The impacts of liquid precipitation on sea ice surface ablation

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>

scholarly journals A key factor initiating surface ablation of Arctic sea ice: earlier and increasing liquid precipitation

2019 ◽  
Vol 13 (4) ◽  
pp. 1233-1246 ◽  
Author(s):  
Tingfeng Dou ◽  
Cunde Xiao ◽  
Jiping Liu ◽  
Wei Han ◽  
Zhiheng Du ◽  
...  
Keyword(s):  
Sea Ice ◽  
Water Content ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
Surface Energy Budget ◽  
The Arctic ◽  
Strong Impact ◽  
Rapid Reduction ◽  
Arctic Sea ◽  
Liquid Precipitation

Abstract. 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 Utqiaġvik, Alaska, reveal rapid reduction in snow depth linked to late-spring rain-on-snow events. Liquid precipitation is key 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 0.3 mm 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 Utqiaġvik 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.

scholarly journals A key factor initiating surface ablation of Arctic sea ice: Earlier and increasing liquid precipitation

2018 ◽  
Author(s):  
Tingfeng Dou ◽  
Cunde Xiao ◽  
Jiping Liu ◽  
Wei Han ◽  
Zhiheng Du ◽  
...  
Keyword(s):  
Sea Ice ◽  
Water Content ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
Surface Energy Budget ◽  
The Arctic ◽  
Strong Impact ◽  
Snow Pack ◽  
Arctic Sea ◽  
Liquid Precipitation

Abstract. 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 Utqiaġvik, Alaska reveal rapid reduction in snow depth linked to late-spring rain-on-snow events. Liquid precipitation is key 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 snow pack, with even small amounts (such as 0.3 mm recorded on May 24, 2017) triggering the transition from the warming phase into the ripening phase. Subsequently, direct heat input drives snow melt, with water content of the snow pack 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 snow pack. First spring rainfall in Utqiaġvik 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.

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

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
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.

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.

scholarly journals Inter-comparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

2017 ◽  
Author(s):  
Ron Kwok ◽  
Nathan T. Kurtz ◽  
Ludovic Brucker ◽  
Alvaro Ivanoff ◽  
Thomas Newman ◽  
...  
Keyword(s):  
Sea Ice ◽  
Data Quality ◽  
Snow Depth ◽  
Radar Data ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea ◽  
Retrieval Algorithms ◽  
Snow Thickness

Abstract. Since 2009, the ultra-wideband snow-radar on Operation IceBridge has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air-snow and snow-ice interfaces are detected and localized in the radar returns, and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affect snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two ground-based campaigns, including the BRomine Ozone Mercury EXperiment (BROMEX) at Barrow, Alaska and a field program by Environment and Climate Change Canada (ECCC) at Eureka, Nunavut, available climatology and snowfall from ERA-Interim reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice.

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 Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

2017 ◽  
Vol 11 (6) ◽  
pp. 2571-2593 ◽  
Author(s):  
Ron Kwok ◽  
Nathan T. Kurtz ◽  
Ludovic Brucker ◽  
Alvaro Ivanoff ◽  
Thomas Newman ◽  
...  
Keyword(s):  
Sea Ice ◽  
Data Quality ◽  
Snow Depth ◽  
Radar Data ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea ◽  
Retrieval Algorithms ◽  
Snow Thickness

Abstract. Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air–snow (a–s) and snow–ice (s–i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two ground-based campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice.

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 The relation between sea ice thickness and freeboard in the Arctic

2010 ◽  
Vol 4 (2) ◽  
pp. 641-661 ◽  
Author(s):  
V. Alexandrov ◽  
S. Sandven ◽  
J. Wahlin ◽  
O. M. Johannessen
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Empirical Relation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Radar Altimeter ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. Retrieval of Arctic sea ice thickness from radar altimeter freeboard data, to be provided by CryoSat-2, requires observational data to verify the relation between the two variables. In this study in-situ ice and snow data from 689 observation sites obtained during the Sever expeditions in the 1980s have been used to establish an empirical relation between ice thickness and freeboard. Estimates of mean and variability of snow depth, snow density and ice density were produced based on many field observations, and have been used in the isostatic equilibrium equation to estimate ice thickness as a function of ice freeboard, snow depth and snow/ice density. The accuracy of the ice thickness retrieval has been calculated from the estimated variability in ice and snow parameters and error of ice freeboard measurements. It is found that uncertainties of ice density and freeboard are the major sources of error in ice thickness calculation. For FY ice, retrieval of ≈1.0 m (2.0 m) thickness has an uncertainty of 60% (41%). For MY ice the main uncertainty is ice density error, since the freeboard error is relatively smaller than for FY ice. Retrieval of 2.4 m (3.0 m) thick MY ice has an error of 24% (21%). The freeboard error is ±0.05 m for both the FY and MY ice. If the freeboard error can be reduced to 0.01 m by averaging a large number of measurements from CryoSat, the error in thickness retrieval is reduced to about 32% for a 1.0 m thick FY floe and to about 18% for a 2.3 m thick MY floe. The remaining error is dominated by uncertainty in ice density. Provision of improved ice density data is therefore important for accurate retrieval of ice thickness from CryoSat data.

Parameter calibration and uncertainty analysis for snow depths from the NASA Eulerian Snow On Sea Ice Model and derived sea ice thickness from ICESat-2

2021 ◽  
Author(s):  
Alex Cabaj ◽  
Paul Kushner ◽  
Alek Petty
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Blowing Snow ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Arctic Sea ◽  
Ice Model

<p><span>Snow on Arctic sea ice plays many, sometimes contrasting roles in Arctic climate feedbacks. During the sea ice growth season, the presence of snow on sea ice can enhance ice growth by increasing the sea ice albedo, or conversely, inhibit sea ice growth by insulating the ice from the cold atmosphere. Furthermore, estimates of snow depth on Arctic sea ice are also a key input for deriving sea ice thickness from altimetry measurements, such as satellite lidar altimetry measurements from ICESat-2. Due to the logistical challenges of making measurements in as remote a region as the Arctic, snow depth on Arctic sea ice is difficult to observationally constrain.<br><br>The NASA Eulerian Snow On Sea Ice Model (NESOSIM) can be used to provide snow depth and density estimates over Arctic sea ice with pan-Arctic coverage within a relatively simple framework. The latest version of NESOSIM, version 1.1, is a 2-layer model with simple representations of the processes of accumulation, wind packing, loss due to blowing snow, and redistribution due to sea ice motion. Relative to version 1.0, NESOSIM 1.1 features an extended model domain, and reanalysis snowfall input scaled to observed snowfall retrieved from CloudSat satellite radar reflectivity measurements.<br><br>In this work, we present a systematic calibration, and an accompanying estimate in the uncertainty of the free parameters in NESOSIM, targeting airborne snow radar measurements from Operation IceBridge. We further investigate uncertainties in snow depth and the resulting uncertainties in derived sea ice thickness from ICESat-2 altimetry measurements using NESOSIM snow depths.</span></p>

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