Abstract
Stratospheric aerosol geoengineering has the potential to offset greenhouse-gas–induced warming by reflecting incoming solar radiation. While global warming is projected to increase global mean precipitation and weaken tropical circulation, the extent to which stratospheric aerosol injection (SAI) may offset or modify these changes, and the underlying mechanisms, are unclear. This study investigates the projected changes in the Indian summer monsoon using simulations from the Community Earth System Model (CESM1) under a high-emission scenario (RCP8.5) and an SAI scenario from the Geoengineering Large Ensemble Project (GLENS), in which SO₂ is injected at multiple latitudes (15°N, 15°S, 30°N, and 30°S) in the lower stratosphere. The large aerosol forcing in GLENS enhances the signal-to-noise ratio, enabling a clear identification of the responses; however, it should not be considered directly relevant to near-term policy choices because of uncertainties due to structural differences among climate models.
In CESM1, Indian summer monsoon precipitation increases by approximately 14% during 2071–2090 relative to 2011–2030 under RCP8.5, whereas GLENS simulations show a ~10% reduction over the same period despite near-stabilization of global mean surface temperature. This indicates an overcompensation of precipitation under SAI. Water vapor budget analysis reveals that the precipitation increases under RCP8.5 is primarily driven by enhanced atmospheric water vapor (thermodynamic effect), with only modest circulation changes. In contrast, the precipitation decrease under SAI relative to the baseline is dominated by circulation weakening (dynamic effect), with the dynamical contribution nearly four times larger than under RCP8.5. We caution that these findings discussed in this paper are specific to the Indian monsoon domain, CESM1 model, high emission background scenario and the GLENS injection strategy. A similar analysis across models, SRM options, strategies and background emission scenarios is needed in the future to assess the robustness of our findings and quantification of the uncertainties.
