Articles | Volume 10, issue 11
https://doi.org/10.5194/gmd-10-4285-2017
https://doi.org/10.5194/gmd-10-4285-2017
Methods for assessment of models
 | 
27 Nov 2017
Methods for assessment of models |  | 27 Nov 2017

The Cloud Feedback Model Intercomparison Project (CFMIP) Diagnostic Codes Catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models

Yoko Tsushima, Florent Brient, Stephen A. Klein, Dimitra Konsta, Christine C. Nam, Xin Qu, Keith D. Williams, Steven C. Sherwood, Kentaroh Suzuki, and Mark D. Zelinka

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Cited articles

Anderberg, M.: Cluster analysis for applications, Academic Press, New York, 359 pp., 1973.
Barnes, E. and Polvani, L.: Response of the Midlatitude Jets, and of Their Variability, to Increased Greenhouse Gases in the CMIP5 Models, J. Climate, 26, 7117–7135, https://doi.org/10.1175/JCLI-D-12-00536.1, 2013.
Bennhold, F. and Sherwood, S.: Erroneous relationships among humidity and cloud forcing variables in three global climate models, J. Climate, 21, 4190–4206, https://doi.org/10.1175/2008JCLI1969.1, 2008.
Bodas-Salcedo, A., Webb, M., Bony, S., Chepfer, H., Dufresne, J., Klein, S., Zhang, Y., Marchand, R., Haynes, J., Pincus, R., and John, V.: COSP Satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011.
Bodas-Salcedo, A., Williams, K., Field, P., and Lock, A.: The Surface Downwelling Solar Radiation Surplus over the Southern Ocean in the Met Office Model: The Role of Midlatitude Cyclone Clouds, J. Climate, 25, 7467–7486, https://doi.org/10.1175/JCLI-D-11-00702.1, 2012.
Short summary
Cloud feedback is the largest uncertainty associated with estimates of climate sensitivity. Diagnostics have been developed to evaluate cloud processes in climate models. For this understanding to be reflected in better estimates of cloud feedbacks, it is vital to continue to develop such tools and to exploit them fully during the model development process. Code repositories have been created to store and document the programs which will allow climate modellers to compute these diagnostics.
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