The boundary layer is the lowest layer of the atmosphere that connects the free troposphere with the surface through exchange of momentum, heat, gases and particles. Transport is achieved by turbulent motions and the thickness of the boundary layer depends on the turbulence intensity. Its depth ranges from a few meters in stably stratified weak-wind clear-sky winter cases, to several hundred meters in the cloud-capped well-mixed boundary layer prevailing in summer, but also common in winter. Turbulence over sea ice, as elsewhere, is generated through mechanical production by vertical wind shear and buoyancy effects. Key differences include the presence of sea ice and a weak diurnal but pronounced seasonal cycle.

While climate change is a global issue, the change in itself is not homogeneously distributed over the globe. It is well established that near-surface Arctic warming is on average 3-4 times larger than the global-average warming. This so-called Arctic Amplification is due to a number of positive feedbacks in the Arctic, some of which are poorly understood. On a basic level, Arctic climate is determined by a balance between inflows of energy from the south and the net loss of energy by radiation at the top of the Arctic atmosphere. Both are large while their difference is small. The inflow of heat from the south occurs in both the atmosphere and ocean. From a numerical modeling perspective it occurs on sufficiently large spatial and temporal scales that it is considered resolved, however, a disproportionately large fraction of the heat transport into the Arctic happens in discrete localized events, sometimes referred to as atmospheric rivers. The net energy flux at the top of the atmosphere also has a very large annual cycle: positive but small in summer, when the solar radiation is at its maximum, but large and negative in winter when the sun is absent. The net radiative flux at TOA depends on a number of processes, including sea-ice cover, surface temperature and albedo, atmospheric chemical composition, clouds and aerosols etc. All of these have in common that they are not resolved in numerical models and hence have to be parameterized, described parametrically as functions of larger-scale resolved variables. Different in different models, models typically have substantial systematic but sometimes compensating errors in these descriptions and to a large extent this explains the spread in climate model projections of future climate and systematic errors in weather forecasts. Arctic Ocean near-surface air temperature, as a proxy for climate, goes through a substantial annual cycle with two main states; these can be characterized as either freezing or melting. Physically, in some sense, the Arctic Ocean surface only has two seasons – the melt season and the freeze season. In winter with surface temperature below the melting point, the surface temperature reacts to changes in the surface energy budget, hence, it features large and fast changes in response to changes mainly in incoming radiation. In summer, or the melt season, the surface temperature is prevented from increasing above the melting point by the phase change of melting, as long as there is substantial ice and snow remaining, and all excess energy goes into melting rather than into warming. Consequently, the summer near-surface air temperature varies only a little. How much ice melts over the melt season is directly related to the length of the melt period but also indirectly to what happens in winter. If the melt season becomes longer it follows that sea ice extent at its minimum in September will decrease. The length of the melt season is therefore one important component of the Arctic climate system, and studying and understanding the so-called shoulder seasons – the transition between melt and freeze both in spring and autumn – is of great interest in order to understand the Arctic climate system. Historically, icebreaker-based expeditions, capable of performing scientific-grade process-level observations have occurred in summer or early autumn because the ice is easier to navigate in the Arctic Ocean, when melting. Hence, a number of Oden expeditions have been able to observe the transition from surface melt to surface freeze in late August or early September. However, only a few have collected such observations at the melt onset. Hence, on a process level, there are no relevant observations of the melt onset. The ARTofMELT expedition was conceived to rectify this, studying the relationship between this onset and atmospheric rivers.

In Arctic warm-air intrusions, air masses undergo a series of radiative, turbulent, cloud, and precipitation processes, the sum of which constitutes the air mass transformation. During the Arctic air mass transformation, heat and moisture are transferred from the air mass to the Arctic environment, melting the sea ice and potentially reinforcing feedback mechanisms responsible for the amplified Arctic warming. We tackle this complex, poorly understood phenomenon from a Lagrangian perspective using the warm-air intrusion event on 12–14 March captured by the 2022 HALO-(𝒜𝒞)³ campaign. Our trajectory analysis of the event suggests that the intruding air mass can be treated as a cohesive air column, therefore justifying the use of a single-column model. In this study, we test this hypothesis using the Atmosphere–Ocean Single-Column Model (AOSCM). The rates of heat and moisture depletion vary along the advection path due to the changing surface properties and large-scale vertical motion. Cloud radiative cooling and turbulent mixing in the stably stratified boundary layer are constant sinks of heat throughout the air mass transformation. Boundary layer cooling intensifies over the marginal ice zone and forces the development of a low-level cloud underneath the advected one. As the air mass flows past the marginal ice zone, large-scale updrafts dominate the temperature and moisture changes through adiabatic cooling and condensation. The ability of the Lagrangian AOSCM framework to simulate elements of the air mass transformation seen in aircraft observations, reanalysis, and operational forecast data makes it an attractive tool for future model analysis and diagnostics development. Our findings can benefit the understanding of the timescales and driving mechanisms of Arctic air mass transformation and help determine the contribution of warm-air intrusions in Arctic amplification.

The Arctic is experiencing a warming much faster than the global average and aerosol–cloud–sea–ice interactions are considered to be one of the key features of the Arctic climate system. It is therefore crucial to identify particle sources and sinks to study their impact on cloud formation and cloud properties in the Arctic. Near-surface particle and sensible heat fluxes were measured using the gradient method during the ARTofMELT Arctic Ocean Expedition 2023. A gradient system was deployed to calculate sensible heat and particle fluxes over three different surface conditions: wide lead, narrow lead, and closed ice. To evaluate the gradient measurements, sensible heat fluxes and friction velocities were compared with eddy covariance data. The strongest mean sensible heat fluxes, ranging from 16 to 51 W m⁻², were observed over wide lead surfaces, aligning with measurements from the icebreaker. In contrast, closed ice surfaces had weak, often negative, sensible heat fluxes. Wide leads acted as a particle source, with median net particle emission fluxes of 0.09 × 10⁶ m⁻² s⁻¹. Narrow lead surfaces exhibited both net emission and net deposition, though the particle fluxes were weaker. Closed ice surfaces acted as a particle sink, with normalized fluxes around 0.06 cm s⁻¹. The gradient method was found to be effective for measuring both sensible heat and particle fluxes, allowing flexible deployment over different surface types. This study addresses the critical need for improved quantification of turbulent vertical particle fluxes and related processes that influence the local particle number budget in the high Arctic.

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.

During the ill-fated 1897 Andrée balloon expedition, Nils Strindberg allegedly dropped a small tin containing a last message for his fiancée onto the island Fuglesongen in northwestern Svalbard, as the expedition crew passed over it in their hydrogen balloon, Örnen. Despite at least one lengthy search on Fuglesongen, the tin has never been found. This paper investigates the hypothesis that the tin was accidentally dropped onto Klovningen, a neighbouring island similar in size and shape, situated approximately 2.4 km east of Fuglesongen. A re-analysis of Strindberg’s original handwritten notes from the balloon flight, along with other primary sources and meteorological analyses, suggests that a targeted search for the tin on Klovningen could be a promising next step in solving this enduring mystery.

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.

Climate change is particularly noticeable in the Arctic. The most common type of cloud at these latitudes is mixed-phase stratocumulus. These clouds occur frequently and persistently during all seasons and play a critical role in the Arctic energy budget. Previous observations in the central (north of 80^∘ N) Arctic have shown a high occurrence of prolonged periods of a shallow, single-layer mixed-phase stratocumulus at the top of the boundary layer (BL; altitudes ∼ 300 to 400 m). However, recent observations from the summer of 2018 instead showed a prevalence of a two-layer boundary-layer cloud system. Here we use large-eddy simulation to examine the maintenance of one of the cloud systems observed in the summer of 2018 and the sensitivity of the cloud layers to different micro- and macro-scale parameters. We find that the model generally reproduces the observed thermodynamic structure well, with two near-neutrally stratified layers in the BL caused by a low cloud (located within the first few hundred meters) capped by a lower-altitude temperature inversion and an upper cloud layer (based around one kilometer or slightly higher) capped by the main temperature inversion of the BL. The simulated cloud structure is persistent unless there are low aerosol number concentrations (≤ 5 cm⁻³), which cause the upper cloud layer to dissipate, or high large-scale wind speeds (≥ 8.5 m s⁻¹), which erode the lower inversion and the related cloud layer. The changes in cloud structure alter both the short- and longwave cloud radiative effect at the surface. This results in changes in the net radiative effect of the modeled cloud system, which can impact the surface melting or freezing. The findings highlight the importance of better understanding and representing aerosol sources and sinks over the central Arctic Ocean. Furthermore, they underline the significance of meteorological parameters, such as the large-scale wind speed, for maintaining the two-layer boundary-layer cloud structure encountered in the lower atmosphere of the central Arctic.

The Arctic climate system is host to many processes which interact vertically over the tightly coupled atmosphere, sea ice and ocean. The coupled Atmosphere-Ocean Single-Column Model (AOSCM) allows to decouple local small-scale and large-scale processes to investigate the model performance in an idealized setting. Here, an observed Arctic warm air intrusion event is used to show how to identify model deficiencies using the AOSCM. The AOSCM allows us to effectively produce a large number of perturbation simulations, around 1,000, to map sensitivities of the model results due to changes in physical and model properties as well as to the large-scale tendencies. The analysis of the summary diagnostics, that is, aggregated results from sensitivity experiments evaluated against modeled physical properties, such as surface energy budget and mean sea ice thickness, reveals sensitivities to the chosen parameters. Further, we discuss how the conclusions can be used to understand the behavior of the global host model. The simulations confirm that the horizontal advection of heat and moisture plays an important role for maintaining a low-level cloud cover, as in earlier studies. The combined cloud layers increase the energy input to the surface, which in turn enhances the ongoing melt. The clouds present an additional sensitivity in terms of how they are represented but also their interaction with the large-scale advection and the model time step. The methodology can be used for a variety of other regions, where the coupling to the ocean is important.

The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development.