03 May 2023
 | 03 May 2023
Status: this preprint is currently under review for the journal BG.

Underestimation of global O2 loss in optimally interpolated historical ocean observations

Takamitsu Ito, Hernan E. Garcia, Zhankun Wang, Shoshiro Minobe, Matthew C. Long, Just Cebrian, James Reagan, Tim Boyer, Christopher Paver, Courtney Bouchard, Yohei Takano, Seth Bushinsky, Ahron Cervania, and Curtis A. Deutsch

Abstract. 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 an optimal interpolation method is applied to fill data gaps. Sub-sampled results are compared to full model output, revealing the biases in global and basin-wise oxygen content trends. The optimal interpolation underestimates the modeled global deoxygenation trends, capturing approximately two-thirds of the full model trends. North Atlantic and Subpolar North Pacific are relatively well sampled, and the optimal interpolation is capable of reconstructing more than 80 % of the oxygen trend. In contrast, pronounced biases are found in the equatorial oceans and the Southern Ocean, where the sampling density is relatively low. Optimal interpolation of the historical dataset estimated the global oxygen loss of 1.5 % over the past 50 years. However, the ratio of global oxygen trend between the subsampled and full model output, increases the estimated loss rate to 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-fill methods to estimate oxygen content trends and its uncertainties due to sampling sparseness.

Takamitsu Ito et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2023-72', Anonymous Referee #1, 12 Jun 2023
    • AC1: 'Reply on RC1', Taka Ito, 09 Sep 2023
  • RC2: 'Comment on bg-2023-72', Anonymous Referee #2, 20 Aug 2023
    • AC2: 'Reply on RC2', Taka Ito, 09 Sep 2023

Takamitsu Ito et al.

Takamitsu Ito et al.


Total article views: 511 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
348 144 19 511 49 12 10
  • HTML: 348
  • PDF: 144
  • XML: 19
  • Total: 511
  • Supplement: 49
  • BibTeX: 12
  • EndNote: 10
Views and downloads (calculated since 03 May 2023)
Cumulative views and downloads (calculated since 03 May 2023)

Viewed (geographical distribution)

Total article views: 497 (including HTML, PDF, and XML) Thereof 497 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 24 Sep 2023
Short summary
This study aims to estimate how much oceanic oxygen has been lost and its uncertainties. One major source of uncertainties comes from the statistical gap-fill methods. Outputs from earth system models are used to generate synthetic observations where oxygen data is extracted from the model output at the location and time of historical oceangraphic cruises. Reconstructed oxygen trend is approximately two thirds of the true trend.