Supporting Data for "Regional Sensitivity Patterns of Arctic Ocean Acidification Revealed With Machine Learning"
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_...
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ftdatacite:10.5281/zenodo.6245224 2023-05-15T14:56:07+02:00 Supporting Data for "Regional Sensitivity Patterns of Arctic Ocean Acidification Revealed With Machine Learning" Krasting, John P. De Palma, Maurizia Sonnewald, Maike Dunne, John P. John, Jasmin G. 2022 https://dx.doi.org/10.5281/zenodo.6245224 https://zenodo.org/record/6245224 unknown Zenodo https://dx.doi.org/10.5281/zenodo.6245223 Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY Dataset dataset 2022 ftdatacite https://doi.org/10.5281/zenodo.6245224 https://doi.org/10.5281/zenodo.6245223 2022-03-10T14:56:10Z 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. Dataset Arctic Arctic Ocean Arctic Ocean Acidification Ocean acidification DataCite Metadata Store (German National Library of Science and Technology) Arctic Arctic Ocean |
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DataCite Metadata Store (German National Library of Science and Technology) |
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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. |
format |
Dataset |
author |
Krasting, John P. De Palma, Maurizia Sonnewald, Maike Dunne, John P. John, Jasmin G. |
spellingShingle |
Krasting, John P. De Palma, Maurizia Sonnewald, Maike Dunne, John P. John, Jasmin G. Supporting Data for "Regional Sensitivity Patterns of Arctic Ocean Acidification Revealed With Machine Learning" |
author_facet |
Krasting, John P. De Palma, Maurizia Sonnewald, Maike Dunne, John P. John, Jasmin G. |
author_sort |
Krasting, John P. |
title |
Supporting Data for "Regional Sensitivity Patterns of Arctic Ocean Acidification Revealed With Machine Learning" |
title_short |
Supporting Data for "Regional Sensitivity Patterns of Arctic Ocean Acidification Revealed With Machine Learning" |
title_full |
Supporting Data for "Regional Sensitivity Patterns of Arctic Ocean Acidification Revealed With Machine Learning" |
title_fullStr |
Supporting Data for "Regional Sensitivity Patterns of Arctic Ocean Acidification Revealed With Machine Learning" |
title_full_unstemmed |
Supporting Data for "Regional Sensitivity Patterns of Arctic Ocean Acidification Revealed With Machine Learning" |
title_sort |
supporting data for "regional sensitivity patterns of arctic ocean acidification revealed with machine learning" |
publisher |
Zenodo |
publishDate |
2022 |
url |
https://dx.doi.org/10.5281/zenodo.6245224 https://zenodo.org/record/6245224 |
geographic |
Arctic Arctic Ocean |
geographic_facet |
Arctic Arctic Ocean |
genre |
Arctic Arctic Ocean Arctic Ocean Acidification Ocean acidification |
genre_facet |
Arctic Arctic Ocean Arctic Ocean Acidification Ocean acidification |
op_relation |
https://dx.doi.org/10.5281/zenodo.6245223 |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.6245224 https://doi.org/10.5281/zenodo.6245223 |
_version_ |
1766328147456819200 |