Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 4
Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1595-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-1595-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 4
Methods for assessment of models
 | Highlight paper
 | 
23 Feb 2022
Methods for assessment of models | Highlight paper |  | 23 Feb 2022

Using neural network ensembles to separate ocean biogeochemical and physical drivers of phytoplankton biogeography in Earth system models

Christopher Holder, Anand Gnanadesikan, and Marie Aude-Pradal

Data sets

Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models" Christopher Holder, Anand Gnanadesikan, and Marie Aude-Pradal http://doi.org/10.5281/zenodo.4774437

Model code and software

Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models" Christopher Holder, Anand Gnanadesikan, and Marie Aude-Pradal http://doi.org/10.5281/zenodo.4774437

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Short summary
It can be challenging to understand why Earth system models (ESMs) produce specific results because one can arrive at the same result simply by changing the values of the parameters. In our paper, we demonstrate that it is possible to use machine learning to figure out how and why particular components of an ESM (such as biology or ocean circulations) affect the output. This work could be applied to observations to improve the accuracy of the formulations used in ESMs.
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Special issue
Benchmark datasets and machine learning algorithms for Earth...