B. J. Shipway

Ice in Clouds Experiment-Layer Clouds. Part II: Testing Characteristics of Heterogeneous Ice Formation in Lee Wave Clouds

AbstractHeterogeneous ice nucleation is a source of uncertainty in models that represent ice clouds. The primary goal of the Ice in Clouds Experiment–Layer Clouds (ICE-L) field campaign was to determine if a link can be demonstrated between ice concentrations and the physical and chemical characteristics of the ambient aerosol. This study combines a 1D kinematic framework with lee wave cloud observations to infer ice nuclei (IN) concentrations that were compared to IN observations from the same flights. About 30 cloud penetrations from six flights were modeled. The temperature range of the observations was −16° to −32°C. Of the three simplified ice nucleation representations tested (deposition, evaporation freezing, and condensation/immersion droplet freezing), condensation/immersion freezing reproduced the lee wave cloud observations best. IN concentrations derived from the modeling ranged from 0.1 to 13 L−1 compared to 0.4 to 6 L−1 from an IN counter. A better correlation was found between temperature a...

Cloud Microphysical Factors Affecting Simulations of Deep Convection During the Presummer Rainy Season in Southern China

The sensitivity of subtropical deep convection to the parameterization of cloud microphysics is elucidated through high-resolution modeling of extreme presummer rainfall over southern China. An ensemble of physics configuration experiments is used to identify several drivers of model errors in comparison to radar observations from the South China Monsoon Rainfall Experiment (SCMREX) and remotely sensed estimates of cloud, precipitation, and radiation from satellites in the A-train constellation. The benefits of increasing the number of prognostic variables in the microphysics scheme is assessed, relative to the effects of the parameterization of cloud microphysical properties and cloud fraction diagnosis. By matching individual parameterizations between the microphysical configurations, it is shown that a small subset of the parameterization changes can reproduce most of the dependence of model performance on physics configuration. In particular, biases that are due to the low-level clouds and rain are strongly influenced by cloud fraction diagnosis and raindrop size distribution, whereas variations in the effects of high clouds are strongly influenced by differences in the parameterization of ice crystal sedimentation. Hence, for the case studied here, these parameterizations give more insight into the causes of variability in model performance than does the number of model prognostics per se.