PNNL

Abstract

This study quantitatively evaluates the overall performance of 9 singlecolumn models (SCMs) and 4 cloud resolving models (CRMs) in simulating a strong midlatitude frontal cloud system taken from the Spring 2000 Cloud Intensive Observational Period at the ARM Southern Great Plains site. The evaluation data are an analysis product of Constrained Variational Analysis of the ARM-Observations and the cloud data collected from the ARM ground active remote sensors (i.e., cloud radar, lidar, and laser ceilometers) and satellite retrievals. Both the selected SCMs and CRMs can typically capture the bulk characteristics of the frontal system and the frontal precipitation. However, there are significant differences in detailed structures of the frontal clouds. Both CRMs and SCMs overestimate high thin cirrus clouds before the main frontal passage. This is likely caused by the application of grid-scale upward motion in the upper troposphere when in reality only cloud streaks exist in narrow regions of upward sub-grid scale motion. During the passage of a front with strong upward motion, CRMs underestimate middle and low clouds while SCMs overestimate clouds at the levels above 765 hPa. The underestimation in the CRMs is presumably due to the lack of organized stratiform processes that are replaced by convections in the models under strong forcing. The overestimation in the SCMs is likely related to the uniform application of grid-averaged cooling and moistening associated with strong upward motion. All CRMs and some SCMs also underestimated the middle clouds after the frontal passage. This could be related to the lack of organized mesoscale cyclonic advection of hydrometeors behind the moving cyclone. Some of the SCMs simulated more middle clouds after frontal passage due to the long lifetime of cloud ice or prognostic cloud amount in the models. There are also large differences in the model simulations of cloud condensates due to differences in parameterizations, however, the differences among inter-compared models are smaller in the CRMs than the SCMs. While the CRM-produced clouds are highly correlated to the simulated cloud liquid and ice water contents, the SCM-simulated clouds are closely associated with their relative humidity fields. The CRM-simulated cloud water and ice are comparable with observations, while most SCMs underestimated cloud water. SCMs show huge biases varying from large overestimates to equally large underestimates of cloud ice. The partitions between cloud water and cloud ice in the SCMs are also very different when they are compared with observations and CRM simulations. The results point out the need to find ways to improve both the treatment of subgrid scale dynamics and cloud microphysical parameterizations in cloud parameterizations for climate models.