Sea-Ice Melt-Pond Fraction as Determined from Low Level Aerial Photographs

Cloud response to synoptic conditions over the Beaufort and Chukchi seasonal ice zone is examined. Four synoptic states with distinct thermodynamic and dynamic signatures are identified using ERA-Interim reanalysis data from 2000 to 2014. CloudSat and CALIPSO observations suggest control of clouds by synoptic states. Warm continental air advection is associated with the fewest low-level clouds, while cold air advection generates the most low-level clouds. Low-level clouds are related to lower-tropospheric stability and both are regulated by synoptic conditions. High-level clouds are associated with humidity and vertical motions in the upper atmosphere. Observed cloud vertical and spatial variability is reproduced well in ERA-Interim, but winter low-level cloud fraction is overestimated. This suggests that synoptic conditions constrain the spatial extent of clouds through the atmospheric structure, while the parameterizations for cloud microphysics and boundary layer physics are critical for the life cycle of clouds in numerical models. Sea ice melt onset is related to synoptic conditions. Melt onsets occur more frequently and earlier with warm air advection. Synoptic conditions with the highest temperatures and precipitable water are most favorable for melt onsets even though fewer low-level clouds are associated with these conditions.

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Sea ice melt pond fraction estimation from dual-polarisation C-band SAR – Part 2: Scaling in situ to Radarsat-2

The Cryosphere Discussions ◽

10.5194/tcd-8-845-2014 ◽

2014 ◽

Vol 8 (1) ◽

pp. 845-885 ◽

Cited By ~ 1

Author(s):

R. K. Scharien ◽

K. Hochheim ◽

J. Landy ◽

D. G. Barber

Keyword(s):

Sea Ice ◽

Large Scale ◽

Temporal Frequency ◽

Model Performance ◽

Free Water ◽

Light Transmittance ◽

The Arctic ◽

Ice Melt ◽

Melt Pond

Abstract. Observed changes in the Arctic have motivated efforts to understand and model its components as an integrated and adaptive system at increasingly finer scales. Sea ice melt pond fraction, an important summer sea ice component affecting surface albedo and light transmittance across the ocean-sea ice–atmosphere interface, is inadequately parameterized in models due to a lack of large scale observations. In this paper, results from a multi-scale remote sensing program dedicated to the retrieval of pond fraction from satellite C-band synthetic aperture radar (SAR) are detailed. The study was conducted on first-year sea (FY) ice in the Canadian Arctic Archipelago during the summer melt period in June 2012. Approaches to retrieve the subscale FY ice pond fraction from mixed pixels in RADARSAT-2 imagery, using in situ, surface scattering theory, and image data are assessed. Each algorithm exploits the dominant effect of high dielectric free-water ponds on the VV/HH polarisation ratio (PR) at moderate to high incidence angles (about 40° and above). Algorithms are applied to four images corresponding to discrete stages of the seasonal pond evolutionary cycle, and model performance is assessed using coincident pond fraction measurements from partitioned aerial photos. A RMSE of 0.07, across a pond fraction range of 0.10 to 0.70, is achieved during intermediate and late seasonal stages. Weak model performance is attributed to wet snow (pond formation) and synoptically driven pond freezing events (all stages), though PR has utility for identification of these events when considered in time series context. Results demonstrate the potential of wide-swath, dual-polarisation, SAR for large-scale observations of pond fraction with temporal frequency suitable for process-scale studies and improvements to model parameterizations.

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Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters

Remote Sensing ◽

10.3390/rs10101603 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1603 ◽

Cited By ~ 1

Author(s):

Saroat Ramjan ◽

Torsten Geldsetzer ◽

Randall Scharien ◽

John Yackel

Keyword(s):

Sea Ice ◽

Regression Models ◽

Incidence Angle ◽

Early Summer ◽

Aerial Photographs ◽

Late Winter ◽

First Year ◽

Melt Pond ◽

Texture Parameters ◽

Snow Thickness

Early-summer melt pond fraction is predicted using late-winter C-band backscatter of snow-covered first-year sea ice. Aerial photographs were acquired during an early-summer 2012 field campaign in Resolute Passage, Nunavut, Canada, on smooth first-year sea ice to estimate the melt pond fraction. RADARSAT-2 Synthetic Aperture Radar (SAR) data were acquired over the study area in late winter prior to melt onset. Correlations between the melt pond fractions and late-winter linear and polarimetric SAR parameters and texture measures derived from the SAR parameters are utilized to develop multivariate regression models that predict melt pond fractions. The results demonstrate substantial capability of the regression models to predict melt pond fractions for all SAR incidence angle ranges. The combination of the most significant linear, polarimetric and texture parameters provide the best model at far-range incidence angles, with an R 2 of 0.62 and a pond fraction RMSE of 0.09. Near- and mid- range incidence angle models provide R 2 values of 0.57 and 0.61, respectively, with an RMSE of 0.11. The strength of the regression models improves when SAR parameters are combined with texture parameters. These predictions also serve as a proxy to estimate snow thickness distributions during late winter as higher pond fractions evolve from thinner snow cover.

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First-year sea ice melt pond fraction estimation from dual-polarisation C-band SAR – Part 1: In situ observations

The Cryosphere ◽

10.5194/tc-8-2147-2014 ◽

2014 ◽

Vol 8 (6) ◽

pp. 2147-2162 ◽

Cited By ~ 15

Author(s):

R. K. Scharien ◽

J. Landy ◽

D. G. Barber

Keyword(s):

Sea Ice ◽

Climate Model ◽

Morphological Changes ◽

Weather Forecast ◽

Equation Model ◽

Arctic Sea Ice ◽

First Year ◽

Ice Melt ◽

Melt Pond

Abstract. Understanding the evolution of melt ponds on Arctic sea ice is important for climate model parameterisations, weather forecast models and process studies involving mass, energy and biogeochemical exchanges across the ocean–sea ice–atmosphere interface. A field campaign was conducted in a region of level first-year sea ice (FYI) in the central Canadian Arctic Archipelago (CAA), during the summer of 2012, to examine the potential for estimating melt pond fraction (fp) from satellite synthetic aperture radar (SAR). In this study, 5.5 GHz (C-band) dual co- (HH + VV – horizontal transmit and horizontal receive + vertical transmit and vertical receive) and cross-polarisation (HV + HH – horizontal transmit and vertical receive + horizontal transmit and horizontal receive) radar scatterometer measurements of melt-pond-covered FYI are combined with ice and pond properties to analyse the effects of in situ physical and morphological changes on backscatter parameters. Surface roughness statistics of ice and ponds are characterised and compared to the validity domains of the Bragg and integral equation model (IEM) scattering models. Experimental and model results are used to outline the potential and limitations of the co-polarisation ratio (VV / HH) for retrieving melt pond information, including fp, at large incidence angles (≥35°). Despite high variability in cross-polarisation ratio (HV / HH) magnitudes, increases at small incidence angles (<30°) are attributed to the formation of ice lids on ponds. Implications of the results for pond information retrievals from satellite C-, L- and P-band SARs are discussed.

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Sea-ice melt CO<sub>2</sub>–carbonate chemistry in the western Arctic Ocean: meltwater contributions to air–sea CO<sub>2</sub> gas exchange, mixed-layer properties and rates of net community production under sea ice

Biogeosciences ◽

10.5194/bg-11-6769-2014 ◽

2014 ◽

Vol 11 (23) ◽

pp. 6769-6789 ◽

Cited By ~ 16

Author(s):

N. R. Bates ◽

R. Garley ◽

K. E. Frey ◽

K. L. Shake ◽

J. T. Mathis

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Mixed Layer ◽

Pond Water ◽

Carbonate Chemistry ◽

Western Arctic Ocean ◽

Ice Melt ◽

Melt Pond ◽

Melt Ponds ◽

Western Arctic

Abstract. The carbon dioxide (CO2)-carbonate chemistry of sea-ice melt and co-located, contemporaneous seawater has rarely been studied in sea-ice-covered oceans. Here, we describe the CO2–carbonate chemistry of sea-ice melt (both above sea-ice as "melt ponds" and below sea-ice as "interface waters") and mixed-layer properties in the western Arctic Ocean in the early summer of 2010 and 2011. At 19 stations, the salinity (∼0.5 to <6.5), dissolved inorganic carbon (DIC; ∼20 to <550 μmol kg−1) and total alkalinity (TA; ∼30 to <500 μmol kg−1) of above-ice melt pond water was low compared to the co-located underlying mixed layer. The partial pressure of CO2 (pCO2) in these melt ponds was highly variable (∼<10 to >1500 μatm) with the majority of melt ponds acting as potentially strong sources of CO2 to the atmosphere. The pH of melt pond waters was also highly variable ranging from mildly acidic (6.1 to 7) to slightly more alkaline than underlying seawater (>8.2 to 10.8). All of the observed melt ponds had very low (<0.1) saturation states (Ω) for calcium carbonate (CaCO3) minerals such as aragonite (Ωaragonite). Our data suggest that sea-ice generated alkaline or acidic type melt pond water. This melt water chemistry dictates whether the ponds are sources of CO2 to the atmosphere or CO2 sinks. Below-ice interface water CO2–carbonate chemistry data also indicated substantial generation of alkalinity, presumably owing to dissolution of CaCO3 in sea-ice. The interface waters generally had lower pCO2 and higher pH/Ωaragonite than the co-located mixed layer beneath. Sea-ice melt thus contributed to the suppression of mixed-layer pCO2, thereby enhancing the surface ocean's capacity to uptake CO2 from the atmosphere. Our observations contribute to growing evidence that sea-ice CO2–carbonate chemistry is highly variable and its contribution to the complex factors that influence the balance of CO2 sinks and sources (and thereby ocean acidification) is difficult to predict in an era of rapid warming and sea-ice loss in the Arctic Ocean.

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