A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall?
Over the last decade, advanced statistical inference and machine learning have been used to fill the gaps in sparse surface ocean CO 2 measurements (Rödenbeck et al., 2015). The estimates from these methods have been used to constrain seasonal, interannual and decadal variability in sea–air CO 2 flu...
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ftcopernicus:oai:publications.copernicus.org:gmd74662 2023-05-15T18:25:58+02:00 A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? Gregor, Luke Lebehot, Alice D. Kok, Schalk Scheel Monteiro, Pedro M. 2019-12-10 application/pdf https://doi.org/10.5194/gmd-12-5113-2019 https://gmd.copernicus.org/articles/12/5113/2019/ eng eng doi:10.5194/gmd-12-5113-2019 https://gmd.copernicus.org/articles/12/5113/2019/ eISSN: 1991-9603 Text 2019 ftcopernicus https://doi.org/10.5194/gmd-12-5113-2019 2020-07-20T16:22:32Z Over the last decade, advanced statistical inference and machine learning have been used to fill the gaps in sparse surface ocean CO 2 measurements (Rödenbeck et al., 2015). The estimates from these methods have been used to constrain seasonal, interannual and decadal variability in sea–air CO 2 fluxes and the drivers of these changes (Landschützer et al., 2015, 2016; Gregor et al., 2018). However, it is also becoming clear that these methods are converging towards a common bias and root mean square error (RMSE) boundary: “the wall”, which suggests that p CO 2 estimates are now limited by both data gaps and scale-sensitive observations. Here, we analyse this problem by introducing a new gap-filling method, an ensemble average of six machine-learning models (CSIR-ML6 version 2019a, Council for Scientific and Industrial Research – Machine Learning ensemble with Six members), where each model is constructed with a two-step clustering-regression approach. The ensemble average is then statistically compared to well-established methods. The ensemble average, CSIR-ML6, has an RMSE of 17.16 µ atm and bias of 0.89 µ atm when compared to a test dataset kept separate from training procedures. However, when validating our estimates with independent datasets, we find that our method improves only incrementally on other gap-filling methods. We investigate the differences between the methods to understand the extent of the limitations of gap-filling estimates of p CO 2 . We show that disagreement between methods in the South Atlantic, southeastern Pacific and parts of the Southern Ocean is too large to interpret the interannual variability with confidence. We conclude that improvements in surface ocean p CO 2 estimates will likely be incremental with the optimisation of gap-filling methods by (1) the inclusion of additional clustering and regression variables (e.g. eddy kinetic energy), (2) increasing the sampling resolution and (3) successfully incorporating p CO 2 estimates from alternate platforms (e.g. floats, gliders) into existing machine-learning approaches. Text Southern Ocean Copernicus Publications: E-Journals Pacific Southern Ocean Geoscientific Model Development 12 12 5113 5136 |
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Open Polar |
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Copernicus Publications: E-Journals |
op_collection_id |
ftcopernicus |
language |
English |
description |
Over the last decade, advanced statistical inference and machine learning have been used to fill the gaps in sparse surface ocean CO 2 measurements (Rödenbeck et al., 2015). The estimates from these methods have been used to constrain seasonal, interannual and decadal variability in sea–air CO 2 fluxes and the drivers of these changes (Landschützer et al., 2015, 2016; Gregor et al., 2018). However, it is also becoming clear that these methods are converging towards a common bias and root mean square error (RMSE) boundary: “the wall”, which suggests that p CO 2 estimates are now limited by both data gaps and scale-sensitive observations. Here, we analyse this problem by introducing a new gap-filling method, an ensemble average of six machine-learning models (CSIR-ML6 version 2019a, Council for Scientific and Industrial Research – Machine Learning ensemble with Six members), where each model is constructed with a two-step clustering-regression approach. The ensemble average is then statistically compared to well-established methods. The ensemble average, CSIR-ML6, has an RMSE of 17.16 µ atm and bias of 0.89 µ atm when compared to a test dataset kept separate from training procedures. However, when validating our estimates with independent datasets, we find that our method improves only incrementally on other gap-filling methods. We investigate the differences between the methods to understand the extent of the limitations of gap-filling estimates of p CO 2 . We show that disagreement between methods in the South Atlantic, southeastern Pacific and parts of the Southern Ocean is too large to interpret the interannual variability with confidence. We conclude that improvements in surface ocean p CO 2 estimates will likely be incremental with the optimisation of gap-filling methods by (1) the inclusion of additional clustering and regression variables (e.g. eddy kinetic energy), (2) increasing the sampling resolution and (3) successfully incorporating p CO 2 estimates from alternate platforms (e.g. floats, gliders) into existing machine-learning approaches. |
format |
Text |
author |
Gregor, Luke Lebehot, Alice D. Kok, Schalk Scheel Monteiro, Pedro M. |
spellingShingle |
Gregor, Luke Lebehot, Alice D. Kok, Schalk Scheel Monteiro, Pedro M. A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? |
author_facet |
Gregor, Luke Lebehot, Alice D. Kok, Schalk Scheel Monteiro, Pedro M. |
author_sort |
Gregor, Luke |
title |
A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? |
title_short |
A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? |
title_full |
A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? |
title_fullStr |
A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? |
title_full_unstemmed |
A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? |
title_sort |
comparative assessment of the uncertainties of global surface ocean co2 estimates using a machine-learning ensemble (csir-ml6 version 2019a) – have we hit the wall? |
publishDate |
2019 |
url |
https://doi.org/10.5194/gmd-12-5113-2019 https://gmd.copernicus.org/articles/12/5113/2019/ |
geographic |
Pacific Southern Ocean |
geographic_facet |
Pacific Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_source |
eISSN: 1991-9603 |
op_relation |
doi:10.5194/gmd-12-5113-2019 https://gmd.copernicus.org/articles/12/5113/2019/ |
op_doi |
https://doi.org/10.5194/gmd-12-5113-2019 |
container_title |
Geoscientific Model Development |
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12 |
container_issue |
12 |
container_start_page |
5113 |
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5136 |
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