would not be able to substitute for mitigation of greenhouse gas (GHG) emissions as a response to the risks of climate change, as they would only mask some of the effects of global warming. They might, however, eventually be applied as a complementary approach to reduce climate risks. Thus, the Earth system consequences of solar geoengineering are central to understanding its potentials and risks. Here we review the state‐of‐the‐art knowledge about stratospheric sulfate aerosol injection and an idealized proxy for this, ‘sunshade geoengineering,’ in which the intensity of incoming sunlight is directly reduced in models. Studies are consistent in suggesting that sunshade geoengineering and stratospheric aerosol injection would generally offset the climate effects of elevated GHG concentrations. However, it is clear that a solar geoengineered climate would be novel in some respects, one example being a notably reduced hydrological cycle intensity. Moreover, we provide an overview of nonclimatic aspects of the response to stratospheric aerosol injection, for example, its effect on ozone, and the uncertainties around its consequences. We also consider the issues raised by the partial control over the climate that solar geoengineering would allow. Finally, this overview highlights some key research gaps in need of being resolved to provide sound basis for guidance of future decisions around solar geoengineering. WIREs Clim Change 2016, 7:815–833. doi: 10.1002/wcc.423 This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models
Surface air temperature (K; top panels), precipitation (mm day−1; middle panels), and precipitation minus‐evaporation (mm day−1; bottom panels) differences averaged over years 11–50 of simulation. Panels show an average of 12 models participating in the Geoengineering Model Intercomparison Project (GeoMIP) 15. Stippling indicates when fewer than 9 of 12 models agree on the sign of change. See Kravitz, et al. for additional details.
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Range of global mean temperature response to two different SAI scenarios, as described by the Geoengineering Model Intercomparison Project (GeoMIP). Solid lines show the model mean, and shading shows the model spread for each experiment. Results for G3 include three models, while G4 include six models.
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Zonally averaged stratospheric sulfate aerosol total column burden (kg m−2) above 200 mb for three models participating in GeoMIP experiment G4. This experiment involves a sustained injection rate amounting to 5 Tg SO2 (~10 Tg H2SO4) per year. (a) GISS‐E2‐R, (b) HadGEM2‐ES and (c) MIROC‐ESM
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Global mean temperature (a) and sea‐level rise (b) response for the RCP 8.5 high GHG emissions scenario and a range of solar geoengineering scenarios. The different colored lines show scenarios of solar geoengineering deployment with background GHG emissions from RCP 8.5 that achieve different total radiative forcing values at year 2100, that is, GEO 1.5 (yellow line) has a net radiative forcing of +1.5 Wm−2 in 2100. The methods used to estimate the sea‐level response to these scenarios is described in Irvine, et al.. (Reprinted with permission from Ref . Copyright 2012 Nature Publishing Group)
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Zonally averaged terrestrial net primary productivity (NPP) for piControl (black), 4xCO2 (red), and G1 (blue). x‐axis is plotted as the sine of latitude to account for different areas of latitude bands. Thick lines indicate an 8‐model average. Thin vertical lines indicate the range of model spread at five different latitudes. See Kravitz, et al. and Glienke, et al. for more details.
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Changes (from piControl) in global mean temperature (K; y‐axis) and global mean precipitation (%; x‐axis) for the 4xCO2 (red) and G1 (blue) simulations. Points represent a 12‐model mean, and ellipses represent the range of model responses. All values are averaged over years 11–50 of simulation.
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