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.

scholarly journals The color of melt ponds on Arctic sea ice

2017 ◽  
Author(s):  
Peng Lu ◽  
Matti Leppäranta ◽  
Bin Cheng ◽  
Zhijun Li ◽  
Larysa Istomina ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Colorimetric Method ◽  
Arctic Sea Ice ◽  
Radiative Transfer Model ◽  
Ice Thickness ◽  
Transfer Model ◽  
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 in this study. A two-stream radiative transfer model is used for ponded sea ice: the upwelling irradiance from the pond surface is determined, and then the upwelling spectrum is transformed into the RGB color space through 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 components of pond color, whereas the red component is mostly sensitive to Hi for thin 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 level. The pond color changes from dark blue to brighter blue with increasing scattering in ice, but the influence of absorption in ice on pond color is limited. The pond color reproduced by the model agrees well with field observations on Arctic sea ice in summer, which supports the validity of this study. More importantly, 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. The results show that retrievals of Hi for thin ice agree better with field measurements than retrievals for thick ice, but that retrievals of Hp are not good. Color has been shown to be a new potential method to obtain ice thickness information, especially for melting sea ice in summer, although more validation data and improvements to the radiative transfer model will be needed in future.

scholarly journals Metamorphism of Arctic marine snow during the melt season. Impact on albedo

2019 ◽  
Author(s):  
Gauthier Verin ◽  
Florent Dominé ◽  
Marcel Babin ◽  
Ghislain Picard ◽  
Laurent Arnaud
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Snow Cover ◽  
Near Infrared ◽  
Surface Melting ◽  
Arctic Sea Ice ◽  
Phase Iii ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Snow Layer

Abstract. The energy budget of Arctic sea ice is strongly affected by the snow cover. Intensive sampling of snow properties was conducted near Qikiqtarjuak in Baffin Bay on typical landfast sea ice during two melt seasons in 2015 and 2016. The sampling included stratigraphy, vertical profiles of snow specific surface area (SSA), density and surface spectral albedo. Both seasons feature four main phases: I) dry snow cover, II) surface melting, III) ripe snowpack and IV) melt pond formation. Each of them was characterized by distinctive physical and optical properties. Highest SSA of 49.3 m2 kg−1 was measured during phase I on surface windslab together with a high broadband albedo of 0.87. The next phase was marked by alternative episodes of surface melting which dramatically decreased the SSA below 3 m2 kg−1 and episodes of snowfall reestablishing the pre-melt conditions. Albedo was highly time variable especially in the near-infrared with minimum values around 0.45 at 1000 nm. At some point, the melt progressed leading to a fully ripe snowpack composed of clustered rounded grains in phase III. Albedo began to decrease in the visible as snow thickness decreased but remained steady at longer wavelengths. Moreover, its spatial variability clearly appeared for the first time following snow depth heterogeneity. The impacts on albedo of both snow SSA and thickness were quantitatively investigated using a radiative transfer model. Comparisons between albedo measurements and simulations show that our data on snow physical properties are relevant for radiative transfer modeling. They also point out to the importance of the properties of the very surface snow layer for albedo computation, especially during phase II when several distinctive layers of snow superimposed following snowfalls, melt or diurnal cycles.

scholarly journals SMOS derived sea ice thickness: algorithm baseline, product specifications and initial verification

2013 ◽  
Vol 7 (6) ◽  
pp. 5735-5792 ◽  
Author(s):  
X. Tian-Kunze ◽  
L. Kaleschke ◽  
N. Maaß ◽  
M. Mäkynen ◽  
N. Serra ◽  
...  
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Remote Sensing Data ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Ice Thickness ◽  
Transfer Model ◽  
Validation Data ◽  
Data Set ◽  
Sea Ice Thickness

Abstract. Following the launch of ESA's Soil Moisture and Ocean salinity (SMOS) mission it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In a first demonstration study, sea ice thickness has been derived using a semi-empirical algorithm with constant tie-points. Here we introduce a novel iterative retrieval algorithm that is based on a sea ice thermodynamic model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within a SMOS footprint are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS based sea ice thickness data set from 2010 on. This data set is compared and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study.

scholarly journals SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

2014 ◽  
Vol 8 (3) ◽  
pp. 997-1018 ◽  
Author(s):  
X. Tian-Kunze ◽  
L. Kaleschke ◽  
N. Maaß ◽  
M. Mäkynen ◽  
N. Serra ◽  
...  
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Remote Sensing Data ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Ice Thickness ◽  
Transfer Model ◽  
Validation Data ◽  
Data Set ◽  
Sea Ice Thickness

Abstract. Following the launch of ESA's Soil Moisture and Ocean Salinity (SMOS) mission, it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In the first demonstration study, sea ice thickness up to 50 cm has been derived using a semi-empirical algorithm with constant tie-points. Here, we introduce a novel iterative retrieval algorithm that is based on a thermodynamic sea ice model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within the SMOS spatial resolution are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS-based sea ice thickness data set from 2010 on. The data set is compared to and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study (Kaleschke et al., 2012).

scholarly journals The effects of additional black carbon on the albedo of Arctic sea ice: variation with sea ice type and snow cover

2013 ◽  
Vol 7 (4) ◽  
pp. 1193-1204 ◽  
Author(s):  
A. A. Marks ◽  
M. D. King
Keyword(s):  
Sea Ice ◽  
Black Carbon ◽  
Cross Sections ◽  
Arctic Sea Ice ◽  
Radiative Transfer Model ◽  
First Year ◽  
Transfer Model ◽  
Snow Layer ◽  
Arctic Sea ◽  
Visible Wavelengths

Abstract. The response of the albedo of bare sea ice and snow-covered sea ice to the addition of black carbon is calculated. Visible light absorption and light-scattering cross-sections are derived for a typical first-year and multi-year sea ice with both "dry" and "wet" snow types. The cross-sections are derived using data from a 1970s field study that recorded both reflectivity and light penetration in Arctic sea ice and snow overlying sea ice. The variation of absorption cross-section over the visible wavelengths suggests black carbon is the dominating light-absorbing impurity. The response of first-year and multi-year sea ice albedo to increasing black carbon, from 1 to 1024 ng g−1, in a top 5 cm layer of a 155 cm-thick sea ice was calculated using a radiative-transfer model. The albedo of the first-year sea ice is more sensitive to additional loadings of black carbon than the multi-year sea ice. An addition of 8 ng g−1 of black carbon causes a decrease to 98.7% of the original albedo for first-year sea ice compared to a decrease to 99.7% for the albedo of multi-year sea ice, at a wavelength of 500 nm. The albedo of sea ice is surprisingly unresponsive to additional black carbon up to 100 ng g−1 . Snow layers on sea ice may mitigate the effects of black carbon in sea ice. Wet and dry snow layers of 0.5, 1, 2, 5 and 10 cm depth were added onto the sea ice surface. The albedo of the snow surface was calculated whilst the black carbon in the underlying sea ice was increased. A layer of snow 0.5 cm thick greatly diminishes the effect of black carbon in sea ice on the surface albedo. The albedo of a 2–5 cm snow layer (less than the e-folding depth of snow) is still influenced by the underlying sea ice, but the effect of additional black carbon in the sea ice is masked.

scholarly journals Improved Sea Ice Shortwave Radiation Physics in CCSM4: The Impact of Melt Ponds and Aerosols on Arctic Sea Ice

2012 ◽  
Vol 25 (5) ◽  
pp. 1413-1430 ◽  
Author(s):  
Marika M. Holland ◽  
David A. Bailey ◽  
Bruce P. Briegleb ◽  
Bonnie Light ◽  
Elizabeth Hunke
Keyword(s):  
Twentieth Century ◽  
Radiative Transfer ◽  
Sea Ice ◽  
Aerosol Deposition ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Climate System Model ◽  
Melt Ponds ◽  
Arctic Sea ◽  
The Impact

The Community Climate System Model, version 4 has revisions across all components. For sea ice, the most notable improvements are the incorporation of a new shortwave radiative transfer scheme and the capabilities that this enables. This scheme uses inherent optical properties to define scattering and absorption characteristics of snow, ice, and included shortwave absorbers and explicitly allows for melt ponds and aerosols. The deposition and cycling of aerosols in sea ice is now included, and a new parameterization derives ponded water from the surface meltwater flux. Taken together, this provides a more sophisticated, accurate, and complete treatment of sea ice radiative transfer. In preindustrial CO2 simulations, the radiative impact of ponds and aerosols on Arctic sea ice is 1.1 W m−2 annually, with aerosols accounting for up to 8 W m−2 of enhanced June shortwave absorption in the Barents and Kara Seas and with ponds accounting for over 10 W m−2 in shelf regions in July. In double CO2 (2XCO2) simulations with the same aerosol deposition, ponds have a larger effect, whereas aerosol effects are reduced, thereby modifying the surface albedo feedback. Although the direct forcing is modest, because aerosols and ponds influence the albedo, the response is amplified. In simulations with no ponds or aerosols in sea ice, the Arctic ice is over 1 m thicker and retains more summer ice cover. Diagnosis of a twentieth-century simulation indicates an increased radiative forcing from aerosols and melt ponds, which could play a role in twentieth-century Arctic sea ice reductions. In contrast, ponds and aerosol deposition have little effect on Antarctic sea ice for all climates considered.

scholarly journals Snow Depth Retrieval on Arctic Sea Ice Using Under-Ice Hyperspectral Radiation Measurements

2021 ◽  
Vol 9 ◽  
Author(s):  
Philipp Anhaus ◽  
Christian Katlein ◽  
Marcel Nicolaus ◽  
Stefanie Arndt ◽  
Arttu Jutila ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
Radiative Transfer Model ◽  
Ice Thickness ◽  
First Year ◽  
Transfer Model ◽  
Remotely Operated Vehicle ◽  
Transmitted Radiation ◽  
Extinction Coefficients

Radiation transmitted through sea ice and snow has an important impact on the energy partitioning at the atmosphere-ice-ocean interface. Snow depth and ice thickness are crucial in determining its temporal and spatial variations. Under-ice surveys using autonomous robotic vehicles to measure transmitted radiation often lack coincident snow depth and ice thickness measurements so that direct relationships cannot be investigated. Snow and ice imprint distinct features on the spectral shape of transmitted radiation. Here, we use those features to retrieve snow depth. Transmitted radiance was measured underneath landfast level first-year ice using a remotely operated vehicle in the Lincoln Sea in spring 2018. Colocated measurements of snow depth and ice thickness were acquired. Constant ice thickness, clear water conditions, and low in-ice biomass allowed us to separate the spectral features of snow. We successfully retrieved snow depth using two inverse methods based on under-ice optical spectra with 1) normalized difference indices and 2) an idealized two-layer radiative transfer model including spectral snow and sea ice extinction coefficients. The retrieved extinction coefficients were in agreement with previous studies. We then applied the methods to continuous time series of transmittance and snow depth from the landfast first-year ice and from drifting, melt-pond covered multiyear ice in the Central Arctic in autumn 2018. Both methods allow snow depth retrieval accuracies of approximately 5 cm. Our results show that atmospheric variations and absolute light levels have an influence on the snow depth retrieval.

scholarly journals Arctic Sea Ice Retreat in 2007 Follows Thinning Trend

2009 ◽  
Vol 22 (1) ◽  
pp. 165-176 ◽  
Author(s):  
R. W. Lindsay ◽  
J. Zhang ◽  
A. Schweiger ◽  
M. Steele ◽  
H. Stern
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Extent ◽  
Pacific Side ◽  
Arctic Sea ◽  
The Arctic Ocean

Abstract The minimum of Arctic sea ice extent in the summer of 2007 was unprecedented in the historical record. A coupled ice–ocean model is used to determine the state of the ice and ocean over the past 29 yr to investigate the causes of this ice extent minimum within a historical perspective. It is found that even though the 2007 ice extent was strongly anomalous, the loss in total ice mass was not. Rather, the 2007 ice mass loss is largely consistent with a steady decrease in ice thickness that began in 1987. Since then, the simulated mean September ice thickness within the Arctic Ocean has declined from 3.7 to 2.6 m at a rate of −0.57 m decade−1. Both the area coverage of thin ice at the beginning of the melt season and the total volume of ice lost in the summer have been steadily increasing. The combined impact of these two trends caused a large reduction in the September mean ice concentration in the Arctic Ocean. This created conditions during the summer of 2007 that allowed persistent winds to push the remaining ice from the Pacific side to the Atlantic side of the basin and more than usual into the Greenland Sea. This exposed large areas of open water, resulting in the record ice extent anomaly.

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 Near Real Time Arctic sea ice thickness and volume from CryoSat-2

2016 ◽  
Author(s):  
R. L. Tilling ◽  
A. Ridout ◽  
A. Shepherd
Keyword(s):  
Sea Ice ◽  
Real Time ◽  
Arctic Sea Ice ◽  
Arctic Climate ◽  
The Arctic ◽  
Ice Thickness ◽  
Satellite Orbits ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. Timely observations of sea ice thickness help us to understand Arctic climate, and can support maritime activities in the Polar Regions. Although it is possible to calculate Arctic sea ice thickness using measurements acquired by CryoSat-2, the latency of the final release dataset is typically one month, due to the time required to determine precise satellite orbits. We use a new fast delivery CryoSat-2 dataset based on preliminary orbits to compute Arctic sea ice thickness in near real time (NRT), and analyse this data for one sea ice growth season from October 2014 to April 2015. We show that this NRT sea ice thickness product is of comparable accuracy to that produced using the final release CryoSat-2 data, with an average thickness difference of 5 cm, demonstrating that the satellite orbit is not a critical factor in determining sea ice freeboard. In addition, the CryoSat-2 fast delivery product also provides measurements of Arctic sea ice thickness within three days of acquisition by the satellite, and a measurement is delivered, on average, within 10, 7 and 6 km of each location in the Arctic every 2, 14 and 28 days respectively. The CryoSat-2 NRT sea ice thickness dataset provides an additional constraint for seasonal predictions of Arctic climate change, and will allow industries such as tourism and transport to navigate the polar oceans with safety and care.

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