On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles
By M. Webb, C. Senior, D. Sexton, W. Ingram, K. Williams, M. Ringer, B. Mcavaney, R. Colman, Brian J. Soden, R. Gudgel and 9 more. 2006
422 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/609466a8d8bbc1c1bd996d97f5971923060a8498
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In Atmospheric Science
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Citations in the corpus
- Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model (2011, methodology): “…applied MVFSA to optimize six parameters related to the cloud process in a Global Climate Model (GCM) because cloud processes play a critical role in the hydrological cycle and uncertainty of climate response to doubling of CO2 forcing (Colman, 2003; Webb et al., 2006; Medeiros and Stevens, 2011).”
- Tropospheric Adjustment Induces a Cloud Component in CO2 Forcing (2008, methodology): “This produces opposing FLC and FSC at low latitudes (Webb et al., 2006; Sokolov, 2006), which is particularly clear in the Pacific in the regression method.”
- 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].”
- The ‘too few, too bright’ tropical low‐cloud problem in CMIP5 models (2012, neutral): “The response of low-level clouds has been identified as a key source of uncertainty for model cloud feedbacks under climate change [ Bony and Dufresne , 2005; Webb et al. , 2006; Wyant et al. , 2006; Medeiros et al. , 2008].”
- The vertical distribution of cloud feedback in coupled ocean‐atmosphere models (2011, methodology): “[1] We assess the vertical distribution of cloud feedbacks in coupled climate models, taking care to distinguish between cloud feedbacks and a change in cloud forcing.”
- Using the Radiative Kernel Technique to Calculate Climate Feedbacks in NCAR's Community Atmospheric Model (2008, methodology): “The first is to use offline radiative transfer calculations with cloud fields from two model simulations to determine the change in TOA fluxes, and then divide by the global-average surface temperature change, as is done for other variables (Wetherald and Manabe 1988).”
- GCM intercomparison of global cloud regimes: present-day evaluation and climate change response (2007, background): “Comparison studies suggest that differences in the radiative feedback from clouds account for much of the variation in climate sensitivity between current GCMs (Cess et al. 1990; Senior and Mitchell 1993; Webb et al. 2006; Soden and Held 2006; Ringer et al. 2006).”
- Error Reduction and Convergence in Climate Prediction (2008, background): “The leading cause of the intermodel differences in sensitivity to CO2 forcing is related to differences in the treatment of clouds (Cess et al. 1990, 1996; Held and Soden 2000; Colman 2003; Webb et al. 2006).”
- Origins of differences in climate sensitivity, forcing and feedback in climate models (2013, methodology): “As pointed out by Soden et al. (2004), the change in the CRE is not the same as the cloud feedback diagnosed using the partial radiative perturbation (PRP) method (e.g. Wetherald and Manabe 1988; Colman 2003), or approximate versions of it (e.g. Soden and Held 2006; Taylor et al. 2007).”