A radiative transfer model for sea ice with vertical structure variations

1991 ◽  
Vol 96 (C9) ◽  
pp. 16991 ◽  
Author(s):  
Thomas C. Grenfell
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Vertical Structure ◽  
Radiative Transfer Model ◽  
Transfer Model

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 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 A combined radiative transfer model for sea ice, open ocean, and atmosphere

Radio Science ◽  
1998 ◽  
Vol 33 (2) ◽  
pp. 303-316 ◽  
Author(s):  
Rolf Fuhrhop ◽  
Thomas C. Grenfell ◽  
Georg Heygster ◽  
Klaus-Peter Johnsen ◽  
Peter Schlüssel ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Open Ocean ◽  
Radiative Transfer Model ◽  
Transfer Model

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 Primary production calculations for sea ice from bio-optical observations in the Baltic Sea

Elem Sci Anth ◽  
2016 ◽  
Vol 4 ◽  
Author(s):  
Susann Müller ◽  
Anssi V. Vähätalo ◽  
Jari Uusikivi ◽  
Markus Majaneva ◽  
Sanna Majaneva ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Primary Production ◽  
Baltic Sea ◽  
Gulf Of Finland ◽  
Radiative Transfer Model ◽  
Quantum Yields ◽  
Transfer Model ◽  
The Gulf Of Finland

Abstract Bio-optics is a powerful approach for estimating photosynthesis rates, but has seldom been applied to sea ice, where measuring photosynthesis is a challenge. We measured absorption coefficients of chromophoric dissolved organic matter (CDOM), algae, and non-algal particles along with solar radiation, albedo and transmittance at four sea-ice stations in the Gulf of Finland, Baltic Sea. This unique compilation of optical and biological data for Baltic Sea ice was used to build a radiative transfer model describing the light field and the light absorption by algae in 1-cm increments. The maximum quantum yields and photoadaptation of photosynthesis were determined from 14C-incorporation in photosynthetic-irradiance experiments using melted ice. The quantum yields were applied to the radiative transfer model estimating the rate of photosynthesis based on incident solar irradiance measured at 1-min intervals. The calculated depth-integrated mean primary production was 5 mg C m–2 d–1 for the surface layer (0–20 cm ice depth) at Station 3 (fast ice) and 0.5 mg C m–2 d–1 for the bottom layer (20–57 cm ice depth). Additional calculations were performed for typical sea ice in the area in March using all ice types and a typical light spectrum, resulting in depth-integrated mean primary production rates of 34 and 5.6 mg C m–2 d–1 in surface ice and bottom ice, respectively. These calculated rates were compared to rates determined from 14C incorporation experiments with melted ice incubated in situ. The rate of the calculated photosynthesis and the rates measured in situ at Station 3 were lower than those calculated by the bio-optical algorithm for typical conditions in March in the Gulf of Finland by the bio-optical algorithm. Nevertheless, our study shows the applicability of bio-optics for estimating the photosynthesis of sea-ice algae.

Towards improving radiometry-derived snow depths with SNOWPACK and SMRT

2020 ◽  
Author(s):  
Robbie Mallett ◽  
Julienne Stroeve ◽  
Michel Tsamados ◽  
Glen Liston
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Model Development ◽  
Light Flux ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Gradient Ratio ◽  
Strong Control

&lt;p&gt;The depth of overlying snow on sea ice exerts a strong control on atmosphere-ocean heat and light flux and introduces major uncertainties in the remote sensing of sea ice thickness. Satellite-mounted microwave radiometers have enabled retrieval of snow depths over first year ice, but such retrievals are subject to a wide margin of error due to spatial variation in snow stratigraphy and roughness.&lt;/p&gt;&lt;p&gt;Here we model the microwave signature of snow on sea ice using a recently released sea ice variant of the snowpack evolution model, SNOWPACK (Wever et al., 2020). By advecting parcels of sea ice using ice motion vectors and exposing them to the relevant atmospheric forcing using ERA5 reanalysis, we model the accumulation of snow and the development of snowpack stratigraphy.&lt;/p&gt;&lt;p&gt;We then pass these modelled snowpacks to the Snow Microwave Radiative Transfer model (Picard et al., 2018) to estimate their microwave emission characteristics. By using relationships from the literature relating the ratios of the 37GHz and 19GHz channels, we calculate whether the traditional &amp;#8220;gradient ratio&amp;#8221; method (Markus and Cavalieri, 1998) over- or underestimates the depth of snow at a particular point based on our modelling. We then adjust the observed gradient ratio based on the model results in an attempt to better characterise snow depths.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References&lt;/p&gt;&lt;p&gt;Wever, Nander, et al. &quot;Version 1 of a sea ice module for the physics-based, detailed, multi-layer SNOWPACK model.&quot; &lt;em&gt;Geoscientific Model Development&lt;/em&gt; 13.1 (2020): 99-119.&lt;/p&gt;&lt;p&gt;Picard, Ghislain, Melody Sandells, and Henning L&amp;#246;we. &quot;SMRT: An active&amp;#8211;passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1. 0).&quot; &lt;em&gt;Geoscientific Model Development &lt;/em&gt;11.7 (2018): 2763-2788.&lt;/p&gt;&lt;p&gt;Markus, Thorsten, and Donald J. Cavalieri. &quot;Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data.&quot; &lt;em&gt;Antarctic sea ice: physical processes, interactions and variability &lt;/em&gt;74 (1998): 19-39.&lt;/p&gt;

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.

Direct Assimilation of AMSR-E Brightness Temperatures for Estimating Sea Ice Concentration

2012 ◽  
Vol 140 (3) ◽  
pp. 997-1013 ◽  
Author(s):  
K. Andrea Scott ◽  
Mark Buehner ◽  
Alain Caya ◽  
Tom Carrieres
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Ocean Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Sea Ice Concentration ◽  
Brightness Temperatures ◽  
Ice Concentration

Abstract In this paper a method to directly assimilate brightness temperatures from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) to produce ice concentration analyses within a three-dimensional variational data assimilation system is investigated. To assimilate the brightness temperatures a simple radiative transfer model is used as the forward model that maps the state vector to the observation space. This allows brightness temperatures to be modeled for all channels as a function of the total ice concentration, surface wind speed, sea surface temperature, ice temperature, vertically integrated water vapor, and vertically integrated cloud liquid water. The brightness temperatures estimated by the radiative transfer model are sensitive to the specified values for the sea ice emissivity. In this paper, two methods of specifying the sea ice emissivity are compared. The first uses a constant value for each polarization and frequency, while the second uses a simple emissivity parameterization. The emissivity parameterization is found to significantly improve the fit to the observations, reducing both the bias and the standard deviation. Results from the assimilation of brightness temperatures are compared with those from assimilating a retrieved ice concentration in the context of initializing a coupled ice–ocean model for an area along the east coast of Canada. It is found that with the emissivity parameterization the assimilation of brightness temperatures produces ice concentration analyses that are in slightly better agreement with operational ice charts than when assimilating an ice concentration retrieval, with the most significant improvements during the melt season.

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