Although cloud profiling lidars provide the most accurate data on cloud amount (CA), cloud top height (CTH) and cloud optical thickness (COT), they only sample cloud fields along a one-dimensional transect. This approach introduces uncertainty into studies that interpret cloud layers as two- or three-dimensional. The present study verified the accuracy of mean annual CA, COT, and CTH when calculated based on transect sampling, and when spatiotemporally averaged over grid cells measuring 1°, 2.5°, and 5° in latitude and longitude. The study evaluates the sampling scheme used by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar mission. In this study for the very first time, transect observations are investigated globally, at the level of climatological means, and for actual cloud regimes. Lidar data were simulated based on MODIS observations for 2005, resulting in 3,762,678 individual laser transects, and 58,647 annual means for each parameter under study. The results of this study demonstrate that, for most locations on Earth, CALIPSO sampling does report ‘true’ mean annual CA, COT, and CTH. At 1° resolution, statistically significant (α ≤ 0.05) differences in CA were only noted for 11% of cells, and for 2–3% of cells in the case of COT and CTH. Polar regions were identified as the most affected by transect sampling. Mean global errors (RMSE) observed at the annual timescale were within or close to the accuracy expected for satellite data used in climate change studies: 1.9% (CA), 2.4% (COT), and 187 m (CTH). This study also found that the spatial resolution of the grid impacts the magnitude of errors: an increase in cell size increased the underestimation of cloud parameters, increased the extent of areas where statistically significant errors/ differences occurred, and increased uncertainty in the climatological mean estimation (wider confidence intervals). However, at the cell level, errors were up to ±5% (CA), ±25% (COT) and ± 400 m (CTH); moreover, the magnitude of error was difficult to estimate a priori due to the noisy spatial distribution. These findings suggest that a dedicated uncertainty analysis should be made a requirement in CALIPSO lidar-based studies at fine spatial resolution.
Kotarba, A.Z. (2022) Errors in global cloud climatology due to transect sampling with the CALIPSO satellite lidar mission