Strengths and weaknesses of three Machine Learning methods for pCO2 interpolation
Using the Large Enemble Testbed, a collection of 100 members from four independent Earth system models, we test three general-purpose Machine Learning (ML) approaches to understand their strengths and weaknesses in statistically reconstructing full-coverage surface ocean pCO 2 from sparse in situ da...
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ftcopernicus:oai:publications.copernicus.org:gmdd89747 2023-05-15T18:26:00+02:00 Strengths and weaknesses of three Machine Learning methods for pCO2 interpolation Stamell, Jake Rustagi, Rea R. Gloege, Lucas McKinley, Galen A. 2020-10-22 application/pdf https://doi.org/10.5194/gmd-2020-311 https://gmd.copernicus.org/preprints/gmd-2020-311/ eng eng doi:10.5194/gmd-2020-311 https://gmd.copernicus.org/preprints/gmd-2020-311/ eISSN: 1991-9603 Text 2020 ftcopernicus https://doi.org/10.5194/gmd-2020-311 2020-10-26T17:22:13Z Using the Large Enemble Testbed, a collection of 100 members from four independent Earth system models, we test three general-purpose Machine Learning (ML) approaches to understand their strengths and weaknesses in statistically reconstructing full-coverage surface ocean pCO 2 from sparse in situ data. To apply the Testbed, we sample the full-field model pCO 2 as real-world pCO 2 collected from 1982–2016 for each ensemble member. We then use ML approaches to reconstruct the full-field and compare with the original model full-field pCO 2 to assess reconstruction skill. We use feed forward neural network (NN), XGBoost (XGB), and random forest (RF) approaches to perform the reconstructions. Our baseline is the NN, since this approach has previously been shown to be a successful method for pCO 2 reconstruction. The XGB and RF allow us to test tree-based approaches. We perform comparisons to a test set, which consists of 20% of the real-world sampled data that are withheld from training. Statistical comparisons with this test set are equivalent to that which could be derived using real-world data. Unique to the Testbed is that it allows for comparison to all the "unseen" points to which the ML algorithms extrapolate. When compared to the test set, XGB and RF both perform better than NN based on a suite of regression metrics. However, when compared to the unseen data, degradation of performance is large with XGB and even larger with RF. Degradation is comparatively small with NN, indicating a greater ability to generalize. Despite its larger degradation, in the final comparison to unseen data, XGB slightly outperforms NN and greatly outperforms RF, with lowest mean bias and more consistent performance across Testbed members. All three approaches perform best in the open ocean and for seasonal variability, but performance drops off at longer time scales and in regions of low sampling, such as the Southern Ocean and coastal zones. For decadal variability, all methods overestimate the amplitude of variability and have moderate skill in reconstruction of phase. For this timescale, the greater ability of the NN to generalize allows it to slightly outperform XGB. Taking into account all comparisons, we find XGB to be best able to reconstruct surface ocean pCO 2 from the limited available data. Text Southern Ocean Copernicus Publications: E-Journals Southern Ocean |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
Using the Large Enemble Testbed, a collection of 100 members from four independent Earth system models, we test three general-purpose Machine Learning (ML) approaches to understand their strengths and weaknesses in statistically reconstructing full-coverage surface ocean pCO 2 from sparse in situ data. To apply the Testbed, we sample the full-field model pCO 2 as real-world pCO 2 collected from 1982–2016 for each ensemble member. We then use ML approaches to reconstruct the full-field and compare with the original model full-field pCO 2 to assess reconstruction skill. We use feed forward neural network (NN), XGBoost (XGB), and random forest (RF) approaches to perform the reconstructions. Our baseline is the NN, since this approach has previously been shown to be a successful method for pCO 2 reconstruction. The XGB and RF allow us to test tree-based approaches. We perform comparisons to a test set, which consists of 20% of the real-world sampled data that are withheld from training. Statistical comparisons with this test set are equivalent to that which could be derived using real-world data. Unique to the Testbed is that it allows for comparison to all the "unseen" points to which the ML algorithms extrapolate. When compared to the test set, XGB and RF both perform better than NN based on a suite of regression metrics. However, when compared to the unseen data, degradation of performance is large with XGB and even larger with RF. Degradation is comparatively small with NN, indicating a greater ability to generalize. Despite its larger degradation, in the final comparison to unseen data, XGB slightly outperforms NN and greatly outperforms RF, with lowest mean bias and more consistent performance across Testbed members. All three approaches perform best in the open ocean and for seasonal variability, but performance drops off at longer time scales and in regions of low sampling, such as the Southern Ocean and coastal zones. For decadal variability, all methods overestimate the amplitude of variability and have moderate skill in reconstruction of phase. For this timescale, the greater ability of the NN to generalize allows it to slightly outperform XGB. Taking into account all comparisons, we find XGB to be best able to reconstruct surface ocean pCO 2 from the limited available data. |
format |
Text |
author |
Stamell, Jake Rustagi, Rea R. Gloege, Lucas McKinley, Galen A. |
spellingShingle |
Stamell, Jake Rustagi, Rea R. Gloege, Lucas McKinley, Galen A. Strengths and weaknesses of three Machine Learning methods for pCO2 interpolation |
author_facet |
Stamell, Jake Rustagi, Rea R. Gloege, Lucas McKinley, Galen A. |
author_sort |
Stamell, Jake |
title |
Strengths and weaknesses of three Machine Learning methods for pCO2 interpolation |
title_short |
Strengths and weaknesses of three Machine Learning methods for pCO2 interpolation |
title_full |
Strengths and weaknesses of three Machine Learning methods for pCO2 interpolation |
title_fullStr |
Strengths and weaknesses of three Machine Learning methods for pCO2 interpolation |
title_full_unstemmed |
Strengths and weaknesses of three Machine Learning methods for pCO2 interpolation |
title_sort |
strengths and weaknesses of three machine learning methods for pco2 interpolation |
publishDate |
2020 |
url |
https://doi.org/10.5194/gmd-2020-311 https://gmd.copernicus.org/preprints/gmd-2020-311/ |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_source |
eISSN: 1991-9603 |
op_relation |
doi:10.5194/gmd-2020-311 https://gmd.copernicus.org/preprints/gmd-2020-311/ |
op_doi |
https://doi.org/10.5194/gmd-2020-311 |
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1766207764632174592 |