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Reliable boundary‐layer turbulence parametrizations for polar conditions are needed to reduce uncertainty in projections of Arctic sea ice melting rate and its potential global repercussions. Surface turbulent fluxes of sensible and latent heat are typically represented in weather/climate models using bulk formulae based on the Monin‐Obukhov Similarity Theory, sometimes finely tuned to high stability conditions and the potential presence of sea ice. In this study, we test the performance of new, machine‐learning (ML) flux parametrizations, using an advanced polar‐specific bulk algorithm as a baseline. Neural networks, trained on observations from previous Arctic campaigns, are used to predict surface turbulent fluxes measured over sea ice as part of the recent MOSAiC expedition. The ML parametrizations outperform the bulk at the MOSAiC sites, with RMSE reductions of up to 70 percent. We provide a plug‐in Fortran implementation of the neural networks for use in models. Plain Language Summary Heat can make its way into or out of sea ice via unpredictable air movements, known as turbulence, near the sea surface. In order to predict how quickly Arctic sea ice will melt in the future, we need to know how much heat the turbulence can transport in different weather conditions. Traditionally, turbulence calculations have been performed using sophisticated mathematical formulae from physics. In this study, we test an alternative method for predicting turbulent heat exchange: a computer algorithm known as an artificial neural network. By showing turbulence data, measured in the Arctic during previous scientific expeditions, to the network, it can be “trained” to make predictions in a process known as machine learning. We compare turbulence measurements, taken above sea ice in the recent MOSAiC expedition, with predictions from trained neural networks. We find that the neural networks are better than the traditional physics at predicting what the scientists at MOSAiC observed. The trained neural networks have been made publicly available so that they can be used by scientists for predicting climate change. Key Points Neural networks trained on previous Arctic campaigns predict surface turbulent fluxes from MOSAiC more accurately than bulk methods Updated parametrizations using the MOSAiC data have been developed and implemented in Fortran for deployment in weather/climate models Modest performance gains (up to +7% R2) from recalibration on MOSAiC indicate good generalizability to the pan‐Arctic sea ice domain

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The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) was a yearlong expedition supported by the icebreaker R/V Polarstern , following the Transpolar Drift from October 2019 to October 2020. The campaign documented an annual cycle of physical, biological, and chemical processes impacting the atmosphere-ice-ocean system. Of central importance were measurements of the thermodynamic and dynamic evolution of the sea ice. A multi-agency international team led by the University of Colorado/CIRES and NOAA-PSL observed meteorology and surface-atmosphere energy exchanges, including radiation; turbulent momentum flux; turbulent latent and sensible heat flux; and snow conductive flux. There were four stations on the ice, a 10 m micrometeorological tower paired with a 23/30 m mast and radiation station and three autonomous Atmospheric Surface Flux Stations. Collectively, the four stations acquired ~928 days of data. This manuscript documents the acquisition and post-processing of those measurements and provides a guide for researchers to access and use the data products.

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The surface energy budget plays a critical role in determining the mass balance of the Greenland Ice Sheet, which in turn has significant implications for global sea levels. Nearly three years of data (January 2011–October 2013) are used to characterize the annual cycle of surface radiative fluxes and cloud radiative forcing (CRF) from the central Greenland Ice Sheet at Summit Station. The annual average CRF is 33 W m−2, representing a substantial net cloud warming of the central Greenland surface. Unlike at other Arctic sites, clouds warm the surface during the summer. The surface albedo is high at Summit throughout the year, limiting the cooling effect of the shortwave CRF and thus the total CRF is dominated by cloud longwave warming effects in all months. All monthly mean CRF values are positive (warming), as are 98.5% of 3-hourly cases. The annual cycle of CRF is largely driven by the occurrence of liquid-bearing clouds, with a minimum in spring and maximum in late summer. Optically thick liquid-bearing clouds [liquid water path (LWP) > 30 g m−2] produce an average longwave CRF of 85 W m−2. Shortwave CRF is sensitive to solar zenith angle and LWP. When the sun is well above the horizon (solar zenith angle < 65°), a maximum cloud surface warming occurs in the presence of optically thin liquid-bearing clouds. Ice clouds occur frequently above Summit and have mean longwave CRF values ranging from 10 to 60 W m−2, dependent on cloud thickness.

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The wintertime central Arctic atmosphere comprises a radiatively clear and a radiatively opaque state, which are linked to synoptic forcing and mixed‐phase clouds. Weather and climate models often lack process representations surrounding these states, but prior work mostly treated the problem as an aggregate of synoptic conditions, resulting in partially overlapping biases. Here, we disaggregate the Arctic states and confront ERA5 reanalysis with observations from the MOSAiC campaign over the central Arctic sea ice during winter 2019/2020. Low‐level winds and liquid water path (LWP) are combined to derive different synoptic classes. Results show that the clear state is primarily formed by weak/moderate winds and the absence of liquid‐bearing clouds, while strong winds and enhanced LWP primarily form the radiatively opaque state. ERA5 struggles to reproduce these basic statistics, shows too weak sensitivity of thermal radiation to synoptic forcing, and overestimates thermal radiation for similar LWP amounts. The latter is caused by a warm bias, which has a pronounced inversion structure and is largest in clear and calm conditions. Under strong synoptic forcing, the warm bias is constant with height and discrepancies in mixed‐phase cloud altitude appear. Separating synoptic conditions is regarded as useful for process‐oriented evaluation of the Arctic troposphere in models. Large‐scale synoptic forcing has a strong impact on the thermodynamic structure, clouds, and thermal radiation in the central Arctic during winter. This study investigates those processes using observations and compares their representation in an atmospheric reanalysis under different synoptic conditions for the MOSAiC campaign, winter 2019/2020. Results show that radiative fluxes and thermodynamic structure, along with their biases in the reanalysis, are sensitive to both liquid cloud water and near‐surface winds, suggesting the consideration of synoptic forcing for process evaluation.

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The amount of snow on Arctic sea ice impacts the ice mass budget. Wind redistribution of snow into open water in leads is hypothesized to cause significant wintertime snow loss. However, there are no direct measurements of snow loss into Arctic leads. We measured the snow lost in four leads in the Central Arctic in winter 2020. We find, contrary to expectations, that under typical winter conditions, minimal snow was lost into leads. However, during a cyclone that delivered warm air temperatures, high winds, and snowfall, 35.0 ± 1.1 cm snow water equivalent (SWE) was lost into a lead (per unit lead area). This corresponded to a removal of 0.7–1.1 cm SWE from the entire surface—∼6%–10% of this site's annual snow precipitation. Warm air temperatures, which increase the length of time that wintertime leads remain unfrozen, may be an underappreciated factor in snow loss into leads. Plain Language Summary The amount of snow on Arctic sea ice impacts how quickly the ice grows in the winter and melts in the summer. Cracks in the ice, known as leads, expose ocean water that snow can be blown into, reducing the amount of snow on the ice and thus impacting ice growth and melt. We found that in typical wintertime conditions, very little snow is blown into leads. However, if there is fresh snowfall, it is uncommonly warm and it is very windy at the same time when leads are forming, a large amount of snow can be blown into the ocean. Accounting for the impacts of air temperature on this process will enable scientists to better understand how much snow is on Arctic sea ice, and hence how quickly the ice grows in the winter and melts in the summer, and how this might change in a future, warmer, Arctic. Key Points Minimal snow was lost into leads in observations of three cases in typical wintertime, cold, moderately windy conditions on Arctic sea ice In an atmospheric advection event with air temperature above −10°C, high wind, and fresh snowfall, most recent snowfall was lost into leads Warm air temperatures increase the duration of unfrozen water in leads, which may be an underappreciated factor in snow loss into leads