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Cirrus cloud formation and evolution are subject to the influences of thermodynamic and dynamic conditions and aerosol indirect effects (AIEs). This study developed near global-scale in-situ aircraft observational datasets based on 12 field campaigns that spanned from the polar regions to the tropics, from 2008 to 2016. Cirrus cloud microphysical properties were investigated at temperatures ≤ ‑40 °C, including ice water content (IWC), ice crystal number concentration (Ni), and number-weighted mean diameter (Di). Positive correlations between the fluctuations of ice microphysical properties and the fluctuations of aerosol number concentrations for larger (> 500 nm) and smaller (> 100 nm) aerosols (i.e., Na500 and Na100, respectively) were found, with stronger AIE from larger aerosols than smaller ones. Machine learning (ML) models showed that using relative humidity with respect to ice (RHi) as a predictor significantly increases the accuracy of predicting cirrus occurrences compared with temperature, vertical velocity (w), and aerosol number concentrations. The ML predictions of IWC fluctuations showed higher accuracies when larger aerosols were used as an predictor compared with smaller aerosols, indicating the stronger AIE from larger aerosols than smaller ones, even though their AIEs are more similar when predicting the occurrences of cirrus. It is also important to capture the spatial variabilities of large aerosols at smaller scales as well as those of smaller aerosols at coarser scales to accurately simulate IWC in cirrus. These results can be used to improve understanding of aerosol-cloud interactions and evaluate model parameterizations of cirrus cloud properties and processes. 

Abstract The Chemistry in the Arctic: Clouds, Halogens, and Aerosols (CHACHA) field project aimed to advance the understanding of coupled meteorological and chemical processes in the atmospheric boundary layer during the seasonal increase in sea ice fracturing in spring. CHACHA sought to understand the interactions between this changing snow-covered surface, surface-coupled clouds, sea spray aerosols, multiphase halogen chemistry, and impacts of emissions from oil and gas extraction on atmospheric chemistry. The project measured greenhouse gases, reactive gases, size-resolved aerosol number concentrations, cloud microphysical properties, and meteorological conditions in real time, while also collecting particles for offline analysis. Two instrumented aircraft were deployed: the Purdue University Airborne Laboratory for Atmospheric Research and the University of Wyoming King Air. Flights were conducted out of Utqiaġvik, Alaska, between 21 February and 16 April 2022, sampling air over snow-covered and newly frozen sea ice in the Beaufort and Chukchi Seas, over open leads, and over the snow-covered tundra of the North Slope of Alaska, including the oil and gas extraction region near Prudhoe Bay. Observations showed that reactive bromine gases generally peaked near the snow-covered surface and decayed rapidly within the lowest few hundred meters where ozone was depleted, with concentrations reduced by nitrogen oxides emitted from oil fields. Cloud microphysical measurements revealed that thin clouds over and downwind of leads grew in vertical extent after contact with open water. Results from dropsondes indicated that convective boundary layers developed over leads, with depths ranging from 250 to 850 m depending on the fetch. 

Abstract The secondary ice process (SIP) is a major microphysical process, which can result in rapid enhancement of ice particle concentration in the presence of preexisting ice. SPICULE was conducted to further investigate the effect of collision–coalescence on the rate of the fragmentation of freezing drop (FFD) SIP mechanism in cumulus congestus clouds. Measurements were conducted over the Great Plains and central United States from two coordinated aircraft, the NSF Gulfstream V (GV) and SPEC Learjet 35A, both equipped with state-of-the-art microphysical instrumentation and vertically pointing W- and Ka-band radars, respectively. The GV primarily targeted measurements of subcloud aerosols with subsequent sampling in warm cloud. Simultaneously, the Learjet performed multiple penetrations of the ascending cumulus congestus (CuCg) cloud top. First primary ice was typically detected at temperatures colder than −10°C, consistent with measured ice nucleating particles. Subsequent production of ice via FFD SIP was strongly related to the concentration of supercooled large drops (SLDs), with diameters from about 0.2 to a few millimeters. The concentration of SLDs is directly linked to the rate of collision–coalescence, which depends primarily on the subcloud aerosol size distribution and cloud-base temperature. SPICULE supports previous observational results showing that FFD SIP efficiency could be deduced from the product of cloud-base temperature and maximum diameter of drops measured ∼300 m above cloud base. However, new measurements with higher concentrations of aerosol and total cloud-base drop concentrations show an attenuating effect on the rate of coalescence. The SPICULE dataset provides rich material for validation of numerical schemes of collision–coalescence and SIP to improve weather prediction simulations 

Abstract Observations from the third campaign of the National Aeronautics and Space Administration Airborne Tropical Tropopause Experiment (ATTREX 3) field mission and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations satellite mission are used to evaluate simulations of tropical tropopause layer (TTL) cirrus clouds in the Community Earth System Model's (CESM) Community Atmosphere Model, CAM5. In this study, CAM5 is coupled with a sectional ice cloud model, the Community Aerosol and Radiation Model for Atmospheres (CARMA). We find that both model variants underrepresent cloud frequency along the ATTREX 3 flight path and both poorly represent relative humidity in the TTL. Furthermore, simulated in‐cloud ice size distributions contained erroneous amounts of ice crystals throughout the distribution. In response, we present a modified ice cloud fraction scheme that boosts the cloud fraction within the TTL. Due to coarse vertical model resolution in the TTL, we also prescribe a 2‐K decrease in cold point tropopause temperatures to better align with observed temperatures. Our modifications improve both CAM5 and CAM5/CARMA's in‐cloud ice size and mass distributions. However, only CAM5/CARMA has a significant improvement in cloud frequency and relative humidity. An investigation of cloud extinction in the ATTREX 3 region found that each model variant struggles to reproduce observed extinctions. As a first‐order approximation, we introduce randomly generated temperature perturbations to simulate the effect of gravity waves into the CAM5/CARMA simulation. These gravity waves significantly increase the incidence of low extinction (<0.02 km⁻¹) values, ice cloud fraction between 16 and 18 km, and ice crystal smaller than 100‐μm concentrations but provided only small changes to high extinction values.