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An atmospheric general circulation model (AGCM) is forced with patterns of observed sea surface temperature (SST) change and those output from atmosphere–ocean GCM (AOGCM) climate change simulations to demonstrate a strong dependence of climate feedback on the spatial structure of surface temperature change. Cloud and lapse rate feedbacks are found to vary the most, depending strongly on the pattern of tropical Pacific SST change. When warming is focused in the southeast tropical Pacific—a region of climatological subsidence and extensive marine low cloud cover—warming reduces the lower-tropospheric stability (LTS) and low cloud cover but is largely trapped under an inversion and hence has little remote effect. The net result is a relatively weak negative lapse rate feedback and a large positive cloud feedback. In contrast, when warming is weak in the southeast tropical Pacific and enhanced in the west tropical Pacific—a strong convective region—warming is efficiently transported throughout the free troposphere. The increased atmospheric stability results in a strong negative lapse rate feedback and increases the LTS in low cloud regions, resulting in a low cloud feedback of weak magnitude. These mechanisms help explain why climate feedback and sensitivity change on multidecadal time scales in AOGCM abrupt4xCO₂ simulations and are different from those seen in AGCM experiments forced with observed historical SST changes. From the physical understanding developed here, one should expect unusually negative radiative feedbacks and low effective climate sensitivities to be diagnosed from real-world variations in radiative fluxes and temperature over decades in which the eastern Pacific has lacked warming.

Experiments with CO₂ instantaneously quadrupled and then held constant are used to show that the relationship between the global-mean net heat input to the climate system and the global-mean surface air temperature change is nonlinear in phase 5 of the Coupled Model Intercomparison Project (CMIP5) atmosphere–ocean general circulation models (AOGCMs). The nonlinearity is shown to arise from a change in strength of climate feedbacks driven by an evolving pattern of surface warming. In 23 out of the 27 AOGCMs examined, the climate feedback parameter becomes significantly (95% confidence) less negative (i.e., the effective climate sensitivity increases) as time passes. Cloud feedback parameters show the largest changes. In the AOGCM mean, approximately 60% of the change in feedback parameter comes from the tropics (30°N–30°S). An important region involved is the tropical Pacific, where the surface warming intensifies in the east after a few decades. The dependence of climate feedbacks on an evolving pattern of surface warming is confirmed using the HadGEM2 and HadCM3 atmosphere GCMs (AGCMs). With monthly evolving sea surface temperatures and sea ice prescribed from its AOGCM counterpart, each AGCM reproduces the time-varying feedbacks, but when a fixed pattern of warming is prescribed the radiative response is linear with global temperature change or nearly so. It is also demonstrated that the regression and fixed-SST methods for evaluating effective radiative forcing are in principle different, because rapid SST adjustment when CO₂ is changed can produce a pattern of surface temperature change with zero global mean but nonzero change in net radiation at the top of the atmosphere (∼−0.5W m−2in HadCM3).

We quantify forcing and feedbacks across available CMIP5 coupled atmosphere‐ocean general circulation models (AOGCMs) by analysing simulations forced by an abrupt quadrupling of atmospheric carbon dioxide concentration. This is the first application of the linear forcing‐feedback regression analysis of Gregory et al. (2004) to an ensemble of AOGCMs. The range of equilibrium climate sensitivity is 2.1–4.7 K. Differences in cloud feedbacks continue to be important contributors to this range. Some models show small deviations from a linear dependence of top‐of‐atmosphere radiative fluxes on global surface temperature change. We show that this phenomenon largely arises from shortwave cloud radiative effects over the ocean and is consistent with independent estimates of forcing using fixed sea‐surface temperature methods. We suggest that future research should focus more on understanding transient climate change, including any time‐scale dependence of the forcing and/or feedback, rather than on the equilibrium response to large instantaneous forcing. Key Points Range of eqm climate sensitivity (2.1‐4.7K) is similar to that found in CMIP3 Differences in cloud feedbacks continue to be a large source of this uncertainty Some models show small deviations from linear behaviour

Our understanding of the interactions between clouds, circulation and climate is limited. Four central research questions — now tractable through advances in models, concepts and observations — are proposed to accelerate future progress. Fundamental puzzles of climate science remain unsolved because of our limited understanding of how clouds, circulation and climate interact. One example is our inability to provide robust assessments of future global and regional climate changes. However, ongoing advances in our capacity to observe, simulate and conceptualize the climate system now make it possible to fill gaps in our knowledge. We argue that progress can be accelerated by focusing research on a handful of important scientific questions that have become tractable as a result of recent advances. We propose four such questions below; they involve understanding the role of cloud feedbacks and convective organization in climate, and the factors that control the position, the strength and the variability of the tropical rain belts and the extratropical storm tracks.

Increases in cloud optical depth and liquid water path (LWP) are robust features of global warming model simulations in high latitudes, yielding a negative shortwave cloud feedback, but the mechanisms are still uncertain. Here the importance of microphysical processes for the negative optical depth feedback is assessed by perturbing temperature in the microphysics schemes of two aquaplanet models, both of which have separate prognostic equations for liquid water and ice. It is found that most of the LWP increase with warming is caused by a suppression of ice microphysical processes in mixed-phase clouds, resulting in reduced conversion efficiencies of liquid water to ice and precipitation. Perturbing the temperature-dependent phase partitioning of convective condensate also yields a small LWP increase. Together, the perturbations in large-scale microphysics and convective condensate partitioning explain more than two-thirds of the LWP response relative to a reference case with increased SSTs, and capture all of the vertical structure of the liquid water response. In support of these findings, a very robust positive relationship between monthly mean LWP and temperature in CMIP5 models and observations is shown to exist in mixed-phase cloud regions only. In models, the historical LWP sensitivity to temperature is a good predictor of the forced global warming response poleward of about 45°, although models appear to overestimate the LWP response to warming compared to observations. The results indicate that in climate models, the suppression of ice-phase microphysical processes that deplete cloud liquid water is a key driver of the LWP increase with warming and of the associated negative shortwave cloud feedback.

Using five climate model simulations of the response to an abrupt quadrupling of CO₂, the authors perform the first simultaneous model intercomparison of cloud feedbacks and rapid radiative adjustments with cloud masking effects removed, partitioned among changes in cloud types and gross cloud properties. Upon CO₂ quadrupling, clouds exhibit a rapid reduction in fractional coverage, cloud-top pressure, and optical depth, with each contributing equally to a 1.1 W m−2net cloud radiative adjustment, primarily from shortwave radiation. Rapid reductions in midlevel clouds and optically thick clouds are important in reducing planetary albedo in every model. As the planet warms, clouds become fewer, higher, and thicker, and global mean net cloud feedback is positive in all but one model and results primarily from increased trapping of longwave radiation. As was true for earlier models, high cloud changes are the largest contributor to intermodel spread in longwave and shortwave cloud feedbacks, but low cloud changes are the largest contributor to the mean and spread in net cloud feedback. The importance of the negative optical depth feedback relative to the amount feedback at high latitudes is even more marked than in earlier models. The authors show that the negative longwave cloud adjustment inferred in previous studies is primarily caused by a 1.3 W m−2cloud masking of CO₂ forcing. Properly accounting for cloud masking increases net cloud feedback by 0.3 W m−2K−1, whereas accounting for rapid adjustments reduces by 0.14 W m−2K−1the ensemble mean net cloud feedback through a combination of smaller positive cloud amount and altitude feedbacks and larger negative optical depth feedbacks.

Low cloud feedback in global warming projections by climate models is characterized by its positive sign, the mechanism of which is not well understood. Here we propose that the positive sign is primarily caused by the increase in upward longwave radiation from the sea surface. We devise numerical experiments that enable separation of the feedback into components coming from physically distinct causes. Results of these experiments with a climate model indicate that increases in upward longwave radiation from the sea surface cause warming and absolute drying in the boundary layer, leading to the positive low cloud feedback. The absolute drying results from decrease in surface evaporation, and also from decrease in inversion strength which enhances vertical mixing of drier free tropospheric air into the boundary layer. This mechanism is different from previously proposed understanding that positive low cloud feedback is caused by increases in surface evaporation or vertical moisture contrast. Plain Language Summary We project future climate change induced by atmospheric greenhouse gas increases by conducting numerical simulations using specialized computer codes, namely Global Climate Models. Results of such simulations are characterized by decreases in low cloud with warming at the Earth's surface, which amplifies the warming by reflecting less sunlight back to space and allowing more sunlight to be absorbed at the surface. This amplifying effect, called “positive low cloud feedback,” is important because the amount of future warming affects our living and safety. However, the mechanism of the low cloud decreases with warming is not well understood. Here we propose that the low cloud decrease is primarily caused by increase in upward longwave radiation from the sea surface. We devise numerical simulations that enable the separation of the low cloud feedback into components coming from physically distinct causes. Results of the simulations indicate that increases in upward longwave radiation from the sea surface cause warming and drying near the Earth's surface, leading to the low cloud decrease. This mechanism is different from previously proposed understanding that the low cloud decrease is due to increases in sea surface evaporation or vertical moisture contrast. Key Points The increase in longwave radiation from the sea surface is a leading order cause of the positive low cloud feedback in a climate model This increase in longwave radiation leads to warming and drying in the boundary layer, which contributes to the decrease in the low cloud This mechanism is not associated with increases in surface evaporation or vertical moisture contrast

We diagnose climate feedback parameters and CO 2 forcing including rapid adjustment in twelve atmosphere/mixed-layer-ocean (“slab”) climate models from the CMIP3/CFMIP-1 project (the AR4 ensemble) and fifteen parameter-perturbed versions of the HadSM3 slab model (the PPE). In both ensembles, differences in climate feedbacks can account for approximately twice as much of the range in climate sensitivity as differences in CO 2 forcing. In the AR4 ensemble, cloud effects can explain the full range of climate sensitivities, and cloud feedback components contribute four times as much as cloud components of CO 2 forcing to the range. Non-cloud feedbacks are required to fully account for the high sensitivities of some models however. The largest contribution to the high sensitivity of HadGEM1 is from a high latitude clear-sky shortwave feedback, and clear-sky longwave feedbacks contribute substantially to the highest sensitivity members of the PPE. Differences in low latitude ocean regions (30°N/S) contribute more to the range than those in mid-latitude oceans (30–55°N/S), low/mid latitude land (55°N/S) or high latitude ocean/land (55–90°N/S), but contributions from these other regions are required to account fully for the higher model sensitivities, for example from land areas in IPSL CM4. Net cloud feedback components over the low latitude oceans sorted into percentile ranges of lower tropospheric stability (LTS) show largest differences among models in stable regions, mainly due to their shortwave components, most of which are positive in spite of increasing LTS. Differences in the mid-stability range are smaller, but cover a larger area, contributing a comparable amount to the range in climate sensitivity. These are strongly anti-correlated with changes in subsidence. Cloud components of CO 2 forcing also show the largest differences in stable regions, and are strongly anticorrelated with changes in estimated inversion strength (EIS). This is qualitatively consistent with what would be expected from observed relationships between EIS and low-level cloud fraction. We identify a number of cases where individual models show unusually strong forcings and feedbacks compared to other members of the ensemble. We encourage modelling groups to investigate unusual model behaviours further with sensitivity experiments. Most of the models fail to correctly reproduce the observed relationships between stability and cloud radiative effect in the subtropics, indicating that there remains considerable room for model improvements in the future.

The primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud–climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions “How does the Earth system respond to forcing?” and “What are the origins and consequences of systematic model biases?” and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity.A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO2 forcing and sea surface warming?CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions.How well do clouds and other relevant variables simulated by models agree with observations?What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models?Which models have the most credible representations of processes relevant to the simulation of clouds?How do clouds and their changes interact with other elements of the climate system?

In HadGEM2-A, AMIP experiments forced with observed sea surface temperatures respond to uniform and patterned +4 K SST perturbations with strong positive cloud feedbacks in the subtropical stratocumulus/trade cumulus transition regions. Over the subtropical Northeast Pacific at 137°W/26°N, the boundary layer cloud fraction reduces considerably in the AMIP +4 K patterned SST experiment. The near-surface wind speed and the air-sea temperature difference reduces, while the near-surface relative humidity increases. These changes limit the local increase in surface evaporation to just 3 W/m 2 or 0.6 %/K. Previous studies have suggested that increases in surface evaporation may be required to maintain maritime boundary layer cloud in a warmer climate. This suggests that the supply of water vapour from surface evaporation may not be increasing enough to maintain the low level cloud fraction in the warmer climate in HadGEM2-A. Sensitivity tests which force the surface evaporation to increase substantially in the +4 K patterned SST experiment result in smaller changes in boundary layer cloud and a weaker cloud feedback in HadGEM2-A, supporting this idea. Although global mean surface evaporation in climate models increases robustly with global temperature (and the resulting increase in atmospheric radiative cooling), local values may increase much less, having a significant impact on cloud feedback. These results suggest a coupling between cloud feedback and the hydrological cycle via changes in the patterns of surface evaporation. A better understanding of both the factors controlling local changes in surface evaporation and the sensitivity of clouds to such changes may be required to understand the reasons for inter-model differences in subtropical cloud feedback.