[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.

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.

[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:

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.

This paper compares airborne in situ observations of cloud microphysical parameters with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) simulations, using the Reisner-2 bulk microphysical parameterization, for a heavy precipitation event over the Oregon Cascades on 13–14 December 2001. The MM5 correctly replicated the extent of the snow field and the growth of snow particles by vapor deposition measured along aircraft flight tracks between altitudes of 4.9 and 6 km, but overpredicted the mass concentrations of snow. The model produced a broader number distribution of snow particles than observed, overpredicting the number of moderate-to-large-sized snow particles and underpredicting the number of small particles observed along the aircraft flight track. Over the mountain crest, the model overpredicted depositional growth of snow and mass concentrations of snow, but underpredicted the amount of cloud liquid water and conversion of snow to graupel. The misclassification of graupel as snow and excessive amounts of snow resulted in the model overpredicting precipitation on the lee slopes and in localized areas along the foothills of the Cascades. The model overpredicted cloud liquid water over the lower windward slopes and foothills, where accretion of cloud liquid water by rain was the primary precipitation-producing mechanism.

The boreal summer intraseasonal variability (BSISV), which is characterized by pronounced meridional propagation from the equatorial zone to the Indian Continent, exerts significant modulation of the active/break phases of the south Asian monsoon. This form of variability provides a primary source of subseasonal predictive skill of the Asian summer monsoon. Unfortunately, current general circulation models display large deficiencies in representing this variability. The new cloud observations made available by the CloudSat mission provide an unprecedented opportunity to advance our characterization of the BSISV. In this study, the vertical structures of cloud water content and cloud types associated with the BSISV over the Indian Ocean and subcontinent are analyzed based on CloudSat observations from 2006 to 2008. These cloud structures are also compared to their counterparts as derived from ERA-interim reanalysis. A marked vertical tilting structure in cloud water is illustrated during the northward propagation of the BSISV based on both datasets. Increased cloud liquid water content (LWC) tends to appear to the north of the rainfall maximum, while ice water content (IWC) in the upper troposphere slightly lags the convection. This northward shift of increased LWC, which is in accord with local enhanced moisture as previously documented, may play an important role in the northward propagation of the BSISV. The transition in cloud structures associated with BSISV convection is further demonstrated based on CloudSat, with shallow cumuli at the leading edge, followed by the deep convective clouds, and then upper anvil clouds. Some differences in cloud water structures between CloudSat and ERA-interim are also noted, particularly in the amplitudes of IWC and LWC fields.

[1] The ice cloud estimates in current global models exhibit significant inconsistency, resulting in a significant amount of uncertainties in climate forecasting. Vertically resolved ice water content (IWC) is recently available from new satellite products, such as CloudSat, providing important observational constraints for evaluating the global models. To account for the varied nature of the model parameterization schemes, it is valuable to develop methods to distinguish the cloud versus precipitating ice components from the remotely sensed estimates in order to carry out meaningful model-data comparisons. The present study develops a new technique that partitions CloudSat total IWC into small and large ice hydrometeors, using the ice particle size distribution (PSD) parameters provided by the retrieval algorithm. The global statistics of CloudSat-retrieved PSD are analyzed for the filtered subsets on the basis of convection and precipitation flags to identify appropriate particle size separation. Results are compared with previous partitioning estimates and suggest that the small particles contribute to ∼25-45% of the global mean total IWC in the upper to middle troposphere. Sensitivity measures with respect to the PSD parameters and the retrieval algorithm are presented. The current estimates are applied to evaluate the IWC estimates from the European Centre for Medium-Range Weather Forecasts model and the finite-volume multiscale modeling framework model, pointing to specific areas of potential model improvements. These results are discussed in terms of applications to model diagnostics, providing implications for reducing the uncertainty in the model representation of cloud feedback and precipitation.