Supporting Data for "Regional Sensitivity Patterns of Arctic Ocean Acidification Revealed With Machine Learning"

• Main Authors: Krasting, John P., De Palma, Maurizia, Sonnewald, Maike, Dunne, John P., John, Jasmin G.
• Format: Dataset
• Language: unknown
• Published: Zenodo 2022
• Subjects: Arctic; Arctic Ocean; Arctic Ocean Acidification; Ocean acidification
• Online Access: https://dx.doi.org/10.5281/zenodo.6245224; https://zenodo.org/record/6245224

This repository contains additional model simulation data used in the following paper: Krasting et al., 2022: Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning. Communications Earth & Environment . Description of data files in this repository: GFDL-CM4.c_ant.nc (42M) - NetCDF file of anthropogenic carbon inventory for 3 historical simulation ensemble members performed with the NOAA GFDL-CM4 climate model GFDL-ESM4.c_ant.nc (12M) - NetCDF file of anthropogenic carbon inventory for 3 concentration-driven historical simulation ensemble members performed with the NOAA GFDL-ESM4 Earth system model GFDL-ESM4e.c_ant.nc (12M) - NetCDF file of anthropogenic carbon inventory for 3 emission-driven historical simulation ensemble members performed with the NOAA GFDL-ESM4 Earth system model Notes: Anthropogenic carbon was calculated by vertically-integrating the dissolved inorganic carbon tracer (dissic) simulated at year 2002 and subtracting from the corresponding year of the preindustrial control simulation Results are provided on the models' native tripolar grids. Supporting grid metrics are provided in each NetCDF file All other model simulation data used in Krasting et al. 2022 is available publicly through the Earth System Grid Federation.