Published February 23, 2022
| Version v1.0.0
Dataset
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Supporting Data for "Regional Sensitivity Patterns of Arctic Ocean Acidification Revealed With Machine Learning"
- 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:
- 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.
Files
Files
(20.4 MB)
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md5:9977790a6c8d1630563fdf0419ade2a1
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