the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Amplified bottom water acidification rates on the Bering Sea shelf from 1970–2022
Abstract. The Bering Sea shelf supports a highly productive marine ecosystem that is vulnerable to ocean acidification (OA) due to the cold, carbon rich waters. Previous observational evidence suggests that bottom waters on the shelf are already seasonally undersaturated with respect to aragonite (i.e. Ωarag < 1), and that OA will continue to increase the spatial extent, duration, and intensity of these conditions. Here, we use a regional ocean biogeochemical model to simulate changes in ocean carbon chemistry for the Bering Sea shelf from 1970–2022. Over this timeframe, surface Ωarag decreases by -0.043 decade-1 and surface pH by -0.014 decade-1, comparable to observed global rates of OA. However, bottom water pH decreases at twice the rate of surface pH, while bottom [H+] decreases at nearly three times the rate of surface [H+]. This amplified bottom water acidification emerges over the past 25 years and is likely driven by a combination of anthropogenic carbon accumulation and a trend of increasing primary productivity and increasing subsurface respiration and remineralization. Due to this enhanced bottom water acidification, the spatial extent of bottom waters with Ωarag < 1 has greatly expanded over the past two decades, along with pH conditions harmful to red king crab. Interannual variability in surface and bottom Ωarag, pH, and [H+] has also increased over the past two decades, resulting in part from the increased physical climate variability. We also find that the Bering Sea shelf is a net annual carbon sink of 1.1–7.9 TgC/year, with the range resulting from the difference in the two different atmospheric forcing reanalysis products used. Seasonally, the shelf is a significant carbon sink from April–October but a somewhat weaker carbon source from November–March.
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Status: open (until 06 Nov 2024)
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RC1: 'Comment on egusphere-2024-1096', Anonymous Referee #1, 20 Aug 2024
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Pilcher et al. used an ocean-biogeochemical model to investigate carbonate system variability in the Bering Sea, with a focus on the long-term trends of ocean acidification (OA) variables (Ω, pH, H+, pCO2), motivated by the need of relevant OA indices for marine resources management. They extended the temporal coverage of previous modeling studies, quantifying spatiotemporal trends during 1970-2022. Their model results showed a significant acceleration of the OA trend over the last 25-years. The simulated bottom trends are greater than the surface trends, presumably associated with an increased respiration/remineralization in response to enhanced phytoplankton production. This is an interesting and valuable study that contributes to better understand changes in the carbonate system in the Bering Sea, but I think additional work is required to clarify/improve the model settings and further explain the model results.
Main comments
1) Model forcing
The main results in this study relate to the long-term patterns in OA variables. A relevant question is therefore how robust the derived 1970-2022 trends are, and I have some concern about this. The author recognized potential issues associated with the use of multiple products to derive the surface model forcing (CORE for 1970-1994, NCEP-CFSR for 2011-2021, and NCEP-CFSv2 for 2011-2022) and boundary conditions of physical variables (Northeast Pacific model (NEP) for 1970-1994 and CFS for 1995-2022). Model results show that the CORE-to-CFS transition most likely altered the long-term patterns in the OA progression. Maybe I am missing something, but there are available atmospheric reanalysis products that cover the entire study period (e.g., ERA5: 1950-2022), so I am not clear why the authors decided to use those three different products. Could you clarify this?
An additional CORE-forced hindcast ending in 2003 was intended to clarify the CORE-CFS transition impact on the trends, but I do not think this extra analysis significantly helped to that goal. Instead of comparing if the CORE-CFS trends for 1970-2022 are like the CORE trends for 1970-2003, I would compare if trends derived from CORE and CFS forced experiments are consistent during the overlapping period of these two products. A comparison over the overlapping period could also help to clarify potential impacts on seasonal and interannual variability.
Maybe you could re-run the full hindcast using ERA5 as the only atmospheric forcing, so that the “forcing issue” would be limited to the NEP-to-CFS shift in the boundary conditions. In that case, you can run additional experiments to compare trends over the overlapping periods of NEP and CFS.
2) Salinity trend shift
Figure S1 show a strong trend shift in salinity associated with the change in the model forcing products (CORE to CFS). I suggest reporting the mean alkalinity series at surface and bottom as supplementary figure, since salinity and alkalinity are usually strongly correlated. Did you get a similar trend change in alkalinity? Since salinity and alkalinity are drivers of Ω, pH, and pCO2, then that shift could significantly impact all the reported OA trends. Could you discuss about it?
3) pCO2 patterns
There are not many observations during fall-winter, but the M2 records in 2021 suggests that the model is overestimating pCO2 during those seasons. If that is correct, then you could conclude that the model has an overall positive bias in pCO2 (and negative bias in pH), which could have a strong impact on the air-sea CO2 fluxes. You could discuss about it and tone done your results related to CO2 fluxes.
4) Underlying drivers
A Taylor series decomposition could provide valuable insights about the underlying drivers of OA variability, helping to identify the causes for the carbonate system trend changes, support the hypothesis of a biological driven increase in the bottom OA trends, and identify if salinity and alkalinity play any role on the OA progression changes. Although the authors mentioned that diagnostic mechanisms are beyond the scope of this study, I strongly recommend adding a Taylor decomposition analysis, especially considering that a paper describing historical OA pattern was already published (Pilcher et al., 2019).
Minor comments:
I suggest tone done all the modeling results, considering that an ocean-BGC model rather suggest than demonstrate the OA patterns. For example, in the abstract: “surface Ωarag decreases by -0.043 decade-1 and surface pH by -0.014 decade-1” => “model results suggest that Ωarag decreases by 0.043 decade-1 and surface pH by -0.014 decade-1”
168-171: Could you provide the spatial resolution of the Northeast Pacific model and the CFS products used for the boundary conditions?
174: How did you estimate the empirical climatological profiles for iron?
393: “variables are comparable” what do you mean?
415: “seasonally occurring” => provide the season name.
455: I suggest using the same x-axis range (1970-2022) for the two panels, independently that the M8 station data extend only until the 80s.
Citation: https://doi.org/10.5194/egusphere-2024-1096-RC1
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