Spaceborne lidar observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO) satellite provide the first-ever observations of cloud vertical structure and phase over the entire Greenland Ice Sheet. This study leverages CALIPSO observations over Greenland to pursue two investigations. First, the GCM-Oriented CALIPSO Cloud Product ( CALIPSO-GOCCP) observations are compared with collocated ground-based radar and lidar observations at Summit, Greenland. The liquid cloud cover agrees well between the spaceborne and ground-based observations. In contrast, ground–satellite differences reach 30% in total cloud cover and 40% in cloud fraction below 2 km above ground level, due to optically very thin ice clouds (IWC < 2.5 × 10−3 g m−3) missed by CALIPSO-GOCCP. Those results are compared with satellite cloud climatologies from the GEWEX cloud assessment. Most passive sensors detect fewer clouds than CALIPSO-GOCCP and the Summit ground observations, due to different detection methods. Second, the distribution of clouds over the Greenland is analyzed using CALIPSO-GOCCP. Central Greenland is the cloudiest area in summer, at +7% and +4% above the Greenland-wide average for total and liquid cloud cover, respectively. Southern Greenland contains free-tropospheric thin ice clouds in all seasons and liquid clouds in summer. In northern Greenland, fewer ice clouds are detected than in other areas, but the liquid cloud cover seasonal cycle in that region drives the total Greenland cloud annual variability with a maximum in summer. In 2010 and 2012, large ice-sheet melting events have a positive liquid cloud cover anomaly (from +1% to +2%). In contrast, fewer clouds (−7%) are observed during low ice-sheet melt years (e.g., 2009).
Abstract. Aeolus carries the Atmospheric LAser Doppler INstrument (ALADIN), the first high-spectral-resolution lidar (HSRL) in space. Although ALADIN is optimized to measure winds, its two measurement channels can also be used to derive optical properties of atmospheric particles, including a direct retrieval of the lidar ratio. This paper presents the standard correct algorithm and the Mie correct algorithm, the two main algorithms of the optical properties product called the Level-2A product, as they are implemented in version 3.12 of the processor, corresponding to the data labelled Baseline 12. The theoretical basis is the same as in Flamant et al. (2008). Here, we also show the in-orbit performance of these algorithms. We also explain the adaptation of the calibration method, which is needed to cope with unforeseen variations of the instrument radiometric performance due to the in-orbit strain of the primary mirror under varying thermal conditions. Then we discuss the limitations of the algorithms and future improvements. We demonstrate that the L2A product provides valuable information about airborne particles; in particular, we demonstrate the capacity to retrieve a useful lidar ratio from Aeolus observations. This is illustrated using Saharan dust aerosol observed in June 2020.
Using lidar and radiative flux observations from space and ground, and a lidar simulator, we evaluate clouds simulated by climate models over the Greenland ice sheet, including predicted cloud cover, cloud fraction profile, cloud opacity, and surface cloud radiative effects. The representation of clouds over Greenland is a central concern for the models because clouds impact ice sheet surface melt. We find that over Greenland, most of the models have insufficient cloud cover during summer. In addition, all models create too few nonopaque, liquid-containing clouds optically thin enough to let direct solar radiation reach the surface (−1% to −3.5% at the ground level). Some models create too few opaque clouds. In most climate models, the cloud properties biases identified over all Greenland also apply at Summit, Greenland, proving the value of the ground observatory in model evaluation. At Summit, climate models underestimate cloud radiative effect (CRE) at the surface, especially in summer. The primary driver of the summer CRE biases compared to observations is the underestimation of the cloud cover in summer (−46% to −21%), which leads to an underestimated longwave radiative warming effect (CRELW = −35.7 to −13.6 W m−2 compared to the ground observations) and an underestimated shortwave cooling effect (CRESW = +1.5 to +10.5 W m−2 compared to the ground observations). Overall, the simulated clouds do not radiatively warm the surface as much as observed.
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