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Over a five-month time window between March and July 2020, scientists deployed two small uncrewed aircraft systems (sUAS) to the central Arctic Ocean as part of legs three and four of the MOSAiC expedition. These sUAS were flown to measure the thermodynamic and kinematic state of the lower atmosphere, including collecting information on temperature, pressure, humidity and winds between the surface and 1 km, as well as to document ice properties, including albedo, melt pond fraction, and open water amounts. The atmospheric state flights were primarily conducted by the DataHawk2 sUAS, which was operated primarily in a profiling manner, while the surface property flights were conducted using the HELiX sUAS, which flew grid patterns, profiles, and hover flights. In total, over 120 flights were conducted and over 48 flight hours of data were collected, sampling conditions that included temperatures as low as −35 °C and as warm as 15 °C, spanning the summer melt season.

 

Abstract. This study analyzes turbulent energy fluxes in the Arctic atmospheric boundary layer (ABL) using measurements with a small uncrewed aircraft system (sUAS). Turbulent fluxes constitute a major part of the atmospheric energy budget and influence the surface heat balance by distributing energy vertically in the atmosphere. However, only few in situ measurements of the vertical profile of turbulent fluxes in the Arctic ABL exist. The study presents a method to derive turbulent heat fluxes from DataHawk2 sUAS turbulence measurements, based on the flux gradient method with a parameterization of the turbulent exchange coefficient. This parameterization is derived from high-resolution horizontal wind speed measurements in combination with formulations for the turbulent Prandtl number and anisotropy depending on stability. Measurements were taken during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in the Arctic sea ice during the melt season of 2020. For three example cases from this campaign, vertical profiles of turbulence parameters and turbulent heat fluxes are presented and compared to balloon-borne, radar, and near-surface measurements. The combination of all measurements draws a consistent picture of ABL conditions and demonstrates the unique potential of the presented method for studying turbulent exchange processes in the vertical ABL profile with sUAS measurements. 

As part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), the HELiX uncrewed aircraft system (UAS) was deployed over the sea ice in the central Arctic Ocean during summer 2020. Albedo measurements were obtained with stabilized pyranometers, and melt pond fraction was calculated from orthomosaic imagery from a surface-imaging multispectral camera. This study analyzed HELiX flight data to provide insights on the temporal and spatial evolution of albedo and melt pond fraction of the MOSAiC floe during the melt season as it drifted south through Fram Strait. The surface albedo distributions showed peak values changing from high albedo (0.55–0.6) to lower values (0.3) as the season advanced. Inspired by methods developed for satellite data, an algorithm was established to retrieve melt pond fraction from the orthomosaic images. We demonstrate that the near-surface observations of melt pond fraction were highly dependent on sample area, offering insight into the influence of subgrid scale features and spatial heterogeneity in satellite observations. Vertical observations conducted with the HELiX were used to quantify the influence of melt pond scales on observed surface albedo as a function of sensor footprint. These scaling results were used to link surface-based measurements collected during MOSAiC to broader-scale satellite data to investigate the influence of surface features on observed albedo. Albedo values blend underlying features within the sensor footprint, as determined by the melt pond size and concentration. This study framed the downscaling (upscaling) problem related to the airborne (surface) observations of surface albedo across a variety of spatial scales. 

Abstract Frequency and intensity of warm and moist air-mass intrusions into the Arctic have increased over the past decades and have been related to sea ice melt. During our year-long expedition in the remote central Arctic Ocean, a record-breaking increase in temperature, moisture and downwelling-longwave radiation was observed in mid-April 2020, during an air-mass intrusion carrying air pollutants from northern Eurasia. The two-day intrusion, caused drastic changes in the aerosol size distribution, chemical composition and particle hygroscopicity. Here we show how the intrusion transformed the Arctic from a remote low-particle environment to an area comparable to a central-European urban setting. Additionally, the intrusion resulted in an explosive increase in cloud condensation nuclei, which can have direct effects on Arctic clouds’ radiation, their precipitation patterns, and their lifetime. Thus, unless prompt actions to significantly reduce emissions in the source regions are taken, such intrusion events are expected to continue to affect the Arctic climate. 

With the Arctic rapidly changing, the needs to observe, understand, and model the changes are essential. To support these needs, an annual cycle of observations of atmospheric properties, processes, and interactions were made while drifting with the sea ice across the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. An international team designed and implemented the comprehensive program to document and characterize all aspects of the Arctic atmospheric system in unprecedented detail, using a variety of approaches, and across multiple scales. These measurements were coordinated with other observational teams to explore cross-cutting and coupled interactions with the Arctic Ocean, sea ice, and ecosystem through a variety of physical and biogeochemical processes. This overview outlines the breadth and complexity of the atmospheric research program, which was organized into 4 subgroups: atmospheric state, clouds and precipitation, gases and aerosols, and energy budgets. Atmospheric variability over the annual cycle revealed important influences from a persistent large-scale winter circulation pattern, leading to some storms with pressure and winds that were outside the interquartile range of past conditions suggested by long-term reanalysis. Similarly, the MOSAiC location was warmer and wetter in summer than the reanalysis climatology, in part due to its close proximity to the sea ice edge. The comprehensiveness of the observational program for characterizing and analyzing atmospheric phenomena is demonstrated via a winter case study examining air mass transitions and a summer case study examining vertical atmospheric evolution. Overall, the MOSAiC atmospheric program successfully met its objectives and was the most comprehensive atmospheric measurement program to date conducted over the Arctic sea ice. The obtained data will support a broad range of coupled-system scientific research and provide an important foundation for advancing multiscale modeling capabilities in the Arctic. 

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice. 

A0 level data from HELiX Uncrewed Aircraft System correspond to the raw data in Matlab format collected in the Central Arctic Ocean during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Synchronized and quality-controlled B1 level data are available in the Arctic Data Center. Users are encouraged to primarily use the B1 level data for analysis (doi:10.18739/A2GH9BB0Q). Raw data are the initial inputs in the processing routines to obtain the B1 and A1 level data (doi:10.18739/A2M90243X). Matlab files include hemispheric irradiance measurements from Kipp and Zonen pyranometers and thermodynamic parameters from Vaisala RSS421 sensors. Autopilot positions and attitudes, along with gimbal attitudes are also provided. Each field of measurements has its own time stamped based on a common clock and associated acquisition frequency. As no Coordinated Universal Time (UTC) time was provided in the FlexLogger acquisition files, the additional A0_PixHawk Matlab files obtained directly from the PixHawk autopilot are used to add UTC time for B1 level data. Please contact the authors if you need to use this dataset. More information on the data and method can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2022): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep. 

This dataset includes unprocessed raw data from DataHawk2 fixed-wind uncrewed aircraft system (UAS) flights that were conducted in the central Arctic Ocean over sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Synchronized and quality controlled data are available in the Arctic Data Center at doi:10.18739/A22Z12Q8X for data provided at their native frequency logged on board the aircraft’s secure digital (SD) card (A1 level files), or at doi:10.18739/A2Z60C34R for data interpolated to a common 10 hertz (Hz) clock (B1 level files). Users are encouraged to primarily use the B1 level data for analysis. Please contact the authors if you plan to use this dataset. More information on data collection with the DataHawk2 can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2022): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, submitted. 

Abstract. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, meteorological conditions over the lowest1 km of the atmosphere were sampled with the DataHawk2 (DH2) fixed-wing uncrewed aircraft system (UAS). These in situ observations of the central Arctic atmosphere are some of the most extensive to date and provide unique insight into the atmospheric boundary layer (ABL) structure. The ABL is an important component of the Arctic climate, as it can be closely coupled to cloud properties, surface fluxes, and the atmospheric radiationbudget. The high temporal resolution of the UAS observations allows us to manually identify the ABL height (ZABL) for 65 out of the total89 flights conducted over the central Arctic Ocean between 23 March and 26 July 2020 by visually analyzing profiles of virtual potentialtemperature, humidity, and bulk Richardson number. Comparing this subjective ZABL with ZABL identified by various previouslypublished automated objective methods allows us to determine which objective methods are most successful at accurately identifying ZABL inthe central Arctic environment and how the success of the methods differs based on stability regime. The objective methods we use are theLiu–Liang, Heffter, virtual potential temperature gradient maximum, and bulk Richardson number methods. In the process of testing these objectivemethods on the DH2 data, numerical thresholds were adapted to work best for the UAS-based sampling. To determine if conclusions are robust acrossdifferent measurement platforms, the subjective and objective ZABL determination processes were repeated using the radiosonde profileclosest in time to each DH2 flight. For both the DH2 and radiosonde data, it is determined that the bulk Richardson number method is the mostsuccessful at identifying ZABL, while the Liu–Liang method is least successful. The results of this study are expected to be beneficialfor upcoming observational and modeling efforts regarding the central Arctic ABL. 

Abstract. The DataHawk2 (DH2) is a small, fixed-wing, uncrewed aircraft system, or UAS,developed at the University of Colorado (CU) primarily for taking detailedthermodynamic measurements of the atmospheric boundary layer. The DH2 weighs1.7 kg and has a wingspan of 1.3 m, with a flight endurance of approximately60 min, depending on configuration. In the DH2's most modern form, theaircraft carries a Vaisala RSS-421 sensor for pressure, temperature, andrelative humidity measurements, two CU-developed infrared temperaturesensors, and a CU-developed fine-wire array, in addition to sensors requiredto support autopilot function (pitot tube with pressure sensor, GPSreceiver, inertial measurement unit), from which wind speed and directioncan also be estimated. This paper presents a description of the DH2,including information on its design and development work, and puts the DH2 intocontext with respect to other contemporary UASs. Data from recent field work(MOSAiC, the Multidisciplinary drifting Observatory for the Study of ArcticClimate) is presented and compared with radiosondes deployed during thatcampaign to provide an overview of sensor and system performance. These datashow good agreement across pressure, temperature, and relative humidity aswell as across wind speed and direction. Additional examples of measurementsprovided by the DH2 are given from a variety of previous campaigns inlocations ranging from the continental United States to Japan and northernAlaska. Finally, a look toward future system improvements and upcomingresearch campaign participation is given.