Quantifying the Effectiveness of Multiple SAI Strategies Across Different Dimensions

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Abstract
Stratospheric aerosol injection (SAI) modeling experiments are often designed to keep global mean surface air temperature (GMSAT) close to a reference target, such as early‐century SSP2‐4.5. However, meeting this target does not necessarily restore regional climates or the seasonal and spatial patterns of key variables. We evaluate multiple SAI strategies (ARISE‐SAI‐1.0/1.5, G6‐1.5K‐SAI, and G6‐1.5K‐HiLLA) across four Earth system models (CESM2‐WACCM6, UKESM1, E3SMv3, and MIROC) using a dimensionless, variability‐scaled distance‐to‐target metric applied to the monthly climatological seasonal cycle. We decompose this distance into mean offset, seasonal‐cycle amplitude, and phase/pattern contributions, and use matched baseline‐against‐baseline null distributions to estimate the finite‐sampling floor. SAI substantially reduces temperature distance‐to‐target relative to late‐century SSP2‐4.5, but the majority of SAI model–scenario cases exceed the 97.5th percentile of their matched finite‐sampling null distributions, indicating that residual temperature departures remain detectable even when large‐scale temperature targets are met. These residuals are more strongly associated with differences in seasonal timing than with mean offsets. In contrast, most precipitation distances do not exceed their matched null thresholds, so some apparent precipitation residuals cannot be distinguished from finite‐sampling variability. This framework enables consistent comparisons across intervention strategies, variables, and models without regridding, and identifies where meeting GMSAT targets does or does not restore regional and seasonal climate.
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Funding Information
  • Quadrature Climate Foundation
  • National Center for Atmospheric Research
  • National Science Foundation

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