The diurnal cycle of marine cloud feedback in climate models

Webb, Mark J.; Lock, A. P.; Bodas‐Salcedo, Alejandro; Bony, Sandrine; Cole, Jason N. S.; Koshiro, Tsuyoshi; Kawai, Hideaki; Lacagnina, Carlo; Selten, Frank; Roehrig, Romain; Stevens, Björn

doi:10.1007/s00382-014-2234-1

Cited by 20 publications

(22 citation statements)

References 40 publications

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“…Single‐time step output of nine climate models for a single grid point near Barbados is available through the cfSites initiative from the Cloud Feedback Model Intercomparison Project (CFMIP) [ Webb et al ., ]. The output is produced from Atmospheric Model Intercomparison Project (AMIP) runs from 1976 to 2006, constrained by observed sea surface temperatures and sea ice.…”

Section: Data Model Output and Methodsmentioning

confidence: 99%

“…Because these approaches sample and model clouds across a wide range of conditions, they have successfully led to the identification of systematic biases in modeled physics, such as a compensating error between cloud structure and radiative transfer due to cloud overlap assumptions [ Neggers and Siebesma , ]. A few years ago, the Cloud Feedback Model Intercomparison Project (CFMIP) also initiated the so‐called cfSites output, which includes single‐time step output from many climate models at 120 strategically located sites [ Webb et al ., ].…”

Section: Introductionmentioning

confidence: 99%

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Observed and modeled patterns of covariability between low‐level cloudiness and the structure of the trade‐wind layer

Nuijens

Medeiros

Sandu

et al. 2015

J Adv Model Earth Syst

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We present patterns of covariability between low-level cloudiness and the trade-wind boundary layer structure using long-term measurements at a site representative of dynamical regimes with moderate subsidence or weak ascent. We compare these with ECMWF's Integrated Forecast System and 10 CMIP5 models. By using single-time step output at a single location, we find that models can produce a fairly realistic trade-wind layer structure in long-term means, but with unrealistic variability at shorter-time scales. The unrealistic variability in modeled cloudiness near the lifting condensation level (LCL) is due to stronger than observed relationships with mixed-layer relative humidity (RH) and temperature stratification at the mixed-layer top. Those relationships are weak in observations, or even of opposite sign, which can be explained by a negative feedback of convection on cloudiness. Cloudiness near cumulus tops at the tradewind inversion instead varies more pronouncedly in observations on monthly time scales, whereby larger cloudiness relates to larger surface winds and stronger trade-wind inversions. However, these parameters appear to be a prerequisite, rather than strong controlling factors on cloudiness, because they do not explain submonthly variations in cloudiness. Models underestimate the strength of these relationships and diverge in particular in their responses to large-scale vertical motion. No model stands out by reproducing the observed behavior in all respects. These findings suggest that climate models do not realistically represent the physical processes that underlie the coupling between trade-wind clouds and their environments in present-day climate, which is relevant for how we interpret modeled cloud feedbacks.

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Section: Data Model Output and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Observed and modeled patterns of covariability between low‐level cloudiness and the structure of the trade‐wind layer

Nuijens

Medeiros

Sandu

et al. 2015

J Adv Model Earth Syst

View full text Add to dashboard Cite

show abstract

“…The model runs are 31 years in length (1979–2009). These AMIP series simulations are often used for studies related to cloud feedback; e.g., Webb and Lock () and Webb et al ().…”

Section: Datamentioning

confidence: 99%

Changes in marine fog in a warmer climate

Kawai

Koshiro

Endo

et al. 2016

Atmospheric Science Letters

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Changes in marine fog in a warmer climate are investigated through simulations using the atmospheric component of a global climate model, with both observed and perturbed sea surface temperature forcing. Global changes in marine fog occurrence in different seasons are compared. We show that the changes in marine fog occurrence correspond well to changes in horizontal temperature advection near the surface in a warmer climate. Therefore, the changes in marine fog can be well explained by large-scale circulation changes. Regarding changes in the characteristics of marine fog, we show that the in-cloud liquid water content of marine fog is consistently increased in a warmer climate, for a given horizontal surface temperature advection. It is also confirmed that the contribution of changes in marine fog to cloud feedback is not negligible, but is small.

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“…The first project is the second phase of the Cloud Feedback Model Intercomparison Project (CFMIP‐2, http://cfmip.metoffice.com/), which is an extension to phase 5 of the Coupled Model Intercomparison Project (CMIP5) [ Taylor et al ., ]. One of the key targets of CFMIP‐2 is to make native resolution, high‐frequency data from the CMIP5 simulations available at a set of selected grid points that are of interest to the research community [ Webb et al ., ]. The aim is to facilitate process‐model development and improvement.…”

Section: Introductionmentioning

confidence: 99%

Attributing the behavior of low‐level clouds in large‐scale models to subgrid‐scale parameterizations

Neggers

2015

J Adv Model Earth Syst

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This study explores ways of establishing the characteristic behavior of boundary layer schemes in representing subtropical marine low-level clouds in climate models. To this purpose, parameterization schemes are studied in both isolated and interactive mode with the larger-scale circulation. Results of the EUCLIPSE/GASS intercomparison study for Single-Column Models (SCM) on low-level cloud transitions are compared to General Circulation Model (GCM) results from the CFMIP-2 project at selected grid points in the subtropical eastern Pacific. Low cloud characteristics are plotted as a function of key state variables for which Large-Eddy Simulation results suggest a distinct and reasonably tight relation. These include the Cloud Top Entrainment Instability (CTEI) parameter and the total cloud cover. SCM and GCM results are thus compared and their resemblance is quantified using simple metrics. Good agreement is reported, to such a degree that SCM results are found to be uniquely representative of their GCM, and vice versa. This suggests that the system of parameterized fast boundary layer physics dominates the model state at any given time, even when interactive with the larger-scale flow. This behavior can loosely be interpreted as a unique ''fingerprint'' of a boundary layer scheme, recognizable in both SCM and GCM simulations. The result justifies and advocates the use of SCM simulation for improving weather and climate models, including the attribution of typical responses of low clouds to climate change in a GCM to specific parameterizations. Key Points: The behavior of low-level clouds in climate models is attributed to parameterizations The resemblance between SCM and GCM results is assessed and quantified Boundary layer schemes are found to have a unique, characteristic behavior Correspondence to: R. A. J. Neggers, neggers@meteo.uni-koeln.de Citation: Neggers, R. A. J. (2015), Attributing the behavior of low-level clouds in largescale models to subgrid-scale parameterizations,

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The diurnal cycle of marine cloud feedback in climate models

Cited by 20 publications

References 40 publications

Observed and modeled patterns of covariability between low‐level cloudiness and the structure of the trade‐wind layer

Observed and modeled patterns of covariability between low‐level cloudiness and the structure of the trade‐wind layer

Changes in marine fog in a warmer climate

Attributing the behavior of low‐level clouds in large‐scale models to subgrid‐scale parameterizations

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