Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
doi:10.5194/gmd-2017-69
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Methods for assessment of models
21 Apr 2017
Review status
This discussion paper is under review for the journal Geoscientific Model Development (GMD).
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 Tsushima1, Florent Brient2, Stephen A. Klein3, Dimitra Konsta4, Christine Nam5, Xin Qu6, Keith D. Williams1, Steven C. Sherwood7, Kentaroh Suzuki8, and Mark D. Zelinka3 1Met Office Hadley Centre, Exeter, United Kingdom
2Centre National de Recherches Météorologiques, Toulouse, France
3Program for Climate Model Diagnosis and Intercomparison, Lawrence Liverrmore National Laboratory, Liverrmore, USA
4National Observatory of Athens, Athens, Greece
5Universitaet Leipzig, Leipzig, Germany
6Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, USA
7Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, Australia
8Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Japan
Abstract. The CFMIP Diagnostic Codes Catalogue assembles cloud metrics, diagnostics and methodologies, together with programs to diagnose them from General Circulation Model (GCM) outputs written by various members of the CFMIP community. This aims to facilitate use of the diagnostics by the wider community studying climate and climate change. This paper describes the diagnostics and metrics which are currently in the catalogue, together with examples of their application to model evaluation studies and a summary of some of the insights these diagnostics have provided on the main shortcomings in current GCMs. Analysis of outputs from CFMIP and CMIP6 experiments will also be facilitated by the sharing of diagnostic codes via this catalogue.

Any code which implements diagnostics relevant to analysing clouds – including cloud-circulation interactions and the contribution of clouds to estimates of climate sensitivity in models – and which is documented in peer-reviewed studies can be included in the catalogue. We very much welcome additional contributions to further support community analysis of CMIP6 outputs.


Citation: Tsushima, Y., Brient, F., Klein, S. A., Konsta, D., Nam, C., Qu, X., Williams, K. D., Sherwood, S. C., Suzuki, K., and Zelinka, M. D.: 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, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2017-69, in review, 2017.
Yoko Tsushima et al.
Yoko Tsushima et al.
Yoko Tsushima et al.

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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.
Cloud feedback is the largest uncertainty associated with estimates of climate sensitivity....
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