Quantifying the effectiveness of multiple SAI strategies across different dimensions

[Geoengineering News]

https://essopenarchive.org/doi/full/10.22541/essoar.15002441/v1

Authors: Dr. Cindy Wang, Dr. Daniele Visioni, Dr. Walker Raymond Lee, Mr. Alistair Duffey, Christopher R. Wentland, Lauren Wheeler, Benjamin Wagman, Samantha Turbeville, Dr. Shingo Watanabe, and Dr. Matthew Henry 

27 April 2026

Primary Abstract

Climate intervention approaches such as stratospheric aerosol injection (SAI) have the potential to cool the planet by reflecting more sunlight back to space, thereby reducing Earth's energy imbalance. Climate model experiments investigating SAI have evolved from relatively simple to more complex strategies aimed at meeting specific climate objectives, most commonly keeping global mean surface temperature (GMST) close to a reference target such as early-century SSP2-4.5. Some strategies also aim to maintain large-scale temperature gradients near the target climate. However, successfully constraining global or latitudinal temperature does not necessarily mean that regional climates, or the seasonal and spatial patterns of key variables, are restored to the reference state. Here, we quantify the "effectiveness" of different potential SAI strategies using a dimensionless, variability-scaled distance-to-target metric that measures departures of the monthly climatological seasonal cycle from the targeted baseline. This framework enables direct comparison across variables without requiring regridding to a common resolution. We apply this framework to a suite of coordinated SAI experiments spanning multiple Earth system models (CESM2-WACCM6, UKESM1, E3SMv3 and MIROC) and injection strategies (ARISE-SAI-1.0/1.5, G6-1.5K-SAI, and G6-1.5K-HiLLA). We further decompose this distance-to-target metric into contributions from mean offset (bias), seasonal-cycle amplitude, and phase/pattern differences. This diagnostic framework supports comparisons across intervention strategies, variables, and model ensembles, and helps identify where apparent success in meeting global targets does or does not translate into regional and seasonal restoration.

Source: ESS Open Archive