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.
Background
Robotic-assisted surgery is used with increasing frequency in general surgery for a variety of applications. In spite of this increase in usage, the learning curve is not yet defined. This ...study reviews the literature on the learning curve in robotic general surgery to inform adopters of the technology.
Methods
PubMed and EMBASE searches yielded 3690 abstracts published between July 1986 and March 2016. The abstracts were evaluated based on the following inclusion criteria: written in English, reporting original work, focus on general surgery operations, and with explicit statistical methods.
Results
Twenty-six full-length articles were included in final analysis. The articles described the learning curves in colorectal (9 articles, 35%), foregut/bariatric (8, 31%), biliary (5, 19%), and solid organ (4, 15%) surgery. Eighteen of 26 (69%) articles report single-surgeon experiences. Time was used as a measure of the learning curve in all studies (100%); outcomes were examined in 10 (38%). In 12 studies (46%), the authors identified three phases of the learning curve. Numbers of cases needed to achieve plateau performance were wide-ranging but overlapping for different kinds of operations: 19–128 cases for colorectal, 8–95 for foregut/bariatric, 20–48 for biliary, and 10–80 for solid organ surgery.
Conclusion
Although robotic surgery is increasingly utilized in general surgery, the literature provides few guidelines on the learning curve for adoption. In this heterogeneous sample of reviewed articles, the number of cases needed to achieve plateau performance varies by case type and the learning curve may have multiple phases as surgeons add more complex cases to their case mix with growing experience. Time is the most common determinant for the learning curve. The literature lacks a uniform assessment of outcomes and complications, which would arguably reflect expertise in a more meaningful way than time to perform the operation alone.
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.
Three months of Doppler lidar wind measurements were obtained during the Arctic Cloud Summer Experiment on the icebreaker Oden during the summer of 2014. Such ship-borne Doppler measurements require ...active stabilisation to remove the effects of ship motion. We demonstrate that the combination of a commercial Doppler lidar with a custom-made motion-stabilisation platform enables the retrieval of wind profiles in the Arctic atmospheric boundary layer during both cruising and ice-breaking with statistical uncertainties comparable to land-based measurements. This held true particularly within the atmospheric boundary layer even though the overall aerosol load was very low. Motion stabilisation was successful for high wind speeds in open water and the resulting wave conditions. It allows for the retrieval of vertical winds with a random error below 0.2 m s−1. The comparison of lidar-measured wind and radio soundings gives a mean bias of 0.3 m s−1 (2°) and a mean standard deviation of 1.1 m s−1 (12°) for wind speed (wind direction). The agreement for wind direction degrades with height. The combination of a motion-stabilised platform with a low-maintenance autonomous Doppler lidar has the potential to enable continuous long-term high-resolution ship-based wind profile measurements over the oceans.
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 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.
An Arctic CCN-limited cloud-aerosol regime Mauritsen, T.; Sedlar, J.; Tjernström, M. ...
Atmospheric chemistry and physics,
01/2011, Volume:
11, Issue:
1
Journal Article
Peer reviewed
Open access
On average, airborne aerosol particles cool the Earth's surface directly by absorbing and scattering sunlight and indirectly by influencing cloud reflectivity, life time, thickness or extent. Here we ...show that over the central Arctic Ocean, where there is frequently a lack of aerosol particles upon which clouds may form, a small increase in aerosol loading may enhance cloudiness thereby likely causing a climatologically significant warming at the ice-covered Arctic surface. Under these low concentration conditions cloud droplets grow to drizzle sizes and fall, even in the absence of collisions and coalescence, thereby diminishing cloud water. Evidence from a case study suggests that interactions between aerosol, clouds and precipitation could be responsible for attaining the observed low aerosol concentrations.
Comprehensive aircraft observations are used to characterise surface roughness over the Arctic marginal ice zone (MIZ) and consequently make recommendations for the parametrisation of surface ...momentum exchange in the MIZ. These observations were gathered in the Barents Sea and Fram Strait from two aircraft as part of the Aerosol–Cloud Coupling And Climate Interactions in the Arctic (ACCACIA) project. They represent a doubling of the total number of such aircraft observations currently available over the Arctic MIZ. The eddy covariance method is used to derive estimates of the 10 m neutral drag coefficient (CDN10) from turbulent wind velocity measurements, and a novel method using albedo and surface temperature is employed to derive ice fraction. Peak surface roughness is found at ice fractions in the range 0.6 to 0.8 (with a mean interquartile range in CDN10 of 1.25 to 2.85 × 10−3). CDN10 as a function of ice fraction is found to be well approximated by the negatively skewed distribution provided by a leading parametrisation scheme (Lüpkes et al., 2012) tailored for sea-ice drag over the MIZ in which the two constituent components of drag – skin and form drag – are separately quantified. Current parametrisation schemes used in the weather and climate models are compared with our results and the majority are found to be physically unjustified and unrepresentative. The Lüpkes et al. (2012) scheme is recommended in a computationally simple form, with adjusted parameter settings. A good agreement holds for subsets of the data from different locations, despite differences in sea-ice conditions. Ice conditions in the Barents Sea, characterised by small, unconsolidated ice floes, are found to be associated with higher CDN10 values – especially at the higher ice fractions – than those of Fram Strait, where typically larger, smoother floes are observed. Consequently, the important influence of sea-ice morphology and floe size on surface roughness is recognised, and improvement in the representation of this in parametrisation schemes is suggested for future study.
The radiosounding network in the Arctic, despite being sparse, is a crucial part of the atmospheric observing system for weather prediction and reanalysis. The spatial coverage of the network was ...evaluated using a numerical weather prediction model, comparing radiosonde observations from Arctic land stations and expeditions in the central Arctic Ocean with operational analyses and background fields (12‐hr forecasts) from European Centre for Medium‐Range Weather Forecasts for January 2016 to September 2018. The results show that the impact of radiosonde observations on analyses has large geographical variation. In data‐sparse areas, such as the central Arctic Ocean, high‐quality radiosonde observations substantially improve the analyses, while satellite observations are not able to compensate for the large spatial gap in the radiosounding network. In areas where the network is reasonably dense, the quality of background field is more related to how radiosonde observations are utilized in the assimilation and to the quality of those observations.
Plain Language Summary
The radiosounding network is a crucial part of the atmospheric observing system, because radiosoundings provide accurate direct information on vertical profiles of temperature, humidity and winds. However, the radiosounding network is sparse in the Arctic; 76 sounding stations are located on continents and islands north of 60°N, and no radiosoundings are regularly made over the Arctic Ocean. In this study, the spatial coverage of the network was evaluated using a numerical weather prediction system, comparing radiosonde observations with operational weather forecast system products of the European Centre for Medium‐Range Weather Forecasts. In the forecasting system, data assimilation is used to produce initial conditions for forecasts. In the data assimilation, background information from the previous short forecast is corrected by observations, resulting in an analysis, the initial conditions for the next forecast. Our results show that radiosoundings substantially improve these analyses. Satellite observations cannot compensate for the large spatial gap in the radiosounding network in the central Arctic. Over continents, where the network is reasonably dense, the quality of background field is more related to how radiosoundings are utilized in the data assimilation, and to the quality of those observations, than to the density of the radiosounding network.
Key Points
In the Arctic, radiosonde observations substantially improve the operational analyses and cannot be replaced by satellite observations
The impact of radiosoundings on analyses varies substantially in space and is largest in the northern Atlantic and in the central Arctic
Where the sounding network is sufficiently dense, the background field quality depends on the quality and utilization of radiosoundings
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