Clouds, circulation and climate sensitivity

By Sandrine Bony, Bjorn Stevens, D. Frierson, C. Jakob, Masa Kageyama, Robert Pincus, T. G. Shepherd, S. Sherwood, A. P. Siebesma, A. Sobel and 2 more. 2015

880 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a0b00e8f434937ef4fb461f72003423219c6386d

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Citations in the corpus

Attribution of extreme weather and climate‐related events (2015, background): “While climate models appear to capture thermodynamic changes well, they may struggle to simulate circulation changes(10) and questions remain about what controls convection, changes in which can affect extremes,(11) and the position of the storm tracks and the tropical rain belts.(12) In the light o”
Global‐scale convective aggregation: Implications for the Madden‐Julian Oscillation (2015, background): “Some form of cloud-longwave feedback has generally been found to be essential, although the relative importance of high versus low clouds is disputed [Bretherton et al., 2005; Muller and Held, 2012; Muller and Bony, 2015].”
DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (2019, background): “Their use will shine welcome new light on long-standing questions, such as how tropical rainbands, or other patterns of weather, change with warming (Bony et al. 2015; Marotzke et al. 2017).”
The physics of climate variability and climate change (2019, background): “icular, the largest uncertainty in dening the ECS for the current climate state is associated with the diculty in estimating correctly the strength of the two main feedbacks associated with clouds (Bony et al., 2015; Schneider et al., 2017). A warmer climate leads to increased presence of water vapo”
Coupling between the terrestrial carbon and water cycles—a review (2019, background): “Over the last century, anthropogenic emissions of carbon dioxide, which is the greenhouse gas (GHG) contributing themost to warming, havemodified the Earth’s climate and the coupled hydrologic cycle (Gregory et al2004, Bony et al2015,Knutti andRugenstein 2015, Armour 2016, 2017, Knutti et al 2017).”
Analyzing the dependence of global cloud feedback on the spatial pattern of sea surface temperature change with a Green's function approach (2017, background): “…has large spread across climate models and has been demonstrated to be the primary contributor to intermodel spread of the equilibrium climate sensitivity (ECS) [Dufresne and Bony, 2008; Webb et al., 2006; Caldwell et al., 2016; Bony et al., 2015; Mauritsen and Stevens, 2015; Stevens et al., 2016].”
Uncertainty in Model Climate Sensitivity Traced to Representations of Cumulus Precipitation Microphysics (2016, background): “…Bony et al. 2004; Bony and Dufresne 2005; Bony et al. 2006; Stephens 2005; Wyant et al. 2006; Webb et al. 2006; Soden and Held 2006; Dufresne and Bony 2008; Webb et al. 2013; Zelinka et al. 2013; Zhang et al. 2013; Sherwood et al. 2014; Bretherton et al. 2014; Webb et al. 2015; Bretherton 2015).”
Narrowing of the ITCZ in a warming climate: Physical mechanisms (2016, background): “Thus, there is a close coupling between clouds and the tropical circulation (as highlighted previously [e.g., Bony et al., 2015]), with changes in shortwave radiative fluxes associated with clouds tending to widen the ITCZ substantially (by 7.8% K−1).”
Could Machine Learning Break the Convection Parameterization Deadlock? (2018, background): “…, 2016] so that Cloud Resolving Models (CRMs) have been the tool of choice to understand convective aggregation [ Jeevanjee and Romps , 2013; Wing and Emanuel , 2014; Arnold and Randall , 2015; Bony et al. , 2015; Bretherton and Khairoutdinov , 2015; Coppin and Bony , 2015; Muller and Bony , 2015].”
Fifty Years of Research on the Madden‐Julian Oscillation: Recent Progress, Challenges, and Perspectives (2020, background): “The critical role for cloud‐radiative feedbacks to the MJO is nowwidely recognized (e.g., Bony et al., 2015; D. Kim et al., 2015; M. I. Lee et al., 2001; Raymond, 2001; Sobel & Gildor, 2003; Stephens et al., 2004).”
Deep learning to represent subgrid processes in climate models (2018, background): “However, owing to the sheer complexity of the underlying physical system significant inaccuracies persist in the parameterization of clouds and their interaction with other processes, such as boundary-layer turbulence and radiation (1, 4, 5).”
Physical mechanisms controlling the initiation of convective self‐aggregation in a General Circulation Model (2015, background): “Beyond the question of what explains the self-aggregation phenomenon, the question arises as to whether or not convective aggregation has an impact on climate [Bony et al., 2015].”
Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization (2015, background): “They relate to (1) advancing understanding of the role of clouds in the general atmospheric circulation and climate sensitivity (Bony et al., 2015), (2) assessing the response of the cryosphere to a warming climate and its global consequences, (3) understanding the factors that control water…”
What favors convective aggregation and why? (2015, background): “Given the large changes in large-scale properties accompanying self-aggregation, this may have important implications for climate sensitivity [Bony et al., 2015; Mauritsen and Stevens, 2015].”