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 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 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.

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 Estimating snow depth over Arctic sea ice from calibrated dual-frequency radar freeboards

2018 ◽  
Vol 12 (11) ◽  
pp. 3551-3564 ◽  
Author(s):  
Isobel R. Lawrence ◽  
Michel C. Tsamados ◽  
Julienne C. Stroeve ◽  
Thomas W. K. Armitage ◽  
Andy L. Ridout
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Light Propagation ◽  
Arctic Sea Ice ◽  
Dual Frequency ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Independent Observations ◽  
The Difference ◽  
Snow Thickness

Abstract. Snow depth on sea ice remains one of the largest uncertainties in sea ice thickness retrievals from satellite altimetry. Here we outline an approach for deriving snow depth that can be applied to any coincident freeboard measurements after calibration with independent observations of snow and ice freeboard. Freeboard estimates from CryoSat-2 (Ku band) and AltiKa (Ka band) are calibrated against data from NASA's Operation IceBridge (OIB) to align AltiKa with the snow surface and CryoSat-2 with the ice–snow interface. Snow depth is found as the difference between the two calibrated freeboards, with a correction added for the slower speed of light propagation through snow. We perform an initial evaluation of our derived snow depth product against OIB snow depth data by excluding successive years of OIB data from the analysis. We find a root-mean-square deviation of 7.7, 5.3, 5.9, and 6.7 cm between our snow thickness product and OIB data from the springs of 2013, 2014, 2015, and 2016 respectively. We further demonstrate the applicability of the method to ICESat and Envisat, offering promising potential for the application to CryoSat-2 and ICESat-2, which launched in September 2018.

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 Modulation of Arctic Sea Ice Loss by Atmospheric Teleconnections from Atlantic Multidecadal Variability

2019 ◽  
Vol 32 (5) ◽  
pp. 1419-1441 ◽  
Author(s):  
Frederic S. Castruccio ◽  
Yohan Ruprich-Robert ◽  
Stephen G. Yeager ◽  
Gokhan Danabasoglu ◽  
Rym Msadek ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Atlantic Multidecadal Variability ◽  
Sea Level Pressure ◽  
Atmospheric Teleconnections ◽  
Arctic Sea ◽  
Long Term Trend

Abstract Observed September Arctic sea ice has declined sharply over the satellite era. While most climate models forced by observed external forcing simulate a decline, few show trends matching the observations, suggesting either model deficiencies or significant contributions from internal variability. Using a set of perturbed climate model experiments, we provide evidence that atmospheric teleconnections associated with the Atlantic multidecadal variability (AMV) can drive low-frequency Arctic sea ice fluctuations. Even without AMV-related changes in ocean heat transport, AMV-like surface temperature anomalies lead to adjustments in atmospheric circulation patterns that produce similar Arctic sea ice changes in three different climate models. Positive AMV anomalies induce a decrease in the frequency of winter polar anticyclones, which is reflected both in the sea level pressure as a weakening of the Beaufort Sea high and in the surface temperature as warm anomalies in response to increased low-cloud cover. Positive AMV anomalies are also shown to favor an increased prevalence of an Arctic dipole–like sea level pressure pattern in late winter/early spring. The resulting anomalous winds drive anomalous ice motions (dynamic effect). Combined with the reduced winter sea ice formation (thermodynamic effect), the Arctic sea ice becomes thinner, younger, and more prone to melt in summer. Following a phase shift to positive AMV, the resulting atmospheric teleconnections can lead to a decadal ice thinning trend in the Arctic Ocean on the order of 8%–16% of the reconstructed long-term trend, and a decadal trend (decline) in September Arctic sea ice area of up to 21% of the observed long-term trend.

scholarly journals Estimating snow depth over Arctic sea ice from calibrated dual-frequency radar freeboards

2018 ◽  
Author(s):  
Isobel Lawrence ◽  
Michel Tsamados ◽  
Julienne Stroeve ◽  
Thomas Armitage ◽  
Andy Ridout
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Light Propagation ◽  
Arctic Sea Ice ◽  
Dual Frequency ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Independent Observations ◽  
The Difference ◽  
Snow Thickness

Abstract. Snow depth on sea ice remains one of the largest uncertainties in sea ice thickness retrievals from satellite altimetry. Here we outline an approach for deriving snow thickness that can be applied to any coincident freeboard measurements after calibration with independent observations of snow and ice freeboard. Freeboard estimates from CryoSat-2 (Ku-band) and AltiKa (Ka-band) are calibrated against data from NASA's Operation IceBridge (OIB) to align AltiKa to the snow surface and CryoSat-2 to the ice/snow interface. Snow depth is found as the difference between the two calibrated freeboards, with a correction added for the slower speed of light propagation through snow. We perform an initial evaluation of our derived snow depth product against OIB snow depth data by excluding successive years of OIB data from the analysis. We find a root-mean-square deviation of 4.9, 6.5, 6.7 and 7.6 cm between our snow thickness product and OIB data from the springs of 2013, 2014, 2015 and 2016 respectively. We further demonstrate the applicability of the method to ICESat and Envisat, offering promising potential for the application to CryoSat-2 and ICESat-2, when ICESat-2 is launched in 2018.

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 Estimating snow depth on Arctic sea ice based on reanalysis reconstruction and particle filter assimilation

2021 ◽  
Author(s):  
Haili Li ◽  
Chang-Qing Ke ◽  
Qinghui Zhu ◽  
Xiaoyi Shen
Keyword(s):  
Sea Ice ◽  
Particle Filter ◽  
Snow Depth ◽  
Input Parameter ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Buoy Data

Abstract. The snow depth, an essential metric of snowpacks, can modulate sea ice changes and is a necessary input parameter to obtain altimeter-derived sea ice thickness values. In this study, we propose an innovative snow depth retrieval method with the improved NASA Eulerian Snow on Sea Ice Model (INESOSIM) and the particle filter (PF) approach, namely, INESOSIM-PF. Then, we generate daily snow depth estimates with INESOSIM-PF from 2012 to 2020 at a 50-km resolution. With the use of Operation IceBridge (OIB) data, it can be revealed that compared to the NESOSIM-estimated snow depth, the INESOSIM-PF-estimated snow depth is greatly improved, with a root mean square error (RMSE) decrease of 17.97 % (RMSE: 6.73 cm) and a correlation coefficient increase of 11.85 % (r: 0.71). The INESOSIM-PF-estimated snow depth is close to the satellite-derived snow depth, which is applied in data assimilation. With the use of Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) snow buoy data, it can be verified that INESOSIM-PF performs well in the Central Arctic with an RMSE of 9.23 cm. INESOSIM-PF is robust and the snow depth determined with INESOSIM-PF is less influenced by input parameters with a snow depth uncertainty of 0.74 cm. The variations in the monthly and seasonal snow depth estimates retrieved from INESOSIM-PF agree well with those in the estimates retrieved from two other existing algorithms. Based on the presented snow depth estimates, we can retrieve the sea ice thickness and perform long-term snow depth and sea ice analysis. Snow depth estimates improve the understanding of Arctic environmental change and promote the future development of sea ice models.

Sign in / Sign up

Export Citation Format

Share Document