the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Feb 2025
Assessment of Ocean Bottom Pressure Variations in CMIP6 HighResMIP Simulations
Abstract. Ocean bottom pressure (pb) variations from high-resolution climate model simulations under the CMIP6 (Coupled Model Intercomparison Project Phase 6) HighResMIP protocol are potentially useful for oceanographic and space-geodetic research, but the overall signal content and accuracy of these pb estimates have hitherto not been assessed. Here we compute monthly pb fields from five CMIP6 HighResMIP models at 1/4° grid spacing over both historical and future time spans and compare these data, in terms of temporal variance, against observation-based pb estimates from a 1/4° downscaled GRACE (Gravity Recovery and Climate Experiment) product and 23 bottom pressure recorders, mostly in the Pacific. The model results are qualitatively and quantitatively similar to the GRACE-based pb variances, featuring—aside from eddy imprints—elevated amplitudes on continental shelves and in major abyssal plains of the Southern Ocean. Modeled pb variance in these regions is ∼10–80 % higher and thus overestimated compared to GRACE, whereas underestimation relative to GRACE and the bottom pressure recorders prevails in more quiescent deep-ocean regions. We also form variance ratios of detrended pb signals over 2030–2049 under a high-emission scenario relative to 1980–1999 for three selected models and find statistically significant increases of future pb variance by ∼30–50 % across the Arctic and in eddy-rich regions of the South Atlantic. The strengthening is consistent with projected changes in high-latitude surface winds and, in the case of the South Atlantic, intensified Agulhas leakage. The study thus points to possibly new pathways for relating observed pb variability from (future) satellite gravimetry missions to anthropogenic climate change.
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Notice on discussion status
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-775', Christopher Piecuch, 03 Apr 2025
- AC1: 'Reply on RC1', Le Liu, 28 May 2025
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RC2: 'Comment on egusphere-2025-775', Anonymous Referee #2, 01 May 2025
Assessment of Ocean Bottom Pressure Variations in CMIP6 HighResMIP Simulations by Liu et al., 2025
This paper evaluates ocean bottom pressure (OBP) variability in high-resolution climate model simulations submitted to CMIP6 under the HighResMIP protocol. The authors compare model-derived OBP variance fields at 1/4° resolution with observation-based estimates from downscaled GRACE satellite data and in situ bottom pressure recorders (BPRs). Their results suggest the models overestimate variance in some regions (notably on continental shelves and the Southern Ocean abyssal plains) while underestimating it in more quiescent deep-ocean areas relative to observations. Future scenario analysis indicates a projected increase in OBP variance in high-latitude and eddy-active regions, which the authors link to enhanced wind forcing and intensified Agulhas leakage, suggesting interesting implications for satellite gravimetry and climate change detection.
With new gravity missions planned by ESA and NASA, the paper is timely and addresses a gap in our understanding of how high-resolution climate models represent ocean bottom pressure variability, and will be of interest to the oceanographic and geodetic communities. Overall, the manuscript is well written, the analysis is well-grounded and the figures are of good quality.
A significant concern, however, arises from the choice of reference data used to assess model performance. The GRACE-DS product, while innovative, is downscaled using ocean reanalysis outputs from GLORYS and ORAS5, both of which are based on the NEMO ocean model. Since NEMO also forms the ocean component of all five CMIP6-HR models assessed here, the evaluation may suffer from circularity: structural biases present in NEMO-based models could propagate into both the GRACE-DS product and the CMIP6 simulations. This undermines the independence of the benchmark and makes it difficult to unambiguously attribute over- or underestimation of OBP variance to model error rather than artefacts of the downscaling process. Moreover, the over or underestimations may be greater than given by this analysis. A deeper discussion of this limitation, or a sensitivity test using alternative GRACE products and/or reanalyses, would help clarify the robustness of the findings. This may be beyond the scope of the present work, but potential limitations should be more fully acknowledged. And, in light of these issues, it would be more appropriate to describe the results in the more neutral terms of relative amplitudes rather than the loaded terms ‘overestimation’ and ‘underestimation’. This shift in language would reduce the implication that one dataset is definitively correct and better reflect the comparative nature of the analysis.
A broader concern is the paper’s tendency to offer speculative explanations for inter-model differences and model–observation mismatches without direct supporting evidence. While many of the proposed mechanisms—such as topographic smoothing, wind stress misrepresentation, blocked ice shelf cavities, or changes in eddy activity—are plausible, they are presented more as assertions than as tested hypotheses. For example, differences in bottom pressure variance are attributed to bathymetric constraints or wind forcing without showing comparative diagnostics of wind fields, bathymetric detail, or eddy characteristics across models. One exception is for the Arctic and South Atlantic where there is a rather superficial attempt to relate long-term changes of OBP variances to the wind field, yet this is not convincing or properly developed. Similarly, interpretations of future OBP variance increases invoke processes like Ekman pumping, Rossby waves, or stratification changes, but these remain unexamined. This interpretive style, while common in model evaluation studies, risks overreaching and may give a false sense of causal understanding.
I suggest removing the speculative content currently embedded in the results section, collating and synthesising it in a separate discussion section. This would clarify the distinction between empirical findings and interpretive hypotheses and improve the scientific rigour of the paper. While this restructuring might leave the results section relatively brief, it creates an opportunity to deepen the quantitative analysis — for example, by more systematically evaluating inter-model spread, introducing uncertainty estimates for variance ratios, or providing more regional or temporal breakdowns of the comparisons. A clearer separation between results and interpretation would also help readers better assess the robustness of the conclusions.
Given the novelty and relevance of the topic, and the sound core methodology, I believe the paper has the potential to make a valuable contribution. However, the concerns outlined above—particularly regarding the choice of reference dataset and the interpretive framing—should be addressed through major revision.
Minor comments
Line 56: “valorizations” -> “evaluations” or similar.
Line 96: “sketchy” -> “uncertain”.
Line 142: reflecting net atmospheric pressure variations over the ocean
Line 216: “considerable” -> “somewhat”
Line 214: “This type of…” - this is an important caveat that needs stating upfront.
Line 250: This is a weak justification.
Line 258: Must be the case.
Line 273: Why not discuss in more detail?
Line 274: “signal levels” -> “amplitudes”.
Line 275: pb italicised.
Figure 5: This caption is rather confusing.
Line 347: Why should decelerated winds lead to a reduction in Agulhas transport and eddy activity?
Citation: https://doi.org/10.5194/egusphere-2025-775-RC2 - AC2: 'Reply on RC2', Le Liu, 28 May 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-775', Christopher Piecuch, 03 Apr 2025
A review of "Assessment of Ocean Bottom Pressure Variations in CMIP6 HighResMIP Simulations" by Liu, Schindelegger, Börger, Foth, and Gou
The authors compare ocean bottom pressure (OBP) variability from GRACE/GRACE-FO retrievals, bottom pressure recorder (BPR) observations, and CMIP6 HighResMIP Simulations for past and future periods. They identify regions where observed and simulated OBP time series agree or not, and they also highlight where simulated OBP variability changes substantially from the present to the future, offerring interpretations in terms of physical oceanographic processes or observational considerations. The analysis largely focuses on periods from a couple months to a decade, and the authors perform their calculations both with and without the mean seasonal cycle removed.
This is a nice paper that presents a solid incremental advance in the science. As the authors explain, changes in OBP can arise from a variety of oceanographic and geodetic processes, so a study like this will be of interest to a wide community of geoscientists. What's more, the paper is well written, the methods and approaches are generally sound, and most scientific inferences are reasonable and justified. The paper should be published after minor revisions to address a few places where the reasoning could be clarified or the analyses could be expanded to make a stronger study. I thank the authors for making a satisfying study on a topic that's largely been overlooked in papers analyzing past and future climate model simulations.
Good luck,
Chris Piecuch, Woods HoleGeneral comments
Section 2.2 on calculation of OBP anomalies. Why don't the authors use the standard CMIP diagnostic output for OBP (pbo), which is readily available? I'd recommend them to use the proper model diagnostic output because, as the authors explain, right now they're making various assumptions in their calculation of OBP. The errors they're incurring from these assumptions are unclear. First, they're computing density from monthly temperature and salinity using the McDougall and Barker (2011) equation of state. While this sounds reasonable, it risks using an equation of state that's potentially distinct from what the various models use on line. It's also complicated by the nonlinearity of the equation of state (the monthly average of a density time series computed from instantaneous temperatures and salinities is not the same as the density computed from monthly averages of temperature and salinity time series). Second, the authors base their bathymetry on ETOPO1. Again, this sounds reasonable, but model bathymetry can be modified relative to something like ETOPO. Using standard model output alleviates these issues.
Section 2.3 on downscaling GRACE. I'd like the authors to discuss why they downscale GRACE/GRACE-FO data rather than coarsen CMIP6 model output. The downscaling is based on machine learning algorithms that incorporate eddy-permitting ocean circulation models. It's unclear what the associated uncertainties are. Are the downscaled datasets also based on the NEMO modeling framework? If so, then we have the potential of correlated errors between the downscaled GRACE/GRACE-FO products and the CMIP6 models. I'm not recommending the authors fundamentally change their approach. But I would like them to (1.) justify their decision to downscale rather than coarsen and (2.) discuss the associated uncertainties, biases, and other potential implications.
Equation 2 (the variance ratio). The authors define this as the modeled variance divided by the observed variance. When they show results on a linear vertical scale, this definition will tend to visually overemphasize values >1 and de-emphasize values <1 (i.e., the former will span a greater color range than the latter). Therefore, I'd suggest, whenever they're showing R values, the authors use more of logarithmic color scale or vertical axis. That way, values >1 and <1 would visually communicate comparable emphasis.
In all the figures, the authors compare root mean square (RMS) variability from the models and the observations. Typically, modeled RMS values are computed for a fixed time period (e.g., 1980-2014). However, Figure 5 shows the very interesting and important result that, even under a control simulation with presumably stationary statistics, there can be large apparent changes in RMS amplitues (see left column). Because of this stochastic variability, it's unclear whether any of the R values in the preceding figures are significant or not. Therefore, I'd like the authors to perform a more comprehensive error analysis. Rather than computing modeled RMS values over a single period (e.g., 1980-2014), I'd suggest the authors instead compute RMS values for overlapping but separate periods to approximate a distribution of RMS values that would better quantify uncertainty and permit them to test whether simulated OBP variability really is distinct from what we're seeing in the observations.
Starting on line 228, the authors note that models show stronger OBP variability on shelves compared to observations. They may mention that this could arise partly if the models aren't frictional enough or are too shallow (e.g., you expect barotropic ocean response to scale with the inverse of both friction coefficient and ocean depth).
In section 3.5, the authors argue that increased future OBP variability could be related to changes in zonally averaged wind speeds (Figure 6). To me, this is an apples to oranges comparison. Assuming a linear barotropic adjustment such that OBP responds to winds on the same timescale, the more relevant comparison to make here would be to compare RMS (not mean) zonal wind speeds between the two periods. That is, the authors should quantify if the winds will grow more variable in the future (not just if they grow stronger overall).
Line edits
Line 34: Suggest to delete "mainly because they are arbitrary in time"
Line 50: Suggest to delete "by us"
Line 51: Suggest to change "Given the monthly sampling of the data and the fact that model drift precludes the study of trends" to "Given the monthly sampling of the data and the fact that models drift, we are precluded from studying trends"
Line 68: Suggest to change "Ad hoc short names" to "Abbreviations"
Equation 1: Change "\int_{0}^{\eta} \rho_0 g dz" to "\int_{0}^{\eta} \rho g dz" and change the second equal sign to an approximation symbol (since the authors make the very reasonable approximation that density is constant over the vertical distance between 0 and sea level)
Line 97: Change "sketchy" to "uncertain"
Lines 173: Specify *planetary* potential vorticity
Line 184: Suggest to change "baroclinic instability" simply to "instability" to be more general
Line 208: Suggest to change "are therefore likely" to "may be"
Line 262: The concept of geostrophic modes (resonances) is fairly specific (and esoteric; see Greenspan 1968). Suggest to change "geostrophic modes" to simply "variability".
Line 274: Suggest to change "from this behavior" to "to this behavior"
Line 275: pb should be italicized
Citation: https://doi.org/10.5194/egusphere-2025-775-RC1 - AC1: 'Reply on RC1', Le Liu, 28 May 2025
-
RC2: 'Comment on egusphere-2025-775', Anonymous Referee #2, 01 May 2025
Assessment of Ocean Bottom Pressure Variations in CMIP6 HighResMIP Simulations by Liu et al., 2025
This paper evaluates ocean bottom pressure (OBP) variability in high-resolution climate model simulations submitted to CMIP6 under the HighResMIP protocol. The authors compare model-derived OBP variance fields at 1/4° resolution with observation-based estimates from downscaled GRACE satellite data and in situ bottom pressure recorders (BPRs). Their results suggest the models overestimate variance in some regions (notably on continental shelves and the Southern Ocean abyssal plains) while underestimating it in more quiescent deep-ocean areas relative to observations. Future scenario analysis indicates a projected increase in OBP variance in high-latitude and eddy-active regions, which the authors link to enhanced wind forcing and intensified Agulhas leakage, suggesting interesting implications for satellite gravimetry and climate change detection.
With new gravity missions planned by ESA and NASA, the paper is timely and addresses a gap in our understanding of how high-resolution climate models represent ocean bottom pressure variability, and will be of interest to the oceanographic and geodetic communities. Overall, the manuscript is well written, the analysis is well-grounded and the figures are of good quality.
A significant concern, however, arises from the choice of reference data used to assess model performance. The GRACE-DS product, while innovative, is downscaled using ocean reanalysis outputs from GLORYS and ORAS5, both of which are based on the NEMO ocean model. Since NEMO also forms the ocean component of all five CMIP6-HR models assessed here, the evaluation may suffer from circularity: structural biases present in NEMO-based models could propagate into both the GRACE-DS product and the CMIP6 simulations. This undermines the independence of the benchmark and makes it difficult to unambiguously attribute over- or underestimation of OBP variance to model error rather than artefacts of the downscaling process. Moreover, the over or underestimations may be greater than given by this analysis. A deeper discussion of this limitation, or a sensitivity test using alternative GRACE products and/or reanalyses, would help clarify the robustness of the findings. This may be beyond the scope of the present work, but potential limitations should be more fully acknowledged. And, in light of these issues, it would be more appropriate to describe the results in the more neutral terms of relative amplitudes rather than the loaded terms ‘overestimation’ and ‘underestimation’. This shift in language would reduce the implication that one dataset is definitively correct and better reflect the comparative nature of the analysis.
A broader concern is the paper’s tendency to offer speculative explanations for inter-model differences and model–observation mismatches without direct supporting evidence. While many of the proposed mechanisms—such as topographic smoothing, wind stress misrepresentation, blocked ice shelf cavities, or changes in eddy activity—are plausible, they are presented more as assertions than as tested hypotheses. For example, differences in bottom pressure variance are attributed to bathymetric constraints or wind forcing without showing comparative diagnostics of wind fields, bathymetric detail, or eddy characteristics across models. One exception is for the Arctic and South Atlantic where there is a rather superficial attempt to relate long-term changes of OBP variances to the wind field, yet this is not convincing or properly developed. Similarly, interpretations of future OBP variance increases invoke processes like Ekman pumping, Rossby waves, or stratification changes, but these remain unexamined. This interpretive style, while common in model evaluation studies, risks overreaching and may give a false sense of causal understanding.
I suggest removing the speculative content currently embedded in the results section, collating and synthesising it in a separate discussion section. This would clarify the distinction between empirical findings and interpretive hypotheses and improve the scientific rigour of the paper. While this restructuring might leave the results section relatively brief, it creates an opportunity to deepen the quantitative analysis — for example, by more systematically evaluating inter-model spread, introducing uncertainty estimates for variance ratios, or providing more regional or temporal breakdowns of the comparisons. A clearer separation between results and interpretation would also help readers better assess the robustness of the conclusions.
Given the novelty and relevance of the topic, and the sound core methodology, I believe the paper has the potential to make a valuable contribution. However, the concerns outlined above—particularly regarding the choice of reference dataset and the interpretive framing—should be addressed through major revision.
Minor comments
Line 56: “valorizations” -> “evaluations” or similar.
Line 96: “sketchy” -> “uncertain”.
Line 142: reflecting net atmospheric pressure variations over the ocean
Line 216: “considerable” -> “somewhat”
Line 214: “This type of…” - this is an important caveat that needs stating upfront.
Line 250: This is a weak justification.
Line 258: Must be the case.
Line 273: Why not discuss in more detail?
Line 274: “signal levels” -> “amplitudes”.
Line 275: pb italicised.
Figure 5: This caption is rather confusing.
Line 347: Why should decelerated winds lead to a reduction in Agulhas transport and eddy activity?
Citation: https://doi.org/10.5194/egusphere-2025-775-RC2 - AC2: 'Reply on RC2', Le Liu, 28 May 2025
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Michael Schindelegger
Lara Börger
Judith Foth
Junyang Gou
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(4029 KB) - Metadata XML
-
Supplement
(1875 KB) - BibTeX
- EndNote
- Final revised paper
A review of "Assessment of Ocean Bottom Pressure Variations in CMIP6 HighResMIP Simulations" by Liu, Schindelegger, Börger, Foth, and Gou
The authors compare ocean bottom pressure (OBP) variability from GRACE/GRACE-FO retrievals, bottom pressure recorder (BPR) observations, and CMIP6 HighResMIP Simulations for past and future periods. They identify regions where observed and simulated OBP time series agree or not, and they also highlight where simulated OBP variability changes substantially from the present to the future, offerring interpretations in terms of physical oceanographic processes or observational considerations. The analysis largely focuses on periods from a couple months to a decade, and the authors perform their calculations both with and without the mean seasonal cycle removed.
This is a nice paper that presents a solid incremental advance in the science. As the authors explain, changes in OBP can arise from a variety of oceanographic and geodetic processes, so a study like this will be of interest to a wide community of geoscientists. What's more, the paper is well written, the methods and approaches are generally sound, and most scientific inferences are reasonable and justified. The paper should be published after minor revisions to address a few places where the reasoning could be clarified or the analyses could be expanded to make a stronger study. I thank the authors for making a satisfying study on a topic that's largely been overlooked in papers analyzing past and future climate model simulations.
Good luck,
Chris Piecuch, Woods Hole
General comments
Section 2.2 on calculation of OBP anomalies. Why don't the authors use the standard CMIP diagnostic output for OBP (pbo), which is readily available? I'd recommend them to use the proper model diagnostic output because, as the authors explain, right now they're making various assumptions in their calculation of OBP. The errors they're incurring from these assumptions are unclear. First, they're computing density from monthly temperature and salinity using the McDougall and Barker (2011) equation of state. While this sounds reasonable, it risks using an equation of state that's potentially distinct from what the various models use on line. It's also complicated by the nonlinearity of the equation of state (the monthly average of a density time series computed from instantaneous temperatures and salinities is not the same as the density computed from monthly averages of temperature and salinity time series). Second, the authors base their bathymetry on ETOPO1. Again, this sounds reasonable, but model bathymetry can be modified relative to something like ETOPO. Using standard model output alleviates these issues.
Section 2.3 on downscaling GRACE. I'd like the authors to discuss why they downscale GRACE/GRACE-FO data rather than coarsen CMIP6 model output. The downscaling is based on machine learning algorithms that incorporate eddy-permitting ocean circulation models. It's unclear what the associated uncertainties are. Are the downscaled datasets also based on the NEMO modeling framework? If so, then we have the potential of correlated errors between the downscaled GRACE/GRACE-FO products and the CMIP6 models. I'm not recommending the authors fundamentally change their approach. But I would like them to (1.) justify their decision to downscale rather than coarsen and (2.) discuss the associated uncertainties, biases, and other potential implications.
Equation 2 (the variance ratio). The authors define this as the modeled variance divided by the observed variance. When they show results on a linear vertical scale, this definition will tend to visually overemphasize values >1 and de-emphasize values <1 (i.e., the former will span a greater color range than the latter). Therefore, I'd suggest, whenever they're showing R values, the authors use more of logarithmic color scale or vertical axis. That way, values >1 and <1 would visually communicate comparable emphasis.
In all the figures, the authors compare root mean square (RMS) variability from the models and the observations. Typically, modeled RMS values are computed for a fixed time period (e.g., 1980-2014). However, Figure 5 shows the very interesting and important result that, even under a control simulation with presumably stationary statistics, there can be large apparent changes in RMS amplitues (see left column). Because of this stochastic variability, it's unclear whether any of the R values in the preceding figures are significant or not. Therefore, I'd like the authors to perform a more comprehensive error analysis. Rather than computing modeled RMS values over a single period (e.g., 1980-2014), I'd suggest the authors instead compute RMS values for overlapping but separate periods to approximate a distribution of RMS values that would better quantify uncertainty and permit them to test whether simulated OBP variability really is distinct from what we're seeing in the observations.
Starting on line 228, the authors note that models show stronger OBP variability on shelves compared to observations. They may mention that this could arise partly if the models aren't frictional enough or are too shallow (e.g., you expect barotropic ocean response to scale with the inverse of both friction coefficient and ocean depth).
In section 3.5, the authors argue that increased future OBP variability could be related to changes in zonally averaged wind speeds (Figure 6). To me, this is an apples to oranges comparison. Assuming a linear barotropic adjustment such that OBP responds to winds on the same timescale, the more relevant comparison to make here would be to compare RMS (not mean) zonal wind speeds between the two periods. That is, the authors should quantify if the winds will grow more variable in the future (not just if they grow stronger overall).
Line edits
Line 34: Suggest to delete "mainly because they are arbitrary in time"
Line 50: Suggest to delete "by us"
Line 51: Suggest to change "Given the monthly sampling of the data and the fact that model drift precludes the study of trends" to "Given the monthly sampling of the data and the fact that models drift, we are precluded from studying trends"
Line 68: Suggest to change "Ad hoc short names" to "Abbreviations"
Equation 1: Change "\int_{0}^{\eta} \rho_0 g dz" to "\int_{0}^{\eta} \rho g dz" and change the second equal sign to an approximation symbol (since the authors make the very reasonable approximation that density is constant over the vertical distance between 0 and sea level)
Line 97: Change "sketchy" to "uncertain"
Lines 173: Specify *planetary* potential vorticity
Line 184: Suggest to change "baroclinic instability" simply to "instability" to be more general
Line 208: Suggest to change "are therefore likely" to "may be"
Line 262: The concept of geostrophic modes (resonances) is fairly specific (and esoteric; see Greenspan 1968). Suggest to change "geostrophic modes" to simply "variability".
Line 274: Suggest to change "from this behavior" to "to this behavior"
Line 275: pb should be italicized