Dual-field-of-view high-spectral-resolution lidar: Simultaneous profiling of aerosol and water cloud to study aerosol–cloud interaction

Aerosol–cloud interaction (ACI) is complex and difficult to be well represented in current climate models. Progress on understanding ACI processes, such as the influence of aerosols on water cloud droplet formation, is hampered by inadequate observational capability. Hitherto, high-resolution and simultaneous observations of diurnal aerosol loading and cloud microphysical properties are challenging for current remote-sensing techniques. To overcome this conundrum, we introduce the dual-field-of-view (FOV) high-spectral-resolution lidar (HSRL) for simultaneously profiling aerosol and water cloud properties, especially water cloud microphysical properties. Continuous observations of aerosols and clouds using this instrument, verified by the Monte Carlo simulation and coincident observations of other techniques, were conducted to investigate the interactions between aerosol loading and water cloud microphysical properties. A case study over Beijing highlights the scientific potential of dual-FOV HSRL to become a significant contributor to the ACI investigations. The observed water cloud profiles identify that due to air entrainment its vertical structure is not perfectly adiabatic, as assumed by many current retrieval methods. Our ACI analysis shows increased aerosol loading led to increased droplet number concentration and decreased droplet effective radius—consistent with expectations—but had no discernible increase on liquid water path. This finding supports the hypothesis that aerosol-induced cloud water increase caused by suppressed rain formation can be canceled out by enhanced evaporation. Thus, these observations obtained from the dual-FOV HSRL constitute substantial and significant additions to understanding ACI process. This technique is expected to represent a significant step forward in characterizing ACI.

Aerosol–cloud interaction (ACI) is a crucial aspect of atmospheric research and one of the primary sources of uncertainties in climate predictions (1–3). To assess the credibility of climate projections, it is imperative to improve our understanding of how aerosols interact with clouds (4–6). It has been well known that aerosols can serve as cloud condensation nuclei (CCN) to form cloud droplets, which can further influence the initiation of precipitation (7). However, quantifying the impact of natural and anthropogenic aerosols on the growth and the evolution of water clouds is still challenging (8, 9). The short lifetime, high temporal variability, and complex vertical structure of water cloud layers lead to a major difficulty for ACI studies (3, 10, 11). Despite the advances in the characterization of ACI by ground-based measurements (12–21), satellite retrieved products (22–25), and airborne in situ measurements (26–28), uncertainties remain in the effects of the aerosols on the water cloud properties. The reason for this gap in our knowledge is closely linked to the inadequate observations of the water cloud microphysical properties under various aerosol conditions (3). Current satellites can estimate cloud properties but not the typical aerosol nucleation region beneath clouds (22–25). Moreover, they also bring challenges for ACI studies that the typical revisit time of satellite-based sensors is much longer than the temporal scales of cloud variability (29, 30). Quintessential ground-based remote-sensing techniques for retrieving cloud properties, such as the cloud radar and the microwave radiometer, cannot provide simultaneous aerosol observations for ACI studies. Therefore, ground-based measurements commonly combine those with other remote-sensing or in situ aircraft instruments for characterizing aerosol loading beneath clouds (12–20). However, given the high variability of clouds, differences in perspective or mismatched sampling in space and time would raise uncertainty and bias in the characterization of ACI (15).Lidar, a powerful tool for profiling optical properties of aerosols and clouds, has been widely used in atmospheric studies (31–33). Yet, further progress with lidar-based techniques for ACI studies is hampered by limited observations of the water cloud microphysical properties, mainly due to the difficulties of quantifying the multiple scattering within water clouds (34). The multiple scattering has a significant impact on the water cloud observations of the extinction as well as the depolarization ratio, which is related to the receiver field of view (FOV). In brief, a retrieval of water cloud microphysical properties for lidar-based techniques relies on utilizing different receiver FOVs to provide the necessary observations for characterizing the multiple-scattering effect caused by the water droplets (34, 35). The first multiple-FOV lidars were aimed at investigating the multiple-scattering effect and measured Mie scattering by water droplets (36). However, a complicated behavior of the Mie phase function makes the quantifying of the multiple scattering become an arduous task. It naturally leads to the use of Raman scattering of atmospheric nitrogen, which has an isotropic phase function practically in the backward direction, to allow developing a feasible algorithm for the retrieval of water cloud microphysical properties (35). Moreover, the dual-FOV Raman lidar technique for profiling cloud properties has been experimentally demonstrated (37). With this technique in conjunction with an incoherent Doppler lidar, the ACI findings have been obtained with an ACI index versus vertical air motion (21, 38). However, nitrogen Raman signals are so weak that the observations are usually restricted to nighttime hours, and the signal has to be averaged over tens of minutes to deliver reliable lidar products, while the typical temporal scale of cloud variability is much shorter than that (29). Recently, a dual-FOV polarization lidar technique was reported, which continued and further developed the concept of the dual-FOV Mie lidar (39). However, to assess ACI this method requires a priori assumptions about the lidar ratio and subadiabatic cloud conditions. The impact of the a priori assumptions on the aerosol and cloud retrievals has been widely discussed (10, 40, 41). In general, all existing multiple-FOV lidar-based techniques have their advantages and also limitations.To overcome this conundrum, a dual-FOV high-spectral-resolution lidar (HSRL) technique for profiling aerosol and cloud properties simultaneously is introduced here. It provides lateral observations of aerosols and clouds with high vertical and temporal resolutions during daytime and nighttime. Neither assumptions on thermodynamic conditions nor lidar ratio are required. This work benefited from the range-resolved observations of water clouds with high resolution, revealing that the observed profiles of low-level water cloud microphysical properties are not perfectly adiabatic as assumed by many current retrievals (42–46). Furthermore, the ACI analysis supports the hypothesis that aerosol-induced water decrease by enhanced evaporation can cancel out the increase caused by suppressed rain formation (6, 47), while most current global general circulation models (GCMs) suggest that increased aerosol loading typically causes increased cloud water content (19, 48). Thus, these observations obtained from the dual-FOV HSRL can constitute a substantial and significant addition to our understanding of ACI studies. We believe that this versatile system will not only benefit the quality monitoring of aerosol and cloud properties but also serve as a powerful tool for ACI studies.