Transpolar observations of the morphological properties of Arctic sea ice

Perovich, Donald K.; Grenfell, Thomas C.; Light, Bonnie; Elder, Bruce; Harbeck, J. P.; Polashenski, C.; Tucker, W. B.; Stelmach, Casey

doi:10.1029/2008jc004892

Cited by 103 publications

(118 citation statements)

References 42 publications

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“…Several Arctic field campaigns carried out after SHEBA (including the Tara campaign of DAMOCLES) were crucial to monitor and deepen the understanding of the processes controlling the snow and ice albedo in a rapidly changing environment. Altogether, these observations have shown that the seasonal evolution of the Arctic sea ice albedo follows the surface metamorphism and change of phases, from dry snow to melting snow, pond formation, pond drainage, pond evolution, and autumn freeze-up Nicolaus et al, 2010a;Perovich and Polashenski, 2012). Seasonal ice has a lower albedo than multiyear ice, because (a) it has a thinner and therefore faster-melting snow layer, (b) the ice itself is thinner, containing a much lower fraction of scattering bubbles, and (c) melt ponds are more extensive due to less ice deformation and a smaller freeboard .…”

Section: Snow and Ice Albedo: Observations And Parameterizationsmentioning

confidence: 89%

“…After the onset, the amount of melt is primarily controlled by absorbed shortwave radiation. The albedo of snow evolves following the surface metamorphism and change of phases, from dry snow to melting snow, pond formation, pond drainage, pond evolution, and autumn freezeup Nicolaus et al, 2010a;Perovich and Polashenski, 2012). Numerous studies during and after IPY have addressed snow and sea ice albedo (more than 70 papers cited here), the actual research topics including the spectral differences, spatial variations between various surface types, and the effects of impurities such as black carbon.…”

Section: Main Advances and Remaining Challenges In Individual Researcmentioning

confidence: 99%

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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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Section: Snow and Ice Albedo: Observations And Parameterizationsmentioning

confidence: 89%

Section: Main Advances and Remaining Challenges In Individual Researcmentioning

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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“…2, in this study we decide to distinguish between three surface types: open water (W), melt ponds (M), and snow and ice (I) (Tschudi et al, 2008;Rösel and Kaleschke, 2011). To calculate the albedo of sea ice on a large scale, the surface-based albedo values are weighted with the fraction of their corresponding surface component (Fetterer and Untersteiner, 1998;Tschudi et al, 2008;Perovich et al, 2009). The so-called areally averaged albedo α for larger areas containing three surface types is expressed as…”

Section: Methodsmentioning

confidence: 99%

“…Several field experiments and ship observations at different locations in the Arctic Ocean aimed at the study of albedo and optical properties of melt ponds (Morassutti and LeDrew, 1996;Perovich et al, 2002a), as well as distribution and size of the ponds (El Naggar et al, 1995;Perovich et al, 2002bPerovich et al, , 2009Sankelo et al, 2010;Nicolaus et al, 2010). The Russian ice atlas compiled by Romanov (1993Romanov ( , 1995 includes pan-arctic pond coverage estimates based on Russian overflights and surface observations.…”

Section: A Rösel Et Al: Melt Ponds On Arctic Sea Icementioning

confidence: 99%

Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network

Rösel¹,

2012

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Abstract. Melt ponds on sea ice strongly reduce the surface albedo and accelerate the decay of Arctic sea ice. Due to different spectral properties of snow, ice, and water, the fractional coverage of these distinct surface types can be derived from multispectral sensors like the Moderate Resolution Image Spectroradiometer (MODIS) using a spectral unmixing algorithm. The unmixing was implemented using a multilayer perceptron to reduce computational costs.Arctic-wide melt pond fractions and sea ice concentrations are derived from the level 3 MODIS surface reflectance product. The validation of the MODIS melt pond data set was conducted with aerial photos from the MELTEX campaign 2008 in the Beaufort Sea, data sets from the National Snow and Ice Data Center (NSIDC) for 2000 and 2001 from four sites spread over the entire Arctic, and with ship observations from the trans-Arctic HOTRAX cruise in 2005. The root-mean-square errors range from 3.8 % for the comparison with HOTRAX data, over 10.7 % for the comparison with NSIDC data, to 10.3 % and 11.4 % for the comparison with MELTEX data, with coefficient of determination ranging from R 2 = 0.28 to R 2 = 0.45. The mean annual cycle of the melt pond fraction per grid cell for the entire Arctic shows a strong increase in June, reaching a maximum of 15 % by the end of June. The zonal mean of melt pond fractions indicates a dependence of the temporal development of melt ponds on the geographical latitude, and has its maximum in mid-July at latitudes between 80 • and 88 • N.Furthermore, the MODIS results are used to estimate the influence of melt ponds on retrievals of sea ice concentrations from passive microwave data. Results from a case study comparing sea ice concentrations from ARTIST Sea Ice-, NASA Team 2-, and Bootstrap-algorithms with MODIS sea ice concentrations indicate an underestimation of around 40 % for sea ice concentrations retrieved with microwave algorithms.

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“…Grenfell collected samples of the surface granular layer of melting sea ice, aged snow and newly-fallen snow from mid-August through late September. Preliminary estimates of BC from this voyage were published by Perovich et al (2009).…”

Section: Arctic Ocean (Fig 2)mentioning

confidence: 99%