Simulated and observed horizontal inhomogeneities of optical thickness of Arctic stratus

Abstract

Abstract. Two-dimensional horizontal fields of cloud optical thickness τ derived from airborne measurements of solar spectral, cloud-reflected radiance are compared with semi-idealized large eddy simulations (LESs) of Arctic stratus performed with the Consortium for Small-scale Modeling (COSMO) atmospheric model. The measurements were collected during the Vertical Distribution of Ice in Arctic Clouds (VERDI) campaign carried out in Inuvik, Canada, in April/May 2012. The input for the LESs is obtained from collocated airborne dropsonde observations of a persistent Arctic stratus above the sea-ice-free Beaufort Sea. Simulations are performed for spatial resolutions of 50 m (1.6 km × 1.6 km domain) and 100 m (6.4 km × 6.4 km domain). Macrophysical cloud properties, such as cloud top altitude and vertical extent, are well captured by the COSMO simulations. However, COSMO produces rather homogeneous clouds compared to the measurements, in particular for the simulations with coarser spatial resolution. For both spatial resolutions, the directional structure of the cloud inhomogeneity is well represented by the model. Differences between the individual cases are mainly associated with the wind shear near cloud top and the vertical structure of the atmospheric boundary layer. A sensitivity study changing the wind velocity in COSMO by a vertically constant scaling factor shows that the directional, small-scale cloud inhomogeneity structures can range from 250 to 800 m, depending on the mean wind speed, if the simulated domain is large enough to capture also large-scale structures, which then influence the small-scale structures. For those cases, a threshold wind velocity is identified, which determines when the cloud inhomogeneity stops increasing with increasing wind velocity.

Estimation of cloud optical thickness, single scattering albedo and effective droplet radius using a shortwave radiative closure study in Payerne

“…For Ci-Cs the opposite applies: the absolute U c in COT DSR retrieval is 0.1 for all cases, independent of the COT input value. Similar behaviours of the uncertainties of COT DSR estimations are also presented in Serrano et al (2014).…”

“…A number of studies have presented methods for the retrieval of COT using data from ground-based instruments, for example, from lidars (Gouveia et al, 2017), broadband pyranometers (Leontyeva and Stamnes, 1994; Barnard and Long, 2004;Qiu, 2006), sunphotometers (Min and Harrison, 1996;Chiu et al, 2010) or UV radiometers (Serrano et al, 2014). With ground-based microwave instruments the liquid water path (LWP) is determined (Dupont et al, 2018), which can be used to calculate the cloud optical thickness, knowing or assuming r eff (Stephens, 1994).…”

Small-scale structure of thermodynamic phase in Arctic mixed-phase clouds observed by airborne remote sensing during a cold air outbreak and a warm air advection event

“…Cloud resolving models (1 km horizontal and 30 m vertical resolution; Luo et al, 2008) and large eddy simulations (LESs, below 100 m horizontal and 15 m vertical resolution; Loewe et al, 2017) resolve small-scale cloud processes and are used to improve the subgrid mixed-phase cloud parameterization of GCMs. In order to evaluate the performance of these highresolution simulations, adequately resolved observations are needed (Werner et al, 2014;Roesler et al, 2017;Schäfer et al, 2018;Egerer et al, 2019;Neggers et al, 2019;Schemann and Ebell, 2020).…”

Employing airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the Arctic

“…Nevertheless, a comparison of large eddy simulations to aircraft observations for well-defined situations can give valuable insights into physical processes within the Arctic atmosphere. Such simulations can be particularly useful when the high resolution of a large eddy set-up is explicitly needed to allow for a comparison with small scale phenomena like in-cloud turbulence (Mech et al, 2020) or cloud-top inhomogeneity (Schäfer et al, 2018;Ruiz-Donoso et al, 2020) that are observed from the airborne remote sensing. To avoid the need for large computational resources but still be able to resolve many processes that act on scales that cannot be captured by GCMs, limited area simulations with grid sizes on the order of a few kilometers where (deep) convection does not need to be explicitly parameterized can offer a good compromise.…”