Observations of cold-cloud properties in the Norwegian Arctic using ground-based and spaceborne lidar

Abstract

Abstract. The role of clouds in the surface radiation budget is particularly complex in the rapidly changing Arctic. However, despite their importance, long-term observations of Arctic clouds are relatively sparse. Here, we present observations of cold clouds based on 7 years (2011–2017) of ground-based lidar observations at the Arctic Lidar Observatory for Middle Atmosphere Research (ALOMAR) in Andenes in the Norwegian Arctic. In two case studies, we assess (1) the agreement between a co-located cirrus cloud observations from the ground-based lidar and the spaceborne lidar aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and (2) the ground-based lidar's capability to determine the cloud phase in mixed-phase clouds from depolarization measurements. We then compute multiyear statistics of cold clouds from both platforms with respect to their occurrence, cloud top and base height, cloud top temperature, and thermodynamic phase for the 2011–2017 period. We find that satellite- and ground-based observations agree well with respect to the coincident cirrus measurement and that the vertical phase distribution within a liquid-topped mixed-phase cloud could be identified from depolarization measurements. On average, 8 % of all satellite profiles were identified as single-layer cold clouds with no apparent seasonal differences. The average cloud top and base heights, combining the ground-based and satellite measurements, are 9.1 and 6.9 km, respectively, resulting in an average thickness of 2.2 km. Seasonal differences between the average top and base heights are on the order of 1–2 km and are largest when comparing fall (highest) and spring (lowest). However, seasonal variations are small compared with the observed day-to-day variability. Cloud top temperatures agree well between both platforms, with warmer cloud top temperatures in summer. The presented study demonstrates the capabilities of long-term cloud observations in the Norwegian Arctic from the ground-based lidar at Andenes.

The effects of warm air intrusions in the high arctic on cirrus clouds

“…They report average cirrus cloud depths of 2.1 km during winter with a cloud base around 7.1 km and a cloud top on average at 9.3 km. Finally, Schäfer et al, (2022) use a ground based and space-born lidar and measure cirrus clouds over the Norwegian arctic for a period of seven years. They find a yearly average cloud thickness of 2.2 km, an average cloud base at 6.9 km and cloud top at 9.1 km.…”

Simulation of Compact Spaceborne Lidar with High-Repetition-Rate Laser for Cloud and Aerosol Detection under Different Atmospheric Conditions

“…where Nb is the number of sky background photons received by each pulse; Pb is the sky background radiation intensity (W•m −2 •sr −1 •nm −1 ), and for a 532 nm wavelength, it is generally believed Pb = 0.2 W•m −2 •sr −1 •nm −1 in the daytime; θ is the viewing angle of the telescope (mrad); d ωl is filter bandwidth (nm); A r is the receiving area of the telescope (m 2 ); T r is the optical transmittance of the lidar receiving optical unit; ∆t is the time resolution (s) of the lidar. According to Equation (10), factors affecting the intensity of the sky background radiation signal received by the lidar are also composed of three parts.…”

“…Therefore, it is of positive significance to study the spatial distribution and change process of clouds and aerosols for improving the precision of weather forecast and predicting global climate change [6][7][8][9]. In order to realize the real-time dynamic monitoring of global clouds and aerosols, spaceborne lidar remote sensing has become a hotspot of environmental surveillance research [10][11][12][13]. However, due to the limitation of the orbit and operation mechanism, the measurement data of a single spaceborne lidar cannot accurately describe atmospheric conditions, so it is necessary to adopt other technical means and build the lidar surveillance network to improve the precision of detection.…”