Cloud response to synoptic conditions over the Beaufort and Chukchi seasonal ice zone is examined. Four synoptic states with distinct thermodynamic and dynamic signatures are identified using ERA-Interim reanalysis data from 2000 to 2014. CloudSat and CALIPSO observations suggest control of clouds by synoptic states. Warm continental air advection is associated with the fewest low-level clouds, while cold air advection generates the most low-level clouds. Low-level clouds are related to lower-tropospheric stability and both are regulated by synoptic conditions. High-level clouds are associated with humidity and vertical motions in the upper atmosphere. Observed cloud vertical and spatial variability is reproduced well in ERA-Interim, but winter low-level cloud fraction is overestimated. This suggests that synoptic conditions constrain the spatial extent of clouds through the atmospheric structure, while the parameterizations for cloud microphysics and boundary layer physics are critical for the life cycle of clouds in numerical models. Sea ice melt onset is related to synoptic conditions. Melt onsets occur more frequently and earlier with warm air advection. Synoptic conditions with the highest temperatures and precipitable water are most favorable for melt onsets even though fewer low-level clouds are associated with these conditions.

The authors use the Polar Weather Research and Forecasting (WRF) Model to simulate atmospheric conditions during the Seasonal Ice Zone Reconnaissance Survey (SIZRS) in the summer of 2013 over the Beaufort Sea. With the SIZRS dropsonde data, the performance of WRF simulations and two forcing datasets is evaluated: the Interim ECMWF Re-Analysis (ERA-Interim) and the Global Forecast System (GFS) analysis. General features of observed mean profiles, such as low-level temperature inversion, low-level jet (LLJ), and specific humidity inversion are reproduced by all three models. A near-surface warm bias and a low-level moist bias are found in ERA-Interim. WRF significantly improves the mean LLJ, with a lower and stronger jet and a larger turning angle than the forcing. The improvement in the mean LLJ is likely related to the lower values of the boundary layer diffusion in WRF than in ERA-Interim and GFS, which also explains the lower near-surface temperature in WRF than the forcing. The relative humidity profiles have large differences between the observations, the ERA-Interim, and the GFS. The WRF simulated relative humidity closely resembles the forcings, suggesting the need to obtain more and better-calibrated humidity data in this region. The authors find that the sea ice concentrations in the ECMWF model are sometimes significantly underestimated due to an inappropriate thresholding mechanism. This thresholding affects both ERA-Interim and the ECMWF operational model. The scale of impact of this issue on the atmospheric boundary layer in the marginal ice zone is still unknown.

[1] Millimeter-wavelength cloud radar (MMCR) can provide information on the vertical structure of cloud fields and thereby improve our understanding of the spatial distribution of clouds and their role in the climate system. Here we consider the representativeness of ground-based vertically pointing MMCR observations, which have been used in numerous climate studies. MMCR cloud statistics collected at Darwin, Australia, are compared against CloudSat (spaceborne) observations gathered in the near vicinity of the ground site. Overall, the total cloud occurrence vertical profiles observed by CloudSat and the ground-based MMCR agree on a spatial scale of 4°× 4°, although CloudSat is found to observe more high reflectivity cloud than the ground-based MMCR. Computed radar reflectivity using idealized atmospheric profiles suggests that rain (especially below the melting level) influences the observed reflectivities, and this appears to account for much of the differences in the observed distributions of radar reflectivity. After removal of precipitation profiles, CloudSat and ground-based MMCR observations show reasonable agreement. Sampling uncertainty in the CloudSat observations makes comparison at smaller region spatial scales (e.g., 2.5°) difficult and unfeasible for analysis at the time scale of months. Comparison of CloudSat observations with the ground-based data on scales of 4°and 7.5°works well. Comparison of total cloud occurrence and reflectivity distribution of nonprecipitating cloud from the MMCR and CloudSat at spatial scales from 4°to 7.5°show good agreement. This suggests that the properties of the nonprecipitating cloud are relatively homogeneous at this large scale.Citation: Liu, Z., R. Marchand, and T. Ackerman (2010), A comparison of observations in the tropical western Pacific from ground-based and satellite millimeter-wavelength cloud radars,