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Abstract. Ground-based instruments offer unique capabilities such as detailed atmospheric, thermodynamic, cloud, and aerosol profiling at a high temporal sampling rate. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility provides comprehensive datasets from key locations around the globe, facilitating long-term characterization and process-level understanding of clouds, aerosol, and aerosol–cloud interactions. However, as with other ground-based datasets, the fixed (Eulerian) nature of these measurements often introduces a knowledge gap in relating those observations with air-mass hysteresis. Here, we describe ARMTRAJ (https://doi.org/10.5439/2309851, Silber, 2024a; https://doi.org/10.5439/2309849, Silber, 2024b; https://doi.org/10.5439/2309850, Silber, 2024c; https://doi.org/10.5439/2309848, Silber, 2024d), a set of multipurpose trajectory datasets that helps close this gap in ARM deployments. Each dataset targets a different aspect of atmospheric research, including the analysis of surface, planetary boundary layer, distinct liquid-bearing cloud layers, and (primary) cloud decks. Trajectories are calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model informed by the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis dataset at its highest spatial resolution (0.25°) and are initialized using ARM datasets. The trajectory datasets include information about air-mass coordinates and state variables extracted from ERA5 before and after the ARM site overpass. Ensemble runs generated for each model initialization enhance trajectory consistency, while ensemble variability serves as a valuable uncertainty metric for those reported air-mass coordinates and state variables. Following the description of dataset processing and structure, we demonstrate applications of ARMTRAJ to a case study and a few bulk analyses of observations collected during ARM's Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field deployment. ARMTRAJ will soon become a near real-time product accompanying new ARM deployments and an augmenting product to ongoing and previous deployments, promoting reaching science goals of research relying on ARM observations. 

Abstract High‐latitudinal mixed‐phase clouds significantly affect Earth's radiative balance. Observations of cloud and radiative properties from two field campaigns in the Southern Ocean and Antarctica were compared with two global climate model simulations. A cyclone compositing method was used to quantify “dynamics‐cloud‐radiation” relationships relative to the extratropical cyclone centers. Observations show larger asymmetry in cloud and radiative properties between western and eastern sectors at McMurdo compared with Macquarie Island. Most observed quantities at McMurdo are higher in the western (i.e., post‐frontal) than the eastern (frontal) sector, including cloud fraction, liquid water path (LWP), net surface shortwave and longwave radiation (SW and LW), except for ice water path (IWP) being higher in the eastern sector. The two models were found to overestimate cloud fraction and LWP at Macquarie Island but underestimate them at McMurdo Station. IWP is consistently underestimated at both locations, both sectors, and in all seasons. Biases of cloud fraction, LWP, and IWP are negatively correlated with SW biases and positively correlated with LW biases. The persistent negative IWP biases may have become one of the leading causes of radiative biases over the high southern latitudes, after correcting the underestimation of supercooled liquid water in the older model versions. By examining multi‐scale factors from cloud microphysics to synoptic dynamics, this work will help increase the fidelity of climate simulations in this remote region. 

Abstract. Due to its remote location and extreme weather conditions, atmospheric in situmeasurements are rare in the Southern Ocean. As a result, aerosol–cloudinteractions in this region are poorly understood and remain a major source ofuncertainty in climate models. This, in turn, contributes substantially topersistent biases in climate model simulations such as the well-known positiveshortwave radiation bias at the surface, as well as biases in numericalweather prediction models and reanalyses. It has been shown in previousstudies that in situ and ground-based remote sensing measurements across theSouthern Ocean are critical for complementing satellite data sets due to theimportance of boundary layer and low-level cloud processes. These processesare poorly sampled by satellite-based measurements and are often obscured bymultiple overlying cloud layers. Satellite measurements also do not constrainthe aerosol–cloud processes very well with imprecise estimation of cloudcondensation nuclei. In this work, we present a comprehensive set of ship-basedaerosol and meteorological observations collected on the 6-weekSouthern Ocean Ross Sea Marine Ecosystem and Environment voyage(TAN1802) voyage of RV Tangaroa across the Southern Ocean, from Wellington, New Zealand, tothe Ross Sea, Antarctica. The voyage was carried out from 8 February to21 March 2018. Many distinct, but contemporaneous, data sets were collectedthroughout the voyage. The compiled data sets include measurements from arange of instruments, such as (i) meteorological conditions at the sea surfaceand profile measurements; (ii) the size and concentration of particles; (iii)trace gases dissolved in the ocean surface such as dimethyl sulfide andcarbonyl sulfide; (iv) and remotely sensed observations of low clouds. Here,we describe the voyage, the instruments, and data processing, and provide a briefoverview of some of the data products available. We encourage the scientificcommunity to use these measurements for further analysis and model evaluationstudies, in particular, for studies of Southern Ocean clouds, aerosol, andtheir interaction. The data sets presented in this study are publiclyavailable at https://doi.org/10.5281/zenodo.4060237 (Kremser et al., 2020). 

Abstract A comparative analysis between observational data from McMurdo Station, Antarctica and the Community Atmosphere Model version 6 (CAM6) simulation is performed focusing on cloud characteristics and their thermodynamic conditions. Ka‐band Zenith Radar (KAZR) and High Spectral Resolution Lidar (HSRL) retrievals are used as the basis of cloud fraction and cloud phase identifications. Radiosondes released at 12‐h increments provide atmospheric profiles for evaluating the simulated thermodynamic conditions. Our findings show that the CAM6 simulation consistently overestimates (underestimates) cloud fraction above (below) 3 km in four seasons of a year. Normalized by total in‐cloud samples, ice and mixed phase occurrence frequencies are underestimated and liquid phase frequency is overestimated by the model at cloud fractions above 0.6, while at cloud fractions below 0.6 ice phase frequency is overestimated and liquid‐containing phase frequency is underestimated by the model. The cloud fraction biases are closely associated with concurrent biases in relative humidity (RH), that is, high (low) RH biases above (below) 2 km. Frequencies of correctly simulating ice and liquid‐containing phase increase when the absolute biases of RH decrease. Cloud fraction biases also show a positive correlation with RH biases. Water vapor mixing ratio biases are the primary contributor to RH biases, and hence, likely a key factor controlling the cloud biases. This diagnosis of the evident shortfalls of representations of cloud characteristics in CAM6 simulation at McMurdo Station brings new insight in improving the governing model physics therein.