scholarly journals Snow contribution to first-year and second-year Arctic sea ice mass balance north of Svalbard

2017 ◽  
Vol 122 (3) ◽  
pp. 2539-2549 ◽  
Author(s):  
Mats A. Granskog ◽  
Anja Rösel ◽  
Paul A. Dodd ◽  
Dmitry Divine ◽  
Sebastian Gerland ◽  
...  
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Arctic Sea Ice ◽  
First Year ◽  
Arctic Sea ◽  
Ice Mass Balance ◽  
Second Year

scholarly journals Estimating the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and ice mass balance buoy data

2019 ◽  
Vol 13 (4) ◽  
pp. 1283-1296 ◽  
Author(s):  
Lise Kilic ◽  
Rasmus Tage Tonboe ◽  
Catherine Prigent ◽  
Georg Heygster
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Effective Temperature ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Interface Temperature ◽  
Microwave Frequencies ◽  
Arctic Sea ◽  
Ice Mass Balance

Abstract. Mapping sea ice concentration (SIC) and understanding sea ice properties and variability is important, especially today with the recent Arctic sea ice decline. Moreover, accurate estimation of the sea ice effective temperature (Teff) at 50 GHz is needed for atmospheric sounding applications over sea ice and for noise reduction in SIC estimates. At low microwave frequencies, the sensitivity to the atmosphere is low, and it is possible to derive sea ice parameters due to the penetration of microwaves in the snow and ice layers. In this study, we propose simple algorithms to derive the snow depth, the snow–ice interface temperature (TSnow−Ice) and the Teff of Arctic sea ice from microwave brightness temperatures (TBs). This is achieved using the Round Robin Data Package of the ESA sea ice CCI project, which contains TBs from the Advanced Microwave Scanning Radiometer 2 (AMSR2) collocated with measurements from ice mass balance buoys (IMBs) and the NASA Operation Ice Bridge (OIB) airborne campaigns over the Arctic sea ice. The snow depth over sea ice is estimated with an error of 5.1 cm, using a multilinear regression with the TBs at 6, 18, and 36 V. The TSnow−Ice is retrieved using a linear regression as a function of the snow depth and the TBs at 10 or 6 V. The root mean square errors (RMSEs) obtained are 2.87 and 2.90 K respectively, with 10 and 6 V TBs. The Teff at microwave frequencies between 6 and 89 GHz is expressed as a function of TSnow−Ice using data from a thermodynamical model combined with the Microwave Emission Model of Layered Snowpacks. Teff is estimated from the TSnow−Ice with a RMSE of less than 1 K.

scholarly journals Estimating the snow depth, the snow-ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and Ice Mass Balance buoys data

2018 ◽  
Author(s):  
Lise Kilic ◽  
Rasmus Tage Tonboe ◽  
Catherine Prigent ◽  
Georg Heygster
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Effective Temperature ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Interface Temperature ◽  
Microwave Frequencies ◽  
Arctic Sea ◽  
Ice Mass Balance

Abstract. Mapping Sea Ice Concentration (SIC) and understanding sea ice properties and variability is important especially today with the recent Arctic sea ice decline. Moreover, accurate estimation of the sea ice effective temperature (Teff) at 50 GHz is needed for atmospheric sounding applications over sea ice and for noise reduction in SIC estimates. At low microwave frequencies, the sensitivity to atmosphere is low, and it is possible to derive sea ice parameters due to the penetration of microwaves in the snow and ice layers. In this study, we propose simple algorithms to derive the snow depth, the snow-ice interface temperature (TSnow-Ice) and the Teff of Arctic sea ice from microwave brightness temperatures (TBs). This is achieved using the Round Robin Data Package of the ESA sea ice CCI project, which contains TBs from the Advanced Microwave Scanning Radiometer 2 (AMSR2) collocated with measurements from Ice Mass Balance (IMB) buoys and the NASA Operation Ice Bridge (OIB) airborne campaigns over the Arctic sea ice. The snow depth over sea ice is estimated with an error of ~ 6 cm using a multilinear regression with the TBs at 6 V, 18 V, and 36 V. The TSnow-Ice is retrieved using a linear regression as a function of the snow depth and the TBs at 10 V or 6 V. The Root Mean Square Errors (RMSEs) obtained are 1.69 and 1.95 K respectively, with the 10 V and 6 V TBs. The Teff at microwave frequencies between 6 and 89 GHz is expressed as a function of TSnow-Ice using data from a thermodynamical model combined with the Microwave Emission Model of Layered Snow-packs. Teffs are estimated from the TSnow-Ice with a RMSE of less than 1 K.

scholarly journals Using Arctic ice mass balance buoys for evaluation of modelled ice energy fluxes

2020 ◽  
Vol 13 (10) ◽  
pp. 4845-4868
Author(s):  
Alex West ◽  
Mat Collins ◽  
Ed Blockley
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Arctic Sea Ice ◽  
Surface Radiation ◽  
Rapid Decline ◽  
Energy Fluxes ◽  
Arctic Sea ◽  
Imperfect Knowledge ◽  
Ice Mass Balance ◽  
Arctic Ice

Abstract. A new method of sea ice model evaluation is demonstrated. Data from the network of Arctic ice mass balance buoys (IMBs) are used to estimate distributions of vertical energy fluxes over sea ice in two densely sampled regions – the North Pole and Beaufort Sea. The resulting dataset captures seasonal variability in sea ice energy fluxes well, and it captures spatial variability to a lesser extent. The dataset is used to evaluate a coupled climate model, HadGEM2-ES (Hadley Centre Global Environment Model, version 2, Earth System), in the two regions. The evaluation shows HadGEM2-ES to simulate too much top melting in summer and too much basal conduction in winter. These results are consistent with a previous study of sea ice state and surface radiation in this model, increasing confidence in the IMB-based evaluation. In addition, the IMB-based evaluation suggests an additional important cause for excessive winter ice growth in HadGEM2-ES, a lack of sea ice heat capacity, which was not detectable in the earlier study. Uncertainty in the IMB fluxes caused by imperfect knowledge of ice salinity, snow density and other physical constants is quantified (as is inaccuracy due to imperfect sampling of ice thickness) and in most cases is found to be small relative to the model biases discussed. Hence the IMB-based evaluation is shown to be a valuable tool with which to analyse sea ice models and, by extension, better understand the large spread in coupled model simulations of the present-day ice state. Reducing this spread is a key task both in understanding the current rapid decline in Arctic sea ice and in constraining projections of future Arctic sea ice change.

scholarly journals Ice mass-balance buoys: a tool for measuring and attributing changes in the thickness of the Arctic sea-ice cover

2006 ◽  
Vol 44 ◽  
pp. 205-210 ◽  
Author(s):  
Jacqueline A. Richter-Menge ◽  
Donald K. Perovich ◽  
Bruce C. Elder ◽  
Keran Claffey ◽  
Ignatius Rigor ◽  
...  
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
North Pole ◽  
The Arctic ◽  
Central Basin ◽  
The North ◽  
Arctic Sea ◽  
Ice Mass Balance

AbstractRecent observational and modeling studies indicate that the Arctic sea-ice cover is undergoing significant climate-induced changes, affecting both its extent and thickness. The thickness or, more precisely, the mass balance of the ice cover is a key climate-change indicator since it is an integrator of both the surface heat budget and the ocean heat flux. Accordingly, efforts are underway to develop and deploy in situ observing systems which, when combined with satellite remote-sensing information and numerical models, can effectively monitor and attribute changes in the mass balance of the Arctic sea-ice cover. As part of this effort, we have developed an autonomous ice mass-balance buoy (IMB), which is equipped with sensors to measure snow accumulation and ablation, ice growth and melt, and internal ice temperature, plus a satellite transmitter. The IMB is unique in its ability to determine whether changes in the thickness of the ice cover occur at the top or bottom of the ice cover, and hence provide insight into the driving forces behind the change. Since 2000, IMBs have been deployed each spring from the North Pole Environmental Observatory and in several other areas, including a few in the Beaufort Sea and Central Basin. At this point, the collective time series is too short to draw significant and specific conclusions regarding interannual and regional variability in ice mass balance. Comparisons of available data indicate that ice surface ablation is greater in the Beaufort region (67–80 cm), relative to the North Pole (0–30 cm), consistent with a longer period of melt in the more southerly location. Ablation at the bottom of the ice (22 cm), maximum ice thickness (235 cm) and maximum snow depth (28 cm) were comparable in the two regions.

Arctic sea ice volume budget decomposition satellite product for the CryoSat-2 (2010-2020) period 

2021 ◽  
Author(s):  
Harry Heorton ◽  
Michel Tsamados ◽  
Paul Holland ◽  
Jack Landy
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Volume Growth ◽  
Arctic Sea Ice ◽  
First Year ◽  
Sea Ice Concentration ◽  
Marginal Seas ◽  
Ice Volume ◽  
Arctic Sea ◽  
Ice Concentration

<p><span>We combine satellite-derived observations of sea ice concentration, drift, and thickness to provide the first observational decomposition of the dynamic (advection/divergence) and thermodynamic (melt/growth) drivers of wintertime Arctic sea ice volume change. Ten winter growth seasons are analyzed over the CryoSat-2 period between October 2010 and April 2020. Sensitivity to several observational products is performed to provide an estimated uncertainty of the budget calculations. The total thermodynamic ice volume growth and dynamic ice losses are calculated with marked seasonal, inter-annual and regional variations</span><span>. Ice growth is fastest during Autumn, in the Marginal Seas and over first year ice</span><span>. Our budget decomposition methodology can help diagnose the processes confounding climate model predictions of sea ice. We make our product and code available to the community in monthly pan-Arctic netcdft files for the entire October 2010 to April 2020 period.</span></p>

scholarly journals Modeling the anisotropic brine microstructure in first-year Arctic sea ice

2012 ◽  
Vol 117 (C2) ◽  
pp. n/a-n/a ◽  
Author(s):  
K. A. Jones ◽  
M. Ingham ◽  
H. Eicken
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
First Year ◽  
Arctic Sea

scholarly journals Arctic sea-ice albedo derived from RGPS-based ice-thickness estimates

2001 ◽  
Vol 33 ◽  
pp. 225-229 ◽  
Author(s):  
R.W. Lindsay
Keyword(s):  
Sea Ice ◽  
Empirical Relationship ◽  
Early Spring ◽  
Snow Accumulation ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
First Year ◽  
Large Set ◽  
Radar Images ◽  
Arctic Sea

AbstractThe RADARSAT geophysical processor system (RGPS) uses sequential synthetic aperture radar images of Arctic sea ice taken every 3 days to track a large set of Lagrangian points over the winter and spring seasons. The points are the vertices of cells, which are initially square and 10 km on a side, and the changes in the area of these cells due to opening and closing of the ice are used to estimate the fractional area of a set of first-year ice categories. The thickness of each category is estimated by the RGPS from an empirical relationship between ice thickness and the freezing degree-days since the formation of the ice. With a parameterization of the albedo based on the ice thickness, the albedo may be estimated from the first-year ice distribution. We compute the albedo for the first spring processed by the RGPS, the early spring of 1997. The data include most of the Beaufort and Chukchi Seas. We find that the mean albedo is 0.79 with a standard deviation of 0.04, with lower albedo values near the edge of the perennial ice zone. The biggest source of error is likely the assumed rate of snow accumulation on new ice.

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