the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The need for carbon emissions-driven climate projections in CMIP7
Abstract. Previous phases of the Coupled Model Intercomparison Project (CMIP) have primarily focused on simulations driven by atmospheric concentrations of greenhouse gases (GHGs), both for idealized model experiments, and for climate projections of different emissions scenarios. We argue that although this approach was pragmatic to allow parallel development of Earth System Model simulations and detailed socioeconomic futures, carbon cycle uncertainty as represented by diverse, process-resolving Earth System Models (ESMs) is not manifested in the scenario outcomes, thus omitting a dominant source of uncertainty in meeting the Paris Agreement. Mitigation policy is defined in terms of human activity (including emissions), with strategies varying in their timing of net-zero emissions, the balance of mitigation effort between short-lived and long-lived climate forcers, their reliance on land use strategy and the extent and timing of carbon removals. To explore the response to these drivers, ESMs need to explicitly represent complete cycles of major GHGs, including natural processes and anthropogenic influences. Carbon removal and sequestration strategies, which rely on proposed human management of natural systems, are currently represented upstream of ESMs in an idealized fashion during scenario development. However, proper accounting of the coupled system impacts of and feedback on such interventions requires explicit process representation in ESMs to build self-consistent physical representations of their potential effectiveness and risks under climate change. We propose that CMIP7 efforts prioritize simulations driven by CO2 emissions from fossil fuel use, projected deployment of carbon dioxide removal technologies, as well as land use and management, using the process resolution allowed by state-of-the-art ESMs to resolve carbon-climate feedbacks. Post-CMIP7 ambitions should aim to incorporate modeling of non-CO2 GHGs (in particular sources and sinks of methane) and process-based representation of carbon removal options. Such experiments would allow resources to be allocated to policy-relevant climate projections and better real-time information related to the detectability and verification of emissions reductions and their relationship to expected near-term climate impacts. Such efforts will provide information on the range of possible future climate states including Earth system processes and feedbacks which are increasingly well-represented in ESMs, thus forming a critical and complementary pillar underpinning proposed km-scale climate modeling activities and calls to better utilize novel machine learning approaches.
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RC1: 'Comment on egusphere-2023-2127', Malte Meinshausen, 24 Mar 2024
Review of Sanderson et al. “The need for carbon emissions-driven climate projections in CMIP7”
General:
I congratulate the author group for pulling together this important and timely paper in the preparation of CMIP7. Please consider my below comments and questions in the hope that they strengthen and clarify the manuscript. Without doubt, this is an important paper and should be published after revisions. Some of the outstanding highlights in my view are for example the list of DECK runs and the definition of reversibility metrics (Lines 700ff).
Comments:
In no particular order, I will here provide a few over-arching comments and questions before adding comments with regard to specific lines of the manuscript.RC1: Limitations & New Challenges: I read the paper with great interest and it lists many benefits of making the carbon emission-driven experiments a priority in CMIP7. I agree. I was wondering whether the authors could provide more discussion on the new challenges that arise under such a framework, though. I think a new section that gathers some of these challenges or limitations and how they could possibly be addressed would strengthen the manuscript.
RC1.1: How to deal with additional bias in historical runs? The appropriate reflection of carbon cycle uncertainties in future climate simulations seems to come at the cost of inserting additional bias into historical runs. For example, Figure 5 shows a substantial range of CO2 concentrations by 2020 for the carbon emission driven CMIP6 models. We know what CO2 concentrations were in the past, so any CMIP6 derived historical climate will now be subject to an additional bias – which is not present in the historical concentration-driven runs. The authors seem to briefly make the argument that those differences of +-40ppm do not matter in the bigger scheme of things. Yet, 40ppm approximately equate to 20 years in the historical evolution of CO2 concentrations. Thus, do the authors suggest that for historical D&A studies, one should still use concentration driven runs to avoid that extra error? Or should emission-driven climate output be somewhat bias corrected (e.g. a model that has 40ppm too low CO2 concentrations gets somehow adjusted in terms of its tas, pr etc. output to match actual CO2 concentrations) (and once one is at it, would the authors also suggest an aerosol radiative forcing mismatch bias correction to the extent possible?)? Or would a hybrid approach be worth discussing where ESMs should be CO2-concentration driven for the historical period but then switch over to being emission-driven for the future scenarios? Anyway, I at least suggest some more reflection and detail on that point (see also below comment on Figure 5, line 388).
RC1.2: How will future “multiple-lines of evidence” temperature projections be derived? In IPCC AR6 Chapter 4, for the first time in IPCC history, some weighted CMIP6 ensembles (Lang et al., Ribes et al., Tokarska et al., see Figure 4.11 in IPCC AR6 WG1) were used as one part of the ‘assessed’ surface temperature projections. The constraints were aiming to constrain and weigh the CMIP6 ensemble in terms of the models’ temperature response to deal with the ‘hot model’ problem in a way (simplified speaking). I am not arguing that it is wrong to fully reflect the carbon cycle uncertainties also in these future projections (quite the contrary), but it does pose the challenge of how to undertake this weighting if now one significant extra chain member is included in the ESM results. For example, suppose a model fails to appropriately produce historical CO2 concentrations, but would nevertheless produce ok temperature hindcasts (thanks to possibly offsetting errors), would that model’s temperature projections for the future than be fully included in that CMIP7 ensemble average (even though one knows that probably its future CO2 concentrations are way off?). It would be great to know how the evolution of this Chapter 4 IPCC AR6 WG1 approach to combine multiple lines of evidence for GSAT projections should be adapted or evolve under the carbon-emission driven results. For example, if CMIP7 models would show the same performance to reproduce historical trends, then implied emissions of zero CO2 by 1950 (see e.g. your Figure 2 - or vice versa too high rates of increase in CO2 concentrations, Figure 5) would raise possibly concerns whether future projections are to be trusted?
RC1.3: Comprehensive reflection of uncertainties across the cause-effect chain. I raise this issue not because it is a problem with the manuscript, but because shifting to CO2-emission and landuse-activity-based scenarios does affect this issue. The key question is: How could IPCC (or the scientific community) get to a comprehensive reflection of uncertainties across the full cause effect chain from human activities and emissions to the biogeophysical results that ESMs provide? (i.e. consider it the general case of RC1.2). The climate services sector is (legitimately) demanding such products, for example, a joint probability distribution of a heat wave at location x with a heavy precipitation event at location y. Let’s suppose we have around 50 ESMs, which each are integrating a fully fledged carbon cycle. Suppose that observational constraints on past warming on the one side and past carbon cycle behaviour on the other would render 30% of ESMs in each verification step as ‘outside of observations and judged to be unrepresentative of our best expert knowledge’. Then we are left with roughly half of the ESM models (50*0.7*0.7=24.5) (assuming independence of how good a model does on past warming and past carbon cycle). However, if there is a way to combine all the ‘non-dismissed’ carbon cycle behaviours with all the non-dismissed warming behaviours, we could potentially create a more comprehensive reflection of future climate uncertainties. Anyway, shifting to longer analysis chains (i.e. starting with emissions rather than concentrations) within our gold-star tools (ESMs) creates more opportunities for some components to be ‘non-ideal’ and hence invalidate (invalidate in terms of policy-relevance) the output from components of the ESMs (say: the AOGCM) that would otherwise be ‘good’ (‘good’ in terms of ‘being a valid/useful/plausible reflection of the earth system on the basis of our current understanding). In other words, the welcomed evolutionary step of ‘self-consistency’ across the cause-effect chain that the integration of the carbon cycle in the main experiments brings with it, comes at the potential price of ‘thinning’ the ultimate evidence base (or for having to post-process the ESM results by compartmentalising and re-combining the results of the ‘valid’ components). I am not suggesting this paper has to solve that issue, but as emission-driven runs with ESMs will accentuate the problem of how an overall reflection of uncertainty across all our best ESM components can be gained, it would be great if the authors can briefly point to their thoughts on the issue. This could be part of a new subsection ‘limitations’ or ‘challenges’ (with the other points RC1.1 and RC1.2 above). (PS: and of course, this problem of ‘longer chains get more vulnerable’ is not at all unique to emission- versus concentration-driven runs).
RC2: Crop harvest figure 3. The conclusions drawn from this figure do not seem to be fully convincing at first without further explanation. The overall harvest flux uncertainty across ESMs seems enormous. Thus, the absolute levels of the REMIND-MAgPIE simulations do not seem to be the issue as such. However, the discussion seems to suggest that the difference of crop harvest between the SSP5-85 and SSP-34over seems ‘at worst completely unphysical’ (Lines 201ff). A few questions arise that would be great to clarify for a more compelling case: How do other IAMs perform in this comparison. IMAGE is one model with high BECCS in the past? And how do newer generations of the IAMs fare, when e.g. considering NGFS Phase-4 scenarios? Given that the ESMs should theoretically have been forced with the landuse activity patterns that are consistent with the REMIND-MAgPIE results, how come that those patterns do not show up as harvest difference? What exactly is part of the crop harvest flux estimates of the ESMs and how does it differ to the normal crop harvest plus Bioenergy Harvest or Net BECCS flux from REMIND- MAgPIE? Wouldn’t it be better to show the aggregate REMIND-MAgPIE crop harvest and bioenergy harvest combined? It would be good to see some REMIND- MAgPIE experts involved in the characterisation of this difference (Jessica Strefler, Alexander Popp, Elmar Kriegler, or others).
RC3: Clarification of the ultimate purpose(s). In general, the manuscript does a fantastic job of highlighting a multitude of benefits of moving to an carbon-emission-driven framework. Maybe the authors could consider of systematizing some of their thoughts with regard to the ultimate purpose(s) – possibly contradictory – that can (or cannot) be better achieved with this change. From reading the manuscript, I can see many listed benefits, but am not 100% clear about the author’s view on the ultimate purpose of an enhanced scenario design. I can extract for example the three following:
- Creating more policy-relevant ESM runs.
- Pushing the ESM development
- Better Earth system understanding (e.g. land-based CDR potentials)
It might be worthwhile to flesh those (or similar ones) out more clearly and then discuss the listed benefits (or limitations) with respect to those ultimate purposes. This is just a suggestion that the authors might want to pick up.
RC4: More precision in terminology. I think the manuscript would greatly benefit from a more precise use of terminology, in particular with regard to the following three areas.
- Scenarios generations. The authors seem to use SSPX-RCPY interchangeably with SSPx-y. For example, in Line 195, it says “The ScenarioMIP ensemble SSP5-RCP8.5”. I would suggest to follow Table 1.4 in IPCC AR6 WG1.
- CDR, land-based CDR, DACCS etc. Some statements seem imprecise as they refer to CDR in general, but discuss limitations that are only applicable to, for example land-based CDR, not DACCS or enhance weathering etc.
- Emission-driven, activity-driven and concentration-driven. (Line 359, 361ff and others) In relation to the core issue of the manuscript. If I understand the authors correctly, then what is actually proposed are ESM runs that are:
- Emission-driven for fossil and industrial CO2 emissions.
- Activity-driven for land-use CO2 emissions.
- And concentration-driven (for the time-bring) for all other well-mixed GHGs.
Of course, saying ‘fossil-industrial-CO2-emission-driven, land-use CO2 activity-driven, and other GHG concentration-driven’ is a bit clumsy, so maybe the authors want to create an acronym for that? FIELAOC? (Am sure you can come up with better ones …). Alternatively, in line 261, you refer to it as an ‘hybrid’ approach. That might also be a good solution to consistently use throughout after defining it. While simply saying ‘emission-driven’ is nice and simple and useful for the high-level communication in a talk or press release, I think for many readers it can prolong confusion (congratulations on Figure 7, which nicely clarifies the approach). Maybe provide a table with those different ‘hybrid’ approaches?
RC5: Odd use of R-value statistics for TCRE and TCR in Figure 9 and text. The analysis presented in Figure 9a and b shows two metrics (TCRE and TCR) derived under the two different experiments (flat10 and 1pctCO2). Both in the Figure panels and the text, the high R-value is taken to argue that results from both experiments are interchangeably close (if I read correctly). Suppose that one experiment would result in exactly halved TCRE and TCR values. The R-value could even be 1, but the experiments are suggesting very different TCRE values. Thus, the R-value is not the correct statistics here to make the point. A simple residual plot with the percentage difference between the two TCRE or TCR values would probably be much better suited to argue that differences are a) understandable and b) generally small.
RC6: Better anchor section 4.1 on computational needs of high-resolution modelling. When reading the section, it felt a bit detached. There is a lot of great material in there, but I was not sure (and that might be just me) of how to relate the discussions of convection-resolving / convection-permitting ESMs and their computational needs to the question of the computational burden that interactive gas cycles add to the ESMs. I might have overlooked it, but if the authors could provide the reader with a clear indication of how much additional computational burden is created by integrating (a) the carbon cycle, (b) the methane cycle, (c) the nitrogen cycle and (d) any additional tracer to represent other well-mixed GHGs (another 40 of them are currently summarized in ‘equivalent’ concentrations), then that would better link this section to the rest of the paper IMHO.
RC7: Sketch the advance that is needed to diagnose land-use related emissions in ESMs. The paper touches this issue (e.g. Line 117ff), but I think it deserves a new community push (in particular as you have the world experts together in the author list). How can CMIP7 assist the ESM community to provide standardized diagnostics that tackle the intrinsically difficult issue of separating direct anthropogenic emissions from indirect anthropogenic emissions and natural fluxes? For runs that are communicated as ‘emission-driven’ (although part of it will be ‘activity-driven’), it is still vital for readers and policy-makers to know what the emissions actually are. Correct me, if I am wrong, but if the diagnostic capability stays the same as it is now (in simplified terms: We do not really know how much additional anthropogenic emissions are created by an ESM due to a certain land-use activity and change of land-use patterns – partly due to a lack of consistent diagnostics across ESMs), then we either a) have to think about future workarounds to determine or approximate landuse CO2 direct anthropogenic emissions or b) continue to fly blind in the sense that our headline results will be: “The remaining carbon budget for 1.7C is X GtCO2 fossil fuels and industrial emissions plus landuse activity patterns Y.” (which would not be very catchy to say the least). Anyway, the point I am trying to get at: a clear recipe and renewed community push for either good workarounds or other stabs at this seemingly intractable problem of diagnosing landuse related emissions from ESMs would be great. And I am not saying that this problem is created by shifting to activity-driven runs, but it is drawn onto the stage.
RC8: Figure 5. There are two suggestions for Figure 5.
- Add extra panel with extension to 2100 under esm-ssp5-8.5. For example, similar to Figure 2a in Friedlingstein et al., 2014. (DOI: 10.1175/JCLI-D-12-00579.1). The reason is that back then, the IAM / MAGICC combination tended to be towards the lower end of the range of projected CO2 concentrations. With an adjustment of the carbon cycle dynamics towards the representation of this CMIP5 range of carbon cycle feedbacks, the plot until 2100 should be expected to show the opposite of Figure 2a in Friedlingstein et al. 2014, i.e. that overall the CMIP6 emission driven runs now show lower future CO2 concentrations for the same emission pathway compared to CMIP5 (or a CMIP5 calibrated emulator) (all other things being equal).
- More importantly though: The lower plots b and c are used in the text as justification that the additional uncertainty of 40ppm of historical mismatch of CO2 concentrations does not matter for temperatures. That is a somewhat surprising statement, as it equates to roughly 20 years of climate change trends (of course, there are other radiative forcers influencing that trend as well.). Nevertheless, a difference is indeed hard to decipher by eye in a spaghetti plot with thick lines, so I would ask for an alternative or additional visual, which is a panel with histograms of last 20-years of tas in esm-historical and historical runs, with the two histograms plotted on top of each other plus their relevant medians and ranges … Even more ideal would be a scatter plot that indicates last-20year averages of tas on the x-axis, the last-20years of CO2 concentrations on the other and then having one cloud of dots for ‘esm-historical’ and one assembly of dots (on the same CO2 level horizontal line) for ‘historical’. In that way (and in particular, if the marginal distributions are shown also by histograms), the challenges that can potentially arise from shifting to emission-driven runs could be better anticipated and discussed.
Comments on specific lines:
- Line 60ff: All the references need a good second look for appropriate formatting.
- Line 67: “In CMIP6, scenarios were … “ See RC4a) above. This is confusing. Make consistent with terminology in IPCC AR6 WG1 Table 1.4.
- Line 78: For the CMIP pipeline, add the relevant reference of Kikstra et al. (https://doi.org/10.5194/gmd-15-9075-2022) and Gidden et al. (https://gmd.copernicus.org/articles/12/1443/2019/).
- Line 90: Rather than the Technical Summary, Cross-Chapter Box 7.1 in IPCC AR6 WG1 is probably what is most appropriate here?
- Line 91: Very nice graph. You could refer to similar earlier figures for context for the reader, such as Figure 1 in Cross-Chapter Box 11.1 in IPCC AR6 WG1. Just an option.
- Line 119: Explain what you mean by “these differences are counterintuitively manifested in compatible emissions… “
- Line 123, Figure 2: Check the figure panel numbering, a,a,a,c,d,d
- Line 123, Figure 2: The Figure legend says GCP2022, and the reference in the caption is provided with P. Friedlingstein. Make consistent.
- Line 123, Figure 2: The dotted line says “IAM”. Now, the emissions are from each of the IAMs, but the comparability to the ESM lines is contingent about the SCM/MAGICC step that was used to provide the concentrations from which the ESM inverse emissions are derived. You might want to unpack this.
- Line 123, Figure 2: The difference in historical emissions from 1950 to 1990 is rather large between GCB2022 emissions and most of the ESM inverted emissions. I think a paragraph on this would be worthwhile (see also RC1.1 above).
- Line 131ff: “simulated net-zero dates measured in terms of compatible… “. This sentence seems to suggest that the IAM net-zero date is biased towards early dates. Maybe add clarity here in terms of what your thoughts are: Is that a systematic over-estimation of carbon cycle feedbacks by the IAM/ MAGICC combo? Or are the recent ten years of observations, in which the ESMs also over-estimate anthropogenic emissions for the observed atmospheric concentrations (see peak of the trajectories around 10 GtC in Figure 2 panel SSP1-2.6). indicate a potential issue elsewhere?
- Line 140: As you mention other SCMs by name in the manuscript, maybe here as well ?:-)
- Line 163-164: “while this approach is convenient, bypassing process-resolving … has risks”. I’d suggest a bit more nuance here. If a simple climate model with an uncalibrated carbon cycle is used ‘in the assessment of mitigation strategies’, then I fully agree. However, if an emulator that is properly calibrated to exactly capture the process-resolving carbon cycle uncertainties from ESMs, then an emulator could be a tool to assist translating the limited number of ESM process resolving carbon cycle uncertainties for policy-relevant settings. Or how would the authors suggest the >1000 scenarios assessed in WG3 should be evaluated in terms of their concentration and temperature outcomes. One option is to use properly calibrated emulators / SCMs. Anyway, the authors may want to add nuance to that sentence.
- Line 183: “The plausibility of large scale CDR..” here and elsewhere: make terminology precise – unless you refer to all CDR (see RC4.b above).
- Line 184: “Interventions will cause ….”. Well, probably you refer here to land-based CDR, rather than DACCS or enhanced weathering etc..? Please rephrase/clarify.
- Line 195: “ScenarioMIP ensemble SSP5-RCP8.5”. See comment RC4.a above.
- Line 202: “about available CDR capacity”… see comment RC4.b above. This comparison to crop harvest probably has little relevance for DACCS capacity. Please clarify, e.g. by simply saying ‘land-based CDR’ or similar. See also RC2 above.
- Line 212-214: “Ocean carbon balance is also … (Navarra and Fogli, 2013).” The uninitiated reader might think that this is a particular limitation of land-based CDR. Without additional explanation, I think such statements can be misleading, as they do not invalidate the basic principle that to offset one tonne of emissions you need to have one tonne of permanent removals. The dynamics of recalibrating carbon pools are scientifically interesting but can be misunderstood – as in this non-contextualized sentence. Please clarify.
- Line 220: Change “where SCMs and emulators…” to maybe “which also results in SCMs and emulators… “. You are the native speakers, though.
- Line 225: If the “dynamics between carbon removal and the wider climate state” refer to the sentence in line 212, then I am not quite sure about this statement. Maybe clarify what exactly you refer to?
- Line 226: “As such, concentration-driven … “. Is that an issue with IAMs or with the concentration-driven setup? I would argue it is the former. Certainly, an emission-driven or activity-driven setup within ESMs helps, as thereby this IAM limitation is circumvented and ESMs are ‘forced’ to implement the land-use activities in their framework, but whether ESMs then get emission-driven or concentration-driven should not matter to the outcome (as appropriately derived inverse emissions and activity options within an ESM for a prescribed concentration-driven setup could also work, albeit being slightly complicate). No? Please clarify.
- Line 229: Referring to RC4c above, it would probably be more appropriate to talk here about an “activity-driven” or “pattern-driven” etc. approach, rather than emission-driven?
- Line 243 (see also line 508): While I fully agree that the ‘radiative forcing’ label is well overdue to be replaced by an emission-based metric, such as ‘cumulative CO2 emissions’ or similar, the manuscript could be more nuanced here, clarifying that these radiative forcing labels are of course only indicative/illustrative labels. The radiative forcing within each ESM for the same scenario wildly differs. But it would be good to hear the authors suggestions of how the next generation of scenarios should be labelled. I see the suggestion in line 508 of “should be categorized in terms of their policy strategies (e.g. net-zero dates and land use strategy) but how exactly do that in practice? (and I am sure than all of these suggestions are better than the current RF label ).
- Line 261ff.: It took the Introduction almost 10 pages to reveal to the reader a bit more detail of what you are actually proposing – and very useful additional detail is provided later on in the manuscript. Consider shifting a clear succinct paragraph of your proposal further up in the Introduction.
- Line 310: Well, the issue is a bit graver than presented here. The issue, as highlighted by Grassi et al. and others, is that countries are currently allowed under the emission inventory guidelines to account for natural responses of the carbon cycle (aka ‘CO2 fertilization’ in forests that the countries declare as ‘managed’) to count as removals. Thus, UNFCCC LULUCF emission submissions contain not only ‘directly induced anthropogenic emissions’, but also a part of the natural carbon cycle response. Anyway, see the Allen et al. piece on geological net-zero under preparation (would be good, if timing works out that you can refer to it) to point to the real problem here.. the non-permanence of sinks is one important issue. But the fact that countries count something that is not actually directly anthropogenic is an even bigger issue (as by that logic, one could argue that the non-airborne fraction of CO2 should be counted towards emission inventories, which would then allow countries to be net-zero, while in fact we only halved anthropogenic emissions). Anyway, I would strongly suggest to add this important issue as ESMs with an interactive carbon cycle and realistic land-use patterns could indeed greatly help to separate out direct anthropogenic from indirect anthropogenic (e.g. CO2 fertilization) fluxes and thereby help changing a major shortcoming in IPCC methods for UNFCCC reporting guidelines.
- Line 326f: .. but none provide a fully self-consistent internally generated representation of the chain of causality from emissions to concentrations”, while this is an excellent phrasing of one of the purposes that such ESM scenario design should pursue, maybe there are similarly important ‘purposes’ that either align with this objective or conflict with it (given overall resource constraints). I think of the objective of attempting to end up with a comprehensive fully probabilistic representation of our current knowledge across each of the cause-effect chain links.. that can be addressed by ex-post analysis and emulators, but the mere ‘self-consistent internally generated representation’ cannot be the only ultimate goal, if we end up with just a sparse sample of five-star ESM realisations. Anyway, maybe consider rephrasing. See comment RC1.2 and RC1.3 above.
- Line 366ff: A table would be a great resource that summarizes CMIP6 model capabilities as of now. Also, this table could contain a comparison of the emission-driven concentration hindcasts with observed concentrations as shown in Figure 5, say for the last 20-year averages…
- Line 388: See comment RC8 above.
- Line 388: One could add the reference for the historical CO2 concentrations used in CMIP6 into the list of references at the end …
- Line 395-400: The section “Although this is … biases in CMIP6 (Papalexiou et al. 2020)” seems to suggest that it is already established that 40ppm CO2 differences are ‘not a significant factor’ in historical warming in esm-historical runs. See my comment RC8 (2) above. Maybe I misunderstand, but I do not think that this conclusion can be robustly drawn from the two spaghetti plots in Figure 5, nor does Papalexiou et al. 2020 seem to consider the esm-historical emulations, but rather focusses on the concentration-driven results (if I am not mistaken). Thus, please consider adding more evidence to this section and your conclusion that 40ppm CO2 differences do not matter. If they turn out to matter, please discuss potential solutions (e.g. would a hybrid protocol, where historical runs are performed concentration driven, but future runs would be emission/activity driven work? Why or why not?).
- Line 415: When you say “it is equally important to sample inter-model uncertainties … as for the physical climate response to a single trajectory of CO2”… Please clarify. Do you mean that CMIP7 should contain one emission and one concentration-driven variant of each scenario?
- Line 435ff: Very nice set of suggestions. Please consider whether you can expand with the potential elements of a protocol that would allow the robust delineation between directly anthropogenic, indirectly anthropogenic and natural carbon fluxes (see comment RC7 and on line 310 above).
- Line 461: Not all readers might be familiar with the term “digital twins”. Please explain.
- Line 493, Figure 6: See comment RC6 above. Please consider how to visually integrate the computational burden for carbon, methane and Nitrogen cycles into this graph, which otherwise feels a bit detached from the rest of the paper.
- Line 507: What do you mean by ‘original IAM simulation’? Do you refer to the temperatures assumed within, e.g., IMAGE, or the temperatures that are produced by the ‘harmonising’ SCM MAGICC to produce all temperatures for all the SSP scenarios (and associated concentrations?).
- Line 523, Figure 7: Excellent figure. Two remarks:
- In panel c: If the land-based CDR activities are already part of the top arrow “Land transitions (impacting concentrations)”, then why are “CDR emissions” listed separately in the lower-right arrow description “Fossil CO2 & CDR emissions”?
- Should the latter arrow rather read “Fossil/industrial CO2 emissions and non-land based CDR”, given that land-based CDR is already part of the land cover information?
- In panel d: What is the separation between the “Land transitions” arrow at the top right and the “CDR” part in the “CDR & SRM strategy” part in the lower right? Should the lower right again be called “non-land-based CDR & SRM strategy”.. and rather than “strategy”, should it be called “activities” (as ESMs are unlikely to include a policy module to estimate a level of activity change from a ‘strategy’?!)
- In panel d: The lower right arrow “all emissions”. Should that rather be called “Fossil/industrial CO2, Fossil/industrial CH4 and Fossil/industrial N2O emissions” given that land-use emissions will be generally estimated from within the ESMs based on activity data provided by IAMs?
- In panel d: If there is no arrow any more for some non-CO2 concentrations, what is the suggestion what should happen with the 40 smaller GHGs, i.e. the CFCs, HFCs, PFCs, HCFCs, Halons etc? Is the suggestion that the ESM will have tracers and gas cycles for each of these GHGs? Probably not, I would assume. So some small box for an offline SCM producing those concentrations and equivalent concentrations in the form of 1 or 2 or 3 ‘equivalent’ tracers will probably still be necessary in CMIP8, right?
- Line 525, Figure 7 caption: When stating “a proposed CMIP8 pipeline, with complete emission driven configuration”. Please consider whether you want to be more precise (see comment RC4c and comments on line 523 above. I would assume that you do not suggest to provide 40 emission trajectories of fluorinated gases to each ESM, so ‘complete emissions driven’ is maybe not the appropriate term. It comes back to whether you might want to define those (somewhat complex) hybrid running modes in a table with acronyms or shortcuts, so that you can provide the details once and then refer to here ane elsewhere.
- Line 532: Is “to represent” the correct verb here?
- Line 551, section 4.4.1.: To me, the closed cycles for water and for other GHGs are separate issues which might warrant distinct subsections. Please consider.
- Line 560: Again, what happens to the 40+ smaller GHGs?
- Line 571: Something missing after “… found in “?
- Line 573: Regarding the text “.. are at least representative of those obtained from ESMs”. Now the question is are those land-use fluxes available from ESMs? So far, there is great diagnostic difficulty of estimating the land-use based anthropogenic emissions, so am not sure I understand the sentence of what it means in practice. Please clarify.
- Line 602, Table 1. Consider separating the column “forcing” into “CO2 forcing” and “non-CO2 forcing” for clarity.
- Line 602, Table 1: Should “Metrics provided from simulation” rather read “Metrics derived from simulation”?
- Line 602, Table 1: When it says “Linearly declining emissions by 2 GtC/decade from 10GtC/yr (year 100) to -10 GtC/Yr (year200)…”. Please check the units. I think you can either write “… by 2 GtC/decade from 10GtC to -10GtC…”, i.e. leaving out the “per year”. Or you the first one will need to be “by 2GtC/yr/decade” as you refer to a rate of change to the rate of annual emissions, if I understand correctly.
- Line 602, Table 1: For ‘esm-historical’ last row and fourth column ‘Forcing” it says “historical emissions”. I presume you mean “historical fossil & industrial CO2 emissions plus land-based activities” or sth similar to that? (and again, please specify for non-CO2 in a separate column for clarity).
- Line 617: See comment RC5 above.
- Line 628, Figure 9: see comment RC5 above.
- Line 631: When you list “emissions are constant throughout.. “ as one of the “desirable properties”. It seems more policy-relevant and more desirable than the emission profile under 1pctCO2, but an even more desirable property would be a ‘gradually declining to zero’ shape, which better resembles policy-relevant settings, no? Consider rephrasing.
- Line 638: On the statement “we find both a generally slightly higher… “ – yes, I think that is the actual comparison metric (rather than the R-squared statistic). Please expand on how much higher and why (I think you have the material further below, but not the quantitative numbers, which are important in order to put any CMIP7 TCRE results into context of CMIP6 TCRE results.)
- Line 649, Figure 10. The argument that the correlation in the panel c plot is a closer one compared to (a) and (b) is not fully convincing due to the vastly different scales of the plots. Consider plotting the residuals towards the diagonal instead (or in addition), which would more clearly show whether a ‘higher TCRE’ is partially offsetting a ‘lower ZEC’, or similar. A scatter plot (similar to the one I outline in RC8 (2)) would be even more ideal to make the point.
- Line 657: On the ‘around 10%’. From the plot, it looks that the differences for the higher ZECs are substantially higher than 10%. Is 10% the mean deviation? Maybe provide mean deviation and ranges. Please clarify.
- Line 659: By logical conclusion, a TCRE that is evaluated at the point of zero emissions after 75 years of 10 GtC emissions and then a 50 year ramp-down period to zero would likely create an even better approximation of peak warming (i.e. ZEC after such a scenario would be even smaller?!). I note the nice indicator TNZ that basically addresses this slight remaining shortcoming of deriving TCRE from esm-flat10. Please clarify why you do not propose that design for the TCRE derivation (which would make the TNZ ‘bias’ metric superfluous, it seems).
- Line 703: Excellent set of indicators, very useful. One can already see the nice papers emerging from those diagnostics. Consider putting into a table instead and consider also to ‘explain’ the abbreviations ‘tPW’, ‘TNZ’, does TNZ stand for ‘Temperature bias Net Zero’? Probably not .. .
- Line 740ff: When you say “The ECS could be inferred indirectly by calibrating a simple climate model to the ESM output of esm-CDR-pi-pulse… “ it send shivers down my spine – The precise calibration with a simple SCMs can introduce so many uncertainties when using only an single idealized scenario, that I really doubt the robustness of the derived ECS and the comparability to ECS derived from long-term 2xCO2 or 4xCO2 simulations. Also, how would be characterise time-changing or state-dependent feedbacks over time (Sheerwood et al., https://doi.org/10.1029/2019RG000678; Rugenstein et al., 2019, https://doi.org/10.1029/2019GL083898)? Please reconsider.
- Line 758f: “A decade earlier… “. Yes. Very important sentence.
- Line 766: After “with the right experimental design”, it would be good if you cite the current community considerations on that design, namely the ScenarioMIP draft proposal (e.g. the Reading workshop document, unless the consultation document of ScenarioMIP is available in time), the much beloved () collection of thoughts in https://gmd.copernicus.org/preprints/gmd-2023-176/ (now accepted) and the ones by Pirani et al. 2024 (https://www.nature.com/articles/s44168-023-00082-1).
- Line 809, Appendix A1. The title just says “Additional idealised experiments NOT in the main ESM-DECK proposal”. Please add some text as this is unclear. Do you suggest that these experiments (1) should NOT be considered as part of the ESM-DECK proposal, (2) are alternatives to the ones proposed in the main section of the text or are (3) complementary optional additions to the ones in the main text?
- Tables A1 and A2: For clarity, again split the “Forcing” column into “CO2 forcing” and “Non-CO2 forcing”.
- Appendix B: It is not quite clear to me what the relevance of this table is for the paper – unless the paper provides more details as to how much computational demand arises from running ESMs with an integrated CO2/ methane / nitrogen cycle etc.. Please clarify.
And lastly: Deep apologies for the long review. I know how painful they can be. It was a very interesting read and is an excellent and important paper and I hope my review comments just make a strong paper stronger.
Citation: https://doi.org/10.5194/egusphere-2023-2127-RC1 -
RC2: 'Comment on egusphere-2023-2127', Anonymous Referee #2, 07 Apr 2024
First, I would like to extend my sincere apologies for the delay in reviewing the paper. The topic that the paper addresses, a call for (CO2) emissions-driven simulations for CMIP7 and beyond, is undeniably crucial. It is evident that this paper holds substantial importance and merits publication. I agree with the authors regarding the necessity of improving Earth system processes in ESMs, which is as critical as the pursuit of high-resolution km-scale climate model development. This aspect is often been overlooked recently, particularly as (too much?) attention tends to be directed in high-profile publications towards km-scale climate modeling. However, before I can consider the paper for publication, some comments require attention.
Major comments:
- Prioritization of CO2 emission-driven simulations
While I appreciate the focus on CO2 emissions-driven simulations, I have some reservations about prioritizing them over concentration-driven simulations. As outlined in the manuscript, conducting simulations in CO2 emissions-driven mode introduces additional uncertainties, making it challenging to compare the physical climate, but also the biogeochemical response with observations over the historical period, mainly because the atmospheric CO2 concentration in these simulations does not align with observed levels. The authors seem to argue that a difference of about 40 ppm does not matter. However, this difference matters for historical ocean acidification, or any other variable that strongly depends on the atmospheric CO2 level. In addition, it becomes problematic to evaluate the models' ability to replicate, for example, decadal-scale hiatuses in global temperature, such as the one observed between 1998 and 2012, when changes in atmospheric CO2 concentrations and therefore the radiative forcings are already inaccurate. The same issue arises when assessing, for example, the recent anomalously warm year 2023. Given these concerns, I believe concentration-driven scenarios remain essential and should be prioritized alongside CO2 emissions-driven runs.
- CO2-emission driven simulations with fully coupled ESMs are not a new thing
After reviewing the manuscript, it appears that CO2 emissions-driven simulations are portrayed as a novel concept. However, it's important to note that earlier research has indeed utilized CO2 emissions-driven simulations. For instance, Joos et al. 1999 (https://www.science.org/doi/10.1126/science.284.5413.464) and subsequent works such as Fung et al. 2005 (https://www.pnas.org/doi/full/10.1073/pnas.0504949102)) have employed such simulations (the authors already cite the Cox et al. study). While the authors have partially addressed this concern in lines 360-368, it is worth noting that there are numerous additional studies beyond the CMIP realm that should be acknowledged. The authors should acknowledge more of these earlier studies and incorporate a paragraph addressing this in the manuscript. Otherwise, the paper may inadvertently give the impression of introducing a significant step change in Earth system modeling, when in fact, this approach has been utilized for some time but hasn't just been fully integrated into the CMIP exercise.
Furthermore, Silvy et al. https://egusphere.copernicus.org/preprints/2024/egusphere-2024-488/, which includes several authors from the present paper, recently published a model intercomparison paper, AERA-MIP, involving participation from over 10 ESMs conducting emission-driven simulations. This contribution deserves acknowledgment.
- Benefit of having CO2-emission driven runs
I found it somewhat surprising that Section 2.1 does not touch upon some rather fundamental implications of emissions-driven runs. For instance, these simulations enable a more robust assessment of uncertainties in future projections of ocean acidification. In the IPCC AR6 WG I report, global surface pH projections lacked uncertainties due to the prescription of atmospheric CO2. However, with emissions-driven runs, model uncertainty can now be quantified. This holds for various Earth system processes dependent on atmospheric CO2 concentration.
- Section ‘A proposal for an ESM-DECK’
I found the proposal for the new scenarios lacks a bit of novelty and inspiration. It surprised me that innovative adaptive approaches, developed in recent years and potentially offering more policy-relevant scenarios and simulations, were not considered (see for example Silvy et al. 2024 or Terhaar et al. 2022, NCC). Although it is stated on lines 594-596 that these idealized emission-driven experiments would facilitate the calculation of key carbon-climate metrics for informing climate policy tools like the IPCC remaining carbon budget for climate stabilization, the proposed simulations do not stabilize at predefined global warming levels. This omission makes it challenging to calculate the remaining carbon budget and associated metrics essential for climate policy. Adaptive approaches, in contrast, appear more appealing and novel. I was wondering, why this has not been addressed.
Moreover, a fixed CO2 emission rate of 10 GtC/yr seems excessively high, far exceeding the average historical levels. I am curious about the rationale behind such a high emission rate.
As the main authors themselves demonstrate, zero emissions commitment (ZEC) likely manifests during the emission ramp-down. However, if I understand correctly, ZEC will be diagnosed from simulations branching off from the flat10 experiment. This raises questions about the policy relevance of ZEC in this context.
In addition, Joos et al. (2013) have shown that a pulse of 100 Pg C is relatively small, with the temperature response still containing significant natural variability compared to the signal. Wouldn't it be more beneficial to use a larger pulse?
Furthermore, emission-driven runs are also planned for TIPMIP ESM simulations, aiming for a fixed warming rate rather than a fixed emission rate. What are the similarities and dissimilarities between these two approaches?
Minor comments:
L.44: ‘represented upstream of ESMs’ Unclear what upstream of ESMs means. Try to clarify.
L 60: Reference should be fixed; i.e. n.d.
L 75-76: Reference should be fixed; i.e. n.d.
Figure 1: This figure seems to be inspired by Figure 1 of a paper submitted in September 2023 by Pfleiderer et al. (the main author and some co-authors are also co-authors on the Pfleiderer paper). This needs to be acknowledged.
L447: This is not true for AERA-MIP.
L667: I know that this argument has already been made during the ZECMIP discussion. I still do not understand this. Are the technical challenges substantial (which I doubt)?
Citation: https://doi.org/10.5194/egusphere-2023-2127-RC2
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