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

2014 ◽  
Vol 8 (1) ◽  
pp. 845-885 ◽  
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.

scholarly journals First-year sea ice melt pond fraction estimation from dual-polarisation C-band SAR – Part 2: Scaling in situ to Radarsat-2

2014 ◽  
Vol 8 (6) ◽  
pp. 2163-2176 ◽  
Author(s):  
R. K. Scharien ◽  
K. Hochheim ◽  
J. Landy ◽  
D. G. Barber
Keyword(s):  
Sea Ice ◽  
Model Performance ◽  
Free Water ◽  
Aerial Survey ◽  
Low Noise ◽  
Light Transmittance ◽  
First Year ◽  
Ice Melt ◽  
Melt Pond ◽  
Mean Square Errors

Abstract. Sea ice melt pond fraction (fp), linked with lower sea ice surface albedo and increased light transmittance to the ocean, is inadequately parameterised in sea ice models due to a lack of observations. In this paper, results from a multi-scale remote-sensing program dedicated to the retrieval of level first-year sea ice (FYI) fp from dual co- and cross-polarisation C-band synthetic aperture radar (SAR) backscatter are detailed. Models which utilise the dominant effect of free-water melt ponds on the VV / HH (vertical transmit and vertical receive / horizontal transmit and horizontal receive) polarisation ratio at high incidence angles are tested for their ability to provide estimates of the subscale fp. Retrieved fp from noise-corrected Radarsat-2 quad-polarisation scenes are in good agreement with observations from coincident aerial survey data, with root mean square errors (RMSEs) of 0.05–0.07 obtained during intermediate and late stages of ponding. Weak model performance is attributed to the presence of wet snow and slush during initial ponding, and a synoptically driven freezing event causing ice lids to form on ponds. The HV / HH (horizontal transmit and vertical receive / horizontal transmit and horizontal receive) ratio explains a greater portion of variability in fp, compared to VV / HH, when ice lids are present. Generally low HV channel intensity suggests limited applications using dual cross-polarisation data, except with systems that have exceptionally low noise floors. Results demonstrate the overall potential of dual-polarisation SAR for standalone or complementary observations of fp for process-scale studies and improvements to model parameterisations.

scholarly journals Inorganic carbon dynamics of melt pond-covered first year sea ice in the Canadian Arctic

2014 ◽  
Vol 11 (5) ◽  
pp. 7485-7519 ◽  
Author(s):  
N.-X. Geilfus ◽  
R. J. Galley ◽  
O. Crabeck ◽  
T. Papakyriakou ◽  
J. Landy ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
First Year ◽  
Ice Melt ◽  
Melt Pond ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Melt Water

Abstract. Melt pond formation is a common feature of the spring and summer Arctic sea ice. However, the role of the melt ponds formation and the impact of the sea ice melt on both the direction and size of CO2 flux between air and sea is still unknown. Here we describe the CO2-carbonate chemistry of melting sea ice, melt ponds and the underlying seawater associated with measurement of CO2 fluxes across first year landfast sea ice in the Resolute Passage, Nunavut, in June 2012. Early in the melt season, the increase of the ice temperature and the subsequent decrease of the bulk ice salinity promote a strong decrease of the total alkalinity (TA), total dissolved inorganic carbon (TCO2) and partial pressure of CO2 (pCO2) within the bulk sea ice and the brine. Later on, melt pond formation affects both the bulk sea ice and the brine system. As melt ponds are formed from melted snow the in situ melt pond pCO2 is low (36 μatm). The percolation of this low pCO2 melt water into the sea ice matrix dilutes the brine resulting in a strong decrease of the in situ brine pCO2 (to 20 μatm). As melt ponds reach equilibrium with the atmosphere, their in situ pCO2 increase (up to 380 μatm) and the percolation of this high concentration pCO2 melt water increase the in situ brine pCO2 within the sea ice matrix. The low in situ pCO2 observed in brine and melt ponds results in CO2 fluxes of −0.04 to −5.4 mmol m–2 d–1. As melt ponds reach equilibrium with the atmosphere, the uptake becomes less significant. However, since melt ponds are continuously supplied by melt water their in situ pCO2 still remains low, promoting a continuous but moderate uptake of CO2 (~ −1mmol m–2 d–1). The potential uptake of atmospheric CO2 by melting sea ice during the Arctic summer has been estimated from 7 to 16 Tg of C ignoring the role of melt ponds. This additional uptake of CO2 associated to Arctic sea ice needs to be further explored and considered in the estimation of the Arctic Ocean's overall CO2 budget.

scholarly journals Inorganic carbon dynamics of melt-pond-covered first-year sea ice in the Canadian Arctic

2015 ◽  
Vol 12 (6) ◽  
pp. 2047-2061 ◽  
Author(s):  
N.-X. Geilfus ◽  
R. J. Galley ◽  
O. Crabeck ◽  
T. Papakyriakou ◽  
J. Landy ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
First Year ◽  
Co2 Fluxes ◽  
Ice Melt ◽  
Melt Pond ◽  
Melt Ponds ◽  
Arctic Sea

Abstract. Melt pond formation is a common feature of spring and summer Arctic sea ice, but the role and impact of sea ice melt and pond formation on both the direction and size of CO2 fluxes between air and sea is still unknown. Here we report on the CO2–carbonate chemistry of melting sea ice, melt ponds and the underlying seawater as well as CO2 fluxes at the surface of first-year landfast sea ice in the Resolute Passage, Nunavut, in June 2012. Early in the melt season, the increase in ice temperature and the subsequent decrease in bulk ice salinity promote a strong decrease of the total alkalinity (TA), total dissolved inorganic carbon (TCO2) and partial pressure of CO2 (pCO2) within the bulk sea ice and the brine. As sea ice melt progresses, melt ponds form, mainly from melted snow, leading to a low in situ melt pond pCO2 (36 μatm). The percolation of this low salinity and low pCO2 meltwater into the sea ice matrix decreased the brine salinity, TA and TCO2, and lowered the in situ brine pCO2 (to 20 μatm). This initial low in situ pCO2 observed in brine and melt ponds results in air–ice CO2 fluxes ranging between −0.04 and −5.4 mmol m−2 day−1 (negative sign for fluxes from the atmosphere into the ocean). As melt ponds strive to reach pCO2 equilibrium with the atmosphere, their in situ pCO2 increases (up to 380 μatm) with time and the percolation of this relatively high concentration pCO2 meltwater increases the in situ brine pCO2 within the sea ice matrix as the melt season progresses. As the melt pond pCO2 increases, the uptake of atmospheric CO2 becomes less significant. However, since melt ponds are continuously supplied by meltwater, their in situ pCO2 remains undersaturated with respect to the atmosphere, promoting a continuous but moderate uptake of CO2 (~ −1 mmol m−2 day−1) into the ocean. Considering the Arctic seasonal sea ice extent during the melt period (90 days), we estimate an uptake of atmospheric CO2 of −10.4 Tg of C yr−1. This represents an additional uptake of CO2 associated with Arctic sea ice that needs to be further explored and considered in the estimation of the Arctic Ocean's overall CO2 budget.

scholarly journals Reflective properties of melt ponds on sea ice

2018 ◽  
Vol 12 (6) ◽  
pp. 1921-1937 ◽  
Author(s):  
Aleksey Malinka ◽  
Eleonora Zege ◽  
Larysa Istomina ◽  
Georg Heygster ◽  
Gunnar Spreen ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Atmospheric Correction ◽  
Scattering Coefficient ◽  
Spherical Particles ◽  
The Arctic ◽  
Melt Pond ◽  
Melt Ponds ◽  
Bottom Albedo

Abstract. Melt ponds occupy a large part of the Arctic sea ice in summer and strongly affect the radiative budget of the atmosphere–ice–ocean system. In this study, the melt pond reflectance is considered in the framework of radiative transfer theory. The melt pond is modeled as a plane-parallel layer of pure water upon a layer of sea ice (the pond bottom). We consider pond reflection as comprising Fresnel reflection by the water surface and multiple reflections between the pond surface and its bottom, which is assumed to be Lambertian. In order to give a description of how to find the pond bottom albedo, we investigate the inherent optical properties of sea ice. Using the Wentzel–Kramers–Brillouin approximation approach to light scattering by non-spherical particles (brine inclusions) and Mie solution for spherical particles (air bubbles), we conclude that the transport scattering coefficient in sea ice is a spectrally independent value. Then, within the two-stream approximation of the radiative transfer theory, we show that the under-pond ice spectral albedo is determined by two independent scalar values: the transport scattering coefficient and ice layer thickness. Given the pond depth and bottom albedo values, the bidirectional reflectance factor (BRF) and albedo of a pond can be calculated with analytical formulas. Thus, the main reflective properties of the melt pond, including their spectral dependence, are determined by only three independent parameters: pond depth z, ice layer thickness H, and transport scattering coefficient of ice σt.The effects of the incident conditions and the atmosphere state are examined. It is clearly shown that atmospheric correction is necessary even for in situ measurements. The atmospheric correction procedure has been used in the model verification. The optical model developed is verified with data from in situ measurements made during three field campaigns performed on landfast and pack ice in the Arctic. The measured pond albedo spectra were fitted with the modeled spectra by varying the pond parameters (z, H, and σt). The coincidence of the measured and fitted spectra demonstrates good performance of the model: it is able to reproduce the albedo spectrum in the visible range with RMSD that does not exceed 1.5 % for a wide variety of melt pond types observed in the Arctic.

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

2014 ◽  
Vol 8 (6) ◽  
pp. 2147-2162 ◽  
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.

scholarly journals A linear model to derive melt pond depth from hyperspectral data

2019 ◽  
Author(s):  
Marcel König ◽  
Natascha Oppelt
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Large Scale ◽  
Hyperspectral Data ◽  
Arctic Sea Ice ◽  
Optical Data ◽  
Melt Pond ◽  
Arctic Sea ◽  
Melt Water

Abstract. Melt ponds are key elements in the energy balance of Arctic sea ice. Observing their temporal evolution is crucial for understanding melt processes and predicting sea ice evolution. Remote sensing is the only technique that enables large-scale observations of Arctic sea ice. However, monitoring vertical melt pond evolution in this way is challenging because most of the optical signal reflected by a pond is defined by the scattering characteristics of the underlying ice. Without knowing the influence of melt water on the reflected signal, the water depth cannot be determined. To solve the problem, we simulated the way melt water changes the reflected spectra of bare ice. We developed a model based on the slope of the log-scaled remote sensing reflectance at 710 nm. We validated the model using 49 in situ melt pond spectra and corresponding depths from ponds on dark and bright ice. Retrieved pond depths are precise (RMSE = 2.81 cm) and highly correlated with in situ measurements (r = 0.89; p = 4.34e−17). The model further explains a large portion of the variation in pond depth (R2 = 0.74). Our results indicate that pond depth is retrievable from optical data under clear sky conditions. This technique is potentially transferrable to hyperspectral remote sensors on UAVs, aircraft and satellites.

scholarly journals A mechanism that produces dichotomy in melt pond coverage in sea ice floes

2016 ◽  
Author(s):  
Predrag Popović ◽  
Dorian S. Abbot
Keyword(s):  
Fixed Point ◽  
Sea Ice ◽  
Large Scale ◽  
Bimodal Distribution ◽  
Melting Behavior ◽  
The Arctic ◽  
Scale Model ◽  
Melt Pond ◽  
Melt Ponds ◽  
Ice Floes

Abstract. Late in the melt season, sea ice floes in the Arctic have been observed to exhibit a large range in melt pond coverage, from heavily ponded to almost pond free. Some of these observations are consistent with a bimodal distribution in pond coverage with few intermediately ponded ice floes. We present a model for the evolution of melt ponds on sea ice floes in which conservation of hydrostatic balance in response to melt creates an unstable fixed point in pond coverage: if the initial pond coverage is below a threshold value the floe becomes unponded, and if it is above the threshold the floe becomes heavily ponded. Whether the fixed point is physically realistic depends on the differential melting rates of different points on the ice: ice at the perimeter of ponds needs to melt sufficiently slower than bare ice on average. Interestingly, this shows that the melting behavior of the narrow boundary between bare ice and melt ponds can govern the melt pond evolution of the entire ice floe. Since melt pond coverage is one of the key parameters controlling the albedo of sea ice, understanding the mechanisms that control the distribution of pond coverage will help us improve large-scale model parameterizations and sea ice forecasts in a warming climate.

scholarly journals The melt pond fraction and spectral sea ice albedo retrieval from MERIS data: validation and trends of sea ice albedo and melt pond fraction in the Arctic for years 2002–2011

2014 ◽  
Vol 8 (5) ◽  
pp. 5227-5292 ◽  
Author(s):  
L. Istomina ◽  
G. Heygster ◽  
M. Huntemann ◽  
P. Schwarz ◽  
G. Birnbaum ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Imaging Spectrometer ◽  
Melt Pond ◽  
Result Show ◽  
Area North ◽  
Ice Floes ◽  
Multiyear Ice

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 on the heat balance and mass balance of sea ice. An algorithm to retrieve melt pond fraction and sea ice albedo (Zege et al., 2014) from the MEdium Resolution Imaging Spectrometer (MERIS) data is validated against aerial, ship borne and in situ campaign data. The result show the best correlation for landfast and multiyear ice of high ice concentrations (albedo: R = 0.92, RMS = 0.068, melt pond fraction: R = 0.6, RMS = 0.065). The correlation for lower ice concentrations, subpixel ice floes, blue ice and wet ice is lower due to complicated surface conditions and ice drift. Combining all aerial observations gives a mean albedo RMS equal to 0.089 and a mean melt pond fraction RMS equal to 0.22. The in situ melt pond fraction correlation is R = 0.72 with an RMS = 0.14. Ship cruise data might be affected by documentation of varying accuracy within the ASPeCT protocol, which is the reason for discrepancy between the satellite value and observed value: mean R = 0.21, 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. The case studies and trend analysis for the whole MERIS period (2002–2011) show pronounced and reasonable spatial features of melt pond fractions and sea ice albedo. The most prominent feature is the melt onset shifting towards spring (starting already in weeks 3 and 4 of June) within the multiyear ice area, north to the Queen Elizabeth Islands and North Greenland.

scholarly journals Regional melt-pond fraction and albedo of thin Arctic first-year drift ice in late summer

2015 ◽  
Vol 9 (1) ◽  
pp. 255-268 ◽  
Author(s):  
D. V. Divine ◽  
M. A. Granskog ◽  
S. R. Hudson ◽  
C. A. Pedersen ◽  
T. I. Karlsen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
First Year ◽  
Surface Type ◽  
Melt Pond ◽  
Melt Ponds

Abstract. The paper presents a case study of the regional (≈150 km) morphological and optical properties of a relatively thin, 70–90 cm modal thickness, first-year Arctic sea ice pack in an advanced stage of melt. The study combines in situ broadband albedo measurements representative of the four main surface types (bare ice, dark melt ponds, bright melt ponds and open water) and images acquired by a helicopter-borne camera system during ice-survey flights. The data were collected during the 8-day ICE12 drift experiment carried out by the Norwegian Polar Institute in the Arctic, north of Svalbard at 82.3° N, from 26 July to 3 August 2012. A set of > 10 000 classified images covering about 28 km2 revealed a homogeneous melt across the study area with melt-pond coverage of ≈ 0.29 and open-water fraction of ≈ 0.11. A decrease in pond fractions observed in the 30 km marginal ice zone (MIZ) occurred in parallel with an increase in open-water coverage. The moving block bootstrap technique applied to sequences of classified sea-ice images and albedo of the four surface types yielded a regional albedo estimate of 0.37 (0.35; 0.40) and regional sea-ice albedo of 0.44 (0.42; 0.46). Random sampling from the set of classified images allowed assessment of the aggregate scale of at least 0.7 km2 for the study area. For the current setup configuration it implies a minimum set of 300 images to process in order to gain adequate statistics on the state of the ice cover. Variance analysis also emphasized the importance of longer series of in situ albedo measurements conducted for each surface type when performing regional upscaling. The uncertainty in the mean estimates of surface type albedo from in situ measurements contributed up to 95% of the variance of the estimated regional albedo, with the remaining variance resulting from the spatial inhomogeneity of sea-ice cover.

Evaluation of Oil Spill Modeling in Ice Against In Situ Drifter Data from the Beaufort Sea

2017 ◽  
Vol 2017 (1) ◽  
pp. 1523-1542 ◽  
Author(s):  
Deborah French-McCay ◽  
Tayebeh Tajalli Bakhsh ◽  
Malcolm L. Spaulding
Keyword(s):  
Oil Spill ◽  
Large Scale ◽  
Model Performance ◽  
Beaufort Sea ◽  
The Arctic ◽  
Time Averaging ◽  
Modeling Approach ◽  
Pack Ice ◽  
Ice Model

ABSTRACT While coupled ice-ocean models provide reliable hindcasts and large-scale predictions of ice conditions and movements in the Arctic, to date, operational models have not been implemented with sufficient spatial resolution or skill to define sea ice characteristics and dynamics needed for high resolution oil spill trajectory forecast modeling. Recently (2015) Nansen Environmental and Remote Sensing Centre (NERSC) researchers updated their modeling approach and rheology used for pack ice. They found that using the newly developed Elasto-Brittle (EB) model showed significant improvement in performance over the present Elastic-Viscous-Plastic (EVP) modeling approach used in the operational forecast and reanalysis versions of their TOPAZ4 coupled ice-ocean model. NERSC also integrated a wave-in-ice model (WIM) into a newly updated version of TOPAZ, to characterize waves in the Marginal Ice Zone (MIZ). RPS ASA’s oil transport and fate models OILMAP and SIMAP (OIL/Spill Impact Model Application Package) were updated, integrating the NERSC ice modeling products for use in transport and oil weathering algorithms. Oil trajectory model simulations, using the existing publically-available TOPAZ4 and updated ice model products, were compared with available in situ drifter data for the Beaufort Sea from the International Arctic Buoy Programme (IABP). The goal was to evaluate model performance (skill) against drifters that were trapped in the pack ice where the EB/EVP rheology applies. The comparisons show that model-based trajectories increasingly diverged from observations over days and weeks due to cumulative errors. The model using EB rheology more closely agreed with the IABP observations than TOPAZ with EVP, and the updated TOPAZ showed improved model performance over TOPAZ4. However, model skill was degraded by time-averaging of ocean and ice model vectors before input to the oil spill model. Demonstrated improvement of oil-in-ice spill modeling would help meet the needs for Arctic oil spill response in the coming decades. While the accuracy of individual oil model trajectories projected weeks to months into the future would be expected to be low, in the event of a spill, forecasts could be updated frequently (on a time scale of hours to days) with satellite information, aircraft observations, drifter data, and other observations to improve reliability. The overall transport patterns and results of an ensemble of trajectories would provide useful information for planning and risk assessments based on typical current and ice movement patterns.

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