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A Strategy for Process-Oriented Validation of Coupled Chemistry-Climate Models

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Eyring, V. , Harris, N. R. P. , Rex, M. , Sheperd, T. G. , Fahey, D. W. , Amanatidis, G. T. , Austin, J. , Chipperfield, M. P. , Dameris, M. , Forster, P. M. D. F. , Gettleman, A. , Graf, H. F. , Nagashima, T. , Newman, P. A. , Pawson, S. , Prather, M. J. , Pyle, J. A. , Salawitch, R. J. , Santer, B. and Waugh, D. W. (2005): A Strategy for Process-Oriented Validation of Coupled Chemistry-Climate Models , Bulletin of the american meteorological society, Vol. 86, No. 81133, 1117 . doi: 10.1175/BAMS-86-8-1117
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Accurate and reliable predictions and an understanding of future changes in the stratosphere are of major importance to our understanding of climate change. Simulating the interaction between chemistry and climate is of particular importance, because continued increases in greenhouse gases and a slow decrease in halogen loading are expected. These both influence the abundance of stratospheric ozone. In recent years a number of coupled chemistry climate models (CCMs) with different levels of complexity have been developed. They produce a wide range of results concerning the timing and extent of ozone-layer recovery. Interest in reducing this range has created a need to address how the main dynamical, chemical, and physical processes that determine the long-term behavior of ozone are represented in the models and to validate these model processes through comparisons with observations and other models. A set of core validation processes structured around four major topics (transport, dynamics, radiation, and stratospheric chemistry and microphysics) has been developed. Each process is associated with one or more model diagnostics and with relevant datasets that can be used for validation. This approach provides a coherent framework for validating CCMs and can be used as a basis for future assessments. Similar efforts would benefit other model communities allowing development in our understanding of the various processes as models increase their degree of complexity.

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