https://meetingorganizer.copernicus.org/EGU23/EGU23-8496.html

*Abstract*
Eshaan Agrawal and Christian Schroder de Witt

How to cite: Agrawal, E. and Schroder de Witt, C.: Utilizing AI emulators
to Model Stratospheric Aerosol Injections and their Effect on Climate, EGU
General Assembly 2023 , Vienna, Austria, 24–28 Apr 2023, EGU23-8496,
https://doi.org/10.5194/egusphere-egu23-8496, 2023.

*Abstract*
With no end to anthropogenic greenhouse gas emissions in sight,
policymakers are increasingly debating artificial mechanisms to cool the
earth's climate. One such solution is stratospheric atmospheric injections
(SAI), a method of solar geoengineering where particles are injected into
the stratosphere in order to reflect the sun’s rays and lower global
temperatures. Past volcanic events suggest that SAI can lead to fast
substantial surface temperature reductions, and it is projected to be
economically feasible. Research in simulation, however, suggests that SAI
can lead to catastrophic side effects. It is also controversial among
politicians and environmentalists because of the numerous challenges it
poses geopolitically, environmentally, and for human health. Nevertheless,
SAI is increasingly receiving attention from policymakers. In this research
project, we use deep reinforcement learning to study if, and by how much,
carefully engineered temporally and spatially varying injection strategies
can mitigate catastrophic side effects of SAI. To do this, we are using the
HadCM3 global circulation model to collect climate system data in response
to artificial longitudinal aerosol injections. We then train a neural
network emulator on this data, and use it to learn optimal injection
strategies under a variety of objectives by alternating model updates with
reinforcement learning. We release our dataset and code as a benchmark
dataset to improve emulator creation for solar aerosol engineering
modeling.

Source: EGU General Assembly 2023

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