Quantifying the Impact of Internal Variability on the CESM2 Control Algorithm for Stratospheric Aerosol Injection

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https://essopenarchive.org/users/558657/articles/697851-quantifying-the-impact-of-internal-variability-on-the-cesm2-control-algorithm-for-stratospheric-aerosol-injection

Authors 

Charlotte J Connolly,Emily M Prewett,Elizabeth A. Barnes,James Wilson Hurrell

24 Jan 2024

Cite as: Charlotte J Connolly, Emily M Prewett, Elizabeth A. Barnes, et al. Quantifying the Impact of Internal Variability on the CESM2 Control Algorithm for Stratospheric Aerosol Injection. ESS Open Archive . January 24, 2024.

DOI:10.22541/essoar.170612815.57714351/v1

Abstract

Earth system models are a powerful tool to simulate the response to hypothetical climate intervention strategies, such as stratospheric aerosol injection (SAI). Recent simulations of SAI implement tools from control theory, called “controllers”, to determine the quantity of aerosol to inject into the stratosphere to reach or maintain specified global temperature targets, such as limiting global warming to 1.5\textdegree C above pre-industrial temperatures. This work explores how internal (unforced) climate variability can impact controller-determined injection amounts using the Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) simulations. Since the ARISE-SAI controller determines injection amounts by comparing global annual-mean surface temperature to predetermined temperature targets, internal variability that impacts temperature can impact the total injection amount as well. Using an offline version of the ARISE-SAI controller and data from CESM2 earth system model simulations, we quantify how internal climate variability and volcanic eruptions impact injection amounts. While idealized, this approach allows for the investigation of a large variety of climate states without additional simulations and can be used to attribute controller sensitivities to specific modes of internal variability.

Source: ESS OPEN ARCHIVE