Permeability of growing sea ice - observations, modelling and some implications for thinning Arctic sea ice

2020 ◽  
Author(s):  
Sönke Maus
Keyword(s):  
Sea Ice ◽  
Pore Space ◽  
Arctic Sea Ice ◽  
Heat Fluxes ◽  
Onset Of Convection ◽  
Permeability Model ◽  
Electrically Conducting ◽  
Melt Pond ◽  
Starting Point

<p>The permeability of sea ice is an important property with regard to the role of sea ice in the earth system. It controls fluid flow within sea ice, and thus processes like melt pond drainage, desalination and to some degree heat fluxes between the ocean and the atmosphere. It also impacts the role of sea ice in hosting sea ice algae and organisms, and the uptake and release of nutrients and pollutants from Arctic surface waters. However, as it is difficult to measure in the field, observations of sea ice permeability are sparse and vary, even for similar porosity, over orders of magnitude. Here I present progress on this topic in three directions. First, I present results from numerical simulations of the permeability of young sea ice based on 3-d X-ray microtomographic images (XRT). These results provide a relationship between permeability and brine porosity of young columnar sea ice for the porosity range 2 to 25 %. The simulations also show that this ice type is permeable and electrically conducting down to a porosity of 2 %, considerably lower than what has been proposed in previous work. Second, the XRT-based simulations are compared to predictions based on a novel crystal growth modelling approach, finding good agreement. Third, the permeability model provides a relationship between sea ice growth velocity and permeability. Based on this relationshiop interesting aspects of the growth of permeable sea ice can be deduced: The predictions consistently explain observations of the onset of convection from growing sea ice. They also allow for an evaluation of expected permeability changes for a thinning sea ice cover in a warmer climate. As the model is strictly valid for growing and cooling sea ice, the results are mostly relevant for sea ice desalination processes during winter. Modelling permeability of summer ice (and melt pond drainage) will require more observations of the pore space evolution in warming sea ice, for which the present results can be considered as a resonable starting point.</p>

A directed percolation model for the permeability of young sea ice

2021 ◽  
Author(s):  
Sönke Maus
Keyword(s):  
Sea Ice ◽  
Pore Space ◽  
Thermal Fluctuations ◽  
Percolation Model ◽  
First Year ◽  
Directed Percolation ◽  
Pore Networks ◽  
Melt Pond ◽  
Starting Point

<p>The permeability of sea ice is an important property with regard to the role of sea ice in the earth system. It controls fluid flow within sea ice, and thus affects processes like desalination and melt pond drainage. It also impacts the role of sea ice in hosting sea ice algae and organisms, and the uptake and release of nutrients and pollutants from Arctic surface waters. However, as sea ice permeability is difficult to measure in the field, observations are sparse and vary, even for similar porosity, over orders of magnitude. This range is related to the evolution of the sea ice pore space during aging from young ice to thick first year ice. In young ice, the pore network is dominated by primary pores constrained by brine layers and the near-interface microstructure. In older sea ice, the ongoing desalination and thermal fluctuations have created wider secondary brine channels, implying a several orders of magnitude higher permeability. It is a challenge to understand and model these changes in pore space and permeability. Here a directed percolation model for the permeability of young sea ice is proposed. The model describes the dependence of sea ice permeability and electrical conductivity on brine porosity, and its critical behaviour and percolation transition due to necking of pores, and related disconnection of pore networks. Its parameters are based on 3D X-ray micro-tomographic imaging of young sea ice and direct numerical simulation of its transport properties, that strongly support the application of directed percolation theory to young sea ice, with a threshold porosity (impermeable ice) of 2 to 3 percent. Combined to an approach to predict the crystal structure at the ice-ocean interface, the model also the growth-velocity dependence and evolution of permeability near the ice-ocean interface. As the model is strictly valid for growing and cooling sea ice, without present of wider secondary brine channels, it is mostly relevant for sea ice desalination processes during winter. Modelling permeability of older and summer ice (and melt pond drainage) will require more observations of the pore space evolution in warming sea ice, for which the present results can be considered as a starting point.</p>

The Role of Atmospheric Feedbacks in Abrupt Winter Arctic Sea-Ice Loss in Future Warming Scenarios

2021 ◽  
pp. 1-38
Author(s):  
Camille Hankel ◽  
Eli Tziperman
Keyword(s):  
Sea Ice ◽  
Freezing Point ◽  
Feedback Mechanism ◽  
Arctic Sea Ice ◽  
Heat Fluxes ◽  
Global Climate Models ◽  
Lower Atmosphere ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

AbstractWinter Arctic sea-ice loss has been simulated with varying degrees of abruptness across Global Climate Models (GCMs) run in the Coupled Model Intercomparison Project 5 (CMIP5) under the high-emissions extended RCP8.5 scenario. Previous studies have proposed various mechanisms to explain modeled abrupt winter sea-ice loss, such as the existence of a wintertime convective cloud feedback or the role of the freezing point as a natural threshold, but none have sought to explain the variability of the abruptness of winter sea-ice loss across GCMs. Here we propose a year-to-year local positive feedback cycle, in which warm, open oceans at the start of winter allow for the moistening and warming of the lower atmosphere, which in turn increases the downwards clear-sky longwave radiation at the surface and suppresses ocean freezing. This leads to delayed and diminished winter sea-ice growth, and allows for increased shortwave absorption due to lowered surface albedo during springtime. Finally, the ocean stores this additional heat throughout the summer and fall seasons, setting up even warmer ocean conditions that lead to further sea-ice reduction. We show that the strength of this feedback, as measured by the partial temperature contributions of the different surface heat fluxes, correlates strongly with the abruptness of winter sea-ice loss across models. Thus, we suggest that this feedback mechanism may explain inter-model spread in the abruptness of winter sea-ice loss. In models where the feedback mechanism is strong, this may indicate the possibility of hysteresis and thus irreversibility of sea-ice loss.

Trends in Arctic sea ice and the role of atmospheric circulation

2013 ◽  
Vol 14 (2) ◽  
pp. 97-101 ◽  
Author(s):  
Masayo Ogi ◽  
Ignatius G. Rigor
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Arctic Sea Ice ◽  
Arctic Sea

Derivation of melt pond coverage on Arctic sea ice using MODIS observations

2008 ◽  
Vol 112 (5) ◽  
pp. 2605-2614 ◽  
Author(s):  
Mark A. Tschudi ◽  
James A. Maslanik ◽  
Donald K. Perovich
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Melt Pond ◽  
Arctic Sea

scholarly journals Investigation of spatiotemporal variability of melt pond fraction and its relationship with sea ice extent during 2000–2017 using a new data

2019 ◽  
Author(s):  
Yifan Ding ◽  
Xiao Cheng ◽  
Jiping Liu ◽  
Fengming Hui ◽  
Zhenzhan Wang
Keyword(s):  
Sea Ice ◽  
Arctic Basin ◽  
Beaufort Sea ◽  
Arctic Sea Ice ◽  
Spatiotemporal Variability ◽  
First Year ◽  
Spatial And Temporal Variability ◽  
Sea Ice Extent ◽  
Melt Pond ◽  
Melt Ponds

Abstract. The accurate knowledge of variations of melt ponds is important for understanding Arctic energy budget due to its albedo-transmittance-melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) from the MODIS surface reflectance. We construct an ensemble-based deep neural network and use in-situ observations of MPF from multi-sources to train the network. The results show that our derived MPF is in good agreement with the observations, and relatively outperforms the MPF retrieved by University of Hamburg. Built on this, we create a new MPF data from 2000 to 2017 (the longest data in our knowledge), and analyze the spatial and temporal variability of MPF. It is found that the MPF has significant increasing trends from late July to early September, which is largely contributed by the MPF over the first-year sea ice. The analysis based on our MPF during 2000–2017 confirms that the integrated MPF to late June does promise to improve the prediction skill of seasonal Arctic sea ice minimum. However, our MPF data shows concentrated significant correlations first appear in a band, extending from the eastern Beaufort Sea, through the central Arctic, to the northern East Siberian and Laptev Seas in early-mid June, and then shifts towards large areas of the Beaufort Sea, Canadian Arctic, the northern Greenland Sea and the central Arctic basin.

scholarly journals New insight from CryoSat-2 sea ice thickness for sea ice modelling

2018 ◽  
Author(s):  
David Schröder ◽  
Danny L. Feltham ◽  
Michel Tsamados ◽  
Andy Ridout ◽  
Rachel Tilling
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Atmospheric Conditions ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Melt Pond ◽  
The Impact

Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50 % of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

Arctic sea ice variability and trends in the last four decades: role of ocean-atmospheric forcing

2021 ◽  
pp. 301-324
Author(s):  
Avinash Kumar ◽  
Juhi Yadav ◽  
Rohit Srivastava ◽  
Rahul Mohan
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Atmospheric Forcing ◽  
Arctic Sea

scholarly journals Monitoring evolution of melt ponds on first-year and multiyear sea ice in the Canadian Arctic Archipelago with optical satellite data

2020 ◽  
Vol 61 (82) ◽  
pp. 154-163
Author(s):  
Qing Li ◽  
Chunxia Zhou ◽  
Lei Zheng ◽  
Tingting Liu ◽  
Xiaotong Yang
Keyword(s):  
Sea Ice ◽  
Canadian Arctic ◽  
Arctic Sea Ice ◽  
First Year ◽  
Landsat 8 ◽  
Canadian Arctic Archipelago ◽  
Melt Pond ◽  
Melt Ponds ◽  
Validation Experiment ◽  
Multiyear Ice

AbstractThe evolution of melt ponds on Arctic sea ice in summer is one of the main factors that affect sea-ice albedo and hence the polar climate system. Due to the different spectral properties of open water, melt pond and sea ice, the melt pond fraction (MPF) can be retrieved using a fully constrained least-squares algorithm, which shows a high accuracy with root mean square error ~0.06 based on the validation experiment using WorldView-2 image. In this study, the evolution of ponds on first-year and multiyear ice in the Canadian Arctic Archipelago was compared based on Sentinel-2 and Landsat 8 images. The relationships of pond coverage with air temperature and albedo were analysed. The results show that the pond coverage on first-year ice changed dramatically with seasonal maximum of 54%, whereas that on multiyear ice changed relatively flat with only 30% during the entire melting period. During the stage of pond formation, the ponds expanded rapidly when the temperature increased to over 0°C for three consecutive days. Sea-ice albedo shows a significantly negative correlation (R = −1) with the MPF in melt season and increases gradually with the refreezing of ponds and sea ice.

scholarly journals The role of cyclone activity in snow accumulation on Arctic sea ice

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
M. A. Webster ◽  
C. Parker ◽  
L. Boisvert ◽  
R. Kwok
Keyword(s):  
Sea Ice ◽  
Snow Accumulation ◽  
Arctic Sea Ice ◽  
Surface Energy Budget ◽  
The Arctic ◽  
Cyclone Activity ◽  
In Situ Data ◽  
Arctic Sea ◽  
Spatio Temporal

AbstractIdentifying the mechanisms controlling the timing and magnitude of snow accumulation on sea ice is crucial for understanding snow’s net effect on the surface energy budget and sea-ice mass balance. Here, we analyze the role of cyclone activity on the seasonal buildup of snow on Arctic sea ice using model, satellite, and in situ data over 1979–2016. On average, 44% of the variability in monthly snow accumulation was controlled by cyclone snowfall and 29% by sea-ice freeze-up. However, there were strong spatio-temporal differences. Cyclone snowfall comprised ~50% of total snowfall in the Pacific compared to 83% in the Atlantic. While cyclones are stronger in the Atlantic, Pacific snow accumulation is more sensitive to cyclone strength. These findings highlight the heterogeneity in atmosphere-snow-ice interactions across the Arctic, and emphasize the need to scrutinize mechanisms governing cyclone activity to better understand their effects on the Arctic snow-ice system with anthropogenic warming.

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