A quantitative performance assessment of cloud regimes in climate models

By K. Williams, M. Webb. 2009

196 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/0c750c89f65945e39e9b0ec04eb8ca8d31ef095e

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In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

Satellite-Based Atmospheric Infrared Sounder Development and Applications (2017, background): “However, there are large uncertainties in the cloud feedback mechanisms due to the various cloud types, complex structures, and nonisotropic radiance distribution, as well as the variety of cloud depictions in climate models (Sun et al. 2006; Williams and Webb 2009; Wu and Zhou 2011).”
Cloud feedback mechanisms and their representation in global climate models (2017, unknown): “Furthermore, climate models systematically underestimate the relative frequency of occurrence of tropical anvil and extratropical cirrus regimes.(74,75) Taken alone, such biases would lead to models systematically underestimating the cloud altitude feedback.”
Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels (2012, methodology): “Finally, Williams and Tselioudis (2007) and Williams and Webb (2009) employed a clustering technique to deﬁne several primary cloud regimes from International Satellite Cloud Climatology Project (ISCCP) simulator output (described below) and assessed the contributions to cloud feedback from changes…”
Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth (2012, background): “) 408 and changes in the properties of the individual cloud types, as shown in Williams and Webb 409 (2009). A striking feature that is apparent from comparing panels a and b of Figs.”
The Evolution of Climate Sensitivity and Climate Feedbacks in the Community Atmosphere Model (2012, methodology): “While Williams and Webb (2009) stress the importance of low clouds for cloud feedback, half of their global signal is from outside of the tropical regions.”
Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization (2015, methodology): “, 2014), or diagnostics such as the cloud regime metric (Williams and Webb, 2009) developed by the Cloud Feedback MIP (CFMIP) community.”
Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator (2012, background): “These measures may be useful as metrics for assessing the skill of climate models in reproducing the present-day distribution clouds and their properties [Gleckler et al., 2008; Pincus et al., 2008; Williams and Webb, 2009].”
Origins of differences in climate sensitivity, forcing and feedback in climate models (2013, methodology): “However, Williams and Webb (2009) examined cloud feedbacks in 10 CO2 doubling experiments with atmosphere-mixedlayer ocean ‘‘slab’’ models from the Cloud Feedback Model Intercomparison Project, and found that stratocumulus and stratocumulus/trade-cumulus transition clouds make a larger contribution…”
Exposing Global Cloud Biases in the Community Atmosphere Model (CAM) Using Satellite Observations and Their Corresponding Instrument Simulators (2012, background): “…to see if similar COSP-identiﬁed low subtropical marine cloud biases exist because these clouds have been repeatedly identiﬁed as the largest contributors to intermodel spread in climate sensitivity (e.g., Cess et al. 1990; Bony and Dufresne 2005; Williams and Webb 2009; Medeiros et al. 2008).”