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Clouds exert multifaceted radiative effects on Earth's energy budget, acting as both insulators and reflectors that profoundly influence regional and global climate dynamics. Since 2006, spaceborne active sounders have monitored clouds with unprecedented vertical and horizontal resolution. Yet comparing cloud data from different lidars remains problematic - variations in wavelength, pulse energy, detector type, and observation times create discontinuities that complicate our understanding of long-term cloud behavior.This study presents a methodology to reconcile cloud observations from multiple spaceborne lidar platforms: CALIPSO (2006–2023), ALADIN/Aeolus (2018–2023), IceSat-2 (2018–present), ACDL/Daqi-1 (2022–present), and ATLID/EarthCARE (2024–present). We have already demonstrated this approach works for CALIOP and ALADIN (Feofilov et al., 2024); here we apply it to bridge CALIOP and ATLID. The approachWe use the Scattering Ratio at 532 nm (SR532) as our common language across all lidars. For measurements at other wavelengths, we convert the retrieved optical properties to SR532 and ATB532 (Attenuated Total Backscatter at 532 nm), enabling direct comparison. Since different signal-to-noise ratios between instruments can affect cloud detection near the detection threshold, we pay close attention to these differences.When satellites don't share the same viewing times - even with nearly identical equator crossings - we apply a diurnal cycle correction using climatology derived from CATS measurements as in (Feofilov and Stubenrauch, 2019; Feofilov et al., 2014). Since the satellites fly in opposite directions, they observe extratropical zones at different local times, and we must account for this.For missions that overlap in time, we fine-tune our cloud detection parameters until the datasets transition seamlessly. We then scrutinize collocated data across latitudes, altitudes, and seasons, hunting for differences and correcting for them where we find instrument sensitivity or noise effects.When instruments don't overlap that is the case for CALIOP and ATLID, we use a different strategy: we identify geographical zones characterized by minimal interannual variability and trends. These "stable" zones become our reference for intercalibration, allowing us to anchor ATLID to CALIOP without a shared observational period.What we getWe take ATLID's complete baseline, apply the wavelength conversion, perform diurnal cycle corrections, run our detection algorithm with the thresholds we've defined, generate global cloud distributions for the entire mission, and discuss its key properties with respect to CALIOP. References:Feofilov, A. G. and Stubenrauch, C. J.: Diurnal variation of high-level clouds from the synergy of AIRS and IASI space-borne infrared sounders, Atmos. Chem. Phys., 19, 13957–13972, https://doi.org/10.5194/acp-19-13957-2019, 2019.Feofilov, A., Chepfer, H., Noël, V., and Hajiaghazadeh-Roodsari, M.: Towards Establishing a Long-Term Cloud Record from Space-Borne Lidar Observations, Springer aerospace technology, 57–72, https://doi.org/10.1007/978-3-031-53618-2_6, 2024.