The central Arctic Ocean (CAO) plays an important role in the global carbon cycle, but the current and future exchange of the climate-forcing trace gases methane (CH4) and carbon dioxide (CO2) between the CAO and the atmosphere is highly uncertain. In particular, there are very few observations of near-surface gas concentrations or direct air-sea CO2 flux estimates and no previously reported direct air-sea CH4 flux estimates from the CAO. Furthermore, the effect of sea ice on the exchange is not well understood. We present direct measurements of the air-sea flux of CH4 and CO2, as well as air-snow fluxes of CO2 in the summertime CAO north of 82.5 N from the Synoptic Arctic Survey (SAS) expedition carried out on the Swedish icebreaker Oden in 2021. Measurements of air-sea CH4 and CO2 flux were made using floating chambers deployed in leads accessed from sea ice and from the side of Oden, and air-snow fluxes were determined from chambers deployed on sea ice. Gas transfer velocities determined from fluxes and surface-water-dissolved gas concentrations exhibited a weaker wind speed dependence than existing parameterisations, with a median sea-ice lead gas transfer rate of 2.5cmh-1 applicable over the observed 10m wind speed range (1-11ms-1). The average observed air-sea CO2 flux was -7.6mmolm-2d-1, and the average air-snow CO2 flux was -1.1mmolm-2d-1. Extrapolating these fluxes and the corresponding sea-ice concentrations gives an August and September flux for the CAO of -1.75mmolm-2d-1, within the range of previous indirect estimates. The average observed air-sea CH4 flux of 3.5μmolm-2d-1, accounting for sea-ice concentration, equates to an August and September CAO flux of 0.35μmolm-2d-1, lower than previous estimates and implying that the CAO is a very small (‰ 1%) contributor to the Arctic flux of CH4 to the atmosphere.

Turbulent exchanges between sea ice and the atmosphere are known to influence the melting rate of sea ice, the development of atmospheric circulation anomalies and, potentially, teleconnections between polar and non-polar regions. Large model errors remain in the parametrization of turbulent heat fluxes over sea ice in climate models, resulting in significant uncertainties in projections of future climate. Fluxes are typically calculated using bulk formulae, based on Monin-Obukhov similarity theory, which have shown particular limitations in polar regions. Parametrizations developed specifically for polar conditions (e.g. representing form drag from ridges or melt ponds on sea ice) rely on sparse observations and thus may not be universally applicable. In this study, new data-driven parametrizations have been developed for surface turbulent fluxes of momentum, sensible heat and latent heat in the Arctic. Machine learning has already been used outside the polar regions to provide accurate and computationally inexpensive estimates of surface turbulent fluxes. To investigate the feasibility of this approach in the Arctic, we have fitted neural-network models to a reference dataset (SHEBA). Predictive performance has been tested using data from other observational campaigns. For momentum and sensible heat, performance of the neural networks is found to be comparable to, and in some cases substantially better than, that of a state-of-the-art bulk formulation. These results offer an efficient alternative to the traditional bulk approach in cases where the latter fails, and can serve to inform further physically based developments.

By synthesising remote-sensing measurements made in the central Arctic into a model-gridded Cloudnet cloud product, we evaluate how well the Met Office Unified Model (UM) and the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS) capture Arctic clouds and their associated interactions with the surface energy balance and the thermodynamic structure of the lower troposphere. This evaluation was conducted using a 4-week observation period from the Arctic Ocean 2018 expedition, where the transition from sea ice melting to freezing conditions was measured. Three different cloud schemes were tested within a nested limited-area model (LAM) configuration of the UM – two regionally operational single-moment schemes (UM_RA2M and UM_RA2T) and one novel double-moment scheme (UM_CASIM-100) – while one global simulation was conducted with the IFS, utilising its default cloud scheme (ECMWF_IFS).

Consistent weaknesses were identified across both models, with both the UM and IFS overestimating cloud occurrence below 3 km. This overestimation was also consistent across the three cloud configurations used within the UM framework, with >90 % mean cloud occurrence simulated between 0.15 and 1 km in all the model simulations. However, the cloud microphysical structure, on average, was modelled reasonably well in each simulation, with the cloud liquid water content (LWC) and ice water content (IWC) comparing well with observations over much of the vertical profile. The key microphysical discrepancy between the models and observations was in the LWC between 1 and 3 km, where most simulations (all except UM_RA2T) overestimated the observed LWC.

Despite this reasonable performance in cloud physical structure, both models failed to adequately capture cloud-free episodes: this consistency in cloud cover likely contributes to the ever-present near-surface temperature bias in every simulation. Both models also consistently exhibited temperature and moisture biases below 3 km, with particularly strong cold biases coinciding with the overabundant modelled cloud layers. These biases are likely due to too much cloud-top radiative cooling from these persistent modelled cloud layers and were consistent across the three UM configurations tested, despite differences in their parameterisations of cloud on a sub-grid scale. Alarmingly, our findings suggest that these biases in the regional model were inherited from the global model, driving a cause–effect relationship between the excessive low-altitude cloudiness and the coincident cold bias. Using representative cloud condensation nuclei concentrations in our double-moment UM configuration while improving cloud microphysical structure does little to alleviate these biases; therefore, no matter how comprehensive we make the cloud physics in the nested LAM configuration used here, its cloud and thermodynamic structure will continue to be overwhelmingly biased by the meteorological conditions of its driving model.

Coastal ecosystems can efficiently remove carbon dioxide (CO₂) from the atmosphere and are thus promoted for nature-based climate change mitigation. Natural methane (CH₄) emissions from these ecosystems may counterbalance atmospheric CO₂ uptake. Still, knowledge of mechanisms sustaining such CH₄ emissions and their contribution to net radiative forcing remains scarce for globally prevalent macroalgae, mixed vegetation, and surrounding depositional sediment habitats. Here we show that these habitats emit CH₄ in the range of 0.1 – 2.9 mg CH₄ m⁻² d⁻¹ to the atmosphere, revealing in situ CH₄ emissions from macroalgae that were sustained by divergent methanogenic archaea in anoxic microsites. Over an annual cycle, CO₂-equivalent CH₄ emissions offset 28 and 35% of the carbon sink capacity attributed to atmospheric CO₂ uptake in the macroalgae and mixed vegetation habitats, respectively, and augment net CO₂ release of unvegetated sediments by 57%. Accounting for CH₄ alongside CO₂ sea-air fluxes and identifying the mechanisms controlling these emissions is crucial to constrain the potential of coastal ecosystems as net atmospheric carbon sinks and develop informed climate mitigation strategies.

Polar oceans and sea ice cover 15% of the Earth's ocean surface, and the environment is changing rapidly at both poles. Improving knowledge on the interactions between the atmospheric and oceanic realms in the polar regions, a Surface Ocean-Lower Atmosphere Study (SOLAS) project key focus, is essential to understanding the Earth system in the context of climate change. However, our ability to monitor the pace and magnitude of changes in the polar regions and evaluate their impacts for the rest of the globe is limited by both remoteness and sea-ice coverage. Sea ice not only supports biological activity and mediates gas and aerosol exchange but can also hinder some in-situ and remote sensing observations. While satellite remote sensing provides the baseline climate record for sea-ice properties and extent, these techniques cannot provide key variables within and below sea ice. Recent robotics, modeling, and in-situ measurement advances have opened new possibilities for understanding the ocean-sea ice-atmosphere system, but critical knowledge gaps remain. Seasonal and long-term observations are clearly lacking across all variables and phases. Observational and modeling efforts across the sea-ice, ocean, and atmospheric domains must be better linked to achieve a system-level understanding of polar ocean and sea-ice environments. As polar oceans are warming and sea ice is becoming thinner and more ephemeral than before, dramatic changes over a suite of physicochemical and biogeochemical processes are expected, if not already underway. These changes in sea-ice and ocean conditions will affect atmospheric processes by modifying the production of aerosols, aerosol precursors, reactive halogens and oxidants, and the exchange of greenhouse gases. Quantifying which processes will be enhanced or reduced by climate change calls for tailored monitoring programs for high-latitude ocean environments. Open questions in this coupled system will be best resolved by leveraging ongoing international and multidisciplinary programs, such as efforts led by SOLAS, to link research across the ocean-sea ice-atmosphere interface.

A major source of uncertainty in both climate projections and seasonal forecasting of sea ice is inadequate representation of surface–atmosphere exchange processes. The observations needed to improve understanding and reduce uncertainty in surface exchange parameterizations are challenging to make and rare. Here we present a large dataset of ship-based measurements of surface momentum exchange (surface drag) in the vicinity of sea ice from the Arctic Clouds in Summer Experiment (ACSE) in July–October 2014, and the Arctic Ocean 2016 experiment (AO2016) in August–September 2016. The combined dataset provides an extensive record of momentum flux over a wide range of surface conditions spanning the late summer melt and early autumn freeze-up periods, and a wide range of atmospheric stabilities. Surface exchange coefficients are estimated from in situ eddy covariance measurements. The local sea-ice fraction is determined via automated processing of imagery from ship-mounted cameras. The surface drag coefficient, C_D10n, peaks at local ice fractions of 0.6–0.8, consistent with both recent aircraft-based observations and theory. Two state-of-the-art parameterizations have been tuned to our observations, with both providing excellent fits to the measurements.

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.

Aircraft observations from two Arctic field campaigns are used to characterize and model surface heat and moisture exchange over the marginal ice zone (MIZ). We show that the surface roughness lengths for heat and moisture over uninterrupted sea ice vary with roughness Reynolds number (R*; itself a function of the roughness length for momentum, z0, and surface wind stress), with a peak at the transition between aerodynamically smooth (R*<0.135) and aerodynamically rough (R*>2.5) regimes. A pre-existing theoretical model based on surface-renewal theory accurately reproduces this peak, in contrast to the simple parameterizations currently employed in two state-of-the-art numerical weather prediction models, which are insensitive to R*. We propose a new, simple parameterization for surface exchange over the MIZ that blends this theoretical model for sea ice with surface exchange over water as a function of sea ice concentration. In offline tests, this new scheme performs much better than the existing schemes for the rough conditions observed during the 'Iceland Greenland Seas Project' field campaign. The bias in total turbulent heat flux across the MIZ is reduced to only 13 W m(-2) for the new scheme, from 48 and 80 W m(-2) for the Met Office Unified Model and ECMWF Integrated Forecast System schemes, respectively. It also performs marginally better for the comparatively smooth conditions observed during the 'Aerosol-Cloud Coupling and Climate Interactions in the Arctic' field campaign. The new surface exchange scheme has the benefit of being physically-motivated, comparatively accurate and straightforward to implement, although to reap the full benefits an improvement to the representation of sea ice topography via z0 is required.

The air‐water exchange of trace gases such as CO₂ is usually parameterized in terms of a gas transfer velocity, which can be derived from direct measurements of the air‐sea gas flux. The transfer velocity of poorly soluble gases is driven by near‐surface ocean turbulence, which may be enhanced or suppressed by the presence of sea ice. A lack of measurements means that air‐sea fluxes in polar regions, where the oceanic sink of CO₂ is poorly known, are generally estimated using open‐ocean transfer velocities scaled by ice fraction. Here, we describe direct determinations of CO₂ gas transfer velocity from eddy covariance flux measurements from a mast fixed to ice adjacent to a sea‐ice lead during the summer‐autumn transition in the central Arctic Ocean. Lead water CO₂ uptake is determined using flux footprint analysis of water‐atmosphere and ice‐atmosphere flux measurements made under conditions (low humidity and high CO₂ signal) that minimize errors due to humidity cross‐talk. The mean gas transfer velocity is found to have a quadratic dependence on wind speed: k₆₆₀ = 0.179 U₁₀², which is 30% lower than commonly used open‐ocean parameterizations. As such, current estimates of polar ocean carbon uptake likely overestimate gas exchange rates in typical summertime conditions of weak convective turbulence. Depending on the footprint model chosen, the gas transfer velocities also exhibit a dependence on the dimension of the lead, via its impact on fetch length and hence sea state. Scaling transfer velocity parameterizations for regional gas exchange estimates may therefore require incorporating lead width data.

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 similar to 100-m(2) 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 similar to 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).