Review of Lise Kilic et al. 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.

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.

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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

10.5194/tc-2018-223 ◽

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.

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Review of "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" by Kilic, L., et al, TC-2018-223

10.5194/tc-2018-223-rc2 ◽

2018 ◽

Author(s):

Anonymous

Keyword(s):

Sea Ice ◽

Mass Balance ◽

Effective Temperature ◽

Snow Depth ◽

Arctic Sea Ice ◽

Interface Temperature ◽

Arctic Sea ◽

Ice Mass Balance

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Fate of sea ice in the 'New Arctic': A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions

10.5194/tc-2021-297 ◽

2021 ◽

Author(s):

Sean Horvath ◽

Linette Boisvert ◽

Chelsea Parker ◽

Melinda Webster ◽

Patrick Taylor ◽

...

Keyword(s):

Time Series ◽

Sea Ice ◽

Mass Balance ◽

Air Temperature ◽

Snow Depth ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Ice Mass Balance ◽

Temperature And Pressure

Abstract. Since the early 2000s, sea ice has experienced an increased rate of decline in thickness and extent and transitioned to a seasonal ice cover. This shift to thinner, seasonal ice in the 'New Arctic' is accompanied by a reshuffling of energy flows at the surface. Understanding the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with daily sea ice parcel drift tracks produced in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states. Building on previous work, this dataset includes remotely sensed radiative and turbulent fluxes from which the surface energy budget can be calculated. Additionally, flags indicate when sea ice parcels travel within cyclones, recording distance and direction from the cyclone center. The database drift track was evaluated by comparison with sea ice mass balance buoys. Results show ice parcels generally remain within 100km of the corresponding buoy, with a mean distance of 82.6 km and median distance of 54 km. The sea ice mass balance buoys also provide recordings of sea ice thickness, snow depth, and air temperature and pressure which were compared to this database. Ice thickness and snow depth typically are less accurate than air temperature and pressure due to the high spatial variability of the former two quantities when compared to a point measurement. The correlations between the ice parcel and buoy data are high, which highlights the accuracy of this Lagrangian database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic. Applications such as these may shed light on the atmosphere-snow-sea ice interactions in the changing Arctic environment.

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

Journal of Geophysical Research Oceans ◽

10.1002/2016jc012398 ◽

2017 ◽

Vol 122 (3) ◽

pp. 2539-2549 ◽

Cited By ~ 41

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

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

Geoscientific Model Development ◽

10.5194/gmd-13-4845-2020 ◽

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.

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Assimilating satellite concentration data into an Arctic sea ice mass balance model, 1979-1985

Journal of Geophysical Research Atmospheres ◽

10.1029/96jc01690 ◽

1996 ◽

Vol 101 (C9) ◽

pp. 20849-20868 ◽

Cited By ~ 37

Author(s):

D. Thomas ◽

S. Martin ◽

D. Rothrock ◽

M. Steele

Keyword(s):

Sea Ice ◽

Mass Balance ◽

Arctic Sea Ice ◽

Balance Model ◽

Mass Balance Model ◽

Concentration Data ◽

Arctic Sea ◽

Ice Mass Balance

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

Annals of Glaciology ◽

10.3189/172756406781811727 ◽

2006 ◽

Vol 44 ◽

pp. 205-210 ◽

Cited By ~ 85

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.

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Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

Remote Sensing ◽

10.3390/rs11232864 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2864 ◽

Cited By ~ 4

Author(s):

Jiping Liu ◽

Yuanyuan Zhang ◽

Xiao Cheng ◽

Yongyun Hu

Keyword(s):

Neural Network ◽

Sea Ice ◽

Snow Depth ◽

Deep Neural Network ◽

Arctic Basin ◽

Temporal Variations ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Thickness ◽

Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

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