Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models

By Sandrine Bony, J. Dufresne. 2005

1,248 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/93f77ba753c58159927168749a04f333c063ff5d

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Citations in the corpus

GCM intercomparison of global cloud regimes: present-day evaluation and climate change response (2007, background): “The inter-model variance in DNCRF for the tropics can be seen to be dominated by differences in the response of stratocumulus, consistent with Webb et al. (2006) and Bony and Dufresne (2005).”
Deep learning to represent subgrid processes in climate models (2018, background): “These inaccuracies manifest themselves in stubborn model biases (6–10) and large uncertainties about how much the earth will warm as a response to increased greenhouse gas concentrations (1, 11, 12).”
On the Growth of Layers of Nonprecipitating Cumulus Convection (2007, background): “In addition, layers of fair-weather cumuli, such as those found in the trades, are increasingly being recognized as a key point of departure among models of the climate system, with much of the variance in model-based estimates of climate sensitivity being attributed to differences in the modeled re”
Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels (2012, methodology): “(2004), Bony and Dufresne (2005), and Wyant et al. (2006) used 500-hPa vertical motion as a proxy for large-scale circulation to separate the response of tropical clouds to an imposed climate change into two components: a thermodynamic component due to intrinsic temperature dependence of cloud radia”
Presentation and Evaluation of the IPSL‐CM6A‐LR Climate Model (2020, background): “The cloud feedback map shows that, geographically, positive values are found in regions of large‐scale subsidence, which cover large parts of the tropical ocean and are associated with marine boundary layer cloud such as stratocumulus and shallow cumulus (Bony & Dufresne, 2005).”
Can aerosol decrease cloud lifetime? (2009, background): “…of small cumulus also is crucial to global climate models, as climate sensitivity (i.e., the global-mean temperature response to a given radiative forcing) in such models is strongly dependent on their representation of shallow convective clouds [Bony and Dufresne, 2005; Medeiros et al., 2008].”
Neural general circulation models for weather and climate (2023, background): “Tuning GCMs to match historical data remains a manual process 5 , and GCMs retain many persistent errors and biases 6–8 .”
A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challenges (2015, background): “As long as these clouds remain unresolved, their parameterization remains key as the radiative effects of such warm cloud decks are considerable [Hartmann et al., 1992; Bony and Dufresne, 2005].”
Causes of Higher Climate Sensitivity in CMIP6 Models (2020, background): “This may indicate the long-standing uncertainty in this feedback (Bony & Dufresne, 2005) is decreasing as tighter observational and high resolution modeling targets are established (Bretherton, 2015; Klein et al., 2017).”
Evaluating Emergent Constraints on Equilibrium Climate Sensitivity (2018, background): “Each link in our chain of logic is imperfect, but subtropical stratocumulus are known to be a key factors for climate sensitivity (Bony and Dufresne 2005) so we consider this potential constraint worth testing.”
Controls on precipitation and cloudiness in simulations of trade‐wind cumulus as observed during RICO (2011, background): “Shallow cumulus clouds such as these have come to be appreciated as a critical piece of the climate puzzle (Bony and Dufresne, 2006; Medeiros et al., 2008), they prevail in the tradewind regions and, as far as clouds go, their dynamics are simple.”
Mechanisms of marine low cloud sensitivity to idealized climate perturbations: A single‐LES exploration extending the CGILS cases (2013, result): “Our findings seem broadly consistent with the subtropical low cloud feedbacks simulated by the CMIP3 suite of global climate models, which were found to mostly be positive, though with large intermodel scatter in amplitude [Bony and DuFresne, 2005; Soden and Vecchi , 2011].”
The CloudSat radar‐lidar geometrical profile product (RL‐GeoProf): Updates, improvements, and selected results (2014, background): “9456 model estimates of cloud feedback [Bony and Dufresne, 2005; Medeiros et al., 2008; Soden and Vecchi, 2011].”