A comparison of climate feedbacks in general circulation models

By R. Colman. 2003

357 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/aa7f0a41a75e10c04bee4a3ef8ee2885dc27b14b

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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).”
Cloud feedback mechanisms and their representation in global climate models (2017, neutral): “The most accurate procedure involves running the GCM radiation code offline—replacing instantaneous cloud fields from a control climatology with those from a perturbed climatology, while keeping other fields unchanged—to obtain the radiative perturbation due to changes in clouds.(36,37) This method ”
An Assessment of Climate Feedbacks in Coupled Ocean–Atmosphere Models (2006, methodology): “…of feedback assessment aliases noncloud feedbacks into the cloud-forcing term; thus, scatter in the cloud-forcing response can partially result from differences in the total-sky components of the other feedback variables (Zhang et al. 1994; Colman and McAvaney 1997; Colman 2003; Soden et al. 2004).”
The Climate Sensitivity and Its Components Diagnosed from Earth Radiation Budget Data (2005, background): “A significant part of this uncertainty is due to different cloud feedbacks between models, although differences in other feedbacks, such as water vapor, also play a role (Houghton et al. 1990; Cess et al. 1996; Colman 2003).”
Cloud Feedbacks in the Climate System: A Critical Review (2005, methodology): “Because clouds affect the clear-sky portions of the radiation budget, it is not possible to use this clear-sky component of the budget to identify the system without cloud feedback and this is a source of misrepresentation of cloud feedback in the analysis of these 2 SST experiments (Colman 2003; So”
Feedbacks, Timescales, and Seeing Red (2009, methodology): “(b) A Gaussian spread of uncertainties in feedback strength ( green shading), ¯ f = 0.65, σ f = 0.13, consistent with the GCM studies of Colman (2003) and Soden & Held (2006), produces uncertainty in the system response (orange shading) that is highly skewed.”
Using the Radiative Kernel Technique to Calculate Climate Feedbacks in NCAR's Community Atmospheric Model (2008, methodology): “In particular, the water vapor and atmospheric temperature flux changes tend to cancel each other spatially, suggesting that the lapse rate and water vapor feedbacks operate in CAM as is found elsewhere (Randall et al. 2007; Allan et al. 2002; Colman 2003; Soden and Held 2006).”
On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles (2006, methodology): “A global mean feedback analysis shows that the largest contribution to inter-model differences in climate sensitivity in both ensembles is due to cloud feedbacks, which confirms the findings of studies with earlier models (e.g. Cess et al. 1996; Colman 2003).”
How reliable are climate models? (2007, background): “…studies of the global radiation budget in climate models, is that a majority of the variation in ECS is due to different changes in cloud amount, optical properties and altitude distribution in different models (Cess et al., 1990, 1996; Colman, 2003; Soden and Held, 2006; Webb et al., 2006).”
Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth (2012, background): “This continues to be the case in contemporary climate models (Colman 2003; Soden and Held 2006; Ringer et al. 2006; Webb et al. 2006), and recent evidence has identified the response of marine boundary layer clouds in subsidence regimes of the subtropics as primarily responsible for the intermodel s”
Present-day climate and climate sensitivity in the meteorological research institute coupled GCM version 2.3 (MRI-CGCM2.3) (2006, neutral): “3 indicated the opposite sign of cloud feedback between shortwave and longwave, as do many other models (e.g., Colman 2003; Williams et al. 2003).”
The Evolution of Climate Sensitivity and Climate Feedbacks in the Community Atmosphere Model (2012, methodology): “However, DCRF can be influenced by changes in noncloud variables (Zhang et al. 1994; Colman 2003; Soden et al. 2004).”
Equilibrium Response of an Atmosphere–Mixed Layer Ocean Model to Different Radiative Forcing Agents: Global and Zonal Mean Response (2008, methodology): “We choose to use the so-called partial radiative perturbation (PRP) method pioneered by Wetherald and Manabe (1988) and applied to multiple models for an intercomparison study by Colman (2003).”
Tropospheric Adjustment Induces a Cloud Component in CO2 Forcing (2008, background): “A similar point was made by Colman et al. (2001). This suggests that internal variability of the climate system has its largest effect on the radiation budget through fluctuations in low cloud, which has a shortwave but not a large longwave radiative effect. The same suggestion has been made regardi”
Climate Forcings and Climate Sensitivities Diagnosed from Coupled Climate Model Integrations (2006, methodology): “Nevertheless, we contend that a simple model remains useful, and furthermore such models provide a useful framework for more regionally based feedback analyses, such as the linear global feedback analysis commonly used to examine and diagnose model feedback differences (e.g., Cess et al. 1996; Colma”