Cloud Vertical Distribution across Warm and Cold Fronts in CloudSat–CALIPSO Data and a General Circulation Model

Abstract

Cloud vertical distributions across extratropical warm and cold fronts are obtained using two consecutive winters of CloudSat-Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations and National Centers for Environmental Prediction reanalysis atmospheric state parameters over the Northern and Southern Hemisphere oceans (308-708N/S) between November 2006 and September 2008. These distributions generally resemble those from the original model introduced by the Bergen School in the 1920s, with the following exceptions: 1) substantial low cloudiness, which is present behind and ahead of the warm and cold fronts; 2) ubiquitous high cloudiness, some of it very thin, throughout the warm-frontal region; and 3) upright convective cloudiness near and behind some warm fronts. One winter of GISS general circulation model simulations of Northern and Southern Hemisphere warm and cold fronts at 28 3 2.58 3 32 levels resolution gives similar cloud distributions but with much lower cloud fraction, a shallower depth of cloudiness, and a shorter extent of tilted warm-frontal cloud cover on the cold air side of the surface frontal position. A close examination of the relationship between the cloudiness and relative humidity fields indicates that water vapor is not lifted enough in modeled midlatitude cyclones and this is related to weak vertical velocities in the model. The model also produces too little cloudiness for a given value of vertical velocity or relative humidity. For global climate models run at scales coarser than tens of kilometers, the authors suggest that the current underestimate of modeled cloud cover in the storm track regions, and in particular the 508-608S band of the Southern Oceans, could be reduced with the implementation of a slantwise convection parameterization.

Using satellite and reanalysis data to evaluate the representation of latent heating in extratropical cyclones in a climate model

“…It is clear that the cloud fraction in ISCCP is far greater than for HiGEM and ERAI, indicating that ISCCP observes more cloud around ETCs than in either HiGEM or ERAI. Other studies (see Zhang et al 2005;Kay et al 2012, and references therein) have shown that climate models typically underestimate total cloud amounts globally and this has been shown to extend to biases around ETCs and fronts (e.g., Naud et al 2010Naud et al , 2014. All three datasets have peak cloud fractions in a region which is centred on the peak precipitation and maximum ascent (as shown in Fig.…”

“…The CloudSat (known as Quickbeam) simulator (Haynes et al 2007;Bodas-Salcedo et al 2011) provides the ability to directly compare equivalent fields from CloudSat and reanalysis/forecast/climate models (e.g., Bodas-Salcedo et al 2008;Greenwald et al 2010;Naud et al 2010;Govekar et al 2011;Field et al 2011;Jiang et al 2012;Franklin et al 2013;Govekar et al 2014;Booth et al 2013). The simulator works by distributing model hydrometeors amongst the sub-grid columns with radar reflectivity, including attenuation, calculated at each model level.…”

Impacts of cloud microphysics on trade wind cumulus: which cloud microphysics processes contribute to the diversity in a large eddy simulation?

“…In global scale models (e.g., general circulation model (GCM)) and regional models with coarse grid spacing, shallow clouds are usually expressed by parameterizations (e.g., Tiedtke 1993;Considine et al 1997;Kain 2004), but these parameterizations have not been able to effectively simulate the shallow cloud cover observed from satellites (e.g., Chepfer et al 2008;Naud et al 2010).…”

Horizontal Distance of Each Cumulus and Cloud Broadening Distance Determine Cloud Cover

“…Shallow clouds have been expressed by parameterizations in global-scale models (e.g., Tiedtke 1993;Considine et al 1997). However, the parameterizations have not been able to simulate the shallow cloud cover observed from satellite (e.g., Chepfer et al 2008;Naud et al 2010); thus, it is necessary to improve understanding of the characteristics and parameterizations of shallow clouds to reduce uncertainties in climate prediction.…”