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The structure and evolution of the atmospheric boundary layer (ABL) under clear-sky fair weather conditions over mountainous terrain is dominated by the diurnal cycle of the surface energy balance and thus strongly depends on surface snow cover. We use data from three passive ground-based infrared spectrometers deployed in the East River Valley in Colorado’s Rocky Mountains to investigate the response of the thermal ABL structure to changes in surface energy balance during the seasonal transition from snow-free to snow-covered ground. Temperature profiles were retrieved from the infrared radiances using the optimal estimation physical retrieval TROPoe. A nocturnal surface inversion formed in the valley during clear-sky days, which was subsequently mixed out during daytime with the development of a convective boundary layer during snow-free periods. When the ground was snow covered, a very shallow convective boundary layer formed, above which the inversion persisted through the daytime hours. We compare these observations to NOAA’s operational High-Resolution-Rapid-Refresh (HRRR) model and find large warm biases on clear-sky days resulting from the model’s inability to form strong nocturnal inversions and to maintain the stable stratification in the valley during daytime when there was snow on the ground. A possible explanation for these model shortcomings is the influence of the model’s relatively coarse horizontal grid spacing (3 km) and its impact on the model’s ability to represent well-developed thermally driven flows, specifically nighttime drainage flows.

Mixed-phase clouds are ubiquitous in the Arctic. These clouds can persist for days and dissipate in a matter of hours. It is sometimes unknown what causes this sudden dissipation, but aerosol–cloud interactions may be involved. Arctic aerosol concentrations can be low enough to affect cloud formation and structure, and it has been hypothesized that, in some instances, concentrations can drop below some critical value needed to maintain a cloud. We use observations from a Department of Energy ARM site on the northern slope of Alaska at Oliktok Point (OLI), the Arctic Summer Cloud Ocean Study (ASCOS) field campaign in the high Arctic Ocean, and the Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit – Aerosol Cloud Experiment (ICECAPS-ACE) project at the NSF (National Science Foundation) Summit Station in Greenland (SMT) to identify one case per site where Arctic boundary layer clouds dissipated coincidentally with a decrease in surface aerosol concentrations. These cases are used to initialize idealized large eddy simulations (LESs) in which aerosol concentrations are held constant until, at a specified time, all aerosols are removed instantaneously – effectively creating an extreme case of aerosol-limited dissipation which represents the fastest a cloud could possibly dissipate via this process. These LESs are compared against the observed data to determine whether cases could, potentially, be dissipating due to insufficient aerosol. The OLI case's observed liquid water path (LWP) dissipated faster than its simulation, indicating that other processes are likely the primary drivers of the dissipation. The ASCOS and SMT observed LWP dissipated at similar rates to their respective simulations, suggesting that aerosol-limited dissipation may be occurring in these instances. We also find that the microphysical response to this extreme aerosol forcing depends greatly on the specific case being simulated. Cases with drizzling liquid layers are simulated to dissipate by accelerating precipitation when aerosol is removed while the case with a non-drizzling liquid layer dissipates quickly, possibly glaciating via the Wegener–Bergeron–Findeisen (WBF) process. The non-drizzling case is also more sensitive to ice-nucleating particle (INP) concentrations than the drizzling cases. Overall, the simulations suggest that aerosol-limited cloud dissipation in the Arctic is plausible and that there are at least two microphysical pathways by which aerosol-limited dissipation can occur.

Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud–radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.

Clear-sky periods across the high latitudes have profound impacts on the surface energy budget and lower atmospheric stratification; however an understanding of the atmospheric processes leading to low-level cloud dissipation and formation events is limited. A method to identify clear periods at Utqiaġvik (formerly Barrow), Alaska, during a 5-year period (2014–2018) is developed. A suite of remote sensing and in situ measurements from the high-latitude observatory are analyzed; we focus on comparing and contrasting atmospheric properties during low-level (below 2 km) cloud dissipation and formation events to understand the processes controlling clear-sky periods. Vertical profiles of lidar backscatter suggest that aerosol presence across the lower atmosphere is relatively invariant during the periods bookending clear conditions, which suggests that a sparsity of aerosol is not frequently a cause for cloud dissipation on the North Slope of Alaska. Further, meteorological analysis indicates two active processes ongoing that appear to support the formation of low clouds after a clear-sky period: namely, horizontal advection, which was dominant in winter and early spring, and quiescent air mass modification, which was dominant in the summer. During summer, the dominant mode of cloud formation is a low cloud or fog layer developing near the surface. This low cloud formation is driven largely by air mass modification under relatively quiescent synoptic conditions. Near-surface aerosol particles concentrations changed by a factor of 2 around summer formation events. Thermodynamic adjustment and increased aerosol presence under quiescent atmospheric conditions are hypothesized as important mechanisms for fog formation.

During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, USA, active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include radar wind profilers, microwave radiometers, atmospheric emitted radiance interferometers, ceilometers, high spectral resolution lidars, Doppler lidars, and collaborative lower-atmospheric mobile profiling systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary layer depth estimations is also investigated. We found that while all instruments are overall able to provide reasonable boundary layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, radar wind profilers perform well during cloud-free conditions, and microwave radiometers and atmospheric emitted radiance interferometers have a very good agreement during all conditions but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from ceilometers and high spectral resolution lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the collaborative lower atmospheric mobile profiling systems can be constricted to a limited height range in low-aerosol conditions.

Aerosol concentrations in the Arctic can get quite low, and recent work has shown that low aerosol concentrations could affect Arctic cloud formation and structure. Arctic mixed-phase clouds have been observed to persist for days at a time and dissipate suddenly, and it has been hypothesized that some instances of cloud dissipation are caused by aerosol concentrations falling below some critical value required to sustain the cloud.We found three cases - from a Department of Energy ARM site on the north slope of Alaska, the ICECAPS-ACE project at the NSF Summit Station in Greenland, and the ASCOS field campaign - where clouds are observed to dissipate coincidentally with a drop of surface aerosol concentration. These cases were used to initialize idealized large eddy simulations in which aerosol concentrations were held constant at observed values before being immediately removed. The resulting simulations are considered to be the fastest possible aerosol-limited dissipation. Comparing simulated liquid water path (LWP) to observations, we find that the ARM case dissipated much faster than our simulations, indicating that the observed dissipation was not driven by lack of available aerosol. The Summit Station and ASCOS simulations dissipate (with respect to LWP) at approximately the same rate as observations, which suggests aerosol-limited dissipation may indeed be occurring in these cases.Furthermore, we find that the microphysical response to aerosol removal varies between the specific cases we simulate. Simulations where the cloud produces constant liquid drizzle dissipate, within 3-4 hours, via an acceleration of precipitation once aerosols are removed. Conversely, the case with a non-precipitating liquid layer dissipates more quickly (< 2 hours), possibly by glaciation via the Wegener-Bergeron-Findeisen (WBF) in which ice grows and precipitates at the expense of liquid droplets. The simulations suggest that aerosol-limited dissipation in the Arctic is plausible, and we present two microphysical pathways by which this dissipation can occur.

Mixed-phase clouds are ubiquitous in the Arctic. These clouds can persist for days and dissipate in a matter of hours. It is sometimes unknown what causes this sudden dissipation, but aerosol-cloud interactions may be involved. Arctic aerosol concentrations can be low enough to affect cloud formation and structure, and it has been hypothesized that, in some instances, concentrations can drop below some critical value needed to maintain a cloud. We use observations from a Department of Energy ARM site on the north slope of Alaska at Oliktok Point (OLI), the ASCOS field campaign in the high Arctic Ocean, and the ICECAPS-ACE project at the NSF Summit Station in Greenland (SMT) to identify one case per site where Arctic boundary-layer clouds dissipated coincidentally with a decrease in surface aerosol concentrations. These cases are used to initialize idealized large eddy simulations (LES) in which aerosol concentrations are held constant until, at a specified time, all aerosols are removed instantaneously – effectively creating an extreme case of aerosol-limited dissipation which represents the fastest a cloud could possibly dissipate via this process. These LES simulations are compared against the observed data to determine whether cases could, potentially, be dissipating due to insufficient aerosol. The OLI case’s observed liquid water path (LWP) dissipated faster than its simulation, indicating that other processes are likely the primary driver of the dissipation. The ASCOS and SMT observed LWP dissipated at similar rates to their respective simulations, suggesting that aerosol-limited dissipation may be occurring in these instances. We also find that the microphysical response to this extreme aerosol forcing depends greatly on the specific case being simulated. Cases with drizzling liquid layers are simulated to dissipate by accelerating precipitation when aerosol is removed while the case with a non-drizzling liquid layer dissipates quickly, possibly glaciating via the Wegener-Bergeron-Findeisen (WBF) process. The non-drizzling case is also more sensitive to INP concentrations than the drizzling cases. Overall, the simulations suggest that aerosol-limited cloud dissipation in the Arctic is plausible and that there are at least two microphysical pathways by which aerosol-limited dissipation can occur.

During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, U.S.A., active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include Radar Wind Profilers, Microwave Radiometers, Atmospheric Emitted Radiance Interferometers, Ceilometers, High Spectral Resolution Lidars, Doppler Lidars, and Collaborative Lower Atmospheric Modelling Profiling Systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary-layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary-layer depth is also investigated. We found that while overall all instruments are able to provide reasonable boundary-layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, Radar Wind Profilers perform well during cloud free conditions, and Microwave Radiometers and Atmospheric Emitted Radiance Interferometers have a very good agreement during all conditions, but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from Ceilometers and High Spectral Resolution Lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the Collaborative Lower Atmospheric Modelling Profiling Systems can be constricted to a limited height range in low aerosol conditions.

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−2of 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.

Horizontal moisture transport has a manifold role in the Arctic climate system as it distributes atmospheric water vapour and thereby shapes the radiative and hydrological conditions. Moisture tran ...

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.

A coordinated regional climate model (RCM) evaluation and intercomparison project based on observations from a July–October 2014 trans‐Arctic Ocean field experiment (ACSE‐Arctic Clouds during Summer Experiment) is presented. Six state‐of‐the‐art RCMs were constrained with common reanalysis lateral boundary forcing and upper troposphere nudging techniques to explore how the RCMs represented the evolution of the surface energy budget (SEB) components and their relation to cloud properties. We find that the main reasons for the modeled differences in the SEB components are a direct consequence of the RCM treatment of cloud and cloud‐radiative interactions. The RCMs could be separated into groups by their overestimation or underestimation of cloud liquid. While radiative and turbulent heat flux errors were relatively large, they often invoke compensating errors. In addition, having the surface sea‐ice concentrations constrained by the reanalysis or satellite observations limited how errors in the modeled radiative fluxes could affect the SEB and ultimately the surface evolution and its coupling with lower tropospheric mixing and cloud properties. Many of these results are consistent with RCM biases reported in studies over a decade ago. One of the six models was a fully coupled ocean‐ice‐atmosphere model. Despite the biases in overestimating cloud liquid, and associated SEB errors due to too optically thick clouds, its simulations were useful in understanding how the fully coupled system is forced by, and responds to, the SEB evolution. Moving forward, we suggest that development of RCM studies need to consider the fully coupled climate system.

The Arctic climate system is complex and clouds are one of its least understood components. Since cloud processes occur from micrometer to synoptic scales, their couplings with the other components of the Arctic climate system and their overall role in modulating the energy budget at different spatio-temporal scales is challenging to quantify. The in-situ measurements, as limited in space and time as they are, still reveal the complex nature of cloud microphysical and thermodynamical processes in the Arctic. However, the synoptic scale variability of cloud systems can only be obtained from the satellite observations. A considerable progress has been made in the last decade in understanding cloud processes in the Arctic due to the availability of valuable data from the multiple campaigns in the Central Arctic and due to the advances in the satellite remote sensing. This chapter provides an overview of this progress.

The second edition of the satellite-derived climate data record CLARA ("The CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data" – second edition denoted CLARA-A2) is described. The data record covers the 34-year period from 1982 until 2015 and consists of cloud, surface albedo and surface radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting, operational meteorological satellites. The data record is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project as part of the operational ground segment. Its upgraded content and methodology improvements since edition 1 are described in detail as well as some major validation results. Some of the main improvements of the data record come from a major effort in cleaning and homogenising the basic AVHRR level 1 radiance record and a systematic use of CALIPSO-CALIOP cloud information for development and validation purposes. Examples of applications studying decadal changes in Polar Summer surface albedo and cloud conditions, as well as global cloud redistribution patterns, are provided.

While the atmospheric greenhouse effect always results in a warming at the surface, outgoing longwave radiation (OLR) to space always represents a cooling. During events of heat and moisture advection into the Arctic, increases in tropospheric temperature and moisture impact clouds, in turn impacting longwave (LW) radiation. State-of-the-art satellite measurements and atmospheric reanalysis consistently reveal an enhancement of summer Arctic monthly OLR cooling ranging 1.5 – 4 W m-2 during months with anomalously high thermodynamic advection. This cooling anomaly is found to be of the same magnitude, or slightly larger than, associated downwelling LW surface warming anomalies. We identify a relationship between large-scale circulation variability and changing cloud properties permitting LW radiation at both the surface and TOA to respond to variability in atmospheric thermodynamics. Driven by anomalous advection of warm air, the corresponding enhanced OLR cooling signal on monthly timescales represents an important buffer to regional Arctic warming.

The Arctic Clouds in Summer Experiment (ACSE) was conducted during summer and early autumn 2014, providing a detailed view of the seasonal transition from ice melt into freeze-up. Measurements were taken over both ice-free and ice-covered surfaces near the ice edge, offering insight into the role of the surface state in shaping the atmospheric conditions. The initiation of the autumn freeze-up was related to a change in air mass, rather than to changes in solar radiation alone; the lower atmosphere cooled abruptly, leading to a surface heat loss. During melt season, strong surface inversions persisted over the ice, while elevated inversions were more frequent over open water. These differences disappeared during autumn freeze-up, when elevated inversions persisted over both ice-free and ice-covered conditions. These results are in contrast to previous studies that found a well-mixed boundary layer persisting in summer and an increased frequency of surface-based inversions in autumn, suggesting that knowledge derived from measurements taken within the pan-Arctic area and on the central ice pack does not necessarily apply closer to the ice edge. This study offers an insight into the atmospheric processes that occur during a crucial period of the year; understanding and accurately modeling these processes is essential for the improvement of ice-extent predictions and future Arctic climate projections.

Arctic sea ice is declining rapidly and its annual ice extent minima reached record lows twice during the last decade. Large environmental and socioeconomic implications related to sea ice reduction in a warming world necessitate realistic simulations of the Arctic climate system, not least to formulate relevant environmental policies on an international scale. However, despite considerable progress in the last few decades, future climate projections from numerical models still exhibit the largest uncertainties over the polar regions. The lack of sufficient observations of essential climate variables is partly to blame for the poor representation of key atmospheric processes, and their coupling to the surface, in climate models. Observations from the hyperspectral Atmospheric Infrared Sounder (AIRS) instrument on board the National Aeronautics and Space Administration (NASA)’s Aqua satellite are contributing toward improved understanding of the vertical structure of the atmosphere over the poles since 2002, including the lower troposphere. This part of the atmosphere is especially important in the Arctic, as it directly impacts sea ice and its short-term variability. Although in situ measurements provide invaluable ground truth, they are spatially and temporally inhomogeneous and sporadic over the Arctic. A growing number of studies are exploiting AIRS data to investigate the thermodynamic structure of the Arctic atmosphere, with applications ranging from understanding processes to deriving climatologies—all of which are also useful to test and improve parameterizations in climate models. As the AIRS data record now extends more than a decade, a select few of many such noteworthy applications of AIRS data over this challenging and rapidly changing landscape are highlighted here.

Mixed-phase clouds are an integral part of the Arctic climate system, for precipitation and for their interactions with radiation and thermodynamics. Mixed-phase processes are often poorly represented in global models and many use an empirically based diagnostic partition between the liquid and ice phases that is dependent solely on temperature. However, increasingly more complex microphysical parametrizations are being implemented allowing a more physical representation of mixed-phase clouds. This study uses in situ observations from the Arctic Summer Cloud Ocean Study (ASCOS) field campaign in the central Arctic to assess the impact of a change from a diagnostic to a prognostic parametrization of mixed-phase clouds and increased vertical resolution in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The newer cloud scheme improves the representation of the vertical structure of mixed-phase clouds, with supercooled liquid water at cloud top and ice precipitating below, improved further with higher vertical resolution. Increased supercooled liquid water and decreased ice content are both in closer agreement with observations. However, these changes do not result in any substantial improvement in surface radiation, and a warm and moist bias in the lowest part of the atmosphere remains. Both schemes also fail to capture the transitions from overcast to cloud-free conditions. Moreover, whereas the observed cloud layer is frequently decoupled from the surface, the modelled clouds remain coupled to the surface most of the time. The changes implemented to the cloud scheme are an important step forward in improving the representation of Arctic clouds, but improvements in other aspects such as boundary-layer turbulence, cloud radiative properties, sensitivity to low aerosol concentrations and representation of the sea-ice surface may also need to be addressed.

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 similar to 15Wm(-2) for a week. Satellite images before and after the episode show sea-ice concentrations decreasing from > 90% to similar 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.

This study investigates aerosol particle transport from the free troposphere to the boundary layer in the summertime high Arctic. Observations from the Arctic Summer Cloud Ocean Study field campaign show several occurrences of high aerosol particle concentrations above the boundary layer top. Large-eddy simulations suggest that when these enhanced aerosol concentrations are present, they can be an important source of aerosol particles for the boundary layer. Most particles are transported to the boundary layer by entrainment. However, it is found that mixed-phase stratocumulus clouds, which often extend into the inversion layer, also can mediate the transport of particles into the boundary layer by activation at cloud top and evaporation below cloud base. Finally, the simulations also suggest that aerosol properties at the surface sometimes may not be good indicators of aerosol properties in the cloud layer.

The Arctic climate is changing; temperature changes in the Arctic are greater than at mid-latitudes, and changing atmospheric conditions influence Arctic mixed-phase clouds, which are important for the Arctic surface energy budget. These low-level clouds are frequently observed across the Arctic. They impact the turbulent and radiative heating of the open water, snow and sea-ice covered surfaces, and are influencing the boundary layer structure. Therefore the processes that affect mixed-phase cloud life cycles are extremely important, yet relatively poorly understood. In this study, we present sensitivity studies using semi-idealized large eddy simulations (LES) to identify processes contributing to the dissipation of Arctic mixed-phase clouds. We found that one potential main contributor to the dissipation of an observed Arctic mixed-phase cloud, during the Arctic Summer Cloud Ocean Study (ASCOS) field campaign, was a low cloud droplet number concentration (CDNC) of about 2 cm−3. Introducing a high ice crystal concentration of 10 l−1 also resulted in cloud dissipation, but such high ice crystal concentrations were deemed unlikely for the present case. Sensitivity studies simulating the advection of dry-air above the boundary layer inversion, as well as a modest increase in ice crystal concentration of 1 l−1, did not lead to cloud dissipation. As a requirement for small droplet numbers, pristine aerosol conditions in the Arctic environment are therefore considered an important factor determining the lifetime of Arctic mixed-phase clouds.

Observations of cloud properties and thermodynamics from two Arctic locations, Barrow, Alaska, and Surface Heat Budget of the Arctic (SHEBA), are examined. A comparison of in-cloud thermodynamic mixing characteristics for low-level, single-layer clouds from nearly a decade of data at Barrow and one full annual cycle over the sea ice at SHEBA is performed. These cloud types occur relatively frequently, evident in 27%–30% of all cloudy cases. To understand the role of liquid water path (LWP), or lack thereof, on static in-cloud mixing, cloud layers are separated into optically thin and optically thick LWP subclasses. Clouds with larger LWPs tend to have a deeper in-cloud mixed layer relative to optically thinner clouds. However, both cloud LWP subclasses are frequently characterized by an in-cloud stable layer above the mixed layer top. The depth of the stable layer generally correlates with an increased temperature gradient across the layer. This layer often contains a specific humidity inversion, but it is more frequently present when cloud LWP is optically thinner (LWP < 50 g m−2). It is suggested that horizontal thermodynamic advection plays a key role modifying the vertical extent of in-cloud mixing and likewise the depth of in-cloud stable layers. Furthermore, longwave atmospheric opacity above the cloud top is generally enhanced during cases with optically thinner clouds. Thermodynamic advection, cloud condensate distribution within the stable layer, and enhanced atmospheric radiation above the cloud are found to introduce a thermodynamic–radiative feedback that potentially modifies the extent of LWP and subsequent in-cloud mixing.

The vertical structure of Arctic low-level clouds and Arctic boundary layer is studied, using observations from ASCOS (Arctic Summer Cloud Ocean Study), in the central Arctic, in late summer 2008. Two general types of cloud structures are examined: the "neutrally stratified" and "stably stratified" clouds. Neutrally stratified are mixed-phase clouds where radiative-cooling near cloud top produces turbulence that generates a cloud-driven mixed layer. When this layer mixes with the surface-generated turbulence, the cloud layer is coupled to the surface, whereas when such an interaction does not occur, it remains decoupled; the latter state is most frequently observed. The decoupled clouds are usually higher compared to the coupled; differences in thickness or cloud water properties between the two cases are however not found. The surface fluxes are also very similar for both states. The decoupled clouds exhibit a bimodal thermodynamic structure, depending on the depth of the sub-cloud mixed layer (SCML): clouds with shallower SCMLs are disconnected from the surface by weak inversions, whereas those that lay over a deeper SCML are associated with stronger inversions at the decoupling height. Neutrally stratified clouds generally precipitate; the evaporation/sublimation of precipitation often enhances the decoupling state. Finally, stably stratified clouds are usually lower, geometrically and optically thinner, non-precipitating liquid-water clouds, not containing enough liquid to drive efficient mixing through cloud-top cooling.

Over the Arctic Ocean, little is known on cloud-generated buoyant overturning vertical motions within mixed-phase stratocumulus clouds. Characteristics of such motions are important for understanding the diabatic processes associated with the vertical motions, the lifetime of the cloud layer and its micro- and macrophysical characteristics. In this study, we exploit a suite of surface-based remote sensors over the high-Arctic sea ice during a weeklong period of persistent stratocumulus in August 2008 to derive the in-cloud vertical motion characteristics. In-cloud vertical velocity skewness and variance profiles are found to be strikingly different from observations within lower-latitude stratocumulus, suggesting these Arctic mixed-phase clouds interact differently with the atmospheric thermodynamics (cloud tops extending above a stable temperature inversion base) and with a different coupling state between surface and cloud. We find evidence of cloud-generated vertical mixing below cloud base, regardless of surface–cloud coupling state, although a decoupled surface–cloud state occurred most frequently. Detailed case studies are examined, focusing on three levels within the cloud layer, where wavelet and power spectral analyses are applied to characterize the dominant temporal and horizontal scales associated with cloud-generated vertical motions. In general, we find a positively correlated vertical motion signal amongst vertical levels within the cloud and across the full cloud layer depth. The coherency is dependent upon other non-cloud controlled factors, such as larger, mesoscale weather passages and radiative shielding of low-level stratocumulus by one or more cloud layers above. Despite the coherency in vertical velocity across the cloud, the velocity variances were always weaker near cloud top, relative to cloud middle and base. Taken in combination with the skewness, variance and thermodynamic profile characteristics, we observe vertical motions near cloud top that behave differently than those from lower within the cloud layer. Spectral analysis indicates peak cloud-generated w variance timescales slowed only modestly during decoupled cases relative to coupled; horizontal wavelengths only slightly increased when transitioning from coupling to decoupling. The similarities in scales suggests that perhaps the dominant forcing for all cases is generated from the cloud layer, and it is not the surface forcing that characterizes the time- and space scales of in-cloud vertical velocity variance. This points toward the resilient nature of Arctic mixed-phase clouds to persist when characterized by thermodynamic regimes unique to the Arctic.

Observations from the Arctic Summer Cloud Ocean Study (ASCOS), in the central Arctic sea-ice pack in late summer 2008, provide a detailed view of cloud–atmosphere–surface interactions and vertical mixing processes over the sea-ice environment. Measurements from a suite of ground-based remote sensors, near-surface meteorological and aerosol instruments, and profiles from radiosondes and a helicopter are combined to characterize a week-long period dominated by low-level, mixed-phase, stratocumulus clouds. Detailed case studies and statistical analyses are used to develop a conceptual model for the cloud and atmosphere structure and their interactions in this environment. Clouds were persistent during the period of study, having qualities that suggest they were sustained through a combination of advective influences and in-cloud processes, with little contribution from the surface. Radiative cooling near cloud top produced buoyancy-driven, turbulent eddies that contributed to cloud formation and created a cloud-driven mixed layer. The depth of this mixed layer was related to the amount of turbulence and condensed cloud water. Coupling of this cloud-driven mixed layer to the surface boundary layer was primarily determined by proximity. For 75% of the period of study, the primary stratocumulus cloud-driven mixed layer was decoupled from the surface and typically at a warmer potential temperature. Since the near-surface temperature was constrained by the ocean–ice mixture, warm temperatures aloft suggest that these air masses had not significantly interacted with the sea-ice surface. Instead, back-trajectory analyses suggest that these warm air masses advected into the central Arctic Basin from lower latitudes. Moisture and aerosol particles likely accompanied these air masses, providing necessary support for cloud formation. On the occasions when cloud–surface coupling did occur, back trajectories indicated that these air masses advected at low levels, while mixing processes kept the mixed layer in equilibrium with the near-surface environment. Rather than contributing buoyancy forcing for the mixed-layer dynamics, the surface instead simply appeared to respond to the mixed-layer processes aloft. Clouds in these cases often contained slightly higher condensed water amounts, potentially due to additional moisture sources from below.

The record sea ice minimum (SIM) extents observed during the summers of 2007 and 2012 in the Arctic are stark evidence of accelerated sea ice loss during the last decade. Improving our understanding of the Arctic atmosphere and accurate quantification of its characteristics becomes ever more crucial, not least to improve predictions of such extreme events in the future. In this context, the Atmospheric Infrared Sounder (AIRS) instrument onboard NASA's Aqua satellite provides crucial insights due to its ability to provide 3-D information on atmospheric thermodynamics. Here, we facilitate comparisons in the evolution of the thermodynamic state of the Arctic atmosphere during these two SIM events using a decade-long AIRS observational record (2003–2012). It is shown that the meteorological conditions during 2012 were not extreme, but three factors of preconditioning from winter through early summer played an important role in accelerating sea ice melt. First, the marginal sea ice zones along the central Eurasian and North Atlantic sectors remained warm throughout winter and early spring in 2012 preventing thicker ice build-up. Second, the circulation pattern favoured efficient sea ice transport out of the Arctic in the Atlantic sector during late spring and early summer in 2012 compared to 2007. Third, additional warming over the Canadian archipelago and southeast Beaufort Sea from May onward further contributed to accelerated sea ice melt. All these factors may have lead the already thin and declining sea ice cover to pass below the previous sea ice extent minimum of 2007. In sharp contrast to 2007, negative surface temperature anomalies and increased cloudiness were observed over the East Siberian and Chukchi seas in the summer of 2012. The results suggest that satellite-based monitoring of atmospheric preconditioning could be a critical source of information in predicting extreme sea ice melting events in the Arctic.

Cloud and thermodynamic characteristics from three Arctic observation sites are investigated to understand the collocation between low-level clouds and temperature inversions. A regime where cloud top was 100–200 m above the inversion base [cloud inside inversion (CII)] was frequently observed at central Arctic Ocean sites, while observations from Barrow, Alaska, indicate that cloud tops were more frequently constrained to inversion base height [cloud capped by inversion (CCI)]. Cloud base and top heights were lower, and temperature inversions were also stronger and deeper, during CII cases. Both cloud regimes were often decoupled from the surface except for CCI over Barrow. In-cloud lapse rates differ and suggest increased cloud-mixing potential for CII cases. Specific humidity inversions were collocated with temperature inversions for more than 60% of the CCI and more than 85% of the CII regimes. Horizontal advection of heat and moisture is hypothesized as an important process controlling thermodynamic structure and efficiency of cloud-generated motions. The portion of CII clouds above the inversion contains cloud radar signatures consistent with cloud droplets. The authors test the longwave radiative impact of cloud liquid above the inversion through hypothetical liquid water distributions. Optically thin CII clouds alter the effective cloud emission temperature and can lead to an increase in surface flux on the order of 1.5 W m−2 relative to the same cloud but whose top does not extend above the inversion base. The top of atmosphere impact is even larger, increasing outgoing longwave radiation up to 10 W m−2. These results suggest a potentially significant longwave radiative forcing via simple liquid redistributions for a distinctly dominant cloud regime over sea ice.

Data collected during July and August from the Arctic Ocean Experiment 2001 illustrated a common occurrence of specific-humidity (q) inversions, where moisture increases with height, coinciding with temperature inversions in the central Arctic boundary layer and lower troposphere. Low-level stratiform clouds and their relationship to temperature inversions are examined using radiosonde data and data from a suite of remote sensing instrumentation. Two low-level cloud regimes are identified: the canonical case of stratiform clouds, where the cloud tops are capped by the temperature inversion base (CCI—Clouds Capped by Inversion) and clouds where the cloud tops were found well inside the inversion (CII—Clouds Inside Inversion). The latter case was found to occur more than twice as frequently than the former. The characteristic of the temperature inversion is shown to have an influence on the cloud regime that was supported. Statistical analyses of the cloud regimes using remote sensing instruments suggest that CCI cases tend to be dominated by single-phase liquid cloud droplets; radiative cooling at the cloud top limits the vertical extent of such clouds to the inversion base height. The CII cases, on the other hand, display characteristics that can be divided into two situations—(1) clouds that only slightly penetrate the temperature inversion and exhibit a microphysical signal similar to CCI cases, or (2) clouds that extend higher into the inversion and show evidence of a mixed-phase cloud structure. An important interplay between the mixed-phase structure and an increased potential for turbulent mixing across the inversion base appears to support the lifetime of CII cases existing within the inversion layer.

Energy balance based glacier melt models require accurate estimates of incoming longwave radiation but direct measurements are often not available. Multi-year near-surface meteorological data from Storglaciären, Northern Sweden, were used to evaluate commonly used longwave radiation parameterizations in a glacier environment under clear-sky and all-sky conditions. Parameterizations depending solely on air temperature performed worse than those which include water vapor pressure. All models tended to overestimate incoming longwave radiation during periods of low longwave radiation, while incoming longwave was underestimated when radiation was high. Under all-sky conditions root mean square error (RMSE) and mean bias error (MBE) were 17 to 20 W m−2 and −5 to 1 W m−2, respectively. Two attempts were made to circumvent the need of cloud cover data. First cloud fraction was parameterized as a function of the ratio, τ, of measured incoming shortwave radiation and calculated top of atmosphere radiation. Second, τ was related directly to the cloud factor (i.e. the increase in sky emissivity due to clouds). Despite large scatter between τ and both cloud fraction and the cloud factor, resulting calculations of hourly incoming longwave radiation for both approaches were only slightly more variable with RMSE roughly 3 W m−2 larger compared to using cloud observations as input. This is promising for longwave radiation modeling in areas where shortwave radiation data are available but cloud observations are not.

Downwelling radiation in six regional models from the Arctic Regional Climate Model Intercomparison (ARCMIP) project is systematically biased negative in comparison with observations from the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment, although the correlations with observations are relatively good. In this paper, links between model errors and the representation of clouds in these models are investigated. Although some modeled cloud properties, such as the cloud water paths, are reasonable in a climatological sense, the temporal correlation of model cloud properties with observations is poor. The vertical distribution of cloud water is distinctly different among the different models; some common features also appear. Most models underestimate the presence of high clouds, and, although the observed preference for low clouds in the Arctic is present in most of the models, the modeled low clouds are too thin and are displaced downward. Practically all models show a preference to locate the lowest cloud base at the lowest model grid point. In some models this happens also to be where the observations show the highest occurrence of the lowest cloud base; it is not possible to determine if this result is just a coincidence. Different factors contribute to model surface radiation errors. For longwave radiation in summer, a negative bias is present both for cloudy and clear conditions, and intermodel differences are smaller when clouds are present. There is a clear relationship between errors in cloud-base temperature and radiation errors. In winter, in contrast, clear-sky cases are modeled reasonably well, but cloudy cases show a very large intermodel scatter with a significant bias in all models. This bias likely results from a complete failure in all of the models to retain liquid water in cold winter clouds. All models overestimate the cloud attenuation of summer solar radiation for thin and intermediate clouds, and some models maintain this behavior also for thick clouds.

The climate in the Arctic is changing faster than anywhere else on earth. Poorly understood feedback processes relating to Arctic clouds and aerosol–cloud interactions contribute to a poor understanding of the present changes in the Arctic climate system, and also to a large spread in projections of future climate in the Arctic. The problem is exacerbated by the paucity of research-quality observations in the central Arctic. Improved formulations in climate models require such observations, which can only come from measurements in situ in this difficult-to-reach region with logistically demanding environmental conditions. The Arctic Summer Cloud Ocean Study (ASCOS) was the most extensive central Arctic Ocean expedition with an atmospheric focus during the International Polar Year (IPY) 2007–2008. ASCOS focused on the study of the formation and life cycle of low-level Arctic clouds. ASCOS departed from Longyearbyen on Svalbard on 2 August and returned on 9 September 2008. In transit into and out of the pack ice, four short research stations were undertaken in the Fram Strait: two in open water and two in the marginal ice zone. After traversing the pack ice northward, an ice camp was set up on 12 August at 87°21' N, 01°29' W and remained in operation through 1 September, drifting with the ice. During this time, extensive measurements were taken of atmospheric gas and particle chemistry and physics, mesoscale and boundary-layer meteorology, marine biology and chemistry, and upper ocean physics. ASCOS provides a unique interdisciplinary data set for development and testing of new hypotheses on cloud processes, their interactions with the sea ice and ocean and associated physical, chemical, and biological processes and interactions. For example, the first-ever quantitative observation of bubbles in Arctic leads, combined with the unique discovery of marine organic material, polymer gels with an origin in the ocean, inside cloud droplets suggests the possibility of primary marine organically derived cloud condensation nuclei in Arctic stratocumulus clouds. Direct observations of surface fluxes of aerosols could, however, not explain observed variability in aerosol concentrations, and the balance between local and remote aerosols sources remains open. Lack of cloud condensation nuclei (CCN) was at times a controlling factor in low-level cloud formation, and hence for the impact of clouds on the surface energy budget. ASCOS provided detailed measurements of the surface energy balance from late summer melt into the initial autumn freeze-up, and documented the effects of clouds and storms on the surface energy balance during this transition. In addition to such process-level studies, the unique, independent ASCOS data set can and is being used for validation of satellite retrievals, operational models, and reanalysis data sets., Atmospheric Chemistry and Physics, 14 (6), ISSN:1680-7375, ISSN:1680-7367

The Arctic climate system includes numerous highly interactive small-scale physical processes in the atmosphere, sea ice, and ocean. During and since the International Polar Year 2007–2009, significant advances have been made in understanding these processes. Here, these recent advances are reviewed, synthesized, and discussed. In atmospheric physics, the primary advances have been in cloud physics, radiative transfer, mesoscale cyclones, coastal, and fjordic processes as well as in boundary layer processes and surface fluxes. In sea ice and its snow cover, advances have been made in understanding of the surface albedo and its relationships with snow properties, the internal structure of sea ice, the heat and salt transfer in ice, the formation of superimposed ice and snow ice, and the small-scale dynamics of sea ice. For the ocean, significant advances have been related to exchange processes at the ice–ocean interface, diapycnal mixing, double-diffusive convection, tidal currents and diurnal resonance. Despite this recent progress, some of these small-scale physical processes are still not sufficiently understood: these include wave–turbulence interactions in the atmosphere and ocean, the exchange of heat and salt at the ice–ocean interface, and the mechanical weakening of sea ice. Many other processes are reasonably well understood as stand-alone processes but the challenge is to understand their interactions with and impacts and feedbacks on other processes. Uncertainty in the parameterization of small-scale processes continues to be among the greatest challenges facing climate modelling, particularly in high latitudes. Further improvements in parameterization require new year-round field campaigns on the Arctic sea ice, closely combined with satellite remote sensing studies and numerical model experiments.

Unique measurements of vertical size-resolved aerosol particle concentrations, trace gas concentrations and meteorological data were obtained during the Arctic Summer Cloud Ocean Study (ASCOS, www.ascos.se), an International Polar Year project aimed at establishing the processes responsible for formation and evolution of low-level clouds over the high Arctic summer pack ice. The experiment was conducted from on board the Swedish icebreaker Oden, and provided both ship- and helicopter-based measurements. This study focuses on the vertical helicopter profiles and onboard measurements obtained during a three-week period when Oden was anchored to a drifting ice floe, and sheds light on the characteristics of Arctic aerosol particles and their distribution throughout the lower atmosphere. Distinct differences in aerosol particle characteristics within defined atmospheric layers are identified. Within the lowermost couple hundred metres, transport from the marginal ice zone (MIZ), condensational growth and cloud processing develop the aerosol population. During two of the four representative periods defined in this study, such influence is shown. At altitudes above about 1 km, long-range transport occurs frequently. However, only infrequently does large-scale subsidence descend such air masses to become entrained into the mixed layer in the high Arctic, and therefore long-range transport plumes are unlikely to directly influence low-level stratiform cloud formation. Nonetheless, such plumes can influence the radiative balance of the planetary boundary layer (PBL) by influencing formation and evolution of higher clouds, as well as through precipitation transport of particles downwards. New particle formation was occasionally observed, particularly in the near-surface layer. We hypothesize that the origin of these ultrafine particles could be in biological processes, both primary and secondary, within the open leads between the pack ice and/or along the MIZ. In general, local sources, in combination with upstream boundary-layer transport of precursor gases from the MIZ, are considered to constitute the origin of cloud condensation nuclei (CCN) particles and thus be of importance for the formation, Atmospheric Chemistry and Physics, 13 (24), ISSN:1680-7375, ISSN:1680-7367

A new satellite-derived climate dataset – denoted CLARA-A1 ("The CM SAF cLoud, Albedo and RAdiation dataset from AVHRR data") – is described. The dataset covers the 28-yr period from 1982 until 2009 and consists of cloud, surface albedo and radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar orbiting operational meteorological satellites. Its content, anticipated accuracies, limitations and potential applications are described. The dataset is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project. The dataset has its strengths in the long duration, its foundation upon a homogenized AVHRR radiance data record, and in some unique features, e.g. the availability of 28 yr of summer surface albedo and cloudiness parameters over the polar regions. Quality characteristics are also well investigated and particularly useful results can be found over the tropics, mid- to high-latitudes and over nearly all oceanic areas. Being the first CM SAF dataset of its kind, an intensive evaluation of the quality of the datasets was performed and major findings wrt. to merits and shortcomings of the datasets are reported. However, the CM SAF's long-term commitment to perform two additional reprocessing events within the time frame 2013–2017 will allow a proper handling of limitations as well as upgrading the dataset with new features (e.g. uncertainty estimates) and extension of the temporal coverage.

A new satellite-derived climate dataset – denoted CLARA-A1 ("The CM SAF cLoud, Albedo and RAdiation dataset from AVHRR data") – is described. The dataset covers the 28 yr period from 1982 until 2009 and consists of cloud, surface albedo, and radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting operational meteorological satellites. Its content, anticipated accuracies, limitations, and potential applications are described. The dataset is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project. The dataset has its strengths in the long duration, its foundation upon a homogenized AVHRR radiance data record, and in some unique features, e.g. the availability of 28 yr of summer surface albedo and cloudiness parameters over the polar regions. Quality characteristics are also well investigated and particularly useful results can be found over the tropics, mid to high latitudes and over nearly all oceanic areas. Being the first CM SAF dataset of its kind, an intensive evaluation of the quality of the datasets was performed and major findings with regard to merits and shortcomings of the datasets are reported. However, the CM SAF's long-term commitment to perform two additional reprocessing events within the time frame 2013–2018 will allow proper handling of limitations as well as upgrading the dataset with new features (e.g. uncertainty estimates) and extension of the temporal coverage.

[1] Monthly clear-sky anomalies of atmospheric temperature and water vapor over the East Siberian and Laptev Sea regions of the Arctic for 2003–2010 are examined here. This region experiences significant interannual variations in sea ice concentration and is also where ice loss was most apparent in the record year 2007. Clear-sky thermodynamic profiles come from the Atmospheric Infrared Sounder (AIRS) sensor onboard the Aqua satellite. Associated longwave (LW) and shortwave (SW) radiation-flux anomalies are estimated through radiative transfer modeling. Anomalies of temperature (±10 K) and water vapor (±1 g kg−1) often positively covary, resulting in distinct signatures in the clear-sky downwelling LW (LWD) anomalies, occasionally larger than ±10 W m−2around the 2003–2010 climatology. Estimates of mean greenhouse anomalies indicate a shift from negative to positive anomalies midway through the 8-year record. Sensitivity tests suggest that temperature anomalies are the strongest contributor to both LWD and greenhouse anomalies, relative to water-vapor anomalies; monthly averaging of column precipitable water yields relatively small anomalies (order 1 mm) that produce a linear response in greenhouse anomalies. Finally the clear-sky contribution to 2007 monthly ice thickness is estimated. Anomalous clear-sky radiation retards the total 2007 ice thickness by 0.3 m (15–30% of ice-thickness climatology), and anomalous LW radiation is most important for preconditioning the ice during the months prior to, and after, the summer melt season. A highly sensitive interaction between cloud fraction, surface albedo and LWD anomalies is found, and we develop a metric for determining clear-sky anomalous ice melt potential.

Understanding the rapidly changing climate in the Arctic is limited by a lack of understanding of underlying strong feedback mechanisms that are specific to the Arctic. Progress in this field can only be obtained by process-level observations; this is the motivation for intensive ice-breaker-based campaigns such as the Arctic Summer Cloud-Ocean Study (ASCOS), described here. However, detailed field observations also have to be put in the context of the larger-scale meteorology, and short field campaigns have to be analysed within the context of the underlying climate state and temporal anomalies from this. To aid in the analysis of other parameters or processes observed during this campaign, this paper provides an overview of the synoptic-scale meteorology and its climatic anomaly during the ASCOS field deployment. It also provides a statistical analysis of key features during the campaign, such as key meteorological variables, the vertical structure of the lower troposphere and clouds, and energy fluxes at the surface. In order to assess the representativity of the ASCOS results, we also compare these features to similar observations obtained during three earlier summer experiments in the Arctic Ocean: the AOE-96, SHEBA and AOE-2001 expeditions. We find that these expeditions share many key features of the summertime lower troposphere. Taking ASCOS and the previous expeditions together, a common picture emerges with a large amount of low-level cloud in a well-mixed shallow boundary layer, capped by a weak to moderately strong inversion where moisture, and sometimes also cloud top, penetrate into the lower parts of the inversion. Much of the boundary-layer mixing is due to cloud-top cooling and subsequent buoyant overturning of the cloud. The cloud layer may, or may not, be connected with surface processes depending on the depths of the cloud and surface-based boundary layers and on the relative strengths of surface-shear and cloud-generated turbulence. The latter also implies a connection between the cloud layer and the free troposphere through entrainment at cloud top.

Understanding the rapidly changing climate in the Arctic is limited by a lack of understanding of underlying strong feedback mechanisms that are specific to the Arctic. Progress in this field can only be obtained by process-level observations; this is the motivation for intensive ice-breaker-based campaigns such as that described in this paper: the Arctic Summer Cloud-Ocean Study (ASCOS). However, detailed field observations also have to be put in the context of the larger-scale meteorology, and short field campaigns have to be analysed within the context of the underlying climate state and temporal anomalies from this. To aid in the analysis of other parameters or processes observed during this campaign, this paper provides an overview of the synoptic-scale meteorology and its climatic anomaly during the ASCOS field deployment. It also provides a statistical analysis of key features during the campaign, such as some key meteorological variables, the vertical structure of the lower troposphere and clouds, and energy fluxes at the surface. In order to assess the representativity of the ASCOS results, we also compare these features to similar observations obtained during three earlier summer experiments in the Arctic Ocean, the AOE-96, SHEBA and AOE-2001 expeditions. We find that these expeditions share many key features of the summertime lower troposphere. Taking ASCOS and the previous expeditions together, a common picture emerges with a large amount of low-level cloud in a well-mixed shallow boundary layer, capped by a weak to moderately strong inversion where moisture, and sometimes also cloud top, penetrate into the lower parts of the inversion. Much of the boundary-layer mixing is due to cloud-top cooling and subsequent buoyant overturning of the cloud. The cloud layer may, or may not, be connected with surface processes depending on the depths of the cloud and surface-based boundary layers and on the relative strengths of surface-shear and cloud-buoyancy turbulence generation. The latter also implies a connection between the cloud layer and the free troposphere through entrainment at cloud top.

Observations made during late summer in the central Arctic Ocean, as part of the Arctic Summer Cloud Ocean Study (ASCOS), are used to evaluate cloud and vertical temperature structure in the Met Office Unified Model (MetUM). The observation period can be split into 5 regimes; the first two regimes had a large number of frontal systems, which were associated with deep cloud. During the remainder of the campaign a layer of low-level cloud occurred, typical of central Arctic summer conditions, along with two periods of greatly reduced cloud cover. The short-range operational NWP forecasts could not accurately reproduce the observed variations in near-surface temperature. A major source of this error was found to be the temperature-dependant surface albedo parameterisation scheme. The model reproduced the low-level cloud layer, though it was too thin, too shallow, and in a boundary-layer that was too frequently well-mixed. The model was also unable to reproduce the observed periods of reduced cloud cover, which were associated with very low cloud condensation nuclei (CCN) concentrations (−3). As with most global NWP models, the MetUM does not have a prognostic aerosol/cloud scheme but uses a constant CCN concentration of 100 cm−3 over all marine environments. It is therefore unable to represent the low CCN number concentrations and the rapid variations in concentration frequently observed in the central Arctic during late summer. Experiments with a single-column model configuration of the MetUM show that reducing model CCN number concentrations to observed values reduces the amount of cloud, increases the near-surface stability, and improves the representation of both the surface radiation fluxes and the surface temperature. The model is shown to be sensitive to CCN only when number concentrations are less than 10–20 cm−3.

Influx of aerosols from the mid-latitudes has a wide range of impacts on the Arctic atmosphere. In this study, the capability of the CALIPSO-CALIOP instrument to provide accurate observations of aerosol layers is exploited to characterize their vertical distribution, probability density functions (PDFs) of aerosol layer thickness, base and top heights, and optical depths over the Arctic for the 4-yr period from June 2006 to May 2010. It is shown that the bulk of aerosols, from about 65% in winter to 45% in summer, are confined below the lowermost kilometer of the troposphere. In the middle troposphere (3-5 km), spring and autumn seasons show slightly higher aerosol amounts compared to other two seasons. The relative vertical distribution of aerosols shows that clean continental aerosol is the largest contributor in all seasons except in summer, when layers of polluted continental aerosols are almost as large. In winter and spring, polluted continental aerosols are the second largest contributor to the total number of observed aerosol layers, whereas clean marine aerosol is the second largest contributor in summer and autumn. The PDFs of the geometrical thickness of the observed aerosol layers peak about 400-700 m. Polluted continental and smoke aerosols, which are associated with the intrusions from mid-latitudes, have much broader distributions of optical and geometrical thicknesses, suggesting that they appear more often optically thicker and higher up in the troposphere.

An accurate characterization of the vertical structure of the Arctic atmosphere is useful in climate change and attribution studies as well as for the climate modelling community to improve projections of future climate over this highly sensitive region. Here, we investigate one of the dominant features of the vertical structure of the Arctic atmosphere, i.e. water-vapour inversions, using eight years of Atmospheric Infrared Sounder data (2002–2010) and radiosounding profiles released from the two Arctic locations (North Slope of Alaska at Barrow and during SHEBA). We quantify the characteristics of clear-sky water vapour inversions in terms of their frequency of occurrence, strength and height covering the entire Arctic for the first time. We found that the frequency of occurrence of water-vapour inversions is highest during winter and lowest during summer. The inversion strength is, however, higher during summer. The observed peaks in the median inversion-layer heights are higher during the winter half of the year, at around 850 hPa over most of the Arctic Ocean, Siberia and the Canadian Archipelago, while being around 925 hPa during most of the summer half of the year over the Arctic Ocean. The radiosounding profiles agree with the frequency, location and strength of water-vapour inversions in the Pacific sector of the Arctic. In addition, the radiosoundings indicate that multiple inversions are the norm with relatively few cases without inversions. The amount of precipitable water within the water-vapour inversion structures is estimated and we find a distinct, two-mode contribution to the total column precipitable water. These results suggest that water-vapour inversions are a significant source to the column thermodynamics, especially during the colder winter and spring seasons. We argue that these inversions are a robust metric to test the reproducibility of thermodynamics within climate models. An accurate statistical representation of water-vapour inversions in models would mean that the large-scale coupling of moisture transport, precipitation, temperature and water vapour vertical structure and radiation are also essentially captured well in such models.

On average, airborne aerosol particles cool the Earth’s surface directly by absorbingand scattering sunlight and indirectly by influencing cloud reflectivity, life time, thicknessor extent. Here we show that over the central Arctic Ocean, where there is frequentlya lack of aerosol particles upon which clouds may form, a small increase in aerosol5loading may enhance cloudiness thereby likely causing a climatologically significantwarming at the ice-covered Arctic surface. Under these low concentration conditionscloud droplets grow to drizzle sizes and fall, even in the absence of collisions andcoalescence, thereby diminishing cloud water. Evidence from a case study suggeststhat interactions between aerosol, clouds and precipitation could be responsible for10attaining the observed low aerosol concentrations., Atmospheric Chemistry and Physics, 10 (7), ISSN:1680-7375, ISSN:1680-7367