Published February 23, 2022 | Version v1.0.0
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Supporting Data for "Regional Sensitivity Patterns of Arctic Ocean Acidification Revealed With Machine Learning"

Creators

  • 1. NOAA/OAR/Geophysical Fluid Dynamics Laboratory
  • 2. Cooperative Institute for Modeling the Earth System (CIMES), Princeton University

Description

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:

  1. 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 
  2. 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
  3. 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.

 

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