As global greenhouse gas emissions continue to rise, the use of stratospheric aerosol injection (SAI), a form of solar geoengineering, is increasingly considered in order to artificially mitigate climate change effects. However, initial research in simulation suggests that naive SAI can have catastrophic regional consequences, which may induce serious geostrategic conflicts. Current geo-engineering research treats SAI control in low-dimensional approximation only. 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.
Investigation of optimal SAI control within the climate community is currently constrained to low-dimensional injection pattern parametrisations @cite_10 or manual grid search over edge cases of interest @cite_2 . Even in simple settings, it has been shown that regional climate response is sensitive to the choice of SAI policy @cite_6 . In addition, super-regional impacts on El Nino Southern Oscillation have been demonstrated @cite_19 . This suggests that climate response to SAI is sensitive enough to warrant a high-dimensional treatment.
@cite_19 @cite_10 @cite_6 @cite_2
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To examine the impact of proposed stratospheric geoengineering schemes on the amplitude and frequency of El Nino Southern Oscillation (ENSO) variations we ex- amine climate model simulations from the Geoengineering Model Intercomparison Project (GeoMIP) G1-G4 experi- ments. Here we compare tropical Pacific behavior under anthropogenic global warming (AGW) using several sce- narios: an instantaneous quadrupling of the atmosphere's CO2 concentration, a 1 annual increase in CO2 concentra- tion, and the representative concentration pathway resulting in 4.5 W m 2 radiative forcing at the end of the 21st cen- tury, the Representative Concentration Pathway 4.5 scenario, with that under G1-G4 and under historical model simu- lations. Climate models under AGW project relatively uni- form warming across the tropical Pacific over the next sev- eral decades. We find no statistically significant change in ENSO frequency or amplitude under stratospheric geoengi- neering as compared with those that would occur under on- going AGW, although the relative brevity of the G1-G4 sim- ulations may have limited detectability of such changes. We also find that the amplitude and frequency of ENSO events do not vary significantly under either AGW scenarios or G1- G4 from the variability found within historical simulations or observations going back to the mid-19th century. Finally, while warming of the Nino3.4 region in the tropical Pacific is fully offset in G1 and G2 during the 40-year simulations, the region continues to warm significantly in G3 and G4, which both start from a present-day climate. There is increasing evidence that Earth's climate is currently warming, primarily due to emissions of greenhouse gases from human activities, and Earth has been projected to continue warming throughout this century. Scientists have begun to investigate the potential for geoengineering options for reducing surface temperatures and whether such options could possibly contribute to environmental risk reduction. One proposed method involves deliberately increasing aerosol loading in the stratosphere to scatter additional sunlight to space. Previous modeling studies have attempted to predict the climate consequences of hypothetical aerosol additions to the stratosphere. These studies have shown that this method could potentially reduce surface temperatures, but could not recreate a low-CO2 climate in a high-CO2 world. In this study, we attempt to determine the latitudinal distribution of stratospheric aerosols that would most closely achieve a low-CO2 climate despite high CO2 levels. Using the NCAR CAM3.1 general circulation model, we find that having a stratospheric aerosol loading in polar regions higher than that in tropical regions leads to a temperature distribution that is more similar to the low-CO2 climate than that yielded by a globally uniform loading. However, such polar weighting of stratospheric sulfate tends to degrade the degree to which the hydrological cycle is restored, and thus does not markedly contribute to improved recovery of a low-CO2 climate. In the model, the optimal latitudinally varying aerosol distributions diminished the rms zonal mean land temperature change from a doubling of CO2 by 94 and the rms zonal mean land precipitation minus evaporation change by 74 . It is important to note that this idealized study represents a first attempt at optimizing the engineering of climate using a general circulation model; uncertainties are high and not all processes that are important in reality are modeled. Aerosols could be injected into the upper atmosphere to engineer the climate by scattering incident sunlight so as to produce a cooling tendency that may mitigate the risks posed by the accumulation of greenhouse gases. Analysis of climate engineering has focused on sulfate aerosols. Here I examine the possibility that engineered nanoparticles could exploit photophoretic forces, enabling more control over particle distribution and lifetime than is possible with sulfates, perhaps allowing climate engineering to be accomplished with fewer side effects. The use of electrostatic or magnetic materials enables a class of photophoretic forces not found in nature. Photophoretic levitation could loft particles above the stratosphere, reducing their capacity to interfere with ozone chemistry; and, by increasing particle lifetimes, it would reduce the need for continual replenishment of the aerosol. Moreover, particles might be engineered to drift poleward enabling albedo modification to be tailored to counter polar warming while minimizing the impact on equatorial climates. In an assessment of how Arctic sea ice cover could be remediated in a warming world, we simulated the injection of SO2 into the Arctic stratosphere making annual adjustments to injection rates. We treated one climate model realization as a surrogate “real world” with imperfect “observations” and no rerunning or reference to control simulations. SO2 injection rates were proposed using a novel model predictive control regime which incorporated a second simpler climate model to forecast “optimal” decision pathways. Commencing the simulation in 2018, Arctic sea ice cover was remediated by 2043 and maintained until solar geoengineering was terminated. We found quantifying climate side effects problematic because internal climate variability hampered detection of regional climate changes beyond the Arctic. Nevertheless, through decision maker learning and the accumulation of at least 10 years time series data exploited through an annual review cycle, uncertainties in observations and forcings were successfully managed.