Marine low cloud sensitivity to an idealized climate change: The CGILS LES intercomparison

Blossey, Peter N.; Bretherton, Christopher S.; Zhang, Minghua; Cheng, Anning; Endo, Satoshi; Heus, Thijs; Liu, Yangang; Lock, A. P.; Roode, Stephan R. de; Xu, Kuan‐Man

doi:10.1002/jame.20025

Cited by 151 publications

(315 citation statements)

References 65 publications

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“…LES is most frequently applied to 3D simulations of boundary layer clouds, with dominant updraft and downdraft scales of a few hundred meters. A typical LES might have a horizontal grid spacing of 25-100 m, a vertical grid spacing of 5-50 m, and a doubly periodic horizontal domain of 5-100 km on a side, simulating a period of hours to days with a time step of around 1 s (29,30). CRMs are often applied to deep cumulonimbus convection with updraft and downdraft scales of several kilometers, using horizontal grids of several hundred meters to a few kilometers and vertical grid spacing of a few hundred meters, typically over domains of 100-1,000 km on a side using time steps on the order of 10 s for simulated periods of hours to tens of days (31).…”

Section: Process-scale Modeling Of Aerosol−cloud Relationshipsmentioning

confidence: 99%

Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system

Seinfeld

Bretherton

Carslaw

et al. 2016

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

611

474

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The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth's clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol−cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions, but significant challenges exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol−cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. We suggest strategies for improving estimates of aerosol−cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty.climate | aerosol−cloud effects | general circulation models | radiative forcing | satellite observations Clouds play a key role in Earth's radiation budget, and aerosols serve as the seeds upon which cloud droplets form. Anthropogenic activity has led to an increase in aerosol particle concentrations globally and an increase in those particles that act as cloud condensation nuclei (CCN) and ice nucleating particles (INP). The effect of an increase in aerosols on cloud optical properties, and associated radiative forcing, is the most uncertain component of historical radiative forcing of Earth's climate caused by greenhouse gases (GHGs) and aerosols. The Intergovernmental Panel on Climate Change (IPCC) AR5 assessment of climate forcing factors (Fig. S1) ascribes "high" confidence to the estimate of direct aerosol radiative forcing (mean

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Section: Process-scale Modeling Of Aerosol−cloud Relationshipsmentioning

confidence: 99%

Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system

Seinfeld

Bretherton

Carslaw

et al. 2016

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

611

474

View full text Add to dashboard Cite

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“…One approach is to use large-eddy simulations that resolve low-cloud processes to predict the low-cloud changes forced by the climate changes in the environment (Rieck et al 2012;Zhang et al 2012;Blossey et al 2013;Bretherton 2015). A second approach relies on observations of clouds to predict how they will respond to changes in the large-scale environment typical of climate warming.…”

Section: Seeking Observational Constraints On Low-cloud Feedbacksmentioning

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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“…''FTh'' for halving this modification by warming the local SST by half as much as the remote SST). The setup of the control simulation and cases P2S and P2 are described by Blossey et al [2013]. In case dRH, the RH of the reference state is decreased uniformly in the free troposphere, and the free-tropospheric horizontal moisture advection is adjusted to maintain this new humidity profile.…”

Section: Setup Of Simulationsmentioning

confidence: 99%

“…[2] This paper is a companion to Blossey et al [2013], which describes the large-eddy simulation (LES) intercomparison component of the Cloud Feedback Model Intercomparison Project-Global Atmospheric System Study Intercomparison of Large-Eddy Simulation and Single Column Models (CGILS), and to M. Zhang et al (First results from an international project to understand the physical mechanisms of low cloud feedbacks in general circulation models, submitted to Bulletin of the American Meteorological Society, 2012), which gives an overview of CGILS. The goal of CGILS was to develop prototype cases for comparing the response of subtropical cloud-topped boundary layers to idealized climate perturbations in both single-column models and LES.…”

Section: Introductionmentioning

confidence: 99%

Mechanisms of marine low cloud sensitivity to idealized climate perturbations: A single‐LES exploration extending the CGILS cases

Bretherton

Blossey

Jones

2013

J Adv Model Earth Syst

Self Cite

206

358

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[1] Climate change sensitivities of subtropical cloud-topped marine boundary layers are analyzed using large-eddy simulation (LES) of three CGILS cases of well-mixed stratocumulus, cumulus under stratocumulus, and shallow cumulus cloud regimes, respectively. For each case, a steadily forced control simulation on a small horizontally doubly periodic domain is run 10-20 days into quasi-steady state. The LES is rerun to steady state with forcings perturbed by changes in temperature, free-tropospheric relative humidity (RH), CO 2 concentration, subsidence, inversion stability, and wind speed; cloud responses to combined forcings superpose approximately linearly. For all three cloud regimes and 23 CO 2 forcing perturbations estimated from the CMIP3 multimodel mean, the LES predicts positive shortwave cloud feedback, like most CMIP3 global climate models. At both stratocumulus locations, the cloud remains overcast but thins in the warmer, moister, CO 2 -enhanced climate, due to the combined effects of an increased lower-tropospheric vertical humidity gradient and an enhanced free-tropospheric greenhouse effect that reduces the radiative driving of turbulence. Reduced subsidence due to weakening of tropical overturning circulations partly counteracts these two factors by raising the inversion and allowing the cloud layer to deepen. These compensating mechanisms may explain the large scatter in low cloud feedbacks predicted by climate models. CMIP3-predicted changes in wind speed, inversion stability, and free-tropospheric RH have lesser impacts on the cloud thickness. In the shallow cumulus regime, precipitation regulates the simulated boundary-layer depth and vertical structure. Cloud-droplet (aerosol) concentration limits the boundary-layer depth and affects the simulated cloud feedbacks.Citation: Bretherton, C. S., P. N. Blossey, and C. R. Jones (2013), Mechanisms of marine low cloud sensitivity to idealized climate perturbations: A single-LES exploration extending the CGILS cases, J. Adv. Model. Earth Syst., 5, 316-337,

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Marine low cloud sensitivity to an idealized climate change: The CGILS LES intercomparison

Cited by 151 publications

References 65 publications

Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system

Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system

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

Mechanisms of marine low cloud sensitivity to idealized climate perturbations: A single‐LES exploration extending the CGILS cases

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