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Lidar observations collected during the Lidar In-space Technology Experiment experiment in conjunction with the Meteosat and European Centre for Medium-Range Weather Forecasts data have been used not only to validate the Saharan dust plume conceptual model constructed from the GARP (Global Atmospheric Research Programme) Atlantic Tropical Experiment data, but also to examine the vicissitudes of the Saharan aerosol including their optical depths across the west Africa and east Atlantic regions. Optical depths were evaluated from both the Meteosat and lidar data. Back trajectory calculations were also made along selected lidar orbits to verify the characteristic anticyclonic rotation of the dust plume over the eastern Atlantic as well as to trace the origin of a dust outbreak over West Africa. A detailed synoptic analysis including the satellite-derived optical depths, vertical lidar backscattering cross section profiles, and back trajectories of the 16-19 September 1994 Saharan dust outbreak over the eastern Atlantic and its origin over West Africa during the 12-15 September period have been presented. In addition, lidar-derived backscattering profiles and optical depths were objectively analyzed to investigate the general features of the dust plume and its geographical variations in optical thickness. These analyses validated many of the familiar characteristic features of the Saharan dust plume conceptual model such as (i) the lifting of the aerosol over central Sahara and its subsequent transport to the top of the Saharan air layer (SAL), (ii) the westward rise of the dust layer above the gradually deepening marine mixed layer and the sinking of the dust-layer top, (iii) the anticyclonic gyration of the dust pulse between two consecutive trough axes, (iv) the dome-shaped structure of the dust-layer top and bottom, (v) occurrence of a middlelevel jet near the southern boundary of the SAL, (vi) transverse-vertical circulations across the SAL front including their possible role in the initiation of a squall line to the southside of the jet that ultimately developed into a tropical storm, and (vii) existence of satellite-based high optical depths to the north of the middle-level jet in the ridge region of the wave. Furthermore, the combined analyses reveal a complex structure of the dust plume including its origin over North Africa and its subsequent westward migration over the Atlantic Ocean. The dust plume over the west African coastline appears to be composed of two separate but narrow plumes originating over the central Sahara and Lake Chad regions, in contrast to one single large plume shown in the conceptual model. Lidar observations clearly show that the Saharan aerosol over North Africa not only consist of background dust (Harmattan haze) but also wind-blown aerosol from fresh dust outbreaks. They further exhibit maximum dust concentration near the middle-level jet axis with downward extension of heavy dust into the marine boundary layer including a clean dust-free trade wind inversion to the north of the d...

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[1] To simulate the impact of drifting snow on the lower atmosphere, surface characteristics and surface mass balance (SMB) of the Antarctic ice sheet regional atmospheric climate model (RACMO2.1/ANT) with horizontal resolution of 27 km is coupled to a drifting snow routine and forced by ERA-Interim fields at its lateral boundaries . This paper evaluates the near-surface and drifting snow climate of RACMO2.1/ANT. Modeled near-surface wind speed (squared correlation coefficient R 2 = 0.64) and temperature (R 2 = 0.93) agree well with observations. Wind speed is underestimated in topographically complex areas, where observed wind speeds are locally very high (>20 m s À1 ). Temperature is underestimated in winter in coastal areas due to an underestimation of downward longwave radiation. Near-surface temperature and wind speed are not significantly affected by the inclusion of drifting snow in the model. In contrast, relative humidity with respect to ice increases in regions with strong drifting snow and becomes more consistent with the observations. Drifting snow frequency is the only observable parameter to directly validate drifting snow results; therefore, we derived an empirical relation for fresh snow density, as a function of wind speed and temperature, which determines the threshold wind speed for drifting snow. Modeled drifting snow frequencies agree well with in situ measurements and novel estimates from remote sensing. Finally, we show that including drifting snow is essential to obtaining a realistic extent and spatial distribution of ablation (SMB < 0) areas.