the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Jan 2025
The Scenario Model Intercomparison Project for CMIP7 (ScenarioMIP-CMIP7)
Abstract. Scenarios represent a critical tool in climate change analysis, enabling the exploration of future evolution of the climate system, climate impacts, and the human system (including mitigation and adaptation actions). This paper describes the scenario framework for ScenarioMIP as part of CMIP7. The design process, initiated in June 2023, has involved various rounds of interaction with the research community and user groups at large. The proposal covers a set of scenarios exploring high levels of climate change (to explore high-end climate risks), medium levels of climate change (anchored to current policy action), and low levels of climate change (aligned with current international agreements). These scenarios follow very different trajectories in terms of emissions, with some likely to experience peaks and subsequent declines in greenhouse gas concentrations. An important innovation is that most scenarios are intended to be run, if possible, in emission-driven mode, providing a better representation of the earth system uncertainty space. The proposal also includes plans for long-term extensions (up to 2500 AD) to study slow climate change-related processes, and (ir)reversibility. This proposal forms the basis for further implementation of the framework in terms of the derivation of climate forcing pathways for use by earth system models and additional variants for adaptation and mitigation studies.
Competing interests: Some authors are members of the editorial board of the journal. Most authors will be involved in subsequent research based on the ScenarioMIP protocol.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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Status: open (until 27 Mar 2025)
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CC1: 'Comment on egusphere-2024-3765', Sungbo Shim, 20 Feb 2025
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According to line 242-250, it is stated that CMIP will provide CO2 concentration data for GCM without carbon cycle module. Do you mean that CMIP will provide concentration data for all 6 future scenarios (Tier1) in Table 1?? Or do CMIP only provide the concentration required for the 3 experimental types (HC, MC, LC) in Table 1?? These sentences are not clear. I would like to express it in detail.
Citation: https://doi.org/10.5194/egusphere-2024-3765-CC1 -
AC1: 'Reply on CC1', Detlef van Vuuren, 22 Mar 2025
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We will further specify this... it is meant for all scenarios as we realize not all models can be run emission driven.
Citation: https://doi.org/10.5194/egusphere-2024-3765-AC1
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AC1: 'Reply on CC1', Detlef van Vuuren, 22 Mar 2025
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RC1: 'Comment on egusphere-2024-3765', Jean-Francois Lamarque, 20 Feb 2025
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This paper discusses a proposal for the upcoming CMIP7 ScenarioMIP. It provides a presentation of the main aspects driving the design for the main experiments. This paper is designed to be of use by both the IAMs and ESMs communities, which tends to make the paper somewhat confusing at times, as identified in some of my comments below. Maybe the paper would be easier to read if the discussions for the two communities were separated? Furthermore, while it is vying to provide a complete picture, I have found many instances of unclear statements and unjustified decisions. The paper will be significantly improved by considering the following comments, listed as they appear in the paper
- The title should indicate that this is a proposal, since that is made very clear in multiple places in the article
- Lines 111-114: this seems like a much bigger role than what this paper describes, which is a set of scenarios. Similarly, line 199 should be “participants in ScenarioMIP”
- Line 137: instead of “IAM scenarios”, should it be “IAM-generated data based on the scenarios in this proposal”? Otherwise “IAM scenarios” should be defined.
- Lines 163-165: this is an interesting point, although it seems that the end result is still with a high RCP8.5-like (or close) scenario. So, were the critiques unfounded and/or ignored?
- Line 195: what is the rationale for this “agnostic” position? This seems like a step back towards the RCPs
- Line 231: this should be qualified as “CO2 concentrations”.
- Lines 236-238: this is similar to when, for example, aerosol emissions and cloud-aerosol interactions were included. What is the approach suggested to help with the challenge in the interpretation of the results? Who will be helping?
- Lines 252-254: shouldn’t there be a definition of which scenario? Is the implication that the concentration-emission comparison is scenario independent? That seems hardly justifiable.
- Lines 260-262: this would be a step backwards for most climate models since cloud-aerosol emissions are now a critical process that needs to be represented. There are multiple implementations of reduced chemistry relevant to aerosol only.
- Line 264: what is proposed here in terms of AerChemMIP? To use the output of ESMs run with full chemistry? Very confusing sentence.
- Lines 268-269: observations of what? CO2?
- Line 272: why still stop at 2100? IAMs have been used over the last couple of decades and have always had 2100 as their end point. One would therefore conclude that IAMs could be used for 100-year projections. Why not here?
- Line 278: emissions are not observed, only estimated.
- Line 285: why that request of 2150? Is there the expectation that something interesting will happen between 2100 and 2150? This needs to be justified
- Line 292: why is there a need for a new high emission scenario? Can’t we just re-use SSP5-85 (or SSP3-70) with updated harmonization? Looking at Fig 1a, the upward bend in CO2 emissions for this scenario seems hard to justify, unlike the M and ML scenarios
- Line 315: it is never “exact”, unfortunately
- Line 335: what will be the process to decide which emissions to use? Will ESMs be allowed to use whichever IAM output they want?
- Lines 356-360: the recent IMO regulations of sulfur emissions have been shown to exert a very significant radiative forcing. See https://doi.org/10.5194/acp-24-13361-2024 for example
- Line 367: who will be in charge of creating this set of emissions? SSP3-70 and SSP3-LowNTCF played that role in CMIP6
- Lines 383-384: why only involve the IAM community?
- Lines 424-442: this discussion does not seem to add anything useful to this paper. I suggest deleting
- Table 2: does this table suggest that ScenarioMIP will be in charge of generating concentrations of all gases listed, aerosol optical depth (which by the way is not a very useful diagnostic to drive a climate model) and ozone?
- Lines 461-463: But the DECK only looks at the response to CO2 forcing and associated feedbacks. Other MIPs should be in charge of other elements of comparison.
- Lines 517-519: what kind of extreme events would lead to high emission future?
- Line 532: what is meant by “strong” here?
- Lines 664-667: why is CDR measures as part of this discussion? If ESM produced temperature signals can’t tell the difference between the scenarios, why would those scenarios be run? This seems more like a topic of interest to the IAM community, but not ScenarioMIP. Maybe part of another MIP?
- Line 734: how will forcings be harmonized? Or is it meant to be emissions and LULCC that will be harmonized?
- Line 743: replace “small” with “simplified”
- Line 834: I don’t believe CDR is part of GeoMIP
- Line 856: how would ESMs use BECCS resulting concentrations? ESMs don’t have a CO2 tracer just for BECCS.
- Lines 867-869: doesn’t enhanced weathering depend on plant productivity (https://www.nature.com/articles/s41586-024-08429-2)?
- Lines 874: do IAMs have a representation of wildfires? ESMs will definitely have that in the emission-driven runs. This could lead to a significant mismatch
- Lines 949: actually, this paper has proposed a limited set of scenarios (section 3), which, once modeled by IAMs, will lead to emissions, concentrations and LULLC. Not the way it is stated
- Lines 971-976: again, this ignores the fact that many ESMs have an explicit representation of aerosols. Furthermore, why would “a single atmospheric chemistry model” be the right approach? Which one would ScenarioMIP pick? It seems that a tighter collaboration with AerChemMIP is required
- Lines 977-982: are the authors arguing for the use of RCP8.5 as it was designed for CMIP5, or for SSP5-85 for CMIP6? Are those going to be harmonized to the same 2025 emissions/concentration data? What about their extension to 2500? Is this part of CMIP7? Very confusing
Citation: https://doi.org/10.5194/egusphere-2024-3765-RC1 -
AC2: 'Reply on RC1', Detlef van Vuuren, 22 Mar 2025
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Thank you for your thoughtful comments... we will consider them carefully. Regarding the more general point about speaking to 2 communities (and I would argue even the impact community as well): you are right that this might be a complicating factor - but that is also what makes ScenarioMIP unique. So we hope to keep that aspect (and think that the more detailed comments made by you can help us to make the text clearer).
Citation: https://doi.org/10.5194/egusphere-2024-3765-AC2
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CC2: 'Comment on egusphere-2024-3765', Nathan Gillett, 26 Feb 2025
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Overall I found the article very interesting, and I think the overall experimental design of ScenarioMIP for CMIP7 is good. I think the proposal to focus on emissions-driven simulations is a good one. I applaud the co-chairs for the comprehensive consultation process and their diverse author team. My comments focus on particular aspects of experimental design and narrative.
The authors include a box (Box 1) on characterizing scenario likelihood. The box includes reference to likely scenarios, which have a relatively high probability of occurring. However, the authors state on lines 539-540 that the medium emissions scenario should not be considered a most likely scenario. The authors do not cite or discuss the substantial recent literature on probabilistic scenarios (e.g. Sarofim et al. (2024, https://www.nature.com/articles/s41467-024-52437-9), Moore et al. (2022, https://www.nature.com/articles/s41586-022-04423-8,), in which probabilities are assigned to future emissions, including papers which explicitly aim to characterize the likelihood of previous-generation ScenarioMIP scenarios e.g. Huard et al. (2022, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022EF002715). The latter study calculates likelihoods for each of the SSP-based scenarios used in the previous generation of ScenarioMIP, based on the results of other probabilistic scenario studies, and for example finds that intermediate scenarios, like the medium scenario, are more likely than some other scenarios. Note that such probabilistic scenarios offer substantial potential benefits for use in adaptation planning, since they can be used to make probabilistic projections which are not conditional on particular emissions scenarios. I am not suggesting that ScenarioMIP actually includes such ensembles of probabilistic scenarios, but I think it would be good to cite and discuss this literature to situate the MIP relative to this recent research. For example the authors could cite and discuss this literature and the potential benefits of such probabilistic scenarios for informing adaptation, and say that while they encourage individual modelling groups to run such scenarios, computational constraints preclude running ensembles of emissions across multiple models in ScenarioMIP – and the focus here is on sampling over model uncertainties for a limited range of plausible scenarios.
While I agree with the point, raised in Box 2, that different assumptions about inequality between regions, reflected in different regional patterns of emissions, will not strongly affect the climate outcome, other aspects of physical climate change might be more sensitive to equity and justice assumptions. For example, integrated assessment modellers typically choose a cost optimization approach to reaching a certain level of radiative forcing in 2100, based on a discount rate which embodies intergenerational justice assumptions, and assumptions about the availability of CDR approaches in the future, many of which require extensive land (for example for afforestation, or growing bioenergy crops for use in BECCS), which may in turn impact food security or Indigenous land rights (e.g. Rubiano Rivadeneira and Carton, 2022 https://www.sciencedirect.com/science/article/pii/S2214629622002845). These scenarios typically offset relatively higher fossil fuel emissions in the near term with relatively larger CDR towards the end of the century. Different assumptions about intergenerational equity (i.e. discount rate), or availability of land for CDR, might change these scenarios in a way which would affect global climate evolution. Moreover the use of extensive CDR in the SSP-based scenarios consistent with Paris targets used in the previous iteration of ScenarioMIP is itself often used as a justification for the development of CDR – even though as just described other assumptions in the IAMs might have resulted in less CDR. Similarly, some papers argue that the strong use of BECCS in IAM scenarios is a product of the IAM structure and assumptions, and directly led to the stronger consideration of BECCS by the policy community (e.g. Creutzig et al., 2021 https://onlinelibrary.wiley.com/doi/10.1111/gcbb.12798). Overall, the current draft slightly gives the impression that the authors are trying to sidestep equity and justice considerations by arguing that that the physical climate is not very sensitive to these assumptions. But even the selection of certain scenarios for inclusion in ScenarioMIP, with certain ranges of emissions, CDR, BECCS etc can send a message to the policy community, even if this isn’t the authors’ intention. So I suggest that the authors include a stronger caveat in this box saying that the scenarios selected for inclusion in ScenarioMIP reflect certain assumptions in the underlying IAMs, particularly with regard to equity and justice issues, and should not be seen as a full range of possible futures, particularly with regard to technologies such as CDR and BECCS.
I was interested to see that the VLLO scenario may include ocean-based CDR strategies (ln 643-644), by which the authors presumably are thinking of geoengineering techniques such as ocean fertilisation or ocean alkalinisation (given that the potential carbon uptake of coastal measures is limited). Such measures stimulate uptake of CO2 by the ocean by altering ocean biogeochemistry on the large scale, and in ways which might interact with climate change induced changes. Also such measures could have implications for ocean ecosystems and ocean chemical/physical properties, including changes which could be simulated by ESMs. However, according to lines 247-250 “only afforestation and reforestation will be based on endogenous representation of land -based mitigation solutions in ESMs. For all other CDR options we will include their emission impact within the IAM emission output”. This seems like a potential miss from a science perspective to me. Also, omitting direct biogeochemical effects of ocean-based CDR in the simulations would mean that variables such as ocean pH or ocean carbon uptake, which are regularly examined and reported on including in IPCC reports, would not be correct in these scenario simulations, because they would be missing important biogeochemical processes (ocean alkalinization directly alters ocean pH, and both alkalinization and fertilization aim to directly increase ocean carbon uptake – whereas specifying the effects of these measures by simply reducing net global CO2 emissions would reduce ocean carbon uptake). I suggest either not including ocean-based CDR in this scenario, or if it is included, then either it should be modelled explicitly in ESMs, or if not then the authors need to include discussion of the limitations of not doing so. Separately, I think the inclusion of such measures in the VLLO scenario would need to be treated carefully to avoid the interpretation that only scenarios with ocean alkalinization/fertilization were able to keep global temperatures below 1.5°C by the end of the century.
The text on lines 709-732 motivates extending simulations beyond 2100 based on understanding long-term dynamics of the earth system and exploring reversibility. Based on these considerations idealized extensions of the scenarios are proposed to 2500. These idealized scenarios include unrealistic features such as abrupt changes in the rate of changes of emissions, and extremely high levels of cumulative negative emissions (around 6000 GtCO2 in H-ext-OS) (Figure 2). But increasingly climate projections beyond 2100 are needed for climate adaptation (e.g. Lyon et al., 2024; https://pubmed.ncbi.nlm.nih.gov/34558764/ ; Park et al., 2025 https://www.science.org/doi/10.1126/sciadv.adn8819; Easterling et al., 2024 https://www.nature.com/articles/s41558-024-02085-0). And 2100 will only be about 70 years away by the time much literature based on the ScenarioMIP simulations is published. Literature containing plausible emissions scenarios beyond 2100 does exist - for example Sarofim et al. (2024) https://www.nature.com/articles/s41467-024-52437-9 contains a probabilistic scenario to 2300 based on expert elicitation. Meinshausen et al. (2020) contained projections to 2500 for the SSP scenarios https://gmd.copernicus.org/articles/13/3571/2020/, based on assumptions of emissions evolution for each sector. Even the previous generation of ScenarioMIP used more plausible scenario extensions to 2300 than those proposed here (O’Neill et al., 2016; https://gmd.copernicus.org/articles/9/3461/2016/gmd-9-3461-2016.pdf). Even though uncertainties are no doubt larger for scenarios extending beyond 2100, I think it would be a disservice to the impacts and adaptation communities to choose scenario extensions beyond 2100 which are obviously implausible – for example imagine trying to defend coastal defense or mine reclamation plans post-2100 based on one of the scenarios in Figure 2. I encourage the authors to reconsider the extensions beyond 2100 and update with scenarios which are plausible and consistent with the assumptions underlying the scenarios up to 2100. And/or, if the authors retain some idealized scenarios which are not intended to be plausible, then I suggest that they clearly flag these so that they can be separated from those scenarios which are intended to be plausible post-2100. (I notice that Jean-Francois Lamarque raises a related issue in his comments).
Detailed comments:
Ln 268-269: How are modelling groups expected to “keep deviations from observations in the historical period to a minimum”? Does this refer to CO2 concentration alone, or CO2 concentration and temperature? As written it sounds like the authors might be suggesting nudging temperature and CO2 towards observations or similar, but I don’t think this is part of the historical experimental design. Probably the authors mean through model tuning – if so, state this explicitly. Also, the use of historical climate change to tune climate models is controversial, with many groups aiming to only tune the present day climate, and future warming being an emergent property of the model arising from the physics of the system, not something which is set by tuning (e.g. Hourdin et al., 2017 https://journals.ametsoc.org/view/journals/bams/98/3/bams-d-15-00135.1.pdf). Are the authors endorsing tuning models to reproduce historical warming here?
Ln 269-270: The authors argue that it is expected that analysis of future projections will focus on deviations compared to the start year of the simulations. This was not generally the case for analysis of future projections in the IPCC AR6 WGI report, because of the interest in climate change relative to preindustrial, as referenced in the Paris Agreement. For example, Figures SPM 4, 5, 7, 8 and 10 in the summary for policymakers of the AR6 WGI report all use quasi-preindustrial baselines for projections.
Ln 299: The text refers to the high emissions scenario as reflecting “slow development of mitigation technologies and diffusion”. My understanding is that high emissions scenarios such as SSP3-7.0 or SSP5-8.5 reflect no new mitigation actions at all, and as just stated on line 293, a reversal of trends towards mitigation. I recommend repeating the language concerning a reversal of current trends towards mitigation here.
Ln 378-388: The timeline for provision of forcings from the IAMs seems a bit vague here, and the text makes it sound hypothetical and far in the future. But on lines 474-475 the authors say that the IAMs are running over the period September 2024 – June/August 2025, in order to meet the needs of IPCC AR7. So the IAM runs are presumably well underway. Can the authors add more here on the progress on the IAM runs so far? Are any outputs of early IAM runs available for inclusion in this paper? I find it a bit unsatisfactory not to have actual IAM outputs included in this paper – the equivalent description paper for CMIP6 (O’Neill et al. 2016) did include actual forcing timeseries from IAMs.
Ln 512: A ‘competition scenario case’ is mentioned here, but this term hasn’t been introduced, and there is no description of what this means.
Ln 517-518: Are the extreme events referred to here climate/weather extreme events? If so, this statement that extreme events could lead to high emissions seems to contradict what the authors say on lines 210-211, and again immediately following this on lines 521-524 – namely that all the scenarios used assume no climate impacts.
Ln 589-591: This text references uncertainty in the carbon cycle response as a source of uncertainty in whether or not the global mean temperature can be returned to 1.5°C. But uncertainty in the physical climate response is also very important here, given for example the still relatively large uncertainty in equilibrium climate sensitivity. I would guess that physical climate uncertainty might be even more important than carbon cycle uncertainty for determining whether or not 1.5°C at the end of the century can be achieved.
Ln 665-667: The text here refers to additional impacts from CDR measures. But according to lines 247-250 only afforestation and reforestation will be represented endogenously. Do the authors just mean additional impacts of afforestation and reforestation here? Or if not, how will these impacts be represented in ScenarioMIP? Clarify.
Ln 682: The text here says “the mechanisms and extent of CDR deployment will have ESM-specific efficacies”, but lines 247-250 say “only afforestation and reforestation will be based on endogenous representation of land -based mitigation solutions in ESMs. For all other CDR options we will include their emission impact within the IAM emission output”. If these statements are both true, then the first statement must surely refer only to afforestation and reforestation, since all other CDR options are apparently just represented by reducing anthropogenic CO2 emissions. Please clarify.
Ln 748-751: Is H-ext really plausible as stated here? There is now extensive literature on the low likelihood of SSP5-8.5 (see Chen et al. (2021), https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-1/, Hausfather (2025) https://journals.sagepub.com/doi/10.1177/29768659241304854, Sarofim et al. (2024) https://www.nature.com/articles/s41467-024-52437-9 among many other sources). This scenario has a similar level of radiative forcing in 2300 as SSP5-8.5 (Meinshausen et al., 2020).
Ln 748-749: I would encourage the authors to consider making the M-ext simulation a high priority simulation. Recent literature suggests that emissions of around the medium level are most likely (e.g. Sarofim et al. (2024) (https://www.nature.com/articles/s41467-024-52437-9), Moore et al. (2022) https://www.nature.com/articles/s41586-022-04423-8, Huard et al. (2022) (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022EF002715), Hausfather (2025) https://journals.sagepub.com/doi/10.1177/29768659241304854). And as discussed above, many adaptation planners will require scenarios post-2100. Relying only on H-ext and VLLO-ext (the two planned high priority extensions) means relying on two low likelihood/less plausible scenarios.
Ln 977-982: The authors might also note that consideration of concentration-driven SSP5-8.5 simulations was in the past motivated by the possibility that stronger carbon-climate feedbacks than those included in MAGIC might give rise to SSP5-8.5 CO2 concentrations even if emissions were lower (e.g. Chen et al., 2021, IPCC AR6 WGI). This time around ScenarioMIP is recommending emissions-driven simulations, so even though the High scenario emissions are below those of SSP5-8.5, the CO2 concentrations could be at similar levels in models with a high airborne fraction/strong carbon-climate feedbacks.
Ln 1028-1033: Another area where emulators may be useful is for simulating future climate change under probabilistic ensembles of scenarios. The authors could mention this here.
Citation: https://doi.org/10.5194/egusphere-2024-3765-CC2 -
AC3: 'Reply on CC2', Detlef van Vuuren, 22 Mar 2025
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Thank you for the thoughtful comments. I am happy to add a more detailed response to several of them - but would like to consult the full author team (as some of your comments will also be looked at differently by different research communities and authors). Responding now a bit more open... thanks for pointing out to the probabilistic scenario literature and I agree we should add some references. At the same time, also these papers don't solve the problem that these are conditional probabilities at best (based on our current (political) situation. Regarding the role of CDR: I agree with the point made - but please note that it will be increasingly difficult to design scenarios without CDR use.
Citation: https://doi.org/10.5194/egusphere-2024-3765-AC3
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AC3: 'Reply on CC2', Detlef van Vuuren, 22 Mar 2025
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CC3: 'Comment on section 5) Representation of carbon dioxide removal in ScenarioMIP', Florian Humpenöder, 01 Mar 2025
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The current ScenarioMIP proposal introduces an inconsistency in how CO₂ emissions from land-based Carbon Dioxide Removal (CDR) methods are handled. Specifically, while ESMs take IAM-derived CO₂ emissions for Bioenergy with Carbon Capture and Storage (BECCS) as given, they recalculate CO₂ emissions related to afforestation and reforestation instead of using IAM-derived estimates. This approach creates an internal inconsistency in the treatment of land-based CDR options.
Explanation of the Inconsistency:
IAMs generate internally consistent land-use and emissions pathways, balancing land competition between food production, bioenergy crops, re/afforestation, and conservation while also projecting associated CO₂ fluxes. However, in the proposed framework:
- BECCS-related CO₂ emissions and removals are directly taken from IAMs and used in ESMs without modification.
- Afforestation and reforestation CO₂ emissions/removals are recalculated dynamically in ESMs, considering soil carbon dynamics, nutrient limitations, and climate-vegetation interactions.
This discrepancy creates an inconsistent approach to land-based CDR—while BECCS emissions are assumed to follow IAM projections exactly, afforestation and reforestation are subject to biophysical recalculations that may significantly alter the land-use CO₂ emissions trajectory. As a result, the total carbon budget and net-negative emissions estimates may diverge from what IAMs initially projected.
Additionally, many IAMs already include a process-based representation of forest growth and carbon sequestration(i.e., the biogeochemical effects of afforestation and reforestation), meaning that they dynamically simulate carbon uptake by vegetation. However, IAMs generally lack a representation of biophysical effects such as changes in albedo, evapotranspiration, and surface energy fluxes, which can significantly impact regional and global climate. These biophysical effects are the strength of ESMs and should be incorporated in a way that complements, rather than overrides, IAM-based carbon sequestration estimates.
Suggested Improvement:
To ensure internal consistency in the treatment of land-based CDR, while still leveraging the complementary strengths of IAMs and ESMs, I suggest the following refinements to the proposal:
- Use IAMs as the primary source for all land-use CO₂ emissions projections, including both BECCS and afforestation/reforestation, ensuring coherence in how CDR is represented.
- Use ESMs to simulate the biophysical feedbacks of land-use changes (e.g., climate-vegetation interactions, albedo effects, surface temperature changes, and hydrological responses) without recalculating CO₂ emissions from afforestation and reforestation.
This approach would ensure a more coherent and transparent integration of land-based CDR into ScenarioMIP, preventing discrepancies in carbon budget estimates while maintaining the policy relevance of IAM scenarios and the biophysical accuracy of ESM outputs. I strongly recommend incorporating this refinement into the ScenarioMIP proposal.
Beyond ScenarioMIP, an iterative feedback mechanism, where ESM-derived insights on biophysical feedback effects are used to refine IAM land-use and emissions projections could be established in the future.
Citation: https://doi.org/10.5194/egusphere-2024-3765-CC3 -
AC4: 'Reply on CC3', Detlef van Vuuren, 22 Mar 2025
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Thanks... I see the issue - and we should discuss within the author team how to solve this (which might very well follow your suggestion).
Citation: https://doi.org/10.5194/egusphere-2024-3765-AC4
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CC4: 'Comment on egusphere-2024-3765: adding a seventh scenario', Massimo Tavoni, 04 Mar 2025
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The current framework nicely explores a wide range of emissions and associated temperatures. However, political turmoil has intensified since it was conceived a year ago, with significant backlash against mitigation measures emerging. As a result, I think it would be great to expand the scenario by reflecting an additional narrative that explores higher ranges of climate overshoot.
Currently, temperature peak and decline is explored in the VL scenarios and possibly also in ML. Both are important, but they currently cover a limited range of future possibilities. The VL scenarios are in the context of Paris-compliant policies stabilizing temperatures at 1.5°C. Though clearly important, they do not reflect a scenario with very limited mitigation action in the next years to decades. The ML scenario is a very important one, but the extent of the overshoot is limited and mainly occurs during the next century. Though extensions beyond 2100 explore various degrees of overshoot, the timescales are so long that they will not be policy-relevant. Also the uncertainties on socio-economic dynamics over such timeframes are so significant that they make it hard to interpret results.
Based on these considerations, adding a seventh scenario with sustained emissions followed by a late awakening of the gravity of climate change followed by rapid action to bring emissions towards zero would be important. Such a HL scenario could have the following characteristics:
- Follow H till after mid-century (e.g. 2060/70), then try to hit net zero CO2 by 2100/2120
- This will result in a significant overshoot of 1.5°C warming followed by very rapid drop in temperature (e.g. 0.5°C in a few decades)
- Such a scenario will provide an interesting, disruptive scenario narrative illuminating the risks and consequences of a sustained political backlash on mitigation
- The scenario will be relevant for earth system science because it will highlight the carbon cycle and climate repercussions of rapid CO2 drawdown and the climatic risks of overshoot
- It will also be relevant for the climate impact community to help quantify the physical risks of high-temperature overshoot
- And it will be relevant for the socio-economic communities to understand the social and economic consequences of overshoot (including for key issues such as adaptation needs and loss&damages)
Citation: https://doi.org/10.5194/egusphere-2024-3765-CC4 -
CC5: 'Reply on CC4', Roberto Schaeffer, 05 Mar 2025
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I fully agree with Prof. Tavoni that the current ScenarioMip framework nicely explores a wide range of scenarios that can lead to a broad spectrum of emissions and associated temperatures. And I fully agree with him that adding a seventh scenario with sustained emissions followed by a late rapid decline in emissions towards net zero would add a lot of new features to the emissions space we want to cover. But in addition to the six characteristics pinpointed by Prof. Tavon, I think such a seventh scenario (High-Low (HL)) could also benefit from two additional features:
1. It would also be relevant for the climate mitigation community to help it quantify the transition risks of rapid increase and then decrease in emissions, and
2. It would also be relevant for the climate mitigation community to better explore some equity implications of such a scenario.
Roberto Schaeffer, Full Professor of Energy Economics, Federal University of Rio de Janeiro, Brazili
Citation: https://doi.org/10.5194/egusphere-2024-3765-CC5 -
AC5: 'Reply on CC5', Detlef van Vuuren, 22 Mar 2025
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Thanks - would be really nice to see even more comments on this issue.
Citation: https://doi.org/10.5194/egusphere-2024-3765-AC5
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AC5: 'Reply on CC5', Detlef van Vuuren, 22 Mar 2025
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RC2: 'Comment on egusphere-2024-3765', Colin Jones, 07 Mar 2025
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Review of Van Vuuren et al. The Scenario Model Intercomparison Project for CMIP7 (ScenarioMIP-CMIP7).
This paper outlines a proposal for a set of emission and land-use scenarios to be developed by the international Integrated Assessment Modelling (IAM) community, which will act as input to Earth system models (ESMs) in CMIP7. The emission scenarios (and therefore ScenarioMIP) are a central component of CMIP7 and one of the primary routes that new ESMs are used, sampling a common set of future emission and land use scenarios, to generate an internationally coordinated set of future climate projections. The resulting projections support numerous downstream activities across science, policy, and real-world decision-making. ScenarioMIP is therefore a key foundational activity for climate research, for climate policy, and for climate change decision making (mitigation, adaptation, and increasingly modification).
The ScenarioMIP project, with this paper as one important outcome, is the result of a long, detailed, open, globally inclusive, and highly responsive consultation. For this the leaders of ScenarioMIP deserve enormous credit as it has required significant time, effort, patience, and expertise. The degree of inclusivity, and the willingness to incorporate views from numerous disciplines and from regions of the world, will make the resulting emission scenarios and the projections based on them significantly stronger and of great utility across the world. The paper therefore clearly needs to be published and as it stands now is very close to publication. I have a few concerns and suggestions that I outline below. I hope these contribute to making the paper stronger and the resulting scenarios of use to the various communities that will build on this effort over the coming years.
Major Points
- As the authors say, this is a proposal for a set of emission and land use scenarios that IAM teams are invited to develop. These will ultimately form the emission and land-use scenarios to be used by ESMs in CMIP7. I have two linked concerns with this.
First, while the different scenarios are well well-motivated and well described qualitatively, there appears to be only weak geophysical constraints on them. For some scenarios there are relatively clear constraints (such as the magnitude and timing of maximum warming in an overshoot scenario and the target long-term temperature post-overshoot). But, for others there does not appear to be clear geophysical constraints (e.g. global warming bounds, global mean radiative forcing bounds) for the IAM teams to aim for. Figure 1 gives one example of some geophysical outcomes for the proposed example scenarios Should the results there be taken as constraints by the IAM teams? In CMIP6 (SSPs) and CMIP5 (RCPs) the main geophysical constraint was global mean top of atmosphere (TOA) radiative forcing anomaly at 2100. So, I wonder whether a clear set of geophysical guardrails (upper and lower targets at certain time points) need to be provided for each scenario? There are numerous places in the paper with comments such as “IAM teams should explore measures that minimize trade-offs and exploit synergies” or AM teams are encouraged to explore VLLO emission trends under different equity assumptions”. Isn’t there a risk that the resulting IAM scenarios span too broad a range of geophysical outcomes?
Linked to my first concern, it is not clear what the decision-making process is for deciding which of a potentially large number of different IAM scenarios produced for each proposed scenario, will ultimately be used in CMIP7. ESMs will want one emission and land use data set per ScenarioMIP scenario. Given the relatively weak geophysical constraints on the different scenarios and the need for one “winner” a clearer explanation of the decision-making process for selecting the “winner” for each scenario would be helpful.
Linked to both concerns, I see at (Lines 137-139) “the IAM scenarios based on this proposal are developed in the period Sept 2024 to summer 20205, so climate model simulations can start after summer 2025.” I note the word “after” summer 2025. Is the aim that the ESM-ready scenarios will be available by “summer 2025” or just the mix of IAM scenario submissions? If the ESM-ready scenarios, this seems ambitious but if it is the aim then I think some more guidance on (i) geophysical constraints per scenario and (ii) the decision-making processes to go from; numerous IAM scenario per scenario pathway to the single data set to be used by ESMs, needs to be included.
Minor Points
- Why was 2100 chosen as the end data for the IAM-based scenario? Previously IAMs have generated scenarios for 85 (CMIP6) and ~100 years. This seems to be reduced now to ~79 years. I am very supportive of the scenario extension plans, and understand the IAM scenarios become increasingly less well-founded with time into the future, but based on past CMIP cycles, I wonder why 2125 was not chosen as the end date?
- Lines 191-193 talks about the CMIP6 emission scenarios being linked to different socio-economic futures (SSPs). At the time this was deemed to be beneficial for use of projections and scenarios linked through to societal impacts. So will the final CMIP7 scenarios (those chosen as the single representative emission scenario) also be linked to an assumed socoi-economic (SSP) future?
- Lines 267-269 the authors say: “Finally, it should be noted that models running emission-driven simulations can have different temperatures and concentration levels in the start year of the experiments. The ESM teams are strongly encouraged to keep deviations from observations in the historical period to a minimum.” I assume this is encouraging ESMs to try and have an accurate simulation of historical temperatures and CO2. This they generally try to do, without overtly tuning to every up and down in the historical records. As this paper is about the IAM scenarios I find this comment somewhat out of place here and I recommend dropping it.
- Linked to my main point above, should IAM teams treat Figure 1 as a “target” for their scenarios? This is not really made clear. I also think global mean TOA radiation anomaly would be helpful to add to Figure 1.
- With respect to the high emission scenario (H): Given recent geopolitical developments (e.g. US leaving the Paris Agreement), which of course happened after this paper was written, is there now a need for an increased sampling of the medium to high emission scenario space?
- Lines 512-513 discusses a so-called “competition scenario” that sounds like a particular SSP world, which again begs the question as to whether a range of SSPs will be represented in the resulting scenarios used in CMIP7 or whether this aspect of the scenario development is being abandoned?
- Text at lines 686 to 694, discussing options for the VLLO scenario, again reads to me as though there is a large amount of flexibility for what the resulting scenario is made up of. Without clear geophysical constraints I struggle to see what the decision process will be for selecting the single IAM scenario to be used by the CMIP7 ESMs.
- Scenario extensions: These are important. It would be helpful to have a timeline for the availability of these as well.
- With reference to table 6. Is there a target resolution for the gridded BECCS and Afforestation data sets? And how is the DACCS data set delivered in terms of spatial distribution?
- A lot of what comes under Discussion and Conclusions is really discussing things that would be nice to do in the future and could be removed. For example Lines 977 to 982 discusses high emission scenarios and high climate sensitivity models. This doesn’t really seem to be part of this paper on emission scenarios. This is also the case for e.g. lines 990 to 994 and lines 1021 to 1025. I recommend shortening the text discussing future work here and instead more clearly summarize the main recommendations from the paper to give IAM teams a concise summary of what is aimed for and by when.
Citation: https://doi.org/10.5194/egusphere-2024-3765-RC2 -
CC6: 'Comment on egusphere-2024-3765', David Huard, 17 Mar 2025
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Thanks for the paper, really appreciated. My comments below are from the perspective of a climate services provider. To give a bit of background, my office interacts daily with practitioners from multiple fields, looking to integrate climate projections into their work. Those practitioners are not climate experts, yet have to make decisions about climate adaptation options, and need clear guidance from the climate science community on the use of scenarios. As climate service providers, our job is to translate the science being done in the research community into actionable information. ScenarioMIP is our main source of information, so the design decisions outlined in the paper will be critical to our work for the next 10 years. We have a strong vested interest in the experimental protocol of ScenarioMIP being understandable to a wide public.
Box 1
I disagree with Box 1' line of reasoning. The first paragraph mentions that the question under discussion is the "likelihood of a scenario approximating reality for a number of key output variables". But then the second paragraph states that for a scenario to be plausible, it has to be feasible based on the five dimensions of feasibility, which arguably cast a much wider net than "key output variables". I imagine a scenario could have an unfeasible demography with unfeasible technologies, that combined together would yield feasible emissions. I would argue that, from the ScenarioMIP perspective, the only dimensions that matters are the variables used as forcings for climate models, and assessments of plausibility should only look at those.
Stylized Pathways (L726)
The paper justifies the decision to use idealized extensions beyond 2100 by the "uncertainties that increasingly affect the socio-economic drivers of these trajectories". This feels arbitrary, and I believe the 2100 switch to stylized pathways is deeply problematic for multiple reasons.
- It breaks the first design principle outlined in the introduction: "internally consistent socio-economic and technological scenarios";
- It will damage the credibility of all climate impact assessments post-2100. Just imagine yourself explaining to a corporate board or elected officials that your climate risk analysis is based on a "stylized emission pathway";
- It creates a break in the provenance of climate projections. All regional, national and international web portals, including the IPCC Interactive Atlas, will have to explain why in 2100 there are abrupt and unrealistic changes in emissions. Communicating climate change projections is hard enough already, please don't make it harder by creating artificial breaks in the series;
- If modeling centers are also skeptical of these stylized pathways, they're less likely to run simulations, which will reduce ensemble size post-2100, hamper our ability to assess hazards, and lower the value of the whole exercise.
An alternative to stylized pathways would be to generate large Monte-Carlo IAM ensembles of emission pathways, and pick those that match best the intended stylized pathways. This would preserve internal consistency and would not muddy communications with end users.
Minor suggestions170: I would add "internally consistent" to the first design principle, as in line 180.
173: As discussed above, I object to this "design principle", which anyway feels more like an implementation detail than a principle.
Box 1: The first paragraph mixes two different ideas: the infinite number of potential futures, and the "dimensions" of each of those futures. It clarifies the latter by saying that we're mostly interested in key forcing variables, but does not address the former. One option would be to focus on the relative likelihoods of ScenarioMIP scenarios (a small, finite, set).
451: Not sure I understand what the message from this table is, and whom it is intended for. I think a list of data that ScenarioMIP plans to make available, irrespective of whether it is intended for climate models or VIA, would suffice.
498: Iea -> IEA
947: I suggest to include among further research directions something about the relative likelihood of key forcing variables, e.g., something like "The IAM community is encouraged to develop probabilistic assessments of ScenarioMIP' emission pathways to inform climate risk assessments and adaptation decision-making."
To expand on scenario likelihood, what decision makers are looking for can be translated as the (Bayesian) probability of the following statement being true: "Climate model drivers from scenario S are an accurate description of future conditions", for all S in the set of available scenarios. This is beyond the scope of this paper, but I hope the scientific community can finally tackle that question head-on. It's been 25 years since Moss and Schneider (2000) wrote: "We believe it is more rational for scientists debating the specifics of a topic in which they are acknowledged experts to provide their best estimates of probability distributions and possible outliers based on their assessment of the literature than to have users less expert in such topics make their own determinations."
Thanks again for your work on this, I can't overstate how important this is.
Citation: https://doi.org/10.5194/egusphere-2024-3765-CC6 -
CC7: 'Comment on egusphere-2024-3765', May M. M. Chim, 21 Mar 2025
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I would like to raise a discussion regarding volcanic forcing in the proposed CMIP7 ScenarioMIP framework. The current preprint does not address volcanic forcing, which I believe merits consideration given recent advances in our understanding of its importance for climate projections.
In CMIP6, ScenarioMIP simulations used a constant volcanic forcing based on the 1850-2014 historical average, which is biased by the under-recording of small-magnitude eruptions prior to 1978 (i.e., the start of the satellite era), and it does not account for eruptions larger in magnitude prior to 1850. Our recent study (paper accepted, preprint at https://www.researchsquare.com/article/rs-4938494/v1) demonstrates that volcanic forcing uncertainties contribute substantially to overall uncertainties in global mean surface air temperature projections – at a magnitude comparable to internal variability.
I recommend that CMIP7 ScenarioMIP incorporate an improved volcanic forcing representation that accounts for the climate uncertainty arising from future volcanic eruptions. This could be implemented through prescribing a constant volcanic forcing with magnitude equivalent to:
- A historically-averaged mean that considers the missing sulfur dioxide flux from small-magnitude eruptions prior to 1978; or
- The median stratospheric aerosol optical depth based on stochastic scenarios resampled from the latest ice-core and satellite volcanic emission records.
This approach improves the magnitude of volcanic forcing and the mean climate state in climate projections. I suggest modelling groups perform projections with constant volcanic forcing at the 5th and 95th percentiles of the stratospheric aerosol optical depth distribution of the stochastic scenarios to account for the climate uncertainty arising from volcanic eruptions.
I would also like to highlight that a constant volcanic forcing approach in ScenarioMIP does not consider the sporadic nature of volcanic eruptions. The use of stochastic volcanic forcings in climate models and projections is necessary to allow the assessment of the abrupt climatic changes caused by large-magnitude volcanic eruptions, and the associated climatic risks and socio-economic impacts.
Citation: https://doi.org/10.5194/egusphere-2024-3765-CC7 -
CC8: 'Comment on egusphere-2024-3765', Gareth S. Jones, 25 Mar 2025
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I have a few comments I hope the authors will consider.
Lines 323-332
Figure 1 gives an illustration of the possible CO2 trajectories, and the global temperature response based on a FaIR model. But does it give a realistic estimate what would be expected from ESMs? Figure 7c in Sanderson et al 2024, shows an estimate of ESMs warming from CMIP6 models driven with CO2 emissions with a present day central 50% range of about 0.4C. This is much bigger than is suggested in Figure 1b.
I suggest that this is addressed, so that readers will have some awareness of how much larger temperature ranges will be in the CO2 emission driven experiments, compared to the CO2 concentration experiments.L268-269
How do ESM teams practically keep "deviations from observations in the historical period to a minimum", especially if extra radiative forcing uncertainty (from CO2 emission configurations) is present?
In O'Neill et al. (2016), there was a recommendation about the solar and volcanic forcing in the 21st century. What is the recommendation for CMIP7?
ReferencesB.C. O'Neill et al., 2016, "The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6", GMD
B.M. Sanderson et al., 2024, "The need for carbon-emissions-driven climate projections in CMIP7", GMD
Citation: https://doi.org/10.5194/egusphere-2024-3765-CC8
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