the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Contrasting projection of the ENSO-driven CO2 flux variability in the Equatorial Pacific under high warming scenario
- 1NORCE Norwegian Research Centre AS, Bjerknes Centre for Climate Research, Bergen, Norway
- 2LMD-IPSL, Ecole Normale Supérieure / Université PSL, CNRS, Ecole Polytechnique, Sorbonne Université, Paris, PSL University, Paris, France
- 3Canadian Centre for Climate Modelling and Analysis, Victoria, BC, CA
- 4Max Planck Institute for Meteorology, Hamburg, Germany
- 5NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey 08540, USA
- 6CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
- 7JMA Meteorological Research Institute, Tsukuba, Ibaraki, Japan
- 8Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25, Showa-machi, Kanazawa-ku, Yokohama, Kanagawa, 236-0001, Japan
- 9National Oceanography Centre, Southampton, UK
- 1NORCE Norwegian Research Centre AS, Bjerknes Centre for Climate Research, Bergen, Norway
- 2LMD-IPSL, Ecole Normale Supérieure / Université PSL, CNRS, Ecole Polytechnique, Sorbonne Université, Paris, PSL University, Paris, France
- 3Canadian Centre for Climate Modelling and Analysis, Victoria, BC, CA
- 4Max Planck Institute for Meteorology, Hamburg, Germany
- 5NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey 08540, USA
- 6CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
- 7JMA Meteorological Research Institute, Tsukuba, Ibaraki, Japan
- 8Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25, Showa-machi, Kanazawa-ku, Yokohama, Kanagawa, 236-0001, Japan
- 9National Oceanography Centre, Southampton, UK
Abstract. The El Niño Southern Oscillation (ENSO) widely modulates the global carbon cycle, in particular, by altering the net uptake of carbon in the tropical ocean. Indeed, over the tropics less carbon is released by oceans during El Niño while it is the opposite for La Niña. Here, the skill of Earth System Models (ESM) from the latest Coupled Model Intercomparison Project (CMIP6) to simulate the observed tropical Pacific CO2 flux variability in response to ENSO is assessed. The temporal amplitude and spatial extent of CO2 flux anomalies vary considerably among models, while the surface temperature signals of El Niño and La Niña phases are generally well represented. Under historical conditions followed by the high warming Shared Socio-economic Pathway (SSP5-8.5) scenarios, about half the ESMs simulate a reversal in ENSO-CO2 flux relationship. This gradual shift, which occurs as early as the first half of the 21st century, is associated with a high CO2-induced increase in Revelle factor that leads to stronger sensitivity of partial pressure of CO2 (pCO2) to changes in surface temperature between ENSO phases. At the same time, uptake of anthropogenic CO2 substantially increases upper ocean dissolved inorganic carbon (DIC) concentrations, reducing its vertical gradient in the thermocline, and weakening the ENSO-modulated surface DIC variability. The response of ENSO-CO2 flux relationship to future climate change is sensitive to the contemporary mean state of the carbonate ion concentration in the tropics. Models that simulate shift in ENSO-CO2 flux relationship simulate positive bias in surface carbonate concentration.
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Pradeebane Vaittinada Ayar et al.
Status: open (until 27 May 2022)
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RC1: 'Comment on esd-2022-12', Anonymous Referee #1, 17 May 2022
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Vaittinada Ayar et al. examined a divergent projection of the ENSO-CO2 flux relationship between CMIP6 ESMs in the SSP5-8.5 scenario. Half of the ESMs simulate a reversal ENSO-CO2 flux relationship while the other half EMSs have a consistent ENSO-CO2 flux relationship from historical to future period. They found the reversal relationship in the first half ESMs is due to a faster-increasing surface DIC, enhanced primary production variability, and pCO2 increase. The manuscript is well written and well structured. These findings are very interesting and have significant implications for understanding the different CMIP6 model projections and model bias. However, I have some concerns about the missing terms in the analysis of the contribution to the CO2 flux and ocean pCO2 variability. I would recommend it for publication in ESD after the concerns and comments are considered by the authors.
General comments
- Since the air-sea CO2 flux is related to three terms: ocean pCO2, air pCO2, and wind-solubility coefficient, the authors only analyze the ocean pCO2 in the manuscript. I am very interested in the role of wind-solubility coefficient and air pCO2 in explaining the divergence in two groups of ESMs. Different models might have different wind and temperature variability which might contribute to the CO2 flux variability. It is worth quantifying and discussing the wind and solubility terms. All the ESMs might use the same air pCO2, so the air pCO2 might have a very little contribution. However, it is needed to be at least discussed in the manuscript.
- Ocean pCO2 is sensitive to four terms: temperature, DIC, alkalinity, and salinity (Takahashi et al., 1993). The authors only discuss the temperature and DIC. Although the DIC is dominant in the ocean pCO2 variability, the alkalinity has a very large compensation. The alkalinity might partly contribute to the model divergence. In addition, the precipitation probably changes a lot under global warming (Cai et al., 2015), this might drive a relatively large variability of alkalinity and salinity in the future. It would be convincing if the author could discuss/quantify the contribution of alkalinity and salinity to the model divergence?
- Line 245-247. The authors found two differences between two group of ESMs (Large increase of surface DIC and lower range of DIC changes). Fig.9 could show the large increase in surface DIC. However, I could not see a figure showing the lower range of DIC changes during ENSO phases. I would suggest one such figure in the main text or supporting information.
- Fig. 9. Except for surface DIC difference between preserved and reversed models, I also see the difference in subsurface DIC (e.g., 200-300 m) between two groups of ESMs. What is the role of subsurface DIC difference in the model CO2 flux-ENSO relationship divergence? Why is the subsurface DIC also different in the two groups of models?
Minor comments:
- Line 41-43. The tropical Pacific ocean CO2 flux anomaly is not only related to the upwelling strength but also related to the poleward Ekman transport driven by easterly trade wind. One more reference (Liao et al., 2020 GBC) is suggested.
- Line 80. What is the re-grid method?
- Line 91. The ENSO variability is usually an interannual variability ranging from 3 to 7 years. Could the author plot the total CO2 flux and CO2 flux anomaly at a sample point to show how well is the detrend method? Could the detrend method remove the decadal variability?
- Line 100. Why do you use 1981-2010 as the climatological period instead of 1985-2014 which is the contemporary period defined in the manuscript.
- 2 Caption. What is the observed data of SST and CO2 flux? I know the authors state them in the method section. However, it would be clearer for the readers if the authors could detail them in the caption. For example, SST is JRA.
- 3. Why do the authors use 5N-5S instead of 2N-2S? This is not consistent with the method section.
- What is the CMIP6 ensemble anomalies one standard deviation?
- Line 379. The text reads like Liao et al. (2021) selected the model subjectively and got a partial and biased conclusion. Actually, Liao et al., (2021) use a strict and reasonable constrain method to select the model. The results are physically rational and convincing. I would suggest rephrasing the words. A suggested way would be: “With a strict constrain method based on contemporary observations, the model tends to show a weaker future CO2 flux anomalies during ENSO phases (Liao et al., 2021).”
- Lines 381-382. The increasing Revelle factor and ocean pCO2 sensitivity to temperature would be a general result in my opinion. This point is discussed by many studies. I would rephrase or delete this point.
References:
Cai, W., Borlace, S., Lengaigne, M., van Rensch, P., Collins, M., Vecchi, G., et al. (2014), Increasing frequency of extreme El Niño events due to greenhouse warming, Nature Climate Change, 4, 111.
Liao, E., Resplandy, L., Liu, J., and Bowman, K. W. (2020), Amplification of the Ocean Carbon Sink During El Niños: Role of Poleward Ekman Transport and Influence on Atmospheric CO2, Global Biogeochemical Cycles, 34(9), e2020GB006574.
Takahashi, T., Olafsson, J., Goddard, J. G., Chipman, D. W., and Sutherland, S. C. (1993), Seasonal variation of CO2 and nutrients in the high-latitude surface oceans: A comparative study, Global Biogeochemical Cycles, 7(4), 843-878.
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RC2: 'Comment on esd-2022-12', Anonymous Referee #2, 23 May 2022
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Firstly, Vaittinada Ayar et al. evaluated the skill of 16 ESMs from CMIP6 to reproduce the ENSO-CO2 flux relationship in the Pacific Equatorial. For this, model outputs of chemical, physical and biological variables were compared to observational datasets. Secondly, they analysed how the simulated ENSO-CO2 flux relationship evolved in the future (i.e., 2071-2100) using model projections under the SSP5-8.5 scenario. They found that half of the ESMs projected a positive correlation between ENSO-associated warming and sea-air CO2 flux anomalies, instead of the current negative correlation. According to their findings, the future reversal of the ENSO-CO2 flux relationship is induced by the thermal component of pCO2 becoming more important than the non-thermal components. However, they concluded that this reversal is unlikely because it could be related to model biases in the historical period.
Such comparative study of CMIP6 models improve our understanding of the influence of climate modes on the carbon cycle and could provide useful metrics to evaluate ESMs. The manuscript is clear and well written. However, I have some questions about the evolution of the simulated CO2 flux anomaly variability which is different between ESMs identified as “reversed” or “preserved”. Therefore, the paper will likely be a significant scientific contribution with minor revisions.
General comments:
1) Figure 5 and Line 206: “This reversal is thus independent on the performance of the model over the contemporary period, though the models in the first row tend to simulate lower than observed CO2 flux anomaly variability.”
Firstly, after reading the manuscript, the results suggested that the reversal behavior was indeed induced by the model performance in the contemporary period. Authors should modify or clarify this sentence.
Secondly, when looking at figure 5, the lower CO2 flux variability in the “reversed” ESMs than in the “preserved” ESMs is a striking feature. I would like to see some discussion about the influence (or the relationship) of this feature with the conclusions. For example, could the historical low CO2 flux variability in the “reversed” ESMs be related to their higher carbon uptake than in the “preserved” ESMs? Authors focused on the understanding of the correlation between the annual CO2 flux and the ENSO index, but could some of their findings explain the variability in the amplitude of the simulated CO2 flux anomalies? As a reminder, most models underestimated the CO2 flux variability (line 197 and Table 3) and according to the figure 5 this is more visible in the “reversed” ESMs.
2) Authors estimated the depth of the thermocline (line 105) but their discussion and conclusions focused on the stratification (or the vertical gradient), which are two different concepts. Although there is no difference between the two ESM groups in term of “thermocline depth” (line 306) the vertical stratification might be different. Therefore, could authors replace their “thermocline depth” estimate with a stratification estimate.
Minor comments:
3) Line 28: “…the Equatorial Pacific CO2 flux represents the dominant mode of variability of the global oceanic CO2 flux variations (Wetzel et al., 2005; Resplandy et al., 2015…”. According to Resplandy et al. (2015), for some ESMs, the Southern Ocean can also be the dominant mode of variability of the global oceanic CO2 flux variations.
4) Line 110 – At which temporal resolution is the thermocline depth estimated? Monthly?
5) Line 175: “Note that the observed average is the result of the climatology over the 2004-2017 period while the average for CMIP6 is computed over 30 years (1985-2014).” Could authors calculate the CMIP6 climatology using the same period (i.e., 2004-2017)? If not, this information should be included in Figure 3.
6) Line 188: “The correlation between annual CO2 flux anomaly and annual ENSO index is given for the models for each 30-year sliding window over the 1850-2100 period.” Why did author choose a 30-year sliding window? Is it the observational period? This information needs to be added.
Pradeebane Vaittinada Ayar et al.
Pradeebane Vaittinada Ayar et al.
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