The Spring-Time Boundary Layer in the Central Arctic Observed during PAMARCMiP 2009

Lampert, Astrid; Maturilli, Marion; Ritter, Christoph; Hoffmann, Anne; Stock, M.; Herber, Andreas; Birnbaum, Gerit; Neuber, Roland; Dethloff, Klaus; Orgis, Thomas; Stone, Robert S.; Brauner, Ralf; Kässbohrer, Johannes; Haas, Christian; Makshtas, Alexander; Sokolov, Vladimir; Liu, Peter

doi:10.3390/atmos3030320

Cited by 18 publications

(14 citation statements)

References 60 publications

(51 reference statements)

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“…The radiation sensors were mounted on the aircraft in a fixed position. For clear-sky conditions, data of the upward facing pyranometer, which receives direct solar radiation, were corrected for the misalignment of the instrument (based on a method described by Bannehr and Schwiesow, 1993) and the roll and pitch angles of the aircraft to derive downwelling hemispheric radiation flux densities for horizontal exposition of the sensor (see Lampert et al, 2012).…”

Section: Aerial Validationmentioning

confidence: 99%

Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 1: Validation against in situ, aerial, and ship cruise data

et al. 2015

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Abstract. The presence of melt ponds on the Arctic sea ice strongly affects the energy balance of the Arctic Ocean in summer. It affects albedo as well as transmittance through the sea ice, which has consequences for the heat balance and mass balance of sea ice. An algorithm to retrieve melt pond fraction and sea ice albedo from Medium Resolution Imaging Spectrometer (MERIS) data is validated against aerial, shipborne and in situ campaign data. The results show the best correlation for landfast and multiyear ice of high ice concentrations. For broadband albedo, R 2 is equal to 0.85, with the RMS (root mean square) being equal to 0.068; for the melt pond fraction, R 2 is equal to 0.36, with the RMS being equal to 0.065. The correlation for lower ice concentrations, subpixel ice floes, blue ice and wet ice is lower due to ice drift and challenging for the retrieval surface conditions. Combining all aerial observations gives a mean albedo RMS of 0.089 and a mean melt pond fraction RMS of 0.22. The in situ melt pond fraction correlation is R 2 = 0.52 with an RMS = 0.14. Ship cruise data might be affected by documentation of varying accuracy within the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol, which may contribute to the discrepancy between the satellite value and the observed value: mean R 2 = 0.044, mean RMS = 0.16. An additional dynamic spatial cloud filter for MERIS over snow and ice has been developed to assist with the validation on swath data.

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Section: Aerial Validationmentioning

confidence: 99%

Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 1: Validation against in situ, aerial, and ship cruise data

et al. 2015

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“…On the basis of airborne observations and high-resolution modelling, Lüpkes et al (2008bLüpkes et al ( , 2012b concluded that convection over 1-2 km wide leads reached altitudes of 50-300 m depending on the boundary layer structure on the upstream side of leads. On the basis of aircraft in situ, drop sonde, and lidar observations, Lampert et al (2012) observed that over areas with many leads, the potential temperature decreased with height in the lowermost 50 m and then was nearly constant due to convective mixing up to the height of 100-200 m. When the leads were frozen and their fraction was small, however, an SBL extended up to a height of 200-300 m. Ebner et al (2011) showed in a modelling study that convective plumes generated over the Laptev Sea polynya influence atmospheric turbulence even 500 km downstream of the polynya, and Hebbinghaus et al (2006) found that cyclonic vortices can be generated or intensified over polynyas due to convective processes. Such processes over large polynyas may be important with respect to the drastic changes in sea ice cover observed in recent years.…”

Section: Atmosmentioning

confidence: 99%

Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review

et al. 2014

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Abstract. 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.

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“…It was possible to document the strong convective regime, which is unusual for the region north of Svalbard (e.g., Brümmer and Pohlmann, 2000), by dropsondes released along flight tracks roughly parallel to the convection rolls. A detailed description of the dropsonde unit can be found in Lampert et al (2012). In the following, we discuss two cases of CAOs causing convection over the polynya area on the basis of the dropsonde measurements.…”

Section: Cold Air Outbreaks North Of Svalbard In March 2013mentioning

confidence: 99%

Brief Communication: Trends in sea ice extent north of Svalbard and its impact on cold air outbreaks as observed in spring 2013

et al. 2014

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Abstract. An analysis of Special Sensor Microwave/Imager (SSM/I) satellite data reveals that the Whaler's Bay polynya north of Svalbard was considerably larger in the three winters from 2012 to 2014 compared to the previous 20 years. This increased polynya size leads to strong atmospheric convection during cold air outbreaks in a region north of Svalbard that was typically ice-covered in the last decades. The change in ice cover can strongly influence local temperature conditions. Dropsonde measurements from March 2013 show that the unusual ice conditions generate extreme convective boundary layer heights that are larger than the regional values reported in previous studies.

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The Spring-Time Boundary Layer in the Central Arctic Observed during PAMARCMiP 2009

Cited by 18 publications

References 60 publications

Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 1: Validation against in situ, aerial, and ship cruise data

Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 1: Validation against in situ, aerial, and ship cruise data

Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review

Brief Communication: Trends in sea ice extent north of Svalbard and its impact on cold air outbreaks as observed in spring 2013

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