Seasonal Variation of Cloud Systems over ARM SGP

Abstract

Increased observational analyses provide a unique opportunity to perform years-long cloud-resolving model (CRM) simulations and generate long-term cloud properties that are very much in demand for improving the representation of clouds in general circulation models (GCMs). A year 2000 CRM simulation is presented here using the variationally constrained mesoscale analysis and surface measurements. The year-long (3 January–31 December 2000) CRM surface precipitation is highly correlated with the Atmospheric Radiation Measurement (ARM) observations with a correlation coefficient of 0.97. The large-scale forcing is the dominant factor responsible for producing the precipitation in summer, spring, and fall, but the surface heat fluxes play a more important role during winter when the forcing is weak. The CRM-simulated year-long cloud liquid water path and cloud (liquid and ice) optical depth are also in good agreement (correlation coefficients of 0.73 and 0.64, respectively) with the ARM retrievals over the Southern Great Plains (SGP). The simulated cloud systems have 50% more ice water than liquid water in the annual mean. The vertical distributions of ice and liquid water have a single peak during spring (March–May) and summer (June–August), but a second peak occurs near the surface during winter (December–February) and fall (September–November). The impacts of seasonally varied cloud water are very much reflected in the cloud radiative forcing at the top-of-atmosphere (TOA) and the surface, as well as in the vertical profiles of radiative heating rates. The cloudy-sky total (shortwave and longwave) radiative heating profile shows a dipole pattern (cooling above and warming below) during spring and summer, while a second peak of cloud radiative cooling appears near the surface during winter and fall.

Evaluation of high‐level clouds in cloud resolving model simulations with ARM and KWAJEX observations

“…Previous studies on the differences between the 2‐D and the three dimensional (3‐D) CRM simulations showed that the 3‐D simulations had superior performances over the 2‐D simulations in various aspects [e.g., Donner et al ., ; Petch and Gray , ; Phillips and Donner , ; Petch et al ., ]. However, the cost‐effectiveness of the 2‐D CRM simulation is still a major advantage and the 2‐D approach is still adopted in some recent studies for year‐long or multiyear simulations [e.g., Wu and Park , ; Henderson and Pincus , ; Blossey et al ., ]. In addition, the 2‐D CRMs are still used in most MMF configurations so the 2‐D CRM simulations are appropriate and relevant in this context.…”

Weather Research and Forecasting Model simulations of extended warm-season heavy precipitation episode over the US Southern Great Plains: data assimilation and microphysics sensitivity experiments

“…Ferrier et al, 1995;Tao et al, 2003;Hong et al, 2004;Leung et al, 2006;Wu et al, 2008). However, even in highly sophisticated models, the representation of clouds remains a challenge (e.g.…”

Understanding and Improving Cloud and Radiation Processes Using Year-Long Cloud-Resolving Model Simulations

“…The horizontal grid size is 3 km and the vertical grid of 52 levels is 100 m at the surface, 550-850 m between 5 and 12 km, and 1500 m at the model top. The simulation time step is 15 s. The model setting of the year-long (i.e., January 3-December 31, 2000) CRM simulation is basically the same as the CRM simulation by Wu et al (2008). The year-long CRM simulation is forced by the evolving temperature and moisture forcing which is kept constant for an hour around the observed time.…”

“…The CRM simulations have been assessed with various observations in terms of atmospheric thermodynamic properties, atmospheric radiation, precipitation, and surface fluxes in the tropics during the Global Atmospheric Research Program Atlantic Tropical Experiment (GATE) (e.g., Xu and Randall 1996;Grabowski et al 1996), Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment (TOGA COARE) (e.g., Wu et al 1998;Li et al 1999;Wu and Moncrieff 2001), and ARM (e.g., Xu et al 2002;Wu et al 2007Wu et al , 2008. Grabowski et al (1996Grabowski et al ( , 1998Grabowski et al ( , 1999 conducted 2-dimensional (2D) and 3-dimensional (3D)…”

“…Recently, Wu et al (2008) conducted a year-long Iowa State University (ISU) CRM simulation using the large-scale forcing over ARM SGP during year 2000 to investigate the seasonal variation of radiative and cloud properties. The year-long simulation provides a physically consistent long-term dataset for understanding the processes of convection, cloud and radiation and the interaction among them.…”