Articles | Volume 17, issue 2
https://doi.org/10.5194/tc-17-1053-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-1053-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Mar 2023
Research article |
| 03 Mar 2023
| 03 Mar 2023
A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation
Yingzhen Zhou
CORRESPONDING AUTHOR
Light and Life Laboratory, Department of Physics, Stevens Institute of Technology, Hoboken, NJ 07307, USA
Wei Li
Light and Life Laboratory, Department of Physics, Stevens Institute of Technology, Hoboken, NJ 07307, USA
Nan Chen
Light and Life Laboratory, Department of Physics, Stevens Institute of Technology, Hoboken, NJ 07307, USA
Yongzhen Fan
Cooperative Institute for Satellite Earth System Studies (CISESS),
Earth System Science Interdisciplinary Center (ESSIC),
University of Maryland, College Park, MD 20740, USA
Knut Stamnes
Light and Life Laboratory, Department of Physics, Stevens Institute of Technology, Hoboken, NJ 07307, USA
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Gabriel Harris Myers, Nan Chen, and Matteo Ottaviani
EGUsphere, https://doi.org/10.5194/egusphere-2023-3023, https://doi.org/10.5194/egusphere-2023-3023, 2024
Short summary
Short summary
We analyze simulated polarization observations over snow, to investigate the capabilities of remote sensing to determine surface and atmospheric properties in snow-covered regions. Polarization measurements are demonstrated to aid in the determination of snow grain shape, ice crystal roughness, and the vertical distribution of impurities in the snow-atmosphere system, data critical to estimate snow albedo for use in climate models.
Related subject area
Discipline: Sea ice | Subject: Energy Balance Obs/Modelling
Understanding model spread in sea ice volume by attribution of model differences in seasonal ice growth and melt
On the statistical properties of sea-ice lead fraction and heat fluxes in the Arctic
New insights into radiative transfer within sea ice derived from autonomous optical propagation measurements
Sunlight, clouds, sea ice, albedo, and the radiative budget: the umbrella versus the blanket
Alex West, Edward Blockley, and Matthew Collins
The Cryosphere, 16, 4013–4032, https://doi.org/10.5194/tc-16-4013-2022, https://doi.org/10.5194/tc-16-4013-2022, 2022
Short summary
Short summary
In this study we explore a method of examining model differences in ice volume by looking at the seasonal ice growth and melt. We use simple physical relationships to judge how model differences in key variables affect ice growth and melt and apply these to three case study models with ice volume ranging from very thin to very thick. Results suggest that differences in snow and melt pond cover in early summer are most important in causing the sea ice differences for these models.
Einar Ólason, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 15, 1053–1064, https://doi.org/10.5194/tc-15-1053-2021, https://doi.org/10.5194/tc-15-1053-2021, 2021
Short summary
Short summary
We analyse the fractal properties observed in the pattern of the long, narrow openings that form in Arctic sea ice known as leads. We use statistical tools to explore the fractal properties of the lead fraction observed in satellite data and show that our sea-ice model neXtSIM displays the same behaviour. Building on this result we then show that the pattern of heat loss from ocean to atmosphere in the model displays similar fractal properties, stemming from the fractal properties of the leads.
Christian Katlein, Lovro Valcic, Simon Lambert-Girard, and Mario Hoppmann
The Cryosphere, 15, 183–198, https://doi.org/10.5194/tc-15-183-2021, https://doi.org/10.5194/tc-15-183-2021, 2021
Short summary
Short summary
To improve autonomous investigations of sea ice optical properties, we designed a chain of multispectral light sensors, providing autonomous in-ice light measurements. Here we describe the system and the data acquired from a first prototype deployment. We show that sideward-looking planar irradiance sensors basically measure scalar irradiance and demonstrate the use of this sensor chain to derive light transmittance and inherent optical properties of sea ice.
Donald K. Perovich
The Cryosphere, 12, 2159–2165, https://doi.org/10.5194/tc-12-2159-2018, https://doi.org/10.5194/tc-12-2159-2018, 2018
Short summary
Short summary
The balance of longwave and shortwave radiation plays a central role in the summer melt of Arctic sea ice. It is governed by clouds and surface albedo. The basic question is what causes more melting, sunny skies or cloudy skies. It depends on the albedo of the ice surface. For snow-covered or bare ice, sunny skies always result in less radiative heat input. In contrast, the open ocean always has, and melt ponds usually have, more radiative input under sunny skies than cloudy skies.
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Short summary
We present a method to compute albedo (percentage of the light reflected) of the cryosphere surface using observations from optical satellite sensors. This method can be applied to sea ice, snow-covered ice, melt pond, open ocean, and mixtures thereof. Evaluation of the albedo values calculated using this approach demonstrated excellent agreement with observations. In addition, we have included a statistical comparison of the proposed method's results with those derived from other approaches.
We present a method to compute albedo (percentage of the light reflected) of the cryosphere...
