ATLID Cloud Climate Product
Authors: Feofilov Artem , Chepfer Hélène , Noël Vincent , Szczap Frederic 
Published: November 2022

Abstract. Despite significant advances in atmospheric measurements and modeling, clouds response to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. Documenting how the cloud detailed vertical structure, the cloud cover and opacity evolve on a global scale over several decades is a necessary step towards understanding and predicting the cloud response to climate warming. Among satellite-based remote sensing techniques, active sounding plays a special role, owing to its high vertical and horizontal resolution and high sensitivity. The launch of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) in 2006 started the era of space-borne optical active sounding of the Earth’s atmosphere, which continued with the CATS (Cloud-Aerosol Transport System) lidar on-board ISS in 2015 and the Atmospheric Laser Doppler INstrument (ALADIN) lidar on-board Aeolus in 2018. The next important step is the ATmospheric LIDar (ATLID) instrument from the EarthCARE mission expected to launch in 2023. With ATLID, the scientific community will continue receiving invaluable vertically resolved information of atmospheric optical properties needed for the estimation of cloud occurrence frequency, thickness, and height. In this article, we define the ATLID Climate Product, Short-Term (CLIMP-ST) and ATLID Climate Product, Long-Term (CLIMP-LT). The purpose of CLIMP-ST is to help evaluate the description of cloud processes in climate models, beyond what is already done with existing space lidar observations, thanks to ATLID new capabilities. The CLIMP-LT will merge the ATLID cloud observations with previous space lidar observations to build a long-term cloud lidar record useful to evaluate the cloud climate variability predicted by climate models. We start with comparing the cloud detection capabilities of ATLID and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) in day- and night-time, on a profile-to-profile basis in analyzing virtual ATLID and CALIOP measurements over synthetic cirrus and stratocumulus cloud scenes. We show that solar background noise affects the cloud detectability in daytime conditions differently for ATLID and CALIPSO. We found that the simulated daytime ATLID measurements have lower noise than the CALIOP day-time simulated measurements. This allows lowering the cloud detection thresholds for ATLID compared to CALIOP and enables ATLID to detect optically thinner clouds than CALIOP in daytime at high horizontal resolution without false cloud detection. These lower threshold values will be used to build the ATLID-ST. Therefore, CLIMP-ST should provide an advance to evaluate optically thin clouds like cirrus or ice polar clouds in climate models compared to the current existing capability. We also found that ATLID and CALIPSO may detect similar clouds if we convert ATLID 355 nm profiles to 532 nm profiles and apply the same cloud detection thresholds as the ones used in GOCCCP (GCM Oriented Calipso Cloud Product). Therefore, this approach will be used to build the CLIMP-LT. The CLIMP-LT data will be merged with the GOCCP data to get a long-term (2006–2030’s) cloud climate record. Finally, we investigate the detectability of cloud changes induced by human-caused climate warming within a virtual long-term cloud monthly gridded lidar dataset over the 2008–2034 period that we obtained from two ocean-atmosphere-coupled climate models coupled with a lidar simulator. We found that a long-term trend of opaque cloud cover should emerge from short-term natural climate variability after 4 to 7 years of ATLID measurements (merged with CALIPSO measurements) according to predictions from the considered climate models. We conclude that a long-term lidar cloud record build from the merge of the actual ATLID-LT data with CALIPSO-GOCCP data will be a useful tool to monitor cloud changes and to evaluate the realism of the cloud changes predicted by climate models.