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

2013 ◽  
Vol 7 (2) ◽  
pp. 943-973
Author(s):  
A. A. Marks ◽  
M. D. King
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Black Carbon ◽  
Surface Albedo ◽  
Arctic Sea Ice ◽  
Radiative Transfer Model ◽  
First Year ◽  
Transfer Model ◽  
Ice Surface ◽  
Carbon Additions

Abstract. Black carbon in sea ice will decrease sea ice surface albedo through increased absorption of incident solar radiation, exacerbating sea ice melting. Previous literature has reported different albedo responses to additions of black carbon in sea ice and has not considered how a snow cover may mitigate the effect of black carbon in sea ice. Sea ice is predominately snow covered. Visible light absorption and light scattering coefficients are calculated for a typical first year and multi-year sea ice and "dry" and "wet" snow types that suggest black carbon is the dominating absorbing impurity. The albedo response of first year and multi-year sea ice to increasing black carbon, from 1–1024 ng g−1, in a top 5 cm layer of a 155 cm thick sea ice was calculated using the radiative transfer model: TUV-snow. Sea ice albedo is surprisingly unresponsive to black carbon additions up to 100 ng g−1 with a decrease in albedo to 98.7% of the original albedo value due to an addition of 8 ng g−1 of black carbon in first year sea ice compared to an albedo decrease to 99.6% for the same black carbon mass ratio increase in multi-year sea ice. The first year sea ice proved more responsive to black carbon additions than the multi-year ice. Comparison with previous modelling of black carbon in sea ice suggests a more scattering sea ice environment will be less responsive to black carbon additions. 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 were added onto the sea ice surface and the snow surface albedo calculated with the same increase in black carbon in the underlying sea ice. Just a 0.5 cm layer of snow greatly diminishes the effect of black carbon on surface albedo, and a 2–5 cm layer (less than half the e-folding depth of snow) is enough to "mask" any change in surface albedo owing to additional black carbon in sea ice, but not thick enough to ignore the underlying sea ice.

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 Optical properties of sea ice doped with black carbon – an experimental and radiative-transfer modelling comparison

2017 ◽  
Vol 11 (6) ◽  
pp. 2867-2881 ◽  
Author(s):  
Amelia A. Marks ◽  
Maxim L. Lamare ◽  
Martin D. King
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Black Carbon ◽  
Cross Sections ◽  
Extinction Coefficient ◽  
Radiative Transfer Model ◽  
First Year ◽  
Transfer Model ◽  
Ice Fabrics ◽  
Scattering Cross Sections

Abstract. Radiative-transfer calculations of the light reflectivity and extinction coefficient in laboratory-generated sea ice doped with and without black carbon demonstrate that the radiative-transfer model TUV-snow can be used to predict the light reflectance and extinction coefficient as a function of wavelength. The sea ice is representative of first-year sea ice containing typical amounts of black carbon and other light-absorbing impurities. The experiments give confidence in the application of the model to predict albedo of other sea ice fabrics. Sea ices,  ∼  30 cm thick, were generated in the Royal Holloway Sea Ice Simulator ( ∼  2000 L tanks) with scattering cross sections measured between 0.012 and 0.032 m2 kg−1 for four ices. Sea ices were generated with and without  ∼  5 cm upper layers containing particulate black carbon. Nadir reflectances between 0.60 and 0.78 were measured along with extinction coefficients of 0.1 to 0.03 cm−1 (e-folding depths of 10–30 cm) at a wavelength of 500 nm. Values were measured between light wavelengths of 350 and 650 nm. The sea ices generated in the Royal Holloway Sea Ice Simulator were found to be representative of natural sea ices. Particulate black carbon at mass ratios of  ∼  75,  ∼  150 and  ∼  300 ng g−1 in a 5 cm ice layer lowers the albedo to 97, 90 and 79 % of the reflectivity of an undoped clean sea ice (at a wavelength of 500 nm).

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 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 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.

Multi-frequency polarimetric microwave observations of snow cover on first-year Arctic sea ice

2015 ◽  
Author(s):  
Vishnu Nandan ◽  
John J. Yackel ◽  
Jagvijay P. S. Gill ◽  
Torsten Geldsetzer ◽  
Mark C. Fuller
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Arctic Sea Ice ◽  
First Year ◽  
Arctic Sea

scholarly journals Snow Surface Albedo Sensitivity to Black Carbon: Radiative Transfer Modelling

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1077
Author(s):  
Nicholas D. Beres ◽  
Magín Lapuerta ◽  
Francisco Cereceda-Balic ◽  
Hans Moosmüller
Keyword(s):  
Radiative Transfer ◽  
Black Carbon ◽  
Surface Albedo ◽  
Mass Density ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Snow Surface ◽  
Snow Albedo ◽  
Carbon Mass ◽  
The Relationship

The broadband surface albedo of snow can greatly be reduced by the deposition of light-absorbing impurities, such as black carbon on or near its surface. Such a reduction increases the absorption of solar radiation and may initiate or accelerate snowmelt and snow albedo feedback. Coincident measurements of both black carbon concentration and broadband snow albedo may be difficult to obtain in field studies; however, using the relationship developed in this simple model sensitivity study, black carbon mass densities deposited can be estimated from changes in measured broadband snow albedo, and vice versa. Here, the relationship between the areal mass density of black carbon found near the snow surface to the amount of albedo reduction was investigated using the popular snow radiative transfer model Snow, Ice, and Aerosol Radiation (SNICAR). We found this relationship to be linear for realistic amounts of black carbon mass concentrations, such as those found in snow at remote locations. We applied this relationship to measurements of broadband albedo in the Chilean Andes to estimate how vehicular emissions contributed to black carbon (BC) deposition that was previously unquantified.

scholarly journals Comparison of a coupled snow thermodynamic and radiative transfer model with in situ active microwave signatures of snow-covered smooth first-year sea ice

2015 ◽  
Vol 9 (6) ◽  
pp. 2149-2161 ◽  
Author(s):  
M. C. Fuller ◽  
T. Geldsetzer ◽  
J. Yackel ◽  
J. P. S. Gill
Keyword(s):  
Sea Ice ◽  
Reanalysis Data ◽  
Physical Models ◽  
Radiative Transfer Model ◽  
Wave Radiation ◽  
First Year ◽  
Transfer Model ◽  
Long Wave ◽  
Microwave Backscatter

Abstract. Within the context of developing data inversion and assimilation techniques for C-band backscatter over sea ice, snow physical models may be used to drive backscatter models for comparison and optimization with satellite observations. Such modeling has the potential to enhance understanding of snow on sea-ice properties required for unambiguous interpretation of active microwave imagery. An end-to-end modeling suite is introduced, incorporating regional reanalysis data (NARR), a snow model (SNTHERM89.rev4), and a multilayer snow and ice active microwave backscatter model (MSIB). This modeling suite is assessed against measured snow on sea-ice geophysical properties and against measured active microwave backscatter. NARR data were input to the SNTHERM snow thermodynamic model in order to drive the MSIB model for comparison to detailed geophysical measurements and surface-based observations of C-band backscatter of snow on first-year sea ice. The NARR variables were correlated to available in situ measurements with the exception of long-wave incoming radiation and relative humidity, which impacted SNTHERM simulations of snow temperature. SNTHERM snow grain size and density were comparable to observations. The first assessment of the forward assimilation technique developed in this work required the application of in situ salinity profiles to one SNTHERM snow profile, which resulted in simulated backscatter close to that driven by in situ snow properties. In other test cases, the simulated backscatter remained 4–6 dB below observed for higher incidence angles and when compared to an average simulated backscatter of in situ end-member snow covers. Development of C-band inversion and assimilation schemes employing SNTHERM89.rev4 should consider sensitivity of the model to bias in incoming long-wave radiation, the effects of brine, and the inability of SNTHERM89.Rev4 to simulate water accumulation and refreezing at the bottom and mid-layers of the snowpack. These impact thermodynamic response, brine wicking and volume processes, snow dielectrics, and thus microwave backscatter from snow on first-year sea ice.

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 Observations of Sea Ice Melt from Operation IceBridge Imagery

2019 ◽  
Author(s):  
Nicholas C. Wright ◽  
Chris M. Polashenski ◽  
Scott T. McMichael ◽  
Ross A. Beyer
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
First Year ◽  
Ice Melt ◽  
Melt Pond ◽  
Ice Surface ◽  
Optical Images ◽  
Arctic Sea ◽  
Ice Floes ◽  
Multiyear Ice

Abstract. The summer albedo of Arctic sea ice is heavily dependent on the fraction and color of melt ponds that form on the ice surface. This work presents a new dataset of sea ice surface fractions along Operation IceBridge (OIB) flight tracks derived from the Digital Mapping System optical imagery set. This dataset was created by deploying version 2 of the Open Source Sea-ice Processing (OSSP) algorithm to NASA’s Advanced Supercomputing Pleiades System. These new surface fraction results are then analyzed to investigate the behavior of meltwater on first-year ice in comparison to multiyear ice. Observations herein show that first-year ice does not ubiquitously have a higher melt pond fraction than multiyear ice under the same forcing conditions, contrary to established knowledge in the sea ice community. We discover and document a larger possible spread of pond fractions on first year ice leading to both high and low pond coverage, in contrast to the uniform melt evolution that has been previously observed on multiyear ice floes. We also present a selection of optical images that captures both the typical and atypical ice types, as observed from the OIB dataset. We hope to demonstrate the power of this new dataset and to encourage future collaborative efforts to utilize the OIB data to explore the behavior of melt pond formation Arctic sea ice.

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