Publications

Export 2 results:
Sort by: Author Title Type [ Year  (Asc)]
2000
Ghan, S, Randall D, Xu KM, Cederwall R, Cripe D, Hack J, Iacobellis S, Klein S, Krueger S, Lohmann U, Pedretti J, Robock A, Rotstayn L, Somerville R, Stenchikov G, Sud Y, Walker G, Xie SC, Yio J, Zhang MH.  2000.  A comparison of single column model simulations of summertime midlatitude continental convection. Journal of Geophysical Research-Atmospheres. 105:2091-2124.   Doi 10.1029/1999jd900971   AbstractWebsite

Eleven different single-column models (SCMs) and one cloud ensemble model (CEM) are driven by boundary conditions observed at the Atmospheric Radiation Measurement (ARM) program southern Great Plains site for a 17 day period during the summer of 1995. Comparison of the model simulations reveals common signatures identifiable as products of errors in the boundary conditions. Intermodel differences in the simulated temperature, humidity, cloud, precipitation, and radiative fluxes reflect differences in model resolution or physical parameterizations, although sensitive dependence on initial conditions can also contribute to intermodel differences. All models perform well at times but poorly at others. Although none of the SCM simulations stands out as superior to the others, the simulation by the CEM is in several respects in better agreement with the observations than the simulations by the SCMs. Nudging of the simulated temperature and humidity toward observations generally improves the simulated cloud and radiation fields as well as the simulated temperature and humidity but degrades the precipitation simulation for models with large temperature and humidity biases without nudging. Although some of the intermodel differences have not been explained, others have been identified as model problems that can be or have been corrected as a result of the comparison.

2005
Xie, SC, Zhang MH, Branson M, Cederwall RT, Delgenio AD, Eitzen ZA, Ghan SJ, Iacobellis SF, Johnson KL, Khairoutdinov M, Klein SA, Krueger SK, Lin WY, Lohmann U, Miller MA, Randall DA, Somerville RCJ, Sud YC, Walker GK, Wolf A, Wu XQ, Xu KM, Yio JJ, Zhang G, Zhang JH.  2005.  Simulations of midlatitude frontal clouds by single-column and cloud-resolving models during the Atmospheric Radiation Measurement March 2000 cloud intensive operational period. Journal of Geophysical Research-Atmospheres. 110   10.1029/2004jd005119   AbstractWebsite

[1] This study quantitatively evaluates the overall performance of nine single-column models (SCMs) and four cloud-resolving models (CRMs) in simulating a strong midlatitude frontal cloud system taken from the spring 2000 Cloud Intensive Observational Period at the Atmospheric Radiation Measurement ( 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. 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. All CRMs and some SCMs also underestimated the middle clouds after the frontal passage. There are also large differences in the model simulations of cloud condensates owing to differences in parameterizations; however, the differences among intercompared models are smaller in the CRMs than the SCMs. In general, 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. Many of these model biases could be traced to the lack of subgrid-scale dynamical structure in the applied forcing fields and the lack of organized mesoscale hydrometeor advections. Other potential reasons for these model errors are also discussed in the paper.