Data from: Quantifying the impact of internal variability on the CESM2 control algorithm for stratospheric aerosol injection dataset
Data files
Mar 29, 2024 version files 10.31 GB
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ENSO_anomalies_LE.nc
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ENSO_anomalies_SSP45.nc
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NAO_anomalies_LE.nc
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NAO_anomalies_SSP45.nc
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README.md
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SAM_anomalies_LE.nc
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SAM_anomalies_SSP45.nc
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TREFHT_anomalies_LE.nc
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TREFHT_anomalies_SSP45.nc
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TREFHT_Basestates_SSP45.nc
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VOLC_anomalies.nc
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 1.5C 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.
README: Data from: Quantifying the impact of internal variability on the CESM2 control algorithm for stratospheric aerosol injection dataset
(Connolly et al. 2024)
TREFHT_Basestates_SSP45: ensemble mean calculated from the ARISE-SAI 10 member ensemble and smoothed by fitting a 3rd order polynomial to the time series at each grid point.
TREFHT_anomalies_SSP45.nc: anomalies from the ARISE-SAI control simulation which is used when making composites based off the modes of internal variability.
TREFHT_anomalies_LE.nc: anomalies from the CESM Large Ensemble which is used when making composites based off the modes of internal variability.
ENSO_anomalies_LE.nc: contains the ENSO index calculated from the CESM Large Ensemble.
ENSO_anomalies_SSP45:contains the ENSO index calculated from the ARISE-SAI control simulations.
NAO_anomalies_LE.nc: contains the NAO index calculated from the CESM Large Ensemble.
NAO_anomalies_SSP45:contains the NAO index calculated from the ARISE-SAI control simulations.
SAM_anomalies_LE.nc: contains the SAM index calculated from the CESM Large Ensemble.
SAM_anomalies_SSP45.nc: contains the SAM index calculated from the ARISE-SAI control simulations.
VOLC_anomalies.nc: contains the average temperature anomalies two years after the 1991 Pinatubo eruption.
Code/Software
The CESM2-LE is available at the Climate Data Gateway https://climatedata.ibs.re.kr/data/cesm2-lens. The ARISE-SAI data is available at https://www.cesm.ucar.edu/community-projects/arise-sai. Code used in this work can be found at https://github.com/connollyc152/ExploreARISEcontroller