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

<p>This repository contains additional model simulation data used in the following paper:</p> <p>Krasting et al., 2022:&nbsp;Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning.&nbsp;<em>Communications Earth &amp; Environment</em>.</p> <p><strong>Description of data files in this repository:</strong></p> <ol> <li>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&nbsp;</li> <li>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&nbsp;Earth system model</li> <li>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&nbsp;model</li> </ol> <p>Notes:</p> <ul> <li>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</li> <li>Results are provided on the models&#39; native tripolar grids. Supporting grid metrics are provided in each NetCDF file</li> <li>All other model simulation data used in Krasting et al. 2022 is available publicly through the Earth System Grid Federation.</li> </ul> <p>&nbsp;</p>