the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 31 May 2023
Intra-scenario variability of trends and controls of near-bed oxygen concentration on the Northwest European Continental Shelf under climate change
Abstract. We present an analysis of the evolution of near-bed oxygen in the next century in the Northwest European Continental Shelf in a three-member ensemble of coupled physics-biogeochemistry models. The comparison between model results helps highlighting the biogeochemical mechanisms responsible for the observed deoxygenation trends and their response to climate drivers.
While all models predict a decrease in near bed oxygen proportional to climate change intensity, the response is spatially heterogeneous, with hotspots of oxygen decline in the members with the most intense change, as well as areas where compensating mechanisms mitigate change.
We separate the components of oxygen change associated to the warming effect on oxygen solubility from those due to the effects of changes in transport and ecosystem processes. We find that while warming is responsible for a mostly uniform decline throughout the shelf, changes in transport and ecosystem processes account for the detected heterogeneity.
Hotspots of deoxygenation are associated with enhanced stratification that greatly reduces vertical transport. A major change in circulation in the North Sea is responsible for the onset of one such hotspot in the members characterised by intense climate change.
Conversely, relatively shallow and well mixed coastal areas in the south experience an increase in net primary production that partially mitigates oxygen decline in all members.
This work represents the first multi-model comparison addressing deoxygenation in the Northwest European Shelf and contributes to the understanding of the processes that drive deoxygenation in continental shelf ecosystems.
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RC1: 'Comment on egusphere-2023-1049', Anonymous Referee #1, 12 Jun 2023
The manuscript investigates the processes driving near-bed oxygen changes on the Northwest European Continental Shelf under a high-emissions climate change scenario, with a focus on the intermodel uncertainties in these processes and their effects on oxygen. This work extends and qualifies the results of a previous study (Wakelin et al., 2020) by adding two additional sets of regionally downscaled model projections within the high-emissions forcing scenario (RCP 8.5).
Ocean deoxygenation and coastal hypoxia under climate change pose a serious threat to marine ecosystems. Robust understanding and projection of these processes is important for effective adaptation of ecosystem services. Given the lack of skill of coarse resolution global ESMs in coastal regions, regional downscaling of ESM projections will likely play a critical role in exploring this topic.
Although these additional model simulations provide valuable new insights into the fate of the oxygen in the region, some of the main conclusions reached by the authors are not well supported by the evidence presented. The scope of the study is not well defined and the manuscript overall lacks focus and rigor. While the scientific premise of the study is valuable, major revisions are required for this work to be fit for publication in Biogeosciences.
Specific comments:
- A major result of the paper is the attribution of the deoxygenation hotspot in the Norwegian Trench to a relaxation/reversal of the Norwegian Trench Current; but this interpretation is not well supported or well argued. The authors argue that (1) a relaxation of the advective current causes a freshening of the shelf region causing increased stratification, and (2) correlation suggests that the increased stratification is responsible for deoxygenation. Holt et al (2018) argue that changes in stratification are responsible for the relaxation of the current, opposite to the authors’ explanation. In most cases, an increase in stratification would come from surface warming and precipitation changes; this null hypothesis should be disproved before seeking alternative explanations. It is also not clear in the results whether vertical mixing or horizontal advective transport is dominating oxygen supply to the Norwegian Trench region, which should guide the conclusions made. Note that Wakelin et al (2020) do link reduced current to a recoupling of export with near-bed respiration; perhaps this is connected to the change in sign of correlation between SS and stratification (320). Lastly, ‘tight coupling’ in Figure 11 is not necessarily convincing by eye. A stronger link has to be made.
- The title of the manuscript suggests that the focus of the paper is on ‘intra-scenario variability’; however, it is unclear what the scope of this is and how effectively it can actually be investigated with available tools. Uncertainty in ESM climate projections (and by extension, downscaled projections) fall broadly into three categories, regarding (1) internal model variability, (2) intermodel uncertainty, (3) and scenario uncertainty. The term ‘intra-scenario’ would suggest that you look at both internal variability and intermodel uncertainty, which is not really the case. Due to the small sample size (three models) and inconsistencies in the model and methods used for downscaling in the older HADGEM run versus the IPSL and GFDL simulations, neither internal variability nor intermodel uncertainty is well sampled nor well isolated. Perhaps the term ‘multi-model comparison’ used in the abstract is more appropriate here. This is already addressed somewhat in the introduction (125-135), but should be clarified and given more thought. Claims like “we added an intra-scenario variability dimension (375)” are unclear and misleading, and should be changed.
- Throughout the study, the authors claim that oxygen changes in the study region across the three simulations scale with the climate sensitivity of the parent ESMs. If quantifications of these sensitivities are available, they should be presented here. Additionally, an issue with this claim is that the differences in downscaling model/methods for HADGEM vs the IPSL and GFDL simulations provide uncontrolled degrees of freedom. The authors should provide an argument whether the differences in downscaling techniques should significantly impact the magnitude of oxygen changes. If possible, the authors could run some short sensitivity experiments using the new (used for IPSL, GFDL) setup to test sensitivity to e.g. vertical resolution.
- In the model used by Wakelin et al (2020), oxygen is not included in open boundary conditions of the regional model so that changes in open ocean oxygen is not included. Is this the case here? This is very important for how the results may be interpreted and should be documented carefully.
- The authors need to be careful when interpreting correlation as causation. Correlations are only meaningful when there is a process that can explain the relationship. Please be thorough about when a physical/biogeochemical mechanism can explain a correlation and when a correlation cannot be explained. For example, why would you have a positive correlation between SS and stratification in some regions (Fig. 7)? If strong but erroneous correlations are prevalent, why can we still trust the results? The authors should also provide a discussion of any covariances that may influence the results (e.g. between temperature, stratification, respiration, NPP)
- In calculating correlations, the long-term trend is removed. I see how this avoids false positives, but how can you assess the drivers of forced changes after removing the long term trends? In this case, it seems that correlations just classify the drivers of short-term variability, which is not what you purport to be investigating. Please explain/clarify.
- What is gained by decomposition in section 2.3 as opposed to a traditional O2sat, AOU decomposition? Why is O2phys-ch (O2sat scaled by the initial saturation state) a more meaningful metric than O2sat? The authors end up using O2sat and SS (a.k.a 1-AOU) anyway, so this section can be removed entirely.
- How are there negative values in the root-mean-square distance calculation (Fig. 2)? Need to provide formulae here for nbias and nurmsd.
- Bias-correction for hypoxia measurements should be included in methods
Technical Corrections:
- Grammatical errors and inconsistent capitalization throughout. Please proofread carefully.
- In all figures, panel labels need to be included
- Use consistent terminology for region names. Is the Danish strait the same as Skagerrak? Eastern North Sea is referenced throughout but not delineated on the map in Fig 1.
- Nearly all instances of ‘in fact’ can be removed
Citation: https://doi.org/10.5194/egusphere-2023-1049-RC1 -
AC1: 'Reply on RC1', giovanni galli, 28 Jul 2023
Please find attached our responses to Reviewer1's questions. Some samples of results with detrending retained in the analysis, as per one of Reviewer1's comments are attached at the end of the document.
Regards,
Giovanni Galli on behalf of co-authors Sarah Wakelin, James Harle, Jason Holt and Yuri Artioli
-
RC2: 'Comment on egusphere-2023-1049', Anonymous Referee #2, 20 Jun 2023
-
AC2: 'Reply on RC2', giovanni galli, 28 Jul 2023
Please find attached our answers to Reviewer2's comments. An analysis of the Western Norwegian Trench current flux compared to warming levels, suggested in one of Reviewer2's questions, is attached at the end of the document.
Regards,
Giovanni Galli on behalf of co-authors Sarah Wakelin, James Harle, Jason Holt and Yuri Artioli
-
AC2: 'Reply on RC2', giovanni galli, 28 Jul 2023
-
RC3: 'Comment on egusphere-2023-1049', Anonymous Referee #3, 03 Jul 2023
Galli et al. investigate the drivers of near-bed [O2] change on the Northwest European Continental Shelf during the 21st century using output from 3 regional marine biogeochemistry model runs. While the other referees have already pointed out several strengths and issues with the manuscript that I will not repeat here, I believe that the major methodological flaw has not been fully addressed to this point. The authors attribute changes in [O2] to either changes in in situ solubility, [O2]sat, or changes in "all other processes". The flawed general premise is that somehow in situ [O2]sat controls in situ [O2], while other mechanisms drive the saturation state. However, in the ocean interior and particularly near the seabed, far away from the surface, changes in solubility alone (from changes in temperature or salinity) should have zero effect on in situ [O2], except in the case where the solubility is reduced below the in situ [O2]. If a parcel of water with salinity S, temperature T, and oxygen concentration [O2] was artificially cooled down, its O2 solubility would increase, its O2 saturation state would decrease, but its O2 content would remain unchanged. While Δ[O2]sat may correlate well with Δ[O2] in the real ocean and in marine biogeochemistry models, there is no causation. (While both previous referees have cautioned the authors about "causation", none of them have pointed out that the main equation, from which most conclusions are drawn, is misinterpreted throughout as if it were causal.)Overall, I recommend rejection of this manuscript. The main drivers for [O2] change (sections 3.5–3.8) are not quantified rigorously and the conclusions thus remain conjectural. Teasing out the controls of deep Δ[O2] requires a rigorous mathematical and physical decomposition of [O2] accross all possible trajectories and histories from air–sea exchange or biological production to subduction and respiration (which appears beyond the scope of this manuscript).I provide some minor comments below.Comment on notationThe authors notation is sometimes hard to parse and confusing, particularly when triple subscripts are used. I would recommend using a different notation, which I hope helps clarifying the comments presented here. (Note that the authors' notation is already different from the preceding work by Wakelin et al. (2020).) I recommend using a simpler symbol, such as f = [O2] / [O2]sat for the saturation state. Below I use the "0" subscript for "at t0", i.e., the 1990–2019 average, and the "1" subscript means "at t", i.e., the 2070–2099 average, so that for any quantity X, its 21st-century change is denoted by ΔX = X1 − X0.Comment on the product-rule approximationGalli et al. and Wakelin et al. (2020) before them are not explicitly discussing a likely important term in their decomposition. In both works, the authors are simply applying the discrete approximation of the product rule (https://en.wikipedia.org/wiki/Product_rule) to Δ[O2] = f × [O2]sat, which is:Δ[O2] = f0 × Δ[O2]sat + Δf × [O2]sat0 + Δf × Δ[O2]sat.In the manuscript under review,ΔO2,phy-ch = f0 × Δ[O2]satandΔO2,other = Δf × [O2]sat1 = Δf × [O2]sat0 + Δf × Δ[O2]sat.Note that ΔO2,other arbitrarily combines a 1st-order term with the 2nd-order term. The authors thus implicitly account for the 2nd-order term, which precisely quantifies spatial correlations between Δf and Δ[O2]sat, but no explicit analysis of that term is provided. In particular, changes in solubility "contribute" to the authors' ΔO2,other term via Δf × Δ[O2]sat (hidden in Δf × [O2]sat1), but this effect is never mentioned in both this manuscript and in Wakelin et al. (2020). Were the major flaw of the decomposition addressed, this contribution would need to be quantified separately along with its effect on the results and conclusions.Citation: https://doi.org/
10.5194/egusphere-2023-1049-RC3 - AC3: 'Reply on RC3', giovanni galli, 28 Jul 2023
Status: closed
-
RC1: 'Comment on egusphere-2023-1049', Anonymous Referee #1, 12 Jun 2023
The manuscript investigates the processes driving near-bed oxygen changes on the Northwest European Continental Shelf under a high-emissions climate change scenario, with a focus on the intermodel uncertainties in these processes and their effects on oxygen. This work extends and qualifies the results of a previous study (Wakelin et al., 2020) by adding two additional sets of regionally downscaled model projections within the high-emissions forcing scenario (RCP 8.5).
Ocean deoxygenation and coastal hypoxia under climate change pose a serious threat to marine ecosystems. Robust understanding and projection of these processes is important for effective adaptation of ecosystem services. Given the lack of skill of coarse resolution global ESMs in coastal regions, regional downscaling of ESM projections will likely play a critical role in exploring this topic.
Although these additional model simulations provide valuable new insights into the fate of the oxygen in the region, some of the main conclusions reached by the authors are not well supported by the evidence presented. The scope of the study is not well defined and the manuscript overall lacks focus and rigor. While the scientific premise of the study is valuable, major revisions are required for this work to be fit for publication in Biogeosciences.
Specific comments:
- A major result of the paper is the attribution of the deoxygenation hotspot in the Norwegian Trench to a relaxation/reversal of the Norwegian Trench Current; but this interpretation is not well supported or well argued. The authors argue that (1) a relaxation of the advective current causes a freshening of the shelf region causing increased stratification, and (2) correlation suggests that the increased stratification is responsible for deoxygenation. Holt et al (2018) argue that changes in stratification are responsible for the relaxation of the current, opposite to the authors’ explanation. In most cases, an increase in stratification would come from surface warming and precipitation changes; this null hypothesis should be disproved before seeking alternative explanations. It is also not clear in the results whether vertical mixing or horizontal advective transport is dominating oxygen supply to the Norwegian Trench region, which should guide the conclusions made. Note that Wakelin et al (2020) do link reduced current to a recoupling of export with near-bed respiration; perhaps this is connected to the change in sign of correlation between SS and stratification (320). Lastly, ‘tight coupling’ in Figure 11 is not necessarily convincing by eye. A stronger link has to be made.
- The title of the manuscript suggests that the focus of the paper is on ‘intra-scenario variability’; however, it is unclear what the scope of this is and how effectively it can actually be investigated with available tools. Uncertainty in ESM climate projections (and by extension, downscaled projections) fall broadly into three categories, regarding (1) internal model variability, (2) intermodel uncertainty, (3) and scenario uncertainty. The term ‘intra-scenario’ would suggest that you look at both internal variability and intermodel uncertainty, which is not really the case. Due to the small sample size (three models) and inconsistencies in the model and methods used for downscaling in the older HADGEM run versus the IPSL and GFDL simulations, neither internal variability nor intermodel uncertainty is well sampled nor well isolated. Perhaps the term ‘multi-model comparison’ used in the abstract is more appropriate here. This is already addressed somewhat in the introduction (125-135), but should be clarified and given more thought. Claims like “we added an intra-scenario variability dimension (375)” are unclear and misleading, and should be changed.
- Throughout the study, the authors claim that oxygen changes in the study region across the three simulations scale with the climate sensitivity of the parent ESMs. If quantifications of these sensitivities are available, they should be presented here. Additionally, an issue with this claim is that the differences in downscaling model/methods for HADGEM vs the IPSL and GFDL simulations provide uncontrolled degrees of freedom. The authors should provide an argument whether the differences in downscaling techniques should significantly impact the magnitude of oxygen changes. If possible, the authors could run some short sensitivity experiments using the new (used for IPSL, GFDL) setup to test sensitivity to e.g. vertical resolution.
- In the model used by Wakelin et al (2020), oxygen is not included in open boundary conditions of the regional model so that changes in open ocean oxygen is not included. Is this the case here? This is very important for how the results may be interpreted and should be documented carefully.
- The authors need to be careful when interpreting correlation as causation. Correlations are only meaningful when there is a process that can explain the relationship. Please be thorough about when a physical/biogeochemical mechanism can explain a correlation and when a correlation cannot be explained. For example, why would you have a positive correlation between SS and stratification in some regions (Fig. 7)? If strong but erroneous correlations are prevalent, why can we still trust the results? The authors should also provide a discussion of any covariances that may influence the results (e.g. between temperature, stratification, respiration, NPP)
- In calculating correlations, the long-term trend is removed. I see how this avoids false positives, but how can you assess the drivers of forced changes after removing the long term trends? In this case, it seems that correlations just classify the drivers of short-term variability, which is not what you purport to be investigating. Please explain/clarify.
- What is gained by decomposition in section 2.3 as opposed to a traditional O2sat, AOU decomposition? Why is O2phys-ch (O2sat scaled by the initial saturation state) a more meaningful metric than O2sat? The authors end up using O2sat and SS (a.k.a 1-AOU) anyway, so this section can be removed entirely.
- How are there negative values in the root-mean-square distance calculation (Fig. 2)? Need to provide formulae here for nbias and nurmsd.
- Bias-correction for hypoxia measurements should be included in methods
Technical Corrections:
- Grammatical errors and inconsistent capitalization throughout. Please proofread carefully.
- In all figures, panel labels need to be included
- Use consistent terminology for region names. Is the Danish strait the same as Skagerrak? Eastern North Sea is referenced throughout but not delineated on the map in Fig 1.
- Nearly all instances of ‘in fact’ can be removed
Citation: https://doi.org/10.5194/egusphere-2023-1049-RC1 -
AC1: 'Reply on RC1', giovanni galli, 28 Jul 2023
Please find attached our responses to Reviewer1's questions. Some samples of results with detrending retained in the analysis, as per one of Reviewer1's comments are attached at the end of the document.
Regards,
Giovanni Galli on behalf of co-authors Sarah Wakelin, James Harle, Jason Holt and Yuri Artioli
-
RC2: 'Comment on egusphere-2023-1049', Anonymous Referee #2, 20 Jun 2023
-
AC2: 'Reply on RC2', giovanni galli, 28 Jul 2023
Please find attached our answers to Reviewer2's comments. An analysis of the Western Norwegian Trench current flux compared to warming levels, suggested in one of Reviewer2's questions, is attached at the end of the document.
Regards,
Giovanni Galli on behalf of co-authors Sarah Wakelin, James Harle, Jason Holt and Yuri Artioli
-
AC2: 'Reply on RC2', giovanni galli, 28 Jul 2023
-
RC3: 'Comment on egusphere-2023-1049', Anonymous Referee #3, 03 Jul 2023
Galli et al. investigate the drivers of near-bed [O2] change on the Northwest European Continental Shelf during the 21st century using output from 3 regional marine biogeochemistry model runs. While the other referees have already pointed out several strengths and issues with the manuscript that I will not repeat here, I believe that the major methodological flaw has not been fully addressed to this point. The authors attribute changes in [O2] to either changes in in situ solubility, [O2]sat, or changes in "all other processes". The flawed general premise is that somehow in situ [O2]sat controls in situ [O2], while other mechanisms drive the saturation state. However, in the ocean interior and particularly near the seabed, far away from the surface, changes in solubility alone (from changes in temperature or salinity) should have zero effect on in situ [O2], except in the case where the solubility is reduced below the in situ [O2]. If a parcel of water with salinity S, temperature T, and oxygen concentration [O2] was artificially cooled down, its O2 solubility would increase, its O2 saturation state would decrease, but its O2 content would remain unchanged. While Δ[O2]sat may correlate well with Δ[O2] in the real ocean and in marine biogeochemistry models, there is no causation. (While both previous referees have cautioned the authors about "causation", none of them have pointed out that the main equation, from which most conclusions are drawn, is misinterpreted throughout as if it were causal.)Overall, I recommend rejection of this manuscript. The main drivers for [O2] change (sections 3.5–3.8) are not quantified rigorously and the conclusions thus remain conjectural. Teasing out the controls of deep Δ[O2] requires a rigorous mathematical and physical decomposition of [O2] accross all possible trajectories and histories from air–sea exchange or biological production to subduction and respiration (which appears beyond the scope of this manuscript).I provide some minor comments below.Comment on notationThe authors notation is sometimes hard to parse and confusing, particularly when triple subscripts are used. I would recommend using a different notation, which I hope helps clarifying the comments presented here. (Note that the authors' notation is already different from the preceding work by Wakelin et al. (2020).) I recommend using a simpler symbol, such as f = [O2] / [O2]sat for the saturation state. Below I use the "0" subscript for "at t0", i.e., the 1990–2019 average, and the "1" subscript means "at t", i.e., the 2070–2099 average, so that for any quantity X, its 21st-century change is denoted by ΔX = X1 − X0.Comment on the product-rule approximationGalli et al. and Wakelin et al. (2020) before them are not explicitly discussing a likely important term in their decomposition. In both works, the authors are simply applying the discrete approximation of the product rule (https://en.wikipedia.org/wiki/Product_rule) to Δ[O2] = f × [O2]sat, which is:Δ[O2] = f0 × Δ[O2]sat + Δf × [O2]sat0 + Δf × Δ[O2]sat.In the manuscript under review,ΔO2,phy-ch = f0 × Δ[O2]satandΔO2,other = Δf × [O2]sat1 = Δf × [O2]sat0 + Δf × Δ[O2]sat.Note that ΔO2,other arbitrarily combines a 1st-order term with the 2nd-order term. The authors thus implicitly account for the 2nd-order term, which precisely quantifies spatial correlations between Δf and Δ[O2]sat, but no explicit analysis of that term is provided. In particular, changes in solubility "contribute" to the authors' ΔO2,other term via Δf × Δ[O2]sat (hidden in Δf × [O2]sat1), but this effect is never mentioned in both this manuscript and in Wakelin et al. (2020). Were the major flaw of the decomposition addressed, this contribution would need to be quantified separately along with its effect on the results and conclusions.Citation: https://doi.org/
10.5194/egusphere-2023-1049-RC3 - AC3: 'Reply on RC3', giovanni galli, 28 Jul 2023
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