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The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) produced a wealth of observational data along the drift of the R/V Polarstern in the Arctic Ocean from October 2019 to September 2020. These data can further process-level understanding and improvements in models. However, the observational records contain temporal gaps and are provided in different formats. One goal of the MOSAiC Single Column Model Working Group (MSCMWG: https://mosaic-expedition.org/science/cross-cutting_groups/) is to provide consistently-formatted, gap-filled, merged datasets representing the conditions at the MOSAiC Central Observatory (the intensively studied region within a few km of R/V Polarstern) that are suitable for driving models on this spatial domain (e.g., single column models, large eddy simulations, etc). The MSCMWG is an open group, please contact the dataset creators if you would like to contribute to future versions of these merged datasets (including new variables). This dataset contains version 1 of these merged datasets, and comprises the variables necessary to force a single column ice model (e.g., Icepack: https://zenodo.org/doi/10.5281/zenodo.1213462). The atmospheric variables are primarily derived from Met City (~66 percent (%) of record, https://doi.org/10.18739/A2PV6B83F), with temporal gaps filled by bias and advection corrected data from Atmospheric Surface Flux Stations ( https://doi.org/10.18739/A2XD0R00S, https://doi.org/10.18739/A25X25F0P, https://doi.org/10.18739/A2FF3M18K). Some residual gaps in shortwave radiation were filled with ARM ship-board radiometer data. Three different options for snowfall precipitation rate (prsn) are provided, based on in-situ observations that precipitation greatly exceeded accumulation on level ice, and accumulation rates varied on different ice types. MOSAiC_kazr_snow_MDF_20191005_20201001.nc uses 'snowfall_rate1' derived from the vertically-pointing, ka-band radar on the vessel (https://doi.org/10.5439/1853942). MOSAiC_Raphael_snow_fyi_MDF_20191005_20201001.nc and MOSAiC_Raphael_snow_syi_MDF_20191005_20201001.nc use snow accumulation measurements from manual mass balance sites (https://doi.org/10.18739/A2NK36626) to derived a pseudo-precipitation. MOSAiC_Raphael_snow_fyi_MDF_20191005_20201001.nc is based on the First Year Ice (fyi) sites. MOSAiC_Raphael_snow_syi_MDF_20191005_20201001.nc is based on the Second Year Ice (syi) sites. The other atmospheric variables for these files are identical. Oceanic variables are in MOSAiC_ocn_MDF_20191006_20200919.nc and are derived from https://doi.org/10.18739/A21J9790B. The data are netCDF files formatted according to the Merged Data File format (https://doi.org/10.5194/egusphere-2023-2413, https://gitlab.com/mdf-makers/mdf-toolkit). The code 'recipes' that were used to produce these data are available at: https://doi.org/10.5281/zenodo.10819497. If you use these datasets, please also cite the appropriate publications: Meteorological variables (excluding precipitation): Cox et al., 2023 (https://doi.org/10.1038/s41597-023-02415-5) Oceanographic variables: Schulz et al., 2023 (https://doi.org/10.31223/X5TT2W) KAZR-derived precipitation: Matrosov et al., 2022 (https://doi.org/10.1525/elementa.2021.00101) Accumulation-derived pseudo-precipitation: Raphael et al., in review. The following are known issues that will be addressed in future dataset releases: 1. Residual gaps occupy approximately 20% of the data record (see addendum) 2. Some transitions to shiprad downwelling shortwave are unreasonable abrupt 3. MDF format does not currently include a field for point-by-point data source Addendum: For atmospheric variables, below indicates the percentage sourced from each dataset (and the amount missing a.k.a NaN) Air Temperature metcity 0.661943 NaN 0.193333 asfs30 0.134910 asfs40 0.008607 asfs50 0.001207 Specific Humidity metcity 0.658890 NaN 0.196298 asfs40 0.008695 Wind Velocity metcity 0.666334 NaN 0.255003 asfs30 0.068828 asfs40 0.008630 asfs50 0.001205 Downwelling Longwave metcity 0.549417 asfs30 0.241502 NaN 0.209081 Downwelling Shortwave metcity 0.674166 NaN 0.158814 asfs30 0.140794 shipradS1 0.026226 Note that the 21 day gap from the end of Central Observatory 2 to the start of Central Observatory 3 occupies 5.8% of the record. 

Most smaller asteroids (1 km diameter) are granular material loosely bound together primarily by self-gravity known as rubble piles. In an effort to better understand the evolution of rubble-pile asteroids, we performed bulk measurements using granular simulant to study the effects of the presence of fine grains on the strength of coarse grains. Our laboratory samples consisted of fine–coarse mixtures of varying percentages of fine grains by volume of the sample. We measured the material’s angle of repose, Young’s Modulus, angle of internal friction, cohesion, and tensile strength by subjecting the samples to compressive and shear stresses. The coarse grains comprising the fine–coarse mixtures ranged from 1 mm to 20 mm (2 cm) and the fines were sieved to sub-millimeter sizes (1 mm). The measured angles of repose varied between 32–45 which increased with increasing fine percentage. In compression, samples generally increased in strength with increasing fine percentage for both confined and unconfined environments. In all cases, the peak strengths were not for purely fine grains but for a mixture of fine and coarse grains. Shear stress measurements yielded angles of internal friction ranging between 25 and 45 with a trend opposite that of the angle of repose, 300–550 Pa for bulk cohesion, and 0.5–1.1 kPa for tensile strength. Using other published works that include data from telescopic and in-situ observations as well as numerical simulations, we discussed the implications of our findings regarding rubble-pile formation, composition, evolution, and disruption. We find that the presence of fine grains in subsurface layers of regolith on an asteroid (confined environment) aids the avoidance of disruption due to impact. However these same fines increase an asteroid’s chance to disrupt or deform from high rotation speeds due to reduced grain interlocking. In surface layers (unconfined environments), we find that the presence of fine grains between coarse ones generates stronger cohesion and aids in the prevention of mass loss and surface shedding. 

During the Arctic winter, the conductive heat flux through the sea ice and snow balances the radiative and turbulent heat fluxes at the surface. Snow on sea ice is a thermal insulator that reduces the magnitude of the conductive flux. The thermal conductivity of snow, that is, how readily energy is conducted, is known to vary significantly in time and space from observations, but most forecast and climate models use a constant value. This work begins with a demonstration of the importance of snow thermal conductivity in a regional coupled forecast model. Varying snow thermal conductivity impacts the magnitudes of all surface fluxes, not just conduction, and their responses to atmospheric forcing. Given the importance of snow thermal conductivity in models, we use observations from sea ice mass balance buoys installed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition to derive the profiles of thermal conductivity, density, and conductive flux. From 13 sites, median snow thermal conductivity ranges from 0.33 W m−1 K−1 to 0.47 W m−1 K−1 with a median from all data of 0.39 W m−1 K−1 from October to February. In terms of surface energy budget closure, estimated conductive fluxes are generally smaller than the net atmospheric flux by as much as 20 W m−2, but the average residual during winter is −6 W m−2, which is within the uncertainties. The spatial variability of conductive heat flux is highest during clear and cold time periods. Higher surface temperature, which often occurs during cloudy conditions, and thicker snowpacks reduce temporal and spatial variability. These relationships are compared between observations and the coupled forecast model, emphasizing both the importance and challenge of describing thermodynamic parameters of snow cover for modeling the Arctic as a coupled system. 

Abstract. Observations collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) provide an annual cycle of the vertical thermodynamic and kinematic structure of the atmospheric boundary layer (ABL) in the central Arctic. A self-organizing map (SOM) analysis conducted using radiosonde observations shows a range in the Arctic ABL vertical structure from very shallow and stable, with a strong surface-based virtual potential temperature (θv) inversion, to deep and near neutral, capped by a weak elevated θv inversion. The patterns identified by the SOM allowed for the derivation of criteria to categorize stability within and just above the ABL, which revealed that the Arctic ABL during MOSAiC was stable and near neutral with similar frequencies, and there was always a θv inversion within the lowest 1 km, which usually had strong to moderate stability. In conjunction with observations from additional measurement platforms, including a 10 m meteorological tower, ceilometer, and microwave radiometer, the radiosonde observations and SOM analysis provide insight into the relationships between atmospheric vertical structure and stability, as well as a variety of atmospheric thermodynamic and kinematic features. A low-level jet was observed in 76 % of the radiosondes, with stronger winds and low-level jet (LLJ) core located more closely to the ABL corresponding with weaker stability. Wind shear within the ABL was found to decrease, and friction velocity was found to increase, with decreasing ABL stability. Clouds were observed within the 30 min preceding the radiosonde launch 64 % of the time. These were typically low clouds, corresponding to weaker stability, where high clouds or no clouds largely coincided with a stable ABL. 

Abstract Neurocognitive models of semantic memory have proposed that the ventral anterior temporal lobes (vATLs) encode a graded and multidimensional semantic space—yet neuroimaging studies seeking brain regions that encode semantic structure rarely identify these areas. In simulations, we show that this discrepancy may arise from a crucial mismatch between theory and analysis approach. Utilizing an analysis recently formulated to investigate graded multidimensional representations, representational similarity learning (RSL), we decoded semantic structure from ECoG data collected from the vATL cortical surface while participants named line drawings of common items. The results reveal a graded, multidimensional semantic space encoded in neural activity across the vATL, which evolves over time and simultaneously expresses both broad and finer-grained semantic structure among animate and inanimate concepts. The work resolves the apparent discrepancy within the semantic cognition literature and, more importantly, suggests a new approach to discovering representational structure in neural data more generally. 

Abstract. Atmospheric measurements taken over the span of an entire year between October 2019 and September 2020 during the icebreaker-based Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provide insight into processes acting in the Arctic atmosphere. Through the merging of disparate, yet complementary in situ observations, we can derive information about these thermodynamic and kinematic processes with great detail. This paper describes methods used to create a lower atmospheric properties dataset containing information on several key features relating to the central Arctic atmospheric boundary layer, including properties of temperature inversions, low-level jets, near-surface meteorological conditions, cloud cover, and the surface radiation budget. The lower atmospheric properties dataset was developed using observations from radiosondes launched at least four times per day, a 10 m meteorological tower and radiation station deployed on the sea ice near the Research Vessel Polarstern, and a ceilometer located on the deck of the Polarstern. This lower atmospheric properties dataset, which can be found at *insert DOI when published*, contains metrics which fall into the overarching categories of temperature, wind, stability, clouds, and radiation at the time of each radiosonde launch. The purpose of the lower atmospheric properties dataset is to provide a consistent description of general atmospheric boundary layer conditions throughout the MOSAiC year which can aid in research applications with the overall goal of gaining a greater understanding of the atmospheric processes governing the central Arctic and how they may contribute to future climate change. 

A sign pattern is an array with entries in $$\{+,-,0\}$$. A real matrix $$Q$$ is row orthogonal if $QQ^T = I$. The Strong Inner Product Property (SIPP), introduced in [B.A. Curtis and B.L. Shader, Sign patterns of orthogonal matrices and the strong inner product property, Linear Algebra Appl. 592: 228-259, 2020], is an important tool when determining whether a sign pattern allows row orthogonality because it guarantees there is a nearby matrix with the same property, allowing zero entries to be perturbed to nonzero entries, while preserving the sign of every nonzero entry. This paper uses the SIPP to initiate the study of conditions under which random sign patterns allow row orthogonality with high probability. Building on prior work, $$5\times n$$ nowhere zero sign patterns that minimally allow orthogonality are determined. Conditions on zero entries in a sign pattern are established that guarantee any row orthogonal matrix with such a sign pattern has the SIPP. 

Abstract. Observations collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) provide a detailed description of the impact of thermodynamic and kinematic forcings on atmospheric boundary layer (ABL) stability in the central Arctic. This study reveals that the Arctic ABL is stable and near-neutral with similar frequencies, and strong stability is the most persistent of all stability regimes. MOSAiC radiosonde observations, in conjunction with observations from additional measurement platforms, including a 10 m meteorological tower, ceilometer, microwave radiometer, and radiation station, provide insight into the relationships between atmospheric stability and various atmospheric thermodynamic and kinematic forcings of ABL turbulence and how these relationships differ by season. We found that stronger stability largely occurs in low-wind (i.e., wind speeds are slow), low-radiation (i.e., surface radiative fluxes are minimal) environments; a very shallow mixed ABL forms in low-wind, high-radiation environments; weak stability occurs in high-wind, moderate-radiation environments; and a near-neutral ABL forms in high-wind, high-radiation environments. Surface pressure (a proxy for synoptic staging) partially explains the observed wind speeds for different stability regimes. Cloud frequency and atmospheric moisture contribute to the observed surface radiation budget. Unique to summer, stronger stability may also form when moist air is advected from over the warmer open ocean to over the colder sea ice surface, which decouples the colder near-surface atmosphere from the advected layer, and is identifiable through observations of fog and atmospheric moisture.