CGILS: Results from the first phase of an international project to understand the physical mechanisms of low cloud feedbacks in single column models

Zhang, Minghua; Bretherton, Christopher S.; Blossey, Peter N.; Austin, Philip H.; Bacmeister, Julio T.; Bony, Sandrine; Brient, Florent; Cheedela, S. K.; Cheng, Anning; Genio, Anthony D. Del; Roode, Stephan R. de; Endo, Satoshi; Franklin, Charmaine; Golaz, Jean‐Christophe; Hannay, Cécile; Heus, Thijs; Isotta, Francesco Alessandro; Dufresne, Jean‐Louis; Kang, In‐Sik; Kawai, Hideaki; Köhler, Martin; Larson, Vincent E.; Liu, Yangang; Lock, A. P.; Lohmann, Ulrike; Khairoutdinov, Marat; Molod, Andrea; Neggers, Roel; Rasch, Philip J.; Sandu, Irina; Senkbeil, Ryan; Siebesma, A. Pier; Drian, Colombe Siegenthaler-Le; Stevens, Björn; Suárez, Max J.; Xu, Kuan‐Man; Salzen, Knut von; Webb, Mark J.; Wolf, Allison; Zhao, Ming

doi:10.1002/2013ms000246

Cited by 155 publications

(199 citation statements)

References 85 publications

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“…This type of environment, including everything from the weakly convective to moderately subsiding regimes considered here, poses a somewhat different challenge to parameterized physics suites in that there is a delicate balance and interplay between the boundary layer turbulence and shallow convective mixing. These are usually separate parameterizations in climate models, and the cloud feedback in any particular model is linked to which process is dominant (or if the dominant process changes under climate change) (58,59). As has been described here, the models can achieve some fairly realistic representations of the current climate, but with compensating errors and biases in structure and relative susceptibility to the environment.…”

Section: Discussionmentioning

confidence: 99%

Clouds at Barbados are representative of clouds across the trade wind regions in observations and climate models

Medeiros

Nuijens

2016

Proc. Natl. Acad. Sci. U.S.A.

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Trade wind regions cover most of the tropical oceans, and the prevailing cloud type is shallow cumulus. These small clouds are parameterized by climate models, and changes in their radiative effects strongly and directly contribute to the spread in estimates of climate sensitivity. This study investigates the structure and variability of these clouds in observations and climate models. The study builds upon recent detailed model evaluations using observations from the island of Barbados. Using a dynamical regimes framework, satellite and reanalysis products are used to compare the Barbados region and the broader tropics. It is shown that clouds in the Barbados region are similar to those across the trade wind regions, implying that observational findings from the Barbados Cloud Observatory are relevant to clouds across the tropics. The same methods are applied to climate models to evaluate the simulated clouds. The models generally capture the cloud radiative effect, but underestimate cloud cover and show an array of cloud vertical structures. Some models show strong biases in the environment of the Barbados region in summer, weakening the connection between the regional biases and those across the tropics. Even bearing that limitation in mind, it is shown that covariations of cloud and environmental properties in the models are inconsistent with observations. The models tend to misrepresent sensitivity to moisture variations and inversion characteristics. These model errors are likely connected to cloud feedback in climate projections, and highlight the importance of the representation of shallow cumulus convection.trade wind clouds | climate models | shallow cumulus | Barbados I s the cloud structure and variability observed at a single location over a long time representative of more general regimes of clouds? This is the conceit of long-term observational "super sites" but has rarely been evaluated (1, 2). This work focuses on clouds found in trade wind regions, which are mostly shallow cumulus, and asks whether findings from the Barbados Cloud Observatory (BCO) are generalizable to the broader tropics in observations and climate models. We will evaluate cloud properties and the environment in which the clouds form across 12 climate models within these regions. This emphasis on the trades is motivated by several factors: (i) Previous studies suggest these are the regions that lead to much of the spread in estimates of climate sensitivity (3, 4), (ii) there is a dearth of evaluation of shallow cumulus in climate models (5), and (iii) space-based and ground-based observations of the trades have recently become available (6-8). This focus on trade wind regions is unique but builds upon previous, broader model evaluations that have, among other things, shown improvements in near-global cloud properties in the latest generation of climate models (9) and provided an overview of the vertical structure of cloud fraction (CF) and cloud water content over tropical oceans (10). Ref. 11 explores low-level, tropical clou...

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Section: Discussionmentioning

confidence: 99%

Clouds at Barbados are representative of clouds across the trade wind regions in observations and climate models

Medeiros

Nuijens

2016

Proc. Natl. Acad. Sci. U.S.A.

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“…Although some climate models are in agreement with our estimate of the tropical low-cloud feedback, it remains to be seen if they are in agreement for the right reasons. This motivates additional research to understand the physical basis for the cloud sensitivities (particularly for oC oSST ) through both observations ) and large-eddy simulations (Bretherton and Blossey 2014), and whether the physics is correctly modeled in global climate models (Zhang et al 2013;Sherwood et al 2014;Vial et al 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.…”

Section: Summary and Final Remarksmentioning

confidence: 99%

Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review

et al. 2017

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The response to warming of tropical low-level clouds including both marine stratocumulus and trade cumulus is a major source of uncertainty in projections of future climate. Climate model simulations of the response vary widely, reflecting the difficulty the models have in simulating these clouds. These inadequacies have led to alternative approaches to predict low-cloud feedbacks. Here, we review an observational approach that relies on the assumption that observed relationships between low clouds and the ''cloud-controlling factors'' of the large-scale environment are invariant across time-scales. With this assumption, and given predictions of how the cloud-controlling factors change with climate warming, one can predict low-cloud feedbacks without using any model simulation of low clouds. We discuss both fundamental and implementation issues with this approach and suggest steps that could reduce uncertainty in the predicted low-cloud feedback. Recent studies using this approach predict that the tropical low-cloud feedback is positive mainly due to the observation that reflection of solar radiation by low clouds decreases as temperature increases, holding all other cloud-controlling factors fixed. The positive feedback from temperature is partially offset by a negative feedback from the tendency for the inversion strength to increase in a warming world, with other cloudcontrolling factors playing a smaller role. A consensus estimate from these studies for the contribution of tropical low clouds to the global mean cloud feedback is

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“…It will help us to test the model physics further, and also better understand the cloud feedbacks produced by these models. Indeed, there is ample evidence that single-column simulations of shallow cumuli can help understand low-cloud feedback processes and their dependence on process representations (e.g., Bony 2012, 2013;Zhang and Bretherton 2008;Zhang et al 2013a;Dal Gesso et al 2015;Brient et al 2016). The link to observations, and the comparison between LES and SCM simulations, will allow us to investigate the relationship between the response of shallow cumuli to prescribed climate change perturbations and the realism of the simulated clouds in the present-day climate, which will help answer questions such as: How does the cloud cover depend on the strength of convective mixing?…”

Section: A Simulation and Modelling Testbedmentioning

confidence: 99%

EUREC4A: A Field Campaign to Elucidate the Couplings Between Clouds, Convection and Circulation

et al. 2017

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Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. 123Surv Geophys (2017) 38:1529-1568 https://doi.org/10.1007/s10712-017-9428-0 cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of tradecumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air-sea interactions and convective organization.

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CGILS: Results from the first phase of an international project to understand the physical mechanisms of low cloud feedbacks in single column models

Cited by 155 publications

References 85 publications

Clouds at Barbados are representative of clouds across the trade wind regions in observations and climate models

Clouds at Barbados are representative of clouds across the trade wind regions in observations and climate models

Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review

EUREC4A: A Field Campaign to Elucidate the Couplings Between Clouds, Convection and Circulation

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