SUMMARYLarge-eddy simulations of the development of shallow cumulus convection over land are presented. Many characteristics of the cumulus layer previously found in simulations of quasi-steady convection over the sea are found to be reproduced in this more strongly forced, unsteady case. Furthermore, the results are shown to be encouragingly robust, with similar results obtained with eight independent models, and also across a range of numerical resolutions. The datasets produced are already being used in the development and evaluation of parametrizations used in numerical weather-prediction and climate models.

[1] To assess the current status of climate models in simulating clouds, basic cloud climatologies from ten atmospheric general circulation models are compared with satellite measurements from the International Satellite Cloud Climatology Project (ISCCP) and the Clouds and Earth's Radiant Energy System (CERES) program. An ISCCP simulator is employed in all models to facilitate the comparison. Models simulated a four-fold difference in high-top clouds. There are also, however, large uncertainties in satellite high thin clouds to effectively constrain the models. The majority of models only simulated 30-40% of middle-top clouds in the ISCCP and CERES data sets. Half of the models underestimated low clouds, while none overestimated them at a statistically significant level. When stratified in the optical thickness ranges, the majority of the models simulated optically thick clouds more than twice the satellite observations. Most models, however, underestimated optically intermediate and thin clouds. Compensations of these clouds biases are used to explain the simulated longwave and shortwave cloud radiative forcing at the top of the atmosphere. Seasonal sensitivities of clouds are also analyzed to compare with observations. Models are shown to simulate seasonal variations better for high clouds than for low clouds. Latitudinal distribution of the seasonal variations correlate with satellite measurements at >0.9, 0.6-0.9, and À0.2-0.7 levels for high, middle, and low clouds, respectively. The seasonal sensitivities of cloud types are found to strongly depend on the basic cloud climatology in the models. Models that systematically underestimate middle clouds also underestimate seasonal variations, while those that overestimate optically thick clouds also overestimate their seasonal sensitivities. Possible causes of the systematic cloud biases in the models are discussed.

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Numerical weather prediction methods show promise for improving parameterizations in climate GCMs.C limate simulations performed with general circulation models (GCMs) are widely viewed as the principal scientific basis for developing policies to address potential future global climate change (Houghton et al. 2001). In order to reduce uncertainties in these GCM projections of future climate, there is a compelling need to improve the simulation of processes that produce the present climate. This undertaking demands close attention to systematic errors in GCM simulations.Systematic errors are persistent (average) departures of the model solution from an appropriate observational standard. For example, the GCM sys-AFFILIATIONS:

SUMMARYThis paper reports an intercomparison study of midlatitude continental cumulus convection simulated by eight two-dimensional and two three-dimensional cloud-resolving models (CRMs), driven by observed large-scale advective temperature and moisture tendencies, surface turbulent uxes, and radiative-heating pro les during three sub-periods of the summer 1997 Intensive Observation Period of the US Department of Energy's Atmospheric Radiation Measurement (ARM) program. Each sub-period includes two or three precipitation events of various intensities over a span of 4 or 5 days. The results can be summarized as follows.CRMs can reasonably simulate midlatitude continental summer convection observed at the ARM Cloud and Radiation Testbed site in terms of the intensity of convective activity, and the temperature and speci c-humidity evolution. Delayed occurrences of the initial precipitation events are a common feature for all three sub-cases among the models. Cloud mass uxes, condensate mixing ratios and hydrometeor fractions produced by all CRMs are similar. Some of the simulated cloud properties such as cloud liquid-water path and hydrometeor fraction are rather similar to available observations. All CRMs produce large downdraught mass uxes with magnitudes similar to those of updraughts, in contrast to CRM results for tropical convection. Some inter-model differences in cloud properties are likely to be related to those in the parametrizations of microphysical processes.There is generally a good agreement between the CRMs and observations with CRMs being signi cantly better than single-column models (SCMs), suggesting that current results are suitable for use in improving parametrizations in SCMs. However, improvements can still be made in the CRM simulations; these include the proper initialization of the CRMs and a more proper method of diagnosing cloud boundaries in model outputs for comparison with satellite and radar cloud observations.