Cloud Feedback Research Articles

Forcings, Feedbacks, and Climate Sensitivity in HadGEM3‐GC3.1 and UKESM1

AbstractClimate forcing, sensitivity, and feedback metrics are evaluated in both the United Kingdom's physical climate model HadGEM3‐GC3.1 at low (‐LL) and medium (‐MM) resolution and the United Kingdom's Earth System Model UKESM1. The effective climate sensitivity (EffCS) to a doubling of CO2 is 5.5 K for HadGEM3.1‐GC3.1‐LL and 5.4 K for UKESM1. The transient climate response is 2.5 and 2.8 K, respectively. While the EffCS is larger than that seen in the previous generation of models, none of the model's forcing or feedback processes are found to be atypical of models, though the cloud feedback is at the high end. The relatively large EffCS results from an unusual combination of a typical CO2 forcing with a relatively small feedback parameter. Compared to the previous U.K. climate model, HadGEM3‐GC2.0, the EffCS has increased from 3.2 to 5.5 K due to an increase in CO2 forcing, surface albedo feedback, and midlatitude cloud feedback. All changes are well understood and due to physical improvements in the model. At higher atmospheric and ocean resolution (HadGEM3‐GC3.1‐MM), there is a compensation between increased marine stratocumulus cloud feedback and reduced Antarctic sea‐ice feedback. In UKESM1, a CO2 fertilization effect induces a land surface vegetation change and albedo radiative effect. Historical aerosol forcing in HadGEM3‐GC3.1‐LL is −1.1 W m−2. In HadGEM3‐GC3.1‐LL historical simulations, cloud feedback is found to be less positive than in abrupt‐4xCO2, in agreement with atmosphere‐only experiments forced with observed historical sea surface temperature and sea‐ice variations. However, variability in the coupled model's historical sea‐ice trends hampers accurate diagnosis of the model's total historical feedback.

Modulation of Indian monsoon by water vapor and cloud feedback over the past 22,000 years

To predict how monsoons will evolve in the 21st century, we need to understand how they have changed in the past. In paleoclimate literature, the major focus has been on the role of solar forcing on monsoons but not on the amplification by feedbacks internal to the climate system. Here we have used the results from a transient climate simulation to show that feedbacks amplify the effect of change in insolation on the Indian summer monsoon. We show that during the deglacial (22 ka to 10 ka) monsoons were predominantly influenced by rising water vapor due to increasing sea surface temperature, whereas in the Holocene (10 ka to 0 ka) cloud feedback was more important. These results are consistent with another transient simulation, thus increasing confidence despite potential model biases. We have demonstrated that insolation drives monsoon through different pathways during cold and warm periods, thereby highlighting the changing role of internal factors.

The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

Abstract. Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates. Depending on whether a region is dominated by cumulus (Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback is somewhat different in both spaceborne and large-eddy simulation studies. Therefore, simulating the correct amount and variation of the Cu and Sc cloud distributions could be crucial to predict future cloud feedbacks. Here we document spatial distributions and profiles of Sc and Cu clouds derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat measurements. For this purpose, we create a new dataset called the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiform outflow and Cu. To separate the Cu from Sc, we design an original method based on the cloud height, horizontal extent, vertical variability and horizontal continuity, which is separately applied to both CALIPSO and combined CloudSat–CALIPSO observations. First, the choice of parameters used in the discrimination algorithm is investigated and validated in selected Cu, Sc and Sc–Cu transition case studies. Then, the global statistics are compared against those from existing passive- and active-sensor satellite observations. Our results indicate that the cloud optical thickness – as used in passive-sensor observations – is not a sufficient parameter to discriminate Cu from Sc clouds, in agreement with previous literature. Using clustering-derived datasets shows better results although one cannot completely separate cloud types with such an approach. On the contrary, classifying Cu and Sc clouds and the transition between them based on their geometrical shape and spatial heterogeneity leads to spatial distributions consistent with prior knowledge of these clouds, from ground-based, ship-based and field campaigns. Furthermore, we show that our method improves existing Sc–Cu classifications by using additional information on cloud height and vertical cloud fraction variation. Finally, the CASCCAD datasets provide a basis to evaluate shallow convection and stratocumulus clouds on a global scale in climate models and potentially improve our understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana, 2019, https://doi.org/10.5281/zenodo.2667637) is available on the Goddard Institute for Space Studies (GISS) website at https://data.giss.nasa.gov/clouds/casccad/ (last access: 5 November 2019) and on the zenodo website at https://zenodo.org/record/2667637 (last access: 5 November 2019).

Evaluation of cloud base height in the North American Regional Reanalysis using ceilometer observations

AbstractFuture climate change predictions by global climate models or earth system models diverge significantly, most likely due to their different cloud responses to global warming. There is an uncertainty as to how the cloud frequency (or cloud fraction) and height will change, in turn, affecting the sign, and amount of cloud feedbacks. While satellite observations have been very useful in augmenting information on clouds, it is mostly related to cloud tops, and there is a lack of information on cloud base height (CBH). In this study, a unique record of CBH information was collected at 706 Automated Surface Observing System (ASOS) ceilometer stations to evaluate the ability of the North American Regional Reanalysis (NARR) model to correctly simulate similar information. It was found that NARR can capture the geographical distribution and the seasonal variation of CBH and cloud base frequency (CBF). On average, the CBF values of NARR were 7% fewer and the CBH values of NARR were 631 m lower than those from observations that span the distance from the surface to 7,600 m. NARR simulates CBH better in arid area in the west of the contiguous United States (CONUS) than in humid areas, where NARR frequently predicted cloud bases too low compared with observations. In the west coast area of the CONUS, the discontinuity between high cloud bases over arid inland areas and low marine cloud layers from the Pacific Ocean over coastal areas produced especially large deviations between the NARR‐simulated and ASOS‐observed CBHs.

Uncertainty in the Evolution of Climate Feedback Traced to the Strength of the Atlantic Meridional Overturning Circulation

AbstractIn most coupled climate models, effective climate sensitivity increases for a few decades following an abrupt CO2 increase. The change in the climate feedback parameter between the first 20 years and the subsequent 130 years is highly model dependent. In this study, we suggest that the intermodel spread of changes in climate feedback can be partially traced to the evolution of the Atlantic Meridional Overturning Circulation. Models with stronger Atlantic Meridional Overturning Circulation recovery tend to project more amplified warming in the Northern Hemisphere a few decades after a quadrupling of CO2. Tropospheric stability then decreases as the Northern Hemisphere gets warmer, which leads to an increase in both the lapse‐rate and shortwave cloud feedbacks. Our results suggest that constraining future ocean circulation changes will be necessary for accurate climate sensitivity projections.

Quantifying the Cloud Particle‐Size Feedback in an Earth System Model

AbstractPhysical process‐based two‐moment cloud microphysical parameterizations, in which effective cloud particle size evolves prognostically with climate change, have recently been incorporated into global climate models. The impacts of cloud particle‐size change on the cloud feedback, however, have never been explicitly quantified. Here we develop a partial radiative perturbation‐based method to estimate the cloud feedback associated with particle‐size changes in the Community Earth System Model. We find an increase of cloud particle size in the upper troposphere in response to an instantaneous doubling of atmospheric CO2. The associated net, shortwave, and longwave cloud feedbacks are estimated to be 0.18, 0.33, and −0.15 Wm−2 K−1, respectively. The cloud particle‐size feedback is dominated by its shortwave component with a maximum greater than 1.0 Wm−2 K−1 in the tropics and the Southern Ocean. We suggest that the cloud particle‐size feedback is an underappreciated contributor to the spread of cloud feedback and climate sensitivity among current models.

Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation

Abstract. The Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) is used to diagnose model performance and physical processes via an apple-to-apple comparison to satellite measurements. Although the COSP provides useful information about clouds and their climatic impact, outputs that have a subcolumn dimension require large amounts of data. This can cause a bottleneck when conducting sets of sensitivity experiments or multiple model intercomparisons. Here, we incorporate two diagnostics for warm rain microphysical processes into the latest version of the simulator (COSP2). The first one is the occurrence frequency of warm rain regimes (i.e., non-precipitating, drizzling, and precipitating) classified according to CloudSat radar reflectivity, putting the warm rain process diagnostics into the context of the geographical distributions of precipitation. The second diagnostic is the probability density function of radar reflectivity profiles normalized by the in-cloud optical depth, the so-called contoured frequency by optical depth diagram (CFODD), which illustrates how the warm rain processes occur in the vertical dimension using statistics constructed from CloudSat and MODIS simulators. The new diagnostics are designed to produce statistics online along with subcolumn information during the COSP execution, eliminating the need to output subcolumn variables. Users can also readily conduct regional analysis tailored to their particular research interest (e.g., land–ocean differences) using an auxiliary post-process package after the COSP calculation. The inline diagnostics are applied to the MIROC6 general circulation model (GCM) to demonstrate how known biases common among multiple GCMs relative to satellite observations are revealed. The inline multi-sensor diagnostics are intended to serve as a tool that facilitates process-oriented model evaluations in a manner that reduces the burden on modelers for their diagnostics effort.

Attribution of Recent Trends in Temperature Extremes over China: Role of Changes in Anthropogenic Aerosol Emissions over Asia

ABSTRACT Observations indicate large changes in temperature extremes over China during the last four decades, exhibiting as significant increases in the amplitude and frequency of hot extremes and decreases in the amplitude and frequency of cold extremes. An ensemble of transient experiments with the fully coupled atmosphere–ocean model HadGEM3-GC2, including both anthropogenic forcing and natural forcing, successfully reproduces the spatial pattern and magnitude of observed historical trends in both hot and cold extremes. The model-simulated trends in temperature extremes primarily come from the positive trends in clear-sky longwave radiation, which is mainly due to the increases in greenhouse gases (GHGs). An ensemble of sensitivity experiments with Asian anthropogenic aerosol (AA) emissions fixed at their 1970s levels tends to overestimate the trends in temperature extremes, indicating that local AA emission changes have moderated the trends in these temperature extremes over China. The recent increases in Asian AA drive cooling trends over China by inducing negative clear-sky shortwave radiation directly through the aerosol–radiation interaction, which partly offsets the strong warming effect by GHG changes. The cooling trends induced by Asian AA changes are weaker over northern China during summer, which is due to the warming effect by the positive shortwave cloud radiative effect through the AA-induced atmosphere–cloud feedback. This accounts for the observed north–south gradients of the historical trends in some temperature extremes over China, highlighting the importance of local Asian AA emission changes on spatial heterogeneity of trends in temperature extremes.

Radiative Feedbacks Associated with the Madden–Julian Oscillation

AbstractRadiative kernels derived from CloudSat/CALIPSO measurements are used to diagnose radiative feedbacks induced by the Madden–Julian oscillation (MJO). Over the Indo-Pacific warm pool, positive cloud and water vapor feedbacks are coincident with the convective envelope of the MJO during its active phases, whereas the lapse rate feedback shows faster eastward propagation than the convective envelope. During phase 2/3, when the convective envelope is over the Indian Ocean, water vapor exhibits a vertically coherent response, with the largest anomalies and strongest feedback in the midtroposphere. Though spatial structures of the feedbacks vary, the most prominent difference lies in the magnitude. Cloud changes induce the largest radiative perturbations associated with the MJO. It is also found that the strength of the cloud feedback per unit of precipitation is greater for strong MJO events, suggesting that the strength of individual MJO events is largely dictated by the magnitude of cloud radiative heating of the atmosphere. In addition, stronger radiative heating due to water vapor and clouds helps the MJO survive the barrier effect of the Maritime Continent, leading to farther eastward propagation. These results offer process-oriented metrics that could help to improve model simulations and predictions of the MJO in the future.

Contributions of atmospheric and oceanic feedbacks to subtropical northeastern sea surface temperature variability

Previous studies show that the dominant mode of variability in the Northeastern subtropical Pacific and Atlantic are analogous. Most attention has been given to the wind-evaporation-sea surface temperature (WES) feedback, but more recent studies suggest that clouds and ocean play a role. Here, it is shown that, while the mode of variability is similar, the quantitative role of clouds and ocean are different. Using Community Earth System Model, version 1.2, cloud feedbacks and interactive ocean dynamics are disabled separately to diagnose the relative contributions of each to sea surface temperature (SST) variability in subtropical northeastern ocean basins. Results from four experiments show that the relative contributions from WES and cloud radiative feedback depend on the role of the ocean. Positive cloud radiative feedback is evident in both basins but has less impact on SST variance in the Atlantic than in the Pacific. The reason for this is that ocean processes strongly damp SST anomalies in the Pacific and weakly enhance SST anomalies in the Atlantic. When cloud feedbacks are disabled, ocean processes become a larger driver of SST variability in the Atlantic. In line with previous studies, the Northeast Pacific SST variability may be understood as a white-noise-forced linear stochastic system with positive feedback from cloud and damping by latent heat flux and ocean processes, while Atlantic SST is driven partially by variations in ocean circulation and requires vertical mixing for rendition. Between these two regions, different ocean dynamics lead to different roles for atmospheric feedbacks but still result in similar patterns of SST variability.

Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks.

The Early Eocene, a period of elevated atmospheric CO2 (>1000 ppmv), is considered an analog for future climate. Previous modeling attempts have been unable to reproduce major features of Eocene climate indicated by proxy data without substantial modification to the model physics. Here, we present simulations using a state-of-the-art climate model forced by proxy-estimated CO2 levels that capture the extreme surface warmth and reduced latitudinal temperature gradient of the Early Eocene and the warming of the Paleocene-Eocene Thermal Maximum. Our simulations exhibit increasing equilibrium climate sensitivity with warming and suggest an Eocene sensitivity of more than 6.6°C, much greater than the present-day value (4.2°C). This higher climate sensitivity is mainly attributable to the shortwave cloud feedback, which is linked primarily to cloud microphysical processes. Our findings highlight the role of small-scale cloud processes in determining large-scale climate changes and suggest a potential increase in climate sensitivity with future warming.

Retrieval of ice cloud microphysical properties at the SACOL

Ice clouds are one of the most widely distributed cloud types in the world and play an important role in the radiation budget. The microphysical parameters of ice clouds, such as ice water content, effective radius and particle concentration, have a significant effect on the artificial influences on weather, the calculation of the radiation effect and climate feedback of ice clouds. Because of their unique physical and optical properties, ice clouds play an important and unique role in the radiation budget. Understanding and recognizing the microphysical properties of ice clouds are of great significance for studying the effects of ice clouds on climate change. Ka-band zenith radar (KAZR), which has been deployed at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) since July 2013, has continuously operated for many years. By using the observation data from KAZR and micropulse lidar (MPL), we compare the microphysical properties of ice clouds, including ice effective particle size (Dge) and ice water content (IWC), which were derived from three algorithms: a traditional algorithm (Microbase), the Z-IWC-T relationship algorithm (Hogan) and the KAZR-MPL retrieval algorithm (Combined). Then, we compute and examine the radiative fluxes using each set of cloud properties as inputs to a radiative transfer model and compare the top-of-atmosphere (TOA) radiative fluxes to the satellite measurements to assess the impact of the differences in the microphysical quantities of different ice clouds on radiation. Our analysis shows that ice clouds mainly appear from 2 to 14 km and peak by approximately 13.02% at 7 km. The ice water contents retrieved from the three different inversion methods show a unimodal distribution. The ice water content is mainly concentrated in the range of 0.01−10 mg/m3, and the corresponding maximum probability of ice water content is concentrated in the vicinity of 0.1 mg/m3. However, there are some differences between the degrees of data concentration and the peak probabilities of the different algorithms. The effective particle size distributions obtained by the three methods are quite different. The effective particle size of the ice crystal inversion by the Microbase algorithm is unimodal, and the data are mainly concentrated in the small particle radius region of 0−100 μm. The KAZR-MPL algorithm has a wider range of particle distributions, with two peaks at 40 and 96 μm. However, the distribution of the ice effective particle radius shows significant discrepancies, which sometimes translate into large differences in the cloud shortwave radiative effect. Using the results of three different microphysical properties, the longwave and shortwave radiation fluxes at the top of the atmosphere are calculated through the Fu-Liou radiation transfer model. The results show that the longwave radiation fluxes calculated by using different cloud microphysical characteristics are in good agreement with the satellite observation data. The average error is 5.04%, and the correlation coefficients are all greater than 0.6. Compared with the observed longwave radiation, the Hogan algorithm that considers the temperature effect has a better simulation effect, the correlation coefficient is larger, and the root mean square error is smaller. The shortwave radiation is smaller than the observed values in the case of clouds, and the maximum error reaches 24.61%. The shortwave radiation flux simulated by the traditional empirical algorithm is closest to the satellite observations, and its root mean square error is significantly reduced compared with that of the other methods.

A Conception for Identifying Trust Service Providers in Collaboration Cloud Computing

cloud computing model has proven to be effective and popular in offering the benefits such as virtually limitless storage volume, availability and scalability. Hence the cloud environment is managed by varied trust management models. Choosing the most suitable trust management model by the user is often a difficult task. Since there is multiple cloud services offering different level of performances with same functionalities it becomes hard for the user to detect and select the apt cloud service. Csps tend to offer cloud related services to the cloud users keeping sla in consideration. Depending on sort of csp administration offered the payment by the consumer end, each transaction is being evidently distinguished. Regrettably, since qos (quality of service) is not achieved, sla is not looked upon seriously. In various situations and recommendations, administration presence doesn’t fulfill the standards and reliability is compromised. The current research paper deals with the difficulty in choosing qualitative csp (cloud service provider). The proposal is of a methodology to reveal the csps trustworthiness pertaining to a cloud scenario. The various levels listed in this mechanism or methodology being: building up of cloud cluster environment, broker creation, machine agent, feedback provider and trust validation. The mentioned system of trust management gains fewer machine agents, feedback is given by external resources, highly effective in trust computing, cpu’s of machine won’t be left idle or sluggish. It can be stated that the proposed mechanism yield to be effective and feasible as validated by the performance analysis and experimental outcomes. The outcome demonstrates the effectiveness of the approach concerning services matching. With effective use of trust model specific service can be chosen by the cloud user.

Separating the Impact of Individual Land Surface Properties on the Terrestrial Surface Energy Budget in both the Coupled and Uncoupled Land–Atmosphere System

AbstractChanges in the land surface can drive large responses in the atmosphere on local, regional, and global scales. Surface properties control the partitioning of energy within the surface energy budget to fluxes of shortwave and longwave radiation, sensible and latent heat, and ground heat storage. Changes in surface energy fluxes can impact the atmosphere across scales through changes in temperature, cloud cover, and large-scale atmospheric circulation. We test the sensitivity of the atmosphere to global changes in three land surface properties: albedo, evaporative resistance, and surface roughness. We show the impact of changing these surface properties differs drastically between simulations run with an offline land model, compared to coupled land–atmosphere simulations that allow for atmospheric feedbacks associated with land–atmosphere coupling. Atmospheric feedbacks play a critical role in defining the temperature response to changes in albedo and evaporative resistance, particularly in the extratropics. More than 50% of the surface temperature response to changing albedo comes from atmospheric feedbacks in over 80% of land areas. In some regions, cloud feedbacks in response to increased evaporative resistance result in nearly 1 K of additional surface warming. In contrast, the magnitude of surface temperature responses to changes in vegetation height are comparable between offline and coupled simulations. We improve our fundamental understanding of how and why changes in vegetation cover drive responses in the atmosphere, and develop understanding of the role of individual land surface properties in controlling climate across spatial scales—critical to understanding the effects of land-use change on Earth’s climate.

Efficacy of black carbon aerosols: the role of shortwave cloud feedback

Using idealized climate model simulations, we investigate the effectiveness of black carbon (BC) aerosols in warming the planet relative to CO2 forcing. We find that a 60-fold increase in the BC aerosol mixing ratio from the present-day levels leads to the same equilibrium global mean surface warming (∼4.1 K) as for a doubling of atmospheric CO2 concentration. However, the radiative forcing is larger (∼5.5 Wm−2) in the BC case relative to the doubled CO2 case (∼3.8 Wm−2) for the same surface warming indicating the efficacy (a metric for measuring the effectiveness) of BC aerosols to be less than CO2. The lower efficacy of BC aerosols is related to the differences in the shortwave (SW) cloud feedback: negative in the BC case but positive in the CO2 case. In the BC case, the negative SW cloud feedback is related to an increase in the tropical low clouds which is associated with a northward shift (∼7°) of the Intertropical Convergence Zone (ITCZ). Further, we show that in the BC case fast precipitation suppression offsets the surface temperature mediated precipitation response and causes ∼8% net decline in the global mean precipitation. Our study suggests that a feedback between the location of ITCZ and the interhemispheric temperature could exist, and the consequent SW cloud feedback could be contributing to the lower efficacy of BC aerosols. Therefore, an improved representation of low clouds in climate models is likely the key to understand the global climate sensitivity to BC aerosols.

CASI: A Convolutional Neural Network Approach for Shell Identification

We utilize techniques from deep learning to identify signatures of stellar feedback in simulated molecular clouds. Specifically, we implement a deep neural network with an architecture similar to U-Net and apply it to the problem of identifying wind-driven shells and bubbles using data from magneto-hydrodynamic simulations of turbulent molecular clouds with embedded stellar sources. The network is applied to two tasks, dense regression and segmentation, on two varieties of data, simulated density and synthetic 12 CO observations. Our Convolutional Approach for Shell Identification (CASI) is able to obtain a true positive rate greater than 90\%, while maintaining a false positive rate of 1\%, on two segmentation tasks and also performs well on related regression tasks. The source code for CASI is available on GitLab.

A Positive Iris Feedback: Insights from Climate Simulations with Temperature-Sensitive Cloud–Rain Conversion

AbstractEstimates for equilibrium climate sensitivity from current climate models continue to exhibit a large spread, from 2.1 to 4.7 K per carbon dioxide doubling. Recent studies have found that the treatment of precipitation efficiency in deep convective clouds—specifically the conversion rate from cloud condensate to rain Cp—may contribute to the large intermodel spread. It is common for convective parameterization in climate models to carry a constant Cp, although its values are model and resolution dependent. In this study, we investigate how introducing a potential iris feedback, the cloud–climate feedback introduced by parameterizing Cp to increase with surface temperature, affects future climate simulations within a slab ocean configuration of the Community Earth System Model. Progressively stronger dependencies of Cp on temperature unexpectedly increase the equilibrium climate sensitivity monotonically from 3.8 to up to 4.6 K. This positive iris feedback puzzle, in which a reduction in cirrus clouds increases surface temperature, is attributed to changes in the opacity of convectively detrained cirrus. Cirrus clouds reduced largely in ice content and marginally in horizontal coverage, and thus the positive shortwave cloud radiative feedback dominates. The sign of the iris feedback is robust across different cloud macrophysics schemes, which control horizontal cloud cover associated with detrained ice. These results suggest a potentially strong but highly uncertain connection among convective precipitation, detrained anvil cirrus, and the high cloud feedback in a climate forced by increased atmospheric carbon dioxide concentrations.

High Climate Sensitivity in the Community Earth System Model Version 2 (CESM2)

AbstractThe Community Earth System Model Version 2 (CESM2) has an equilibrium climate sensitivity (ECS) of 5.3 K. ECS is an emergent property of both climate feedbacks and aerosol forcing. The increase in ECS over the previous version (CESM1) is the result of cloud feedbacks. Interim versions of CESM2 had a land model that damped ECS. Part of the ECS change results from evolving the model configuration to reproduce the long‐term trend of global and regional surface temperature over the twentieth century in response to climate forcings. Changes made to reduce sensitivity to aerosols also impacted cloud feedbacks, which significantly influence ECS. CESM2 simulations compare very well to observations of present climate. It is critical to understand whether the high ECS, outside the best estimate range of 1.5–4.5 K, is plausible.

Is Arctic Amplification Dominated by Regional Radiative Forcing and Feedbacks: Perspectives From the World‐Avoided Scenario

AbstractArctic amplification (AA) is typically associated with Planck, lapse rate, and ice albedo feedbacks. However, the relative importance of poleward energy transport on AA remains uncertain. Here, we analyze integrations from a Chemistry Climate Model to investigate the impact of the Montreal Protocol on forcing, feedback, and transport contributions to AA. Two ensembles of future integrations are considered—one projecting decreasing ozone‐depleting substance concentrations and stratospheric ozone recovery and another assuming that ozone‐depleting substances are not regulated (the “World Avoided”). We find similar degrees of AA in both ensembles, despite a negative radiative forcing over the Arctic in the “World Avoided” from massive ozone loss. That negative radiative forcing is primarily balanced from positive atmospheric energy flux convergence and long‐wave cloud feedbacks. Our results highlight the impact of inhomogeneous radiative forcing on regional differences in forcing and feedback strength and the importance of radiative forcing meridional structure on poleward energy transport.

Cloud Radiative Feedbacks and El Niño–Southern Oscillation

Abstract Cloud radiative feedbacks are disabled via “cloud-locking” in the Community Earth System Model, version 1.2 (CESM1.2), to result in a shift in El Niño–Southern Oscillation (ENSO) periodicity from 2–7 years to decadal time scales. We hypothesize that cloud radiative feedbacks may impact the periodicity in three ways: by 1) modulating heat flux locally into the equatorial Pacific subsurface through negative shortwave cloud feedback on sea surface temperature anomalies (SSTA), 2) damping the persistence of subtropical southeast Pacific SSTA such that the South Pacific meridional mode impacts the duration of ENSO events, or 3) controlling the meridional width of off-equatorial westerly winds, which impacts the periodicity of ENSO by initiating longer Rossby waves. The result of cloud-locking in CESM1.2 contrasts that of another study, which found that cloud-locking in a different global climate model led to decreased ENSO magnitude across all time scales due to a lack of positive longwave feedback on the anomalous Walker circulation. CESM1.2 contains this positive longwave feedback on the anomalous Walker circulation, but either its influence on the surface is decoupled from ocean dynamics or the feedback is only active on interannual time scales. The roles of cloud radiative feedbacks in ENSO in other global climate models are additionally considered. In particular, it is shown that one cannot predict the role of cloud radiative feedbacks in ENSO through a multimodel diagnostic analysis. Instead, they must be directly altered.