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Cloud phase defines many cloud properties and determines the ways in which clouds interact with other aspects of the climate system. The occurrence fraction and characteristics of clouds distinguished by their phase are examined at three Arctic atmospheric observatories. Each observatory has the basic suite of instruments that are necessary to identify cloud phase, namely, cloud radar, depolarization lidar, microwave radiometer, and twice-daily radiosondes. At these observatories, ice clouds are more prevalent than mixed-phase clouds, which are more prevalent than liquid-only clouds. Cloud ice occurs 60%–70% of the time over a typical year, at heights up to 11 km. Liquid water occurs at temperatures above −40°C and is increasingly more likely as temperatures increase. Within the temperature range from −40° to −30°C, liquid water occurs in 3%–5% of the observed cloudiness. Liquid water is found higher in the atmosphere when accompanied by ice; there are few liquid-only clouds above 3 km, although liquid in mixed-phase clouds occurs at heights up to about 7–8 km. Regardless of temperature or height, liquid water occurs 56% of the time at Barrow, Alaska, and at a western Arctic Ocean site, but only 32% of the time at Eureka, Nunavut, Canada. This significant difference in liquid occurrence is due to a relatively dry lower troposphere during summer at Eureka in addition to warmer cloud temperatures with more persistent liquid water layers at the far western locations. The most persistent liquid clouds at these locations occur continuously for more than 70 h in the autumnand more than 30 h in the winter. Ice clouds persist for much longer than do liquid clouds at Eureka and occur more frequently in the winter season, leading to a total cloud occurrence annual cycle that is distinct from the other observatories.

Low-level mixed-phase clouds (MPCs) are common in the Arctic. Both local and large-scale phenomena influence the properties and lifetime of MPCs. Arctic fjords are characterized by complex terrain and large variations in surface properties. Yet, not many studies have investigated the impact of local boundary layer dynamics and their relative importance on MPCs in the fjord environment. In this work, we used a combination of ground-based remote sensing instruments, surface meteorological observations, radiosoundings, and reanalysis data to study persistent low-level MPCs at Ny-Ålesund, Svalbard, for a 2.5-year period. Methods to identify the cloud regime, surface coupling, and regional and local wind patterns were developed. We found that persistent low-level MPCs were most common with westerly winds, and the westerly clouds had a higher mean liquid (42 g m−2) and ice water path (16 g m−2) compared to those with easterly winds. The increased height and rarity of persistent MPCs with easterly free-tropospheric winds suggest the island and its orography have an influence on the studied clouds. Seasonal variation in the liquid water path was found to be minimal, although the occurrence of persistent MPCs, their height, and their ice water path all showed notable seasonal dependency. Most of the studied MPCs were decoupled from the surface (63 %–82 % of the time). The coupled clouds had 41 % higher liquid water path than the fully decoupled ones. Local winds in the fjord were related to the frequency of surface coupling, and we propose that katabatic winds from the glaciers in the vicinity of the station may cause clouds to decouple. We concluded that while the regional to large-scale wind direction was important for the persistent MPC occurrence and properties, the local-scale phenomena (local wind patterns in the fjord and surface coupling) also had an influence. Moreover, this suggests that local boundary layer processes should be described in models in order to present low-level MPC properties accurately.

The Arctic sea‐ice surface energy budget is characterized by two radiative states driven by the presence or absence of opaque clouds that warm the surface. Ground‐based observations from the 1998 SHEBA campaign collected over sea‐ice suggested that the two radiative states correspond to distinct surface pressure values. Using satellite data, we search for such relationship over 13 years across all sea‐ice‐covered Arctic basins in winter and spring when radiative states influence sea‐ice growth and can trigger early melt. We find a consistent, statistically significant pressure influence across basins, with higher surface pressure associated with the transmissive state and lower pressure with the opaque state. The pressure offset is weaker (∼4hPa)$(\\sim 4hPa)$than the SHEBA‐based estimate (∼15hPa)$(\\sim 15hPa)$ , likely caused by an anomalous circulation during spring 1998. These results indicate that surface pressure weakly but systematically modulates radiative‐state occurrence, helping to explain small regional differences in surface cloud radiative warming.

Using 13 years of space-based lidar cloud observations over the Arctic sea-ice, we show that the low cloud cover increases from 34 % to 71 % between 7 April and 7 May, mainly due to the increase of liquid-containing clouds below 1 km altitude. This study assesses the relative roles of moisture transport from the mid-latitudes and the spring warming driven by the seasonal increase of solar radiation in setting this transition called “spring cloud onset”. We highlight that daily moisture mass advected over the sea-ice in March represents 14 % of the moisture mass already present, exceeding the increase of atmospheric moisture carrying capacity due to the rising temperatures. Consistently, MOSAiC campaign data suggest that moisture is not limiting the spring cloud onset as most of the radiosoundings in spring contained layers saturated with respect to ice (96 %). Instead, we identify a temperature dependency in the ratio of ice to liquid-containing layer occurrence, confirmed by ground-based MOSAiC lidar. While the proportion of ice layers over all atmospheric layers is poorly dependent on temperature below 0 °C, the occurrence of liquid-containing layers increase steeply between −20 and −10 °C. As a result, March lower troposphere temperatures (−20 °C) favor more ice clouds, while May (−13 °C) favors more liquid-containing clouds. Overall, our results suggest that moisture availability is not the limiting factor and that spring warming induced by solar radiation sets the transition toward more liquid-containing low clouds.

Pulses of warm and moist air from lower latitudes provide energy to the Arctic and form its main energy source outside of the summer months. These pulses can cause substantial surface warming and trigger ice melt. Air-mass transport in the opposite direction, away from the Arctic, leads to cold-air outbreaks. The outbreaks are often associated with cold extremes over continents, and extreme surface heat fluxes and occasional polar lows over oceans. Air masses advected across the strong Arctic-to-mid-latitude temperature gradient are rapidly transformed into colder and dryer or warmer and moister air masses by clouds, radiative and turbulent processes, particularly in the boundary layer. Phase changes from liquid to ice within boundary-layer clouds are critical in these air-mass transformations. The presence of liquid water determines the radiative effects of these clouds, whereas the presence of ice is crucial for subsequent cloud decay or dissipation, processes that are poorly represented in weather and climate models. We argue that a better understanding of how air masses are transformed on their way into and out of the Arctic is essential for improved prediction of weather and climate in the Arctic and mid-latitudes. Observational and modelling exercises should take an air-mass-following Lagrangian approach to attain these goals.

The Arctic is warming faster than anywhere else on Earth, prompting glacial melt, permafrost thaw, and sea ice decline. These severe consequences induce feedbacks that contribute to amplified warming, affecting weather and climate globally. Aerosols and clouds play a critical role in regulating radiation reaching the Arctic surface. However, the magnitude of their effects is not adequately quantified, especially in the central Arctic where they impact the energy balance over the sea ice. Specifically, aerosols called ice nucleating particles (INPs) remain understudied yet are necessary for cloud ice production and subsequent changes in cloud lifetime, radiative effects, and precipitation. Here, we report observations of INPs in the central Arctic over a full year, spanning the entire sea ice growth and decline cycle. Further, these observations are size-resolved, affording valuable information on INP sources. Our results reveal a strong seasonality of INPs, with lower concentrations in the winter and spring controlled by transport from lower latitudes, to enhanced concentrations of INPs during the summer melt, likely from marine biological production in local open waters. This comprehensive characterization of INPs will ultimately help inform cloud parameterizations in models of all scales. The Arctic is changing faster than anywhere else on Earth. Interactions between clouds and aerosols play a role in these changes. We report how the quantities and origins of aerosols that affect cloud ice formation change over a full sea ice cycle

Frequency and intensity of warm and moist air-mass intrusions into the Arctic have increased over the past decades and have been related to sea ice melt. During our year-long expedition in the remote central Arctic Ocean, a record-breaking increase in temperature, moisture and downwelling-longwave radiation was observed in mid-April 2020, during an air-mass intrusion carrying air pollutants from northern Eurasia. The two-day intrusion, caused drastic changes in the aerosol size distribution, chemical composition and particle hygroscopicity. Here we show how the intrusion transformed the Arctic from a remote low-particle environment to an area comparable to a central-European urban setting. Additionally, the intrusion resulted in an explosive increase in cloud condensation nuclei, which can have direct effects on Arctic clouds’ radiation, their precipitation patterns, and their lifetime. Thus, unless prompt actions to significantly reduce emissions in the source regions are taken, such intrusion events are expected to continue to affect the Arctic climate. Warm and moist air-mass intrusions into the Arctic are more frequent than the past decades. Here, the authors show that warm air mass intrusions from northern Eurasia inject record amounts of aerosols into the central Arctic Ocean strongly impacting atmospheric chemistry and cloud properties.

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

The Arctic warms nearly four times faster than the global average, and aerosols play an increasingly important role in Arctic climate change. In the Arctic, sea salt is a major aerosol component in terms of mass concentration during winter and spring. However, the mechanisms of sea salt aerosol production remain unclear. Sea salt aerosols are typically thought to be relatively large in size but low in number concentration, implying that their influence on cloud condensation nuclei population and cloud properties is generally minor. Here we present observational evidence of abundant sea salt aerosol production from blowing snow in the central Arctic. Blowing snow was observed more than 20% of the time from November to April. The sublimation of blowing snow generates high concentrations of fine-mode sea salt aerosol (diameter below 300 nm), enhancing cloud condensation nuclei concentrations up to tenfold above background levels. Using a global chemical transport model, we estimate that from November to April north of 70° N, sea salt aerosol produced from blowing snow accounts for about 27.6% of the total particle number, and the sea salt aerosol increases the longwave emissivity of clouds, leading to a calculated surface warming of +2.30 W m−2 under cloudy sky conditions.Fine sea salt aerosols produced by blowing snow in the Arctic impact cloud properties and warm the surface, according to observations from the MOSAiC expedition.

During the Arctic Clouds in Summer Experiment (ACSE) in summer 2014 a weeklong period of warm-air advection over melting sea ice, with the formation of a strong surface temperature inversion and dense fog, was observed. Based on an analysis of the surface energy budget, we formulated the hypothesis that, because of the airmass transformation, additional surface heating occurs during warm-air intrusions in a zone near the ice edge. To test this hypothesis, we explore all cases with surface inversions occurring during ACSE and then characterize the inversions in detail. We find that they always occur with advection from the south and are associated with subsidence. Analyzing only inversion cases over sea ice, we find two categories: one with increasing moisture in the inversion and one with constant or decreasing moisture with height. During surface inversions with increasing moisture with height, an extra 10–25 W m−2 of surface heating was observed, compared to cases without surface inversions; the surface turbulent heat flux was the largest single term. Cases with less moisture in the inversion were often cloud free and the extra solar radiation plus the turbulent surface heat flux caused by the inversion was roughly balanced by the loss of net longwave radiation.