Building a Long-Term Cloud Record from Spaceborne Lidars: Bridging CALIOP to ATLID

Clouds exert multifaceted radiative effects on Earth's energy budget, serving as both insulators and reflectors of incoming solar radiation while also trapping outgoing infrared radiation. Consequently, clouds contribute to both surface cooling and warming processes, profoundly influencing regional and global climate dynamics. Despite their crucial role in Earth's energy balance, uncertainties persist regarding their feedback mechanisms.

A comprehensive understanding of clouds, including their spatial coverage, vertical distribution, and optical properties, is imperative for accurate climate prediction. Satellite-based observations, particularly those from active sounders, have offered continuous monitoring of clouds with high vertical and horizontal resolution since 2006. However, comparing cloud data from different spaceborne lidars presents challenges due to variations in wavelength, pulse energy, detector type, and local observation times.

This study discusses a methodology aimed at reconciling cloud data derived from several disparate spaceborne lidar platforms: CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation), which operated from 2006 to 2023; ALADIN/Aeolus (Atmospheric Laser Doppler Instrument), which operated from 2018 to 2023; IceSat-2, operational since 2018; and ATLID/EarthCARE (ATmospheric LIDar), launched last year.

For historical reasons, we use the Scattering Ratio at 532 nm (SR532) as a baseline for defining clouds across all lidars. The numerator contains the Attenuated Total Backscatter at 532 nm (ATB532), while the denominator includes a calculated Attenuated Molecular Backscatter at 532 nm (AMB532), assuming a cloud-free atmospheric profile. For measurements at other wavelengths, we convert the retrieved optical properties to SR532 and ATB532 to enable direct comparison. We demonstrate that this approach facilitates the retrieval of comparable cloud data for CALIOP and ALADIN using real measurements and for CALIOP and ATLID using synthetic measurements.

For lidars overlapping in time, the aforementioned cloud detection parameters can be fine-tuned to ensure a seamless transition between datasets. Collocated data are analyzed with respect to cloud fraction at different latitudes, altitudes, and seasons, and any differences are explored and corrected for, potentially accounting for instrument sensitivity or noise. However, when instruments do not overlap in time, an additional inter-calibrational procedure is necessary. We show how IceSat-2 can serve as a reference to align CALIOP and ATLID cloud datasets.