the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Jan 2026
The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP7
Abstract. Cloud processes constitute one of the key uncertainties for climate change projections. The fourth iteration of the Cloud Feedback Model Intercomparison Project, CFMIP4, contributes to the Coupled Model Intercomparison Project phase 7 (CMIP7), by providing a set of global climate model experiments aiming to enhance our understanding of clouds, circulation and climate sensitivity, thereby informing improved projections of future climate change. CFMIP4 targets four knowledge gaps: (1) Physical mechanisms of cloud feedback and adjustment; (2) Dependence of cloud feedback and adjustment on climate base state and on the nature of the forcing; (3) Coupled mechanisms of the sea-surface temperature "pattern effect"; and (4) Coupling of clouds with circulation and precipitation. CFMIP4 contributes four CMIP7 Assessment Fast Track experiments that are central to the quantification of climate feedback and sensitivity in past, present and future climates, essential for process understanding and model evaluation. Furthermore, CFMIP4 supports the joint analysis of models and observations through a data request that includes process and satellite simulator output.
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Status: open (until 28 Mar 2026)
- CC1: 'Comment on egusphere-2026-398', Chen Zhou, 30 Jan 2026 reply
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RC1: 'Comment on egusphere-2026-398', Anonymous Referee #1, 30 Jan 2026
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This paper describes the protocols and reasoning for the experiments requested as part of CFMIP4, which is in turn part of of CMIP7. Given the importance of cloud feedbacks in climate system and their role in the structural diversity across models, this is an important initiative to get right. This paper is timely, given that CMIP7 simulations are now beginning and is publishable with only a few minor revisions.l.85-90 The variation in ECS between 2xCO2 and 4xCo2 is not that large (except for a few models, like CESM2, which already have too high an ECS), and so I am unsure how important this will turn out to be in the real world. It is worth looking into more (as suggested here). I note that some papers referenced use 8xCO2 runs as well, and I start to get a little nervous since most models are starting to push their basic assumptions at such high forcing levels (high water vapor effects on the equation of state or radiative transfer, mis-match of the ozone layer location and the rising troposphere etc.)
l. 141. The amip-piForcing experiment seems to be basically the same as the amip-sst experiment in CERESMIP in CMIP6Plus. Probably better to have the same name.
l. 141. The choice of SST/SIC driving file is problematic (for reasons acknowledged in the text). It's possible that there will be an update to HadISST2.x relatively soon which would be better - and so maybe leave this ambiguous for now? Or allow for an update?
Table 1. Can the authors ask for simulations up to Dec 2025 for all historical runs? CMIP is endeavoring to update forcing fields by the fall of the following year. So Dec 2025 forcing (not just for SST) should be available by the end of this calendar year. (Hewitt et al, 2025; https://doi.org/10.1371/journal.pclm.0000708). Data to the end of 2024 should already be available (though it probably isn't). We should at least be aspiring to keep things as up-to-date as possible.
l. 168. The "true" value of ECS? I think this is a little optimistic. The longer-term model-specific ECS is more achievable!
Citation: https://doi.org/10.5194/egusphere-2026-398-RC1
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This paper is very well written, which provides an important framework for understanding the cloud feedback processes. Its contribution to the CMIP7 ensures it will be important for future research and the next generation of climate projections.
Nevertheless, I have a suggestion to address a potentially inaccurate statement in the paper: According to Figure 1, the pattern effect analyzed using amip-piForcing is induced by the evolving spatial patterns of both SST and SIC. Although the SST pattern effect dominates, a relatively smaller SIC pattern effect also exists when analyzing the amip-piForcing experiment according to our previous study. In contrast, the piClim-deltaSST experiment appears to include the SST pattern effect only, excluding the SIC pattern effect. Therefore, I suggest adding a discussion on whether the SIC pattern effect is accounted for in the pattern effect analyses across these different experiments.