Underestimation of multi-decadal global O 2 loss due to an optimal interpolation method
The global ocean's oxygen content has declined significantly over the past several decades and is expected to continue decreasing under global warming, with far-reaching impacts on marine ecosystems and biogeochemical cycling. Determining the oxygen trend, its spatial pattern, and uncertainties...
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ftncar:oai:drupal-site.org:articles_27124 2024-05-19T07:45:25+00:00 Underestimation of multi-decadal global O 2 loss due to an optimal interpolation method Ito, Takamitsu (author) Garcia, Hernan E. (author) Wang, Zhankun (author) Minobe, Shoshiro (author) Long, Matthew C. (author) Cebrian, Just (author) Reagan, James (author) Boyer, Tim (author) Paver, Christopher (author) Bouchard, Courtney (author) Takano, Yohei (author) Bushinsky, Seth (author) Cervania, Ahron (author) Deutsch, Curtis A. (author) 2024-02-12 https://doi.org/10.5194/bg-21-747-2024 en eng Biogeosciences--Biogeosciences--1726-4189 articles:27124 doi:10.5194/bg-21-747-2024 ark:/85065/d72v2m9m Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. article Text 2024 ftncar https://doi.org/10.5194/bg-21-747-2024 2024-05-02T00:23:33Z The global ocean's oxygen content has declined significantly over the past several decades and is expected to continue decreasing under global warming, with far-reaching impacts on marine ecosystems and biogeochemical cycling. Determining the oxygen trend, its spatial pattern, and uncertainties from observations is fundamental to our understanding of the changing ocean environment. This study uses a suite of CMIP6 Earth system models to evaluate the biases and uncertainties in oxygen distribution and trends due to sampling sparseness. Model outputs are sub-sampled according to the spatial and temporal distribution of the historical shipboard measurements, and the data gaps are filled by a simple optimal interpolation method using Gaussian covariance with a constant e -folding length scale. Sub-sampled results are compared to full model output, revealing the biases in global and basin-wise oxygen content trends. The simple optimal interpolation underestimates the modeled global deoxygenation trends, capturing approximately two-thirds of the full model trends. The North Atlantic and subpolar North Pacific are relatively well sampled, and the simple optimal interpolation is capable of reconstructing more than 80 % of the oxygen trend in the non-eddying CMIP models. In contrast, pronounced biases are found in the equatorial oceans and the Southern Ocean, where the sampling density is relatively low. The application of the simple optimal interpolation method to the historical dataset estimated the global oxygen loss to be 1.5 % over the past 50 years. However, the ratio of the global oxygen trend between the sub-sampled and full model output has increased the estimated loss rate in the range of 1.7 % to 3.1 % over the past 50 years, which partially overlaps with previous studies. The approach taken in this study can provide a framework for the intercomparison of different statistical gap-filling methods to estimate oxygen content trends and their uncertainties due to sampling sparseness. Article in Journal/Newspaper North Atlantic Southern Ocean OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) Biogeosciences 21 3 747 759 |
institution |
Open Polar |
collection |
OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) |
op_collection_id |
ftncar |
language |
English |
description |
The global ocean's oxygen content has declined significantly over the past several decades and is expected to continue decreasing under global warming, with far-reaching impacts on marine ecosystems and biogeochemical cycling. Determining the oxygen trend, its spatial pattern, and uncertainties from observations is fundamental to our understanding of the changing ocean environment. This study uses a suite of CMIP6 Earth system models to evaluate the biases and uncertainties in oxygen distribution and trends due to sampling sparseness. Model outputs are sub-sampled according to the spatial and temporal distribution of the historical shipboard measurements, and the data gaps are filled by a simple optimal interpolation method using Gaussian covariance with a constant e -folding length scale. Sub-sampled results are compared to full model output, revealing the biases in global and basin-wise oxygen content trends. The simple optimal interpolation underestimates the modeled global deoxygenation trends, capturing approximately two-thirds of the full model trends. The North Atlantic and subpolar North Pacific are relatively well sampled, and the simple optimal interpolation is capable of reconstructing more than 80 % of the oxygen trend in the non-eddying CMIP models. In contrast, pronounced biases are found in the equatorial oceans and the Southern Ocean, where the sampling density is relatively low. The application of the simple optimal interpolation method to the historical dataset estimated the global oxygen loss to be 1.5 % over the past 50 years. However, the ratio of the global oxygen trend between the sub-sampled and full model output has increased the estimated loss rate in the range of 1.7 % to 3.1 % over the past 50 years, which partially overlaps with previous studies. The approach taken in this study can provide a framework for the intercomparison of different statistical gap-filling methods to estimate oxygen content trends and their uncertainties due to sampling sparseness. |
author2 |
Ito, Takamitsu (author) Garcia, Hernan E. (author) Wang, Zhankun (author) Minobe, Shoshiro (author) Long, Matthew C. (author) Cebrian, Just (author) Reagan, James (author) Boyer, Tim (author) Paver, Christopher (author) Bouchard, Courtney (author) Takano, Yohei (author) Bushinsky, Seth (author) Cervania, Ahron (author) Deutsch, Curtis A. (author) |
format |
Article in Journal/Newspaper |
title |
Underestimation of multi-decadal global O 2 loss due to an optimal interpolation method |
spellingShingle |
Underestimation of multi-decadal global O 2 loss due to an optimal interpolation method |
title_short |
Underestimation of multi-decadal global O 2 loss due to an optimal interpolation method |
title_full |
Underestimation of multi-decadal global O 2 loss due to an optimal interpolation method |
title_fullStr |
Underestimation of multi-decadal global O 2 loss due to an optimal interpolation method |
title_full_unstemmed |
Underestimation of multi-decadal global O 2 loss due to an optimal interpolation method |
title_sort |
underestimation of multi-decadal global o 2 loss due to an optimal interpolation method |
publishDate |
2024 |
url |
https://doi.org/10.5194/bg-21-747-2024 |
genre |
North Atlantic Southern Ocean |
genre_facet |
North Atlantic Southern Ocean |
op_relation |
Biogeosciences--Biogeosciences--1726-4189 articles:27124 doi:10.5194/bg-21-747-2024 ark:/85065/d72v2m9m |
op_rights |
Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
op_doi |
https://doi.org/10.5194/bg-21-747-2024 |
container_title |
Biogeosciences |
container_volume |
21 |
container_issue |
3 |
container_start_page |
747 |
op_container_end_page |
759 |
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1799485466408386560 |