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.

No abstract

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.

[1] This study proposes another approach to develop multiyear single-column model (SCM) and cloud system-resolving model (CSRM) forcing data from numerical weather prediction (NWP) model analyses constrained with the observed surface and top of the atmosphere measurements by using a variational analysis approach. In the approach the atmospheric state variables from NWP analyses are adjusted to balance the observed column budgets of mass, heat, moisture, and momentum rather than the NWP modelproduced budgets. The derived constrained NWP forcing data from the National Oceanic and Atmospheric Administration rapid update cycle (RUC) analyses are evaluated by the ''observed'' forcing data derived from radiosonde and wind profiler data collected at the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains site under three selected cases: A strong convective case in the ARM summer 1997 intensive operational period (IOP), a moderate synoptic-scale process-dominated precipitation period in the spring 2000 IOP, and a nonprecipitation period in the late fall 2000 IOP. We show that the forcing data derived from the RUC analyses using ARM column constraints agree with the observed forcing reasonably well. The largest improvements are seen during precipitation periods since precipitation is a strong constraint used in the proposed approach. During the nonprecipitation period the improvements are moderate because the constraints are weak in the absence of precipitation. The constrained NWP forcing and the observed forcing, however, show better agreement during the moderate precipitation period and the nonprecipitation period than during the strong convective period. In SCM tests we show that most model errors revealed by the SCM driven by the observed forcing can be seen in the SCM driven by the constrained NWP forcing. These results suggest the feasibility of using the derived constrained NWP forcing data from RUC analyses for statistical studies of SCM/CSRM results over long time periods.

gauStad, long, MatheR, McfaRl ane, and Shi-Pacific northwest national laboratory, Richland, Washington; golaz and lin-noAA geophysical Fluid dynamics laboratory, Princeton, new Jersey; JenSen, JohnSon, and wiScoMbe-Brookhaven national laboratory,

SUMMARYThis study reports the Single-Column Model (SCM) part of the Atmospheric Radiation Measurement (ARM)/the Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS) joint SCM and Cloud-Resolving Model (CRM) Case 3 intercomparison study, with a focus on evaluation of cumulus parametrizations used in SCMs. Fifteen SCMs are evaluated under summertime midlatitude continental conditions using data collected at the ARM Southern Great Plains site during the summer 1997 Intensive Observing Period. Results from ten CRMs are also used to diagnose problems in the SCMs.It is shown that most SCMs can generally capture well the convective events that were well-developed within the SCM domain, while most of them have dif culties in simulating the occurrence of those convective events that only occurred within a small part of the domain. All models signi cantly underestimate the surface stratiform precipitation. A third of them produce large errors in surface precipitation and thermodynamic structures. De ciencies in convective triggering mechanisms are thought to be one of the major reasons. Using a triggering mechanism that is based on the vertical integral of parcel buoyant energy without additional appropriate constraints results in overactive convection, which in turn leads to large systematic warm/dry biases in the troposphere. It is also shown that a non-penetrative convection scheme can underestimate the depth of instability for midlatitude convection, which leads to large systematic cold/moist biases in the troposphere.SCMs agree well quantitatively with CRMs in the updraught mass uxes, while most models signi cantly underestimate the downdraught mass uxes. Neglect of mesoscale updraught and downdraught mass uxes in the SCMs contributes considerably to the discrepancies between the SCMs and the CRMs. In addition, uncertainties in the diagnosed mass uxes in the CRMs and de ciencies with cumulus parametrizations are not negligible.Similar results are obtained in the sensitivity tests when different forcing approaches are used. Finally, sensitivity tests from an SCM indicate that its simulations can be greatly improved when its triggering mechanism and closure assumption are improved.