Park, Chang‐Eui, Su‐Jong Jeong, Yuanchao Fan, Jerry Tjiputra, Helene Muri, and Chunmiao Zheng (2019): Inequal Responses of Drylands to Radiative Forcing Geoengineering Methods. Geophysical Research Letters, December. https://doi.org/10.1029/2019gl084210.

‌"We show noticeable inequality in the responses of drylands when three radiative forcing geoengineering (RFG) methodologies—cirrus cloud thinning (CCT), marine sky brightening (MSB), and stratospheric aerosol injection (SAI)—individually reduce the radiative forcing of the representative concentration pathway 8.5 scenario using a set of the Norwegian Earth system model (NorESM1‐ME) experiments."

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Klaus, Geraldine; Ernst, Andreas; Oswald, Lisa (2019): Psychological factors influencing laypersons’ acceptance of climate engineering, climate change mitigation and business as usual scenarios. In Technology in Society, p. 101222. DOI: 10.1016/j.techsoc.2019.101222.

"In July 2017, we assessed public reactions from the German population in a representative online survey. Participants were given a brief text informing them about climate change and one of four different scenarios in a between-subject design (N = 678). Two of the scenarios described the use of climate engineering technologies - stratospheric aerosol injection (SAI) and bio-energy with carbon capture and storage (BECCS) - one of them covered conventional mitigation strategies and the fourth group was given an outline of the business-as-usual (BAU) approach."

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Jiang, Jiu, Long Cao, Douglas G. MacMartin, Isla R. Simpson, Ben Kravitz, Wei Cheng, Daniele Visioni, Simone Tilmes, Jadwiga H. Richter, and Michael J. Mills (2019): Stratospheric Sulfate Aerosol Geoengineering Could Alter the High Latitude Seasonal Cycle. Geophysical Research Letters, December. https://doi.org/10.1029/2019gl085758.

"Here we use the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS) simulations to explore the effects of SAG on the seasonal cycle of climate change. Simulations show that relative to the present‐day climate, SAG diminishes the amplitude of the seasonal cycle of temperature at many high‐latitude locations, with warmer winters and cooler summers."

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Schroeder, Christian, and Thomas Hornigold. 2019. “Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem.” ArXiv.Org. 2019. https://arxiv.org/abs/1905.07366v1.

‌"We suggest treating SAI as a high-dimensional control problem, with policies trained according to a context-sensitive reward function within the Deep Reinforcement Learning (DRL) paradigm. In order to facilitate training in simulation, we suggest to emulate HadCM3, a widely used General Circulation Model, using deep learning techniques. We believe this is the first application of DRL to the climate sciences."

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