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Regional climate models are a valuable tool for the study of the climate processes and climate change in polar regions, but the performance of the models has to be evaluated using experimental data. The regional climate model CCLM was used for simulations for the MOSAiC period with a horizontal resolution of 14 km (whole Arctic). CCLM was used in a forecast mode (nested in ERA5) and used a thermodynamic sea ice model. Sea ice concentration was taken from AMSR2 data (C15 run) and from a high-resolution data set (1 km) derived from MODIS data (C15MOD0 run). The model was evaluated using radiosonde data and data of different profiling systems with a focus on the winter period (November–April). The comparison with radiosonde data showed very good agreement for temperature, humidity, and wind. A cold bias was present in the ABL for November and December, which was smaller for the C15MOD0 run. In contrast, there was a warm bias for lower levels in March and April, which was smaller for the C15 run. The effects of different sea ice parameterizations were limited to heights below 300 m. High-resolution lidar and radar wind profiles as well as temperature and integrated water vapor (IWV) data from microwave radiometers were used for the comparison with CCLM for case studies, which included low-level jets. LIDAR wind profiles have many gaps, but represent a valuable data set for model evaluation. Comparisons with IWV and temperature data of microwave radiometers show very good agreement.

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.

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 an

In the Arctic, the aerosol budget plays a particular role in determining the behaviour of clouds, which are important for the surface energy balance and thus for the region's climate. A key question is the extent to which cloud condensation nuclei in the high Arctic summertime boundary layer are controlled by local emission and formation processes as opposed to transport from outside. Each of these sources is likely to respond differently to future changes in ice cover. Here we use a global model and observations from ship and aircraft field campaigns to understand the source of high Arctic aerosol in late summer. We find that particles formed remotely, i.e. at latitudes outside the Arctic, are the dominant source of boundary layer Aitken mode particles during the sea ice melt period up to the end of August. Particles from such remote sources, entrained into the boundary layer from the free troposphere, account for nucleation and Aitken mode particle concentrations that are otherwise underestimated by the model. This source from outside the high Arctic declines as photochemical rates decrease towards the end of summer and is largely replaced by local new particle formation driven by iodic acid created during freeze-up. Such a local source increases the simulated Aitken mode particle concentrations by 2 orders of magnitude during sea ice freeze-up and is consistent with strong fluctuations in nucleation mode concentrations that occur in September. Our results suggest a high-Arctic aerosol regime shift in late summer, and only after this shift do cloud condensation nuclei become sensitive to local aerosol processes.

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, CD10n, 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.

With the Arctic rapidly changing, the needs to observe, understand, and model the changes are essential. To support these needs, an annual cycle of observations of atmospheric properties, processes, and interactions were made while drifting with the sea ice across the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. An international team designed and implemented the comprehensive program to document and characterize all aspects of the Arctic atmospheric system in unprecedented detail, using a variety of approaches, and across multiple scales. These measurements were coordinated with other observational teams to explore cross-cutting and coupled interactions with the Arctic Ocean, sea ice, and ecosystem through a variety of physical and biogeochemical processes. This overview outlines the breadth and complexity of the atmospheric research program, which was organized into 4 subgroups: atmospheric state, clouds and precipitation, gases and aerosols, and energy budgets. Atmospheric variability over the annual cycle revealed important influences from a persistent large-scale winter circulation pattern, leading to some storms with pressure and winds that were outside the interquartile range of past conditions suggested by long-term reanalysis. Similarly, the MOSAiC location was warmer and wetter in summer than the reanalysis climatology, in part due to its close proximity to the sea ice edge. The comprehensiveness of the observational program for characterizing and analyzing atmospheric phenomena is demonstrated via a winter case study examining air mass transitions and a summer case study examining vertical atmospheric evolution. Overall, the MOSAiC atmospheric program successfully met its objectives and was the most comprehensive atmospheric measurement program to date conducted over the Arctic sea ice. The obtained data will support a broad range of coupled-system

The bubbles generated by breaking waves are of considerable scientific interest due to their influence on air–sea gas transfer, aerosol production, and upper ocean optics and acoustics. However, a detailed understanding of the processes creating deeper bubble plumes (extending 2–10 m below the ocean surface) and their significance for air–sea gas exchange is still lacking. Here, we present bubble measurements from the HiWinGS expedition in the North Atlantic in 2013, collected during several storms with wind speeds of 10–27 m s−1. A suite of instruments was used to measure bubbles from a self-orienting free-floating spar buoy: a specialised bubble camera, acoustical resonators, and an upward-pointing sonar. The focus in this paper is on bubble void fractions and plume structure. The results are consistent with the presence of a heterogeneous shallow bubble layer occupying the top 1–2 m of the ocean, which is regularly replenished by breaking waves, and deeper plumes which are only formed from the shallow layer at the convergence zones of Langmuir circulation. These advection events are not directly connected to surface breaking. The void fraction distributions at 2 m depth show a sharp cut-off at a void fraction of 10−4.5 even in the highest winds, implying the existence of mechanisms limiting the void fractions close to the surface. Below wind speeds of 16 m s−1 or a wind-wave Reynolds number of , the probability distribution of void fraction at 2 m depth is very similar in all conditions but increases significantly above either threshold. Void fractions are significantly different during periods of rising and falling winds, but there is no distinction with wave age. There is a complex near-surface flow structure due to Langmuir circulation, Stokes drift, and wind-induced current shear which influences the spatial distribution of bubbles within the top few metres. We do not see evidence for slow bubble dissolution as bubbles are carried downwards, implying that col

Bubbles formed by breaking waves in the open ocean influence many surface processes but are poorly understood. We report here on detailed bubble size distributions measured during the High Wind Speed Gas Exchange Study (HiWinGS) in the North Atlantic, during four separate storms with hourly averaged wind speeds from 10–27 m s−1. The measurements focus on the deeper plumes formed by advection downwards (at 2 m depth and below), rather than the initial surface distributions. Our results suggest that bubbles reaching a depth of 2 m have already evolved to form a heterogeneous but statistically stable population in the top 1–2 m of the ocean. These shallow bubble populations are carried downwards by coherent near-surface circulations; bubble evolution at greater depths is consistent with control by local gas saturation, surfactant coatings and pressure. We find that at 2 m the maximum bubble radius observed has a very weak wind speed dependence and is too small to be explained by simple buoyancy arguments. For void fractions greater than 10−6, bubble size distributions at 2 m can be fitted by a two-slope power law (with slopes of −0.3 for bubbles of radius <80 µm and −4.4 for larger sizes). If normalised by void fraction, these distributions collapse to a very narrow range, implying that the bubble population is relatively stable and the void fraction is determined by bubbles spreading out in space rather than changing their size over time. In regions with these relatively high void fractions we see no evidence for slow bubble dissolution. When void fractions are below 10−6, the peak volume of the bubble size distribution is more variable and can change systematically across a plume at lower wind speeds, tracking the void fraction. Relatively large bubbles (80 µm in radius) are observed to persist for several hours in some cases, following periods of very high wind. Our results suggest that local gas supersaturation around the bubble plume may have a strong influence on

Detailed knowledge of the physical and chemical properties and sources of particles that form clouds is especially important in pristine areas like the Arctic, where particle concentrations are often low and observations are sparse. Here, we present in situ cloud and aerosol measurements from the central Arctic Ocean in August–September 2018 combined with air parcel source analysis. We provide direct experimental evidence that Aitken mode particles (particles with diameters ≲70 nm) significantly contribute to cloud condensation nuclei (CCN) or cloud droplet residuals, especially after the freeze-up of the sea ice in the transition toward fall. These Aitken mode particles were associated with air that spent more time over the pack ice, while size distributions dominated by accumulation mode particles (particles with diameters ≳70 nm) showed a stronger contribution of oceanic air and slightly different source regions. This was accompanied by changes in the average chemical composition of the accumulation mode aerosol with an increased relative contribution of organic material toward fall. Addition of aerosol mass due to aqueous-phase chemistry during in-cloud processing was probably small over the pack ice given the fact that we observed very similar particle size distributions in both the whole-air and cloud droplet residual data. These aerosol–cloud interaction observations provide valuable insight into the origin and physical and chemical properties of CCN over the pristine central Arctic Ocean.

The amount of ice versus supercooled water in clouds is important for their radiative properties and role in climate feedbacks. Hence, knowledge of the concentration of ice-nucleating particles (INPs) is needed. Generally, the concentrations of INPs are found to be very low in remote marine locations allowing cloud water to persist in a supercooled state. We had expected the concentrations of INPs at the North Pole to be very low given the distance from open ocean and terrestrial sources coupled with effective wet scavenging processes. Here we show that during summer 2018 (August and September) high concentrations of biological INPs (active at >−20°C) were sporadically present at the North Pole. In fact, INP concentrations were sometimes as high as those recorded at mid-latitude locations strongly impacted by highly active biological INPs, in strong contrast to the Southern Ocean. Furthermore, using a balloon borne sampler we demonstrated that INP concentrations were often different at the surface versus higher in the boundary layer where clouds form. Back trajectory analysis suggests strong sources of INPs near the Russian coast, possibly associated with wind-driven sea spray production, whereas the pack ice, open leads, and the marginal ice zone were not sources of highly active INPs. These findings suggest that primary ice production, and therefore Arctic climate, is sensitive to transport from locations such as the Russian coast that are already experiencing marked climate change.

We present an annual characterization of low-level jets (LLJs) over the Arctic Ocean using wind profiles from radiosondes launched during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition, from October 2019 through September 2020. Our results show LLJs to be common throughout the entire year, with a mean annual frequency of occurrence of more than 40%, a typical height below 400 m, peaking at 120–180 m, and speed between 6 and 14 m s–1. Jet characteristics show some seasonal variability: During winter and the freeze-up period, they are more common and faster, with an average occurrence of 55% and speeds of 8–16 m s–1, while in summer and the transition period, they have a mean occurrence of 46% and speeds of 6–10 m s–1. They have a similar height all year, with a peak between 120 and 180 m. The ERA5 reanalysis shows a similar frequency of occurrence, but a 75 m high bias in altitude, and a small, 0.28 m s–1, slow bias in speed. The height biases are greater in the transition period, more than 130 m, while the bias in speed is similar all year. Examining jets in ERA5 over the full year and whole Arctic Ocean, we find that the frequency of occurrence depends strongly on both the season and the distance to the sea-ice edge.

With the Arctic rapidly changing, the needs to observe, understand, and model the changes are essential. To support these needs, an annual cycle of observations of atmospheric properties, processes, and interactions were made while drifting with the sea ice across the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. An international team designed and implemented the comprehensive program to document and characterize all aspects of the Arctic atmospheric system in unprecedented detail, using a variety of approaches, and across multiple scales. These measurements were coordinated with other observational teams to explore cross- cutting and coupled interactions with the Arctic Ocean, sea ice, and ecosystem through a variety of physical and biogeochemical processes. This overview outlines the breadth and complexity of the atmospheric research program, which was organized into 4 subgroups: atmospheric state, clouds and precipitation, gases and aerosols, and energy budgets. Atmospheric variability over the annual cycle revealed important influences from a persistent large-scale winter circulation pattern, leading to some storms with pressure and winds that were outside the interquartile range of past conditions suggested by long-term reanalysis. Similarly, the MOSAiC location was warmer and wetter in summer than the reanalysis climatology, in part due to its close proximity to the sea ice edge.The comprehensiveness of the observational program for characterizing and analyzing atmospheric phenomena is demonstrated via a winter case study examining air mass transitions and a summer case study examining vertical atmospheric evolution. Overall, the MOSAiC atmospheric program successfully met its objectives and was the most comprehensive atmospheric measurement program to date conducted over the Arctic sea ice. The obtained data will support a broad range of coupled-system

With the Arctic rapidly changing, the needs to observe, understand, and model the changes are essential. To support these needs, an annual cycle of observations of atmospheric properties, processes, and interactions were made while drifting with the sea ice across the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. An international team designed and implemented the comprehensive program to document and characterize all aspects of the Arctic atmospheric system in unprecedented detail, using a variety of approaches, and across multiple scales. These measurements were coordinated with other observational teams to explore crosscutting and coupled interactions with the Arctic Ocean, sea ice, and ecosystem through a variety of physical and biogeochemical processes. This overview outlines the breadth and complexity of the atmospheric research program, which was organized into 4 subgroups: atmospheric state, clouds and precipitation, gases and aerosols, and energy budgets. Atmospheric variability over the annual cycle revealed important influences from a persistent large-scale winter circulation pattern, leading to some storms with pressure and winds that were outside the interquartile range of past conditions suggested by long-term reanalysis. Similarly, the MOSAiC location was warmer and wetter in summer than the reanalysis climatology, in part due to its close proximity to the sea ice edge. The comprehensiveness of the observational program for characterizing and analyzing atmospheric phenomena is demonstrated via a winter case study examining air mass transitions and a summer case study examining vertical atmospheric evolution. Overall, the MOSAiC atmospheric program successfully met its objectives and was the most comprehensive atmospheric measurement program to date conducted over the Arctic sea ice. The obtained data will support a broad range of coupled-system s