Choice of Forecast Scenario Impacts the Carbon Allocation at the Same Global Warming Levels

de Mora, Lee; Swaminathan, Ranjini; Allan, Richard P.; Blackford, Jeremy; Kelley, Douglas I.; Harris, Phil; Jones, Chris D.; Jones, Colin G.; Liddicoat, Spencer; Parker, Robert J.; Quaife, Tristan; Walton, Jeremy; Yool, Andrew

doi:https://doi.org/10.5194/egusphere-2022-1483

Preprints

https://doi.org/10.5194/egusphere-2022-1483

Preprints

04 Jan 2023

| 04 Jan 2023

Choice of Forecast Scenario Impacts the Carbon Allocation at the Same Global Warming Levels

Lee de Mora, Ranjini Swaminathan, Richard P. Allan, Jeremy Blackford, Douglas I. Kelley, Phil Harris, Chris D. Jones, Colin G. Jones, Spencer Liddicoat, Robert J. Parker, Tristan Quaife, Jeremy Walton, and Andrew Yool

Abstract. The anthropogenic carbon distribution between the atmosphere, land surface and ocean varies significantly with the choice of scenario for identical changes in mean global surface temperature. Moving to a lower CO₂ emissions scenario means that warming levels occur later, and with significantly less carbon in the three main carbon reservoirs. After 2 °C of warming, the multi-model mean ocean allocation can be up to 3 % different between scenarios, or 36 Pg in total with an even larger difference in some single model means. For the UKESM1 model, the difference between the minimum and maximum atmospheric fraction at the 2 °C Global Warming Level (GWL) is 3.6 %. This is equivalent to 50 Pg of additional carbon in the atmosphere, or the equivalent of five years of our current global total emissions.

In the lower CO₂ concentration scenarios, SSP1-1.9 and SSP1-2.6, the ocean fraction grows over time while the the land surface fraction remains constant. In the higher CO₂ concentration scenarios, SSP2-4.5, SSP3-7.0 and SSP5-8.5, the ocean fraction remains constant over time while the the land surface fraction decreases over time.

Higher equilibrium climate sensitivity (ECS) models reach the GWLs sooner, and with lower atmospheric CO₂ than lower sensitivity models. However, the choice of scenario has a much larger impact on the percentage carbon allocation at a given warming level than the individual model's ECS.

Received: 21 Dec 2022 – Discussion started: 04 Jan 2023

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Journal article(s) based on this preprint

13 Dec 2023

Scenario choice impacts carbon allocation projection at global warming levels

Lee de Mora, Ranjini Swaminathan, Richard P. Allan, Jerry C. Blackford, Douglas I. Kelley, Phil Harris, Chris D. Jones, Colin G. Jones, Spencer Liddicoat, Robert J. Parker, Tristan Quaife, Jeremy Walton, and Andrew Yool

Earth Syst. Dynam., 14, 1295–1315, https://doi.org/10.5194/esd-14-1295-2023,https://doi.org/10.5194/esd-14-1295-2023, 2023

Short summary

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-1483', Anonymous Referee #1, 03 Feb 2023

General Comments:
This article explores the carbon allocation with different choice of scenarios, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, at three different global warming levels (2, 3 and 4 degree celsius). Authors comprehensively include a wide range of ESM outputs and design a quantitative analysis framework to calculate carbon fractions in different reservoirs. The current version of the manuscript matches the scope of ESD and the presentation of methodology is enough. However, the main finding from this manuscript is not clear to me. Authors also need to heavily revise their results and discussion sections to provide logical and robust analysis and cross validation and comparison to previous studies.
Specific comments:
I have the following major comments:
1. You have some discussions about the implication of your study on the future carbon management and relevant studies in the discussion section, which is good. But the same information in the introduction part is missing. It would be nice to see more introduction about how carbon allocation is important for relevant research. For example, 1) how the calculated parameter can be important to the next stage of model intercomparison, benchmarking and 2) if this parameter can be helpful to indicate the strategy of carbon management for the next stage.
2. Contents in Results and Discussion sections are stacked in a whole block and require more revisions to streamline your manuscript structure. Please summarize 2-3 subtitles and split your context and fill in these sub-sections.
3. Line 231: “In summary, fig. 3 shows that a model’s sensitivity to CO2 concentration significantly affects the total carbon allocation between the atmosphere, ocean and land at global warming levels, but is less impactful on the percentage allocation……the scenario has a much larger impact on the percentage carbon allocation at a given warming level than the ECS.” But as I found in fig. 3, the carbon allocation fraction after normalization (left pane) are quite similar to each other under different scenarios at least for GWLs at 2 and 3 degree celsius. To the opposite, certain models show very large discrepancy, e.g. EC-Earth3-CC compared to other models. Please explain how you get this conclusion?
4. My understanding is that the authors plan to use UKESM as one of the examples to help understand how different processes in ESMs can influence the calculated carbon fraction. But I only find qualitative speculation instead of quantitative analysis. For example, in Line 340, “The UKESM1’s higher AF at the year 2100 is likely due to the model limiting carbon uptake more than the other models. This could be Nitrogen limitation in the land surface or could be due to the model's higher ECS and thus warmer temperatures at 2100 than the multi-model mean.” I expect to see more analysis, figures or tables to list evidence and prove these statements. Otherwise, there’s no need to specifically highlight the result from one model and these conclusions from this manuscript are not robust.
5. In the discussion section, the manuscript lacks enough cross-validation or comparison against other similar published studies. There are published studies discussing carbon storage, residence time and feedbacks in land and ocean components under different future scenarios. Just to name a few here:
Friend, A. D., Lucht, W., Rademacher, T. T., Keribin, R., Betts, R., Cadule, P., et al. (2014). Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proceedings of the National Academy of Sciences, 111(9), 3280–3285. https://doi.org/10.1073/pnas.1222477110
Jiang, L., Yan, Y., Hararuk, O., Mikle, N., Xia, J., Shi, Z., et al. (2015). Scale-Dependent Performance of CMIP5 Earth System Models in Simulating Terrestrial Vegetation Carbon. Journal of Climate, 28(13), 5217–5232. https://doi.org/10.1175/JCLI-D-14-00270.1
Katavouta, A., & Williams, R. G. (2021). Ocean carbon cycle feedback in CMIP6 models: contributions from different basins. Biogeosciences, 18(10), 3189–3218. https://doi.org/10.5194/bg-18-3189-2021
6. Your key findings are not properly highlighted. To improve this draft, authors need to conclude a more solid and informative key finding, for example, “choice of forecast scenario impacts the carbon allocation at the same global warming levels more than model’s ECS/TCRE”. At the same time, provide more qualitative analysis to prove your key findings.
Technical corrections and minor comments:
Line 25: “and the land surface via primary production”. Here “primary production” can be replaced by “terrestrial carbon fixation”.
Line 27: “known as carbon allocation”. To avoid confusion with the “carbon allocation” widely used in terrestrial ecosystem modeling, I would suggest clarifying this point here, such as “known as carbon allocation in the Earth Systems (we simply use carbon allocation in the rest of the text)”.
Line 92: “land use emissions” contains how many different components? This LUE calculation may not contain the feedback from the settings of different ensembles.
Line 125: “can gives” shall be “can give”
Line 131: “Individual component models can be used by” can be clarified as “Same Individual component model can be used by”.
Line 137: Please clarify “All quoted values”. What are these values?
Line 148: “In addition, several models may share contributing component models” seems to be a repetition of the content in Line 131. Shall think about how to merge them.
Line 165: “These tools include quick ways to standardise, slice, re-grid, and apply statistical operators to datasets.” Can you provide a table or figure to summarize and explain the mathematical algorithms of the operators you applied in this paper through using ESMValTool for data pre-processing? I think this is necessary information to understand your methodology.
Line 193: “Figure 2 only shows the multi-model means, not single models.” It will be helpful to add the spread of carbon allocation fraction using the results from single models in figure 2.
Line 302: “Therefore, SSP3-7.0 can reaches” shall be “reach”.
Line 302: “Therefore, SSP3-7.0 can reaches the GWLs earlier than other scenarios at the same CO2 concentration”. I’m not quite sure about this conclusion. If we take a look at figure 4, SSP3-7.0 is later than SSP5-8.5 to reach all 3 GWLs.
Line 315: “Higher CO2 is causes” shall be “Higher CO2 causes”
Line 323: “the rate at which surface waters and dissolved CO2 is mixed downward will slow. This reduction is downward mixing reduces the overall absorption rate of CO2 into the ocean” This statement is confusing. Please rephrase.
Figure 1: It’s better to clarify that your prescribed DCO2 has accounted for the anthropogenic fossil fuel exploitation and the subsequent C emission from application.
Figure 4: “the historical observations from Raupach et al. (2014) & Watson et al. (2020),” It will be better if you can clarify in which year(s) these observations represent.
There are plenty of other typos and confusing statements in this draft and the authors shall be responsible to double check the whole document before resubmission.

Citation: https://doi.org/10.5194/egusphere-2022-1483-RC1
- AC1: 'Reply on RC1', Lee de Mora, 20 Apr 2023
  
  Please find attached the authors response to RC1.
  
  Citation: https://doi.org/10.5194/egusphere-2022-1483-AC1
RC2:
'Comment on egusphere-2022-1483', John Dunne, 08 Feb 2023

The manuscript “Choice of Forecast Scenario Impacts the Carbon Allocation at the Same Global Warming Levels” by de Mora et al provides an analysis of the carbon allocation across land, atmosphere, and ocean across a subset of CMIP6 models. While I was somewhat surprised at the degree of model agreement, The analysis and conclusions are fairly straightforward and of value to the broad audience of carbon cycle researchers. I have detailed many specific examples of technical questions and points of clarification that I thought should be addressed before publication. It would also be helpful to add more information on caveats that might lead to an underestimation of the overall uncertainty. For example, while the CMIP6 historical simulations start in 1850, it is understood that changes to the carbon cycle began well beforehand which has implications for ongoing partitioning (Bronselaer et al., 2017 https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2017GL074435; Le Quere et al. 2018 https://essd.copernicus.org/articles/10/2141/2018/). Similarly, representation of dynamic vegetation, soil carbon and fire response is most likely undersampled in this ensemble (Arora et al., 2020 https://bg.copernicus.org/articles/17/4173/2020/bg-17-4173-2020.pdf; Koch et al., 2021 https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020EF001874).
Specific comments:
Title – “forecast”, which implies an initial value problem is inappropriate and should be “projection” which implies a boundary value problem.
Abstract, line 13 – Albeit not having read the rest of the manuscript at this point, after hearing that the range of carbon allocation between scenarios towards 2C varies by only 3%, I find the conclusion, “However, the choice of scenario has a much larger impact on the percentage carbon allocation at a given warming level than the individual model’s ECS”. Difficult to understand/believe…are the authors only referring the ECS as an indicator of the differing model approach to 2C, or to the overall ECS over CO2 doubling, which might vary from 2-5C or more? I believe the authors are only referring to the pace of attaining 2C which is far more specific than the current statement conveys. For example, approaching the equilibrium temperature at CO2 doubling or even 3C could have very different implications for carbon allocation than the scenario approach to 2C. (Note, upon finishing the manuscript, I felt like this issue was not resolved).
36 – “(“ belongs before “Ukkola”
37 – “that we have” is unnecessary
37 – add comma after “fuels”
38 – “tool that we have to make forecasts of the future climate” should be “tools capable of projecting the future coupled carbon-climate system”
42 – “This means that the model outputs must use a common format and meet the minimum quality requirements.” Adds nothing beyond the previous sentence
44 – “…drift in the global volume mean ocean temperature of less than 0.1 degrees per year.” Are you sure about this? A mean ocean temperature change of 0.1 C per year corresponds to a global radiative imbalance at the ocean surface of about 60 W per m2… about 100 times greater than the present day imbalance… are you sure that isn’t supposed to be “0.1 degrees per century”?
54 – “forecast” should be “scenario”
74 – “breaks” should be “break”
75 – comma after “year”
85 – While the statement “and several members of the authorship team contributed to the development of the UKESM1 model” may be relevant to the execution of the manuscript and important to establish author contributions, it is not appropriate to provide in the manuscript content.
101 – The sentence “This is typically expressed as an annual total, so the total cumulative flux is calculated as the cumulative sum of the global annual total fluxes along the time dimension” is redundant in invoking “total” 3 times, and “annual” and cumulative” twice.
112 – The statement “Here, we take land-use emissions from the scenario, so they are not in balance with run-time model behaviour: this means that SLAND is only an approximation.” Is unclear as to the need for an approximation. More information on how land use fluxes are treated is warranted. Why is a precise budget not possible? How much uncertainty is there in this “approximation”?
132 – “may appear in several of the earth system models”… The word “may” here is inappropriate. In which of the models used in the present study is the same version of the NEMO circulation model used? This should be specific. How does the model diversity sampled here, in weighting the NEMO model. impact the overall diversity captured in the larger ensemble in CMIP5 and CMIP6, for example, including the GFDL results in the idealized experiments as was done in Arora et al., 2020 (https://bg.copernicus.org/articles/17/4173/2020/bg-17-4173-2020.pdf)
139 – The word “weighted” is inappropriately vague here, since the “one-model one-vote” approach was used. The word should be “mean”, or “median” as appropriate.
Table 1 – Why wasn’t the GFDL-ESM4 model included? It has among the most sophisticated treatments of vegetation/land use and ocean biogeochemistry and is the highest performer in reproducing historical warming (Brunner et al., 2020; https://esd.copernicus.org/articles/11/995/2020/).
147 - What is the support for “These model pairs are likely only to have slight differences.”? Similar to the assertion that multiply models use the same ocean, these characteristics should be justified. There are many previous intermodal comparisons on “uniqueness” and “independence” including the Brunner paper mentioned above that could be referenced on this.
178, 183 – Should “SSP1-2.5” be “SSP1-2.6”?
195 – I don’t know what is being referred to as “This is known as survivor bias”. What is “This” The lack of some models to meet a metric?
226 – What do the authors mean by “strange behavior”?
230 – The phrase “and if the atmospheric carbon concentration were allowed to rise sufficiently high” is not a necessary condition for warming based on TCRE – as long as emissions are positive, temperatures are expected to rise even if concentrations are declining. The statement should rather be “and if net CO2 emissions are positive”
264 – The assertion that ocean variability is larger than land variability in “The variability in the ocean is likely due to the wider range of circulation behavior in the scenarios.” Seems very difficult to believe given the dominant role of land variability in historical interannual variability in carbon uptake as documented by the Global Carbon Project and IPCC… is this an indication of a lack of realism in the UKESM1 representation of interannual carbon variability on land, either through lack of ENSO variability or the land response? Perhaps I don’t understand well enough how this is being calculated to average out land carbon internal variability, or if the models chosen do not have reasonable amount of historical variability. More explanation is warranted.
268 – comma after “land”
292 – The end of the sentence is confusing to me as I do not understand how some models achieve “similar atmospheric CO2 concentrations” with “faster atmospheric CO2 growth” than others… “This means that even though two scenarios may reach the same warming level with similar atmospheric CO2 concentrations, the ocean and the land surface absorb less carbon in the scenario with faster atmospheric CO2 growth.” Are the authors saying that the same GWL can bey achieved at the same atmospheric CO2 concentration by both a high ECS model early in SSP585 as well as a low ECS model in SSP245? Some explanation and examples are necessary.
303 – Given that representation of methane and aerosol precursor emissions have been studied for decades and played a major role in both CMIP5 and CMIP6 (much of the focus of AR6 WGI Ch6), I do not think the word “infancy is accurate in the sentence “The impact of different methane and aerosol precursor emissions on the climate response is still in its infancy in terms of realism in CMIP6.” Rather I think it would be more accurate to stay that these topics remain highly uncertain.
314 – move “(“ to before “Wang”
315 – remove “is”
324 – “reduction is” should be “reduction in”
325 – The logic here is reversed – “more saline surface layers” decreases stratification rather than increasing it.
329 – move “(“ to before “Zeebe”, also, remove “together”
331 – remove “which”
340 – remove second “could be”

Citation: https://doi.org/10.5194/egusphere-2022-1483-RC2
- AC2: 'Reply on RC2', Lee de Mora, 20 Apr 2023
  
  Please find attached the authors response to John Dunne (RC2).
  
  Citation: https://doi.org/10.5194/egusphere-2022-1483-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-1483', Anonymous Referee #1, 03 Feb 2023

General Comments:
This article explores the carbon allocation with different choice of scenarios, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, at three different global warming levels (2, 3 and 4 degree celsius). Authors comprehensively include a wide range of ESM outputs and design a quantitative analysis framework to calculate carbon fractions in different reservoirs. The current version of the manuscript matches the scope of ESD and the presentation of methodology is enough. However, the main finding from this manuscript is not clear to me. Authors also need to heavily revise their results and discussion sections to provide logical and robust analysis and cross validation and comparison to previous studies.
Specific comments:
I have the following major comments:
1. You have some discussions about the implication of your study on the future carbon management and relevant studies in the discussion section, which is good. But the same information in the introduction part is missing. It would be nice to see more introduction about how carbon allocation is important for relevant research. For example, 1) how the calculated parameter can be important to the next stage of model intercomparison, benchmarking and 2) if this parameter can be helpful to indicate the strategy of carbon management for the next stage.
2. Contents in Results and Discussion sections are stacked in a whole block and require more revisions to streamline your manuscript structure. Please summarize 2-3 subtitles and split your context and fill in these sub-sections.
3. Line 231: “In summary, fig. 3 shows that a model’s sensitivity to CO2 concentration significantly affects the total carbon allocation between the atmosphere, ocean and land at global warming levels, but is less impactful on the percentage allocation……the scenario has a much larger impact on the percentage carbon allocation at a given warming level than the ECS.” But as I found in fig. 3, the carbon allocation fraction after normalization (left pane) are quite similar to each other under different scenarios at least for GWLs at 2 and 3 degree celsius. To the opposite, certain models show very large discrepancy, e.g. EC-Earth3-CC compared to other models. Please explain how you get this conclusion?
4. My understanding is that the authors plan to use UKESM as one of the examples to help understand how different processes in ESMs can influence the calculated carbon fraction. But I only find qualitative speculation instead of quantitative analysis. For example, in Line 340, “The UKESM1’s higher AF at the year 2100 is likely due to the model limiting carbon uptake more than the other models. This could be Nitrogen limitation in the land surface or could be due to the model's higher ECS and thus warmer temperatures at 2100 than the multi-model mean.” I expect to see more analysis, figures or tables to list evidence and prove these statements. Otherwise, there’s no need to specifically highlight the result from one model and these conclusions from this manuscript are not robust.
5. In the discussion section, the manuscript lacks enough cross-validation or comparison against other similar published studies. There are published studies discussing carbon storage, residence time and feedbacks in land and ocean components under different future scenarios. Just to name a few here:
Friend, A. D., Lucht, W., Rademacher, T. T., Keribin, R., Betts, R., Cadule, P., et al. (2014). Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proceedings of the National Academy of Sciences, 111(9), 3280–3285. https://doi.org/10.1073/pnas.1222477110
Jiang, L., Yan, Y., Hararuk, O., Mikle, N., Xia, J., Shi, Z., et al. (2015). Scale-Dependent Performance of CMIP5 Earth System Models in Simulating Terrestrial Vegetation Carbon. Journal of Climate, 28(13), 5217–5232. https://doi.org/10.1175/JCLI-D-14-00270.1
Katavouta, A., & Williams, R. G. (2021). Ocean carbon cycle feedback in CMIP6 models: contributions from different basins. Biogeosciences, 18(10), 3189–3218. https://doi.org/10.5194/bg-18-3189-2021
6. Your key findings are not properly highlighted. To improve this draft, authors need to conclude a more solid and informative key finding, for example, “choice of forecast scenario impacts the carbon allocation at the same global warming levels more than model’s ECS/TCRE”. At the same time, provide more qualitative analysis to prove your key findings.
Technical corrections and minor comments:
Line 25: “and the land surface via primary production”. Here “primary production” can be replaced by “terrestrial carbon fixation”.
Line 27: “known as carbon allocation”. To avoid confusion with the “carbon allocation” widely used in terrestrial ecosystem modeling, I would suggest clarifying this point here, such as “known as carbon allocation in the Earth Systems (we simply use carbon allocation in the rest of the text)”.
Line 92: “land use emissions” contains how many different components? This LUE calculation may not contain the feedback from the settings of different ensembles.
Line 125: “can gives” shall be “can give”
Line 131: “Individual component models can be used by” can be clarified as “Same Individual component model can be used by”.
Line 137: Please clarify “All quoted values”. What are these values?
Line 148: “In addition, several models may share contributing component models” seems to be a repetition of the content in Line 131. Shall think about how to merge them.
Line 165: “These tools include quick ways to standardise, slice, re-grid, and apply statistical operators to datasets.” Can you provide a table or figure to summarize and explain the mathematical algorithms of the operators you applied in this paper through using ESMValTool for data pre-processing? I think this is necessary information to understand your methodology.
Line 193: “Figure 2 only shows the multi-model means, not single models.” It will be helpful to add the spread of carbon allocation fraction using the results from single models in figure 2.
Line 302: “Therefore, SSP3-7.0 can reaches” shall be “reach”.
Line 302: “Therefore, SSP3-7.0 can reaches the GWLs earlier than other scenarios at the same CO2 concentration”. I’m not quite sure about this conclusion. If we take a look at figure 4, SSP3-7.0 is later than SSP5-8.5 to reach all 3 GWLs.
Line 315: “Higher CO2 is causes” shall be “Higher CO2 causes”
Line 323: “the rate at which surface waters and dissolved CO2 is mixed downward will slow. This reduction is downward mixing reduces the overall absorption rate of CO2 into the ocean” This statement is confusing. Please rephrase.
Figure 1: It’s better to clarify that your prescribed DCO2 has accounted for the anthropogenic fossil fuel exploitation and the subsequent C emission from application.
Figure 4: “the historical observations from Raupach et al. (2014) & Watson et al. (2020),” It will be better if you can clarify in which year(s) these observations represent.
There are plenty of other typos and confusing statements in this draft and the authors shall be responsible to double check the whole document before resubmission.

Citation: https://doi.org/10.5194/egusphere-2022-1483-RC1
- AC1: 'Reply on RC1', Lee de Mora, 20 Apr 2023
  
  Please find attached the authors response to RC1.
  
  Citation: https://doi.org/10.5194/egusphere-2022-1483-AC1
RC2:
'Comment on egusphere-2022-1483', John Dunne, 08 Feb 2023

The manuscript “Choice of Forecast Scenario Impacts the Carbon Allocation at the Same Global Warming Levels” by de Mora et al provides an analysis of the carbon allocation across land, atmosphere, and ocean across a subset of CMIP6 models. While I was somewhat surprised at the degree of model agreement, The analysis and conclusions are fairly straightforward and of value to the broad audience of carbon cycle researchers. I have detailed many specific examples of technical questions and points of clarification that I thought should be addressed before publication. It would also be helpful to add more information on caveats that might lead to an underestimation of the overall uncertainty. For example, while the CMIP6 historical simulations start in 1850, it is understood that changes to the carbon cycle began well beforehand which has implications for ongoing partitioning (Bronselaer et al., 2017 https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2017GL074435; Le Quere et al. 2018 https://essd.copernicus.org/articles/10/2141/2018/). Similarly, representation of dynamic vegetation, soil carbon and fire response is most likely undersampled in this ensemble (Arora et al., 2020 https://bg.copernicus.org/articles/17/4173/2020/bg-17-4173-2020.pdf; Koch et al., 2021 https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020EF001874).
Specific comments:
Title – “forecast”, which implies an initial value problem is inappropriate and should be “projection” which implies a boundary value problem.
Abstract, line 13 – Albeit not having read the rest of the manuscript at this point, after hearing that the range of carbon allocation between scenarios towards 2C varies by only 3%, I find the conclusion, “However, the choice of scenario has a much larger impact on the percentage carbon allocation at a given warming level than the individual model’s ECS”. Difficult to understand/believe…are the authors only referring the ECS as an indicator of the differing model approach to 2C, or to the overall ECS over CO2 doubling, which might vary from 2-5C or more? I believe the authors are only referring to the pace of attaining 2C which is far more specific than the current statement conveys. For example, approaching the equilibrium temperature at CO2 doubling or even 3C could have very different implications for carbon allocation than the scenario approach to 2C. (Note, upon finishing the manuscript, I felt like this issue was not resolved).
36 – “(“ belongs before “Ukkola”
37 – “that we have” is unnecessary
37 – add comma after “fuels”
38 – “tool that we have to make forecasts of the future climate” should be “tools capable of projecting the future coupled carbon-climate system”
42 – “This means that the model outputs must use a common format and meet the minimum quality requirements.” Adds nothing beyond the previous sentence
44 – “…drift in the global volume mean ocean temperature of less than 0.1 degrees per year.” Are you sure about this? A mean ocean temperature change of 0.1 C per year corresponds to a global radiative imbalance at the ocean surface of about 60 W per m2… about 100 times greater than the present day imbalance… are you sure that isn’t supposed to be “0.1 degrees per century”?
54 – “forecast” should be “scenario”
74 – “breaks” should be “break”
75 – comma after “year”
85 – While the statement “and several members of the authorship team contributed to the development of the UKESM1 model” may be relevant to the execution of the manuscript and important to establish author contributions, it is not appropriate to provide in the manuscript content.
101 – The sentence “This is typically expressed as an annual total, so the total cumulative flux is calculated as the cumulative sum of the global annual total fluxes along the time dimension” is redundant in invoking “total” 3 times, and “annual” and cumulative” twice.
112 – The statement “Here, we take land-use emissions from the scenario, so they are not in balance with run-time model behaviour: this means that SLAND is only an approximation.” Is unclear as to the need for an approximation. More information on how land use fluxes are treated is warranted. Why is a precise budget not possible? How much uncertainty is there in this “approximation”?
132 – “may appear in several of the earth system models”… The word “may” here is inappropriate. In which of the models used in the present study is the same version of the NEMO circulation model used? This should be specific. How does the model diversity sampled here, in weighting the NEMO model. impact the overall diversity captured in the larger ensemble in CMIP5 and CMIP6, for example, including the GFDL results in the idealized experiments as was done in Arora et al., 2020 (https://bg.copernicus.org/articles/17/4173/2020/bg-17-4173-2020.pdf)
139 – The word “weighted” is inappropriately vague here, since the “one-model one-vote” approach was used. The word should be “mean”, or “median” as appropriate.
Table 1 – Why wasn’t the GFDL-ESM4 model included? It has among the most sophisticated treatments of vegetation/land use and ocean biogeochemistry and is the highest performer in reproducing historical warming (Brunner et al., 2020; https://esd.copernicus.org/articles/11/995/2020/).
147 - What is the support for “These model pairs are likely only to have slight differences.”? Similar to the assertion that multiply models use the same ocean, these characteristics should be justified. There are many previous intermodal comparisons on “uniqueness” and “independence” including the Brunner paper mentioned above that could be referenced on this.
178, 183 – Should “SSP1-2.5” be “SSP1-2.6”?
195 – I don’t know what is being referred to as “This is known as survivor bias”. What is “This” The lack of some models to meet a metric?
226 – What do the authors mean by “strange behavior”?
230 – The phrase “and if the atmospheric carbon concentration were allowed to rise sufficiently high” is not a necessary condition for warming based on TCRE – as long as emissions are positive, temperatures are expected to rise even if concentrations are declining. The statement should rather be “and if net CO2 emissions are positive”
264 – The assertion that ocean variability is larger than land variability in “The variability in the ocean is likely due to the wider range of circulation behavior in the scenarios.” Seems very difficult to believe given the dominant role of land variability in historical interannual variability in carbon uptake as documented by the Global Carbon Project and IPCC… is this an indication of a lack of realism in the UKESM1 representation of interannual carbon variability on land, either through lack of ENSO variability or the land response? Perhaps I don’t understand well enough how this is being calculated to average out land carbon internal variability, or if the models chosen do not have reasonable amount of historical variability. More explanation is warranted.
268 – comma after “land”
292 – The end of the sentence is confusing to me as I do not understand how some models achieve “similar atmospheric CO2 concentrations” with “faster atmospheric CO2 growth” than others… “This means that even though two scenarios may reach the same warming level with similar atmospheric CO2 concentrations, the ocean and the land surface absorb less carbon in the scenario with faster atmospheric CO2 growth.” Are the authors saying that the same GWL can bey achieved at the same atmospheric CO2 concentration by both a high ECS model early in SSP585 as well as a low ECS model in SSP245? Some explanation and examples are necessary.
303 – Given that representation of methane and aerosol precursor emissions have been studied for decades and played a major role in both CMIP5 and CMIP6 (much of the focus of AR6 WGI Ch6), I do not think the word “infancy is accurate in the sentence “The impact of different methane and aerosol precursor emissions on the climate response is still in its infancy in terms of realism in CMIP6.” Rather I think it would be more accurate to stay that these topics remain highly uncertain.
314 – move “(“ to before “Wang”
315 – remove “is”
324 – “reduction is” should be “reduction in”
325 – The logic here is reversed – “more saline surface layers” decreases stratification rather than increasing it.
329 – move “(“ to before “Zeebe”, also, remove “together”
331 – remove “which”
340 – remove second “could be”

Citation: https://doi.org/10.5194/egusphere-2022-1483-RC2
- AC2: 'Reply on RC2', Lee de Mora, 20 Apr 2023
  
  Please find attached the authors response to John Dunne (RC2).
  
  Citation: https://doi.org/10.5194/egusphere-2022-1483-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Reconsider after major revisions (21 Apr 2023) by Somnath Baidya Roy

AR by Lee de Mora on behalf of the Authors (25 May 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (11 Jul 2023) by Somnath Baidya Roy

RR by John Dunne (24 Jul 2023)

Suggestions for revision or reasons for rejection

The manuscript “Scenario Choice Impacts Carbon Allocation Projection at Global Warming Levels” by de Mora et al provides a useful analysis framework for interpreting carbon emissions allocation between atmosphere, land and ocean across the Shared Socioeconomic Pathway (SSP) future projections out to 2100 used in the Sixth phase of the Coupled Model Intercomparison Project, quantifying the tendency of more carbon remaining in the atmosphere under scenarios of faster emissions to achieve the same Global Warming Level (GWL). What I missed was the value of this approach in achieving consistency among carbon cycle responses relative to what the authors point as the more common approach of reference time frames (2050, or 2100). For example, how much larger would the variance in Figure 4 have been if the models were grouped by time rather than warming level for each scenario? The answer to this question would seem a critical justification for this approach in being able to simplify the interpretation of the carbon cycle response– in the same way that the Transient Cumulative Response to Emissions (TCRE) concept helped simplify communication of the coupled carbon-climate response. Currently, the main justification appears to salvage information from the models with unrealistically high ECS, which is much less compelling. The discussion was somewhat wandering, and I suggested some areas that could be cut if space is an issue. My big frustration with this manuscript was the number of grammar mistakes, particularly given the number of English Institutions and native English speakers represented. I eventually stopped pointing out grammar mistakes in my technical comments in interest of time, but at least one of the co-authors should commit to a detailed look at the grammar before it is resubmitted. It is often much harder for the primary author to find them.

Technical comments
13 – missing comma before “and”
17 - “report” used twice
32 - “allocation” used twice
52-55 – it is difficult for the reader to understand and quantify the assertion that something “appears” to be the case by looking at a figure in an IPCC Assessment – Did the Chapter assess the degree that TCRE was pathway dependent? I do not believe that was a conclusion of that chapter and went back to that figure and saw, what I would say is “negligible” pathway dependence when contextualized against the overall uncertainties.
67 – need “projected” before “increased”
68 – “forecasting” should be “projected”
69 – I am not sure what makes this statement “On the other hand”
77 – “simulat” is used twice
77-79 – While CMIP6 indeed had data standards was not a requirement that ESMs “meet certain model quality” standards other the ones provided. As such “including” should be “were”.
80 – “remove second “a drift in the”
91 – Whether SSP5-8.5 is still “feasible” as a future projection is debatable. I would remove the word.
114 – Why is “This allows us to maintain model democracy” a goal? Shouldn’t we want to chose the model(s) that is structurally best? Why should models that have been falsified for ECS be considered at all? I believe the argument here is that the GWL approach accounts for ECS bias to make use of other dimensions of potential usefulness such as the carbon cycle.
115-119 – This paragraph is highly repetitive with the first sentence misdirected. Suggest removing “The 2, 3 and 4 ◦C GWLs were chosen because”
123 – I believe “climate” is needed before “sensitivity”
126-131 – I am unclear as to how each of these terms relates quantitatively to the net carbon flux across the atmos-land interface… is that NBP? If so, that should be made explicit.
130 – as a flux, NBP cannot be a “prognostic variable” in the common usage (e.g. all the carbon pools) but is rather a “diagnostic variable”
163 – “response” is necessary after “change”
217 – add comma before “and”
223,224 – “allocation” should be “allocated”
265 – “correct” is an odd word to use here, as it connotes the potential for the analysis to be in error, whereas I believe “appropriate” is intended.
274 – is this “total carbon” the total or just the change from preindustrial? If the former, than the total in the control should be added. If the latter, than “change” should be added after “carbon”
299 – remove “we can assume that”
302 – again, “total carbon” is used where I believe “change” is intended as they are associated with “allocation” of emissions.
309 – I don’t know what “is likely to be not correlated with ECS.” Means… either it is correlated or it is not based on the statistic the authors define.
310-312 – I do not see the value of this paragraph and suggest it be removed, along with the “hollow” symbols in figure 5.
333 – “Due to results like these, it is widely thought that” is not a scientific statement. To what past hypotheses, assumptions, conclusions and assessments are the authors referring?
367 – The statement, “This highlights the significant role that ECS plays in the uncertainty of warming projections.” Suggests that the high ECS models should be considered as an uncertainty in the target rather than a deficiency in the model development process that requires fixing. Rather the point is that the willingness of some modeling centers to contribute such easily falsifiable climate responses makes for a fundamental challenge in interpretation of other aspects of the models and requires the translation into GWL.
370-399 – This section is fairly superficial and speculative and could be removed or placed in the introduction for context.
400-414 – Again, these points were made earlier in the analysis and don’t seem to add anything.
444-445 – I don’t know why the authors would say “…whereas the ocean heat content anomaly is less widely accepted outside earth system sciences.” As an argument against choosing a more appropriate/objective metric than GWL if they think that is the case. It is an odd statement from a science perspective.
456 – Why is the response in SSP3-7.0 “may have a noticeably different warming response to CO2”… shouldn’t this already be known as part of the analysis above? What about the response was different? Perhaps a statement should be made about this in the discussion of Figure 4.`
490 – “on the way down as it did on the way up.” Is not clear – I believe the authors are referring to reversibility and/or overshoot experiments, but both SSP1-1.9 and SSP1-2.6 include negative CO2 emissions.
502-505 – I do not understand what is being advocated with these sentences, “While the impact of ensemble bias is a small effect here, the multi-model means could have had a much wider range of mean ECS values between scenario groups. In the future, any investigation using the multi-model means needs to be careful with handling the equilibrium climate sensitivity bias of the ensemble. Two ensembles constituted of differing sets of models may not always be directly comparable.” How can one infer that ensemble bias is “small” without having performed the data denial test of removing some of the available members to assess the effect? How could the mean “have had a much wider range of mean ECS” when that range is already outside of the assessed ECS range? In what way do people need to be “careful”? The section should be removed or rewritten with a clear recommendation in mind.
507 – “that” should be “how”… everyone expected that the allocations would differ. The value of the present study is in quantifying those difference and assessing the implications.

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RR by Anonymous Referee #1 (28 Jul 2023)

Suggestions for revision or reasons for rejection

I appreciate the authors' work to respond to my concerns (especially the cross-validation against published studies in the results and discussion sections) and improve the quality and readability of this manuscript. I have no further general questions but one major request, to simplify the languages used in this manuscript. The following minor revision suggestions (marked in italic format) are based on the line number in the manuscript version with tracking changes and hopefully can help authors to find directions about how to squeeze out unnecessary wordings, but authors shall double check their writings, especially the newly added content in discussion section:

Line 66: “There may also be a flux of fossil fuels directly into the ocean or land surface via for instance fossil fuel extraction and other leaks (Roser and Ritchie, 2022), but these are not generally included in Earth system models.”
can be simplified.
“There may also be a direct flux of fossil fuel extraction and other leaks (Roser and Ritchie, 2022) into the ocean or land surface, but are not included in Earth system models.”

Line 71: “Figure 5.31 of Canadell et al. (2021) shows the cumulative carbon emissions against global mean temperature change for several projections. That figure shows a strong correspondence between emissions and warming which appears to be scenario independent. “
These two sentences can be merged.
“Figure 5.31 of Canadell et al. (2021) shows the cumulative carbon emissions against global mean temperature change for several projections, but the strong correspondence between emissions and warming appears to be scenario independent.”

Line 83: “For instance, atmospheric carbon allocation is 30% in SSP1-1.9 of the carbon remaining in the atmosphere in the year 2100, but in SSP5-8.5, that value is 62%. While the land and ocean carbon uptake are expected to remain approximately equal, the uncertainty is much larger for the land carbon sink than the ocean. In the land, some of the uncertainty is due to the balance of increased land carbon accumulation in the high latitudes and loss of land carbon in the tropics (Canadell et al., 2021). Further uncertainty arises from the challenges of forecasting the water cycle, including droughts that reduce carbon absorption potential of the land surface. On the other hand, the ocean CO2 sink is strongly dependent on the emissions scenario. This absorption of carbon into the ocean reduces the mean global buffering capacity and drives changes in the global ocean’s carbonate chemistry (Jiang et al., 2019; Katavouta and Williams, 2021). These projections are based on data from the Coupled Model Inter-comparison Project (CMIP), and the most recent CMIP round, CMIP6, is described in sec. 1.1.”
can be simplified.
“For instance, projected 2100 atmospheric carbon allocation from CMIP6 is 30% in SSP1-1.9 but rises to 62% in SSP5-8.5. While the land and ocean carbon uptake are expected to remain approximately equal, the uncertainty is much larger for the land carbon sink than the ocean. Uncertainty from the land sink is a tradeoff between the accumulated land carbon in the high latitudes and loss of land carbon in the tropics (Canadell et al., 2021) and the challenges of forecasting the water cycle, including droughts that reduce carbon absorption potential of the land surface. On the other hand, continuous absorption of carbon into the ocean reduces the mean global buffering capacity and drives changes in the global ocean’s carbonate chemistry (Jiang et al., 2019; Katavouta and Williams, 2021), building a strong dependency on the choice of scenarios.”

Line 98: “Earth System models (ESMs) are one of the main tools to study the climatic impact of the combustion of fossil fuels, and they are the only tools capable of projecting the future coupled carbon-climate system. The Sixth Coupled Model Inter-comparison Project (CMIP6) (Eyring et al., 2016) is the most recent in a series of global efforts to standardise, share and study ESM simulations. To participate in CMIP6, models must simulate a suite of standard simulations and meet certain model quality and data standards. These standard simulations (also known as the Deck) include a pre-industrial control, a historical simulation, a gradual 1% CO2 growth experiment and a rapid 4xCO2 experiment. The quality requirements include a drift in the air-sea flux of CO2 of less than 10 PgC per century, and a drift in the global volume mean ocean temperature of less than 0.1 degrees per century (Jones et al., 2011; Eyring et al., 2016; Yool et al., 2020).”
can be shortened.
“Earth System models (ESMs) are the only tools capable of projecting the future coupled carbon-climate system. The Sixth Coupled Model Inter-comparison Project (CMIP6) (Eyring et al., 2016) is the most recent global effort to standardise, share and study ESM simulations. CMIP6 designed standard simulation protocols (also known as the Deck) include a pre-industrial control, a historical simulation, a gradual 1% CO2 growth experiment and a rapid 4xCO2 experiment. For quality assurance, only results with a global drift per century lower than 10 PgC in the air-sea CO2 flux and lower than 0.1 degrees in the volume mean ocean temperature are accepted (Jones et al., 2011; Eyring et al., 2016; Yool et al., 2020).”

Line 108: “In order to make projections of the future anthropogenic climate drivers, multiple scenarios were proposed in the ScenarioMIP project to cover a wide range of potential futures. ScenarioMIP expands upon the CMIP6 core simulations and multiple scenarios are available for modellers to use to generate simulations (O’Neill et al., 2016). We include the scenarios: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 (O’Neill et al., 2016; Riahi et al., 2017). Scenario names in CMIP6 are comprised of a general future pathway (SSP1-SSP5) followed by an estimate of the radiative forcing at the year 2100 in units of Wm−2. These scenarios cover a wide range of possible futures, including sustainable development in the SSP1-1.9 and SSP1-2.6 scenarios. The intermediate emissions scenario or “middle of the road” pathway in SSP2-4.5 extrapolates historic and current global development into the future with a medium radiative forcing by the end of the century. The regional ri90 valry scenario, SSP3-7.0, revives nationalism and regional conflicts, pushing global issues into the background and resulting in higher emissions. Then finally, the enhanced fossil fuel development in SSP5-8.5 is a scenario with the highest feasible fossil fuel deployment and atmospheric CO2 concentration (Riahi et al., 2017).”
can be more succinct.
“ScenarioMIP expands upon the CMIP6 core simulations with multiple scenarios of the future anthropogenic climate drivers to cover a wide range of potential futures (O’Neill et al., 2016). Scenario names in CMIP6 are a general future pathway (SSP1-SSP5) followed by an estimate of the radiative forcing at the year 2100 in units of Wm−2. These scenarios (O’Neill et al., 2016; Riahi et al., 2017) include sustainable development scenarios SSP1-1.9 and SSP1-2.6, the intermediate emissions scenario SSP2-4.5 with a medium radiative forcing by the end of the century, the regional rivalry scenario, SSP3-7.0 pushing global issues into the background and the enhanced fossil fuel development in SSP5-8.5 with the highest feasible fossil fuel deployment and atmospheric CO2 concentration (Riahi et al., 2017).”

Line 121: “Given the same rise in atmospheric CO2 concentration, each ESM will warm by a different amount due to the significant structural and parametric differences between models. The Equilibrium Climate Sensitivity (ECS) is a measure of this sensitivity to CO2. The ECS is given in ◦C Celsius and represents the long-term near-surface air temperature rise that is expected to result from a doubling of the atmospheric CO2 concentration once the model has reached equilibrium. In effect, the ECS is an indicator for how much warming occurs in a model with a doubling of CO2. The most recent 5-95% assessed natural ECS range was between 2 ◦C and 5 ◦C, the likely ECS range was 2.5 - 4 ◦C, and the most likely value was 3 ◦C (Arias et al., 2021, TS6). The wide spread ECS values in climate models is one of the causes of uncertainty for the timing of when forecasts reach certain warming levels. The “allowable emissions” that keep global temperature rise within policy targets are equally impacted (United Nations Treaty Collection, 2015). This has been exacerbated in the latest round of CMIP, as the CMIP6 generation of ESMs has a broader range of sensitivities than previous generations. Several CMIP6 models have a stronger response to atmospheric carbon than any CMIP5 model, and many sit above the likely ECS range (Arias et al., 2021, TS6.)”
can be more succinct.
“The Equilibrium Climate Sensitivity (ECS) is a measure defined as the near-surface air temperature rise in ◦C Celsius from a doubling of the atmospheric CO2 concentration once the model has reached equilibrium. The wide spread ECS values in climate models is one of the indicators of uncertainty for the timing of when forecasts reach certain warming levels. Due to the demand to keep global temperature rise within policy targets (United Nations Treaty Collection, 2015), more scenarios based on “allowable emissions” exacerbated a broader range of ECS in CMIP6 models than previous generations. Several CMIP6 models have a stronger response to atmospheric carbon than any CMIP5 model, and many sit above the likely ECS range. The most recent 5-95% assessed natural ECS range was between 2 ◦C and 5 ◦C, the likely ECS range was 2.5 - 4 ◦C, and the most likely value was 3 ◦C (Arias et al., 2021, TS6).”

Line 140: “Climate change policy can often focus on the climate at specific target years, like 2050 or 2100 (United Nations Treaty Collection, 2015; IPCC, 2021a). However, due to the wide range of ECS values in ESMs, this can mean that ensembles at the year 2100 are composed of a set of models with significantly different behaviours. This wide range in the temperatures and warming rates at a given point in time has knock-on effects on feedbacks and may inhibit the realism and representivity of the ensemble 110 multi-model mean (Hausfather et al., 2022; Swaminathan et al., 2022). Instead of specific target years, we can alternatively focus on model behaviour at specific Global Warming Levels (GWL), such as 2 ◦C, 3 ◦C or 4 ◦C of warming relative to the pre-industrial period. By investigating the system’s behaviour at specific warming levels instead of target years, we can account for the impact of climate sensitivity and make policy relevant assessments while still exploiting the full ensemble of CMIP6 models. This allows us to maintain model democracy, even in a so-called “hot model” ensemble. The 2, 3 and 4 ◦C GWLs were chosen because the 2 ◦ C GWL is a key target set in the 2015 Paris Agreement (United Nations Treaty Collection, 2015) and thought to be a threshold for potentially dangerous climate change.”
Need to be polished.
“Climate change policy focuses on the climate at specific target years, like 2050 or 2100 (United Nations Treaty Collection, 2015; IPCC, 2021a). However, due to the wide range of ECS values in ESMs, models with significantly different behaviours projected wide range of warming rates at a given point in time, which has knock-on effects on carbon feedback and reduce realism and representativeness of the multi-model ensemble mean (Hausfather et al., 2022; Swaminathan et al., 2022). Instead of specific target years, we alternatively focus on model behaviour at specific Global Warming Levels (GWL), including 2 ◦C, 3 ◦C or 4 ◦C of warming relative to the pre-industrial period for policy relevant assessments while still exploiting the full ensemble of CMIP6 models. This allows us to maintain model democracy, even in a so-called “hot model” ensemble. The 2 ◦ C GWL is a key target set in the 2015 Paris Agreement (United Nations Treaty Collection, 2015) and thought to be a threshold for potentially dangerous climate change.”

Line 186: “The NBP is an prognostic variable calculated” shall be “a prognostic variable”

Line 297: “The left side shows the percentage allocation, and the right side shows the totals in PgC.” Move figure explanation to the figure caption.

Line 299/300: “More carbon is allocation” shall be “More carbon is allocated”

Line 300: revise to “is allocate”

Line 335: “Figure 2 only shows the multi-model means, not single models. This means that multi-model means that do not reach the GWL are not included in this figure.”
Move this to the corresponding figure caption.

Line 387” “This figure includes a pair of panes for each experiment scenario. For each pair, the top pane is the cumulative carbon in PgC and the bottom pane shows the percentage.”
Move to the figure caption.

Line 413: “This results in the saw-tooth pattern on the right of this figure. However, this saw-tooth pattern does not appear on the left side of the figure, as the ratios of carbon allocation between land, ocean and atmosphere at a given GWL are not dependent on ECS. ”
can be simplified.
“However, the ratios of carbon allocation between land, ocean and atmosphere at a given GWL are not dependent on ECS, since the pattern is relatively smooth comparing to the C emission.”

Line 426: “Firstly, at a given GWL, higher emission scenarios have a higher atmospheric fraction. In effect, the SSP5-8.5 scenarios have a higher atmospheric fraction than SSP1-1.9 and SSP1-2.6 scenarios, even at the same GWL. Similarly, higher emission scenarios have a smaller land fraction, while the ocean fraction is similar across scenarios at the same GWL. Secondly, warmer GWLs have a larger atmospheric fraction than cooler GWLs. Thirdly warmer GWLs have a smaller land fraction than cooler GWLs. Finally, the ocean fraction is relatively consistent between GWLs and scenarios.”
The expression shall be simplified.
“At a given GWL, higher emission scenarios have a higher atmospheric fraction, but a lower land fraction and a relatively consistent ocean fraction. When comparing the allocation fractions from different GWLs, warmer GWLs have larger atmospheric fractions, lower land fractions and consistent ocean fractions than colder GWLs.”

Line 432: ”The data from fig.4 is re-framed in fig.5 as a series of scatter plots. In this figure, each row represents a different scenario, and each column is a different dataset. These datasets are: the GWL threshold year, the total carbon allocated, the carbon allocation for each domain and the fractional carbon allocation to each domain. The y-axis shows the model’s ECS, and each point is a different GWL, where the squares are 2 ◦C GWL, the circles are 3 ◦C GWL, and the triangles are 4 ◦C GWL.” Move to the figure caption.

Line 437: “For each group of data, the line of best fit is shown and the absolute value of the fitting error (Err) of the slope (M) over the slope is shown in the legend, as Err/M. The fitting error, Err, here is the standard error of the estimated gradient under the assumption of residual normality. This value indicates whether the slope crosses the origin within the 95% confidence limit. If the uncertainty on the slope is greater than the slope itself (and Err/M exceeds unity), then we can assume that the fit is not statistically significant. All groups with three models or fewer that reach the GWL were excluded as this is not enough data points to draw meaningful conclusions.
The goal of this figure is to highlight in broad strokes the ways that ECS interacts with carbon allocation in these models. In most of the fits, the data and the ECS are inversely correlated such that lower ECS models have higher values. This appears to be true for GWL year, total carbon and the individual total carbon fields in the atmosphere, ocean and land. The GWL threshold year and the total carbon allocations both have all absolute Err/M values lower than unity and as such are both related to ECS. In both the ocean and the atmosphere’s total carbon, the absolute value of Err/M is always smaller than one. This means that the total carbon in both the ocean and the atmosphere are linked to ECS with 95% confidence. However, this is not the case for the ocean or the atmosphere’s carbon allocation as a percentage and in many cases absolute Err/M is greater than unity. This means that we can not say that the fraction of carbon allocated to the ocean or to the atmosphere is related to the ECS with 95% confidence. Similarly, this absolute Err/M ratio is not consistently below unity for the land ensembles at all GWLs. This implies that the total or percentage land carbon allocation is likely to be not correlated with ECS.”
This part is way too verbose. I suggest to revise:
“For each group of data, the line of best fit is shown and the absolute value of the fitting error (Err, the standard error of the estimated gradient under the assumption of residual normality) over the slope (M) is shown in the legend, as Err/M. This value indicates whether the slope crosses the origin within the 95% confidence limit (< 1) or not (> 1). All groups with three models or fewer that reach the GWL were excluded as not enough data points to draw meaningful conclusions.
GWL year, total carbon and the individual total carbon allocation fractions are inversely correlated to ECSs. The GWL threshold year and the total carbon allocations both have all absolute Err/M values lower than unity and as such are both related to ECS. The total carbon in both the ocean and the atmosphere are linked to ECS, as their Err/M are smaller than 1. However, the correlations between carbon allocation fraction of the ocean or the atmosphere and ECS are not statistically significant. For land, both total carbon sink and allocation fraction are not significantly correlated to ECS at all GWLs.”

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ED: Reconsider after major revisions (31 Jul 2023) by Somnath Baidya Roy

AR by Lee de Mora on behalf of the Authors (11 Sep 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (13 Sep 2023) by Somnath Baidya Roy

RR by Anonymous Referee #1 (25 Sep 2023)

ED: Publish subject to minor revisions (review by editor) (26 Sep 2023) by Somnath Baidya Roy

AR by Lee de Mora on behalf of the Authors (05 Oct 2023) Author's response Author's tracked changes Manuscript

ED: Publish as is (10 Oct 2023) by Somnath Baidya Roy

AR by Lee de Mora on behalf of the Authors (18 Oct 2023) Author's response Manuscript

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13 Dec 2023

Scenario choice impacts carbon allocation projection at global warming levels

Earth Syst. Dynam., 14, 1295–1315, https://doi.org/10.5194/esd-14-1295-2023,https://doi.org/10.5194/esd-14-1295-2023, 2023

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We investigated the flux of carbon from the atmosphere into the land surface and the ocean for multiple models and over a range of future scenarios. We did this by comparing simulations after the same amount of change in the global mean near surface temperature. Using this method, we show that the choice of scenario can have a big impact. Scenarios with higher emissions reach the same warming levels sooner, but also with relatively more carbon in the atmosphere.


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Choice of Forecast Scenario Impacts the Carbon Allocation at the Same Global Warming Levels

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