Well-Posed Global 3-D Retrieval of Background Aerosol Optical Properties From the DQ-1/ACDL

Aerosols critically influence Earth’s energy balance and climate through direct radiation interactions and indirect cloud condensation nuclei (CCN) effects. Background aerosols, typically undetectable due to low optical depths, hinder precise radiative forcing quantification. China’s DQ-1 satellite, equipped with the high-spectral-resolution aerosol and carbon-detection lidar (ACDL), enables the retrieval of these faint aerosols, but conventional algorithms struggle with weak signals. In this study, we address these challenges through two primary innovations: 1) the development of a uniquely tailored multiscale denoising chain for high-spectral-resolution lidar (HSRL) data and 2) the identification and subsequent correction of a systematic negative bias that occurs during the retrieval of backscatter data under low signal-to-noise ratio (SNR) conditions. These advancements facilitate the first well-posed global 3-D retrieval of background aerosol extinction, backscatter coefficients, and lidar ratios from spaceborne lidar, eliminating traditional assumptions. Validation against SAGE III/ISS occultation data confirms strong agreement. Bias correction reduces the absolute mean profile deviations by 7.2% and 26.3% for nighttime and daytime, with mean absolute error (MAE) values of 6.06×10⁻⁴ and 7.71×10⁻⁴ for nighttime and daytime, respectively. Global stratospheric background aerosols average a lidar ratio of 45.0 sr, peaking near 30^◦N and reaching its minimum at 55^◦S. Tropospheric background aerosols average 36.9 sr, with significantly higher values over land than ocean, driven by seasonal biomass burning, human activities, and phytoplankton cycles. Crucially, the algorithm captures the spatiotemporal evolution of highly diluted sulfate aerosols from the 2022 Hunga Tonga–Hunga Ha’apai eruption, demonstrating high sensitivity. This work provides a global 3-D dataset to quantify background aerosol radiative forcing and constrain aerosol–cloud interactions in climate models.