Abstract

GMD

Geoscientific Model Development

GMD

Geosci. Model Dev.

1991-9603

Copernicus Publications

Göttingen, Germany

10.5194/gmd-19-8693-2026

The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP7

The CFMIP contribution to CMIP7

Ceppi

Paulo

p.ceppi@imperial.ac.uk

https://orcid.org/0000-0002-3754-3506

Bodas-Salcedo

Alejandro

Zelinka

Mark D.

https://orcid.org/0000-0002-6570-5445

Andrews

Timothy

Brient

Florent

https://orcid.org/0000-0001-8485-4705

Chadwick

Robin

https://orcid.org/0000-0001-6767-5414

Dai

An-Zhuo

Gregory

Jonathan M.

https://orcid.org/0000-0003-1296-8644

Hwang

Yen-Ting

https://orcid.org/0000-0002-4084-1408

Kang

Sarah M.

https://orcid.org/0000-0003-4635-275X

Kay

Jennifer E.

Mauritsen

Thorsten

Ogura

Tomoo

Tselioudis

George

https://orcid.org/0000-0002-7145-9113

Watanabe

Masahiro

Webb

Mark J.

Wing

Allison A.

https://orcid.org/0000-0003-2194-8709

1Department of Physics, Imperial College London, London, UK 2Met Office Hadley Centre, Exeter, UK 3Lawrence Livermore National Laboratory, Livermore, CA, USA 4School of Earth and Environment, University of Leeds, Leeds, United Kingdom 5LMD/IPSL, Sorbonne Université, Paris, France 6Institut Universitaire de France (IUF), Paris, France 7Global Systems Institute, Department of Mathematics and Statistics, University of Exeter, Exeter, UK 8National Centre for Atmospheric Science, University of Reading, Reading, UK 9Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan 10Max Planck Institute for Meteorology, Hamburg, Germany 11Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, USA 12Cooperative Institute for Research in Environmental Science, University of Colorado Boulder, Boulder, Colorado, USA 13Department of Meteorology, Stockholm University, Stockholm, Sweden 14Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden 15Earth System Division, National Institute for Environmental Studies, Tsukuba, Japan 16Goddard Institute for Space Studies, NASA, New York, NY, USA 17Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Japan 18Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL, USA

Paulo Ceppi (p.ceppi@imperial.ac.uk)

18September2026

19 18 86938708 23January2026 29January2026 4August2026 5August2026

2026

This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/

This article is available from https://gmd.copernicus.org/articles/19/8693/2026/gmd-19-8693-2026.html

The full text article is available as a PDF file from https://gmd.copernicus.org/articles/19/8693/2026/gmd-19-8693-2026.pdf

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.

UK Research and Innovation

EP/Y036123/1

Natural Environment Research Council

NE/V012045/1

NE/T006250/1

Department for Science, Innovation and Technology

Met Office Hadley Centre Climate Programme

Lawrence Livermore National Laboratory

DE-AC52-07NA27344

Agence Nationale de la Recherche

ANR-22-CE01-0005

National Science and Technology Council

112-2111-M-002-016-MY4

National Science Foundation

AGS-2140419

1Introduction

Clouds play a fundamental role for climate variability and change by modulating the Earth's radiation budget, as well as by coupling with atmospheric circulation and precipitation. These processes are however subject to substantial and long-standing uncertainty in global climate models (GCMs), and they are difficult to constrain observationally. The purpose of the Cloud Feedback Model Intercomparison Project (CFMIP) is to inform improved projections of future climate change, by understanding and evaluating clouds, circulation and climate sensitivity.

The present paper aims to motivate and describe the science questions and experimental protocol of the fourth iteration of CFMIP, hereafter CFMIP4, which will contribute to phase seven of the Coupled Model Intercomparison Project CMIP7;. While the present paper focuses on global climate modelling, we highlight that the CFMIP community's activities and interests extend to field studies e.g.,, analysis of satellite observations e.g.,, and process modelling e.g.,. Correspondingly, the CFMIP experiment protocol and data request are designed to facilitate the validation of global climate model simulations against observations and process-resolving modelling across a range of scales.

A long-standing focus of CFMIP activities has been on understanding and quantifying cloud feedback and adjustment, the two main processes through which clouds affect the climate sensitivity. Cloud feedback and adjustment represent respectively the slow, SST-mediated and the fast, non-SST-mediated components of the cloud-radiative response to forcing. Cloud feedback in particular has dominated inter-model spread in climate sensitivity across generations of GCMs , and also constitutes a key uncertainty in process-based assessments of the climate sensitivity . Radiative feedback is commonly estimated by least-squares regression of top-of-atmosphere radiative anomalies onto global-mean surface temperature, under the assumption that the Earth's global radiative response, R, is approximately linear with respect to surface temperature anomaly ΔT: R≈λΔT, with the climate feedback parameter λ assumed near-constant .

Over the last decade, the CFMIP community has played a leading role in demonstrating that cloud feedback is in fact non-constant, and in particular that it differs substantially between observed historical climate and future projected climate change e.g.,. Analysis of experiments involving different forcing agents and forcing time evolutions has revealed that λ varies with time, forcing agent, forcing magnitude, and the climate base state e.g.,, with cloud feedback often dominating the variations in λ.

Much of this variation in cloud feedback is now understood to result from anomalous patterns of sea-surface temperature (SST), via their effect on lower-tropospheric stability and boundary-layer cloud – a phenomenon known as the “SST pattern effect” . This pattern effect accounts for cloud-radiative variability on timescales ranging from inter-annual to multi-decadal, involving both forced SST responses and unforced coupled climate variability. Beyond the SST pattern effect however, the climate base state also affects cloud feedback (and potentially also cloud adjustment), particularly through a dependence on temperature e.g., – thus further contributing to changes in λ as the climate warms.

The climate impact of clouds occurs not only via the global radiation budget, but also through interactions with regional climate processes. The CFMIP community therefore has a long-standing interest in cloud–circulation coupling across a range of scales , from convective processes to planetary-scale circulations such as the Hadley cells and the midlatitude jets . Recent years have seen an increased focus on interactions between clouds and ocean processes, producing novel insights into how clouds can affect patterns of SST under both natural variability and forced climate change .

These recent advances in the understanding of clouds and their coupling with circulation and climate sensitivity motivate a new set of science questions that underpin the CFMIP4 experimental protocol. Section 2 will introduce the CFMIP4 science questions, review the insights gained from the previous iteration of CFMIP experiments i.e. CFMIP-3;, and discuss new opportunities for progress. The experimental protocol and data request are described in Sects. 3 and 4 respectively.

2CFMIP4 science questions and opportunities for progress

The CFMIP4 science questions are deliberately broad in scope, to encompass the range of current and future research directions within the CFMIP community. We however highlight specific knowledge gaps relevant to our science questions, where we hope the new CFMIP4 experiment protocol and data request will provide new opportunities for progress.

2.1What are the physical mechanisms underlying <italic>cloud feedbacks and adjustments</italic> in nature, and how credibly do models represent these?

Considerable uncertainty remains on feedback mechanisms for individual cloud regimes. While the rise of high clouds with warming is reasonably well understood and observed , there are ongoing efforts to elucidate how high-cloud amount and optical depth respond to warming, and how this affects longwave and shortwave radiation . In particular, further observational and modelling work is needed to verify a hypothesis that predicts a reduction in high-cloud amount as the upper troposphere warms and stabilises . As for low-cloud feedback, while observational evidence of a positive feedback is now strong , the relative importance of various potential physical drivers remains unclear . Open questions also remain regarding the magnitude and microphysical mechanisms of phase-change feedbacks and the possible coupling between aerosol forcing and cloud feedback e.g.,.

The representation of cloud feedback processes in climate models is a long-standing challenge , with uncertainty resulting from a combination of structural and parametric uncertainty e.g.,. Recent trends in clouds and radiation are providing new opportunities to observationally assess the feedback and adjustments of clouds, and to validate the behaviour of GCMs, particularly through the use of satellite simulator output provided as part of CFMIP-3 . Observations indicate a rapid increase in Earth's energy imbalance since the turn of the century, at a rate close to 0.5 W m⁻² decade⁻¹ , with changes in marine low clouds and storm-track clouds making a large contribution to this trend . GCMs appear unable to replicate the magnitude of this energy imbalance increase, whether SSTs are interactive or prescribed ; the reasons for this discrepancy are presently unclear. There is a pressing need to quantify the contributions of cloud feedback and adjustments to the observed trends, and the ability of GCMs to represent these. While much research so far has focused on the cloud response to weakening aerosol emissions , we highlight the need for observational constraints on greenhouse gas adjustments, which may have made a comparably large contribution to the recent cloud-radiative trends .

2.2How and why do cloud feedbacks and adjustments depend on <italic>climate base state</italic> and on the <italic>nature of the climate forcing</italic>?

Analyses of CFMIP-3 experiments forced with different levels of SST (±4 K) and CO₂ (halving, doubling, quadrupling from pre-industrial) have revealed a substantial inter-model spread in cloud feedback state-dependence, with most GCMs simulating a more amplifying feedback as the climate warms . This is a first-order control on climate sensitivity in some GCMs; for example, CESM2 simulates a near-doubling of the climate sensitivity between the abrupt-2xCO2 and abrupt-4xCO2 experiments . State-dependence also affects non-cloud feedbacks e.g., and the degree to which they are masked by clouds . Understanding to what extent this feedback state-dependence is due to changing SST patterns, feedback temperature dependence, or other processes, is an avenue for future research.

It has long been recognised that forcing agents can differ in their “efficacy”, i.e. the amount of temperature change per unit radiative forcing, as a result of differences in climate feedback . Hence, temporal changes in the relative importance of various forcing agents mean that climate feedback may differ between the historical period and future climate change . Several studies have identified a role for the patterns of SST response, and thus cloud feedback, in explaining forcing efficacy differences . The results are however highly model dependent , and it remains therefore uncertain to what extent changes in the relative strength of diverse forcing agents may contribute to time variation in historical climate feedback .

2.3What coupled processes underlie the <italic>SST pattern effect</italic>, and how does this affect cloud feedback?

Understanding the mechanisms of SST pattern formation has been identified as one of four fundamental science questions guiding the activities of CMIP7 . There is compelling evidence that aspects of the observed SST warming pattern in recent decades, for example the east–west contrast across the tropical Pacific Ocean, lie outside of the range of coupled GCM simulations . This has important implications for the time evolution of climate feedback via the pattern effect , as revealed by CFMIP-3 experiment amip-piForcing, in which atmosphere GCMs are forced with observed historical SST and sea-ice but with constant pre-industrial forcing Table 1;. It is presently unclear whether this model bias indicates issues with the GCM representation of natural variability, the forced response, or both.

Of particular relevance to CFMIP is the potential role of subtropical marine stratocumulus clouds, whose feedback GCMs tend to under-represent . Recent modelling evidence suggests that a stronger (and thus more realistic) stratocumulus cloud feedback results in a stronger coupling between Southern Ocean and tropical Pacific SST anomalies . Thus, GCMs with Southern Ocean SSTs nudged towards the observed decadal cooling trend during 1979 to 2013 produce a more realistic tropical Pacific warming pattern, with suppressed East Pacific warming, to the extent that they simulate a realistically strong stratocumulus cloud feedback . Coupled mean-state biases in SSTs, clouds and circulation around the Intertropical Convergence Zone (ITCZ) region may also play an important role for the SST warming pattern through their impact on the trade winds .

2.4What are the mechanisms underlying <italic>cloud–circulation coupling</italic> and <italic>regional precipitation change</italic>, and how credibly do models represent these?

Under global warming, climate models simulate shifts in features of the atmospheric circulation such as the jet streams, the subtropical dry zones, and tropical rainfall – all of which will have substantial impacts on regional climate through their coupling with the radiative budget components and the hydrological cycle. Shifts in these circulation features are however highly uncertain among climate models e.g.,. Cloud–circulation coupling contributes to this uncertainty, with cloud-radiative heating affecting atmospheric temperature gradients through local diabatic effects as well as via coupling with SSTs .

CFMIP-3 included a set of atmosphere-only time-slice experiments (piSST, a4SST, and variants) aimed at decomposing the coupled 4×CO₂ climate response into contributions from SST, sea-ice, and direct responses to CO₂, providing insight into sources of inter-model uncertainty . Analysis of these simulations has revealed that rapid adjustments, uniform SST changes and SST warming patterns all contribute substantially to model uncertainty in tropical circulation and precipitation, with the balance between mechanisms varying by region . This highlights the need for tighter constraints on the coupled response of clouds and circulation to rapid adjustments and SST-mediated warming.

3CFMIP4 experimental protocol

Table summarises the CFMIP4 protocol and the science questions addressed by each experiment. A summary schematic of the experiments is provided in Fig. . In our experiment names, we follow the convention that “4k” has a lower-case k in CMIP7 , whereas it was upper-case K in CMIP6. Compared to the previous iteration, CFMIP-3, the main changes include:

A contribution to the new CMIP7 Assessment Fast Track AFT;, through the following experiments: amip-piForcing for historical feedback and pattern effect; amip-p4k for cloud feedback; abrupt-2xCO2 and abrupt-0p5xCO2 for forcing and feedback state-dependence.

Three new experiments, described in greater detail in the subsections below: amip-p4k-rad and amip-p4k-turb (cloud feedback processes); piClim-deltaSST (CO₂-forced pattern effect).

An additional amip-piForcing variant forced with HadISST1 SST and sea-ice concentration SIC;, and extending to December 2025.

An overall more compact set of experiments: we have discontinued the aquaplanet experiments, amip-4xCO2, amip-future4K, the abrupt solar forcing experiments, and the lwoff experiments with longwave cloud-radiative effects switched off . The piSST and a4SST set of experiments has also been reduced from eight to three, to focus on the processes identified as most important in previous analyses.

Figure 1

Schematic of the CFMIP4 and related CMIP7 DECK experiments. Experiments are grouped horizontally according to pre-industrial, present-day, or perturbed climates; experiments are also grouped according to the key science questions they address (see Table for additional details).

Note that the former amip-4xCO2 experiment has been superseded by Radiative Forcing Model Intercomparison Project RFMIP; experiments piClim-4xCO2 and piClim-4xCO2-rad. By comparison with piClim-control, both experiments quantify the effective radiative forcing of CO₂, respectively with and without plant physiological responses. piClim-4xCO2-rad is therefore the closest analogue to amip-4xCO2, which did not include the plant physiological effect.

Contrary to CFMIP-3, the CFMIP4 protocol does not distinguish between mandatory Tier 1 experiments and optional higher tiers. Our hope is that the reduced set of experiments will encourage full participation in our protocol by modelling groups.

We highlight the continuity in CFMIP and related DECK experiments abrupt-4xCO2, amip and amip-p4K, relative to previous iterations of CFMIP and CMIP. This continuity facilitates an evaluation of the drivers of changes in cloud adjustment and feedback (and thus effective radiative forcing and climate sensitivity) across generations of CMIP models, as for example performed by .

Table 1

Summary of CFMIP4 and related CMIP7 DECK experiments. For abrupt CO₂ forcing experiments, we request a minimum of 300 years of simulation, but encourage modelling groups to extend the simulations to 1000 years or longer if possible.

Experiment Description Years Science questions & applications abrupt-4xCO2^a Abrupt quadrupling of CO₂ concentration relative to piControl 300+ (1000) Q1 Climate feedback & sensitivity Q2 Forcing & feedback state-dependence; palaeoclimate feedback Q3 CO₂-forced pattern effects Q4 CO₂-forced circulation & precipitation changes Additional 9+ ensemble members for years 1–10, initialised in 10-year intervals 90+ abrupt-2xCO2^b Abrupt doubling of CO₂ concentration relative to piControl 300+ (1000) abrupt-0p5xCO2^b Abrupt halving of CO₂ concentration relative to piControl 300+ (1000) amip^a Atmosphere-only with observed SST/SIC prescribed and historical forcing 43 Q1 Observed forcing and feedback Q3 Observed pattern effects Q4 Observed circulation & precipitation changes amip-piForcing^b,c,d As amip, but with constant pre-industrial forcing and from January 1870 to December 2021 152 Additional amip-piForcing variant with HadISST1 SST and SIC, January 1870 to December 2025 156 amip-p4k^b amip with uniform 4-K SST increase 43 Q1 Climate feedback amip-m4k amip with uniform 4-K SST decrease 43 Q2 Feedback state-dependence Q4 Circulation & precipitation changes amip-p4k-rad amip with surface radiative emission perturbed according to a 4-K increase in surface skin temperature 43 Q1 Cloud feedback processes amip-p4k-turb amip with surface turbulent energy fluxes perturbed according to a 4-K increase in surface skin temperature 43 piClim-deltaSST^d piClim-control with added monthly time-varying SST anomalies from years 1–20 of a representative set of seven CMIP6 abrupt-4xCO2 simulations 20 × 7 Q2 CO₂-forced pattern effects piSST-pxK Atmosphere-only with monthly time-varying SST and SIC prescribed from 30 years of each model's own piControl simulation, plus a uniform x-K SST increase taken from each model's own global, climatological annual-mean ice-free SST change between abrupt-4xCO2 (years 111–140) and piControl 30 Q4 Decomposition of CO₂-driven circulation & precipitation changes into: CO₂ adjustment; response to uniform SST increase; response to SST pattern and sea-ice change a4SSTice Atmosphere-only with monthly time-varying SST and SIC prescribed from years 111–140 of each model's own abrupt-4xCO2, and pre-industrial CO₂ concentration 30 a4SSTice-4xCO2 a4SSTice with quadrupled CO₂ concentration 30

^a DECK; ^b Assessment Fast Track; ^c Minimum three realisations; ^d SST forcing variants to be denoted by different forcing indices (f1, f2, etc.).

3.1Coupled abrupt CO<sub>2</sub> forcing experiments

Assessments of climate feedback and equilibrium climate sensitivity (ECS) are typically based on the abrupt-4xCO2 experiment e.g.,, part of the Diagnostics, Evaluation and Characterization of Klima (DECK) group of core CMIP7 experiments . To support research on cloud processes, we ask modelling groups to output the CFMIP variables requested as part of our “Baseline” opportunity for this and all other DECK experiments (see Sect. , and note that “opportunities” refer to data requests in CMIP7).

The abrupt-4xCO2 experiment is complemented by CO₂ doubling and halving experiments, abrupt-2xCO2 and abrupt-0p5xCO2, both of which are part of the AFT . Comparing among these experiments will quantify the degree to which climate feedback, ECS and the pattern effect are sensitive to climate state and forcing magnitude. This will be supported by RFMIP experiments piClim-4xCO2, piClim-2xCO2 and piClim-0p5xCO2, addressing the state-dependence of effective radiative forcing, including cloud adjustments . The abrupt-0p5CO2 experiment can also support the assessment of feedback processes in colder palaeoclimates, for example the Last Glacial Maximum .

As a novel aspect of CFMIP4 and CMIP7, all abrupt CO₂ forcing experiments should be run for a minimum of 300 years, and ideally 1000 years or longer . This will facilitate an assessment of the longer timescales of the coupled climate response , including the time evolution of climate feedback and the pattern effect, and thus the ECS .

Another addition to CFMIP4 is the request of an extra nine abrupt-4xCO2 ensemble members (and more if possible) for the first 10 years of the experiment, to support the assessment of the fast timescale of the SST response pattern e.g.,. The choice of 10 years aims to keep the computational burden of the request limited, while also allowing for an accurate characterisation of the early SST response to CO₂ forcing. The ensemble members should be initialised in 10-year intervals from the parent piControl simulation, to ensure variability in ocean conditions is adequately sampled.

3.2Atmosphere-only experiments 3.2.1<italic>amip</italic>

The DECK experiment amip simulates historical climate conditions (including atmospheric composition and insolation) with prescribed observed SST and SIC from January 1979 to December 2021. To support process studies of cloud-radiative trends and feedback, and comparison with observations, for amip and its variants with uniform 4-K SST increase or decrease we request outputs from both our “Baseline” and “Extension for process-level studies” opportunities (Sect. ). The “Extension” outputs should be supplied for at least one ensemble member.

3.2.2<italic>amip-piForcing</italic>

The AFT experiment amip-piForcing follows the same protocol as amip, but with forcing agents set to pre-industrial values. This facilitates the diagnosis of the SST-mediated radiative response, climate feedback and the pattern effect . Comparison of amip and amip-piForcing during their period of overlap also provides an estimate of the historical effective radiative forcing, complementary to the RFMIP experiment piClim-histall .

The CMIP7 protocol for amip-piForcing employs the Atmospheric Model Intercomparison Project (AMIP) II SST and SIC dataset, ending December 2022 . This means that the period since 2023, which saw large anomalies in SST, global-mean surface temperature and the global energy budget , is not covered. We therefore request that participating modelling centres run an additional amip-piForcing variant with HadISST1 SST and SIC , extending up to December 2025 (input files available on 10.5281/zenodo.21164517 , pending final publication on the Earth System Grid Federation). The choice of HadISST1 is motivated by the fact that it is a regularly updated, operational dataset, and that it has been used in previous studies to force atmosphere-only GCMs , despite known shortcomings in e.g. the representation of Southern Ocean SST trends .

Comparing between the AMIP II and the HadISST1 variants of amip-piForcing will provide a measure of the sensitivity of the radiative response to the choice of SST and SIC boundary conditions. (Note however that the HadISST1 and AMIP II datasets are not completely independent: AMIP II uses HadISST1 SST and SIC before 1981, with some post-processing to match the 1971–2000 climatology of the Optimum Interpolation v2 dataset used from November 1981 onwards.) Previous studies have highlighted a substantial dependence of the radiative response on the SST dataset for certain historical periods, although most studies were based on single GCMs .

Although the AFT request is for a single amip-piForcing realisation, we encourage modelling groups to perform a minimum of three realisations with perturbed initial conditions (and for each of the two sets of SST/SIC boundary conditions), as this will permit a more accurate characterisation of the time-varying historical climate feedback. Simulation output should be archived using different forcing indices corresponding to different boundary conditions; we request f1 for AMIP II and f2 for HadISST1. We therefore request a total of six amip-piForcing variants: r1i1p1f1 to r3i1p1f1 for AMIP II SST and SIC, and r1i1p1f2 to r3i1p1f2 for HadISST1. The HadISST1 SST and SIC monthly-mean boundary conditions have been processed to ensure adequate sampling of the seasonal cycle according to the method of .

Note that a new version of HadISST SST, HadISST2, is due to be released soon and will be used in the CERESMIP protocol . Once published on input4MIPs, we encourage modelling centres participating in CFMIP to perform a third set of amip-piForcing simulations with HadISST2 SST and SIC, using the forcing variant f3. This will facilitate comparisons between CERESMIP amip simulations and CFMIP amip-piForcing simulations.

3.2.3<italic>amip-p4k</italic>, <italic>amip-m4k</italic>

Experiments amip-p4k and amip-m4k follow the amip protocol, except that SSTs are uniformly increased or decreased by 4 K over ice-free regions; SIC and SSTs under sea-ice remain unchanged, with SSTs at the freezing point. amip-p4k was adopted into the AFT for the diagnosis of climate feedback. As an atmosphere-only experiment, it is relatively low-cost, and furthermore the use of prescribed SSTs ensures that cloud feedback can be robustly estimated even from short simulations . This makes the protocol highly suitable for high-resolution models, e.g. the highresSST-p4kuni experiment of HighResMIP or the superparameterised simulations of .

Moreover, comparing between amip-p4k and amip-m4k responses provides an estimate of feedback state-dependence with SST patterns held fixed, thus isolating the role of global temperature changes for climate feedback . This is complementary to estimates based on coupled abrupt CO₂ forcing experiments, which additionally include effects from changing SST patterns.

3.2.4<italic>amip-p4k-rad</italic>, <italic>amip-p4k-turb</italic>

The two experiments amip-p4k-rad and amip-p4k-turb, new to CFMIP4, aim to provide a better understanding of low-cloud feedback mechanisms. The idea behind the experiments, introduced by , is that uniform SST warming modifies the atmosphere via two causal pathways: first by increasing upwelling longwave radiation from the sea surface, and second by changing turbulent transport at the air-sea interface, particularly latent and sensible heat fluxes. The experiments isolate the impact of each of these two pathways on low-cloud feedback, motivated by previously hypothesized mechanisms involving changes in surface turbulent fluxes e.g.,.

Following , amip-p4k-rad is run exactly as amip but a 4-K anomaly is added (over ocean regions only) to the SST used in the radiation code for the calculation of surface upwelling longwave radiation. For amip-p4k-turb, the protocol again follows amip but a 4-K anomaly is added to the SST seen by the model's surface turbulent exchange scheme only. We recommend perturbing sensible and latent heat fluxes only, and keeping any other turbulent fluxes (e.g. of momentum or aerosols) unperturbed. Test simulations indicate that perturbing momentum or aerosol fluxes has very little impact on low-cloud properties (T. Ogura, personal communication, 2026).

3.3<italic>piClim-deltaSST</italic>

In previous CFMIP protocols, experiment amip-future4K (or amipFuture in CMIP5) served to assess the global climate response to patterned warming, with the warming pattern taken from the model-mean response in CMIP3 1pctCO2 simulations . Being calculated from a model mean, the amip-future4K warming pattern was muted and underestimated the amplitude of SST anomaly patterns found in individual models. Furthermore, the use of the 1pctCO2 experiment meant the pattern combined fast and slow timescales of the climate response to CO₂ forcing . Because of these issues, amip-future4K proved to be of limited use to interpret the CO₂-forced pattern effect in individual climate models.

In CFMIP4, we replace amip-future4K by the new experiment piClim-deltaSST, whose aim is to represent the climate response to model-specific CO₂-forced SST change. Thus, instead of a single model-mean SST pattern, piClim-deltaSST uses SST anomalies from individual CMIP6 GCMs forced with abrupt CO₂ quadrupling (calculated relative to the corresponding piControl monthly climatology, taken from the contemporaneous period). The chosen GCMs are CanESM5, CESM2, CNRM-ESM2-1, GFDL-CM4, HadGEM3-GC31-LL, MIROC6, and NorESM2-LM. They are selected for their diverse representation of the pattern effect, as measured by the cloud-radiative effect (CRE) feedback simulated in piClim-deltaSST test simulations with the HadAM3 atmosphere-only model (J. M. Gregory, personal communication, 2026), and furthermore these GCMs come from different modelling groups. We use the first 20 years of these GCMs' integrations to calculate a set of monthly time-varying SST anomaly fields, ΔSSTi(x,t), where x is location, t is time (in months), and subscript i refers to one of the seven GCMs listed above.

Modelling centres are requested to perform this experiment following the piClim-control protocol, but with the following modifications:

The monthly time-varying SST anomaly fields ΔSSTi(x,t) should be added to the piClim-control SST monthly climatology. SIC is kept to the piClim-control climatology. SSTs should be kept to freezing (-1.8 °C) wherever SIC is greater than zero, or wherever the ΔSSTi(x,t) anomaly takes SST to below freezing.

The simulations should be run for 20 years, i.e. the time range of the ΔSST datasets.

The simulations with different SST anomaly fields should be saved under different forcing indices (f1 to f7), in alphabetical order of the GCMs used to derive the SST anomaly fields. The recommended forcing indices are provided as part of the filenames of the input datasets (available on 10.5281/zenodo.21164517 , pending final publication on the Earth System Grid Federation).

Note that, by keeping SIC fixed to the control climatology, piClim-deltaSST excludes effects associated with changes in the pattern of SIC .

3.4<italic>piSST</italic> and <italic>a4SSTice</italic> time-slice experiments

This set of three atmosphere-only experiments provides a decomposition of the abrupt-4xCO2 climate response into three main components: direct CO₂ effect; response to uniform SST increase; and response to SST pattern and sea-ice change. The science focus of these experiments is the coupled response of clouds, circulation and precipitation to CO₂ forcing in GCMs. To adequately resolve regional features of circulation and precipitation (and their variability), the experiments here use monthly time-varying SST and SIC fields. This is a key difference from the setup of the piClim experiments.

The three experiments are set up as follows:

piSST-pxK uses monthly time-varying SST, SIC and atmospheric constituents from 30 years of each model's own piControl run, with SSTs uniformly increased by x K in ice-free regions, where x is the global, climatological annual-mean ice-free SST change between years 111–140 of abrupt-4xCO2 and piControl. The 30 years should be chosen to be parallel to years 111–140 of the abrupt-4xCO2 run.

a4SSTice uses monthly time-varying SST and SIC from years 111–140 of each model's own abrupt-4xCO2 run, but keeping atmospheric constituents to pre-industrial levels.

a4SSTice-4xCO2 is set up like a4SSTice, but CO₂ concentration is quadrupled.

Unlike in the previous iteration of CFMIP, there is no piSST experiment with SST and SIC taken directly from piControl. This is because the piSST and piControl climates are sufficiently identical that piControl can be used as a reference for comparison with the atmosphere-only simulations with perturbed SST, SIC and/or CO₂ concentration.

Differences between experiment pairs can be interpreted as follows:

a4SSTice-4xCO2 minus piControl can be compared with the climate response simulated in years 111–140 of abrupt-4xCO2 relative to piControl, to confirm that the atmosphere-only framework can adequately replicate coupled GCM responses. A previous analysis suggests that this is generally the case .

piSST-pxK minus piControl provides the response to uniform SST increase.

a4SSTice minus piSST-pxK provides the response to the (zero-mean) pattern of SST change and the change in SIC.

a4SSTice-4xCO2 minus a4SSTice provides the direct CO₂ effect, including the plant physiological response.

4CFMIP4 data request

The CMIP7 data request is structured into groups of scientific objectives referred to as “Opportunities”, two of which are related to CFMIP. Together, the data requested in these two opportunities includes all fields requested in CFMIP-3, augmented by several new fields. The first is the Clouds, circulation and climate sensitivity: baseline opportunity, which is intended to capture the base set of variables essential for performing analyses to answer the key CFMIP questions listed in Sect. 2. The data requested include the Baseline Climate Variables , monthly-mean 2D and 3D fields, daily-mean 2D fields, and fixed fields. These data are requested from the 10 DECK experiments in addition to the suite of CFMIP experiments listed in Table 1.

Supplementing this is a second opportunity, Clouds, circulation and climate sensitivity: extension for process-level studies, which is intended to capture variables crucial for advanced diagnosis and evaluation of cloud, radiation, and precipitation processes in the present-day and warmed climate. In addition to requesting the same variables as the baseline opportunity, this opportunity requests daily-mean 3D fields; sub-hourly instantaneous fields at specified “cfSites” locations; additional output from the CFMIP Observation Simulator Package COSP;; and monthly climatologies of hourly-resolved top-of-atmosphere (TOA) fluxes. The variables included here also ensure that CFMIP experiment output can be more directly compared with global satellite observations and field campaign data. Furthermore, cfSites output can be used to provide large-scale forcings for regional process-resolving model experiments, for example with single-column models or large-eddy simulations .

Five new cfSites locations have been added to the request since CFMIP-3, corresponding to locations of field campaigns and surface-based observational facilities . Several new COSP outputs are requested, including phase-separated cloud fraction histograms produced by the MODIS simulator, which are useful for diagnosing cloud phase feedbacks (Wall et al., 2025). Some of these COSP variables are only produced by COSP version 2 , but either COSP version can be used to contribute to CFMIP. To keep the data volume reasonable, this second opportunity is applicable only to a subset of five experiments (amip, amip-p4k, amip-m4k, amip-p4k-rad, and amip-p4k-turb) rather than for the full suite of experiments in Table 1.

Producing data from these two opportunities across a large collection of climate models will allow major progress across the topics of interest to the CFMIP community by facilitating advanced diagnosis and understanding of cloud processes, feedbacks, adjustments, and biases. Additional information about the CFMIP data request and how it fits into the broader CMIP7 data request can be found in . The data request database is currently hosted on the Airtable cloud platform (https://bit.ly/CMIP-DR-Opportunities, last access: 8 September 2026, Opportunity IDs 78–79).

5Conclusions

The growing climate change signal means that understanding cloud processes and their impact on Earth's energy imbalance is a critical challenge for the research community. CFMIP plays a central role in this endeavour, by supporting CMIP7 and its Assessment Fast Track with a set of experiments aimed at understanding cloud-radiative processes under past, present and future climate. The CFMIP protocol is also key to understanding the mechanisms of the “SST pattern effect”, one of four fundamental science questions underpinning CMIP7 activities .

The scope of CFMIP extends beyond pure cloud processes: the CFMIP4 science questions and experimental protocol support improved understanding of climate feedback processes, coupled climate variability and change, atmosphere and ocean circulation, and precipitation. The CFMIP science community actively collaborates on these topics, particularly through its annual meeting. We invite interested members of the climate research community to engage with CFMIP through membership of the mailing list (https://groups.google.com/g/cfmip_all/, last access: 8 September 2026) and attendance at the CFMIP annual meeting.

CFMIP science questions are highly complementary to other CMIP-related initiatives. In particular, the Radiative Forcing Model Intercomparison Project RFMIP; provides experiments supporting the diagnosis and process understanding of radiative forcing and thus climate sensitivity. Furthermore, the Regional Aerosol Model Intercomparison Project RAMIP; and the Aerosol and Chemistry Model Intercomparison Project AerChemMIP; support the understanding of aerosol processes, including their interaction with clouds. CFMIP experiments and output variables also support the aim of assessing aerosol processes, for example through the use of satellite simulator output e.g.,.

Beyond the protocol outlined here, CFMIP also supports informal experiments and model intercomparison projects (MIPs) related to the aims of CFMIP. This includes for example the Radiative-Convective Equilibrium Model Intercomparison Project RCEMIP;, the Extratropical–Tropical Interaction Model Intercomparison Project ETIN-MIP;, or the Green's Function Model Intercomparison Project GFMIP;. An up-to-date list of supported informal experiments is available at https://www.cfmip.org/experiments/informal-experiments (last access: 8 September 2026), and the CFMIP committee welcomes additional informal experiment proposals.

Code and data availability

No code or data has been used or is necessary to replicate the work here presented.

Author contributions

PC, ABS, MJW and MDZ jointly designed the protocol, with input from co-authors. PC led the writing of the paper. MDZ led the writing of Sect. 4. AZD processed and uploaded the SST and sea-ice boundary condition files for CFMIP4. All co-authors commented on and edited the draft.

Competing interests

The contact author has declared that none of the authors has any competing interests.

Disclaimer

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 paper. The authors bear the ultimate responsibility for providing appropriate place names. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

Acknowledgements

We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. We also thank the Earth System Grid Federation (ESGF) for archiving the model output and providing access, and we thank the multiple funding agencies who support CMIP and ESGF.

Financial support

PC was supported by UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding Guarantee (grant EP/Y036123/1). PC was additionally supported through UK Natural Environmental Research Council (NERC) grants NE/V012045/1 and NE/T006250/1. MJW, ABS and TA were supported by the Met Office Hadley Centre Climate Programme funded by DSIT. The effort of MDZ was supported by the US Department of Energy (DOE) Regional and Global Model Analysis program area and was performed under the auspices of the DOE by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. FB acknowledges the financial support of grant MOBYDYC ANR-22-CE01-0005. AAW is supported by US National Science Foundation (NSF) Grant AGS-2140419. YTH was supported by the National Science and Technology Council, R.O.C. (112-2111-M-002-016-MY4).

Review statement

This paper was edited by Xianan Jiang and reviewed by Andrew Gettelman, Bjorn Stevens, and two anonymous referees.

References Andrews and Webb(2018)

Andrews, T. and Webb, M. J.: The dependence of global cloud and lapse-rate feedbacks on the spatial structure of tropical Pacific warming, J. Climate, 31, 641–654, 10.1175/JCLI-D-17-0087.1, 2018.

Andrews et al.(2012)Andrews, Gregory, Webb, and Taylor

Andrews, T., Gregory, J. M., Webb, M. J., and Taylor, K. E.: Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models, Geophys. Res. Lett., 39, L09712, 10.1029/2012GL051607, 2012.

Andrews et al.(2015)Andrews, Gregory, and Webb

Andrews, T., Gregory, J. M., and Webb, M. J.: The Dependence of Radiative Forcing and Feedback on Evolving Patterns of Surface Temperature Change in Climate Models, J. Climate, 28, 1630–1648, 10.1175/JCLI-D-14-00545.1, 2015.

Andrews et al.(2018)Andrews, Gregory, Paynter, Silvers, Zhou, Mauritsen, Webb, Armour, Forster, and Titchner

Andrews, T., Gregory, J. M., Paynter, D., Silvers, L. G., Zhou, C., Mauritsen, T., Webb, M. J., Armour, K. C., Forster, P. M., and Titchner, H.: Accounting for Changing Temperature Patterns Increases Historical Estimates of Climate Sensitivity, Geophys. Res. Lett., 45, 8490–8499, 10.1029/2018GL078887, 2018.

Andrews et al.(2022)

Andrews, T., Bodas-Salcedo, A., Gregory, J. M., Dong, Y., Armour, K. C., Paynter, D., Lin, P., Modak, A., Mauritsen, T., Cole, J. N. S., Medeiros, B., Benedict, J. J., Douville, H., Roehrig, R., Koshiro, T., Kawai, H., Ogura, T., Dufresne, J.-L., Allan, R. P., and Liu, C.: On the Effect of Historical SST Patterns on Radiative Feedback, J. Geophys. Res.-Atmos., 127, e2022JD036675, 10.1029/2022JD036675, 2022.

Bellomo et al.(2016)Bellomo, Clement, Murphy, Polvani, and Cane

Bellomo, K., Clement, A. C., Murphy, L. N., Polvani, L. M., and Cane, M. A.: New observational evidence for a positive cloud feedback that amplifies the Atlantic Multidecadal Oscillation, Geophys. Res. Lett., 43, 9852–9859, 10.1002/2016GL069961, 2016.

Bjordal et al.(2020)Bjordal, Storelvmo, Alterskjær, and Carlsen

Bjordal, J., Storelvmo, T., Alterskjær, K., and Carlsen, T.: Equilibrium climate sensitivity above 5 °C plausible due to state-dependent cloud feedback, Nat. Geosci., 13, 718–721, 10.1038/s41561-020-00649-1, 2020.

Bloch-Johnson et al.(2015)Bloch-Johnson, Pierrehumbert, and Abbot

Bloch-Johnson, J., Pierrehumbert, R. T., and Abbot, D. S.: Feedback temperature dependence determines the risk of high warming, Geophys. Res. Lett., 42, 4973–4980, 10.1002/2015GL064240, 2015.

Bloch-Johnson et al.(2021)Bloch-Johnson, Rugenstein, Stolpe, Rohrschneider, Zheng, and Gregory

Bloch-Johnson, J., Rugenstein, M., Stolpe, M. B., Rohrschneider, T., Zheng, Y., and Gregory, J. M.: Climate Sensitivity Increases Under Higher CO₂ Levels Due to Feedback Temperature Dependence, Geophys. Res. Lett., 48, e2020GL089074, 10.1029/2020GL089074, 2021.

Bloch-Johnson et al.(2024)Bloch-Johnson, Rugenstein, Alessi, Proistosescu, Zhao, Zhang, Williams, Gregory, Cole, Dong, Duffy, Kang, and Zhou

Bloch-Johnson, J., Rugenstein, M. A. A., Alessi, M. J., Proistosescu, C., Zhao, M., Zhang, B., Williams, A. I. L., Gregory, J. M., Cole, J., Dong, Y., Duffy, M. L., Kang, S. M., and Zhou, C.: The Green's Function Model Intercomparison Project (GFMIP) Protocol, J. Adv. Model. Earth Sy., 16, e2023MS003700, 10.1029/2023MS003700, 2024.

Bodas-Salcedo et al.(2011)Bodas-Salcedo, Webb, Bony, Chepfer, Dufresne, Klein, Zhang, Marchand, Haynes, Pincus, and John

Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J.-L., Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.: COSP: Satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043, 10.1175/2011BAMS2856.1, 2011.

Bony et al.(2015)Bony, Stevens, Frierson, Jakob, Kageyama, Pincus, Shepherd, Sherwood, Siebesma, Sobel, Watanabe, and Webb

Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity, Nat. Geosci., 8, 261–268, 10.1038/ngeo2398, 2015.

Bony et al.(2016)Bony, Stevens, Coppin, Becker, Reed, Voigt, and Medeiros

Bony, S., Stevens, B., Coppin, D., Becker, T., Reed, K. A., Voigt, A., and Medeiros, B.: Thermodynamic control of anvil cloud amount, P. Natl. Acad. Sci. USA, 113, 8927–32, 10.1073/pnas.1601472113, 2016.

Bony et al.(2017)Bony, Stevens, Ament, Bigorre, Chazette, Crewell, Delanoë, Emanuel, Farrell, Flamant, Gross, Hirsch, Karstensen, Mayer, Nuijens, Ruppert, Sandu, Siebesma, Speich, Szczap, Totems, Vogel, Wendisch, and Wirth

Bony, S., Stevens, B., Ament, F., Bigorre, S., Chazette, P., Crewell, S., Delanoë, J., Emanuel, K., Farrell, D., Flamant, C., Gross, S., Hirsch, L., Karstensen, J., Mayer, B., Nuijens, L., Ruppert, J. H., Sandu, I., Siebesma, P., Speich, S., Szczap, F., Totems, J., Vogel, R., Wendisch, M., and Wirth, M.: EUREC4A: A Field Campaign to Elucidate the Couplings Between Clouds, Convection and Circulation, Surv. Geophys., 38, 1529–1568, 10.1007/s10712-017-9428-0, 2017.

Bony et al.(2020)Bony, Semie, Kramer, Soden, Tompkins, and Emanuel

Bony, S., Semie, A., Kramer, R. J., Soden, B., Tompkins, A. M., and Emanuel, K. A.: Observed Modulation of the Tropical Radiation Budget by Deep Convective Organization and Lower-Tropospheric Stability, AGU Adv., 1, e2019AV000155, 10.1029/2019AV000155, 2020.

Bourdin et al.(2021)Bourdin, Kluft, and Stevens

Bourdin, S., Kluft, L., and Stevens, B.: Dependence of Climate Sensitivity on the Given Distribution of Relative Humidity, Geophys. Res. Lett., 48, e2021GL092462, 10.1029/2021GL092462, 2021.

Breul et al.(2025)Breul, Ceppi, and Nowack

Breul, P., Ceppi, P., and Nowack, P.: The importance of stratocumulus clouds for projected warming patterns and circulation changes, Atmos. Chem. Phys., 25, 11991–12005, 10.5194/acp-25-11991-2025, 2025.

Brown et al.(2016)Brown, Lozier, Zhang, and Li

Brown, P. T., Lozier, M. S., Zhang, R., and Li, W.: The necessity of cloud feedback for a basin-scale Atlantic Multidecadal Oscillation, Geophys. Res. Lett., 43, 3955–3963, 10.1002/2016GL068303, 2016.

Byrne and Zanna(2020)

Byrne, M. P. and Zanna, L.: Radiative Effects of Clouds and Water Vapor on an Axisymmetric Monsoon, J. Climate, 33, 8789–8811, 10.1175/JCLI-D-19-0974.1, 2020.

Ceppi and Gregory(2019)

Ceppi, P. and Gregory, J. M.: A refined model for the Earth's global energy balance, Clim. Dynam., 53, 4781–4797, 10.1007/s00382-019-04825-x, 2019.

Ceppi et al.(2017)Ceppi, Brient, Zelinka, and Hartmann

Ceppi, P., Brient, F., Zelinka, M. D., and Hartmann, D. L.: Cloud feedback mechanisms and their representation in global climate models, WIRes Clim. Change, 8, e465, 10.1002/wcc.465, 2017.

Ceppi et al.(2018)Ceppi, Zappa, Shepherd, and Gregory

Ceppi, P., Zappa, G., Shepherd, T. G., and Gregory, J. M.: Fast and slow components of the extratropical atmospheric circulation response to CO₂ forcing, J. Climate, 31, 1091–1105, 10.1175/JCLI-D-17-0323.1, 2018.

Ceppi et al.(2024)Ceppi, Myers, Nowack, Wall, and Zelinka

Ceppi, P., Myers, T. A., Nowack, P., Wall, C. J., and Zelinka, M. D.: Implications of a Pervasive Climate Model Bias for Low-Cloud Feedback, Geophys. Res. Lett., 51, e2024GL110525, 10.1029/2024GL110525, 2024.

Ceppi et al.(2026a)Ceppi, Wilson Kemsley, Andersen, Andrews, Kramer, Nowack, Wall, and Zelinka

Ceppi, P., Wilson Kemsley, S., Andersen, H., Andrews, T., Kramer, R. J., Nowack, P., Wall, C. J., and Zelinka, M. D.: Emerging low-cloud feedback and adjustment in global satellite observations, Atmos. Chem. Phys., 26, 4153–4171, 10.5194/acp-26-4153-2026, 2026a.

Ceppi et al.(2026b)

Ceppi, P., Gregory, J., and Dai, A.-Z.: SST and sea-ice boundary conditions for CFMIP4 experiments, Zenodo [data set], 10.5281/zenodo.21164518, 2026b.

Cesana and Del Genio(2021)

Cesana, G. V. and Del Genio, A. D.: Observational constraint on cloud feedbacks suggests moderate climate sensitivity, Nat. Clim. Change, 11, 213–218, 10.1038/s41558-020-00970-y, 2021.

Cess et al.(1990)Cess, Potter, Blanchet, Boer, Del Genio, Déqué, Dymnikov, Galin, Gates, Ghan, Kiehl, Lacis, Le Treut, Li, Liang, McAvaney, Meleshko, Mitchell, Morcrette, Randall, Rikus, Roeckner, Royer, Schlese, Sheinin, Slingo, Sokolov, Taylor, Washington, Wetherald, Yagai, and Zhang

Cess, R. D., Potter, G. L., Blanchet, J. P., Boer, G. J., Del Genio, A. D., Déqué, M., Dymnikov, V., Galin, V., Gates, W. L., Ghan, S. J., Kiehl, J. T., Lacis, A. A., Le Treut, H., Li, Z.-X., Liang, X.-Z., McAvaney, B. J., Meleshko, V. P., Mitchell, J. F. B., Morcrette, J.-J., Randall, D. A., Rikus, L., Roeckner, E., Royer, J. F., Schlese, U., Sheinin, D. A., Slingo, A., Sokolov, A. P., Taylor, K. E., Washington, W. M., Wetherald, R. T., Yagai, I., and Zhang, M.-H.: Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models, J. Geophys. Res., 95, 16601, 10.1029/JD095iD10p16601, 1990.

Chadwick et al.(2017)Chadwick, Douville, and Skinner

Chadwick, R., Douville, H., and Skinner, C. B.: Timeslice experiments for understanding regional climate projections: applications to the tropical hydrological cycle and European winter circulation, Clim. Dynam., 1–19, 10.1007/s00382-016-3488-6, 2017.

Charney et al.(1979)Charney, Arakawa, Baker, Bolin, Dickinson, Goody, Leith, Stommel, and Wunsch

Charney, J. G., Arakawa, A., Baker, D. J., Bolin, B., Dickinson, R. E., Goody, R. M., Leith, C. E., Stommel, H. M., and Wunsch, C. I.: Carbon Dioxide and Climate, Tech. rep., National Academy of Science, Washington, D.C., 10.17226/12181, 1979.

Chepfer et al.(2025)

Chepfer, H., Chomette, O., Arouf, A., Noel, V., Winker, D., Feofilov, A., and Alava Baldazo, A.: Variability and Trends in Cloud Properties Over 17 Years From CALIPSO Space Lidar Observations, J. Geophys. Res.-Atmos., 130, e2025JD043764, 10.1029/2025JD043764, 2025.

Collins et al.(2017)Collins, Lamarque, Schulz, Boucher, Eyring, Hegglin, Maycock, Myhre, Prather, Shindell, and Smith

Collins, W. J., Lamarque, J.-F., Schulz, M., Boucher, O., Eyring, V., Hegglin, M. I., Maycock, A., Myhre, G., Prather, M., Shindell, D., and Smith, S. J.: AerChemMIP: quantifying the effects of chemistry and aerosols in CMIP6, Geosci. Model Dev., 10, 585–607, 10.5194/gmd-10-585-2017, 2017.

Cooper et al.(2024)Cooper, Armour, Hakim, Tierney, Osman, Proistosescu, Dong, Burls, Andrews, Amrhein, Zhu, Dong, Ming, and Chmielowiec

Cooper, V. T., Armour, K. C., Hakim, G. J., Tierney, J. E., Osman, M. B., Proistosescu, C., Dong, Y., Burls, N. J., Andrews, T., Amrhein, D. E., Zhu, J., Dong, W., Ming, Y., and Chmielowiec, P.: Last Glacial Maximum pattern effects reduce climate sensitivity estimates, Sci. Adv., 10, eadk9461, 10.1126/sciadv.adk9461, 2024.

Curtis et al.(2020)Curtis, Ceppi, and Zappa

Curtis, P. E., Ceppi, P., and Zappa, G.: Role of the mean state for the Southern Hemispheric jet stream response to CO₂ forcing in CMIP6 models, Environ. Res. Lett., 15, 064011, 10.1088/1748-9326/ab8331, 2020.

Dal Gesso and Neggers(2018)

Dal Gesso, S. and Neggers, R. A. J.: Can We Use Single-Column Models for Understanding the Boundary Layer Cloud-Climate Feedback?, J. Adv. Model. Earth Sy., 10, 245–261, 10.1002/2017MS001113, 2018.

Dingley et al.(2026)Dingley, Anstey, Abalos, Abraham, Bergman, Bock, Fiddes, Hassler, Kramer, Luo, O'Connor, Šácha, Simpson, Wilcox, and Zelinka

Dingley, B., Anstey, J. A., Abalos, M., Abraham, C., Bergman, T., Bock, L., Fiddes, S., Hassler, B., Kramer, R. J., Luo, F., O'Connor, F. M., Šácha, P., Simpson, I. R., Wilcox, L. J., and Zelinka, M. D.: CMIP7 Data Request: atmosphere priorities and opportunities, Geosci. Model Dev., 19, 2945–2984, 10.5194/gmd-19-2945-2026, 2026.

Dong et al.(2026)Dong, Lu, Hwang, Hu, Ceppi, Breul, Roach, and Deser

Dong, Y., Lu, K., Hwang, Y.-T., Hu, R.-J., Ceppi, P., Breul, P., Roach, L. A., and Deser, C.: Tropical Impacts of the Southern Ocean Underestimated by Mean-State Biases, Sci. Adv., 12, eaed1936, 10.1126/sciadv.aed1936, 2026.

Duffy et al.(2023)Duffy, Medeiros, Gettelman, and Eidhammer

Duffy, M. L., Medeiros, B., Gettelman, A., and Eidhammer, T.: Perturbing Parameters to Understand Cloud Contributions to Climate Change, J. Climate, 37, 213–227, 10.1175/JCLI-D-23-0250.1, 2023.

Dunne et al.(2025)Dunne, Hewitt, Arblaster, Bonou, Boucher, Cavazos, Dingley, Durack, Hassler, Juckes, Miyakawa, Mizielinski, Naik, Nicholls, O'Rourke, Pincus, Sanderson, Simpson, and Taylor

Dunne, J. P., Hewitt, H. T., Arblaster, J. M., Bonou, F., Boucher, O., Cavazos, T., Dingley, B., Durack, P. J., Hassler, B., Juckes, M., Miyakawa, T., Mizielinski, M., Naik, V., Nicholls, Z., O'Rourke, E., Pincus, R., Sanderson, B. M., Simpson, I. R., and Taylor, K. E.: An evolving Coupled Model Intercomparison Project phase 7 (CMIP7) and Fast Track in support of future climate assessment, Geosci. Model Dev., 18, 6671–6700, 10.5194/gmd-18-6671-2025, 2025.

Durack et al.(2025)Durack, Taylor, Ames, Po-Chedley, and Mauzey

Durack, P. J., Taylor, K. E., Ames, S. K., Po-Chedley, S., and Mauzey, C.: PCMDI AMIP SST and Sea-Ice Boundary Conditions Version 1.1.10, Earth System Grid Federation [data set], 10.25981/ESGF.input4MIPs.CMIP7/2575015, 2025.

Espinosa et al.(2026)Espinosa, Zelinka, Bitz, and Armour

Espinosa, Z. I., Zelinka, M. D., Bitz, C. M., and Armour, K. C.: The Strength of Coupling to the Southern Ocean Modulates Tropical Eastern Pacific Variability and Forced Response, J. Climate, 39, 1543–1558, 10.1175/JCLI-D-25-0226.1, 2026.

Fan et al.(2025)Fan, Paynter, Kramer, and Lin

Fan, C., Paynter, D., Kramer, R. J., and Lin, P.: Sensitivity of Earth's Radiation Budget to Lower Boundary Condition Data Sets in Historical Climate Simulations, Geophys. Res. Lett., 52, e2025GL115914, 10.1029/2025GL115914, 2025.

Fiedler et al.(2026)Fiedler, O'Connor, Watson-Parris, Allen, Collins, Griffiths, Kasoar, Kikstra, Kok, Murray, Paulot, Sand, Turnock, Weber, Wilcox, and Naik

Fiedler, S., O'Connor, F. M., Watson-Parris, D., Allen, R. J., Collins, W. J., Griffiths, P. T., Kasoar, M., Kikstra, J., Kok, J. F., Murray, L. T., Paulot, F., Sand, M., Turnock, S. T., Weber, J., Wilcox, L. J., and Naik, V.: AerChemMIP2 – unraveling the role of reactive gases, aerosol particles, and land use for air quality and climate change in CMIP7, Geosci. Model Dev., 19, 3477–3508, 10.5194/gmd-19-3477-2026, 2026.

Forster et al.(2021)Forster, Storelvmo, Armour, Collins, Dufresne, Frame, Lunt, Mauritsen, Palmer, Watanabe, Wild, and Zhang

Forster, P. M., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.: The Earth's energy budget, climate feedbacks, and climate sensitivity, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07.pdf (last access: 8 September 2026), 2021.

Geoffroy et al.(2013)Geoffroy, Saint-Martin, Bellon, Voldoire, Olivié, and Tytéca

Geoffroy, O., Saint-Martin, D., Bellon, G., Voldoire, A., Olivié, D. J. L., and Tytéca, S.: Transient Climate Response in a Two-Layer Energy-Balance Model. Part II: Representation of the Efficacy of Deep-Ocean Heat Uptake and Validation for CMIP5 AOGCMs, J. Climate, 26, 1859–1876, 10.1175/JCLI-D-12-00196.1, 2013.

Gettelman et al.(2024)Gettelman, Eidhammer, Duffy, McCoy, Song, and Watson-Parris

Gettelman, A., Eidhammer, T., Duffy, M. L., McCoy, D. T., Song, C., and Watson-Parris, D.: The Interaction Between Climate Forcing and Feedbacks, J. Geophys. Res.-Atmos., 129, e2024JD040857, 10.1029/2024JD040857, 2024.

Goessling et al.(2025)Goessling, Rackow, and Jung

Goessling, H. F., Rackow, T., and Jung, T.: Recent global temperature surge intensified by record-low planetary albedo, Science, 387, 68–73, 10.1126/science.adq7280, 2025.

Good et al.(2011)Good, Gregory, and Lowe

Good, P., Gregory, J. M., and Lowe, J. A.: A step-response simple climate model to reconstruct and interpret AOGCM projections, Geophys. Res. Lett., 38, L01703, 10.1029/2010GL045208, 2011.

Gregory and Andrews(2016)

Gregory, J. M. and Andrews, T.: Variation in climate sensitivity and feedback parameters during the historical period, Geophys. Res. Lett., 43, 3911–3920, 10.1002/2016GL068406, 2016.

Gregory et al.(2004)Gregory, Ingram, Palmer, Jones, Stott, Thorpe, Lowe, Johns, and Williams

Gregory, J. M., Ingram, W. J., Palmer, M. A., Jones, G. S., Stott, P. A., Thorpe, R. B., Lowe, J. A., Johns, T. C., and Williams, K. D.: A new method for diagnosing radiative forcing and climate sensitivity, Geophys. Res. Lett., 31, L03205, 10.1029/2003GL018747, 2004.

Grise and Davis(2020)

Grise, K. M. and Davis, S. M.: Hadley cell expansion in CMIP6 models, Atmos. Chem. Phys., 20, 5249–5268, 10.5194/acp-20-5249-2020, 2020.

Günther et al.(2022)Günther, Schmidt, Timmreck, and Toohey

Günther, M., Schmidt, H., Timmreck, C., and Toohey, M.: Climate Feedback to Stratospheric Aerosol Forcing: The Key Role of the Pattern Effect, J. Climate, 35, 4303–4317, 10.1175/JCLI-D-22-0306.1, 2022.

Hansen et al.(2005)Hansen, Sato, Ruedy, Nazarenko, Lacis, Schmidt, Russell, Aleinov, Bauer, Bauer, Bell, Cairns, Canuto, Chandler, Cheng, Del Genio, Faluvegi, Fleming, Friend, Hall, Jackman, Kelley, Kiang, Koch, Lean, Lerner, Lo, Menon, Miller, Minnis, Novakov, Oinas, Perlwitz, Perlwitz, Rind, Romanou, Shindell, Stone, Sun, Tausnev, Thresher, Wielicki, Wong, Yao, and Zhang

Hansen, J., Sato, M., Ruedy, R., Nazarenko, L., Lacis, A., Schmidt, G. A., Russell, G., Aleinov, I., Bauer, M., Bauer, S., Bell, N., Cairns, B., Canuto, V., Chandler, M., Cheng, Y., Del Genio, A., Faluvegi, G., Fleming, E., Friend, A., Hall, T., Jackman, C., Kelley, M., Kiang, N., Koch, D., Lean, J., Lerner, J., Lo, K., Menon, S., Miller, R., Minnis, P., Novakov, T., Oinas, V., Perlwitz, J., Perlwitz, J., Rind, D., Romanou, A., Shindell, D., Stone, P., Sun, S., Tausnev, N., Thresher, D., Wielicki, B., Wong, T., Yao, M., and Zhang, S.: Efficacy of climate forcings, J. Geophys. Res., 110, D18104, 10.1029/2005JD005776, 2005.

Hartmann and Larson(2002)

Hartmann, D. L. and Larson, K.: An important constraint on tropical cloud–climate feedback, Geophys. Res. Lett., 29, 1951, 10.1029/2002GL015835, 2002.

Harvey et al.(2020)Harvey, Cook, Shaffrey, and Schiemann

Harvey, B. J., Cook, P., Shaffrey, L. C., and Schiemann, R.: The Response of the Northern Hemisphere Storm Tracks and Jet Streams to Climate Change in the CMIP3, CMIP5, and CMIP6 Climate Models, J. Geophys. Res.-Atmos., 125, e2020JD032701, 10.1029/2020JD032701, 2020.

Haugstad et al.(2017)Haugstad, Armour, Battisti, and Rose

Haugstad, A. D., Armour, K. C., Battisti, D. S., and Rose, B. E.: Relative roles of surface temperature and climate forcing patterns in the inconstancy of radiative feedbacks, Geophys. Res. Lett., 44, 7455–7463, 10.1002/2017GL074372, 2017.

Heede et al.(2020)Heede, Fedorov, and Burls

Heede, U. K., Fedorov, A. V., and Burls, N. J.: Time Scales and Mechanisms for the Tropical Pacific Response to Global Warming: A Tug of War between the Ocean Thermostat and Weaker Walker, J. Climate, 33, 6101–6118, 10.1175/JCLI-D-19-0690.1, 2020.

Hodnebrog et al.(2024)Hodnebrog, Myhre, Jouan, Andrews, Forster, Jia, Loeb, Olivié, Paynter, Quaas, Raghuraman, and Schulz

Hodnebrog, Ø., Myhre, G., Jouan, C., Andrews, T., Forster, P. M., Jia, H., Loeb, N. G., Olivié, D. J. L., Paynter, D., Quaas, J., Raghuraman, S. P., and Schulz, M.: Recent reductions in aerosol emissions have increased Earth’s energy imbalance, Commun. Earth Environ., 5, 166, 10.1038/s43247-024-01324-8, 2024.

Hsiao et al.(2022)Hsiao, Hwang, Chen, and Kang

Hsiao, W.-T., Hwang, Y.-T., Chen, Y.-J., and Kang, S. M.: The Role of Clouds in Shaping Tropical Pacific Response Pattern to Extratropical Thermal Forcing, Geophys. Res. Lett., 49, e2022GL098023, 10.1029/2022GL098023, 2022.

Hurrell et al.(2008)Hurrell, Hack, Shea, Caron, and Rosinski

Hurrell, J. W., Hack, J. J., Shea, D., Caron, J. M., and Rosinski, J.: A New Sea Surface Temperature and Sea Ice Boundary Dataset for the Community Atmosphere Model, J. Climate, 21, 5145–5153, 10.1175/2008JCLI2292.1, 2008.

Jansson et al.(2023)Jansson, Janssens, Grönqvist, Siebesma, Glassmeier, Attema, Azizi, Satoh, Sato, Schulz, and Kölling

Jansson, F., Janssens, M., Grönqvist, J. H., Siebesma, A. P., Glassmeier, F., Attema, J., Azizi, V., Satoh, M., Sato, Y., Schulz, H., and Kölling, T.: Cloud Botany: Shallow Cumulus Clouds in an Ensemble of Idealized Large-Domain Large-Eddy Simulations of the Trades, J. Adv. Model. Earth Sy., 15, e2023MS003796, 10.1029/2023MS003796, 2023.

Juckes et al.(2025)Juckes, Taylor, Antonio, Brayshaw, Buontempo, Cao, Durack, Kawamiya, Kim, Lovato, Mackallah, Mizielinski, Nuzzo, Stockhause, Visioni, Walton, Turner, O'Rourke, and Dingley

Juckes, M., Taylor, K. E., Antonio, F., Brayshaw, D., Buontempo, C., Cao, J., Durack, P. J., Kawamiya, M., Kim, H., Lovato, T., Mackallah, C., Mizielinski, M., Nuzzo, A., Stockhause, M., Visioni, D., Walton, J., Turner, B., O'Rourke, E., and Dingley, B.: Baseline Climate Variables for Earth System Modelling, Geosci. Model Dev., 18, 2639–2663, 10.5194/gmd-18-2639-2025, 2025.

Kang et al.(2019)Kang, Hawcroft, Xiang, Hwang, Cazes, Codron, Crueger, Deser, Hodnebrog, Kim, Kim, Kosaka, Losada, Mechoso, Myhre, Seland, Stevens, Watanabe, and Yu

Kang, S. M., Hawcroft, M., Xiang, B., Hwang, Y. T., Cazes, G., Codron, F., Crueger, T., Deser, C., Hodnebrog, Ø., Kim, H., Kim, J., Kosaka, Y., Losada, T., Mechoso, C. R., Myhre, G., Seland, Ø., Stevens, B., Watanabe, M., and Yu, S.: Extratropical–tropical interaction model intercomparison project (ETIN-MIP): Protocol and initial results, B. Am. Meteorol. Soc., 100, 2589–2605, 10.1175/BAMS-D-18-0301.1, 2019.

Kang et al.(2023a)Kang, Ceppi, Yu, and Kang

Kang, S. M., Ceppi, P., Yu, Y., and Kang, I.-S.: Recent global climate feedback controlled by Southern Ocean cooling, Nat. Geosci., 16, 775–780, 10.1038/s41561-023-01256-6, 2023a.

Kang et al.(2023b)Kang, Yu, Deser, Zhang, Kang, Lee, Rodgers, and Ceppi

Kang, S. M., Yu, Y., Deser, C., Zhang, X., Kang, I.-S., Lee, S.-S., Rodgers, K. B., and Ceppi, P.: Global impacts of recent Southern Ocean cooling, P. Natl. Acad. Sci., 120, e2300881120, 10.1073/pnas.2300881120, 2023b.

Kidston and Gerber(2010)

Kidston, J. and Gerber, E. P.: Intermodel variability of the poleward shift of the austral jet stream in the CMIP3 integrations linked to biases in 20th century climatology, Geophys. Res. Lett., 37, L09708, 10.1029/2010GL042873, 2010.

Kim et al.(2022)Kim, Kang, Kay, and Xie

Kim, H., Kang, S. M., Kay, J. E., and Xie, S.-P.: Subtropical clouds key to Southern Ocean teleconnections to the tropical Pacific, P. Natl. Acad. Sci., 119, e2200514119, 10.1073/pnas.2200514119, 2022.

Kluft et al.(2025)Kluft, Stevens, Brath, and Buehler

Kluft, L., Stevens, B., Brath, M., and Buehler, S. A.: A conceptual framework for understanding longwave cloud effects on climate sensitivity, Atmos. Chem. Phys., 25, 9075–9084, 10.5194/acp-25-9075-2025, 2025.

Koll et al.(2023)Koll, Jeevanjee, and Lutsko

Koll, D. D. B., Jeevanjee, N., and Lutsko, N. J.: An Analytic Model for the Clear-Sky Longwave Feedback, J. Atmos. Sci., 80, 1923–1951, 10.1175/JAS-D-22-0178.1, 2023.

Kramer et al.(2025)Kramer, Smith, and Andrews

Kramer, R. J., Smith, C., and Andrews, T.: The Radiative Forcing Model Intercomparison Project (RFMIP2.0) for CMIP7, Geosci. Model Dev., 19, 4447–4466, 10.5194/gmd-19-4447-2026, 2026.

Kuhlbrodt et al.(2024)Kuhlbrodt, Swaminathan, Ceppi, and Wilder

Kuhlbrodt, T., Swaminathan, R., Ceppi, P., and Wilder, T.: A Glimpse into the Future: The 2023 Ocean Temperature and Sea Ice Extremes in the Context of Longer-Term Climate Change, J. Climate, 105, E474–E485, 10.1175/BAMS-D-23-0209.1, 2024.

Lewis and Mauritsen(2021)

Lewis, N. and Mauritsen, T.: Negligible Unforced Historical Pattern Effect on Climate Feedback Strength Found in HadISST-Based AMIP Simulations, J. Climate, 34, 39–55, 10.1175/JCLI-D-19-0941.1, 2021.

Li et al.(2013)Li, von Storch, and Marotzke

Li, C., von Storch, J.-S., and Marotzke, J.: Deep-ocean heat uptake and equilibrium climate response, Clim. Dynam., 40, 1071–1086, 10.1007/s00382-012-1350-z, 2013.

Loeb et al.(2024)Loeb, Ham, Allan, Thorsen, Meyssignac, Kato, Johnson, and Lyman

Loeb, N. G., Ham, S.-H., Allan, R. P., Thorsen, T. J., Meyssignac, B., Kato, S., Johnson, G. C., and Lyman, J. M.: Observational Assessment of Changes in Earth's Energy Imbalance Since 2000, Surv. Geophys., 10.1007/s10712-024-09838-8, 2024.

Marvel et al.(2016)Marvel, Schmidt, Miller, and Nazarenko

Marvel, K., Schmidt, G. A., Miller, R. L., and Nazarenko, L. S.: Implications for climate sensitivity from the response to individual forcings, Nat. Clim. Change, 6, 386–389, 10.1038/nclimate2888, 2016.

Mauritsen et al.(2025)Mauritsen, Tsushima, Meyssignac, Loeb, Hakuba, Pilewskie, Cole, Suzuki, Ackerman, Allan, Andrews, Bender, Bloch-Johnson, Bodas-Salcedo, Brookshaw, Ceppi, Clerbaux, Dessler, Donohoe, Dufresne, Eyring, Findell, Gettelman, Gristey, Hawkins, Heimbach, Hewitt, Jeevanjee, Jones, Kang, Kato, Kay, Klein, Knutti, Kramer, Lee, McCoy, Medeiros, Megner, Modak, Ogura, Palmer, Paynter, Quaas, Ramanathan, Ringer, von Schuckmann, Sherwood, Stevens, Tan, Tselioudis, Sutton, Voigt, Watanabe, Webb, Wild, and Zelinka

Mauritsen, T., Tsushima, Y., Meyssignac, B., Loeb, N. G., Hakuba, M., Pilewskie, P., Cole, J., Suzuki, K., Ackerman, T. P., Allan, R. P., Andrews, T., Bender, F. A.-M., Bloch-Johnson, J., Bodas-Salcedo, A., Brookshaw, A., Ceppi, P., Clerbaux, N., Dessler, A. E., Donohoe, A., Dufresne, J.-L., Eyring, V., Findell, K. L., Gettelman, A., Gristey, J. J., Hawkins, E., Heimbach, P., Hewitt, H. T., Jeevanjee, N., Jones, C., Kang, S. M., Kato, S., Kay, J. E., Klein, S. A., Knutti, R., Kramer, R., Lee, J.-Y., McCoy, D. T., Medeiros, B., Megner, L., Modak, A., Ogura, T., Palmer, M. D., Paynter, D., Quaas, J., Ramanathan, V., Ringer, M., von Schuckmann, K., Sherwood, S., Stevens, B., Tan, I., Tselioudis, G., Sutton, R., Voigt, A., Watanabe, M., Webb, M. J., Wild, M., and Zelinka, M. D.: Earth's Energy Imbalance More Than Doubled in Recent Decades, AGU Adv., 6, e2024AV001636, 10.1029/2024AV001636, 2025.

McCoy et al.(2023)McCoy, Frazer, Mülmenstädt, Tan, Terai, and Zelinka

McCoy, D. T., Frazer, M. E., Mülmenstädt, J., Tan, I., Terai, C. R., and Zelinka, M. D.: Extratropical Cloud Feedbacks, in: Clouds and Their Climatic Impacts, 133–157, American Geophysical Union (AGU), ISBN 978-1-119-70035-7, 10.1002/9781119700357.ch6, 2023.

McKim et al.(2024)McKim, Bony, and Dufresne

McKim, B., Bony, S., and Dufresne, J.-L.: Weak anvil cloud area feedback suggested by physical and observational constraints, Nat. Geosci., 17, 392–397, 10.1038/s41561-024-01414-4, 2024.

Modak and Mauritsen(2023)

Modak, A. and Mauritsen, T.: Better-constrained climate sensitivity when accounting for dataset dependency on pattern effect estimates, Atmos. Chem. Phys., 23, 7535–7549, 10.5194/acp-23-7535-2023, 2023.

Mutton et al.(2024)Mutton, Andrews, Hermanson, Seabrook, Smith, Ringer, Jones, and Webb

Mutton, H., Andrews, T., Hermanson, L., Seabrook, M., Smith, D. M., Ringer, M. A., Jones, G. S., and Webb, M. J.: Feedbacks, Pattern Effects, and Efficacies in a Large Ensemble of HadGEM3-GC3.1-LL Historical Simulations, J. Geophys. Res.-Atmos., 129, e2024JD041137, 10.1029/2024JD041137, 2024.

Mutton et al.(2025)Mutton, Chadwick, Collins, Lambert, Taylor, Geen, Douville, and Saint-Lu

Mutton, H., Chadwick, R., Collins, M., Lambert, F. H., Taylor, C. M., Geen, R., Douville, H., and Saint-Lu, M.: Understanding the Uncertainty in the West African Monsoon Precipitation Response to Increasing CO₂, J. Climate, 38, 3151–3168, 10.1175/JCLI-D-24-0506.1, 2025.

Myers and Mechoso(2020)

Myers, T. A. and Mechoso, C. R.: Relative Contributions of Atmospheric, Oceanic, and Coupled Processes to North Pacific and North Atlantic Variability, Geophys. Res. Lett., 47, 10.1029/2019GL086321, 2020.

Myers et al.(2021)Myers, Scott, Zelinka, Klein, Norris, and Caldwell

Myers, T. A., Scott, R. C., Zelinka, M. D., Klein, S. A., Norris, J. R., and Caldwell, P. M.: Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity, Nat. Clim. Change, 501–507, 10.1038/s41558-021-01039-0, 2021.

Myers et al.(2023)Myers, Vogel, Brient, Parishani, and Scott

Myers, T. A., Vogel, R., Brient, F., Parishani, H., and Scott, R. C.: Tropical Marine Low Clouds: Feedbacks to Warming and on Climate Variability, in: Clouds and Their Climatic Impacts, 159–177, American Geophysical Union (AGU), ISBN 978-1-119-70035-7, 10.1002/9781119700357.ch7, 2023.

Myhre et al.(2024)Myhre, Byrom, Andrews, Forster, and Smith

Myhre, G., Byrom, R. E., Andrews, T., Forster, P. M., and Smith, C. J.: Efficacy of climate forcings in transient CMIP6 simulations, Front. Climate, 6, 10.3389/fclim.2024.1397358, 2024.

Mülmenstädt et al.(2021)

Mülmenstädt, J., Salzmann, M., Kay, J. E., Zelinka, M. D., Ma, P.-L., Nam, C., Kretzschmar, J., Hörnig, S., and Quaas, J.: An underestimated negative cloud feedback from cloud lifetime changes, Nat. Clim. Change, 11, 508–513, 10.1038/s41558-021-01038-1, 2021.

Natchiar et al.(2024)Natchiar, Webb, Lambert, Vallis, Morcrette, Holloway, Sergeev, and Boutle

Natchiar, S. R. M., Webb, M. J., Lambert, F. H., Vallis, G. K., Morcrette, C. J., Holloway, C. E., Sergeev, D. E., and Boutle, I.: Reduction in the Tropical High Cloud Fraction in Response to an Indirect Weakening of the Hadley Cell, J. Adv. Model. Earth Sy., 16, e2023MS003985, 10.1029/2023MS003985, 2024.

Norris et al.(2016)

Norris, J. R., Allen, R. J., Evan, A. T., Zelinka, M. D., O'Dell, C. W., and Klein, S. A.: Evidence for climate change in the satellite cloud record, Nature, 536, 72–75, 10.1038/nature18273, 2016.

Nuijens and Siebesma(2019)

Nuijens, L. and Siebesma, A. P.: Boundary Layer Clouds and Convection over Subtropical Oceans in our Current and in a Warmer Climate, Current Climate Change Reports, 5, 80–94, 10.1007/s40641-019-00126-x, 2019.

Ogura et al.(2023)

Ogura, T., Webb, M. J., and Lock, A. P.: Positive Low Cloud Feedback Primarily Caused by Increasing Longwave Radiation From the Sea Surface in Two Versions of a Climate Model, Geophys. Res. Lett., 50, e2023GL104786, 10.1029/2023GL104786, 2023.

Olonscheck and Rugenstein(2024)

Olonscheck, D. and Rugenstein, M.: Coupled Climate Models Systematically Underestimate Radiation Response to Surface Warming, Geophys. Res. Lett., 51, e2023GL106909, 10.1029/2023GL106909, 2024.

Peng et al.(2025)

Peng, L., Blossey, P. N., Hannah, W. M., Bretherton, C. S., Terai, C. R., Jenney, A. M., Ferretti, S. L., Parishani, H., and Pritchard, M. S.: Resolving Low Cloud Feedbacks Globally With E3SM High-Res MMF: Agreement With LES but Stronger Shortwave Effects, J. Adv. Model. Earth Sy., 17, e2025MS005003, 10.1029/2025MS005003, 2025.

Pincus et al.(2016)Pincus, Forster, and Stevens

Pincus, R., Forster, P. M., and Stevens, B.: The Radiative Forcing Model Intercomparison Project (RFMIP): experimental protocol for CMIP6, Geosci. Model Dev., 9, 3447–3460, 10.5194/gmd-9-3447-2016, 2016.

Poletti et al.(2024)Poletti, Frierson, and Armour

Poletti, A. N., Frierson, D. M. W., and Armour, K. C.: Why Do CO2 Quadrupling Simulations Warm More Than Twice as Much as CO₂ Doubling Simulations in CMIP6?, Geophys. Res. Lett., 51, e2023GL107320, 10.1029/2023GL107320, 2024.

Proistosescu and Huybers(2017)

Proistosescu, C. and Huybers, P. J.: Slow climate mode reconciles historical and model-based estimates of climate sensitivity, Sci. Adv., 3, 10.1126/sciadv.1602821, 2017.

Qin et al.(2022)

Qin, Y., Zelinka, M. D., and Klein, S. A.: On the Correspondence Between Atmosphere-Only and Coupled Simulations for Radiative Feedbacks and Forcing From CO2, J. Geophys. Res.-Atmos., 127, e2021JD035460, 10.1029/2021JD035460, 2022.

Quaas et al.(2022)

Quaas, J., Jia, H., Smith, C., Albright, A. L., Aas, W., Bellouin, N., Boucher, O., Doutriaux-Boucher, M., Forster, P. M., Grosvenor, D., Jenkins, S., Klimont, Z., Loeb, N. G., Ma, X., Naik, V., Paulot, F., Stier, P., Wild, M., Myhre, G., and Schulz, M.: Robust evidence for reversal of the trend in aerosol effective climate forcing, Atmos. Chem. Phys., 22, 12221–12239, 10.5194/acp-22-12221-2022, 2022.

Raghuraman and Medeiros(2026)

Raghuraman, S. P. and Medeiros, B.: Cloud Feedback Overestimated in Coupled CESM2 Simulations Relative to AMIP Simulations and Satellite Observations: Importance of the Pattern Effect and Implications for Climate Sensitivity, Environ. Res. Lett., 21, 084018, 10.1088/1748-9326/ae5fab, 2026.

Raghuraman et al.(2021)Raghuraman, Paynter, and Ramaswamy

Raghuraman, S. P., Paynter, D., and Ramaswamy, V.: Anthropogenic forcing and response yield observed positive trend in Earth's energy imbalance, Nat. Commun., 12, 4577, 10.1038/s41467-021-24544-4, 2021.

Raghuraman et al.(2024)Raghuraman, Medeiros, and Gettelman

Raghuraman, S. P., Medeiros, B., and Gettelman, A.: Observational Quantification of Tropical High Cloud Changes and Feedbacks, J. Geophys. Res.-Atmos., 129, e2023JD039364, 10.1029/2023JD039364, 2024.

Rayner et al.(2003)Rayner, Parker, Horton, Folland, Alexander, Rowell, Kent, and Kaplan

Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res.-Atmos., 108, 10.1029/2002JD002670, 2003.

Reynolds et al.(2002)Reynolds, Rayner, Smith, Stokes, and Wang

Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., and Wang, W.: An Improved In Situ and Satellite SST Analysis for Climate, J. Climate, 15, 1609–1625, 10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2, 2002.

Richardson et al.(2022)Richardson, Roy, and Lebsock

Richardson, M. T., Roy, R. J., and Lebsock, M. D.: Satellites Suggest Rising Tropical High Cloud Altitude: 2002–2021, Geophys. Res. Lett., 49, e2022GL098160, 10.1029/2022GL098160, 2022.

Richardson et al.(2019)Richardson, Forster, Smith, Maycock, Wood, Andrews, Boucher, Faluvegi, Fläschner, Hodnebrog, Kasoar, Kirkevåg, Lamarque, Mülmenstädt, Myhre, Olivié, Portmann, Samset, Shawki, Shindell, Stier, Takemura, Voulgarakis, and Watson-Parris

Richardson, T. B., Forster, P. M., Smith, C. J., Maycock, A. C., Wood, T., Andrews, T., Boucher, O., Faluvegi, G., Fläschner, D., Hodnebrog, Ø., Kasoar, M., Kirkevåg, A., Lamarque, J. F., Mülmenstädt, J., Myhre, G., Olivié, D., Portmann, R. W., Samset, B. H., Shawki, D., Shindell, D., Stier, P., Takemura, T., Voulgarakis, A., and Watson-Parris, D.: Efficacy of Climate Forcings in PDRMIP Models, J. Geophys. Res.-Atmos., 124, 12824–12844, 10.1029/2019JD030581, 2019.

Rieck et al.(2012)Rieck, Nuijens, and Stevens

Rieck, M., Nuijens, L., and Stevens, B.: Marine Boundary Layer Cloud Feedbacks in a Constant Relative Humidity Atmosphere, J. Atmos. Sci., 69, 2538–2550, 10.1175/JAS-D-11-0203.1, 2012.

Ringer et al.(2023)Ringer, Bodas-Salcedo, and Webb

Ringer, M. A., Bodas-Salcedo, A., and Webb, M. J.: Global and Regional Climate Feedbacks in Response to Uniform Warming and Cooling, J. Geophys. Res.-Atmos., 128, e2023JD038861, 10.1029/2023JD038861, 2023.

Roberts et al.(2025)Roberts, Reed, Bao, Barsugli, Camargo, Caron, Chang, Chen, Christensen, Danabasoglu, Frenger, Fučkar, Hasson, Hewitt, Huang, Kim, Kodama, Lai, Leung, Mizuta, Nobre, Ortega, Paquin, Roberts, Scoccimarro, Seddon, Treguier, Tu, Ullrich, Vidale, Wehner, Zarzycki, Zhang, Zhang, and Zhao

Roberts, M. J., Reed, K. A., Bao, Q., Barsugli, J. J., Camargo, S. J., Caron, L.-P., Chang, P., Chen, C.-T., Christensen, H. M., Danabasoglu, G., Frenger, I., Fučkar, N. S., ul Hasson, S., Hewitt, H. T., Huang, H., Kim, D., Kodama, C., Lai, M., Leung, L.-Y. R., Mizuta, R., Nobre, P., Ortega, P., Paquin, D., Roberts, C. D., Scoccimarro, E., Seddon, J., Treguier, A. M., Tu, C.-Y., Ullrich, P. A., Vidale, P. L., Wehner, M. F., Zarzycki, C. M., Zhang, B., Zhang, W., and Zhao, M.: High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7, Geosci. Model Dev., 18, 1307–1332, 10.5194/gmd-18-1307-2025, 2025.

Rugenstein et al.(2019)Rugenstein, Bloch-Johnson, Abe-Ouchi, Andrews, Beyerle, Cao, Chadha, Danabasoglu, Dufresne, Duan, Foujols, Frölicher, Geoffroy, Gregory, Knutti, Li, Marzocchi, Mauritsen, Menary, Moyer, Nazarenko, Paynter, Saint-Martin, Schmidt, Yamamoto, and Yang

Rugenstein, M., Bloch-Johnson, J., Abe-Ouchi, A., Andrews, T., Beyerle, U., Cao, L., Chadha, T., Danabasoglu, G., Dufresne, J.-L., Duan, L., Foujols, M.-A., Frölicher, T., Geoffroy, O., Gregory, J., Knutti, R., Li, C., Marzocchi, A., Mauritsen, T., Menary, M., Moyer, E., Nazarenko, L., Paynter, D., Saint-Martin, D., Schmidt, G. A., Yamamoto, A., and Yang, S.: LongRunMIP: Motivation and Design for a Large Collection of Millennial-Length AOGCM Simulations, B. Am. Meteorol. Soc., 100, 2551–2570, 10.1175/BAMS-D-19-0068.1, 2019.

Rugenstein et al.(2020)Rugenstein, Bloch-Johnson, Gregory, Andrews, Mauritsen, Li, Frölicher, Paynter, Danabasoglu, Yang, Dufresne, Cao, Schmidt, Abe-Ouchi, Geoffroy, and Knutti

Rugenstein, M., Bloch-Johnson, J., Gregory, J., Andrews, T., Mauritsen, T., Li, C., Frölicher, T. L., Paynter, D., Danabasoglu, G., Yang, S., Dufresne, J.-L., Cao, L., Schmidt, G. A., Abe-Ouchi, A., Geoffroy, O., and Knutti, R.: Equilibrium Climate Sensitivity Estimated by Equilibrating Climate Models, Geophys. Res. Lett., 47, e2019GL083898, 10.1029/2019GL083898, 2020.

Rugenstein et al.(2023)Rugenstein, Zelinka, Karnauskas, Ceppi, and Andrews

Rugenstein, M., Zelinka, M., Karnauskas, K. D., Ceppi, P., and Andrews, T.: Patterns of Surface Warming Matter for Climate Sensitivity, Eos, 104, 10.1029/2023EO230411, 2023.

Rugenstein et al.(2016)Rugenstein, Gregory, Schaller, Sedláček, and Knutti

Rugenstein, M. A. A., Gregory, J. M., Schaller, N., Sedláček, J., and Knutti, R.: Multi-annual ocean-atmosphere adjustments to radiative forcing, J. Climate, JCLI-D-16-0312.1, 10.1175/JCLI-D-16-0312.1, 2016.

Rädel et al.(2016)Rädel, Mauritsen, Stevens, Dommenget, Matei, Bellomo, and Clement

Rädel, G., Mauritsen, T., Stevens, B., Dommenget, D., Matei, D., Bellomo, K., and Clement, A.: Amplification of El Niño by cloud longwave coupling to atmospheric circulation, Nat. Geosci., 9, 106–110, 10.1038/ngeo2630, 2016.

Salvi et al.(2022)Salvi, Ceppi, and Gregory

Salvi, P., Ceppi, P., and Gregory, J. M.: Interpreting Differences in Radiative Feedbacks From Aerosols Versus Greenhouse Gases, Geophys. Res. Lett., 49, e2022GL097766, 10.1029/2022GL097766, 2022.

Salvi et al.(2023)Salvi, Gregory, and Ceppi

Salvi, P., Gregory, J. M., and Ceppi, P.: Time-Evolving Radiative Feedbacks in the Historical Period, J. Geophys. Res.-Atmos., 128, e2023JD038984, 10.1029/2023JD038984, 2023.

Scheff and Frierson(2012)

Scheff, J. and Frierson, D.: 21st-century multi-model subtropical precipitation declines are mostly mid-latitude shifts, J. Climate, 120125132609008, 10.1175/JCLI-D-11-00393.1, 2012.

Schmidt(2024)

Schmidt, G.: Climate models can't explain 2023's huge heat anomaly – we could be in uncharted territory, Nature, 627, 467–467, 10.1038/d41586-024-00816-z, 2024.

Schmidt et al.(2023)Schmidt, Andrews, Bauer, Durack, Loeb, Ramaswamy, Arnold, Bosilovich, Cole, Horowitz, Johnson, Lyman, Medeiros, Michibata, Olonscheck, Paynter, Raghuraman, Schulz, Takasuka, Tallapragada, Taylor, and Ziehn

Schmidt, G. A., Andrews, T., Bauer, S. E., Durack, P. J., Loeb, N. G., Ramaswamy, V., Arnold, N. P., Bosilovich, M. G., Cole, J., Horowitz, L. W., Johnson, G. C., Lyman, J. M., Medeiros, B., Michibata, T., Olonscheck, D., Paynter, D., Raghuraman, S. P., Schulz, M., Takasuka, D., Tallapragada, V., Taylor, P. C., and Ziehn, T.: CERESMIP: a climate modeling protocol to investigate recent trends in the Earth's Energy Imbalance, Frontiers in Climate, 5, 10.3389/fclim.2023.1202161, 2023.

Seeley and Jeevanjee(2021)

Seeley, J. T. and Jeevanjee, N.: H2O Windows and CO2 Radiator Fins: A Clear-Sky Explanation for the Peak in Equilibrium Climate Sensitivity, Geophys. Res. Lett., 48, e2020GL089609, 10.1029/2020GL089609, 2021.

Shen et al.(2022)Shen, Sridhar, Tan, Jaruga, and Schneider

Shen, Z., Sridhar, A., Tan, Z., Jaruga, A., and Schneider, T.: A Library of Large-Eddy Simulations Forced by Global Climate Models, J. Adv. Model. Earth Sy., 14, e2021MS002631, 10.1029/2021MS002631, 2022.

Sherwood et al.(2020)Sherwood, Webb, Annan, Armour, Forster, Hargreaves, Hegerl, Klein, Marvel, Rohling, Watanabe, Andrews, Braconnot, Bretherton, Foster, Hausfather, Heydt, Knutti, Mauritsen, Norris, Proistosescu, Rugenstein, Schmidt, Tokarska, and Zelinka

Sherwood, S., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., Heydt, A. S. v. d., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and Zelinka, M. D.: An assessment of Earth's climate sensitivity using multiple lines of evidence, Rev. Geophys., 58, 1–92, 10.1029/2019RG000678, 2020.

Simpson et al.(2025)Simpson, Shaw, Ceppi, Clement, Fischer, Grise, Pendergrass, Screen, Wills, Woollings, Blackport, Kang, and Po-Chedley

Simpson, I. R., Shaw, T. A., Ceppi, P., Clement, A. C., Fischer, E., Grise, K. M., Pendergrass, A. G., Screen, J. A., Wills, R. C. J., Woollings, T., Blackport, R., Kang, J. M., and Po-Chedley, S.: Confronting Earth System Model trends with observations, Sci. Adv., 11, eadt8035, 10.1126/sciadv.adt8035, 2025.

Stevens and Kluft(2023)

Stevens, B. and Kluft, L.: A colorful look at climate sensitivity, Atmos. Chem. Phys., 23, 14673–14689, 10.5194/acp-23-14673-2023, 2023.

Stevens et al.(2016)Stevens, Sherwood, Bony, and Webb

Stevens, B., Sherwood, S. C., Bony, S., and Webb, M. J.: Prospects for narrowing bounds on Earth's equilibrium climate sensitivity, Earth's Future, 4, 512–522, 10.1002/2016EF000376, 2016.

Stevens et al.(2026)Stevens, Bony, Gross, Klocke, Windmiller, Wing, von Bismarck, Brito, David, Delanoë, Farrell, and Wu

Stevens, B., Bony, S., Gross, S., Klocke, D., Windmiller, J. M., Wing, A. A., von Bismarck, J., Brito, E., David, R. O., Delanoë, J., Farrell, D., and Wu, Y.: ORCESTRA: Organized Convection and EarthCARE Studies Over the Tropical Atlantic, Tellus, 79, 10.16993/tellus.4123, 2026.

Swales et al.(2018)Swales, Pincus, and Bodas-Salcedo

Swales, D. J., Pincus, R., and Bodas-Salcedo, A.: The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2, Geosci. Model Dev., 11, 77–81, 10.5194/gmd-11-77-2018, 2018.

Tan et al.(2025)Tan, Zhou, Lamy, and Stauffer

Tan, I., Zhou, C., Lamy, A., and Stauffer, C. L.: Moderate climate sensitivity due to opposing mixed-phase cloud feedbacks, npj Climate and Atmospheric Science, 8, 86, 10.1038/s41612-025-00948-7, 2025.

Taylor et al.(2000)Taylor, Williamson, and Zwiers

Taylor, K. E., Williamson, D., and Zwiers, F.: The sea surface temperature and sea ice concentration boundary conditions for AMIP II simulations, Tech. Rep. 60, Lawrence Livermore National Laboratory, Program for Climate Model Diagnosis and Intercomparison, https://pcmdi.llnl.gov/report/pdf/60.pdf (last access: 8 September 2026), 2000.

Tselioudis et al.(2016)Tselioudis, Lipat, Konsta, Grise, and Polvani

Tselioudis, G., Lipat, B. R., Konsta, D., Grise, K. M., and Polvani, L. M.: Midlatitude cloud shifts, their primary link to the Hadley cell, and their diverse radiative effects, Geophys. Res. Lett., 43, 4594–4601, 10.1002/2016GL068242, 2016.

Tselioudis et al.(2025)Tselioudis, Remillard, Jakob, and Rossow

Tselioudis, G., Remillard, J., Jakob, C., and Rossow, W. B.: Contraction of the World's Storm-Cloud Zones the Primary Contributor to the 21st Century Increase in the Earth's Sunlight Absorption, Geophys. Res. Lett., 52, e2025GL114882, 10.1029/2025GL114882, 2025.

Vogel et al.(2022)Vogel, Albright, Vial, George, Stevens, and Bony

Vogel, R., Albright, A. L., Vial, J., George, G., Stevens, B., and Bony, S.: Strong cloud–circulation coupling explains weak trade cumulus feedback, Nature, 612, 696–700, 10.1038/s41586-022-05364-y, 2022.

Voigt et al.(2021)Voigt, Albern, Ceppi, Grise, Li, and Medeiros

Voigt, A., Albern, N., Ceppi, P., Grise, K., Li, Y., and Medeiros, B.: Clouds, radiation, and atmospheric circulation in the present‐day climate and under climate change, WIREs Clim. Change, 12, 10.1002/wcc.694, 2021.

Wall et al.(2022a)

Wall, C. J., Norris, J. R., Possner, A., McCoy, D. T., McCoy, I. L., and Lutsko, N. J.: Assessing effective radiative forcing from aerosol–cloud interactions over the global ocean, P. Natl. Acad. Sci., 119, e2210481119, 10.1073/pnas.2210481119, 2022a.

Wall et al.(2022b)

Wall, C. J., Storelvmo, T., Norris, J. R., and Tan, I.: Observational Constraints on Southern Ocean Cloud-Phase Feedback, J. Climate, 35, 5087–5102, 10.1175/JCLI-D-21-0812.1, 2022b.

Wang et al.(2020)Wang, Jin, and Liu

Wang, B., Jin, C., and Liu, J.: Understanding Future Change of Global Monsoons Projected by CMIP6 Models, J. Climate, 33, 6471–6489, 10.1175/JCLI-D-19-0993.1, 2020.

Webb(2025)

Webb, M.: CFMIP Cfsites Locations for CFMIP4/CMIP7, Zenodo, 10.5281/zenodo.17966101, 2025.

Webb et al.(2017)

Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R., Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S. A., Marchand, R., Medeiros, B., Siebesma, A. P., Skinner, C. B., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M.: The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6, Geosci. Model Dev., 10, 359–384, 10.5194/gmd-10-359-2017, 2017.

Wendisch et al.(2024)

Wendisch, M., Crewell, S., Ehrlich, A., Herber, A., Kirbus, B., Lüpkes, C., Mech, M., Abel, S. J., Akansu, E. F., Ament, F., Aubry, C., Becker, S., Borrmann, S., Bozem, H., Brückner, M., Clemen, H.-C., Dahlke, S., Dekoutsidis, G., Delanoë, J., De La Torre Castro, E., Dorff, H., Dupuy, R., Eppers, O., Ewald, F., George, G., Gorodetskaya, I. V., Grawe, S., Groß, S., Hartmann, J., Henning, S., Hirsch, L., Jäkel, E., Joppe, P., Jourdan, O., Jurányi, Z., Karalis, M., Kellermann, M., Klingebiel, M., Lonardi, M., Lucke, J., Luebke, A. E., Maahn, M., Maherndl, N., Maturilli, M., Mayer, B., Mayer, J., Mertes, S., Michaelis, J., Michalkov, M., Mioche, G., Moser, M., Müller, H., Neggers, R., Ori, D., Paul, D., Paulus, F. M., Pilz, C., Pithan, F., Pöhlker, M., Pörtge, V., Ringel, M., Risse, N., Roberts, G. C., Rosenburg, S., Röttenbacher, J., Rückert, J., Schäfer, M., Schaefer, J., Schemann, V., Schirmacher, I., Schmidt, J., Schmidt, S., Schneider, J., Schnitt, S., Schwarz, A., Siebert, H., Sodemann, H., Sperzel, T., Spreen, G., Stevens, B., Stratmann, F., Svensson, G., Tatzelt, C., Tuch, T., Vihma, T., Voigt, C., Volkmer, L., Walbröl, A., Weber, A., Wehner, B., Wetzel, B., Wirth, M., and Zinner, T.: Overview: quasi-Lagrangian observations of Arctic air mass transformations – introduction and initial results of the HALO–(AL)³ aircraft campaign, Atmos. Chem. Phys., 24, 8865–8892, 10.5194/acp-24-8865-2024, 2024.

Wilcox et al.(2023)Wilcox, Allen, Samset, Bollasina, Griffiths, Keeble, Lund, Makkonen, Merikanto, O'Donnell, Paynter, Persad, Rumbold, Takemura, Tsigaridis, Undorf, and Westervelt

Wilcox, L. J., Allen, R. J., Samset, B. H., Bollasina, M. A., Griffiths, P. T., Keeble, J., Lund, M. T., Makkonen, R., Merikanto, J., O'Donnell, D., Paynter, D. J., Persad, G. G., Rumbold, S. T., Takemura, T., Tsigaridis, K., Undorf, S., and Westervelt, D. M.: The Regional Aerosol Model Intercomparison Project (RAMIP), Geosci. Model Dev., 16, 4451–4479, 10.5194/gmd-16-4451-2023, 2023.

Wills et al.(2022)Wills, Dong, Proistosescu, Armour, and Battisti

Wills, R. C. J., Dong, Y., Proistosescu, C., Armour, K. C., and Battisti, D. S.: Systematic Climate Model Biases in the Large-Scale Patterns of Recent Sea-Surface Temperature and Sea-Level Pressure Change, Geophys. Res. Lett., 49, e2022GL100011, 10.1029/2022GL100011, 2022.

Wilson Kemsley et al.(2025)Wilson Kemsley, Nowack, and Ceppi

Wilson Kemsley, S., Nowack, P., and Ceppi, P.: Climate Models Underestimate Global Decreases in High-Cloud Amount With Warming, Geophys. Res. Lett., 52, e2024GL113316, 10.1029/2024GL113316, 2025.

Wing et al.(2018)Wing, Reed, Satoh, Stevens, Bony, and Ohno

Wing, A. A., Reed, K. A., Satoh, M., Stevens, B., Bony, S., and Ohno, T.: Radiative–convective equilibrium model intercomparison project, Geosci. Model Dev., 11, 793–813, 10.5194/gmd-11-793-2018, 2018.

Wing et al.(2024)Wing, Silvers, and Reed

Wing, A. A., Silvers, L. G., and Reed, K. A.: RCEMIP-II: mock-Walker simulations as phase II of the radiative–convective equilibrium model intercomparison project, Geosci. Model Dev., 17, 6195–6225, 10.5194/gmd-17-6195-2024, 2024.

Ying and Huang(2016)

Ying, J. and Huang, P.: Cloud–Radiation Feedback as a Leading Source of Uncertainty in the Tropical Pacific SST Warming Pattern in CMIP5 Models, J. Climate, 29, 3867–3881, 10.1175/JCLI-D-15-0796.1, 2016.

Zelinka and Hartmann(2010)

Zelinka, M. D. and Hartmann, D. L.: Why is longwave cloud feedback positive?, J. Geophys. Res., 115, D16117, 10.1029/2010JD013817, 2010.

Zelinka et al.(2020)Zelinka, Myers, McCoy, Po‐Chedley, Caldwell, Ceppi, Klein, and Taylor

Zelinka, M. D., Myers, T. A., McCoy, D. T., Po‐Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47, 10.1029/2019GL085782, 2020.

Zelinka et al.(2026)Zelinka, Myers, Qin, Chao, Klein, Po-Chedley, Ma, Wall, Ceppi, and Gettelman

Zelinka, M. D., Myers, T. A., Qin, Y., Chao, L.-W., Klein, S. A., Po-Chedley, S., Ma, P.-L., Wall, C. J., Ceppi, P., and Gettelman, A.: Recent Cloud Trends and Extremes Reaffirm Established Bounds on Cloud Feedback and Aerosol-Cloud Interactions, Commun. Earth Environ., 10.1038/s43247-026-03461-8, 2026.

Zhang et al.(2023)Zhang, Zhao, He, Soden, Tan, Xiang, and Wang

Zhang, B., Zhao, M., He, H., Soden, B. J., Tan, Z., Xiang, B., and Wang, C.: The Dependence of Climate Sensitivity on the Meridional Distribution of Radiative Forcing, Geophys. Res. Lett., 50, e2023GL105492, 10.1029/2023GL105492, 2023.

Zhang and Kang(2026)

Zhang, J. and Kang, S. M.: Fast Expansion and Slow Contraction of the ITCZ in Response to CO2 Forcing, Sci. Adv., 12, eaef2833, 10.1126/sciadv.aef2833, 2026.

Zhou et al.(2016)Zhou, Zelinka, and Klein

Zhou, C., Zelinka, M. D., and Klein, S. A.: Impact of decadal cloud variations on the Earth's energy budget, Nat. Geosci., 9, 871–874, 10.1038/ngeo2828, 2016.

Zhou et al.(2023)Zhou, Wang, Zelinka, Liu, Dong, and Armour

Zhou, C., Wang, M., Zelinka, M. D., Liu, Y., Dong, Y., and Armour, K. C.: Explaining Forcing Efficacy With Pattern Effect and State Dependence, Geophys. Res. Lett., 50, e2022GL101700, 10.1029/2022GL101700, 2023.

Zhou et al.(2025)Zhou, Wang, Tan, Zhang, Zelinka, Wang, and Bloch-Johnson

Zhou, C., Wang, Q., Tan, I., Zhang, L., Zelinka, M. D., Wang, M., and Bloch-Johnson, J.: Sea Ice Pattern Effect on Earth's Energy Budget Is Characterized by Hemispheric Asymmetry, Sci. Adv., 11, eadr4248, 10.1126/sciadv.adr4248, 2025.