Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI

Given their high albedo and low thermal conductivity, snow and sea ice are considered key reasons for amplified warming in the Arctic. Snow-covered sea ice is a more effective insulator, which greatly limits the energy and momentum exchange between the atmosphere and surface, and further controls the thermal dynamic processes of snow and ice. In this study, using the Microwave Emission Model of Layered Snowpacks (MEMLS), the sensitivities of the brightness temperatures (TBs) from the FengYun-3B/MicroWave Radiometer Imager (FY3B/MWRI) to changes in snow depth were simulated, on both first-year and multiyear ice in the Arctic. Further, the correlation coefficients between the TBs and snow depths in different atmospheric and sea ice environments were investigated. Based on the simulation results, the most sensitive factors to snow depth, including channels of MWRI and their combination form, were determined for snow depth retrieval. Finally, using the 2012–2013 Operational IceBridge (OIB) snow depth data, retrieval algorithms of snow depth were developed for the Arctic on first-year and multiyear ice, separately. Validation using the 2011 OIB data indicates that the bias and standard deviation (Std) of the algorithm are 2.89 cm and 2.6 cm on first-year ice (FYI), respectively, and 1.44 cm and 4.53 cm on multiyear ice (MYI), respectively.

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Variability scaling and consistency in airborne and satellite altimetry measurements of Arctic sea ice

The Cryosphere ◽

10.5194/tc-14-751-2020 ◽

2020 ◽

Vol 14 (2) ◽

pp. 751-767

Author(s):

Shiming Xu ◽

Lu Zhou ◽

Bin Wang

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Snow Depth ◽

Spatial Scales ◽

Ice Cover ◽

Arctic Sea Ice ◽

The Arctic ◽

First Year ◽

Noise Levels ◽

Wide Range

Abstract. Satellite and airborne remote sensing provide complementary capabilities for the observation of the sea ice cover. However, due to the differences in footprint sizes and noise levels of the measurement techniques, as well as sea ice's variability across scales, it is challenging to carry out inter-comparison or consistently study these observations. In this study we focus on the remote sensing of sea ice thickness parameters and carry out the following: (1) the analysis of variability and its statistical scaling for typical parameters and (2) the consistency study between airborne and satellite measurements. By using collocating data between Operation IceBridge and CryoSat-2 (CS-2) in the Arctic, we show that consistency exists between the variability in radar freeboard estimations, although CryoSat-2 has higher noise levels. Specifically, we notice that the noise levels vary among different CryoSat-2 products, and for the European Space Agency (ESA) CryoSat-2 freeboard product the noise levels are at about 14 and 20 cm for first-year ice (FYI) and multi-year ice (MYI), respectively. On the other hand, for Operation IceBridge and NASA's Ice, Cloud, and land Elevation Satellite (ICESat), it is shown that the variability in snow (or total) freeboard is quantitatively comparable despite more than a 5-year time difference between the two datasets. Furthermore, by using Operation IceBridge data, we also find widespread negative covariance between ice freeboard and snow depth, which only manifests on small spatial scales (40 m for first-year ice and about 80 to 120 m for multi-year ice). This statistical relationship highlights that the snow cover reduces the overall topography of the ice cover. Besides this, there is prevalent positive covariability between snow depth and snow freeboard across a wide range of spatial scales. The variability and consistency analysis calls for more process-oriented observations and modeling activities to elucidate key processes governing snow–ice interaction and sea ice variability on various spatial scales. The statistical results can also be utilized in improving both radar and laser altimetry as well as the validation of sea ice and snow prognostic models.

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Sub-kilometre scale distribution of snow depth on Arctic sea ice from Soviet drifting stations

10.31223/x5tg93 ◽

2021 ◽

Author(s):

Robbie Mallett ◽

Julienne Stroeve ◽

Michel Tsamados ◽

Rosemary Willatt ◽

Thomas Newman ◽

...

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Arctic Sea Ice ◽

First Year ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Ice Field ◽

The Mean ◽

Ice Impacts ◽

Multiyear Ice

The sub-kilometre scale distribution of snow depth on Arctic sea ice impacts atmosphere-ice fluxes of heat and light, and is of importance for satellite estimates of sea ice thickness from both radar and lidar altimeters. While information about the mean of this distribution is increasingly available from modelling and remote sensing, the full distribution cannot yet be resolved. We analyse 33539 snow depth measurements from 499 transects taken at Soviet drifting stations between 1955 and 1991 and derive a simple statistical distribution for snow depth over multi-year ice as a function of only the mean snow depth. We then evaluate this snow depth distribution against snow depth transects that span first-year ice to multiyear ice from the MOSAiC, SHEBA and AMSR-Ice field campaigns. Because the distribution can be generated using only the mean snow depth, it can be used in the downscaling of several existing snow depth products for use in flux modelling and altimetry studies.

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Variability of Arctic Sea Ice (1979–2016)

Water ◽

10.3390/w11010023 ◽

2018 ◽

Vol 11 (1) ◽

pp. 23 ◽

Cited By ~ 3

Author(s):

Zhankai Wu ◽

Xingdong Wang

Keyword(s):

Sea Ice ◽

Data Center ◽

Arctic Sea Ice ◽

The Arctic ◽

First Year ◽

Concentration Data ◽

Sea Ice Concentration ◽

Arctic Sea ◽

Ice Concentration ◽

Multiyear Ice

This study is based on the daily sea ice concentration data from the National Snow and Ice Data Center (NSIDC; Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USA) from 1979 to 2016. The Arctic sea ice is analyzed from the total sea ice area, first year ice extent, multiyear ice area, and the variability of sea ice concentration in different ranges. The results show that the total sea ice area decreased by 0.0453 × 106 km2·year−1 (−0.55%/year) between 1979 and 2016, and the variability of the sea ice area from 1997 to 2016 is significantly larger than that from 1979 to 1996. The first-year ice extent increased by 0.0199 × 106 km2·year−1 (0.36%/year) from 1997 to 2016. The multiyear ice area decreased by 0.0711 × 106 km2·year−1 (−0.66%/year) from 1979 to 2016, of which in the last 20 years is about 30.8% less than in 1979–1996. In terms of concentration, we have focused on comparing 1979–1996 and 1997–2016 in different ranges. Sea ice concentration between 0.9–1 accounted for about 39.57% from 1979 to 1996, while from 1997–2016, it accounted for only 27.75%. However, the sea ice of concentration between 0.15–0.4 exhibits no significant trend changes.

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Variability Scaling and Consistency of Airborne and Satellite Altimetry Measurements of Arctic Sea Ice

10.5194/tc-2019-220 ◽

2019 ◽

Author(s):

Shiming Xu ◽

Lu Zhou ◽

Bin Wang

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Snow Depth ◽

Spatial Scales ◽

Ice Cover ◽

Arctic Sea Ice ◽

The Arctic ◽

First Year ◽

Noise Levels ◽

Wide Range

Abstract. Satellite and airborne remote sensing provide complementary capabilities for the observation of the sea ice cover. However, due to the differences in footprint sizes and noise levels of the measurement techniques, as well as sea ice's variability across scales, it is challenging to carry out inter-comparison or consistency study of these observations. In this study we focus on the remote sensing of sea ice thickness parameters, and carry out: (1) the analysis of variability and its statistical scaling for typical parameters, and (2) the consistency study between airborne and satellite measurements. By using collocating data between Operation IceBridge and CryoSat-2 in the Arctic, we show that there exists consistency between the variability of radar freeboard estimations, although CryoSat-2 has higher noise levels. Specifically, we notice that the noise levels vary among different CryoSat-2 products, and for ESA CryoSat-2 freeboard product the noise levels are at about 14 and 20 cm for first-year and multiyear ice, respectively. On the other hand, for Operation IceBridge and ICESat, it is shown that the variability of snow (or total) freeboard is quantitatively comparable, despite over 5 years' the time difference between the two datasets. Furthermore, by using Operation IceBridge data, we also find wide-spread negative covariance between ice freeboard and snow depth, which only manifest at small spatial scales (40 m for first-year ice and about 80 to 120 m for MYI). This statistical relationship highlights that the snow cover reduces the overall topography of the ice cover. Besides, there is prevalent positive covariability between snow depth and snow freeboard across a wide range of spatial scales. The variability and consistency analysis calls for more process-oriented observations and modeling activities to elucidate key processes governing snow-ice interaction and sea ice variability on various spatial scales. The statistical results can also be utilized in improving both radar and laser altimetry, as well as the validation of sea ice and snow prognostic models.

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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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Winter Arctic sea ice freeboard, snow depth and thickness variability from ICESat-2 and NESOSIM

10.5194/egusphere-egu21-13779 ◽

2021 ◽

Author(s):

Alek Petty ◽

Nicole Keeney ◽

Alex Cabaj ◽

Paul Kushner ◽

Nathan Kurtz ◽

...

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Ice Cloud ◽

Ice Drift ◽

Thickness Variability ◽

Arctic Ice

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite&#8208; 2 (ICESat&#8208;2) mission was launched in September 2018 and is now providing routine, very high&#8208;resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA&#8217;s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

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

Annals of Glaciology ◽

10.1017/aog.2020.24 ◽

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.

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Snow Depth Retrieval on Arctic Sea Ice From Passive Microwave Radiometers—Improvements and Extensions to Multiyear Ice Using Lower Frequencies

Journal of Geophysical Research Oceans ◽

10.1029/2018jc014028 ◽

2018 ◽

Vol 123 (10) ◽

pp. 7120-7138 ◽

Cited By ~ 21

Author(s):

Philip Rostosky ◽

Gunnar Spreen ◽

Sinead L. Farrell ◽

Torben Frost ◽

Georg Heygster ◽

...

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Arctic Sea Ice ◽

Passive Microwave ◽

Arctic Sea ◽

Microwave Radiometers ◽

Multiyear Ice ◽

Lower Frequencies

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The impacts of liquid precipitation on sea ice surface ablation

10.5194/egusphere-egu2020-20971 ◽

2020 ◽

Author(s):

Tingfeng Dou

Keyword(s):

Sea Ice ◽

Water Content ◽

Snow Depth ◽

Arctic Sea Ice ◽

Surface Energy Budget ◽

The Arctic ◽

Strong Impact ◽

Rapid Reduction ◽

Arctic Sea ◽

Liquid Precipitation

<p>Snow plays an important role in the Arctic climate system, modulating heat transfer in terrestrial and marine environments and controlling feedbacks. Changes in snow depth over Arctic sea ice, particularly in spring, have a strong impact on the surface energy budget, influencing ocean heat loss, ice growth and surface ponding. Snow conditions are sensitive to the phase (solid or liquid) of deposited precipitation. However, variability and potential trends of rain-on snow events over Arctic sea ice and their role in sea-ice losses are poorly understood. Time series of surface observations at Utqiagvik, Alaska, reveal rapid reduction in snow depth linked to late-spring rain-on-snow events. Liquid precipitation is critical in preconditioning and triggering snow ablation through reduction in surface albedo as well as latent heat release determined by rainfall amount, supported by field observations beginning in 2000 and model results. Rainfall was found to accelerate warming and ripening of the snowpack, with even small amounts (such as 0.3mm recorded on 24 May 2017) triggering the transition from the warming phase into the ripening phase. Subsequently, direct heat input drives snowmelt, with water content of the snowpack increasing until meltwater output occurs, with an associated rapid decrease in snow depth. Rainfall during the ripening phase can further raise water content in the snow layer, prompting onset of the meltwater output phase in the snowpack. First spring rainfall in Utqiagvik has been observed to shift to earlier dates since the 1970s, in particular after the mid-1990s. Early melt season rainfall and its fraction of total annual precipitation also exhibit an increasing trend. These changes of precipitation over sea ice may have profound impacts on ice melt through feedbacks involving earlier onset of surface melt.</p>

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Sea Ice Enhancements to Polar WRF*

Monthly Weather Review ◽

10.1175/mwr-d-14-00344.1 ◽

2015 ◽

Vol 143 (6) ◽

pp. 2363-2385 ◽

Cited By ~ 40

Author(s):

Keith M. Hines ◽

David H. Bromwich ◽

Lesheng Bai ◽

Cecilia M. Bitz ◽

Jordan G. Powers ◽

...

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Snow Depth ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Surface Model ◽

Sea Ice Thickness ◽

Near Surface ◽

The Arctic Ocean

Abstract The Polar Weather Research and Forecasting Model (Polar WRF), a polar-optimized version of the WRF Model, is developed and made available to the community by Ohio State University’s Polar Meteorology Group (PMG) as a code supplement to the WRF release from the National Center for Atmospheric Research (NCAR). While annual NCAR official releases contain polar modifications, the PMG provides very recent updates to users. PMG supplement versions up to WRF version 3.4 include modified Noah land surface model sea ice representation, allowing the specification of variable sea ice thickness and snow depth over sea ice rather than the default 3-m thickness and 0.05-m snow depth. Starting with WRF V3.5, these options are implemented by NCAR into the standard WRF release. Gridded distributions of Arctic ice thickness and snow depth over sea ice have recently become available. Their impacts are tested with PMG’s WRF V3.5-based Polar WRF in two case studies. First, 20-km-resolution model results for January 1998 are compared with observations during the Surface Heat Budget of the Arctic Ocean project. Polar WRF using analyzed thickness and snow depth fields appears to simulate January 1998 slightly better than WRF without polar settings selected. Sensitivity tests show that the simulated impacts of realistic variability in sea ice thickness and snow depth on near-surface temperature is several degrees. The 40-km resolution simulations of a second case study covering Europe and the Arctic Ocean demonstrate remote impacts of Arctic sea ice thickness on midlatitude synoptic meteorology that develop within 2 weeks during a winter 2012 blocking event.

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