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  • Waliser, D.E., J. F. Li, C. Woods, R. Austin, J. Bacmeister, J. Chern, A. Del Genio, J. Jiang, Z. Kuang, H. Meng, P. Minnis, S. Platnick, W. B. Rossow, G. Stephens, S. Sun-Mack, W.K. Tao, A. Tompkins , D. Vane, C. Walker, 2008: Cloud Ice: A Climate Model Challenge With Signs and Expectations of Progress. Journal of Geophysical Research. Accepted.

Present-day shortcomings in the representation of upper-tropospheric ice clouds in general circulation models (GCMs) lead to errors in weather and climate forecasts as well as account for a source of uncertainty in climate change projections. An ongoing challenge in rectifying these shortcomings has been the availability of adequate, high-quality, global observations targeting ice clouds and related precipitating hydrometeors. In addition, the inadequacy of the modeled physics and the often-disjointed nature between model representation and the characteristics of the retrieved/observed values have hampered GCM development and validation efforts from making effective use of the measurements that have been available. Thus, even though parameterizations in GCMs accounting for cloud ice processes have, in some cases, become more sophisticated in recent years, this development has largely occurred independently of the global scale measurements. With the relatively recent addition of satellite-derived products from Aura/MLS and CloudSat, there are now considerably more resources with new and unique capabilities to evaluate GCMs. In this article, we illustrate the shortcomings evident in model representations of cloud ice through a comparison of the simulations assessed in the IPCC Fourth Assessment Report, briefly discuss the range of global observational resources that are available, and describe the essential components of the model parameterizations that characterize their “cloud” ice and related fields. Using this information as background, we discuss some of the main considerations and cautions that must be taken into account in making model-data comparisons related to cloud ice, illustrate present progress and uncertainties in applying satellite cloud ice - namely from MLS and CloudSat - to model diagnosis, show some indications of model improvements, and finally discuss a number of remaining questions and suggestions for pathways forward.


Last Updated: 2008-08-26

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