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Solar radiation management by stratospheric aerosol injection (SRM-SAI) has been proposed as a possible method to counteract anthropogenic global warming, with climate models suggesting it could reduce substantially global temperature and associated impacts. Its effectiveness as simulated by Earth system models exhibits, however, large uncertainties, implying high risks for natural and human ecosystems. Here we identify an emergent relationship linking the long-term global land surface cooling due to SRM-SAI and the short-term cooling following the twentieth century major volcanic eruptions across an Earth system models ensemble. This emergent relationship, combined with observations and reanalysis data, is used to constrain the global land surface temperature (LT) response to reduced downward solar radiation. Based on these constraints, we find a mean decrease in land surface temperature of 0.44KW(-1)m(2), 20% smaller than the unconstrained multimodel mean. This new estimate may affect how trade-offs between cost, risk, and effectiveness of SRM-SAI might be considered.

Plain Language Summary Solar radiation management by stratospheric sulfate aerosol injection has been proposed 10 years ago as means of limiting the harmful impacts of anthropogenic global warming in the absence of strong global efforts toward mitigation of greenhouse gas emissions. However, in absence of real-world experiments, the efficiency of this method to counteract global warming as derived from model simulated remains poorly constrained. Here using state-of-the-art multimodel simulations, we have identified an emergent property of the Earth system between the decrease in solar radiation and the global land surface temperature. First, we relate long-term climate sensitivity to the short-term response of a natural observable analog in large volcanic eruptions. When applied to multiple data sets, which relate the observed temperature decrease to changes in incoming shortwave radiation, we constrain the model response to sulfate geoengineering. We find that state-of-the-art multimodel simulations overestimate the efficiency of this method to counteract global warming by about 20%.