Interpretation of Cloud-Climate Feedback as Produced by 14 Atmospheric General Circulation Models

Cess, R. D.; Potter, Gerald L.; Blanchet, Jean‐Pierre; Boer, G. J.; Ghan, Steven J.; Kiehl, J. T.; Treut, Hervé Le; Li, Z.-X.; Liang, Xin‐Zhong; Mitchell, J. F. B.; Morcrette, Jean‐Jacques; Randall, David A.; Riches, Michael R.; Roeckner, Erich; Schlese, Ulrich; Slingo, A.; Taylor, Karl E.; Washington, Warren M.; Wetherald, R. T.; Yagai, I.

doi:10.1126/science.245.4917.513

Cited by 486 publications

(274 citation statements)

References 13 publications

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“…Similarly, the effective climate sensitivities of AM2.1 (1.7 K), AM3 (2.0 K), and AM4.0 (1.9 K) over the historical period are significantly lower than the ECS of the parent AOGCMs (ESM2M (3.3 K) and CM3(4.8 K), computed from multimillennia equilibrium runs). Much of the uncertainty in the ECS has been attributed to cloud feedbacks (Bony & Dufresne, 2005;Cess et al, 1989). Despite large differences in ECS and effective climate sensitivities among AM2.1, AM3, and AM4.0, this study demonstrates that the cloudy response to changing SST patterns over the historical period is similar for these AGCMs.…”

Section: Discussionmentioning

confidence: 74%

“…We emphasize that the terminology "climate sensitivity" and "climate feedback parameter" here simply refer to measures of the change of radiative balance at TOA with fixed radiative forcing and prescribed SST. This method of computing climate sensitivity is in keeping with previous studies (Cess et al, 1989;Silvers et al, 2016;GA16), and we assume there is useful correspondence between the interaction of clouds and the TOA energy budget found here and in a fully coupled Earth system. For amip-piForcing experiments, F = 0, and the "effective" climate sensitivity can be inferred as F 2× ∕ , where F 2× is an appropriate representative value of radiative forcing due to doubled (relative to preindustrial) CO 2 concentrations (we use F 2× = 3.4 W m −2 , Flato et al, 2013).…”

Section: Methodology Of Analysismentioning

confidence: 96%

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The Diversity of Cloud Responses to Twentieth Century Sea Surface Temperatures

Silvers

Paynter

Zhao

2018

Geophysical Research Letters

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Low‐level clouds are shown to be the conduit between the observed sea surface temperatures (SST) and large decadal fluctuations of the top of the atmosphere radiative imbalance. The influence of low‐level clouds on the climate feedback is shown for global mean time series as well as particular geographic regions. The changes of clouds are found to be important for a midcentury period of high sensitivity and a late century period of low sensitivity. These conclusions are drawn from analysis of amip‐piForcing simulations using three atmospheric general circulation models (AM2.1, AM3, and AM4.0). All three models confirm the importance of the relationship between the global climate sensitivity and the eastern Pacific trends of SST and low‐level clouds. However, this work argues that the variability of the climate feedback parameter is not driven by stratocumulus‐dominated regions in the eastern ocean basins, but rather by the cloudy response in the rest of the tropics.

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Section: Discussionmentioning

confidence: 74%

Section: Methodology Of Analysismentioning

confidence: 96%

The Diversity of Cloud Responses to Twentieth Century Sea Surface Temperatures

Silvers

Paynter

Zhao

2018

Geophysical Research Letters

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“…Clouds tend to dominate radiative fluxes and greatly influence regional and global climate [ Cess et al , 1989]. Cloud depths and droplet number concentrations ( N c ) determine the efficiency of radiation reflection.…”

Section: Introductionmentioning

confidence: 99%

MODIS comparisons with northeastern Pacific in situ stratocumulus microphysics

Noble

Hudson

2015

JGR Atmospheres

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Vertical sounding measurements within stratocumuli during two aircraft field campaigns, Marine Stratus/stratocumulus Experiment (MASE) and Physics of Stratocumulus Top (POST), are used to validate Moderate Resolution Imaging Spectroradiometer (MODIS) cloud optical thickness (COT), cloud liquid water path (LWP), and cloud effective radius (r e). In situ COT, LWP, and r e were calculated using 5 m vertically averaged droplet probe measurements of complete vertical cloud penetrations. MODIS COT, LWP, and r e 1 km pixels were averaged along these penetrations. COT comparisons in POST showed strong correlations and a near 1:1 relationship. In MASE, comparisons showed strong correlations; however, MODIS COT exceeded in situ COT, likely due to larger temporal differences between MODIS and in situ measurements. LWP comparisons between two cloud probes show good agreement for POST but not MASE, giving confidence to POST data. Both projects provided strong LWP correlations but MODIS exceeded in situ by 14–36%. MODIS in situ r e correlations were strong, but MODIS 2.1 µm r e exceeded in situ r e, which contributed to LWP bias; in POST, MODIS r e was 20–30% greater than in situ r e. Maximum in situ r e near cloud top showed comparisons nearer 1:1. Other MODIS r e bands (3.7 µm and 1.6 µm) showed similar comparisons. Temporal differences between MODIS and in situ measurements, airplane speed differences, and cloud probe artifacts were likely causes of weaker MASE correlations. POST COT comparison was best for temporal differences under 20 min. POST data validate MODIS COT but it also implies a positive MODIS r e bias that propagates to LWP while still capturing variability.

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“…Research over at least the past two decades has shown that an uncertain representation of clouds, specifically tropical low-level clouds, is currently the largest source of spread among state-of-the-art climate models [Cess et al, 1989;Bony and Dufresne, 2005;Stevens and Bony, 2013]. Specific limitations of modeled clouds include both features which differ from what one observes (e.g., ''too few, too bright,'' [Webb et al, 2001;Nam et al, 2012]) as well as features which are inadequately understood (e.g., controls on cloud vertical structure [Nuijens et al, 2015], and organization [Bretherton et al, 2005;Muller and Held, 2012;Wing and Emanuel, 2014]).…”

Section: Introductionmentioning

confidence: 99%

Radiative convective equilibrium as a framework for studying the interaction between convection and its large‐scale environment

Silvers

Stevens

Mauritsen

et al. 2016

J Adv Model Earth Syst

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An uncertain representation of convective clouds has emerged as one of the key barriers to our understanding of climate sensitivity. The large gap in resolved spatial scales between General Circulation Models (GCMs) and high resolution models has made a systematic study of convective clouds across model configurations difficult. It is shown here that the simulated atmosphere of a GCM in Radiative Convective Equilibrium (RCE) is sufficiently similar across a range of domain sizes to justify the use of RCE to study both a GCM and a high resolution model on the same domain with the goal of improved constraints on the parameterized clouds. Simulations of RCE with parameterized convection have been analyzed on domains with areas spanning more than two orders of magnitude ( ), all having the same grid spacing of . The simulated climates on different domains are qualitatively similar in their degree of convective organization, the precipitation rates, and the vertical structure of the clouds and water vapor, with the similarity increasing as the domain size increases. Sea surface temperature perturbation experiments are used to estimate the climate feedback parameter for the differently configured experiments, and the cloud radiative effect is computed to examine the role which clouds play in the response. Despite the similar climate states between the domains the feedback parameter varies by more than a factor of two; the hydrological sensitivity parameter is better behaved, varying by a factor of 1.4. The sensitivity of the climate feedback parameter to domain size is related foremost to a nonsystematic response of low‐level clouds as well as an increasingly negative longwave feedback on larger domains.

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Interpretation of Cloud-Climate Feedback as Produced by 14 Atmospheric General Circulation Models

Cited by 486 publications

References 13 publications

The Diversity of Cloud Responses to Twentieth Century Sea Surface Temperatures

The Diversity of Cloud Responses to Twentieth Century Sea Surface Temperatures

MODIS comparisons with northeastern Pacific in situ stratocumulus microphysics

Radiative convective equilibrium as a framework for studying the interaction between convection and its large‐scale environment

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