The Transpose-AMIP II Experiment and its application to the understanding of southern ocean cloud biases in climate models

• Published in: Journal of Climate
• Other Authors: Williams, K. (author), Bodas-Salcedo, A. (author), Deque, M. (author), Fermepin, S. (author), Medeiros, Brian (author), Watanabe, M. (author), Jakob, C. (author), Klein, S. (author), Senior, C. (author), Williamson, David (author)
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Meteorological Society 2013
• Subjects: Southern Ocean
• Online Access: http://nldr.library.ucar.edu/repository/collections/OSGC-000-000-018-891; https://doi.org/10.1175/JCLI-D-12-00429.1

The Transpose-Atmospheric Model Intercomparison Project (AMIP) is an international model intercomparison project in which climate models are run in "weather forecast mode." The Transpose-AMIP II experiment is run alongside phase 5 of the Coupled Model Intercomparison Project (CMIP5) and allows processes operating in climate models to be evaluated, and the origin of climatological biases to be explored, by examining the evolution of the model from a state in which the large-scale dynamics, temperature, and humidity structures are constrained through use of common analyses. The Transpose-AMIP II experimental design is presented. The project requests participants to submit a comprehensive set of diagnostics to enable detailed investigation of the models to be performed. An example of the type of analysis that may be undertaken using these diagnostics is illustrated through a study of the development of cloud biases over the Southern Ocean, a region that is problematic for many models. Several models share a climatological bias for too little reflected shortwave radiation from cloud across the region. This is found to mainly occur behind cold fronts and/or on the leading side of transient ridges and to be associated with more stable lower-tropospheric profiles. Investigation of a case study that is typical of the bias and associated meteorological conditions reveals the models to typically simulate cloud that is too optically and physically thin with an inversion that is too low. The evolution of the models within the first few hours suggests that these conditions are particularly sensitive and a positive feedback can develop between the thinning of the cloud layer and boundary layer structure.