scholarly journals Observing Muostakh Island disappear: erosion of a ground-ice-rich coast in response to summer warming and sea ice reduction on the East Siberian shelf

2013 ◽  
Vol 7 (4) ◽  
pp. 4101-4176 ◽  
Author(s):  
F. Günther ◽  
P. P. Overduin ◽  
A. Baranskaya ◽  
T. Opel ◽  
M. N. Grigoriev
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Open Water ◽  
The Arctic ◽  
Historical Period ◽  
Interannual Variations ◽  
Ground Ice ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Complex

Abstract. Observations of coastline retreat using contemporary very high resolution satellite and historical aerial imagery were compared to measurements of open water fractions and summer air temperatures. We analyzed seasonal and interannual variations of thawing-induced cliff top retreat (thermo-denudation) and marine abrasion (thermo-abrasion) on Muostakh Island in the southern central Laptev Sea. The island is composed of ground-ice-rich permafrost deposits of Ice Complex type that render it particularly susceptible to erosion along the coast, resulting in land loss. Based on topographic reference measurements during field campaigns, we generated digital elevation models using stereophotogrammetry, in order to block adjust and ortho-rectify aerial photographies from 1951 and GeoEye, QuickBird, WorldView-1, and WorldView-2 imagery from 2010 to 2012 for change detection. Coastline retreat for erosive segments ranged from −13 to −585 m and was −109 ± 81 m (–1.8 ± 1.3 m a−1) on average during the historical period. Current seasonal dynamics of cliff top retreat revealed rapid thermo-denudation rates of –10.2 ± 4.5 m a−1 in mid summer and –4.1 ± 2.0 m a−1 on average during the 2010–2012 observation period. Using sea ice concentration data from the Special Sensor Microwave Imager (SSM/I) and air temperature time series from Tiksi, we calculated seasonal duration available for thermo-abrasion, expressed as open water days, and for thermo-denudation, based on thawing degree days. Geomorphometric analysis revealed that total ground ice content on Muostakh is made up of equal amounts of intrasedimentary and macro ground ice, while its vertical hourglass distribution provides favorable local preconditions for subsidence and the acceleration of coastal thermo-erosion under intensifying environmental forcings. Our results showed a~close relationship between mean summer air temperature and coastal thermo-erosion rates, in agreement with observations made for various permafrost coastlines different from East Siberian Ice Complex coasts elsewhere in the Arctic. Seasonality and recent interannual variations of coastline retreat rates suggest that the combination of macro ground ice distribution in the ground and changes in enviromental forcing generate a cyclicity in coastal thermo-erosion, that is currently increasing in frequency.

scholarly journals Observing Muostakh disappear: permafrost thaw subsidence and erosion of a ground-ice-rich island in response to arctic summer warming and sea ice reduction

2015 ◽  
Vol 9 (1) ◽  
pp. 151-178 ◽  
Author(s):  
F. Günther ◽  
P. P. Overduin ◽  
I. A. Yakshina ◽  
T. Opel ◽  
A. V. Baranskaya ◽  
...  
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Open Water ◽  
Aerial Photographs ◽  
The Arctic ◽  
Interannual Variations ◽  
Ground Ice ◽  
Concentration Data ◽  
Water Fraction ◽  
Permafrost Thaw

Abstract. Observations of coastline retreat using contemporary very high resolution satellite and historical aerial imagery were compared to measurements of open water fraction, summer air temperature, and wind. We analysed seasonal and interannual variations of thawing-induced cliff top retreat (thermo-denudation) and marine abrasion (thermo-abrasion) on Muostakh Island in the southern central Laptev Sea. Geomorphometric analysis revealed that total ground ice content on Muostakh is made up of equal amounts of intrasedimentary and macro ground ice and sums up to 87%, rendering the island particularly susceptible to erosion along the coast, resulting in land loss. Based on topographic reference measurements during field campaigns, we generated digital elevation models using stereophotogrammetry, in order to block-adjust and orthorectify aerial photographs from 1951 and GeoEye, QuickBird, WorldView-1, and WorldView-2 imagery from 2010 to 2013 for change detection. Using sea ice concentration data from the Special Sensor Microwave Imager (SSM/I) and air temperature time series from nearby Tiksi, we calculated the seasonal duration available for thermo-abrasion, expressed as open water days, and for thermo-denudation, based on the number of days with positive mean daily temperatures. Seasonal dynamics of cliff top retreat revealed rapid thermo-denudation rates of −10.2 ± 4.5 m a−1 in mid-summer and thermo-abrasion rates along the coastline of −3.4 ± 2.7 m a−1 on average during the 2010–2013 observation period, currently almost twice as rapid as the mean rate of −1.8 ± 1.3 m a−1 since 1951. Our results showed a close relationship between mean summer air temperature and coastal thermo-erosion rates, in agreement with observations made for various permafrost coastlines different to the East Siberian Ice Complex coasts elsewhere in the Arctic. Seasonality of coastline retreat and interannual variations of environmental factors suggest that an increasing length of thermo-denudation and thermo-abrasion process simultaneity favours greater coastal erosion. Coastal thermo-erosion has reduced the island's area by 0.9 km2 (24%) over the past 62 years but shrank its volume by 28 x 106 m3 (40%), not least because of permafrost thaw subsidence, with the most pronounced with rates of ≥− 11 cm a−1 on yedoma uplands near the island's rapidly eroding northern cape. Recent acceleration in both will halve Muostakh Island's lifetime to less than a century.

scholarly journals Sea Ice Monitoring with CFOSAT Scatterometer Measurements Using Random Forest Classifier

2021 ◽  
Vol 13 (22) ◽  
pp. 4686
Author(s):  
Xiaochun Zhai ◽  
Zhixiong Wang ◽  
Zhaojun Zheng ◽  
Rui Xu ◽  
Fangli Dou ◽  
...  
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Open Water ◽  
Global Scale ◽  
Random Forest Classifier ◽  
Summer Season ◽  
The Arctic ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
The Difference

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). This paper performs sea ice monitoring with the CSCAT backscatter measurements in polar areas. The CSCAT measurements have the characteristics of diverse incidence and azimuth angles and separation between open water and sea ice. Hence, five microwave feature parameters, which show different sensitivity to ice or water, are defined and derived from the CSCAT measurements firstly. Then the random forest classifier is selected for sea ice monitoring because of its high overall accuracy of 99.66% and 93.31% in the Arctic and Antarctic, respectively. The difference of features ranked by importance in different seasons and regions shows that the combination of these parameters is effective in discriminating sea ice from water under various conditions. The performance of the algorithm is validated against the sea ice edge data from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) on a global scale in a period from 1 January 2019 to 10 May 2021. The mean sea ice area differences between CSCAT and OSI SAF product in the Arctic and Antarctic are 0.2673 million km2 and −0.4446 million km2, respectively, and the sea ice area relative errors of CSCAT are less than 10% except for summer season in both poles. However, the overall sea ice area derived from CSCAT is lower than the OSI SAF sea ice area in summer. This may be because the CSCAT is trained by radiometer sea ice concentration data while the radiometer measurement of sea ice is significantly affected by melting in the summer season. In conclusion, this research verifies the capability of CSCAT in monitoring polar sea ice using a machine learning-aided random forest classifier. This presented work can give guidance to sea ice monitoring with radar backscatter measurements from other spaceborne scatterometers, particular for the recently launched FY-3E scatterometer (called WindRad).

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

scholarly journals Brief communication: The challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation

2016 ◽  
Vol 10 (2) ◽  
pp. 761-774 ◽  
Author(s):  
Qinghua Yang ◽  
Martin Losch ◽  
Svetlana N. Losa ◽  
Thomas Jung ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Satellite Data ◽  
General Circulation ◽  
The Arctic ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Uncertainty Estimates ◽  
Ice Concentration ◽  
Data Products

Abstract. Data assimilation experiments that aim at improving summer ice concentration and thickness forecasts in the Arctic are carried out. The data assimilation system used is based on the MIT general circulation model (MITgcm) and a local singular evolutive interpolated Kalman (LSEIK) filter. The effect of using sea ice concentration satellite data products with appropriate uncertainty estimates is assessed by three different experiments using sea ice concentration data of the European Space Agency Sea Ice Climate Change Initiative (ESA SICCI) which are provided with a per-grid-cell physically based sea ice concentration uncertainty estimate. The first experiment uses the constant uncertainty, the second one imposes the provided SICCI uncertainty estimate, while the third experiment employs an elevated minimum uncertainty to account for a representation error. Using the observation uncertainties that are provided with the data improves the ensemble mean forecast of ice concentration compared to using constant data errors, but the thickness forecast, based on the sparsely available data, appears to be degraded. Further investigating this lack of positive impact on the sea ice thicknesses leads us to a fundamental mismatch between the satellite-based radiometric concentration and the modeled physical ice concentration in summer: the passive microwave sensors used for deriving the vast majority of the sea ice concentration satellite-based observations cannot distinguish ocean water (in leads) from melt water (in ponds). New data assimilation methodologies that fully account or mitigate this mismatch must be designed for successful assimilation of sea ice concentration satellite data in summer melt conditions. In our study, thickness forecasts can be slightly improved by adopting the pragmatic solution of raising the minimum observation uncertainty to inflate the data error and ensemble spread.

scholarly journals The impact of melt ponds on summertime microwave brightness temperatures and sea ice concentrations

2016 ◽  
Author(s):  
S. Kern ◽  
A. Rösel ◽  
L. T. Pedersen ◽  
N. Ivanova ◽  
R. Saldo ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Water Fraction ◽  
Microwave Brightness Temperature ◽  
Ice Melt ◽  
Melt Ponds ◽  
Retrieval Algorithms ◽  
The Impact

Abstract. The sea ice concentration (SIC) derived from satellite microwave brightness temperature (TB) data are known to be less accurate during summer melt conditions – in the Arctic Ocean primarily because of the impact of melt ponds on sea ice. Using data from June to August 2009, we investigate how TBs and SICs vary as a function of the ice surface fraction (ISF) computed from open water fraction and melt pond fraction both derived from satellite optical reflectance data. SIC is computed from TBs using a set of eight different retrieval algorithms and applying a consistent set of tie points. We find that TB values change during sea ice melt non-linearly and not monotonically as a function of ISF for ISF of 50 to 100 %. For derived parameters such as the polarization ratio at 19 GHz the change is monotonic but substantially smaller than theoretically expected. Changes in ice/snow radiometric properties during melt also contribute to the TB changes observed; these contributions are functions of frequency and polarization and have the potential to partly counter-balance the impact of changing ISF on the observed TBs. All investigated SIC retrieval algorithms overestimate ISF when using winter tie points. The overestimation varies among the algorithms as a function of ISF such that the SIC retrieval algorithms could be categorized into two different classes. These reveal a different degree of ISF overestimation at high ISF and an opposite development of ISF over-estimation as ISF decreases. For one class, correlations between SIC and ISF are ≥ 0.85 and the associated linear regression lines suggest an exploitable relationship between SIC and ISF if reliable summer sea ice tie points can be established. This study shows that melt ponds are interpreted as open water by the SIC algorithms, while the concentration of ice between the melt ponds is in general being overestimated. These two effects may cancel each other out and thus produce seemingly correct SIC for the wrong reasons. This cancelling effect will in general only be "correct" at one specific value of MPF. Based on our findings we recommend to not correct SIC algorithms for the impact of melt ponds as this seems to violate physical principles. Users should be aware that the SIC algorithms available at the moment retrieve a combined parameter presented by SIC in winter and ISF in summer.

scholarly journals Perspectives on future sea ice and navigability in the Arctic

2021 ◽  
Vol 15 (12) ◽  
pp. 5473-5482
Author(s):  
Jinlei Chen ◽  
Shichang Kang ◽  
Wentao Du ◽  
Junming Guo ◽  
Min Xu ◽  
...  
Keyword(s):  
Sea Ice ◽  
Coupled Model ◽  
Open Water ◽  
Circulation Pattern ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Transportation Accessibility ◽  
Decadal Rate

Abstract. The retreat of sea ice has been found to be very significant in the Arctic under global warming. It is projected to continue and will have great impacts on navigation. Perspectives on the changes in sea ice and navigability are crucial to the circulation pattern and future of the Arctic. In this investigation, the decadal changes in sea ice parameters were evaluated by the multi-model from the Coupled Model Inter-comparison Project Phase 6, and Arctic navigability was assessed under two shared socioeconomic pathways (SSPs) and two vessel classes with the Arctic transportation accessibility model. The sea ice extent shows a high possibility of decreasing along SSP5-8.5 under current emissions and climate change. The decadal rate of decreasing sea ice extent will increase in March but decrease in September until 2060, when the oldest ice will have completely disappeared and the sea ice will reach an irreversible tipping point. Sea ice thickness is expected to decrease and transit in certain parts, declining by −0.22 m per decade after September 2060. Both the sea ice concentration and volume will thoroughly decline at decreasing decadal rates, with a greater decrease in volume in March than in September. Open water ships will be able to cross the Northern Sea Route and Northwest Passage between August and October during the period from 2045 to 2055, with a maximum navigable percentage in September. The time for Polar Class 6 (PC6) ships will shift to October–December during the period from 2021 to 2030, with a maximum navigable percentage in October. In addition, the central passage will be open for PC6 ships between September and October during 2021–2030.

scholarly journals Impact of the Assimilation of Sea Ice Concentration Data on an Atmosphere-Ocean-Sea Ice Coupled Simulation of the Arctic Ocean Climate

SOLA ◽  
2011 ◽  
Vol 7 ◽  
pp. 37-40 ◽  
Author(s):  
Takahiro Toyoda ◽  
Toshiyuki Awaji ◽  
Nozomi Sugiura ◽  
Shuhei Masuda ◽  
Hiromichi Igarashi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
The Arctic ◽  
Coupled Simulation ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ocean Climate ◽  
Ice Concentration ◽  
The Arctic Ocean

Retrogressive thaw slumps in areas with tabular ground ice on Kolguev (Barents Sea) and Novaya Sibir’ (East-Siberian Sea) Islands

2020 ◽  
Author(s):  
Alexander Kizyakov ◽  
Frank Günther ◽  
Mikhail Zimin ◽  
Anton Sonyushkin ◽  
Ekaterina Zhdanova ◽  
...  
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Linear Trend ◽  
Open Water ◽  
Wave Action ◽  
Observation Data ◽  
Ground Ice ◽  
Significant Linear Trend ◽  
Ice Conditions ◽  
Kolguev Island

<p>The activation of retrogressive thaw slumps is associated with slope surface stability disturbances, or with an increase in the depth of seasonal thawing, that can reach the top of surface-near ground ice. Most retrogressive thaw slumps are confined to terraced slope surfaces that have been undercut and started to retreat due to lateral river erosion or wave action along lake, river or sea shores. Subsequent long-term retrogressive that slump growth depends on constant removal of material from the slope foot by river water or sea waves.</p><p>We have studied the current dynamics of coastal destruction and retrogressive thaw slumps in the western (Kolguev Island) and one of the eastern-most (Novaya Sibir’ Island) occurrences of tabular ground ice in the Eurasian Arctic. A wide set of multi-temporal optical earth observation data of high and very-high spatial resolution (SPOT 6 & 7, GeoEye, WorldView, Kompsat, Prism, Formosat, and Resurs) was used. We modified the TanDEM-X DEM (12 m) for relief reconstruction of earlier stage relief settings to ensure consistent orthorectification of oblique viewing satellite imagery. All raw images were terrain-corrected and georeferenced using a comprehensive block adjustment.</p><p>In the western part of Kolguev Island retrogressive thaw slump average retreat rates of different thermocirque features varied from 0.7 to 7.9 m/year in 2002-2018. Maximum rates reached 14.5-15.1 m/year. On the Novaya Sibir’ Island thermocirques averaged retreat rates in 2007-2018 varied from 3.3 to 8.5 m/year, maximum rates were up to 15.5 m/year.</p><p>Besides dependence of thermocirque occurrence on local ground ice conditions, external forcing on coastal dynamics and thermocirque retreat has been analysed for air temperature and sea ice fluctuations through sums of positive daily mean air temperature and the duration of the open-water period variability for specific periods bracketed by image acquisition dates. Ice conditions in the coastal zone (app. near 50 km of coastal line) of the studied areas were analyzed according to microwave satellite OSI-450 and OSI-430 datasets. We assumed the open-water season as the period when sea ice concentration was less than 15%. Around Kolguev Island, over the 2006-2018 there has been not statistically significant linear trend for open-water period - median value of linear trend is 2.5 days/year with different sea ice conditions off the south and north coasts of the island. At the same time, an increase in the annual sum of positive daily mean  air temperature is noted. For the period 2006-2018, the linear trend was 23.2 degree/year. That is why, for Kolguev Island, we expect at least a sustained level of substantially stronger retreat rates when compared with the past, if not a further increase in thermal denudation intensity and thermocirque growth, and strong and steady rates of coastal destruction due to wave action. Further research will focus on identifying commonalities and differences between the two study regions with respect to hydrometeorological and permafrost conditions.</p><p>Supported by RFBR grants № 18-05-60080 and 18-05-60221, and by DFG grant № WE4390/7-1.</p>

scholarly journals The effect of changing sea ice on the vulnerability of Arctic coasts

2014 ◽  
Vol 8 (3) ◽  
pp. 2277-2329 ◽  
Author(s):  
K. R. Barnhart ◽  
I. Overeem ◽  
R. S. Anderson
Keyword(s):  
Sea Ice ◽  
Water Level ◽  
Coastal Erosion ◽  
Extreme Values ◽  
Open Water ◽  
Water Levels ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Edge ◽  
Sea Ice Edge

Abstract. Shorefast sea ice prevents the interaction of the land and the ocean in the Arctic winter and influences this interaction in the summer by governing the fetch. In many parts of the Arctic the sea-ice-free season is increasing in duration, and the summertime sea ice extents are decreasing. Sea ice provides a first order control on the vulnerability of Arctic coasts to erosion, inundation, and damage to settlements and infrastructure. We ask how the changing sea ice cover has influenced coastal erosion over the satellite record. First, we present a pan-Arctic analysis of satellite-based sea ice concentration specifically along the Arctic coasts. The median length of the 2012 open water season in comparison to 1979 expanded by between 1.5 and 3-fold by Arctic sea sector which allows for open water during the stormy Arctic fall. Second, we present a case study of Drew Point, Alaska, a site on the Beaufort Sea characterized by ice-rich permafrost and rapid coastal erosion rates where both the duration of the sea ice free season and distance to the sea ice edge, particularly towards the northwest, has increased. At Drew Point, winds from the northwest result in increased water levels at the coast and control the process of submarine notch incision, the rate-limiting step of coastal retreat. When open water conditions exist, the distance to the sea ice edge exerts control on the water level and wave field through its control on fetch. We find that the extreme values of water level set-up have increased, consistent with increasing fetch.

scholarly journals The EUMETSAT sea ice climate record

2016 ◽  
Author(s):  
R. T. Tonboe ◽  
S. Eastwood ◽  
T. Lavergne ◽  
A. M. Sørensen ◽  
N. Rathmann ◽  
...  
Keyword(s):  
Sea Ice ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Open Water ◽  
The Arctic ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Microwave Imager

Abstract. An Arctic and Antarctic sea ice area and extent dataset has been generated by EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSISAF) using the record of American microwave radiometer data from Nimbus 7 Scanning Multichannel Microwave radiometer (SMMR) and the Defense Meteorological satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager and Sounder (SSMIS) satellite sensors. The dataset covers the period from 1978 to 2014 and updates and further developments are planned for the next phase of the project. The methodology is using: 1) numerical weather prediction (NWP) input to a radiative transfer model (RTM) for correction of the brightness temperatures for reduction of atmospheric noise, 2) dynamical algorithm tie-points to mitigate trends in residual atmospheric, sea ice and water emission characteristics and inter-sensor differences/biases, 3) and a hybrid sea ice concentration algorithm using the Bristol algorithm over ice and the Bootstrap algorithm in frequency mode over open water. A new algorithm has been developed to estimate the spatially and temporally varying sea ice concentration uncertainties. A comparison to sea ice charts from the Arctic and the Antarctic shows that ice concentrations are higher in the ice charts than estimated from the radiometer data at intermediate ice concentrations. The sea ice climate dataset is available for download at (www.osisaf.org) including documentation.

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