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Cloud ice: A climate model challenge with signs and expectations of progress
Jet Propulsion Laboratory, Pasadena, California, USA
Jet Propulsion Laboratory, Pasadena, California, USA
Jet Propulsion Laboratory, Pasadena, California, USA
Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
NASA Goddard Institute for Space Studies, New York, New York, USA
Jet Propulsion Laboratory, Pasadena, California, USA
Department of Earth and Planetary Sciences and Division of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
NOAA, Camp Springs, Maryland, USA
NASA Langley Research Center, Hampton, Virginia, USA
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Cooperative Remote Sensing Science and Technology Center, City College of New York, New York, New York, USA
City University of New York, New York, New York, USA
Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
NASA Langley Research Center, Hampton, Virginia, USA
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
European Centre for Medium-Range Weather Forecasts, Reading, UK
Jet Propulsion Laboratory, Pasadena, California, USA
Department of Earth and Planetary Sciences and Division of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
Jet Propulsion Laboratory, Pasadena, California, USA
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/Microwave Limb Sounder (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 Intergovernmental Panel on Climate Change 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 (1) discuss some of the main considerations and cautions that must be taken into account in making model-data comparisons related to cloud ice, (2) illustrate present progress and uncertainties in applying satellite cloud ice (namely from MLS and CloudSat) to model diagnosis, (3) show some indications of model improvements, and finally (4) discuss a number of remaining questions and suggestions for pathways forward.
Received 23 February 2008; accepted 31 October 2008; published 29 January 2009.
Citation: (2009), Cloud ice: A climate model challenge with signs and expectations of progress, J. Geophys. Res., 114, D00A21, doi:10.1029/2008JD010015.
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