[1] 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.

A mesoscale model simulation of a wide cold-frontal rainband observed in the Pacific Northwest during the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-1) field study was used to test the sensitivity of the model-produced precipitation to varied representations of snow particles in a bulk microphysical scheme. Tests of sensitivity to snow habit type, by using empirical relationships for mass and velocity versus diameter, demonstrated the defectiveness of the conventional assumption of snow particles as constant density spheres. More realistic empirical massdiameter relationships result in increased numbers of particles and shift the snow size distribution toward larger particles, leading to increased depositional growth of snow and decreased cloud water production. Use of realistic empirical mass-diameter relationships generally increased precipitation at the surface as the rainband interacted with the orography, with more limited increases occurring offshore. Changes in both the mass-diameter and velocity-diameter relationships significantly redistributed precipitation either windward or leeward when the rainband interacted with the mountain barrier.A method of predicting snow particle habit in a bulk microphysical scheme, and using predicted habit to dynamically determine snow properties in the scheme, was developed and tested. The scheme performed well at predicting the habits present (or not present) in aircraft observations of the rainband. Use of the scheme resulted in little change in the precipitation rate at the ground for the rainband offshore, but significantly increased precipitation when the rainband interacted with the windward slope of the Olympic Mountains. The study demonstrates the promise of the habit prediction approach to treating snow in bulk microphysical schemes.

[1] To assess the fidelity of general circulation models (GCMs) in simulating cloud liquid water, liquid water path (LWP) retrievals from several satellites with passive sensors and the vertically-resolved liquid water content (LWC) from the CloudSat are used. Comparisons are made with ECMWF and MERRA analyses, GCM simulations utilized in the IPCC 4th Assessment, and three GCM simulations. There is considerable disagreement amongst the LWP estimates and amongst the modeled values. The LWP from GCMs are much larger than the observed estimates and the two analyses. The largest values in the CloudSat LWP occur over the boundary-layer stratocumulus regions; this feature is not as evident in the analyses or models. Better agreement is found between the two analyses and CloudSat LWP when cases with surface precipitation are excluded. The upward vertical extent of LWC from the GCMs and analyses is greater than CloudSat estimates. The issues of representing LWC and precipitation consistently between satellite-derived and model values are discussed. Citation:

This paper investigates the microphysical pathways and sensitivities within the Reisner-2 bulk microphysical parameterization (BMP) of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) for the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE)-2 field experiment on 13–14 December 2001. A microphysical budget over the windward slope at 1.33-km horizontal grid spacing was calculated, in which the importance of each microphysical process was quantified relative to the water vapor loss (WVL) rate. Over the windward Cascades, the largest water vapor loss was associated with condensation (73% of WVL) and snow deposition (24%), and the windward surface precipitation resulted primarily from accretion of cloud water by rain (27% of WVL), graupel fallout and melt (19%), and snowmelt (6%). Two-thirds of the snow generated aloft spilled over into the lee in an area of model overprediction, resulting in windward precipitation efficiency of only 50%. Even with the large amount of precipitation spillover, the windward precipitation was still overpredicted in many locations. A series of experiments were completed using different snowfall speeds, cloud water autoconversion, threshold riming values for snow to graupel autoconversion, and slope intercepts for snow. The surface precipitation was most sensitive to those parameters associated with the snow size distribution and fall speed, while decreasing the riming threshold for snow to graupel conversion had the greatest positive impact on the precipitation forecast. All simulations overpredicted cloud water over the lower windward slopes, had too little cloud water over the crest, and had too much ice at moderate-to-large sizes aloft. Riming processes were important, since without supercooled water there were bull’s-eyes of spurious snow spillover over the lee slopes.

Particle size spectra collected by the University of Washington's Convair-580 research aircraft at a variety of altitudes and temperatures in winter frontal and orographic precipitation systems during the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE) are analyzed in this study. The particle size spectra generally appeared to conform to an exponential size distribution, with well-correlated linear fits between the log of the number concentration and particle diameter. When the particle size spectra were grouped according to the habit composition as determined from airborne imagery, significantly improved correlations between the size spectrum parameters and temperature were obtained. This result could potentially be exploited for specifying the size distribution in a single-moment bulk microphysical scheme, if particle habit is predicted by the scheme. Analyses of "spectral trajectories" suggest that the rime-splintering process was likely responsible for the presence of needle and column habit types and the positive shift in both N 0s and s at temperatures warmer than Ϫ10°C.