ABSTRACT:Results are presented from an intercomparison of single-column and cloud-resolving model simulations of a cold-air outbreak mixed-phase stratocumulus cloud observed during the Atmospheric Radiation Measurement (ARM) programme's Mixed-Phase Arctic Cloud Experiment. The observed cloud occurred in a well-mixed boundary layer with a cloud-top temperature of −15 • C. The average liquid water path of around 160 g m −2 was about two-thirds of the adiabatic value and far greater than the average mass of ice which when integrated from the surface to cloud top was around 15 g m −2 .Simulations of 17 single-column models (SCMs) and 9 cloud-resolving models (CRMs) are compared. While the simulated ice water path is generally consistent with observed values, the median SCM and CRM liquid water path is a factor-of-three smaller than observed. Results from a sensitivity study in which models removed ice microphysics suggest that in many models the interaction between liquid and ice-phase microphysics is responsible for the large model underestimate of liquid water path.Despite this underestimate, the simulated liquid and ice water paths of several models are consistent with observed values. Furthermore, models with more sophisticated microphysics simulate liquid and ice water paths that are in better agreement with the observed values, although considerable scatter exists. Although no single factor guarantees a good simulation, these results emphasize the need for improvement in the model representation of mixed-phase microphysics.

An intercomparison of six cloud-resolving and large-eddy simulation models is presented. This case study is based on observations of a persistent mixed-phase boundary layer cloud gathered on 7 May, 1998 from the Surface Heat Budget of Arctic Ocean (SHEBA) and First ISCCP Regional Experiment -Arctic Cloud Experiment (FIRE-ACE). Ice nucleation is constrained in the simulations in a way that holds the ice crystal concentration approximately fixed, with two sets of sensitivity runs in addition to the baseline simulations utilizing different specified ice nucleus (IN) concentrations. All of the baseline and sensitivity simulations group into two distinct quasi-steady states associated with either persistent mixed-phase clouds or all-ice clouds after the first few hours of integration, implying the existence of multiple states for this case. These two states are associated with distinctly different microphysical, thermodynamic, and radiative characteristics. Most but not all of the models produce a persistent mixed-phase cloud qualitatively similar to observations using the baseline IN/crystal concentration, while small increases in the IN/crystal concentration generally lead to rapid glaciation and conversion to the all-ice state. Budget analysis indicates that larger ice deposition rates associated with increased IN/crystal concentrations have a limited direct impact on dissipation of liquid in these simulations. However, the impact of increased ice deposition is greatly enhanced by several interaction pathways that lead to an increased surface precipitation flux, weaker cloud top radiative cooling and cloud dynamics, and reduced vertical mixing, promoting rapid glaciation of the mixed-phase cloud for deposition rates in the cloud layer greater than about 122610 -5 g kg -1 s -1 for this case. These results indicate the critical importance of precipitation-radiative-dynamical interactions in simulating cloud phase, which have been neglected in previous fixed-dynamical parcel studies of the cloud phase parameter space. Large sensitivity to the IN/crystal concentration also suggests the need for improved understanding of ice nucleation and its parameterization in models.

[1] We describe a method to evaluate cloud microphysics simulated with a global cloud-resolving model against CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite data. Output from the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) is run through a satellite-sensor simulator (Joint Simulator for Satellite Sensors), then directly compared to the radar and lidar signals from CloudSat and CALIPSO. The forward approach allows for consistency in cloud microphysical assumption involved in the evaluation. To investigate the dependence of the signals on the temperature, we use temperature extensively as the vertical coordinate. The global statistical analysis of the radar reflectivity shows that the simulation overestimates all the percentiles above À50°C and that snow category contributes significantly to low reflectivity values between À80 and À40°C. The simulated lidar signals have two modes associated with cloud ice and snow categories, though the observations have only one mode. The synergetic use of radar reflectivity and lidar backscatter enables us to determine the relative magnitudes of ice/liquid water contents and effective radii without use of retrievals. The radar-and-lidar diagnosis for cloud tops shows that, due to snow category, NICAM overestimates the mass-equivalent effective radius and underestimates ice water content. Also, the diagnosis was shown to be useful to investigate sensitivities of the parameters of bulk microphysical schemes on the water contents and sizes. The nonspherical scattering of ice particles was shown to affect the above radar-and-lidar diagnosis for large reflectivity ranges but not to alter most of the other diagnoses for this simulation.

Abstract. Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three biascorrection methods for ensemble streamflow volume forecasts. All three adjust the ensemble traces using a transformation derived with simulated and observed flows from a historical simulation. The quality of probabilistic forecasts issued when using the three bias-correction methods is evaluated using a distributions-oriented verification approach. Comparisons are made of retrospective forecasts of monthly flow volumes for a north-central United States basin (Des Moines River, Iowa), issued sequentially for each month over a 48-year record. The results show that all three bias-correction methods significantly improve forecast quality by eliminating unconditional biases and enhancing the potential skill. Still, subtle differences in the attributes of the bias-corrected forecasts have important implications for their use in operational decision-making. Diagnostic verification distinguishes these attributes in a context meaningful for decision-making, providing criteria to choose among bias-correction methods with comparable skill.

For probability forecasts, the Brier score and Brier skill score are commonly used verification measures of forecast accuracy and skill. Using sampling theory, analytical expressions are derived to estimate their sampling uncertainties. The Brier score is an unbiased estimator of the accuracy, and an exact expression defines its sampling variance. The Brier skill score (with climatology as a reference forecast) is a biased estimator, and approximations are needed to estimate its bias and sampling variance. The uncertainty estimators depend only on the moments of the forecasts and observations, so it is easy to routinely compute them at the same time as the Brier score and skill score. The resulting uncertainty estimates can be used to construct error bars or confidence intervals for the verification measures, or perform hypothesis testing. Monte Carlo experiments using synthetic forecasting examples illustrate the performance of the expressions. In general, the estimates provide very reliable information on uncertainty. However, the quality of an estimate depends on both the sample size and the occurrence frequency of the forecast event. The examples also illustrate that with infrequently occurring events, verification sample sizes of a few hundred forecast–observation pairs are needed to establish that a forecast is skillful because of the large uncertainties that exist.

A 14-yr climatology of Tropical Rainfall Measuring Mission (TRMM) collocated multisensor signal statistics reveals a distinct land–ocean contrast as well as geographical variability of precipitation type, intensity, and microphysics. Microphysics information inferred from the TRMM Precipitation Radar and Microwave Imager show a large land–ocean contrast for the deep category, suggesting continental convective vigor. Over land, TRMM shows higher echo-top heights and larger maximum echoes, suggesting taller storms and more intense precipitation, as well as larger microwave scattering, suggesting the presence of more/larger frozen convective hydrometeors. This strong land–ocean contrast in deep convection is invariant over seasonal and multiyear time scales. Consequently, relatively short-term simulations from two global storm-resolving models can be evaluated in terms of their land–ocean statistics using the TRMM Triple-Sensor Three-Step Evaluation Framework via a satellite simulator. The models evaluated are the NASA Multiscale Modeling Framework (MMF) and the Nonhydrostatic Icosahedral Cloud Atmospheric Model (NICAM). While both simulations can represent convective land–ocean contrasts in warm precipitation to some extent, near-surface conditions over land are relatively moister in NICAM than MMF, which appears to be the key driver in the divergent warm precipitation results between the two models. Both the MMF and NICAM produced similar frequencies of large CAPE between land and ocean. The dry MMF boundary layer enhanced microwave scattering signals over land, but only NICAM had an enhanced deep convection frequency over land. Neither model could reproduce a realistic land–ocean contrast in deep convective precipitation microphysics. A realistic contrast between land and ocean remains an issue in global storm-resolving modeling.

This paper describes the Spectral Ice Habit Prediction System (SHIPS), which represents a continuousproperty approach to microphysics simulation in an Eulerian cloud-resolving model (CRM). A two-moment hybrid-bin method is adopted to predict the solid hydrometeor distribution, where the distribution is divided into the mass bins with a simple mass distribution inside each bin. Each bin is characterized by a single representative ice crystal habit and the type of solid hydrometeor. These characteristics are diagnosed based on a series of particle property variables (PPVs) of solid hydrometeors that reflect the history of microphysical processes and the mixing between bins and air parcels in space. Thus, SHIPS allows solid hydrometeors to evolve characteristics and size distribution based on their movement through a cloud.SHIPS was installed into the University of Wisconsin-Nonhydrostatic Modeling System (UW-NMS) and tested for ice nucleation and vapor deposition processes. Two-dimensional idealized simulations were employed to simulate a winter orographic storm observed during the second Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-2) campaign. The simulated vertical distributions of ice crystal habits showed that the dynamic advection of dendrites produces wider dendritic growth region than local atmospheric conditions suggest. SHIPS showed the sensitivities of the habit distribution in the low-and midlevel to the upper-level growth mode (T Ͻ Ϫ20°C) of ice crystals through the sedimentation. Comparison of the results to aircraft observations casts doubt on the role of the columnar growth mode (T Ͻ Ϫ20°C) traditionally thought to be dominant in the literature. The results demonstrated how the complexity of the vapor deposition growth of ice crystals, including dendrites and capped columns, in varying temperature and moisture lead to particular observed habits.

Abstract. Anthropogenic aerosols serve as a source of both cloud condensation nuclei (CCN) and ice nuclei (IN) and affect microphysical properties of clouds. Increasing aerosol number concentrations is hypothesized to retard the cloud droplet coalescence and the riming in mixed-phase clouds, thereby decreasing orographic precipitation.This study presents results from a model intercomparison of 2-D simulations of aerosol-cloud-precipitation interactions in stratiform orographic mixed-phase clouds. The sensitivity of orographic precipitation to changes in the aerosol number concentrations is analysed and compared for various dynamical and thermodynamical situations. Furthermore, the sensitivities of microphysical processes such as coalescence, aggregation, riming and diffusional growth to changes in the aerosol number concentrations are evaluated and compared.The participating numerical models are the model from the Consortium for Small-Scale Modeling (COSMO) with bulk microphysics, the Weather Research and Forecasting (WRF) model with bin microphysics and the University of Wisconsin modeling system (UWNMS) with a spectral ice habit prediction microphysics scheme. All models are operated on a cloud-resolving scale with 2 km horizontal grid spacing.The results of the model intercomparison suggest that the sensitivity of orographic precipitation to aerosol modificaCorrespondence to: A. Muhlbauer (andreasm@atmos.washington.edu) tions varies greatly from case to case and from model to model. Neither a precipitation decrease nor a precipitation increase is found robustly in all simulations. Qualitative robust results can only be found for a subset of the simulations but even then quantitative agreement is scarce. Estimates of the aerosol effect on orographic precipitation are found to range from −19% to 0% depending on the simulated case and the model. Similarly, riming is shown to decrease in some cases and models whereas it increases in others, which implies that a decrease in riming with increasing aerosol load is not a robust result. Furthermore, it is found that neither a decrease in cloud droplet coalescence nor a decrease in riming necessarily implies a decrease in precipitation due to compensation effects by other microphysical pathways.The simulations suggest that mixed-phase conditions play an important role in buffering the effect of aerosol perturbations on cloud microphysics and reducing the overall susceptibility of clouds and precipitation to changes in the aerosol number concentrations. As a consequence the aerosol effect on precipitation is suggested to be less pronounced or even inverted in regions with high terrain (e.g., the Alps or Rocky Mountains) or in regions where mixed-phase microphysics is important for the climatology of orographic precipitation.