| 06 Oct 2025
Uncertain current and future ocean deoxygenation due to internal climate variability and observational gaps
Abstract. Observed declines in oceanic oxygen (O2) over recent decades are subject to substantial uncertainty due to internal climate variability (ICV) and limited observational coverage. Here, we quantify how observational uncertainty affects the assessment of both historical and future ocean deoxygenation by combining multiple observational datasets with a large ensemble simulation of the Max Planck Institute Earth System Model (MPI-ESM). We find that observational biases in ICV can amplify global and regional O2 variability by 150 %–500 % in annual time series over the past 50 years. The combined effect of ICV and sampling bias can also introduce deviations of 5 %–25 % in estimated multi-decadal O2 trends. Moreover, time-dependent changes in observational coverage complicate the interpretation of historical O2 trends. Our results underscore the crucial need for a sustained, globally uniform ocean observing system to monitor long-term deoxygenation, assess its impact on marine ecosystems, and detect the anthropogenic signal in O2 trends. We further show that near-future trend detection will remain sensitive to ICV, and observational gaps may distort the detection of scenario-based projections of O2 trends, especially in the context of climate mitigation efforts.
The authors have presented what is largely a methodological study focused on challenges in trend detection for ocean interior oxygen. Their analysis tools are clearly chosen, and their knowledge and judgment in choosing an ensemble and a range of observational resources is sound, as is their interpretation. So with that I believe that the manuscript is well-organized and well-presented, and overall I think it can meet the standard of Biogeosciences with major revisions. The reason for recommending major revisions is given below, but stems largely from what I think is an illl-advised choice of 300m as a horizon on which to assess observing system design.
Main Points:
There is what I see as a highly problematic decision at the core of the framing of the study in choosing a fixed depth horizon (300m) for evaluating trends in the ocean interior. Though it is technically fine to do this, it should be expected to be a horizon that is characterized by elevated noise due to climate variations and wave activity in the ocean interior. Previous studies have tended to choose two different approaches to deal with the associated signal-to-noise ratio for this horizon being very high: (a) Long et al. (2016) considered changes on isopycnal surfaces in the ocean interior, and (b) Bopp et al. (2013) considered layer integrals for the upper ocean. These choices were made precisely because the signal-to-noise ratio at 300m is unnecessarily high. In short, no one would choose a 300m horizon for detecting anthropogenic trends, so it’s perplexing that this fixed horizon was chosen for this study. Additionally the authors do not provide any evidence of any particular importance of this depth horizon for any species or ecosystem community structure.
The core idea is that even with a perfect and gapless observing system for oxygen, natural variability at a fixed depth of 300m will be so large that it would take hundreds of years to detect a trend.
I believe that for this paper to be of interest to and to find broader applications within the broader community, including the ocean observations community, this problem must be addressed. One way to do this would be to choose a vertical layer spanning the upper ocean (again the approach of Bopp, 2013), if the problem is that the MPI model doesn’t include monthly mean output. A much more challenging but robust approach might be to do a more comprehensive analysis for density layers and then to map back to depth horizons based on the mean depth of isopycnal layers.
More detailed points:
In the Abstract , the authors refer to “observational bias in ICV”, do they mean “undersampling of ICV”? This also applies to the first sentence of the second paragraph of the “Summary and Discussion” section.
It would help if the equations were numbered, but for the second equation (for AOU) the authors should be clear about which terms are saved as annual means and which are not.
For the 4th paragraph of the “Summary and Discussion” (lines 550-561) it would be good if the authors could describe what distinguishes this study from previous studies.