Direct observations during intense warm‐air advection over the East Siberian Sea reveal a period of rapid sea‐ice melt. A semistationary, high‐pressure system north of the Bering Strait forced northward advection of warm, moist air from the continent. Air‐mass transformation over melting sea ice formed a strong, surface‐based temperature inversion in which dense fog formed. This induced a positive net longwave radiation at the surface while reducing net solar radiation only marginally; the inversion also resulted in downward turbulent heat flux. The sum of these processes enhanced the surface energy flux by an average of ~15 W m⁻² for a week. Satellite images before and after the episode show sea‐ice concentrations decreasing from > 90% to ~50% over a large area affected by the air‐mass transformation. We argue that this rapid melt was triggered by the increased heat flux from the atmosphere due to the warm‐air advection.

We demonstrate direct eddy covariance (EC) observations of methane (CH₄) fluxes between the sea and atmosphere from an icebreaker in the eastern Arctic Ocean. EC-derived CH₄ emissions averaged 4.58, 1.74, and 0.14 mg m⁻² day⁻¹ in the Laptev, East Siberian, and Chukchi seas, respectively, corresponding to annual sea-wide fluxes of 0.83, 0.62, and 0.03 Tg year⁻¹. These EC results answer concerns that previous diffusive emission estimates, which excluded bubbling, may underestimate total emissions. We assert that bubbling dominates sea-air CH₄ fluxes in only small constrained areas: A ~100-m² area of the East Siberian Sea showed sea-air CH₄ fluxes exceeding 600 mg m⁻² day⁻¹; in a similarly sized area of the Laptev Sea, peak CH₄ fluxes were ~170 mg m⁻² day⁻¹. Calculating additional emissions below the noise level of our EC system suggests total ESAS CH₄ emissions of 3.02 Tg year⁻¹, closely matching an earlier diffusive emission estimate of 2.9 Tg year⁻¹.

A variety of physical mechanisms are jointly responsible for facilitating air‐sea gas transfer through turbulent processes at the atmosphere‐ocean interface. The nature and relative importance of these mechanisms evolves with increasing wind speed. Theoretical and modeling approaches are advancing, but the limited quantity of observational data at high wind speeds hinders the assessment of these efforts. The HiWinGS project successfully measured gas transfer coefficients (k₆₆₀) with coincident wave statistics under conditions with hourly mean wind speeds up to 24 m s⁻¹ and significant wave heights to 8 m. Measurements of k₆₆₀ for carbon dioxide (CO₂) and dimethylsulfide (DMS) show an increasing trend with respect to 10 m neutral wind speed (U_10N), following a power law relationship of the form: and . Among seven high wind speed events, CO₂ transfer responded to the intensity of wave breaking, which depended on both wind speed and sea state in a complex manner, with increasing as the wind sea approaches full development. A similar response is not observed for DMS. These results confirm the importance of breaking waves and bubble injection mechanisms in facilitating CO₂ transfer. A modified version of the Coupled Ocean‐Atmosphere Response Experiment Gas transfer algorithm (COAREG ver. 3.5), incorporating a sea state‐dependent calculation of bubble‐mediated transfer, successfully reproduces the mean trend in observed k₆₆₀ with wind speed for both gases. Significant suppression of gas transfer by large waves was not observed during HiWinGS, in contrast to results from two prior field programs.

The contemporary air‐sea flux of CO₂ is investigated by the use of an air‐sea flux equation, with particular attention to the uncertainties in global values and their origin with respect to that equation. In particular, uncertainties deriving from the transfer velocity and from sparse upper ocean sampling are investigated. Eight formulations of air‐sea gas transfer velocity are used to evaluate the combined standard uncertainty resulting from several sources of error. Depending on expert opinion, a standard uncertainty in transfer velocity of either ~5% or ~10% can be argued and that will contribute a proportional error in air‐sea flux. The limited sampling of upper ocean fCO₂ is readily apparent in the Surface Ocean CO₂ Atlas databases. The effect of sparse sampling on the calculated fluxes was investigated by a bootstrap method, that is, treating each ship cruise to an oceanic region as a random episode and creating 10 synthetic data sets by randomly selecting episodes with replacement. Convincing values of global net air‐sea flux can only be achieved using upper ocean data collected over several decades but referenced to a standard year. The global annual referenced values are robust to sparse sampling, but seasonal and regional values exhibit more sampling uncertainty. Additional uncertainties are related to thermal and haline effects and to aspects of air‐sea gas exchange not captured by standard models. An estimate of global net CO₂ exchange referenced to 2010 of −3.0 ± 0.6 Pg C/year is proposed, where the uncertainty derives primarily from uncertainty in the transfer velocity.

The Arctic Ocean 2018 (AO2018) took place in the central Arctic Ocean in August and September 2018 on the Swedish icebreaker Oden. An extensive suite of instrumentation provided detailed measurements of surface water chemistry and biology, sea ice and ocean physical and biogeochemical properties, surface exchange processes, aerosols, clouds, and the state of the atmosphere. The measurements provide important information on the coupling of the ocean and ice surface to the atmosphere and in particular to clouds. This paper provides (i) an overview of the synoptic-scale atmospheric conditions and their climatological anomaly to help interpret the process studies and put the detailed observations from AO2018 into a larger context, both spatially and temporally; (ii) a statistical analysis of the thermodynamic and near-surface meteorological conditions, boundary layer, cloud, and fog characteristics; and (iii) a comparison of the results to observations from earlier Arctic Ocean expeditions – in particular AOE1996 (Arctic Ocean Expedition 1996), SHEBA (Surface Heat Budget of the Arctic Ocean), AOE2001 (Arctic Ocean Experiment 2001), ASCOS (Arctic Summer Cloud Ocean Study), ACSE (Arctic Clouds in Summer Experiment), and AO2016 (Arctic Ocean 2016) – to provide an assessment of the representativeness of the measurements. The results show that near-surface conditions were broadly comparable to earlier experiments; however the thermodynamic vertical structure was quite different. An unusually high frequency of well-mixed boundary layers up to about 1 km depth occurred, and only a few cases of the “prototypical” Arctic summer single-layer stratocumulus deck were observed. Instead, an unexpectedly high amount of multiple cloud layers and mid-level clouds were present throughout the campaign. These differences from previous studies are related to the high frequency of cyclonic activity in the central Arctic in 2018.

Forecasts with the European Centre for Medium‐Range Weather Forecasts' numerical weather prediction model are evaluated using an extensive set of observations from the Arctic Ocean 2018 expedition on the Swedish icebreakerOden. The atmospheric model (Cy45r1) is similar to that used for the ERA5 reanalysis (Cy41r2). The evaluation covers 1 month, with the icebreaker moored to drifting sea ice near the North Pole; a total of 125 forecasts issued four times per day were used. Standard surface observations and 6‐hourly soundings were assimilated to ensure that the initial model error is small. Model errors can be divided into two groups. First, variables related to dynamics feature errors that grow with forecast length; error spread also grows with time. Initial errors are small, facilitating a robust evaluation of the second group; thermodynamic variables. These feature fast error growth for 6–12 hr, after which errors saturates; error spread is roughly constant. Both surface and near‐surface air temperatures are too warm in the model. During the summer both are typically above zero in spite of the ongoing melt; however, the warm bias increases as the surface freezes. The warm bias is due to a too warm atmosphere; errors in surface sensible heat flux transfer additional heat from the atmosphere to the surface. The lower troposphere temperature error has a distinct vertical structure: a substantial warm bias in the lowest few 100 m and a large cold bias around 1 km; this structure features a significant diurnal cycle and is tightly coupled to errors in the modelled clouds. Clouds appear too often and in a too deep layer of the lower atmosphere; the lowest clouds essentially never break up. The largest error in cloud presence is aligned with the largest cold bias at around 1 km.