scholarly journals Impact of melt ponds on Arctic sea ice simulations from 1990 to 2007

2012 ◽  
Vol 117 (C9) ◽  
pp. n/a-n/a ◽  
Author(s):  
Daniela Flocco ◽  
David Schroeder ◽  
Daniel L. Feltham ◽  
Elizabeth C. Hunke
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Melt Ponds ◽  
Arctic Sea

scholarly journals Stable Isotope Clues to the Formation and Evolution of Refrozen Melt Ponds on Arctic Sea Ice

2018 ◽  
Vol 123 (12) ◽  
pp. 8887-8901
Author(s):  
L. Tian ◽  
Y. Gao ◽  
S. F. Ackley ◽  
S. Stammerjohn ◽  
T. Maksym ◽  
...  
Keyword(s):  
Stable Isotope ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Formation And Evolution

scholarly journals Light Permeates Seasonally Through Arctic Sea Ice

Eos ◽  
2019 ◽  
Vol 100 ◽  
Author(s):  
Katherine Kornei
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Melt Ponds ◽  
Arctic Sea

The transmission of sunlight through Arctic sea ice depends on the presence of ice, snow, and melt ponds, data collected over 6 years reveal.

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.

scholarly journals The color of melt ponds on Arctic sea ice

2018 ◽  
Vol 12 (4) ◽  
pp. 1331-1345 ◽  
Author(s):  
Peng Lu ◽  
Matti Leppäranta ◽  
Bin Cheng ◽  
Zhijun Li ◽  
Larysa Istomina ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Radiative Transfer Model ◽  
Ice Thickness ◽  
Transfer Model ◽  
Blue Color ◽  
Validation Data ◽  
Melt Ponds ◽  
Arctic Sea

Abstract. Pond color, which creates the visual appearance of melt ponds on Arctic sea ice in summer, is quantitatively investigated using a two-stream radiative transfer model for ponded sea ice. The upwelling irradiance from the pond surface is determined and then its spectrum is transformed into RGB (red, green, blue) color space using a colorimetric method. The dependence of pond color on various factors such as water and ice properties and incident solar radiation is investigated. The results reveal that increasing underlying ice thickness Hi enhances both the green and blue intensities of pond color, whereas the red intensity is mostly sensitive to Hi for thin ice (Hi  <  1.5 m) and to pond depth Hp for thick ice (Hi  >  1.5 m), similar to the behavior of melt-pond albedo. The distribution of the incident solar spectrum F0 with wavelength affects the pond color rather than its intensity. The pond color changes from dark blue to brighter blue with increasing scattering in ice, and the influence of absorption in ice on pond color is limited. The pond color reproduced by the model agrees with field observations for Arctic sea ice in summer, which supports the validity of this study. More importantly, the pond color has been confirmed to contain information about meltwater and underlying ice, and therefore it can be used as an index to retrieve Hi and Hp. Retrievals of Hi for thin ice (Hi  <  1 m) agree better with field measurements than retrievals for thick ice, but those of Hp are not good. The analysis of pond color is a new potential method to obtain thin ice thickness in summer, although more validation data and improvements to the radiative transfer model will be needed in future.

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.

Observations of melt ponds on Arctic sea ice

1998 ◽  
Vol 103 (C11) ◽  
pp. 24821-24835 ◽  
Author(s):  
Florence Fetterer ◽  
Norbert Untersteiner
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Melt Ponds ◽  
Arctic Sea

scholarly journals Meltwater sources and sinks for multiyear Arctic sea ice in summer

2021 ◽  
Vol 15 (9) ◽  
pp. 4517-4525
Author(s):  
Don Perovich ◽  
Madison Smith ◽  
Bonnie Light ◽  
Melinda Webster
Keyword(s):  
Sea Ice ◽  
Heat Budget ◽  
Arctic Sea Ice ◽  
Sources And Sinks ◽  
Ice Melt ◽  
Ice Surface ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Ice Mass Balance ◽  
Surface Heat Budget

Abstract. On Arctic sea ice, the melt of snow and sea ice generate a summertime flux of fresh water to the upper ocean. The partitioning of this meltwater to storage in melt ponds and deposition in the ocean has consequences for the surface heat budget, the sea ice mass balance, and primary productivity. Synthesizing results from the 1997–1998 SHEBA field experiment, we calculate the sources and sinks of meltwater produced on a multiyear floe during summer melt. The total meltwater input to the system from snowmelt, ice melt, and precipitation from 1 June to 9 August was equivalent to a layer of water 80 cm thick over the ice-covered and open ocean. A total of 85 % of this meltwater was deposited in the ocean, and only 15 % of this meltwater was stored in ponds. The cumulative contributions of meltwater input to the ocean from drainage from the ice surface and bottom melting were roughly equal.

Snow topography on undeformed Arctic sea ice captured by an idealized model

2020 ◽  
Author(s):  
Predrag Popovic ◽  
Justin Finkel ◽  
Mary Silber ◽  
Dorian Abbot
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Early Stage ◽  
Arctic Sea Ice ◽  
Conductive Heat ◽  
Melt Pond ◽  
Melt Ponds ◽  
Detailed Surface ◽  
Arctic Sea ◽  
Analytic Expressions

&lt;p&gt;Our ability to predict the future of Arctic sea ice is limited by ice's sensitivity to detailed surface conditions such as the distribution of snow and melt ponds. Snow on top of the ice decreases ice's thermal conductivity, increases its reflectivity, and provides a source of meltwater for melt ponds during summer that decrease the ice's albedo. Here, we develop a simple model of pre-melt ice surface topography that accurately describes snow cover on flat, undeformed ice. The model considers a surface that is a sum of randomly sized and placed ``snow dunes'' represented as Gaussian mounds. This model generalizes the &quot;void model&quot; of Popovic et al. (2018) and, as such, accurately describes the statistics of melt pond geometry. We test this model against detailed LiDAR measurements of the pre-melt snow topography. We show that the model snow-depth distribution is statistically indistinguishable from the measurements on flat ice, while small disagreement exists if the ice is deformed. We then use this model to determine analytic expressions for the conductive heat flux through the ice and for melt pond coverage evolution during an early stage of pond formation. We also formulate a criterion for ice to remain pond-free throughout the summer. Results from our model could be directly included in large-scale models, thereby improving our understanding of energy balance on sea ice and allowing for more reliable predictions of Arctic sea ice in a future climate.&amp;#160;&lt;/p&gt;

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

2012 ◽  
Vol 6 (2) ◽  
pp. 431-446 ◽  
Author(s):  
A. Rösel ◽  
L. Kaleschke ◽  
G. Birnbaum
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Spectral Unmixing ◽  
Coefficient Of Determination ◽  
Strong Increase ◽  
Data Set ◽  
Melt Pond ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Mean Square Errors

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 R2=0.28 to R2=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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