scholarly journals Melt pond distribution and geometry in high Arctic sea ice derived from aerial investigations

2016 ◽  
Vol 57 (73) ◽  
pp. 105-118 ◽  
Author(s):  
W. Huang ◽  
P. Lu ◽  
R. Lei ◽  
H. Xie ◽  
Z. Li
Keyword(s):  
Sea Ice ◽  
Ground Truth ◽  
High Arctic ◽  
Arctic Sea Ice ◽  
Melt Pond ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Areal Fraction ◽  
Pond Size ◽  
Arctic Ice

ABSTRACTAerial photography was conducted in the high Arctic Ocean during a Chinese research expedition in summer 2010. By partitioning the images into three distinct surface categories (sea ice/snow, water and melt ponds), the areal fraction of each category, ice concentration and the size and geometry of individual melt ponds, are determined with high-spatial resolution. The ice concentration and melt pond coverage have large spatial deviations between flights and even between images from the marginal ice zone to the pack ice zone in the central Arctic. Ice concentration and pond coverage over high Arctic (from 84°N to north) was ~75% and ~6.8%, respectively, providing ‘ground truth’ for the unusual transpolar reduction strip of ice indicated concurrently by AMSR-E data and for the regions (north of 88°N) where no passive microwave sensors can cover. Melt pond size and shape distributions are examined in terms of pond area (S), perimeter (P), mean caliper dimension (MCD) (L), roundness (R), convex degree (C), the ratio of P/S and fractal dimension (D). Power-law relationships are developed between pond size and number. Some general trends in geometric metrics are identified as a function of pond area including R, C, P/S and D. The scale separation of pond complexity is demonstrated by analyzing area-perimeter data. The results will potentially help the modelling of melt pond evolution and the determination of heterogeneity of under-ice transmitted light fields.

scholarly journals Application of Aerosondes to Melt-Pond Observations over Arctic Sea Ice

2008 ◽  
Vol 25 (2) ◽  
pp. 327-334 ◽  
Author(s):  
Jun Inoue ◽  
Judith A. Curry ◽  
James A. Maslanik
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
First Year ◽  
Melt Pond ◽  
Earth Observing System ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Aerial Vehicle

Abstract Continuous observation of sea ice using a small robotic aircraft called the Aerosonde was made over the Arctic Ocean from Barrow, Alaska, on 20–21 July 2003. Over a region located 350 km off the coast of Barrow, images obtained from the aircraft were used to characterize the sea ice and to determine the fraction of melt ponds on both multiyear and first-year ice. Analysis of the data indicates that melt-pond fraction increased northward from 20% to 30% as the ice fraction increased. However, the fraction of ponded ice was over 30% in the multiyear ice zone while it was about 25% in the first-year ice zone. A comparison with a satellite microwave product showed that the ice concentration derived from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) has a negative bias of 7% due to melt ponds. These analyses demonstrate the utility of recent advances in unmanned aerial vehicle (UAV) technology for monitoring and interpreting the spatial variations in the sea ice with melt ponds.

Inter-comparison of melt pond products with melt/freeze-up dates and sea ice concentration data

2020 ◽  
Author(s):  
Sanggyun Lee ◽  
Julienne Stroeve ◽  
Michel Tsamados
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Solar Heat ◽  
Melt Pond ◽  
Ice Surface ◽  
Melt Ponds ◽  
Ice Concentration

<p> Melt ponds are a dominant feature on the Arctic sea ice surface in summer, occupying up to about 50 – 60% of the sea ice surface during advanced melt. Melt ponds normally begin to form around mid-May in the marginal ice zone and expand northwards as the summer melt season progresses. Once melt ponds emerge, the scattering characteristics of the ice surface changes, dramatically lowering the sea ice albedo. Since 96% of the total annual solar heat into the ocean through sea ice occurs between May and August, the presence of melt ponds plays a significant role in this transfer of solar heat, influencing not only the sea ice energy balance, but also the amount of light available under the sea ice and ocean primary productivity. Given the importance melt ponds play in the coupled Arctic climate-ecosystem, mapping and quantification of melt pond variability on a Pan-Arctic basin scale are needed. Satellite-based observations are the only way to map melt ponds and albedo changes on a pan-Arctic scale. Rösel et al. (2012) utilized a MODIS 8-day average product to map melt ponds on a pan-Arctic scale and over several years. In another approach, melt pond fraction and surface albedo were retrieved based on the physical and optical characteristics of sea ice and melt ponds without a priori information using MERIS.Here, we propose a novel machine learning-based methodology to map Arctic melt ponds from MODIS 500m resolution data. We provide a merging procedure to create the first pan-Arctic melt pond product spanning a 20-year period at a weekly temporal resolution. Specifically, we use MODIS data together with machine learning, including multi-layer neural network and logistic regression to test our ability to map melt ponds from the start to the end of the melt season. Since sea ice reflectance is strongly dependent on the viewing and solar geometry (i.e. sensor and solar zenith and azimuth angles), we attempt to minimize this dependence by using normalized band ratios in the machine learning algorithms. Each melt pond retrieval algorithm is different and validation ways are different as well producing somewhat dissimilar melt pond results. In this study, we inter-compare melt ponds products from different institutes, including university of Hamburg, university of Bremen, and university college London. The melt pond maps are compared with melt onset and freeze-up dates data and sea ice concentration. The melt pond maps are evaluated by melt pond fraction statistics from high resolution satellite (MEDEA) images that have not been used for the evaluation in melt pond products. </p>

scholarly journals Investigation of spatiotemporal variability of melt pond fraction and its relationship with sea ice extent during 2000–2017 using a new data

2019 ◽  
Author(s):  
Yifan Ding ◽  
Xiao Cheng ◽  
Jiping Liu ◽  
Fengming Hui ◽  
Zhenzhan Wang
Keyword(s):  
Sea Ice ◽  
Arctic Basin ◽  
Beaufort Sea ◽  
Arctic Sea Ice ◽  
Spatiotemporal Variability ◽  
First Year ◽  
Spatial And Temporal Variability ◽  
Sea Ice Extent ◽  
Melt Pond ◽  
Melt Ponds

Abstract. The accurate knowledge of variations of melt ponds is important for understanding Arctic energy budget due to its albedo-transmittance-melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) from the MODIS surface reflectance. We construct an ensemble-based deep neural network and use in-situ observations of MPF from multi-sources to train the network. The results show that our derived MPF is in good agreement with the observations, and relatively outperforms the MPF retrieved by University of Hamburg. Built on this, we create a new MPF data from 2000 to 2017 (the longest data in our knowledge), and analyze the spatial and temporal variability of MPF. It is found that the MPF has significant increasing trends from late July to early September, which is largely contributed by the MPF over the first-year sea ice. The analysis based on our MPF during 2000–2017 confirms that the integrated MPF to late June does promise to improve the prediction skill of seasonal Arctic sea ice minimum. However, our MPF data shows concentrated significant correlations first appear in a band, extending from the eastern Beaufort Sea, through the central Arctic, to the northern East Siberian and Laptev Seas in early-mid June, and then shifts towards large areas of the Beaufort Sea, Canadian Arctic, the northern Greenland Sea and the central Arctic basin.

scholarly journals Monitoring evolution of melt ponds on first-year and multiyear sea ice in the Canadian Arctic Archipelago with optical satellite data

2020 ◽  
Vol 61 (82) ◽  
pp. 154-163
Author(s):  
Qing Li ◽  
Chunxia Zhou ◽  
Lei Zheng ◽  
Tingting Liu ◽  
Xiaotong Yang
Keyword(s):  
Sea Ice ◽  
Canadian Arctic ◽  
Arctic Sea Ice ◽  
First Year ◽  
Landsat 8 ◽  
Canadian Arctic Archipelago ◽  
Melt Pond ◽  
Melt Ponds ◽  
Validation Experiment ◽  
Multiyear Ice

AbstractThe evolution of melt ponds on Arctic sea ice in summer is one of the main factors that affect sea-ice albedo and hence the polar climate system. Due to the different spectral properties of open water, melt pond and sea ice, the melt pond fraction (MPF) can be retrieved using a fully constrained least-squares algorithm, which shows a high accuracy with root mean square error ~0.06 based on the validation experiment using WorldView-2 image. In this study, the evolution of ponds on first-year and multiyear ice in the Canadian Arctic Archipelago was compared based on Sentinel-2 and Landsat 8 images. The relationships of pond coverage with air temperature and albedo were analysed. The results show that the pond coverage on first-year ice changed dramatically with seasonal maximum of 54%, whereas that on multiyear ice changed relatively flat with only 30% during the entire melting period. During the stage of pond formation, the ponds expanded rapidly when the temperature increased to over 0°C for three consecutive days. Sea-ice albedo shows a significantly negative correlation (R = −1) with the MPF in melt season and increases gradually with the refreezing of ponds and sea ice.

scholarly journals Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery

2020 ◽  
Vol 12 (16) ◽  
pp. 2623 ◽  
Author(s):  
Marcel König ◽  
Gerit Birnbaum ◽  
Natascha Oppelt
Keyword(s):  
Sea Ice ◽  
Hyperspectral Imagery ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Arctic Sea Ice ◽  
Atmospheric Conditions ◽  
Melt Pond ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Highly Correlated

Hyperspectral remote-sensing instruments on unmanned aerial vehicles (UAVs), aircraft and satellites offer new opportunities for sea ice observations. We present the first study using airborne hyperspectral imagery of Arctic sea ice and evaluate two atmospheric correction approaches (ATCOR-4 (Atmospheric and Topographic Correction version 4; v7.0.0) and empirical line calibration). We apply an existing, field data-based model to derive the depth of melt ponds, to airborne hyperspectral AisaEAGLE imagery and validate results with in situ measurements. ATCOR-4 results roughly match the shape of field spectra but overestimate reflectance resulting in high root-mean-square error (RMSE) (between 0.08 and 0.16). Noisy reflectance spectra may be attributed to the low flight altitude of 200 ft and Arctic atmospheric conditions. Empirical line calibration resulted in smooth, accurate spectra (RMSE < 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated (r = 0.86) and accurate (RMSE = 4.04 cm), and the model explains a large portion of the variability (R2 = 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne hyperspectral data is possible subject to accurate atmospheric correction. Furthermore, we see the necessity to improve existing approaches with Arctic-specific atmospheric profiles and aerosol models and/or by using multiple reference targets on the ground.

Unexpectedly high dimethyl sulfide concentration in high-latitude Arctic sea ice melt ponds

2019 ◽  
Vol 21 (10) ◽  
pp. 1642-1649 ◽  
Author(s):  
Keyhong Park ◽  
Intae Kim ◽  
Jung-Ok Choi ◽  
Youngju Lee ◽  
Jinyoung Jung ◽  
...  
Keyword(s):  
Sea Ice ◽  
High Latitude ◽  
Dimethyl Sulfide ◽  
Arctic Sea Ice ◽  
Sulfide Concentration ◽  
Sea Ice Concentration ◽  
Ice Melt ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Ice Concentration

Dimethyl sulfide (DMS) production in the northern Arctic Ocean has been considered to be minimal because of high sea ice concentration and extremely low productivity.

scholarly journals Comparison of different retrieval techniques for melt ponds on Arctic sea ice from Landsat and MODIS satellite data

2011 ◽  
Vol 52 (57) ◽  
pp. 185-191 ◽  
Author(s):  
Anja Rösel ◽  
Lars Kaleschke
Keyword(s):  
Sea Ice ◽  
Principal Component ◽  
Arctic Sea Ice ◽  
Additional Advantage ◽  
Melt Pond ◽  
Spectral Bands ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Very High Spatial Resolution ◽  
Landsat 7

AbstractMelt ponds are regularly observed on the surface of Arctic sea ice in late spring and summer. They strongly reduce the surface albedo and accelerate the decay of Actic sea ice. Until now, only a few studies have looked at the spatial extent of melt ponds on Arctic sea ice. Knowledge of the melt-pond distribution on the entire Arctic sea ice would provide a solid basis for the parameterization of melt ponds in existing sea-ice models. Due to the different spectral properties of snow, ice and water, a multispectral sensor such as Landsat 7 ETM+ is generally applicable for the analysis of distribution. an additional advantage of the ETM+ sensor is the very high spatial resolution (30 m). an algorithm based on a principal component analysis (PCA) of two spectral channels has been developed in order to determine the melt-pond fraction. PCA allows differentiation of melt ponds and other surface types such as snow, ice or water. Spectral bands 1 and 4 with central wavelengths at 480 and 770 nm, respectively, are used as they represent the differences in the spectral albedo of melt ponds. A Landsat 7 ETM+ scene from 19 July 2001 was analysed using PCA. the melt-pond fraction determined by the PCA method yields a different spatial distribution of the ponded areas from that developed by others. A MODIS subset from the same date and area is also analysed. the classification of MODIS data results in a higher melt-pond fraction than both Landsat classifications.

scholarly journals Understanding model spread in sea ice volume by attribution of model differences in seasonal ice growth and melt

2021 ◽  
Author(s):  
Alex West ◽  
Ed Blockley ◽  
Mat Collins
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Surface Flux ◽  
Early Summer ◽  
Arctic Sea Ice ◽  
Ice Growth ◽  
Melt Pond ◽  
Ice Volume ◽  
Melt Ponds ◽  
Future Loss

Abstract. Arctic sea ice is declining rapidly, but predictions of its future loss are made difficult by the large spread both in present-day and in future sea ice area and volume; hence, there is a need to better understand the drivers of model spread in sea ice state. Here we present a framework for understanding differences between modelled sea ice simulations based on attributing seasonal ice growth and melt differences. In the method presented, the net downward surface flux is treated as the principal driver of seasonal sea ice growth and melt. A system of simple models is used to estimate the pointwise effect of model differences in key Arctic climate variables on this surface flux, and hence on seasonal sea ice growth and melt. We compare three models with very different historical sea ice simulations: HadGEM2-ES, HadGEM3-GC3.1 and UKESM1.0. The largest driver of differences in ice growth / melt between these models is shown to be the ice area in summer (representing the surface albedo feedback) and the ice thickness distribution in winter (the thickness-growth feedback). Differences in snow and melt-pond cover during the early summer exert a smaller effect on the seasonal growth and melt, hence representing the drivers of model differences in both this and in the sea ice volume. In particular, the direct impacts on sea ice growth / melt of differing model parameterisations of snow area and of melt-ponds are shown to be small but non-negligible.

scholarly journals Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017

2019 ◽  
Vol 65 (253) ◽  
pp. 813-821 ◽  
Author(s):  
Longjiang Mu ◽  
Xi Liang ◽  
Qinghua Yang ◽  
Jiping Liu ◽  
Fei Zheng
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Ocean Prediction ◽  
Arctic Ice

AbstractIn an effort to improve the reliability of Arctic sea-ice predictions, an ensemble-based Arctic Ice Ocean Prediction System (ArcIOPS) has been developed to meet operational demands. The system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model. A localized error subspace transform ensemble Kalman filter is used to assimilate the weekly merged CryoSat-2 and Soil Moisture and Ocean Salinity sea-ice thickness data together with the daily Advanced Microwave Scanning Radiometer 2 (AMSR2) sea-ice concentration data. The weather forecasts from the Global Forecast System of the National Centers for Environmental Prediction drive the sea ice–ocean coupled model. The ensemble mean sea-ice forecasts were used to facilitate the Chinese National Arctic Research Expedition in summer 2017. The forecasted sea-ice concentration is evaluated against AMSR2 and Special Sensor Microwave Imager/Sounder sea-ice concentration data. The forecasted sea-ice thickness is compared to the in-situ observations and the Pan-Arctic Ice-Ocean Modeling and Assimilation System. These comparisons show the promising potential of ArcIOPS for operational Arctic sea-ice forecasts. Nevertheless, the forecast bias in the Beaufort Sea calls for a delicate parameter calibration and a better design of the assimilation system.

scholarly journals Arctic sea-ice conditions and the distribution of solar radiation during summer

1997 ◽  
Vol 25 ◽  
pp. 445-450 ◽  
Author(s):  
Donald K. Perovich ◽  
Walter B. Tucker
Keyword(s):  
Solar Radiation ◽  
Sea Ice ◽  
Open Water ◽  
Ice Cover ◽  
Feedback Mechanism ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Melt Ponds ◽  
Arctic Ice ◽  
Median Area

Understanding the interaction of solar radiation with the ice cover is critical in determining the heat and mass balance of the Arctic ice pack, and in assessing potential impacts due to climate change. Because of the importance of the ice-albedo feedback mechanism, information on the surface state of the ice cover is needed. Observations of the surface slate of sea ice were obtained from helicopter photography missions made during the 1994 Arctic Ocean Section cruise. Photographs from one flight, taken during the height of the melt season (31 July 1994) at 76° N, 172° W, were analyzed in detail. Bare ice covered 82% of the total area, melt ponds 12%, and open water 6%, There was considerable variability in these area fractions on scales &lt; 1 km2. Sample areas &gt;2 3 km2gave representative values of ice concentration and pond fraction. Melt ponds were numerous, with a number density of 1800 ponds km-2. The melt ponds had a mean area of 62 m2a median area of 14 m2, and a size distribution that was well lit by a cumulative lognormal distribution. While leads make up only a small portion of the total area, they are the source of virtually all of the solar energy input to the ocean.

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