OCS Study

BOEM 2024-034

U.S. Department of the Interior

Bureau of Ocean Energy Management

Alaska OCS Region, Anchorage, AK

Landfast Ice Climatology within the 

Arctic OCS
OCS Study

BOEM 2024-034

U.S. Department of the Interior

Bureau of Ocean Energy Management

Alaska OCS Region, Anchorage, AK

Landfast Ice Climatology within the 

Arctic OCS

August 2024

Authors:

Andrew R. Mahoney

Peter Bieniek

Seth Danielson

Andrew H. Einhorn

Kate S. Hedstrom

Joshua M. Jones

Thilo Klenz

Prepared under Cooperative Agreement M19AC00021

By

University of Alaska Fairbanks

Fairbanks, AK, 99775
i 

DISCLAIMER

Study collaboration and funding were provided by the U.S. Department of the Interior, Bureau of 

Ocean Energy Management (BOEM), Environmental Studies Program, Washington, DC, under 

Agreement Number M19AC00021. This report has been technically reviewed by BOEM, and it 

has been approved for publication. The views and conclusions contained in this document are 

those of the authors and should not be interpreted as representing the opinions or policies of 

BOEM, nor does mention of trade names or commercial products constitute endorsement or 

recommendation for use.

REPORT AVAILABILITY

Download a PDF file of this report at https://espis.boem.gov/Final%20Reports/BOEM_2024-

034.pdf. 

To search for other Environmental Studies Program ongoing and completed studies, visit 

https://www.boem.gov/environment/environmental-studies/environmental-studies-information/ 

CITATION

Mahoney AR, Bieniek P, Danielson S, Einhorn AH, Hedstrom KS, Jones JM, Klenz, T 

(University of Alaska, Fairbanks, AK). 2024. Landfast ice climatology within the Arctic 

OCS. Anchorage (AK): U.S. Department of the Interior, Bureau of Ocean Energy 

Management. 121 p. Report No.: OCS Study BOEM 2024-034. 

https://espis.boem.gov/Final%20Reports/BOEM_2024-034.pdf. 

ABOUT THE COVER

A recently refrozen lead at the landfast sea ice edge near Point Barrow on January 17, 2020. 

Photograph by A. Mahoney 

ACKNOWLEDGMENTS

We are grateful to personnel at the U.S. National Ice Center (NIC) and the National Weather 

Service’s Alaska Sea Ice Program (ASIP) for cooperation and assistance with questions 

regarding their sea ice chart products. We also thank the staff of Ukpeagvik Iñupiaq Corporation 

(UIC) Science in Utqiagvik, and Michael Tuzroyluck from the community of Point Hope for 

assistance with field activities. We are also grateful to Hilcorp for assistance with our attempted 

deployment of equipment near Deadhorse in 2020. Lastly, we thank Dr. Wilbert Weijer, from 

Los Alamos National Laboratory for providing the graphic used in Figure 3-37. 
ii

Contents

List of Figures ............................................................................................................................................iv

List of Tables .............................................................................................................................................vii

List of Abbreviations and Acronyms .......................................................................................................ix

Abstract........................................................................................................................................................1

1 Introduction .........................................................................................................................................3

1.1 Note on prior related research ......................................................................................................3

1.2 Background ...................................................................................................................................3

1.3 Regional hydrography, bathymetry, and sea ice characteristics ..................................................4

1.4 Study regions ................................................................................................................................5

2 Data and methods...............................................................................................................................6

2.1 Existing landfast ice extent data sources......................................................................................6

2.1.1 Rationale for using existing data sources .................................................................................6

2.1.2 M2014 landfast ice data............................................................................................................6

2.1.3 U.S. National Ice Center (NIC) ice charts .................................................................................7

2.1.4 Alaska Sea Ice Program (ASIP) ice charts ...............................................................................8

2.2 Standardized analysis of SLIE data ..............................................................................................8

2.2.1 Overview of SLIEalyzer tools ....................................................................................................8

2.2.2 A note on assigning dates to SLIE observations ......................................................................9

2.2.3 Measurement of landfast ice width............................................................................................9

2.2.4 Reconstruction of SLIE images from landfast ice width measurements.................................10

2.2.5 Key events within the landfast ice cycle..................................................................................11

2.2.6 Calculation of monthly SLIE extents .......................................................................................12

2.2.7 Identification of mid-season breakout events..........................................................................12

2.3 Compilation of extended landfast ice climatology.......................................................................13

2.4 InSAR-based detection of landfast ice........................................................................................15

2.4.1 Overview of InSAR and application to sea ice........................................................................15

2.4.2 Interferometric coherence and relationship to landfast ice extent ..........................................15

2.4.3 Measurement of apparent strain .............................................................................................16

2.4.4 Regions used to define stability thresholds.............................................................................17

2.5 In-situ field observations .............................................................................................................19

2.5.1 Seasonal ice mass balance buoys (SIMBs)............................................................................19

2.5.2 Ice tethered acoustic Doppler current profilers (ADCPs)........................................................21

2.6 Atmospheric analysis ..................................................................................................................22

2.6.1 Reanalysis data products........................................................................................................22

2.6.2 Cyclone tracking......................................................................................................................22

2.7 Pan-Arctic modeling of landfast sea ice......................................................................................23

2.7.1 Selection and configuration of Pan-Arctic MOM6-SIS2 (PAMS) model..................................23

2.7.2 Model forcing...........................................................................................................................25

2.7.3 Parameterization of basal shear stress...................................................................................26

2.7.4 Velocity-based definition of modeled landfast ice extent ........................................................26

2.7.5 Selection of model runs...........................................................................................................27

3 Results ...............................................................................................................................................29

3.1 1996–2023 landfast ice climatology............................................................................................29

3.1.1 Assessment of differences and bias between landfast ice data source .................................29

3.1.2 Regional landfast ice variability in the EM2024 dataset .........................................................34
iii

3.1.3 Landfast ice occurrence probabilities......................................................................................34

3.1.4 Monthly landfast ice extents....................................................................................................38

3.1.5 Timing of annual landfast ice events.......................................................................................42

3.1.6 Landfast ice breakout events ..................................................................................................44

3.1.7 Access and availability of climatology dataset........................................................................45

3.2 InSAR-derived landfast ice extent and stability...........................................................................47

3.2.1 Landfast ice extent from coherence masks ............................................................................47

3.2.2 Monthly mean apparent strain.................................................................................................49

3.2.3 Quantification of three landfast ice stability categories...........................................................52

3.3 In situ landfast ice observations..................................................................................................54

3.3.1 Point Hope landfast sea ice mass balance observations .......................................................54

3.3.2 Utqiagvik landfast sea ice mass balance observations ..........................................................57

3.3.3 Under-ice ADCP measurements.............................................................................................58

3.4 Cyclone Activity in the Alaska Arctic OCS ..................................................................................62

3.5 PAMS model results....................................................................................................................65

3.5.1 Pan-Arctic ice concentration ...................................................................................................65

3.5.2 Pan-Arctic Ocean evaluation...................................................................................................66

3.5.3 Simulated landfast ice extent ..................................................................................................75

4 Discussion.........................................................................................................................................78

4.1 Recent changes in landfast ice in the Alaska Arctic OCS ..........................................................78

4.1.1 Changes in landfast ice extent................................................................................................78

4.1.2 Changes in timing of landfast ice season ...............................................................................80

4.1.3 Evidence of changes in landfast ice breakouts.......................................................................82

4.2 InSAR detection of landfast ice extent and stability....................................................................82

4.2.1 Suitability of InSAR for routine identification of landfast sea ice.............................................82

4.2.2 Regional variability and annual evolution of landfast ice stability ...........................................83

4.3 In situ observations of landfast ice..............................................................................................85

4.4 Impact of storms on landfast ice extent ......................................................................................86

4.5 Comparison between simulated and observed landfast sea ice extent......................................86

5 Conclusions.......................................................................................................................................89

5.1 Lessons from an updated climatology of landfast sea ice in the Arctic OCS .............................89

5.2 Application of InSAR for landfast ice mapping............................................................................89

5.3 Impact of the ocean on landfast ice mass balance and stability.................................................90

5.4 Forecastability of breakout events ..............................................................................................90

5.5 Numerical simulation of landfast ice in the Arctic OCS...............................................................90

5.6 Recommendation for future studies ............................................................................................91

5.6.1 Development of Pan-Arctic landfast ice dataset .....................................................................91

5.6.2 Targeted post-detachment in-situ observations......................................................................91

5.6.3 Application of NISAR data to landfast ice ...............................................................................91

5.6.4 Continued development and evaluation of simulated landfast ice in PAMS model................91

6 References.........................................................................................................................................93

Appendix A Catalog of Breakout Events .......................................................................................100

A.1 Chukchi region breakout events................................................................................................100

A.2 Beaufort region breakout events...............................................................................................104
iv

List of Figures

Figure 1-1: The bathymetry of the Chukchi and Beaufort Seas based on the Alaska Regional Digital 

Elevation Model (ARDEM), compiled by Danielson et al., (2015). The red contour 

indicates the location of the 20 m isobath. Colored arrows are schematic 

representations of mean surface currents, from Weingartner (2005)......................................5

Figure 1-2: Map showing extent of the study areas used for analysis of landfast sea ice extent in this 

project. See Sections 1.1 and 1.4 for an explanation of the origins of these study 

regions......................................................................................................................................6

Figure 2-1: Pre-defined set of approximately coast-normal vectors (hereinafter known as coast 

vectors) used to measure the width of landfast ice within our study area. Note that the 

coast vectors are further group into subregions used in later analysis..................................10

Figure 2-2: One annual cycle of landfast ice width at a location in the Beaufort Sea, illustrating the 

definition of the three key events (first ice, breakup, and last ice).........................................11

Figure 2-3: Illustration of detection of a breakout event in Beaufort Sea in January 2009. a) Spatial 

extent of landfast ice from January 21 and January 23, 2009. Areas of retreat over this 

time are shown in red. Coast vectors within the breakout region are shown in blue. 

Those outside are shown in gray. Note that the red region to the west of Pt Barrow does 

not qualify as a breakout event because the reduction in landfast ice width is 

insufficient. b) Seasonal variation in landfast ice width measurements along the coast 

vectors within the region of breakout. The timing of the breakout is illustrated by the 

vertical red line. ......................................................................................................................12

Figure 2-4: Schematic illustrating how the EM2024 dataset is compiled from the three “parent” 

datasets..................................................................................................................................14

Figure 2-5: Spatial extent of bottomfast, sheltered, and not sheltered landfast ice masks. .......................18

Figure 2-6: Schematic showing deployment of SIMB3 and derived measurements. .................................20

Figure 2-7: Map showing locations of SIMBs deployed during this project. ...............................................20

Figure 2-8: Schematic illustrating upward-looking ice-tethered deployment of an ADCP adjacent to a 

SIMB.......................................................................................................................................21

Figure 2-9: Model domain and bathymetry for the PAMS model used in this study...................................23

Figure 3-1: Landfast ice width for the a) Chukchi and b) Beaufort regions for all three datasets 

available for the 2007–2008 season. .....................................................................................30

Figure 3-2: ASIP (A, B, C) and NIC (D, E, F) ice charts from December 2007 and January 2008 with 

the M2014 SLIE image (yellow) measurement of landfast ice during the similar period. 

ASIP ice charts depicted are (A) 12/14/2007, (B) 12/24/2007, and (C) 1/9/2008. NIC ice 

charts depicted are (D) 12/17/2007, (E) 12/31/2007, and (F) 01/14/2008. Red areas 

indicate areas in each ice chart identified as landfast ice by ASIP and NIC. Shades of 

blue indicate various concentrations of sea ice. ....................................................................31

Figure 3-3: Time series of mean landfast ice width in the a) Chukchi and b) Beaufort regions for all 

seasons for the M2014 (black), ASIP (red), and NIC (blue) datasets. For this 

comparison, all datasets are interpolated to daily resolution, while the ASIP and NIC 

datasets also have a 20-day running minimum applied.........................................................33

Figure 3-4: Regional median landfast ice width in the a) Chukchi and b) Beaufort regions. The 

shaded regions represent the spatial variability in daily landfast ice width in each region. 

Note full width of stable extensions that occurred in the Beaufort Sea in 1999, 2000, 

and 2004 extend beyond the limit of the y-axis......................................................................35

Figure 3-5: All SLIEs delineated from the period 1996–2023 stacks so the color value of a line 

indicated the probability of the SLIE lying within 500 m of a given point between 
v

October and July. A near continuous dark blue zone indicates where the SLIE 

commonly stabilizes. Dashed yellow ellipses highlight the location of nodes some 

distance from the coast where the SLIE occurs with greater frequency, and which 

indicate probable locations of recurring grounded ice features. ............................................36

Figure 3-6: The extent of landfast ice shoreward of the SLIE stacked for each 9-year period in the 

EM2024 dataset (1996–2023), such that the shade of blue represents the fraction of the 

304-day annual cycle (October-July) for which that area was occupied by landfast sea 

ice. The red line indicates the 50% probability contour. White areas indicate where 

landfast ice was never observed............................................................................................37

Figure 3-7: Monthly minimum, median, maximum, and mean landfast sea ice extents for the months 

of October and November......................................................................................................38

Figure 3-8: Monthly minimum, median, maximum, and mean landfast sea ice extents for the months 

of December, January, and February. ...................................................................................39

Figure 3-9: Monthly minimum, median, maximum, and mean landfast sea ice extents for the months 

of March, April, and May. .......................................................................................................40

Figure 3-10: Monthly minimum, median, maximum, and mean landfast sea ice extents for the months 

of June and July. ....................................................................................................................41

Figure 3-11: Spatial variability in the median dates of occurrence of 3 key events in the annual cycle 

(First Ice, Breakup, and Ice-Free) over the duration of the EM2024 dataset. Also shown 

are median dates of the onset of freezing and thawing derived from NCEP reanalysis 

data. .......................................................................................................................................43

Figure 3-12: Annual counts of breakout events in the Chukchi and Beaufort study regions......................44

Figure 3-13: a) Average bi-weekly breakout counts and b) probability density distribution of breakout 

size for the Chukchi and Beaufort study regions. ..................................................................45

Figure 3-14: Spatial distribution of breakouts across the combined study region shown as a) mean 

annual breakout, b) breakout probability, and c) conditional breakout probability. See 

text for details of how these values were calculated..............................................................46

Figure 3-15: Mean SLIE position for the month of April by the EM2024 dataset (cyan) and InSARbased method (blue) from 2017 –2021. Yellow regions indicate “shadow” zones outside 

the domain of the coast vectors (see Figure 2-1). .................................................................47

Figure 3-16: Difference in landfast ice width at each coast vector from 2017–2021 between the 

InSAR-derived results and the EM2024 dataset. Positive values indicate that the InSAR 

method overestimate landfast ice extent relative the EM2024 dataset. ................................48

Figure 3-17: Monthly mean apparent strain from 2017–2021 for the months of a) December, b) 

January, and c) February. ......................................................................................................50

Figure 3-18: Monthly mean apparent strain from 2017–2021 for the months of a) March, b) April, and 

c) May.....................................................................................................................................51

Figure 3-19: Distribution of average apparent strain during each month (December, January, 

February, March, April, and May) between 2017–2021.........................................................52

Figure 3-20: Distribution of April mean apparent strain during the period 2017–2021 in the areas 

identified as bottomfast, sheltered, and not-sheltered landfast ice........................................53

Figure 3-21: Categorized landfast ice stability derived from apparent strain threshold applied to 

interferograms from April 2017 corresponding to those used by Dammann et al., (2019). 

Solid shaded areas indicate the stability categories quantitatively derived from apparent 

strain. Outlined areas indicate areas identified qualitatively by Dammann et al., (2019). .....53

Figure 3-22: Snow depth, ice thickness, and temperature profile data from SIMBs deployed in 

landfast ice near Point Hope in a) 2020 and b) 2022.............................................................55
vi

Figure 3-23: (a–e) PlanetScope imagery and (f) Landsat-9 imagery capturing the late season 

evolution of the landfast ice near Point Hope and the trajectory of the SIMB deployed in 

2020. ......................................................................................................................................56

Figure 3-24: SIMB data from 3 deployments in landfast ice near Utqiagvik in a) 2021, b) 2022, and c) 

2024. ......................................................................................................................................57

Figure 3-25: Timeseries of data for the ADCP deployed under landfast sea ice near Utqiagvik in 

2022. a) Water temperature measured by the ADCP; b) ERA5 wind velocity 

interpolated to ADCP location; c) Zonal current velocity profiles; d) Meridional current

velocity profiles; and e) Vertical current velocity profiles. .....................................................59

Figure 3-26: 14-day progressive vector plots derived from depth-average current velocity data from 

the ADCP deployed near Utqiagvik in 2022. White circles indicate the progressive 

pseudo-position at noon UTC each day. Bathymetry is given in meters. ..............................60

Figure 3-27: Time-average vertical profiles of zonal and meridional currents under the landfast ice 

near Utqiagvik, measured in 2021. ........................................................................................61

Figure 3-28: Daily composite anomalies of sea level pressure on landfast breakout days 1997–2023 

for the (a) Beaufort and (b) Chukchi Seas. Units are in hPa and regions significant at 

the 95% level according to a t-test are cross-hatched. Anomalies relative to 1991–2020 

reference period. Locations of breakout events shown in cyan.............................................62

Figure 3-29: Self-organizing map 4×5 matrix of daily sea level pressure anomalies for January-April 

over 1997–2022. The units are in hPa. The node number is shown on each panel along 

with the frequency of occurrence over the record. White areas indicate elevations 

greater than 500 m which are masked (see Section 2.6.2). ..................................................63

Figure 3-30: Composite anomalies of total storm track densities associated with the days of each 

SOM node corresponding to Figure 3-29. The units are in number of storms per day 

and regions significant at the 95% or greater level according to a t-test are crosshatched. Anomalies relative to 1991–2020 reference period. ...............................................64

Figure 3-31: Composite anomalies of wind speed (shaded) and direction (vectors) associated with 

the days of each SOM node corresponding to Figure 3-29. The units of wind speed are 

in m s-1 and regions significant at the 95% or greater level according to a t-test are 

cross-hatched. Anomalies relative to 1991–2020 reference period. The longest wind 

vectors are associated with 4 m s-1 wind anomalous speeds................................................64

Figure 3-32: Comparison of the PAMS model’s ice extent compared to the satellite estimates of 

Comiso et al., (2014) in units of millions of square kilometers. The 25-year span of the 

simulation is split into thirds for clarity. As described in Section 2.7.5, model runs 51 and 

56 differ primarily in the use of an experimental ice-ocean coupler (CIOD) in run 56...........65

Figure 3-33: Difference between the PAMS model temperature World Ocean Atlas 2018 for the 

decade 1995 –2004. On the left is the surface, middle is at 100 m depth and on the 

right is 200 m depth. Three simulations are shown, with run 51 at the top, run 56 in the 

middle and run 73 on the bottom. ..........................................................................................67

Figure 3-34: Difference between PAMS model salinity World Ocean Atlas 2018 for the decade 1995 

–2004. On the left is the surface, middle is at 100 m depth and on the right is 200 m 

depth. Three simulations are shown, with run 51 at the top, run 56 in the middle and 

run73 on the bottom. ..............................................................................................................68

Figure 3-35: Difference between the PAMS model temperature World Ocean Atlas 2018 for the 

decade 2005 –2017. On the left is the surface, middle is at 100 m depth and on the 

right is 200 m depth. Three simulations are shown, with run 51 at the top, run 56 in the 

middle and run73 on the bottom. ...........................................................................................69

Figure 3-36: Difference between the PAMS model salinity World Ocean Atlas 2018 for the decade 

2005 –2017. On the left is the surface, middle is at 100 m depth and on the right is 200 
vii

m depth. Three simulations are shown, with run 51 at the top, run 56 in the middle and 

run73 on the bottom. ..............................................................................................................70

Figure 3-37: The Arctic ocean bathymetry with the Western (yellow) and Eastern (yellow-orange) 

basins delimited......................................................................................................................71

Figure 3-38: Vertical temperature profiles for the eastern Arctic basin by decade for WOA18, and 

yearly for GLORYS and four PAMS model runs....................................................................72

Figure 3-39: Vertical temperature profiles for the western Arctic basin by decade for WOA18, and 

yearly for GLORYS and four PAMS model runs....................................................................73

Figure 3-40: Vertical salinity profiles for the eastern Arctic basin by decade for WOA18, and yearly 

for GLORYS and four PAMS model runs...............................................................................74

Figure 3-41: Vertical salinity profiles for the western Arctic basin by decade for WOA18, and yearly 

for GLORYS and four PAMS model runs...............................................................................75

Figure 3-42: 1996 –2017 median April landfast ice extent derived from the PAMS model from a) run 

51 and b) run 56. In both model runs, we use three different ice velocity thresholds is 

listed in Table 2-6...................................................................................................................76

Figure 3-43: Minimum, lower quartile, median, upper quartile, and maximum April landfast ice 

extents for the period 1996 –2017 derived from the PAMS model from a) run 51 and b) 

run 56 using a velocity threshold of 0.005 cm s-1...................................................................77

Figure 4-1: Daily median landfast ice width in a) the Chukchi region and b) the Beaufort region 

computed for the three periods 1996–2005, 2005–2014, and 2014–2023. Shaded 

regions represent the upper and lower 20th percentile for 1996–2005 and 2014–2023. .......78

Figure 4-2: Monthly median landfast ice width from 1996–2023 in a) the Chukchi region and b) the 

Beaufort region.......................................................................................................................79

Figure 4-3: Timeseries of mean occurrence date for each key event in the landfast ice cycle over the 

period of 1996–2023 calculated for a) the Chukchi region and b) the Beaufort region. 

Dashed lines indicated a statistically significant (P-value < 0.05) trend in the event date. ...81

Figure 4-4: Monthly distribution of interferometric phase gradient in each of the 2 regions, 11 

subregions, and entire study region. Shaded regions indicated the stability categories 

discussed in Section 3.2.3. ....................................................................................................84

Figure 4-5: Timeseries of water temperature below the ice at different depths and SIMB position in 

the final 50 days of the data from the SIMB deployed near Point Hope in 2020. The 

inset shows a 36-hour period starting 12 hours before the detachment................................85

Figure 4-6: Regional mean landfast ice width from the PAMS model for (a-f) runs 51 and (g-i) runs 56 

with the three velocity thresholds described in Section 2.7.4. Lefthand panels 

(a,c,e,g,i,k) show results for the Chukchi Region, while righthand panels (b,d,f,h,j,l) 

show results for the Beaufort Region.....................................................................................88

List of Tables

Table 2-1: Number of SLIE images produced in the Beaufort and Chukchi regions and landfast ice 

seasons for which SLIE were produced from each dataset.....................................................8

Table 2-2: Percentage of cases where calculation of annual landfast ice event date failed ......................12

Table 2-3: Deployment notes for instruments deployed in landfast ice ......................................................21

Table 2-4: Notable parameter choices for MOM6 model used in this study...............................................24

Table 2-5: Notable sea ice parameters used in the SIS2 model ................................................................25
viii

Table 2-6: Mean daily Ice velocity thresholds used to determine landfast sea ice extent in PAMS 

model output...........................................................................................................................26

Table 2-7: Summary of differences between PAMS model runs selected for presentation in this 

report ......................................................................................................................................28

Table 3-1: Comparison of average metrics for the 2007–08 landfast sea ice season between the 

M2014, ASIP, and NIC datasets for the Chukchi and Beaufort regions. Note that the 

uncertainty ranges indicate the spatial variability within each region. ...................................29

Table 3-2: Percent of occurrence of each SOM node (corresponding to Figure 3-29) within 0–5 days 

prior to the landfast breakout events for the Beaufort and Chukchi regions. The 

percentage was calculated by dividing the number of breakout events associated with 

each node by the total number events...................................................................................63

Table 4-1: Linear regression values for regional mean occurrence dates of key landfast ice events........80

Table A-1: Catalog of breakout events identified in the Chukchi region...................................................100

Table A-2: Catalog of breakout events identified in the Beaufort region ..................................................104
ix

List of Abbreviations and Acronyms

ADCP Acoustic Doppler current profiler

ASF Alaska Satellite Facility

ARDEM Alaska Regional Digital Elevation Model

ALOS Advanced Land Observing Satellite

ASIP Alaska Sea Ice Program

BOEM Bureau of Ocean Energy Management

CIOD Coupled ice-ocean driver

EM2024 The Einhorn-Mahoney (2024) landfast sea ice climatology dataset (1996–2023)

ERA5 European Centre for Medium-Range Weather Forecasts reanalysis version 5

GEBCO General bathymetric chart of the oceans

GloFAS Global Flood Advisory System

GLORYS Global Ocean Physics Reanalysis

GNSS Global Navigation Satellite System

IARPC Interagency Arctic Research Policy Committee

InSAR Interferometric synthetic aperture radar

ISRO Indian Space Research Organization

JRA55 Japanese 55-year reanalysis

MMS Minerals Management Service

M2014 The Mahoney et al., (2014) landfast sea ice climatology dataset (1996–2008)

MOM6 Modular ocean model version 6

NIC National Ice Center

NASA North Aeronautics and Space Administration

NISAR NASA ISRO Synthetic Aperture Radar

NCEP National Centers for Environmental Prediction

NWS National Weather Service

OCS Outer Continental Shelf

PAMS Pan-Arctic MOM6-SIS2 model

PAROMS Pan-Arctic Regional Ocean Modelling System

PALSAR Phased Array L-band Synthetic Aperture Radar

ROMS Regional Ocean Modelling System

SAR Synthetic aperture radar

SIMB Seasonal ice mass-balance buoy

SIS2 Sea ice simulator version 2

SLIE Seaward landfast ice edge

SLP Sea Level Pressure

SOM Self organizing map

WMO World Meteorological Organization

WOA World Ocean Atlas
1

Abstract

The Arctic Ocean is currently undergoing a dramatic transition toward a seasonally ice-free state, with 

some of the greatest losses of sea ice in recent decades occurring in the Beaufort and Chukchi Seas. These 

changes are expected to have substantial impacts on landfast ice within the U.S. Arctic Outer Continental 

Shelf (OCS), which is used as a platform for hunting and travel by members of coastal communities and 

for exploration and production operations by the offshore oil and gas industry. Landfast ice also affects 

hydrographic processes in the nearshore zone and serves to protect the coast from mechanical erosion by 

waves. In this report we present findings from a coordinated series of tasks designed to improve our 

understanding of recent changes in landfast ice with the Arctic OCS and their potential drivers while 

improving our ability to simulate and forecast landfast ice. The individual tasks were:

Task 1: Development of a landfast ice climatology database for the BOEM Alaska OCS Region 

Task 2: In-situ instrumentation of landfast ice

Task 3: Analysis of landfast ice change and atmospheric forcing

Task 4: Improved parameterization of landfast ice in an ice/ocean model

Task 5: Long term forecasting of landfast ice extent and seasonality

Task 6: Documentation of human impacts of landfast ice change

Task 7: Establishment of Scientific Review Board

Task 1 involved updating the results from two prior BOEM-supported research projects: MMS OCS 

Study 2005-068 (Mahoney et al., 2006) and BOEM OCS Study 2012-067 (Mahoney et al., 2012). To do 

so, we extracted landfast ice extent identified in ice charts prepared by the U.S. National Ice Center (NIC) 

and the National Weather Service’s Alaska Sea Ice Program (ASIP). In doing so, we have created a 27-

year climatology of landfast ice extent in the Arctic OCS1

, which refer to as the EM2024 dataset. This 

record shows there are significant trends toward later formation of landfast ice across the region. In the 

Chukchi Sea, we find landfast ice is forming an average 1.6 days yr-1 later, while in the Beaufort Sea the 

trend is 1.1 days yr-1

. Landfast ice in the Chukchi Sea also shows significant trends toward earlier break 

up (-0.6 days yr-1

), earlier ice-free conditions (-0.8 days yr-1), and reduced landfast ice width throughout 

the winter (-0.09 to -0.27 km yr-1 from January to May). Trends toward reduced landfast ice width in the 

Beaufort Sea are primarily driven by an absence of stable extensions since 2006, but we caution that this 

result could be an artifact of the use of ice charts in the latter half of the record. As a result of these 

changes in width we note a significant reduction in the fraction of the Arctic OCS occupied by landfast 

ice over the duration of the EM2024 dataset. Over the first nine years (1996–2005) the median April 

landfast ice extent occupied 3.8% of the Arctic OCS area, where in the final nine year (2014–2024) this 

percentage had dropped to 2.0%.

We also explore the use of interferometric synthetic aperture radar (InSAR) as a more automatable 

approach for mapping landfast ice and extending the dataset into the future. Although our results indicate 

this would currently be problematic during the key stages of formation and breakup, we find that InSAR 

1 Please note that our study area includes small regions of the outer continental shelf in Canada and Russia adjacent 

to the U.S. Arctic OCS
2

products can provide useful information about different stability regimes within landfast ice, which may 

help improve our overall understanding of recent and future changes in landfast ice extent.

To further improve our understanding of the processes driving recent changes, we carried out a program 

of in-situ field observations of landfast ice. We investigated the specific role that storms may play in 

destabilizing landfast ice and we simulated 25 years of past landfast ice extent within the Arctic OCS by 

implementing a basal shear stress parameterization in a pan-Arctic coupled ice-ocean model. Despite 

relatively few successful deployments of in-situ instruments, we were able to collect valuable data 

capturing an abrupt change in sub-ice ocean temperature profile associated with a breakout event at Point 

Hope. The sudden increase in water temperature immediately below the ice shortly after it detached from 

the coast suggests that the ice was effectively sheltered from this warm water while it was still landfast. 

This demonstrates that the ocean heat flux below landfast ice may be substantially lower than below 

adjacent drifting ice and illustrates the importance of accurately resolving ocean processes in shallow 

water for modeling landfast ice behavior. However, we acknowledge that without coincident water 

temperature data from further offshore, we cannot rule out the possibility that the detachment coincided 

with the arrival of warm water in the region that would also have found its way under the landfast ice.

We explored the role of storms in promoting mid-season breakouts of landfast ice by compiling a catalog 

of breakout events from the EM2024 dataset and using this as a basis to track storms and analyze patterns 

of sea level pressure (SLP) in ERA5 reanalysis data. Composite SLP fields derived from days associated 

with breakout events shows significantly anomalous low pressures in the Bering Sea. A self-organizing 

map (SOM) analysis shows that the SLP pattern most commonly associated with breakout events 

corresponds to the occurrence of storms in the northern Bering Sea. Together, these results suggest that 

low pressure centers in the southeast Bering Sea may be a key component of some landfast ice breakout 

events, which is consistent with previous suggestions that storm-triggered shelf waves may destabilize 

landfast ice. These waves have length scales over 100 km and can propagate with phase speeds of >1,000 

km day-1

, causing local sea level at the coast to fluctuate by tens of centimeters.

Lastly, we implemented a basal shear stress parameterization in a pan-Arctic coupled ice-ocean model to 

simulate the effect of ridge keels interacting with seafloor in shallow water. We refer this as the “PAMS” 

model, as explained in Section 2.7) We performed multiple models runs forced by JRA55-do atmospheric 

reanalysis data for the 15-year period from 1993 to 2017. Although the PAMS model is able to 

qualitatively reproduce the annual cycle of landfast ice extent, our results cannot be described as realistic 

simulations of landfast ice in the Arctic OCS over this period. Nonetheless, we anticipate continued 

development of the PAMS model and our results demonstrate that the Alaska Arctic OCS is an ideal test 

ground for refining model parameter choices and the EM2024 dataset represents a valuable benchmark 

for quantifying model performance.
3

1 Introduction

1.1 Note on prior related research

This project represents the extension of the landfast ice component of two prior projects supported by the 

Bureau of Ocean Energy Management (BOEM), or its predecessor the Minerals Management Service 

(MMS). Both of these prior projects were titled “Mapping and Characterization of Recurring Spring 

Leads and Landfast Ice in the Beaufort and Chukchi Seas”. The first project (MMS OCS Study 2005-068; 

Eicken et al., 2006) produced a climatology of landfast in the Beaufort and eastern Chukchi Sea from 

1996–2004. Here we used the term climatology in the context of landfast ice to refer to a consistent multidecadal dataset of landfast ice extent that allows the identification of key events with the annual cycle. 

The second project (BOEM OCS Study 2012-067; Mahoney et al., 2012) expanded the spatial domain of 

the landfast ice analysis to include a second study region encompassing the remainder of the Chukchi Sea 

and extended the overall time period up July 2008. In both of these previous studies, the extent of landfast 

sea ice was mapped through a systematic analysis of consecutive synthetic aperture radar (SAR) images. 

In this study we adopt the same two study regions and extend the timeline to July 2023 by incorporating 

landfast ice extent delineated in ice charts compiled by the U.S. National Ice Center (NIC) and the 

National Weather Service (NWS) Alaska Sea Ice Program (ASIP). See below for more details on study 

regions (Section 1.4) and data sources and methods for identifying landfast ice (Sections 2.1–2.4). This 

project exceeds the scope of the preceding work related to landfast ice by including a program of field 

observations (Section 2.5), a reanalysis-based study of storm impacts (Section 2.6), and a pan-Arctic iceocean modeling component designed to improve Arctic-wide simulation of landfast ice (Section 2.7). The 

modeling component is also builds on prior BOEM and MMS projects, including most recently 

“Development of a Very High-Resolution Regional Circulation Model of Beaufort Sea Nearshore Areas” 

(OCS Study BOEM 2018-018; Curchitser et al., 2018 and references therein). 

1.2 Background

Landfast ice is an essential component of the coastal sea ice system and is found along coastlines 

wherever sea ice occurs. In the Arctic as a whole, landfast ice typically reaches an average annual 

maximum extent of 1.8 million km2 (Yu et al., 2014), or approximately 12% of the total Northern 

Hemisphere wintertime sea ice cover. Acting as a floating extension of the land, it is the most accessible 

form of sea ice and the one most often encountered by people. Of particular relevance to BOEM, landfast 

ice serves as a platform for the oil and gas industry, which relies on ice roads across landfast ice to access 

nearshore production and exploration facilities (Masterson 2009; Potter et al., 1981). Additionally, 

members of Arctic coastal communities travel across landfast ice and hunt marine mammals and birds 

that are commonly found at its seaward edge (George et al., 2004; Laidre et al., 2015; Lovvorn et al.,

2018). Additionally, landfast ice serves as an important habitat for ringed seals (pusa hispida; or nachiq

in Iñupiat), which are uniquely adapted to thrive in landfast ice through their ability to create and maintain 

breathing and access holes (Smith et al., 1991) and polar bear (ursus maritimus; or nanuq in Iñupiat) who 

prey upon them (Pilfold et al., 2014).

Landfast ice also plays an important role in the sediment dynamics of coastal waters (Eicken et al., 2005)

and can buffer the coast against the erosive action of waves (Lantuit and Pollard 2008; Ravens et al.,

2023). By isolating the underlying ocean from wind mixing, landfast ice allows river plumes to extend 

farther than they would under open water or pack ice (Granskog et al., 2005; Ingram and Larouche 1987; 

Kasper and Weingartner 2015). In some cases, the hydrographic influence of landfast ice extends well 

beyond the coastal zone (Itkin et al., 2015), affecting the locations of upwelling such as in the Beaufort 

Sea (Pickart et al., 2009) and deep convection on the Eurasian Arctic shelf (Dmitrenko et al., 2005).
4

Landfast sea ice is typically a seasonal phenomenon in the Arctic, forming each fall and breaking up the 

following spring. A quantitative definition of the beginning and end of the landfast ice season is discussed 

in Section 2.2.5, but from hereon when we refer to the landfast ice season in general, we are considering 

the 304-day period from October 1 to July 31 during which landfast ice could conceivably be found 

within our study region. The seasonal maximum extent of landfast ice is largely controlled by coastal 

bathymetry (e.g, Mahoney et al., 2014 and references therein) and, consequently, most previous studies 

have found little or no change over time. One notable exception is the Chukchi Sea, where Mahoney et 

al., (2014) found that the annual maximum width of landfast ice reduced by an average of 13 km (~50%) 

between the periods 1973–76 and 1996–2008. However, the observed landfast ice season has been 

shortening throughout the Arctic as a result of later formation and earlier breakup. For example, ice charts 

from Russia’s Arctic and Antarctic Research Institute (AARI) show a declining trend in Laptev Sea 

landfast ice duration of almost 3 d yr-1 (Selyuzhenok et al., 2015). Similarly, Canadian Ice Service charts 

show similar trends in the Alaska Beaufort Sea and Mackenzie Delta area, and a shorter landfast ice 

season throughout the Canadian Arctic Archipelago (Galley et al., 2012). In Alaska waters, the landfast 

ice season in the Chukchi and Beaufort Seas has shortened by approximately 38 days (~1.4 d yr-1

) and 53 

days (~2 d yr-1

), respectively, between 1973–77 and 1996–2008 (Mahoney et al., 2014; Mahoney et al.,

2012). 

Due to its connection with other components of the changing Arctic system, landfast ice has been 

identified as a potentially valuable indicator of Arctic change for a variety of stakeholders, including the 

oil and gas industry and coastal communities (Mahoney 2018). However, despite the dramatic changes 

that have affected Arctic sea ice in the last decade (Fox-Kemper et al., 2021), there have been very few 

studies of landfast ice that document this period. To facilitate planning and ensure the safety of on-ice 

activities, there is an urgent need for updated information and improved ability to forecast the extent, 

stability, and seasonality of the landfast ice, particular along the U.S. Arctic coast, which have seen some 

of the most extensive changes in seasonal sea ice extent in recent years (Peng and Meier 2018). 

1.3 Regional hydrography, bathymetry, and sea ice characteristics

The Chukchi and Beaufort Seas lie north of Alaska and together contain the entire U.S. Arctic outer 

continental shelf (OCS). Typically, Point Barrow, the northernmost point of the North American 

mainland, is taken as the demarcation between the two bodies of water, which have distinct hydrographic 

and bathymetric characteristics (Figure 1-1). The Chukchi Sea is dominated by a broad, shallow shelf (the 

Chukchi Shelf) mostly less than 60 m deep with shoals such as Hanna Shoal and Herald Shoal rising to 

around 20 m. In contrast, water shallower than 60 m in the Beaufort Sea occupies only a narrow strip less 

than 100 km from the coast. Most of the Beaufort Sea is more than 1,000 m deep and is part of the 

Canada Basin.

Figure 1-1 also shows the predominant current directions. In the Chukchi Sea, there is a net northward 

flow, which enters through Bering Strait and branches into different bathymetrically constrained currents 

(Ovall et al., 2021; Stabeno and McCabe 2023; Weingartner et al., 2005). The heat flux associated with 

this northward flow enhances the early loss of ice in the Chukchi Sea, particular in the vicinity of the 

primary currents illustrated in Figure 1-1 (Danielson et al., 2020a; Woodgate et al., 2010). In comparison, 

ocean circulation in the Beaufort Sea is dominated by the anticyclonic (clockwise) motion of the Beaufort 

Gyre, which transports some of the oldest and thickest ice in the Arctic from the region north of the 

Canadian Archipelago into the Beaufort Sea. This motion is driven by atmospheric circulation around a 

persistent region of high pressure (the Beaufort High). The strength of the Beaufort Gyre can vary from 

year to year and the ocean current and ice motion can sometimes reverse for periods of a few days. 

However, in winter the average ice drift is approximately parallel to the coastline.
5

Figure 1-1: The bathymetry of the Chukchi and Beaufort Seas based on the Alaska Regional Digital Elevation 

Model (ARDEM), compiled by Danielson et al., (2015). The red contour indicates the location of the 20 m isobath. 

Colored arrows are schematic representations of mean surface currents, from Weingartner (2005).

The differences in bathymetry, hydrography, and ocean currents between the Chukchi and Beaufort Seas 

lead to marked differences in the character of sea ice in these two regions. The U.S. Beaufort Sea receives 

an influx of multiyear sea ice from the Canadian Arctic and, although a substantial fraction now melts 

before it can recirculate in the Beaufort Gyre (Mahoney et al., 2019), this multiyear ice can become 

incorporated in landfast ice along the Beaufort Coast. Multiyear sea ice is rarely seen very far south of 

Point Barrow in the Chukchi Sea, which typically has a thinner ice pack than the Beaufort Sea. This is 

due to the Chukchi Sea’s more southerly location and the inflow of heat through Bering Strait, which 

delays freeze-up in the fall and promotes breakup in the spring. In addition, the orientation of the Beaufort 

and Chukchi Sea coastlines relative to the direction of prevailing winds induces a contrasting response of 

ocean and ice circulation behavior between the two regions.

1.4 Study regions

As introduced in Section 1.1, this study uses the same study region used in the previous BOEM study on 

landfast ice in the Arctic OCS (BOEM OCS Study 2012-067; Mahoney et al., 2012). This comprises a 

western region, occupying most of the Alaska Chukchi coast and the northern coastline of the Russian 

Chukotka Peninsula, and an eastern region extending from just west of Wainwright on Alaska’s Chukchi 

coast to the Mackenzie Delta in Canada (Figure 1-2). For convenience, we will refer to the western and 

eastern regions as the Chukchi and Beaufort regions, respectively. We recognize that these regions do not 

align with conventional definitions of the Chukchi and Beaufort seas, but they are effectively a legacy of 

our former work, and we adopt them for historical consistency. By way of explanation, the Beaufort 

region corresponds to the study region for the first SAR-derived landfast sea ice climatology project 

(MMS OCS Study 2005-068; Eicken et al., 2006), while the Chukchi region corresponds to the area that 

was added to the study region in the subsequent project (BOEM OCS Study 2012-067; Mahoney et al.,

2012). 
6

Figure 1-2: Map showing extent of the study areas used for analysis of landfast sea ice extent in this project. See 

Sections 1.1 and 1.4 for an explanation of the origins of these study regions.

2 Data and methods

2.1 Existing landfast ice extent data sources

2.1.1 Rationale for using existing data sources

In this study, we combine three separate observation-based data sources that partly overlap in time to 

produce a single continuous climatology of landfast ice extent. These data sources are described below in 

Sections 2.1.2 to 2.1.4, but two features they all share is that they each identify landfast ice extent without 

any additional interpretation and they existed prior to this study commencing. This allowed us to compile

16 more years of data in considerably less time than it took to create the previous 12-year dataset. Due to 

gaps in satellite data availability in the intervening years, it would have more challenging to produce a 

continuous dataset had we attempted to analyze landfast ice extent ourselves from original imagery.

2.1.2 M2014 landfast ice data

As noted in Section 1.1, the aforementioned BOEM OCS Study 2012-067 project (Mahoney et al., 2012)

resulted in a dataset of landfast ice extent spanning the Chukchi and Beaufort Seas from 1996–2008. 

From here on, we refer to this dataset as the M2014 dataset, in reference to the authorship and date of the 

peer-reviewed publication in which it was first presented and described (Mahoney et al., 2014). In brief, 

the M2014 dataset was produced by analyzing sequences of SAR imagery to identify landfast sea ice 

based on the following two criteria:

1. Sea ice that is contiguous with the shoreline

2. Sea ice that lacks detectable motion for ~20 days
7

Each image sequence consisted of three SAR mosaics spanning a total time period of approximately 20 

days. Motion was detected through visual inspection assisted by the generation of a composite gradient 

difference image, that highlighted edges that remained constant across all three SAR images. This 

resulted in the delineation of the seaward landfast ice edge (SLIE) for each 3-mosaic sequence. The 

region between each SLIE and the coastline was then rasterized to create a binary image that defined the 

extent of landfast with a pixel resolution of 100 m. A mosaic of SAR images spanning the entire study 

area typically requires images acquired several days apart and so separate mosaics were assembled for the 

Chukchi and Beaufort study regions. Accordingly, SLIEs were also delineated separately for these two 

regions. Although each sequence of 3 SAR mosaics spanned ~20 days, SLIEs were delineated every ~10 

days. As a result, there were typically 27–28 SLIEs delineated in each landfast ice season in the Beaufort 

region. In the Chukchi region, where the landfast ice season is somewhat shorter, each season was 

typically represented by 26–27 SLIEs. Up until early 2008, the SAR mosaics were compiled exclusively 

from Radarsat imagery. However, Radarsat ceased to be available March 2008 and from this point on we 

used Envisat SAR instead. This resulted in fewer mosaics overall for the 2007–08 season, with some 

being incomplete. In total, the M2014 dataset consists of 316 SLIE images for the Chukchi region and 

331 SLIE images for the Beaufort region.

2.1.3 U.S. National Ice Center (NIC) ice charts

The National Ice Center (NIC) produces operational ice charts primarily used for navigation in and 

around icy seas. These ice charts are archived in SIGRID-3 format at the National Snow and Ice Data 

Center (U.S. National Ice Center 2022). These ice charts were provided as shape files with vector features 

containing attributes corresponding to sea ice characteristics identified by NIC analysts. The NIC analysis 

used a variety of data including SAR, visible, and infrared satellite imagery to identify sea ice 

characteristics such as ice concentration, stage of development, ice age, and ice thickness. When the 

analysts were identifying a vector feature as landfast, the analyst primarily used SAR to identify areas 

which exhibit no motion over multiple days. In addition to a lack of motion, NIC analysts looked for 

characteristic shapes within landfast ice in Alaska such as ridges, which appear bright in SAR images, 

separated by smooth ice. Areas with homogenous characteristics were outlined by vector features with 

attributes corresponding to each characteristic. Throughout the duration of the dataset, the attributes and 

attribute values vary. Of particular importance to this study, prior to March of 2006 the attribute used to 

identify landfast ice was the form variable CF. When the CF variable was set to “0899” the analysis 

identified the area as landfast ice. We found this definition to be inconsistent within the ice charts 

resulting in the exclusion ice charts prior to March of 2006 in our analysis. From March 2006 onward 

NIC adopted the World Meteorological Organization (WMO) “Egg Code” which defined standard 

attributes and attribute values corresponding to specific sea ice characteristics. The Egg Code identifies 

landfast ice using the primary form (FP or FA) attribute with a value of “08”. Multiple instances were 

found where the primary form attribute indicated the vector feature was landfast ice (FP = 08), but also 

indicated the ice concentration was less than 100%. This error was attributed to mislabeling by the analyst

thus we implemented a secondary attribute requirement for a vector feature to be considered landfast. In 

addition to the primary form being 08, the ice concentration must represent 100% concentration, total 

concentration (CT) is 92 or 93. In addition to the varying attribute values throughout the NIC ice charts, 

the frequency at which an ice chart was released also changed. 

From 2003 through the 2013–2014 season NIC produced one ice chart for the whole Arctic every 2 

weeks. The frequency increased to weekly in 2014. When retrieving the digitized ice charts from NIC an 

issue occurred where the 2017–2018 and 2018–2019 seasons were incomplete, such that charts we absent 

during March–July 2018 and January–April 2019. Since this represented a substantial fraction of the 

season, both seasons were excluded in their entirety. Additionally, we identified a small number of charts 

(24 in total) that contained polygons designated as landfast ice, but were not contiguous with the coast. 
8 

These affected the Beaufort region only, so they were excluded on a regional basis. In total, the NIC ice 

charts yielded 501 SLIE images in the Chukchi region and 477 SLIE images in the Beaufort region. 

2.1.4 Alaska Sea Ice Program (ASIP) ice charts

The National Weather Services’(NWS) Alaska Sea Ice Program (ASIP) produced digitized ice charts for 

the Beaufort and Chukchi Seas from 2007–2023 (Alaska Sea Ice Program (ASIP) 2023). The area which 

ice charts extend is 55–85° N and 150°E–105°W. These ice charts were provided as shape files with 

vector features containing attributes corresponding to sea ice characteristics identified by ASIP analysts. 

At ASIP, analysts use multiple satellite-based sensors, including SAR, visible bands, and infrared, to 

identify landfast ice. The ASIP analyst look for no sea ice motion in response to winds or currents. Sea 

ice which exhibited no motion and was contiguous with the coast was classified as landfast. In general, 

the analysts waited to classify immobile sea ice attached to the coast until it achieves a thickness of 4–12

inches. This additional parameter implemented by the ASIP analyst was done since the majority of ASIP 

ice chart users require information with the intent to travel on or through the sea ice. Similar to the NIC 

ice charts, the attributes which defined sea ice characteristics changed during the duration of the dataset. 

Prior to January 1, 2015, ASIP used the attributes “Form” and “Label Age” to identify vector features as 

landfast. A vector feature was identified as landfast when the Form attribute was “Fast,” and Label Age 

was “Shorefast.” From 2015 onward, ASIP used the WMO Egg Code where landfast ice was identified 

by the primary form “FP” was “08”. We added the secondary attribute requirement of total concentration 

“CT” represents 100% (92 or 93) similar to the NIC ice charts. The year 2015 also marked a change in the 

frequency with which ASIP charts were produced. Up to the end of 2014, charts were produced on a twoto-four-day interval on Mondays, Wednesdays, and Fridays. From 2015 onward ice charts were produced 

daily. In total, the ASIP ice charts consisted of 4,322 SLIE images in the Chukchi region and 4,323 SLIE 

images in the Beaufort region. 

Table 2-1: Number of SLIE images produced in the Beaufort and Chukchi regions and landfast ice seasons for 

which SLIE were produced from each dataset

2.2 Standardized analysis of SLIE data 

2.2.1 Overview of SLIEalyzer tools

SLIEalyzer is the name we have given to a package of software tools written in Matlab that allow 

standardized analysis of SLIE data from each dataset and subregion. The underlying code and associated 

documentation can be found at https://github.com/armahoney/SLIEalyzer and key processes are described 

in detail in the following subsections. However we will first provide an overview of the inputs provided 

to SLIEalyzer, the primary processing involved and the data products it produces.

As its primary input, SLIEalyzer takes a series of GeoTIFF files containing SLIE images together with a 

configuration file that specifies details such as the size of the images, the number seasons to be analyzed 

and the locations of ancillary files necessary for the analysis. These ancillary files include GeoTIFFs 

providing a coast mask and bathymetry as well as ASCII files specifying coast vector information (see 

Section 2.2.3) and subregion boundaries. In practice, SLIEalyzer is configured so that it just needs to be 

Dataset Number of SLIE Images 

in Beaufort Region

Number of SLIE Images

in Chukchi Region

Landfast ice 

Seasons

M2014 331 316 1996–2008

ASIP 4,323 4,322 2007–2023

NIC 477 501 2005–2017, 

2019–2022
9

given the name of the dataset to be analyzed. The first step in the analysis is the calculation of landfast ice 

width in each SLIE image using a predefined set of quasi shore-normal “coast vectors” (see Section

2.2.3). This facilitates temporal analysis of the data including the determination of key events in the 

annual landfast ice cycle (Section 2.2.5), the derivation of monthly landfast ice extents (Section 2.2.5) and 

the detection of mid-season breakout events (Section 2.2.7). At the same time that width of the landfast 

ice is measured, SLIEalyzer also determines the water depth at the SLIE based on a data from the 

ARDEM bathymetric digital elevation model (Danielson 2013; Danielson et al., 2015) regridded to study 

region at 100-m resolution.

2.2.2 A note on assigning dates to SLIE observations

Assigning a specific date to a SLIE observation is not entirely straightforward, since each observation 

spans a multiple-day period of time, rather than a specific day. In the M2014 data set, each SLIE 

represents the extent of landfast ice identified in three consecutive SAR mosaics, each approximately 10 

days apart, spanning a total period of approximately 20 days. Mahoney et al., (2014; 2007) were able to 

narrow down the appropriate date for individual points along a SLIE by determining whether the landfast 

ice was advancing or retreating at the location. If the SLIE was advancing, then the observation ought to 

correspond to a date toward the beginning of the ~20-day period, while if it was retreating then the 

appropriate date should be toward the end of the period. However, there are a number of reasons to 

reconsider this approach for the updated climatology developed in this project.

First, this approach becomes problematic when applied to a gridded landfast ice product, since the SLIE 

might advance or retreat at different locations during the same period. The approach also depends on a 

perfectly consistent dataset in which there should not be any instance of a local minimum in landfast ice 

extent lasting fewer than 20 days. We found a small number of cases where this occurs (<1%), indicating 

a minor level of inconsistency in our subjective technique for determining of landfast ice extent. Lastly, 

unlike the M2014 dataset, the chart-based products we are using post-2008 do not use an overlapping set 

of three consecutive mosaics. 

For these reasons, we have chosen not to follow our previous approach for assigning dates to SLIEs. 

Instead, we simply assign a date to each SLIE based on the rounded-up mid-date of the time period over 

which SLIE is defined. For the M2014 data, this will be mid-point between the dates of the first and last 

mosaic, while for the NIC- and ASIP-derived datasets, we will use the mid-point between the publication 

date of the ice chart and the day following the publication of the previous chart. For daily ice charts, this 

will equate to the publication date of the chart. 

2.2.3 Measurement of landfast ice width

The first step in the process of combining landfast ice extent data from the three different datasets 

described in Section 2.1 is to measure the width of landfast ice width from a series of fixed points along 

the coastline. The approach we follow is described in detail by Mahoney et al., (2014; 2007) and involves 

the use of a discrete set of vectors approximately normal to the coastline (Figure 2-1). Hereinafter we 

refer to these vectors, as “coast vectors” and we defined 8,892 of them in total: 6,956 in the Chukchi 

region and 1,936 in the Beaufort region. Collectively, they serve to establish a common 1-dimensonal 

curvilinear coordinate scheme for defining the position of SLIE in terms of its distance from the origin of 

each vector. This distance equates to the width of the landfast and is generally defined by the 

seawardmost pixel of landfast intersected by each vector. In places where the SLIE or the coastline are 

particularly convoluted, a coast vector may intersect the SLIE more than once and, in such cases, the 

width measurement will therefore include some region on open water or non-landfast ice. In the deep 

embayment of Kotzebue Sound, it is also possible for a coast vector to intersect landfast ice attached to 

the opposite shoreline. In this case, the landfast width is defined by the pixel on the landfast ice edge that 

is farthest from the coast.
10

Notwithstanding the few exceptions described above, the measurement of landfast along the predefined 

set of coast vectors illustrated in Figure 2-1 allows us to reconstruct the SLIE using a fixed set of 1-

dimensional coordinates. This greatly simplifies the task of interpolating over time, which is essential for 

producing a dataset with a common daily sampling frequency (see Section 2.3). The computation of 

landfast ice width also allows us to interpolate across missing segments of data and facilitates temporal 

analysis of the data as described in Sections 2.2.5–2.2.7. By grouping coast vectors into subregions, we 

can also analyze spatial variability in landfast ice. For some of the analysis that follows, we identified 11 

different subregions based on primarily on the orientation of the coastline and separated by prominent 

headlands or other coastal features. These subregions also used in the process reconstructing the SLIE 

from the measurements of landfast ice width (Section 2.2.4).

2.2.4 Reconstruction of SLIE images from landfast ice width measurements

In what is effectively the inverse process of measuring landfast ice width, we can use the coast vectors to 

reconstruct a SLIE image from landfast ice width measurements. This is useful for creating statistically 

derived SLIE images, such as monthly means (Section 2.2.6) or interpolating SLIE data over time 

(Section 2.3). The coast vectors allow the width measurements to be converted into an ordered sequence 

of spatial coordinates defining the vertices of the SLIE. By joining this SLIE with the coastline, we define 

a polygon that can be filled and rasterized to create a SLIE image of the same format as those used as 

inputs to SLIEalyzer. The coastline and associated land mask used here and throughout the study is the 

same as that used in the original landfast climatology project (MMS OCS Study 2005-068; Eicken et al.,

2006). It is a 100-m resolution rasterization of the National Geospatial Intelligence Agency (NGA)’s

World Vector Shoreline product in an Alaska Albers NAD83 projection (EPSG:3338).

This process is relatively straightforward, but there are a few details of worth noting. First, it is necessary 

to group coast vectors into contiguous segments of coast so that the SLIE does not bridge discontinuities 

in the coastline like Bering Strait. This is accomplished using the subregions illustrated in Figure 2-1.

Second, we also had to apply a special rule for subregions 3 (Outer Kotzebue Sound) and 4 (Inner 

Kotzebue Sound), where there are intersecting coast vectors. In such cases, averaging or interpolation on 

a per-vector basis can result in different SLIE locations for each set of coast vectors. We overcome this by 

Figure 2-1: Pre-defined set of approximately coast-normal vectors (hereinafter known as coast vectors) used 

to measure the width of landfast ice within our study area. Note that the coast vectors are further group into 

subregions used in later analysis.
11

using the mid-points between the two SLIEs in these regions. Lastly, there are certain “shadow” zones 

(shown in yellow in Figure 2-1), which represent waters where the coast vectors do not reach due to 

convolutions in the coastline. In SLIE images reconstructed from landfast ice width measurements, these 

regions are treated as unknown. 

2.2.5 Key events within the landfast ice cycle

Following the approach of Mahoney et al., (2014; 2007), we determine the timing of three key events in 

the annual cycle of landfast ice based on seasonal timeseries of width (Figure 2-2): 

First landfast ice on coast (first ice). We define the first occurrence of landfast ice as the date of the first 

non-zero width measurement that immediately precedes a width measurement ≥500 m. This 

allows us to exclude any early season, short-lived landfast ice that fails to reach a width of at least 

500 m before detaching. 

Breakup. We use the term breakup to refer to the season-ending retreat of landfast ice that results in a 

coastline free of landfast ice. We define the timing of breakup as the time of most rapid retreat 

during the tail end of the season once the landfast ice width ceases to make any increases.

Last landfast ice on coasts (last ice). Similar to the definition of first ice above, we define the last 

occurrence of landfast ice as the last non-zero width measurement after the last measurement 

>500 m. Short-lived landfast events are less common at the end of the season than at the 

beginning, but we exclude them for consistency.

Figure 2-2: One 

annual cycle of 

landfast ice width at 

a location in the 

Beaufort Sea, 

illustrating the 

definition of the three 

key events (first ice, 

breakup, and last 

ice). 

On occasion, the annual cycle of landfast ice width measured at a coast vector does not fit the example 

shown in Figure 2-2 and the determination of one or more key events fails. For example, the 

determination of first ice will fail if there is already landfast ice present at a location at the first 

observation for a given landfast ice season. Similarly, if the record for a season ends before the landfast 

ice width reaches zero at a location, the determination of last ice will fail. Also, if the tail end of the 

landfast ice season is not well defined (i.e. landfast ice width does not decline monotonically) it may not 

be possible to uniquely determine the timing of breakup. Overall, these make up a small percentage of all 

cases as listed in Table 2-2. 
12

Table 2-2: Percentage of cases where calculation of annual landfast ice event date failed

Region First ice Breakup Ice free

Chukchi 6.6% 6.5% 6.5%

Beaufort 8.0% 0.5% 0.5%

In addition to these three key annual events derived from the landfast ice width data, we also derived the 

dates of the onset of freezing and thawing air temperatures using data from National Center for 

Environmental Prediction (NCEP) Reanalysis 1 data (NCEP 2023), following the approach of Mahoney 

et al., (2014; 2007). This involved creating timeseries of air temperature at origin point of each coast 

vector by interpolating the 4x daily gridded near surface (sigma level 0.995) air temperature data product. 

For each year, we then find the first day with a daily mean temperature below 0°C that was also the first 

day of a 15-day period with a mean temperature below 0°C. The onset of thawing was calculated in a 

similarly using positive temperatures.

2.2.6 Calculation of monthly SLIE extents

Using the mid-point dates assigned to each SLIE observation (Section 2.2.2) we binned landfast ice width 

measurements by calendar month and computed the minimum, median, mean, maximum and lower and 

upper quartile values for each coast vector. The width values were then used to reconstruct monthly SLIE 

images for each statistical measure, following the approach described in Section 2.2.4. Since these images 

are reconstructed from width measurements, the shadow zones are coded as unknown.

2.2.7 Identification of mid-season breakout events

We use the term breakout to refer to mid-season events in which the landfast ice undergoes a sudden and 

substantial retreat. It is important to note that breakout events are different from breakup (Section 2.2.5), 

which occurs toward the end of every season. Breakout events are identified from the landfast ice width 

measurements based on four criteria, listed below and illustrated in Figure 2-3: 

1. The landfast ice width decreases by at least one third of the median width for a given coast vector

2. The width of remaining ice is less than 50% of climatology median width 

3. The event affects at least 25 consecutive coast vectors (~5 km)

4. The event occurs between January and April (typically the most stable period)

Figure 2-3: Illustration of detection of a breakout event in Beaufort Sea in January 2009. a) Spatial extent of 

landfast ice from January 21 and January 23, 2009. Areas of retreat over this time are shown in red. Coast 

vectors within the breakout region are shown in blue. Those outside are shown in gray. Note that the red region 

to the west of Pt Barrow does not qualify as a breakout event because the reduction in landfast ice width is 

insufficient. b) Seasonal variation in landfast ice width measurements along the coast vectors within the region 

of breakout. The timing of the breakout is illustrated by the vertical red line. 
13

Using these criteria, we developed a catalog of individual breakout events that lists the time, location, and 

size of each breakout, which is specified as total area, coastline length, and mean width. We treat all 

breakouts as separate events, even if events overlap in space and occur within the same season. In total, 

there are 133 such events, but in most cases the spatial extents of the two events are substantially different 

(in 75% of cases there is <50% overlap in extent along the coast). The analysis that follows does not 

require that breakouts are strictly independent events and instead benefits from a more exhaustive catalog. 

Also note that when calculating the size of a breakout, we include any contiguous regions that retreated 

over the same time period, but did not meet criteria 1–3 above. For example, in the two breakout regions 

illustrated in Figure 2-3, criteria 1 and 2 may not be met for all coast vectors, particular at either of their 

coastal extent where the breakout width tapers. However, each region contains at least 25 consecutive 

coast vectors that do meet these criteria, then the region as whole is treated as a breakout. 

2.3 Compilation of extended landfast ice climatology 

We combined the M2014, ASIP and NIC datasets into a single 27-year climatology, which will refer to 

from hereinafter as the EM2024 dataset, in reference to its two primary authors (Einhorn and Mahoney) 

and the year in which it was completed. Compilation of the EM2024 dataset was a three-step process 

performed on the measurements of landfast ice width (Section 2.2.3) derived from each parent dataset 

individually. Since each dataset was sampled at a different rate, the first compilation step was to 

interpolate all observations to a consistent, daily sampling interval. This was achieved through a single 

nearest neighbor scheme, such that if there was no observation on a given date, we assign the value from 

the closest date when there was an observation. We applied this interpolation over time to data from each 

coast vector to create staircase-like timeseries that effectively preserve the underlying temporal 

resolution, which is important when computing the occurrence dates of key events (Section 2.2.5). In 

addition to unifying sampling rates between datasets, interpolation also allows us to fill-in missing data 

from incomplete SAR mosaics in M2014 dataset (Section 2.1.2) and missing polygons in ASIP and NIC 

ice charts (Sections 2.1.3 and 2.1.4).

The second step in the compilation process is to apply a running 20-day minimum filter to the ASIP and 

NIC datasets. As noted in Section 2.1.2, the definition of landfast ice used in the M2014 dataset includes 

an implicit 20-day minimum, so this step allows us to achieve a similar definition across all three datasets. 

The third and final step is to combine the datasets into a single timeseries following a relatively simple set 

of rules, as illustrated in Figure 2-4. The first rule is simply to use the M2014 when available, since this is 

most consistent and well understood of the three datasets. After the 2007–08 season, we then take either 

the average of the ASIP and NIC datasets, if both are available, or just the ASIP data when the NIC charts 

are unavailable. As with the other steps, this averaging takes place on a per-coast vector basis. Daily SLIE 

images are then reconstructed from the resulting landfast ice width data, as per the method described in 

Section 2.2.4.
14

Figure 2-4: Schematic illustrating how the EM2024 dataset is compiled from the three “parent” datasets.
15

2.4 InSAR-based detection of landfast ice

2.4.1 Overview of InSAR and application to sea ice

Synthetic aperture radar interferometry (InSAR) holds promise for both automated identification of 

landfast ice and understanding small-scale deformation that it can undergo. InSAR uses SAR images 

taken from approximately the same location in space at different times to measure the surface 

displacement which occurred between images. Any application of InSAR requires that the surface being 

imaged remains sufficiently coherent between acquisitions. In the case of a moving surface like sea ice, 

this requires the total motion is less than half image resolution, such that at least half of any given pixel 

represents the same ice surface in both images. At typical drift speeds of up to 0.5 ms-1 and typical SAR 

resolutions on the order of 10–100 m, this is only achievable over mobile pack ice when images are 

acquired within a few minutes of each other. However, over relatively immobile landfast ice, InSAR can 

be applied to images acquired weeks or even months apart. In most situations motion of tens of m would 

preclude ice from being classified as landfast.

The Alaska Satellite Facility (ASF) Vertex portal hosts Sentinel-1 A/B SAR images that can be paired to 

perform interferometry. Within the Vertex portal, the Short Baseline Subset tool allowed for the selection 

of Sentinel-1 A/B SAR pairs within specified perpendicular and temporal baselines ranges. SAR pairs 

were limited to pairs with less than a 300 m perpendicular baseline and a 12-day temporal baseline. A 6-

day temporal baseline is possible when using both the Sentinel-1A and Sentinel-1B in tandem. However, 

this was not possible for the whole study period due to Sentinel-1B encountering an issue. The 12-day 

temporal baseline allowed for the same satellite to be used to acquire the primary and secondary images 

in the SAR pairing. In addition, we applied a seasonal filter from November to July of 2017–2022 as 

landfast ice is not typically present within the study region outside of this period. A total of fourteen 

reference scenes were chosen to span the extent of the study area, with 7 required to cover the Chukchi 

and 7 to cover the Beaufort regions. After these constraints were applied to the Sentiel-1 dataset 2,084 

SAR pairs were identified from March 2017 to July 2022. All images were taken in the interferometric 

wide swath (IW) mode. The Sentinel-1 single look complex in IW mode has a spatial resolution and pixel 

spacing of 3.1×22.7 m and 2.3×14.1 m, respectively. Each pixel is then multi-looked, or averaged, by 20 

looks in the range direction by 4 looks in the azimuth direction to reduce speckle noise and improve 

coherence. Multi-looking by 20×4 looks reduced the resolution to 160 m which is down sampled to a 

pixel spacing of 80×80 m. Each SAR pairing was processed by ASF using the HyP3 toolbox (Hogenson 

et al 2016), which produces interferometric data products from the SAR pairings. For this study we used 

the coherence GeoTIFF and wrapped interferometric phase GeoTIFF for analysis.

2.4.2 Interferometric coherence and relationship to landfast ice extent

Coherence in interferometry describes the signal similarity of coregistered pixels in the primary and 

secondary SAR image pairs. In the context of sea ice, reductions in coherence are attributed to drifting sea 

ice, large amount of deformation, surface roughness change, and surface composition changes. Coherence 

has been used to identify and delineate landfast sea ice from pack ice by thresholding the normalized 

coherence values. Using Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic 

Aperture Radar (PALSAR) data, Meyer et al., (2011) determined pixels with a normalized coherence 

value greater than 0.1 were landfast ice. The choice of an L-Band sensor, wavelength of 23.62 cm, 

allowed for longer time between acquisitions and be more robust to late season melting while maintaining 

coherence. PALSAR repeated orbit paths every 45 days. 

This study used SAR pairs acquired by Sentinel-1, a C-Band (5.54 cm), SAR satellite which repeated its 

orbit every 12 days. The shorter wavelength requires less change to reduce coherence. However, the 
16

reduction of the repeat orbit interval is assumed to offset the lower threshold for reductions in coherence. 

This offset allowed for the use of the same normalized coherence threshold, 0.1, to distinguish landfast 

ice from mobile pack ice. On occasion, pack ice returns pixels or small clusters of pixels in the speckle 

which achieve the 0.1 coherence threshold but are not contiguous with the coast nor landfast ice. We 

remove these false positive identifications of landfast ice using morphological filtering. This operation 

removed small clusters of coherent pixels while filling ‘pinholes’ of low coherence within the landfast 

ice. Pixels did not achieve the 0.1 threshold outside of the land area prior to December. In addition, 

suspected surface melting or flooding caused complete loss of coherence in May or June. Interferograms 

from the Beaufort region maintained coherence through the end of May while the Chukchi region would 

lose coherence in early to mid-May. Previous studies using Sentinel-1 experienced similar losses of 

coherence and constrained their data to interferograms from March to May (Dammann et al., 2019). In 

accordance with our coherence-based definition of landfast ice, pixels must have a coherence higher than 

0.1 and be contiguous with the coast; we constrained our analysis to December, January, February, 

March, April, and May.

InSAR products were grouped and mosaicked to generate monthly images for the whole study area. This 

created 24 InSAR-derived SLIE images for the months of December to May over the period 2017–2021. 

We then used the SLIEalyzer toolbox (Section 2.2.1) to measure landfast ice width for each of these SLIE

images and compute monthly means for the whole InSAR period (2017–2021). The monthly landfast ice 

width measurements using coherence were then compared to corresponding data from the EM2024 

dataset for the same period. 

2.4.3 Measurement of apparent strain

InSAR allows for different deformation regimes within landfast ice to be calculated. Dammann et al.,

(2018) and Fedders et al., (2024, in press), show that it is possible to estimate 2-dimensional strain of sea 

ice from the phase gradient under certain circumstances, but without a constellation of satellites to 

provide multiple look directions, a universal approach remains elusive. Hence, to use InSAR to derive 

information about the stability of landfast ice, we simply assume that ice deformation of any kind results 

in a phase gradient and therefore the magnitude of the phase gradient is inversely related to the stability of 

the landfast ice. 

To calculate the interferometric phase gradient, we treat each interferogram as a matrix and convolve it 

with kernels representing finite differences in the x and y directions. Using a 4-pixel long kernel provides 

the calculates the difference in phase between pixels spaced 3 pixel-widths apart. The phase gradient in 

the x direction using (Equation 2-1) and the y direction (Equation 2-2):

∂∂∂∂

∂∂∂∂ ≈ (mm,nn)− (mm−33,nn)

3333 (2-1)

∂∂∂∂

∂∂∂∂ ≈ (mm,nn)− (mm,nn−3)

3Δ (2-2)

Where φφ is the interferometric phase, mm and nn are pixels indices in the x- and y-direction, respectively, 

and Δxx and Δy are the corresponding pixels dimensions. Four-pixel kernels were found to capture the 

phase gradient while mitigating spurious pixels of rapid phase change associated with areas of low 

coherence. 

The cyclic nature of interferometric phase means that there are artificially high phase gradients when the 

phase wraps between 2ππ and 0 at the end of a “fringe”. To account for this, we follow the work of Libert 

et al., (2022) and convert the wrapped phase gradient to complex number using Equations 2-3 and 2-4, 

which invoke Euler’s formula. In this form, the real and imaginary components of the phase gradient are 
17

represented using continuous sine and cosine functions that are insensitive to the discontinuities at phase 

wraps between 2ππ and 0. The magnitude of the unwrapped phase gradient in both the x and y direction 

can be found by taking the inverse tangent of the imaginary and real components of the complex wrapped 

phase gradient (Equation 2-5 and 2-6). The total unwrapped phase gradient, |∇φ|, is the Pythagorean sum 

of the phase gradients in both x and y directions (Equation 2-7). 

aaxx + iibbxx = ee

i

∂∂∂∂

∂∂∂∂ = cccccc ∂∂∂∂

∂∂ + ii ∂∂∂∂∂∂ (2-3) 

aa + iibb = ee

i

∂∂∂∂

∂∂∂∂= cos∂∂∂∂

∂∂∂∂ + ii ∂∂∂∂∂∂∂∂ (2-4) 

∂∂∂∂

∂∂ = tan−1(bbxx, aaxx) (2-5) 

∂∂∂∂

∂∂∂∂ = tan−1(bb , aa ) (2-6) 

|∇φφ| = �∂∂∂∂uu

∂∂

2

+ ∂∂∂∂uu

∂∂∂∂

2

 (2-7) 

A 2ππ difference in φφ between any two pixels corresponds to half a radar wavelength, λλ=0.0564 m,

difference in the line-of-sight displacement of the ice at those two pixels. Here, we define the term 

apparent strain, ∈aa, to describe the magnitude of the horizontal gradient in line-of-sight displacement:

∈aa= λλ

4ππ |∇φφ| (2-8) 

Since Sentinel-1 is a side looking radar with a non-zero incidence angle, ∈aa is sensitive to both horizontal 

and vertical displacement of the ice surface. However, in the horizontal plane, it is only sensitive to 

motion in the look direction. Under certain circumstances, it is possible to identify cases when surface 

strain is dominated by either horizontal or vertical strain and, with sufficient constraint on the mode of 

deformation (e.g., radial divergence or simple shear), it is possible to resolve the 2D strain field through 

inverse modeling (Fedders et al., 2024, in press). However, this typically only applies inside sheltered 

lagoons (when strain is dominated by thermal processes) or when additional information about ice motion 

is available. Hence, it would be impracticable to apply this approach throughout our study domain and so 

instead we use ∈aa, as a conservative approximation of the total amount of strain between acquisitions. We 

should be cautious about using ∈aa to infer stress accumulation in the ice, but we should expect areas 

experiencing more deformation should exhibit higher values of ∈aa than those regions that are sheltered 

from such activitiy, particularly when averaged over multiple interferograms. We therefore use ∈aa here as 

a proxy for the relative stability of the landfast ice. 

The apparent strain images were sorted by year and month, with the date of the primary image in the SAR 

pair being the date of reference. Spatial coverage of Sentinel-1 is inconsistent within the study region. 

This resulted in different numbers of interferograms created for each reference scene. In addition, the 

fourteen SAR reference scenes overlapped in certain areas, also causing different numbers of 

interferograms to be valid during each time period. After aligning all valid apparent strain images, a per 

pixel count of images with a valid pixel was created. This value was then used to average the apparent 

strain value for this pixel on a per month basis from 2017–2022. By counting the number of valid pixels, 

we ensured areas which were occupied by landfast ice for multiple interferograms are not counted 

differently than places with few valid areas of landfast ice. Monthly average apparent strain values were 

calculated for December, January, February, March, April, and May for pixels with valid identification of 

landfast ice. 

2.4.4 Regions used to define stability thresholds

Dammann et al., (2019) used InSAR to define 3 categories of stability within landfast ice, based on a 

qualitative analysis of the density and orientation of interferometric fringes: bottomfast; stabilized; and 
18

non-stabilized. Bottomfast ice is the most stable of these categories and occurs in water shallow enough 

where the entire water column freezes, and the sea ice is frozen to or is resting on the seafloor. This 

depends on the ice thickness (or, more precisely, the draft of the ice), but in our study region bottomfast 

ice will be found in waters up to approximately 1.5 m deep (Pratt 2022). In interferograms, bottomfast ice 

was identified based on “No identifiable phase difference from the adjacent land”. Stabilized landfast ice 

is found seaward of the bottomfast ice zone where the ice is floating but held in place by islands or 

grounded pressure ridges. Dammann et al., (2019) identified stabilized ice based on “Poorly defined, 

widely spaced fringes, or abruptly reduced fringe spacing compared to offshore ice”. Lastly, nonstabilized ice includes all landfast seaward of any island or grounded ridge and was identified according 

to “Well-defined fringe orientation or patterns”. Here, we derived phase gradient thresholds to 

quantitatively define these stability categories.

Figure 2-5: Spatial extent of bottomfast, sheltered, and not sheltered landfast ice masks.

Landfast ice that is anchored by an oceanward grounded feature is more stable than landfast ice 

oceanward of a grounded feature. Grounded pressures ridges do not necessarily occur in the same location 

during each season, leading to different extents of the stabilized region defined by Dammann et al.,

(2019). Unlike grounded pressure ridges, barrier island locations do not vary between seasons. Barrier 

islands shelter the landfast ice shoreward of its location and act similar to a grounded ridge when the 

embayment fills with landfast ice. We created spatial masks for landfast ice related to the landfast ice 

location to barrier islands:

1. Bottomfast ice: The same extent identified by Dammann et al., (2019) (blue hashed areas in 

Figure 2-5)

2. Sheltered ice: Area shoreward of a barrier island or barrier island chain, not including areas of 

bottomfast ice (orange areas in Figure 2-5)

3. Not-sheltered ice: Landfast ice oceanward of a barrier island or barrier island chain (light blue 

areas in Figure 2-5)

The maximum extent of bottomfast ice extent along the Beaufort coast varies little enough between 

seasons (Pratt 2022) that we can use the bottomfast ice extent defined during April of 2017 by Dammann 

et al., (2019) to identify the areas which are likely bottomfast ice during April of all seasons. The landfast 

ice in the sheltered region is guaranteed to have grounded landfast ice oceanward of its location due to the 

presence of the barrier islands. The not-sheltered region may include areas of landfast ice stabilized by 

grounded pressure ridges, but will include all the non-stabilized areas seaward of any stabilizing features. 
19

2.5 In-situ field observations

2.5.1 Seasonal ice mass balance buoys (SIMBs)

Cryosphere Innovations 3rd generation Seasonal Ice Mass balance Buoys (SIMB3s) were deployed to 

measure the growth and melt of landfast ice (Figure 2-6). The SIMB3 is equipped with a downwardlooking snow sounder to measure the distance, zzss, to either the snow surface, or the ice surface if there is 

no snow present. By independently measuring the initial distance from the snow sounder to the ice 

surface, zzss0, when the buoy is deployed, this allows the SIMB3 to observe changes in the snow depth, HHss, 

according to:

HHss = zzss0 − zzss (2-9)

In summer, after the snow pack has melted away and zzss > zzss0, this sensor measures of surface melt of the 

ice. A second sounder looks upward below the ice and measures the distance to the ice bottom, zzbb. The 

ice thickness, HHii, is then given by:

HHii = zzssss − zzss0 for zzss ≤ zzss0 (2-10)

HHii = zzssss − zzss for zzss > zzss0 (2-11)

where zzssss is the distance between the two sounders, which is equal to 4.05 m. 

The SIMB is also equipped with a chain of temperature sensors, spaced at 2-cm intervals, that record the 

temperature profile from the ocean to the atmosphere through the ice and any snow present. There is a 

shielded air temperature sensor at the top of the buoy and a water temperature sensor at the bottom. Data 

are telemetered every hour via an Iridium satellite connection to servers at CryosphereInnovations, where 

they are available in real time. 

SIMBs were deployed in landfast ice near Point Hope and Utqiagvik, Alaska (Figure 2-7) between 2020 

and 2024 (Table 2-3). An attempt was made in early 2020 to deploy a SIMB in Foggy Island Bay, near 

Deadhorse, Alaska but the attempt was unsuccessful due to unanticipated failure of an ice auger used for 

the making the necessary hole in the sea ice. A subsequent storm prevented an immediate return to the 

site and the unfolding COVID-19 pandemic prevented further attempts in 2021 and 2022. Eventually, the 

SIMB was returned to Fairbanks, Alaska and redeployed in Utqiagvik to replace a buoy that was damaged 

during retrieval. This buoy was then damaged by a polar bear in 2022, prematurely ending its data record. 

The SIMB deployed at Utqiagvik in 2024 survived one polar bear encounter shortly after deployment, but 

was unrecoverably damaged by a second encounter on May 10.
20

Figure 2-6: Schematic showing deployment of SIMB3 and derived 

measurements.

Figure 2-7: Map showing locations of SIMBs deployed during this project.
21

Table 2-3: Deployment notes for instruments deployed in landfast ice

Season Point Hope Utqiagvik Foggy Island Bay

2020 SIMB: Mar 7 – Jun 20 

ADCP: lost No deployments Aborted SIMB & ADCP deployments

2021 No deployments SIMB: Mar 4 – Jun 8 No deployments

2022 SIMB: Feb 12 – May 26 SIMB: Jan 28 – Mar 4

ADCP: Jan 28 – Jun 22 No deployments

2023 No deployments No deployments No deployments

2024 No deployments SIMB: Feb 3 – May 10 No deployments

2.5.2 Ice tethered acoustic Doppler current profilers (ADCPs)

To measure the vertical profile of currents beneath landfast ice, we deployed 1,200 kHz acoustic Doppler 

current profilers (ADCPs) in an ice-tether configuration adjacent to SIMBs (Figure 2-8). Suspending the 

ADCP close to the seafloor looking upwards allows measurement of the current profile near the ice-water 

interface with a vertical resolution of 5 cm. However, as noted in Table 2-3, we were only able so 

successfully deploy two such ADCPs. The first of these was deployed as planned in Point Hope, but due 

to the COVID-19 pandemic we were unable to return to the community to recover it before the landfast 

ice detached from the coast. We requested assistance from local residents to search the seafloor for in the 

vicinity of the initial deployment, but the weather never aligned with scheduling windows and so the 

instrument and its data were lost. We were therefore only able to collect one season of under-ice current 

data at Utqiagvik, in 2022. In this instance, the ADCP was deployed at a depth of 6.5 m in approximately 

7 m of water, below sea ice that was initially 0.8 m thick.

Figure 2-8: Schematic illustrating upward-looking ice-tethered deployment of an ADCP adjacent to a SIMB. 
22

2.6 Atmospheric analysis

2.6.1 Reanalysis data products

This study utilized the European Centre for Medium-Range Weather Forecasts reanalysis version 5 

(ERA5; Hersbach et al., 2020) for the analysis of storms, which is publicly available at the Copernicus 

Climate Data Store (https://cds.climate.copernicus.eu/). ERA5 is a gridded (0.25 by 0.25 degree 

resolution) reanalysis dataset that uses a weather model to assimilate station and satellite remote sensing 

products to generate hourly gridded observations of various meteorological variables. This reanalysis was 

selected as it has shown good performance relative to similar products from other modeling centers in the 

Arctic and Alaska (Avila-Diaz et al., 2021; Bachand and Walsh 2022; Graham et al., 2019). 

For our analysis we obtained hourly sea level pressure (SLP), 10-m wind speed and wind direction 

spanning 1991–2023 (Hersbach et al., 2023). This includes years prior to those covered by the EM2024 

landfast ice dataset, but overlaps well with the most recent climate normal period of 1991–2020 used by 

the U.S. National Weather Service and the World Meteorological Organization. This 30-year climatology 

provides a simple long-term average of each parameter over its appropriate timescale (i.e., monthly or

daily). Anomalies were produced by subtracting out the long-term average climatologies based on the 

1991–2020 climate normal period.

2.6.2 Cyclone tracking

Cyclones, often referred to interchangeably as storms, are areas of low pressure that can bring high winds 

and precipitation. These storm systems are often responsible for many extreme events in coastal regions 

of Alaska such as the ex-Typhoon Merbok in September 2022 that brought substantial impacts from 

coastal erosion and flooding. These storms can be tracked either subjectively (i.e., by visual inspection of 

synoptic weather maps) or following an objective method using computer software. In this study we used 

the objective storm identification and tracking algorithm described by Zhang et al., (2004). This algorithm 

is designed to track storms on gridded SLP from atmospheric reanalysis at 6 hourly and 2.5 degree 

temporal and spatial resolution, respectively. For our study we applied this algorithm to the ERA5 SLP 

gridded fields at 6 hour increments and regridded via bilinear interpolation to 2.5 degrees. The result is a 

data set that provides the time, location, and central pressure of each cyclone identified.

To identify links between typical patterns of storm conditions and the occurrence of landfast ice breakout 

events, we used the results of a Self-Organizing Map (SOM) algorithm. SOM is a methodology that uses 

unsupervised neural networks to identify patterns in data (Kohonen 1989; 1990). In our analysis, SOMs 

were used to identify common patterns of SLP in the Alaska region (i.e., synoptic pattern types), each of 

which being tied to specific days during the period of analysis (each day of the period belongs to a SOM 

node). The SOM method has been previously used to identify synoptic pattern types for Alaska (e.g., 

Cassano and Cassano 2010; Cassano et al., 2017; Glisan et al., 2016; Hartl and Stuefer 2020). In this 

study we applied the Python MiniSOM algorithm (Vettigli 2018) to all of the daily average SLP anomaly 

fields from the ERA5 reanalysis for January–April over 19972

–2023 to match the period of the breakouts 

database. The SLP data were regridded to an equal area, 50×50 km grid and grid points with elevations 

greater than 500 m were masked to avoid artifacts stemming from the calculation of SLP at high 

elevations. Although we could have avoided the need to mask these regions by using reference pressure 

level (e.g., 850 hPa) for the SOM analysis, we used SLP for consistency with the cyclone tracking. Our 

analysis used a 4 by 5 SOM map, which provided a set of 20 different possible synoptic map types.

2 Since breakouts are defined as only occurring between January and April and the EM2024 dataset begins in 

October 1996, the first year in the breakout catalog is 1997. 
23

2.7 Pan-Arctic modeling of landfast sea ice

2.7.1 Selection and configuration of Pan-Arctic MOM6-SIS2 (PAMS) model

Previous work using a Pan-Arctic Regional Ocean Modelling System (PAROMS) (Curchitser et al., 2018; 

Danielson et al., 2016; Danielson et al., 2020b) demonstrated that the annual cycle of landfast ice extent 

in the Chukchi and Beaufort Seas could realistically be simulated with Lemieux et al.’s (2016; 2015)

parameterization for basal shear stress resulting from the interaction between ridge keels and the seafloor 

in shallow water. However, PAROMS was found to be less well suited for multi-decadal model runs of 

similar duration to our expanded observational climatology of landfast sea ice. Specifically, it developed 

drifts in the temperature and salinity of the deep ocean that diverged from the known property values over 

extended integration times. At the same time, other partners in the modeling community were moving 

toward turning the world-class Modular Ocean Model v6 (MOM6) climate model (Adcroft et al., 2019)

into a regional ocean model for its improved numerical schemes, including more efficient time-stepping. 

We have since discovered that the deep temperature drifts are sensitive to the restratification 

parameterization used, which is not something that is currently available in PAROMS.

Accordingly, for this study we chose the MOM6 ocean model, with the Sea Ice Simulator v2 (SIS2) sea 

ice model (Adcroft et al., 2019; Ross et al., 2023). This choice required that we set up a new integration 

domain covering the Arctic and some of the marginal seas around the Arctic (Bering Sea, Hudson Bay, 

etc.). The model was developed at roughly 12 km horizontal resolution for computational efficiency and 

for efficient testing, though a 6-km version of the same model formulation is in development and can be 

run later if a higher-resolution is required. Due to the very small scales of the eddies in the Arctic, true 

eddy resolving is beyond our abilities for this pan-Arctic domain. The 1-km or less nested model 

(including sea ice) is a Regional Ocean Modelling System (ROMS) capability that does not yet exist for 

SIS2. Having ice open boundary conditions would involve considerable development effort on top of 

Figure 2-9: Model domain 

and bathymetry for the 

PAMS model used in this 

study.
24

what was done for the ocean open boundary conditions. As a result of the aforesaid limitations, the PAMS 

model we have implemented here is limited to a horizontal resolution of 12 km.

Note that we have chosen to mask out the Sea of Okhotsk as it is not connected to the rest of the domain. 

This domain has open boundaries on all lateral sides, and its bathymetry and extent are shown in Figure 

2-9. Some of the notable parameter choices for MOM6 are shown in Table 2-4. 

Table 2-4: Notable parameter choices for MOM6 model used in this study

Parameter Value Reference

Vertical coordinate 75 layer z* (Adcroft et al., 2019)

Baroclinic time step 600 s -

Thermodynamics, coupling and 

biogeochemistry time step 1,800 s - 

Model time period 1993–2017 (or 2019) -

Vertical mixing

Planetary boundary layer 

parameterization ePBL (Reichl and Hallberg 2018)

Shear mixing Jackson (Jackson et al., 2008)

Bottom boundary layer mixing 

efficiency 0.0 - 

Internal tidal mixing Polzin (Polzin 2009)

Submesoscale restratification 

parameterization Bodner, coefficient of 0.3 (Bodner et al., 2023)

Lateral mixing 

Biharmonic Smagorinsky:

SMAG_BI_CONST = 0.06,

AH_VEL_SCALE = 0.01

(Griffies and Hallberg 2000)

Boundary conditions

Sea level and barotropic velocity Flather scheme (Flather 1976)

Baroclinic velocity

Radiation and nudging

(3 day inflow, 360 day outflow time 

scales)

(Marchesiello et al., 2001); 

(Orlanski 1976)

Tracers Reservoirs with 3 km length scales -

Minimum depth 5 m -

Maximum depth 6,500 m -

Tidal SAL coefficient 0.094 -

Equation of state WRIGHT_FULL (Wright 1997)

Opacity scheme 3-band with chlorophyll from 

COBALT (Manizza et al., 2005)

The SIS2 sea ice model has the option to call the CICE Consortium Icepack library (Hunke et al., 2024)

for ice ridging, which we have found to be helpful for getting realistic ice thickness fields in the central 

Arctic basin. In addition, we have ported two different landfast ice parameterizations from CICE into 

SIS2: those of Lemieux et al., (2016; 2015) and Dupont et al., (2022), though all the results described 

below were derived using only the latter, at the recommendation of the science review board. In brief, the 

Lemieux studies assumed idealized triangular ridges that scaled with the mean thickness in a grid cell and 

applied a basal shear stress where the keel depth of these ridges exceeded the water depth. By 

comparison, Dupont et al’s (2022) approach takes advantage of the sub-grid ice thickness distribution to 

probabilistically estimate the interaction of deep-keeled ice with the seafloor, which can also have subgrid roughness. Here, we use bathymetry from the general bathymetric chart of the oceans (GEBCO) 

2020 (GEBCO Compilation Group 2020) and apply the method of Adcroft (2013) for estimating seafloor 

roughness. Notable parameters used (i.e., differing from the global model of Adcroft et al., 2019) for the 
25

ice are shown in Table 2-5. The SIS2 model has a split-explicit timestep, with the thermodynamics

updating on the slow ice timestep and the ice dynamics updating on the fast ice timestep. The fast ice 

timestep of 2.1 s is needed to resolve the elastic waves of the elastic-viscous-plastic rheology (Bouillon et 

al., 2009; Hunke and Dukowicz 1997). Note that, in this particular context, the term “fast” is used to 

mean rapid or quick, rather than stationary or attached.

Table 2-5: Notable sea ice parameters used in the SIS2 model

Parameter Value Reference

Number of ice categories 10 - 

Slow ice timestep 900 s - 

Timestep ratio for fast ice solver 432 - 

Yield curve ellipticity 1.5 - 

Ridging Yes - 

 Ridge participation old (Thorndike et al., 1975)

 Ridge redistribution old (Hibler 1980)

Landfast ice parameterization ITD (Dupont et al., 2022)

Transmute ice Yes, at “northern” edge - 

2.7.2 Model forcing

The PAMS model is forced at the surface with winds and other atmospheric fields from the Japanese 55-

year Reanalysis (JRA55-do) (Tsujino et al., 2018). The bulk fluxes are computed by the sea ice model and 

passed to the ocean via the coupler. For river forcing, the Global Flood Awareness System (GloFAS)

product is remapped to all the coastal ocean points and applied as a fresh water input, similar to rain. It is 

given a salinity of zero and a temperature matching the 10-m air temperature. For the Gulf of Alaska, we 

replace GloFAS with a high-resolution (1-km) validated runoff product from Hill et al., (2015). 

The model initial conditions are interpolated from the 1/12-degree Global Ocean Physics Reanalysis

(GLORYS) fields (Copernicus Marine Service Information 2023). This includes temperature, salinity, 

free surface, horizontal velocities and the ice thickness and concentration. GLORYS is also used for the 

ocean lateral boundary conditions, applied daily on all four open boundaries. The boundary conditions are 

those of Flather (1976) for the barotropic flows and a combination of radiation and nudging to GloFAS

for the baroclinic velocities. The temperature and salinity boundary conditions are applied via the tracer 

reservoirs. In MOM6, each open boundary has a data structure containing fields on that open boundary 

segment, including a reservoir for each tracer in which values are mixed in from values inside the model 

if the flow is directed outward or from external values if the flow is directed inward. The user provides 

lengthscales for each segment for how large the “reservoir” is for both the inflow and outflow cases, i.e., 

the reservoir can be large with a long memory of resident values, or it can be minute and easily 

overwritten with the incoming values.

The sea ice does not have open boundary conditions and sees the domain boundaries as walls. However, 

there is a TRANSMUTE_SEA_ICE option which we are using to turn ice into fresh water when it 

approaches the southern boundaries near Greenland and Kamchatka to prevent it from accumulating 

there. We are currently nudging to global monthly sea surface salinity climatology. This is only applied 

where there is no sea ice and is used to help correct some salinity biases observed in the Bering Sea. The 

model has tides, coming from both the open boundaries and an internal body force option. The boundary 

tides are interpolated from a global TPXO simulation (Egbert and Erofeeva 2002) as part of the Flather 

boundary condition. In both cases, we are including 10 tidal constituents, 4 semi-diurnal (M2, S2, N2, 

K2), 4 diurnal (K1, O1, P1, Q1), and 2 longer period tides (MF and MM). 
26

2.7.3 Parameterization of basal shear stress

A basal shear stress term to represent the interaction between pressure ridge keels and seafloor was first 

introduced by Lemieux et al., (2016; 2015) to allow for ice to become landfast in shallow waters. In this 

scheme, the momentum equation includes an extra bottom drag term that acts in opposition to the 

resultant horizontal stress when the ice is deep enough to interact with the local bottom. The maximum 

magnitude of this stress is determined by mean ice thickness, the mean water depth, and two parameters 

defining simplified triangular shapes of pressure ridge keels. One advantage of this approach is that the 

landfast ice parameterization applies to the mean ice thickness in the cell and is therefore suitable even for 

a model with a single ice category. However, many sea ice models now allow for multiple ice categories 

in each grid cell, with some memory of the thickness distribution. Hence, to make use of that additional 

information Dupont et al., (2022) designed a probabilistic landfast ice parameterization that considers 

both the ice thickness distribution, gg(HHii), and the water depth distribution, bb(HHww). The basal shear stress, 

ττbbmmmm , is calculated by integrated the product of a normal force applied to the seafloor, FFNN, gg(HHii), and 

bb(HHww), for all thicknesses where the ice draft, DD(HHii), is greater than the water depth, HHww: 

ττbbmmmm = μμss � � FFNNgg(HHii)bb(HHww)

HHww=DD(HHii)

0

∞

0

ddHHii ddHHww (2-12) 

FFNN = gg�(ρρiiHHii − ρρwwHHww) (2-13)

This allows the basal shear stress to be computed using the relatively well constrained physical 

parameters of a seafloor friction coefficient, μμss, and the densities of ice, ρρii, and seawater, ρρww. gg� is the 

acceleration due to gravity. 

Although both the Lemieux and Dupont parameterization schemes are available in SIS2 (via the CICE 

IcePack), here we only use the Dupont parameterization. Note also that while MOM6 supports the 

tracking of icebergs, we have not yet added to this domain and so pressure ridges are the only basal shear 

stress mechanism accounted for in this parameterization.

2.7.4 Velocity-based definition of modeled landfast ice extent

Landfast ice extent is derived from the PAMS model output by applying a simple threshold to the daily 

mean sea ice velocity field followed by a 20-day running minimum to find grid cells with a velocity 

below the threshold for at least 20 days. To test sensitivity of this threshold, we applied three different 

thresholds (Table 2-6) to each of the two model runs to create 6 separate modelled datasets of landfast sea 

ice extent. Sea ice velocity is computed by the model as a daily mean value in units of cm s-1 and we 

selected thresholds based on the cumulative amount of motion over the 20-day period that underlies our 

definition of landfast ice (Section 2.1.2). The middle threshold corresponds to a total 20-day motion of 

around 86 m, which approximately corresponds to the spatial resolution of the SAR images used in the 

M2014 dataset (Mahoney et al., 2014; Mahoney et al., 2007). Thresholds 1 and 3 correspond to twice and 

half this amount of motion, respectively. 

Table 2-6: Mean daily Ice velocity thresholds used to determine landfast sea ice extent in PAMS model output

Threshold cm / s m / 20-days

1 0.010 173

2 0.005 86

3 0.001 17
27

2.7.5 Selection of model runs

As of July 2024, the 80th model run on the 12-km grid described above is now executing. These were 

preceded by 20 experiments on an 18-km grid and prior to that, a few runs were performed on a 6-km grid 

in which we had an error in the velocity boundary conditions. All of the early simulations were performed 

on the University of Alaska Fairbanks’ high-performance computing system known as “chinook”. In 

2021, co-Investigator Hedstrom obtained access to a computer in Oak Ridge known as “gaea”. All of the 

simulations are now being done there using the Flexible Runtime Environment software which manages 

the compilation, resubmission, and file transfers to Princeton. 

Not all of these simulations ran to completion. There have been instabilities within the ice, others within 

the ocean. We have learned much from the Regional MOM6 group meetings, for instance, the North 

Atlantic simulations show that the Gulf Stream path is extremely sensitive to the choice of Coriolis 

option. They have also gotten some odd Gulf Stream paths when trying a hybrid vertical coordinate 

instead of the z-star, even after some open boundary bugs were fixed. The Northeast Pacific group has 

been active in comparing their results to mooring data from the Bering Sea shelf, leading to all of us 

turning off the bottom boundary layer mixing and to reduce the shear mixing as the defaults currently in 

the model are too vigorous.

Not all model runs were focused on improvement of Arctic processes, but among those that did, there was 

an attempt to include a brine plume parameterization from Nguyen et al., (2009). We also changed the 

restratification parameterization scheme in an effort to better resolve sub-grid processes that oppose 

vertical mixing. We began by using the Fox-Kemper scheme (Fox-Kemper et al., 2011; Fox-Kemper et 

al., 2008), which uses constant lengthscale of 500 m for restratification processes. However, we later 

switched to using the Bodner scheme (Bodner et al., 2023), which has a variable lengthscale that depends 

upon latitude and other modeled ocean properties.

We also experimented with the coupler used to represent ice-ocean processes in an effort to reduce 

instabilities. To handle communication between all components of the model, we used the Flexible 

Modeling System (FMS) coupler (http://noaa-gfdl.github.io/FMS). The timestep on which the FMS 

coupler operates is set to half an hour (1,800 s). Accordingly, the atmospheric fields are interpolated 

every half hour, then the ice is advanced for the same interval, taking two steps of 900 s each. Finally, the 

ocean takes three (baroclinic) steps of 600 s each as well as one thermodynamic step of 1,800 s. However, 

ice-ocean instabilities occur at much shorter timesteps, and so we implemented a new coupled ice-ocean 

driver (CIOD), which couples just the ice-ocean processes at the fast ice timesteps (see Table 2-5). It is 

known that there is an instability in the traditional ice-ocean coupler that can show up in the marginal ice 

zone and lead to excessive near-surface mixing. The CIOD is being developed at the Geophysical Fluid 

Dynamics Laboratory, with the goal to more tightly couple the ice and ocean time-stepping in such a way 

as to shut down the instability. Recent experiments in the global model show that the amount of ice in the 

model is sensitive to the time-stepping, even with the traditional coupler. The combined modeling system 

has many timesteps, including the fast and slow ice timesteps (see Section 2.7.1), as well as the coupling 

timestep and the frequency of updating the atmospheric wind fields. The ice can become up to a meter 

thicker in the central Arctic with differing choices in these multi-year runs. The CIOD runs also tend to 

have thicker ice than those without it by roughly half a meter.

To make a quantitative comparison between the landfast ice extent simulated by the PAMS model and 

observations from the EM2024 dataset, we scaled and reprojected the model’s 12-km gridded output to 

the 100-m grid used in the EM2024. This allowed us to the SLIEalyzer toolbox to make a direct 

comparison of the metrics described in Section 2.2. However, this was a time consuming task, requiring 

approximately 72 hours of processing time and the creation of approximately 1 TB of data files to 

complete the process for data from a single velocity threshold (Section 2.7.4) for a one model run. As a 

result, a detailed analysis of the landfast ice simulated by the PAMS model lagged behind the model 
28

development and we had to use model runs that had completed before April 2024 for the analysis to be 

included in this report. 

In the following sections of this report, we present results from mode runs 51, 56, and 73, the differences 

between which are summarized in Table 2-7. Model run 73 was not completed in time for a thorough 

analysis of its simulated landfast ice extent, but is included here as an example of a later iteration of the 

PAMS model that allowed for hourly output, and we evaluate its ocean and non-landfast ice performance

in comparison with model runs 51 and 56. 

Table 2-7: Summary of differences between PAMS model runs selected for presentation in this report

Model run Coupling system Restratification

parameterization EM2024 comparison

51 FMS Fox-Kemper Yes

56 FMS + CIOD Bodner Yes

73 FMS Bodner No
29

3 Results

3.1 1996–2023 landfast ice climatology

3.1.1 Assessment of differences and bias between landfast ice data source

During the assimilation of the EM2024 dataset, the 2007–2008 season was the only season which 

contained data for all datasets. Each SLIE images’ landfast ice width was measured using the SLIEalyzer 

toolbox (Section 2.2.1) and the coast vectors described in Section 2.2.3 in both the Beaufort and Chukchi 

regions. To have a direct comparison between the datasets the landfast ice width measurements were

interpolated to daily resolution for the M2014, NIC, and ASIP datasets during the 2007–2008 season 

using a nearest neighbor interpolation. Each season in all datasets contains 304 days of landfast ice width

measurements. These 304 width measurements represent SLIE locations at each coast vector each day 

from October 1st to July 31st of the following year. Since the M2014 data predates the ice chart datasets 

we treat landfast ice width measurements from the M2014 as the most accurate. After each dataset was 

interpolated to daily resolution an additional precaution was taken for the ASIP and NIC datasets. In 

addition to the daily interpolation of weekly, ASIP, and bi-weekly, NIC, ice charts we applied a 20-day 

running minimum to these datasets to match the methodology used during the creation of the M2014 

dataset. The datasets used for comparison were all daily resolution with a 20-day running minimum 

applied to the width measurements at each coast vector. The daily regional mean landfast ice width in the 

Chukchi and Beaufort regions during the 2007–2008 season are shown in Figure 3-1 and regional mean 

values for maximum width and the dates of first ice and ice free events are listed in Table 3-1. 

Table 3-1: Comparison of average metrics for the 2007–08 landfast sea ice season between the M2014, ASIP, and 

NIC datasets for the Chukchi and Beaufort regions. Note that the uncertainty ranges indicate the spatial 

variability within each region. 

Chukchi Beaufort

Metric M2014 ASIP NIC M2014 ASIP NIC

Average Maximum Width (km) 17.2

± 21.1

18.4

± 14.6

11.0

± 17.65

26.3

± 22.1

35.4

± 30.7

29.3

± 27.5

Average Date of First Ice Dec 28

±38 days

Jan 7

± 35 days

Jan 11

± 69 days

Dec 4

± 60 days

Dec 8

± 38 days

Nov 24

± 36 days

Average Date of Ice-Free May 31

± 21 days

May 22

± 23 days

May 14

± 22 days

Jun 9

± 19 days

Jun 11

± 5 days

May 31

± 21 days
30

Figure 3-1: Landfast ice 

width for the a) Chukchi 

and b) Beaufort regions for 

all three datasets available 

for the 2007–2008 season.
31

Figure 3-2: ASIP (A, B, C) and 

NIC (D, E, F) ice charts from 

December 2007 and January 

2008 with the M2014 SLIE 

image (yellow) measurement 

of landfast ice during the 

similar period. ASIP ice charts 

depicted are (A) 12/14/2007, 

(B) 12/24/2007, and (C) 

1/9/2008. NIC ice charts 

depicted are (D) 12/17/2007, 

(E) 12/31/2007, and (F) 

01/14/2008. Red areas 

indicate areas in each ice chart 

identified as landfast ice by 

ASIP and NIC. Shades of blue 

indicate various concentrations 

of sea ice.
32

Small deviations in the landfast ice width measured by each dataset were expected as the methodology 

and frequency which was used to identify areas of landfast ice differed amongst datasets. Both the ASIP 

and NIC datasets fall within 1 standard deviation of the M2014 dataset for maximum regional mean 

landfast ice with, mean first ice date, and mean last ice date. One exception to this is a stable extension of 

landfast ice reported in the ASIP dataset during the early 2007–2008 season deviated from the other two 

datasets (Figure 3-1). During January and February, an extension of landfast ice was observed in the ASIP 

dataset which was not observed in either the M2014 or NIC datasets (Figure 3-1). The difference in 

landfast ice width observed by the ASIP ice charts can be attributed to specific ice charts (Figure 3-2). 

Along the Beaufort region, mobile pack ice with 90–100% concentration occupied the existing SLIE 

between December 12th, 2007, and December 14th, 2007. These vector features in the ASIP ice charts 

were reclassified as landfast ice as there was no identifiable motion between ice charts. The lack of 

motion and continuity to the previously charted landfast ice likely lead the analyst at ASIP to classify 

these areas as landfast. Later, 1–2 months, in the season similar extents were identified as landfast by the 

M2014 dataset and NIC dataset. This deviation from the other datasets was not a consistent trend found in 

the ASIP ice charts and was treated as an anomaly within the dataset. 

Landfast ice width measured for all seasons in which any dataset had valid data for the whole season, 

described in Table 2-1, are shown in Figure 3-3. As described in Section 2.2.3, we calculated the landfast 

ice width along 8,892 locations across our study region, 6,956 in the Chukchi region and 1,936 in the 

Beaufort. Averaging the daily SLIE measurements across each region highlights interannual variability in 

the landfast ice regime not revealed in the average seasonal. A total of 27 annual cycles covering the 

years 1996–2023, with 12 measured by M2014, 16 by ASIP, and 15 by NIC (Figure 3-3). Each season 

follows a similar pattern with a gradual advance and rapid retreat in both regions. In the Chukchi region, 

the regional mean landfast ice extent typically achieved an annual maximum between 10–25 km for all 

datasets (Figure 3-3a). The 2017–2018 and 2018–2019 seasons were anonymously low landfast ice 

widths (<5 km), and only ASIP data were available. In the Beaufort region, the landfast ice typically 

achieves an annual maximum mean width between 20–30 km for all datasets (Figure 3-3b). Overall, the 

ASIP and NIC datasets agreed well with regards to regional mean landfast ice width. When deviations 

were observed between datasets, the deviations were not consistent in the sense that ASIP was not always 

measuring greater landfast ice width compared to NIC. The inconsistent differences between the NIC and 

ASIP datasets provided no evidence to choose a single dataset to update the M2014 dataset. As such we 

chose to average the ASIP and NIC datasets, described in Section 2.3 to update the M2014 landfast ice 

climatology.
33

Figure 3-3: Time series of mean landfast ice width in the a) Chukchi and b) Beaufort regions for all seasons for the M2014 (black), ASIP (red), and NIC (blue)

datasets. For this comparison, all datasets are interpolated to daily resolution, while the ASIP and NIC datasets also have a 20-day running minimum 

applied.
34

3.1.2 Regional landfast ice variability in the EM2024 dataset

By computing the median daily landfast ice width for the Chukchi and Beaufort regions (Figure 3-4) we 

can summarize and compare landfast ice extent variability over time and between the two regions. In the 

Chukchi Sea (Figure 3-4a) we see that typical maximum mean landfast ice width varies between 10–

25 km, with anomalously low landfast ice widths during the 2017–18 and 2018–19 seasons. In the 

Beaufort region (Figure 3-4b), typical maximum mean landfast ice widths vary between 20 km and 30 km 

with multiple stable extensions in excess of 50 km in the pre-2008 (i.e., M2014) part of the record. 

However, in addition to variation between the region, there is considerable variability in landfast ice 

width within each region, which is illustrated by the shaded regions in Figure 3-4. This local-scale 

variability of landfast ice width along the coast appears to scale with median width such that it is typically 

greater in the Beaufort region than in the Chukchi region and years with a high median landfast ice width

exhibit more intra-regional variability than those with a lower median width. It also notable that the 

differences in mean daily landfast ice width between the different data sources (Figure 3-3) are 

considerably smaller than the spatial variability in landfast ice width within each region, as indicated by 

the extent of the shaded regions in Figure 3-4.

3.1.3 Landfast ice occurrence probabilities

To visualize the spatial variability in the location of the SLIE, we rasterized and stacked all the daily 

SLIEs for all years on a single grid. This allows us to count the number of days that a SLIE occupied any 

given pixel within the grid. By “fattening” each SLIE to a width of 10 pixels (1,000 m) before doing so

and then dividing by the total number of days in the record (7,668), we are able to estimate the probability 

that the SLIE occurred within a 500-m radius of a given point in space (Figure 3-5). The lightest shades of 

blue indicate areas where the SLIE rarely occurred, and these are generally found furthest offshore. The 

typical range of SLIE location is indicated by darker blue shades, while areas of red correspond to 

locations where the SLIE occurred on at least 389 different days (5% of all daily SLIE images). 

Individual regions of high SLIE probability separated from the coast are referred to as “nodes” (Mahoney 

et al., 2014; Mahoney et al., 2007) and are encircled by dashed yellow. These are interpreted to represent 

regions of recurring grounded ice features. 

By stacking the SLIE images (i.e. including the areas shoreward of the SLIE) rather than just the SLIEs 

themselves, we use a similar approach to determine the probability that any given pixel was occupied by 

landfast ice, which can be expressed as a percentage or in days per year. To illustrate variability in 

landfast ice occurrence over the 27-year span of the EM2024 , we calculated landfast ice occurrence 

probability over three 9-year periods (Figure 3-6). Areas shoreward of nodes identified in Figure 3-5

show the highest occurrence probabilities, though there is a notable reduction in the area of high 

probability behind the node in Harrison Bay (which represents the node farthest from the coast) since the 

1996–2005 period (Figure 3-6a). Stable extensions, as described by Mahoney et al., (2014; 2007), are 

represented by widespread areas of low probability indicative of their infrequent and brief occurrences. 

These areas are notably absent from the 2005–14 and 2014–23 periods (Figure 3-6b,c), though it is likely 

that any such unusually extensive areas would not be charted as landfast by NIC and ASIP analysts so 

their absence in data derived from ice charts may not be meaningful.
35

Figure 3-4: Regional median landfast ice width in the a) Chukchi and b) Beaufort regions. The shaded regions represent the spatial variability in daily 

landfast ice width in each region. Note full width of stable extensions that occurred in the Beaufort Sea in 1999, 2000, and 2004 extend beyond the limit 

of the y-axis.
36

Figure 3-5: All SLIEs delineated from the period 1996–2023 stacks so the color value of a line indicated the probability of the SLIE lying within 500 m of a 

given point between October and July. A near continuous dark blue zone indicates where the SLIE commonly stabilizes. Dashed yellow ellipses highlight the

location of nodes some distance from the coast where the SLIE occurs with greater frequency, and which indicate probable locations of recurring grounded 

ice features.
37

Figure 3-6: The extent of landfast ice shoreward of the SLIE stacked for each 9-year period in the EM2024 dataset 

(1996–2023), such that the shade of blue represents the fraction of the 304-day annual cycle (October-July) for 

which that area was occupied by landfast sea ice. The red line indicates the 50% probability contour. White 

areas indicate where landfast ice was never observed.
38

3.1.4 Monthly landfast ice extents

Landfast ice is a seasonal feature in the study region, typically occupying the waters adjacent to coast 

from October or November through June or July, as illustrated by the annual cycles in Figure 3-4. The 

spatial pattern of this seasonal variability is illustrated by monthly plots of the minimum, median, mean, 

and maximum landfast sea ice extent (Figures 3-7–3-10). Over the full duration of the EM2024 landfast 

ice rarely occurred in October such that the median landfast ice width for the month is zero throughout the 

study region, meaning that landfast ice was absent from the entire study area during October more than 

50% of the time. When landfast ice was present in October, the vast majority of it was found in the 

Beaufort Sea, though some was present in sheltered lagoons along the Chukchi coast (as indicated by 

maximum extent in Figure 3-7a). November is the first month of the landfast ice season in which the 

median landfast ice width was non-zero (Figure 3-7b), though the median extent is still confined to 

sheltered regions of the coast. 

Figure 3-7: Monthly minimum, median, maximum, and mean landfast sea ice extents for the months of 

October and November.
39

Figure 3-8: Monthly minimum, median, maximum, and mean landfast sea ice extents for the months of 

December, January, and February.
40

Figure 3-9: Monthly minimum, median, maximum, and mean landfast sea ice extents for the months of March, 

April, and May.
41

Figure 3-10: Monthly minimum, median, maximum, and mean landfast sea ice extents for the months of June 

and July.

By December, the median SLIE position has advance to just beyond the barrier islands in both the 

Chukchi and Beaufort regions and the most sheltered regions of Kotzebue Sound (Figure 3-8b). However, 

with the exception of a small area in Elson Lagoon near Point Barrow, the minimum monthly landfast ice 

width remains zero throughout both regions until January (Figure 3-8b) and it is not until February that 

the minimum landfast ice extent covers at least half the adjacent coastline in the Beaufort Sea (Figure

3-8c). This indicates that throughout most of the study region, there was at least one year in which 

landfast ice either did not form until February or experienced a breakout event that extended all the way 

to the coastline. In most of Chukchi Sea, the minimum landfast ice extent remains zero throughout the 

year.

Overall, the minimum, median, and maximum monthly landfast ice extents achieve maxima in the month 

of April (Figure 3-9b). In May, there was a notable reduction in the maximum extent of landfast ice in the 

Beaufort Sea (Figure 3-9c), but the minimum and median extents closely resemble those of April. This 

implies that interannual variability of landfast ice extent is comparatively low in May. As the landfast 

continues to retreat in June (Figure 3-10a), the minimum monthly extent becomes zero once again 

throughout the study region, with the median landfast ice extent also becoming zero in the Chukchi Sea. 

However, the maximum landfast ice extent is slower to retreat and, in June, resembles that of May. This 

indicates that in some years, breakup did not commence until at least June. In the Chukchi Sea, the 
42

monthly maximum extent remains remarkably consistent from April to June, which suggests there is a 

bathymetric limit on the maximum extent in this region. In July, the minimum and median extents are 

effectively zero throughout both study regions but the maximum extent demonstrates that extensive 

regions of landfast remained until July in the Beaufort Sea and northern Chukchi Sea in at least one year 

(Figure 3-10b).

3.1.5 Timing of annual landfast ice events

Following the methods described in Section 2.2.5, we derived the annual occurrence dates of 3 key events 

based on the timeseries of landfast ice measured width at each coast vector (Figure 3-11). The median 

date on which each event occurred throughout the study region is shown by a solid colored line. The 

shaded region around each line extends between the lower and upper 20th percentiles and illustrates the 

range of interannual variability. The median dates of freezing and thawing onset are shown by dashed 

lines, inside of their respective lower and upper 20th percentiles. These were calculated following the 

method of Mahoney et al., (2014; 2007) from NCEP reanalysis data interpolated to the origin point of 

each coast vector.

Overall, landfast ice forms earliest in regions where freezing temperatures occur earliest, but there is no 

consistent relationship between the dates of first ice and those of freeze onset. Abrupt changes in the date 

formation along the coast correspond to pronounced features in the coastline, such as Point Lay and Icy 

Cape. The landfast ice between these points is among the earliest to form in the study region, along with 

the ice along the Beaufort Sea coast between Point Barrow and Kaktovik, where there are many coastal 

lagoons and barrier islands. The latest place in our study region where landfast ice occurs is between 

Kivalina and Cape Lisburne, which is the only section of coast with a southerly aspect.

The occurrence of breakup is closely followed by ice free conditions and those locations where breakup 

occurs earliest are also the locations that become ice free earliest. In general, there is less variability in the 

timing of these season-ending events than in the date of formation at the beginning of the season. 

However, there are some short sections of the coastline where breakup and ice free conditions occur 

substantially earlier than on the coast to either side. For example, the landfast ice to the east of the 

headland near Enurmino on the northern Chukotka Peninsula typically breaks up in mid-April, while the 

landfast ice on either side remains in place until mid-May or June. Similarly early breakup occurs on the 

east-facing coast immediately west of Bering Strait, the west facing coast immediately south of Cape 

Lisburne, and in the vicinity of Herschel Island. Landfast ice is also later to form in these locations, such 

that the upper bound of first dates ice comes close or even overlaps with the lower bound of breakup dates 

in some cases. One thing these locations all have in common is a relatively narrow landfast ice cover, as 

indicated by the proximity of the SLIE to the coastline in these locations (Figure 3-5). The time period 

between the onset of thawing temperatures and the end of the landfast ice season is much shorter than that 

between the onset of freezing temperatures and the beginning of the landfast ice season. With the 

exception of the aforementioned locations where breakup occurs particularly early, the median date of 

breakup in the Chukchi Sea is almost coincident with the median date of thaw onset. By comparison, the 

median breakup date is 2–3 weeks later than the median thaw onset throughout most of the Beaufort Sea. 

This suggests that surface heating has a less direct relationship with breakup in this section of coastline.
43

Figure 3-11: Spatial variability in the median dates of occurrence of 3 key events in the annual cycle (First Ice, Breakup, and Ice-Free) over the duration of the 

EM2024 dataset. Also shown are median dates of the onset of freezing and thawing derived from NCEP reanalysis data.
44

3.1.6 Landfast ice breakout events

Applying the criteria described in Section 2.2.7 to the EM2024 dataset, we identified a total of 202 

individual breakouts in the Beaufort study region and 161 in the Chukchi region (Appendix A). On 

average, there are 7.5 breakout per year in Beaufort region, compared with 6.0 per year in the Chukchi 

region. However, there is considerable interannual variability and there were 9 seasons when there were 

as many or more breakouts in the Chukchi region than the Beaufort (Figure 3-12). There is also some 

evidence of synchronous peaks and lulls in breakout activity in both regions every 7–10 years. The 

underlying cause for the absence of breakouts in some years (2003–04 and 2021–22 in the Chukchi region 

and 2011–12 in both regions) is unclear. If we relax our breakout criteria, we detect some events in these 

years, but the overall 7–10 year pattern of peaks and lulls remains. The difference in annual average 

breakout counts between the Chukchi and Beaufort regions can be primarily ascribed to breakouts in 

January, when on average the Beaufort region experiences twice as many breakouts as the Chukchi 

(Figure 3-13a). In February, March, and April, breakout events seem equally frequent in both regions. 

Breakouts are of similar area in both regions and the size distribution appears to follow a power law such 

that larger breakouts have a lower likelihood of occurring (Figure 3-13b).

Figure 3-12: Annual counts of breakout events in the Chukchi and Beaufort study regions.

We computed a map of breakout probabilities by accumulating, or stacking, the areas of ice involved in 

each breakout in the catalog (Figure 3-14). By dividing the breakout count, NNbb, by the number of years, 

NN yy = 27, we derive the average number of breakouts per year, NN�bb, on a pixel-by-pixel basis (Equation

3-1). This shows that some areas are more prone to breakout than others (Figure 3-14a). In particular, the 

region eastward of Point Barrow shows breakout counts of over 0.3/yr, indicating that extensive breakouts 

can occur approximately every three years. Other breakout “hotspots” are found in the southernmost 

Chukchi coastline either side of Bering Strait, the area between Kotzebue and Cape Kruzenstern, and the 

section coast between Kaktovik and Herschel Island. 

NN�bb = NNbb/NN yy (3-1)
45

By dividing NN�bb, by the annual likelihood that landfast ice occupied each pixel, PP(ll ll ll), we can also 

obtain a measure of the breakout probability, PP(bb) (Figure 3-14b): 

PP(bb) = NN�bb/PP(ll ll ll) (3-2)

P(b) provides the probability of a breakout occurring if the ice becomes landfast ice in a given region. A 

probability of 1 indicates that every time landfast ice occupied that pixel it underwent a breakout. 

Comparison between Figures 3-14a and 3-14b shows that regions with a breakout probability near 1 

correspond to regions with low overall breakout counts, meaning that landfast ice is rarely found at these 

locations. 

We also computed the conditional breakout probability, PP∗(bb), which represents a measure of the 

likelihood that any given pixel will become landfast between January and April and then subsequently 

breakout (Equation 3-3). 

PP∗(bb) = NN�bb ∙ PP(ll ll ll) (3-3)

This emphasizes the areas of potential breakout risk that overlap with areas where landfast is likely to 

occur (Figure 3-14c), which is primarily in the regions closest to the shore.

3.1.7 Access and availability of climatology dataset

A copy of the EM2024 dataset of landfast ice extent in the Arctic OCS was provided to BOEM with this 

report. We are continuing to explore longer term open online access to the data for public dissemination 

after completion of the project and anticipate that results will be available as a “data story” through the 

Scenarios Network for Alaska and Arctic Planning (SNAP) at https://uaf-snap.org. 

Figure 3-13: a) Average bi-weekly breakout counts and b) probability density distribution of breakout size for 

the Chukchi and Beaufort study regions. 
46

Figure 3-14: Spatial distribution of breakouts across the combined study region shown 

as a) mean annual breakout, b) breakout probability, and c) conditional breakout 

probability. See text for details of how these values were calculated.
47

3.2 InSAR-derived landfast ice extent and stability

3.2.1 Landfast ice extent from coherence masks

The automated methodology for delineating areas of landfast ice from mobile pack ice using 

interferometric coherence derived from Sentinel-1 interferograms, described in Section 2.4.2, was applied 

to 2,084 SAR image pairs from 2017–2021 along the study region coastline. In total, 14 interferometric 

wide reference scenes were identified which covered the study region. During a typical month, 2 

Sentenel-1 SAR pairs satisfied our temporal and perpendicular baseline requirements for each reference 

scene. Certain areas within the study region such as Kotzebue Sound, Colville Delta, and Mackenzie 

Delta, had fewer acquisitions resulting in months with 1 or no acquisitions which satisfied our baseline 

requirements. The thresholded coherence images (Section 2.4.2) were sorted and mosaiced by month and 

year to represent monthly images of InSAR-derived landfast ice extent. This resulted in produced 24 

monthly coherence mosaics representing each month between December and May during the 4 landfast 

ice seasons between 2017 and 2021, which could be used as inputs to the SLIEalyzer toolbox. For 

example, Figure 3-15 compares the InSAR-derived mean landfast ice extent during April (cyan) line with 

that derived from the EM2024 dataset (blue) for the period 2017–2021. This shows generally good 

agreement, with no consistent bias across the study region for this month.

Figure 3-15: Mean SLIE position for the month of April by the EM2024 dataset (cyan) and InSAR-based method 

(blue) from 2017 –2021. Yellow regions indicate “shadow” zones outside the domain of the coast vectors (see 

Figure 2-1).

To compare the InSAR-derived landfast ice extent against the EM2024 dataset for all months from 

December to May, we calculated the difference in landfast ice width for each coast vector (Figure 3-16). 

Here, the difference (blue line) is calculated by subtracting the InSAR-derived width from the EM2024 

values. The normalized difference (gray line) is derived by dividing the difference by the EM2024 width. 

During December, mean landfast ice width was zero throughout much of the study region and both 

datasets generally agree where landfast ice had not yet formed, but small differences in width result in 

large normalized differences. However, where landfast was present (primarily in the Beaufort region), the

InSAR-derived results tend to underestimate the width. This is illustrated by the tendency of the blue line 

to lie below the x-axis in Figure 3-16a. 
48

Figure 3-16: Difference in landfast ice width at each coast vector from 2017–2021 between the InSAR-derived 

results and the EM2024 dataset. Positive values indicate that the InSAR method overestimate landfast ice 

extent relative the EM2024 dataset.
49

There is a similar pattern for the month of January (Figure 3-16b), with the InSAR approach still tending 

to underestimate the width of landfast ice as it expands throughout both study regions. However as the 

season progresses through February, March, and April, the relationship becomes more variable along the 

coast, with the InSAR-based approach indicating over 10 km more landfast ice on average than the 

EM2024 dataset (Figure 3-16b-e). Also, there is notable spatial variability in Kotzebue Sound during 

these months, with the difference in landfast ice width changing from <−10 km to >10 km and back 

again over the span of just a few coast vectors. The cause of this variability is not certain, but examination 

of the parent SAR imagery and interferograms over Kotzebue Sound indicate that the surface of the ice 

loses coherence temporarily without any substantial horizontal motion. This coherence loss could be 

caused by surface flooding, which has been observed to be caused by heavy snow load in this region 

(Mahoney et al., 2021). Kotzebue Sound also has extensive areas of shallow water (≲2 m), in which the 

ice can repeatedly interact with the seafloor as the water level rises and falls under the influence of winds 

and tides. This process can result in flexural fracturing of the ice surface, which can also lead to 

coherence loss.

In the month of May, the landfast ice width difference between the two datasets is more consistently 

negative again, due to extensive areas where no landfast ice was identified by the InSAR-based method, 

but landfast ice was still present in both the Chukchi and Beaufort coasts in the EM2024 dataset. In 

particular, Kotzebue Sound never met the coherence threshold to be considered landfast ice during May 

of any season from 2017–2021. This consistent underestimation by the InSAR-based method is likely due 

to the alteration of the dielectric properties of the ice surface during the early stages of melt, which leads 

to the loss of coherence before any substantial motion of the ice. 

3.2.2 Monthly mean apparent strain

Apparent strain fields were calculated from each of the 2,084 interferograms as described in Section 

2.4.3. These were sorted into months, based on the date of the primary SAR acquisition and used to 

generate monthly mean fields of apparent strain (Figure 3-17 and Figure 3-18). Pixels with coherence 

values below 0.1 were excluded. Although there can be considerable variability between adjacent pixels, 

there is an overall tendency for lower apparent strain values (blue regions) to be found near the coast with

higher apparent strains (yellow regions) found nearer the SLIE. This spatial distribution becomes more 

apparent as the landfast ice season progresses and another tendency emerges whereby the apparent strain

in landfast ice tends to decrease over time. This tendency can be clearly seen in the probability 

distribution of average coherence values for each month (Figure 3-19), which shows that modal value of 

apparent strain decreases monotonically from month to month. This indicates that landfast ice becomes 

more stable the longer it persists.

The greater proportion of low phase gradient landfast ice pixels occurred in April and May. A difference 

between April and May is the presence of landfast ice along the Chukchi coast, specifically in Kotzebue 

Sound. The majority of landfast ice which is coherent during the month of May is along the Beaufort 

coast (Figures 3-17 and 3-18). The landfast ice which remains coherent in May is the most stable. While 

landfast ice is typically present along the Beaufort coast in June (Figure 3-7) no coherent pixels existed 

using the methodology described in Section 2.4.2. 
50

Figure 3-17: Monthly mean apparent strain from 2017–2021 for the months of a) December, b) January, and c) 

February.
51

Figure 3-18: Monthly mean apparent strain from 2017–2021 for the months of a) March, b) April, and c) May.
52

Figure 3-19: Distribution of average apparent strain during each month (December, January, February, 

March, April, and May) between 2017–2021.

3.2.3 Quantification of three landfast ice stability categories

Landfast ice present along the Beaufort coast is typically stable and approaching its seasonal maximum 

width during April. This is also the month for which Dammann et al., (2019) qualitatively defined 

landfast ice stability using InSAR. Hence, we used the distributions of April mean apparent strain in the 

three regions identified in Section 2.4.4 (bottomfast, sheltered, and not-sheltered) to establish strain

thresholds for the 3 associated stability categories (bottomfast, stabilized, and non-stabilized). The 

distributions of apparent strain in each region have well defined and distinct modes (Figure 3-20), with 

the modal value for the bottomfast region being the lowest and that for the not-sheltered region being the 

highest. The bottomfast region distribution is right skewed while the other regions are approximately 

normally distributed. The right skew in the bottomfast distribution is the result of high apparent strains 

being present at the oceanward bottomfast ice boundary, likely associated with tide cracks. The 

distribution of the sheltered region was used to establish the upper and lower bounds for the bottomfast 

and not-sheltered regions respectably.

The 10th percentile of the sheltered region corresponds to a value of apparent strain of 1.0×10-5 and the 

point at which the proportion of pixels sheltered region exceeds that in the bottomfast region. Hence, we 

use this value as the threshold between bottomfast and stabilized ice. For threshold between stabilized and 

non-stabilized ice, we use the 90th percentile of the sheltered region distribution, which corresponds to an 

apparent strain of 2.3×10-5

. We note that this aligns with the mode of the not-sheltered distribution, rather 

than intersection between the sheltered and not-sheltered distributions. However, we expect that areas in

the not-sheltered region may still have been stabilized, but by a grounded ridge rather than a permanent 

barrier island. This likely explains why the mode of the not-sheltered region is broader and is why we did 

not choose the intersection point of the sheltered and non-sheltered distribution as the threshold between 

stabilized and non-stabilized ice. These apparent strain thresholds can now be applied to results from 

single interferograms to identify areas of varying stability within the landfast ice.
53

Figure 3-20: Distribution of April mean apparent strain during the period 2017–2021 in the areas identified as 

bottomfast, sheltered, and not-sheltered landfast ice.

To assess the accuracy of the apparent strain thresholds determined in Figure 3-20, we applied them to 

interferograms created from pairs of SAR images acquire in April 2017 similar to those used by 

Dammann et al., (2019) along the Beaufort coast in Alaska. The exact SAR pairs used by Dammann et 

al., (2019) were not available through ASF’s Vertex portal and so we selected overlapping pairs that were 

acquired within 12 days. By color-coding apparent strain values according to these thresholds, we can 

map the resulting distribution of stability categories within the landfast ice (Figure 3-21). The areas 

defined as bottomfast ice by this method are more expansive than the areas Dammann et al., (2019)

identified as bottomfast. This discrepancy is large, but we can attribute it to abnormally low phase 

gradient across the region at this particular time. The corresponding apparent strain throughout Elson 

lagoon is considerably lower than the mean value for this month (Figure 3-17b) and falls within range 

typically found in bottomfast ice. The boundary between stabilized and non-stabilized landfast ice agrees 

Figure 3-21: Categorized landfast ice stability derived from apparent strain threshold applied to interferograms

from April 2017 corresponding to those used by Dammann et al., (2019). Solid shaded areas indicate the stability 

categories quantitatively derived from apparent strain. Outlined areas indicate areas identified qualitatively by 

Dammann et al., (2019).
54

more closely between our quantitative approach and the qualitative method used by Dammann et al.,

(2019), which provides some validation for our apparent strain threshold of 2.3×10-5

.

3.3 In situ landfast ice observations

3.3.1 Point Hope landfast sea ice mass balance observations

As summarized in Table 2-3, SIMBs were deployed in landfast ice near Point Hope in 2020 and 2022. 

Unfortunately, the bottom sounder on the SIMB deployed in 2020 malfunctioned, but we were able to 

estimate the ice thickness from the temperature profile, following the method outlined by Gough et al.,

(2012). Data from both deployments (Figure 3-22) show sustained growth through March and April, but 

otherwise there are marked differences between the two landfast ice seasons.

In 2020, a maximum ice thickness of 0.95 m was reached around May 1 and bottom melt began 10 days 

later on May 11, which coincided with the arrival of comparatively warm water above its freezing point at 

the base of the ice (Figure 3-22a). From this point forward, it was not possible to estimate the position of 

the ice bottom from the temperature profile. The snow pack started warming and thinning on May 8 and 

had completely melted by May 20, when surface melt began. By May 25, a total of 6 cm of surface melt 

had occurred before the SIMB appeared to shift vertically down, giving rise to an apparent rapid increase 

in snow depth as the snow sounder got closer to the ice surface. On June 8, 2020, the SIMB began drifting 

due to detachment of the landfast ice. This event was identified by the Global Navigation Satellite System 

(GNSS)-derived track of the buoy, but it is also marked by an abrupt warming of the water column 

throughout the full depth of the SIMB. By comparison, in 2022, the ice was already 1.12 m thick by the 

time it had started thinning in 2020 (May 1) and it continued growing until the buoy was recovered on 

May 26. By this time the ice had reached a thickness of 1.16 m. Snow melt in 2022 began on May 21, by 

which date in 2020 the snow had completely melted and surface ablation of the ice had started.

Although we did not intend to let the SIMB deployed in 2020 drift away when the landfast ice detached, 

it provided valuable data of the thermodynamic processes leading up to the detachment and disintegration 

of the landfast ice, which was fortuitously under frequently clear skies allowing it to be captured by 

PlanetScope and Landsat imagery (Figure 3-23). On May 19, the day before the SIMB indicated complete 

melting of the snow pack, PlanetScope imagery shows the landfast around the SIMB was largely still 

snow covered (Figure 3-23a). By May 24, areas of bare ice or melt ponds hundreds of meters in size are 

evident (Figure 3-23b), consistent with approximately 4 cm of surface melt observed by the SIMB. On 

June 7, the day before the SIMB was set adrift, many ponds appear to have drained and a refrozen 

fracture can be seen close to the SIMB location, revealed by snow melt (Figure 3-23c). The next clear-sky 

image on June 9 shows that the landfast ice has retreated back to location of the refrozen fracture (Figure

3-23d). At the time this image was acquired (18:44 UTC), the SIMB had drifted approximately 20 km 

southward, but it's GNSS position began to become intermittent, suggesting the buoy may have been 

tilting in waves. This makes is difficult to identify the buoys precise position in the image (Figure 3-23e), 

but there are no easily resolvable ice floes in the region, suggesting that the SIMB was drifting in open 

water at the time and probably since the detachment event. The SIMB continued to report its position for 

another 11 days as it drifted through largely open water containing isolated fragments of landfast ice 

(Figure 3-23f).
55

Figure 3-22: Snow depth, ice thickness, and temperature profile data from SIMBs deployed in landfast ice near 

Point Hope in a) 2020 and b) 2022.
56

Figure 3-23: (a–e) PlanetScope imagery and (f) Landsat-9 imagery capturing the late season evolution of the 

landfast ice near Point Hope and the trajectory of the SIMB deployed in 2020.
57

3.3.2 Utqiagvik landfast sea ice mass balance observations

SIMBs were deployed in the landfast ice near Utqiagvik in 2021, 2022, and 2024 (Figure 3-24). However, 

due to a thermistor string malfunction, temperature profile data are not available from 2021 and, as noted 

in Section 2.5.1, damage by a polar bear cut the SIMB record short prematurely in early March 2022. 

Nonetheless, we can still usefully compare the early winter ice growth between 2022 and 2024 and the 

Figure 3-24: SIMB data from 3 deployments in landfast ice near Utqiagvik in a) 2021, b) 2022, and c) 2024.
58

late winter ice thicknesses and snow depths between 2021 and 2024. Notably, the sea ice was some 30 cm 

thinner throughout February 2024 than it was during February 2022, and during April and May the 

landfast ice was approximately 20 cm thinner in 2024 than during the same period in 2021. These 

differences can be partly explained by the greater snow depth in 2024 than in either 2021 or 2022, which 

would have insulated the underlying sea ice and slowed growth. However, there is also a notable pause in 

ice growth in late-March to early-April 2024 associated with water temperatures up to -1.4 oC, slightly 

above the expected freezing point (Figure 3-24c). During this time, temperature sensors up to 6 cm above 

the location of the ice bottom detected by the bottom sounder reported water temperature similarly above 

the freezing point. This is similar to the situation that occurs immediately after deployment as the hole 

around the SIMB freezes in. In this case, it suggests that the ice immediately around the buoy may have 

melted, possibly due to turbulence-enhanced heat flux around the protrusion of the buoy below the ice 

bottom. This raises the possibility that increased ocean heat flux could also have contributed to the 

reduced landfast ice thickness measured in Utqiagvik in 2024.

3.3.3 Under-ice ADCP measurements

The one ice-tethered ADCP that was successfully deployed and recovered recorded under-ice velocity 

data near Utqiaġvik from January 29 to June 10, 2022 (Figure 3-25). The instrument was set to record 

velocity data in 150 5-cm vertical bins. Ensemble averages consisted of two pings and were reported in 1-

minute increments. Data was generally available from 0.6 m to 5.5 m above the transducer, with data 

quality being highly dependent on the amount of suitably-size backscatterers present in the water column. 

A lack thereof resulted in periods of insufficient data quality being rejected by the onboard processing of 

the ADCP. In order to reduce noise, the raw velocity data was averaged hourly and in 25 cm vertical bins.

Pronounced current variability was observed on semidiurnal timescales, with a period consistent with the 

semidiurnal M2 tide, and on sub-inertial timescales of 2–7 days on two separate occasions (Figure 3-25ce). Subinertial variability with a period of 2–3 days occurring between 20 February and 28 February was 

strongly baroclinic (Figure 3-25c,d) and was associated with pronounced upward velocities close to the 

transducer (Figure 3-25e). ERA5 wind data indicated that these baroclinic velocities were likely 

associated with a passing storm (Figure 3-25b). Transducer temperature exhibited relatively strong 

variability during this time (Figure 3-25a), suggesting possible advection of offshore waters by the 

baroclinic currents. A second period of subinertial velocity variability (this time with a barotropic vertical 

structure) occurred between 15 May and 22 May. Its period was slightly longer at 5–7 days with no 

apparent wind or temperature association. 

By time-integrating the depth-averaged velocity over 2-week periods, we can visualize the direction and 

scale of spatial scale of water transport under the ice at the location of the ADCP (Figure 3-26). For most 

of the winter, the currents are relatively weak and change direction regularly such that 14-day integrated 

current amounts to less than 10 km of transport, with a general tendency for westward motion. However, 

in early May there is a pronounced change in the direction and magnitude of the flow such that the 14-day 

integrated current velocities exceed 10 km for the remainder of the record (Figure 3-26h,i). This may 

represent a mechanism to transport water under the landfast ice from more southerly latitudes where there 

may have been more opportunity to absorb more solar energy. Upwelling of warm water seems an 

unlikely explanation as this is usually associated with eastward flow (e.g., Hirano et al., 2016).
59

Figure 3-25: Timeseries of data for the ADCP deployed under landfast sea ice near Utqiagvik in 2022. a) Water 

temperature measured by the ADCP; b) ERA5 wind velocity interpolated to ADCP location; c) Zonal current 

velocity profiles; d) Meridional current velocity profiles; and e) Vertical current velocity profiles.
60

Figure 3-26: 14-day progressive vector plots derived from depth-average current velocity data from the ADCP 

deployed near Utqiagvik in 2022. White circles indicate the progressive pseudo-position at noon UTC each day. 

Bathymetry is given in meters

Time-averaged under-ice current velocities (Figure 3-27) were generally small, with average currents on 

the order of 0.6 cm s-1flowing in a northward direction, which is almost parallel to the coastline that is 

oriented approximately SSW-NNE in the vicinity of the ADCP deployment. The vertical structure of the 

cross-shore flow was mostly barotropic, while a slight intensification lower in the water column towards 

the transducer was likely associated with the baroclinic velocity signals mentioned earlier. The 

intensification in the upper 0.5 m underneath the ice is likely an artifact due to data sparsity during 

aforementioned periods of insufficient data quality, and care must be taken when interpreting this 

intensification physically.
61

Figure 3-27: Time-average vertical profiles of zonal and meridional currents 

under the landfast ice near Utqiagvik, measured in 2021.
62

3.4 Cyclone Activity in the Alaska Arctic OCS

Composite anomalies, which is an average of sea level pressure on all breakout days, are shown in Figure 

3-28. The composites for both the Beaufort and Chukchi Sea events reveal an area of anomalous low 

pressure centered in the Bering Sea. This general pattern can be indicative of storm activity in the Bering 

Sea region during the days when landfast breakouts occurred. However, the areas of statistically 

significant patterns were limited implying that there is some variability among the strength or position of 

storms during these events. SOMs (Section 2.6.2) were employed to look for possible groupings of spatial 

patterns in sea level pressure, storm tracks and winds among events.

Figure 3-28: Daily composite anomalies of sea level pressure on landfast breakout days 19973–2023 for the (a) 

Beaufort and (b) Chukchi Seas. Units are in hPa and regions significant at the 95% level according to a t-test are 

cross-hatched. Anomalies relative to 1991–2020 reference period. Locations of breakout events shown in cyan.

The 4×5 SOM matrix of nodes is shown in Figure 3-29. These 20 nodes reveal the relatively common 

nature of storms in and around the Alaska region during January–April, with areas of low pressure found 

in the domain in each node. Over the full period, no SOM node stood out as being more frequent than 

others. The relative frequency of each node occurring within 0–5 days prior to each breakout day are 

shown in Table 3-2. Node 19 has the highest frequency of occurrence of the sea level pressure patterns 

associated with breakouts for both seas. This node is most associated with low pressure in southern 

Bering Sea. Over the 1997–2022 period there is a breakout event in Beaufort or Chukchi Sea 31% or 21% 

of the time when it occurs, respectively. The second most frequent SOM pattern for the Beaufort Sea is 

Node 15 and for the Chukchi it is Node 11. To better understand potential meteorological drivers of the 

breakouts associated with these SOM patterns, the composite anomalies of storm track density and wind 

speeds are shown in Figures 3-30 and 3-31, respectively. Node 19 is associated with storms tracking 

through the Bering Sea. High pressure of the Arctic Ocean results in a pressure gradient that gives rise to 

anomalously strong easterly winds across the Beaufort turning more northward in Chukchi Sea. Nodes 

14–15 are also similarly linked with Bering Sea storm tracks but with the strongest positive wind 

anomalies in Chukchi Sea. Node 11 is tied to weaker winds over the Chukchi Sea region.

3 As noted earlier in Section 2.6.2, breakouts are defined as only occurring between January and April. Since the 

EM2024 dataset begins in October 1996, the first year in the breakout catalog is 1997.
63

Figure 3-29: Self-organizing map 4×5 matrix of daily sea level pressure anomalies for January-April over 

1997–2022. The units are in hPa. The node number is shown on each panel along with the frequency of 

occurrence over the record. White areas indicate elevations greater than 500 m which are masked (see 

Section 2.6.2).

Table 3-2: Percent of occurrence of each SOM node (corresponding to Figure 3-29) within 0–5 days prior to the 

landfast breakout events for the Beaufort and Chukchi regions. The percentage was calculated by dividing the 

number of breakout events associated with each node by the total number events.
64

Figure 3-30: Composite anomalies of total storm track densities associated with the days of each SOM node 

corresponding to Figure 3-29. The units are in number of storms per day and regions significant at the 95% or 

greater level according to a t-test are cross-hatched. Anomalies relative to 1991–2020 reference period.

Figure 3-31: Composite anomalies of wind speed (shaded) and direction (vectors) associated with the days of 

each SOM node corresponding to Figure 3-29. The units of wind speed are in m s-1 and regions significant at 

the 95% or greater level according to a t-test are cross-hatched. Anomalies relative to 1991–2020 reference 

period. The longest wind vectors are associated with 4 m s-1 wind anomalous speeds.
65

3.5 PAMS model results

3.5.1 Pan-Arctic ice concentration

As referenced in Section 2.7.5, we have performed a total of 80 model runs, each producing a 25-year 

simulation of the Arctic domain and . Many of these runs were aimed at reducing biases in the 

temperature and salinity fields and overall ice cover. These biases are quite sensitive to the vertical 

mixing in the model, as well as to the restratification parameterizations of Fox-Kemper et al., (2011; 

2008) and Bodner et al., (2023). We evaluated overall ice extent for model runs 51 and 56 (see Table 2-7)

and compared the results against daily satellite estimates, with both runs showing a bias towards too much 

winter ice (Figure 3-32). Run 56 also has too much ice in the summer. Though there are biases, they are 

not worsening in time, unlike a similar run which used the ERA5 forcing (not shown) instead of JRA55-

do. The ERA5 run with the increasing sea ice extent also exhibited an increasing landfast ice extent.

Figure 3-32: Comparison of the PAMS model’s ice extent compared to the satellite estimates of Comiso et al.,

(2014) in units of millions of square kilometers. The 25-year span of the simulation is split into thirds for clarity. 

As described in Section 2.7.5, model runs 51 and 56 differ primarily in the use of an experimental ice-ocean 

coupler (CIOD) in run 56.
66

3.5.2 Pan-Arctic Ocean evaluation

One of the metrics we have been using to evaluate the PAMS model is to compare the March mixed-layer 

depth with that of de Boyer Montégut et al., (2004). They picked a mixed-layer depth criterion of a 

density difference of 0.003 kg m-3

, one of the metrics that MOM6 can write out. Figure 3-33 shows that 

the model has many of the same patterns, with a mixed layer of less than 50 m in the central Arctic, 

around 100 m in the North Pacific, and with patches of deep mixing south of Greenland and Iceland. 

While there are slight variations from one run to the next, there are persistent biases of well over 100 m, 

with some variability in the strength and exact location of the biases.

Another metric is to compare to the World Ocean Atlas (WOA) 2018 “decadal” averages for 1995 –2004 

and 2005–2017. Figures 3-35–3-36 show the difference between the PAMS model and WOA for both 

temperature and salinity and for both decades. We here examine both the surface and the 100 and 200 m 

depths. The surface temperature of the Greenland-Iceland-Norwegian (GIN) Seas is persistently biased 

too cold in the model while the North Atlantic and North Pacific are biased warm, getting warmer in the 

second decade. The subsurface temperature biases echo the patterns in the mixed-layer biases, with too 

deep mixing bringing cold water down off the Norwegian coast and too shallow mixing preserving the 

warm subsurface water off the Northeast Greenland coast.

For the salinity, we have a persistent bias of too much salt at the surface in the central Arctic basin. We 

have surface salt restoring, but not under the ice. One theory for the excess salt under the ice is that of 

Nguyen et al., (2009). The idea is that in the model, when sea ice is formed in winter, the ice model grows 

ice, taking water from the ocean model. It leaves behind most of the salt, leaving it at the surface of the 

ocean. In reality, the ice would be forming in open leads covering at most 1% of the surface ocean and the 

salt should be rejected in brine plumes which would sink to the bottom of the mixed layer, entraining 

fluid as it goes.

A third metric was introduced by Dr. Wilbert Weijer in an Interagency Arctic Research Policy Committee 

(IARPC) session on Arctic ocean biases. He asked for time- and spatially-averaged profiles for the two 

Arctic basins delineated by the yellow and gold curves in Figure 3-37. In May 2023 (run 27), our PAMS 

model had one of the larger temperature biases in the eastern basin. In order to discover the timescale of 

this temperature drift, we have made plots such as those shown in Figures 3-38–3-41. The eastern basin 

has warm Atlantic layer water inflowing at roughly 300 m depth, mixing and deepening to 400 m as it 

progresses around to the western basin. The WOA profiles show some warming from one decade to the 

next, but nowhere near as much as in some of the model runs, including that of GLORYS.

For salinity, the most notable feature is the too-salty surface bias in the western basin, consistent with our 

surface salt bias under the ice. This feature is also seen in the GLORYS profiles. Surprisingly, adding the 

surface salinity restoring to a climatology has increased that salinity bias while simultaneously reducing 

the temperature bias. The changes between runs 27 and the more recent ones include this surface salinity 

restoring, turning off the bottom boundary layer, and introducing the mixed layer restratification from 

either Fox-Kemper (2011; 2008) or Bodner (2023). Run 73 also has the shear mixing dialed down with a 

new LZ_RESCALE option.
67

Figure 3-33: Difference between the PAMS model temperature World Ocean Atlas 2018 for the decade 1995 –

2004. On the left is the surface, middle is at 100 m depth and on the right is 200 m depth. Three simulations are 

shown, with run 51 at the top, run 56 in the middle and run 73 on the bottom.
68

Figure 3-34: Difference between PAMS model salinity World Ocean Atlas 2018 for the decade 1995 –2004. On the 

left is the surface, middle is at 100 m depth and on the right is 200 m depth. Three simulations are shown, with 

run 51 at the top, run 56 in the middle and run73 on the bottom.
69

Figure 3-35: Difference between the PAMS model temperature World Ocean Atlas 2018 for the decade 2005 –

2017. On the left is the surface, middle is at 100 m depth and on the right is 200 m depth. Three simulations are 

shown, with run 51 at the top, run 56 in the middle and run73 on the bottom.
70

Figure 3-36: Difference between the PAMS model salinity World Ocean Atlas 2018 for the decade 2005 –2017. On 

the left is the surface, middle is at 100 m depth and on the right is 200 m depth. Three simulations are shown, 

with run 51 at the top, run 56 in the middle and run73 on the bottom.
71

Figure 3-37: The Arctic ocean bathymetry with the Western (yellow) and Eastern (yellow-orange) basins 

delimited.
72

Figure 3-38: Vertical temperature profiles for the eastern Arctic basin by decade for WOA18, and yearly for 

GLORYS and four PAMS model runs.
73

Figure 3-39: Vertical temperature profiles for the western Arctic basin by decade for WOA18, and yearly for 

GLORYS and four PAMS model runs.
74

Figure 3-40: Vertical salinity profiles for the eastern Arctic basin by decade for WOA18, and yearly for GLORYS 

and four PAMS model runs.
75

Figure 3-41: Vertical salinity profiles for the western Arctic basin by decade for WOA18, and yearly for GLORYS 

and four PAMS model runs.

3.5.3 Simulated landfast ice extent

Comparing median landfast ice extents for the month of April for all 25 years of the PAMS model time 

domain, model run 51 simulates more landfast ice than run 56 (Figure 3-42). However, the modeled 

extent in both runs is sensitive to the chosen velocity threshold such that Threshold 1 (0.001 cm s-1

) in run 

51 (Figure 3-42a) produces an extent similar to that of Threshold 2 (0.005 cm s-1

) in run 56 (Figure

3-42b). Please refer to Section 2.7.4 for more information about these thresholds. 
76

We also note that the sensitivity to the velocity threshold differs between model runs. In run 51, the 

difference in median extent between thresholds 2 and 3 is much smaller than the difference between 

thresholds 1 and 2, suggesting the model is more sensitive below a threshold of 0.005 cm s-1

. By 

comparison, in model run 56, the differences between adjacent thresholds are more equal. Another 

notable difference in median landfast ice between model runs 51 and 56 is the presence of stationary ice 

offshore. It does not meet our criteria for being considered landfast since it is not contiguous with the 

coast, but model run 51 shows a sizeable area of stationary ice in the vicinity of 72 oN–172 oW, which is 

where Hanna Shoal is located (see Figure 1-1). The existence of this feature, isolated from the shoreline, 

must be a function of the basal shear stress from the parameterized interaction between pressure ridge 

keels and the shallow seafloor (<20 m) in this location.

Figure 3-42: 1996 –2017 median April landfast ice extent derived from the PAMS model from a) 

run 51 and b) run 56. In both model runs, we use three different ice velocity thresholds is listed in 

Table 2-6.
77

Although the median extents shown in Figure 3-42 are broadly comparable to those derived from the 

EM2024 dataset (Figure 3-7), there are a number of occasions where the simulated landfast ice extends 

well beyond anything in the observed record (Figure 3-43). For velocity threshold 2 (0.005 cm s-1

), both 

model runs indicate that in at least 5% of the days in April between 1993 and 2017, landfast ice formed a 

bridge across the Bering Strait. Although ice arches are known to form across Bering Strait (e.g., 

Torgerson and Stringer 1985), it is unusual for them to remain in place for 20 consecutive days. More 

notable is the more regular formation of a bridge of landfast ice between the Alaska coastline and Hanna 

Shoal in model run 51, which also occurs in at least 5% of cases. The existence of these landfast bridges 

is less likely to be related to the basal shear stress parameterization and, instead is more likely to depend 

on the stiffness, or viscosity of the sea ice. 

Figure 3-43: Minimum, lower quartile, median, upper quartile, and maximum April landfast ice 

extents for the period 1996 –2017 derived from the PAMS model from a) run 51 and b) run 56 

using a velocity threshold of 0.005 cm s-1

.
78

4 Discussion

4.1 Recent changes in landfast ice in the Alaska Arctic OCS

4.1.1 Changes in landfast ice extent

For the purpose of understanding how the landfast ice regime has changed from 1996–2023 we 

partitioned the 27-year EM2024 dataset into three 9-year periods (1996–2005; 2005–2014; and 2014–

2023) and calculated regional median annual cycles of landfast ice width for each period for the Chukchi 

and Beaufort regions (Figure 4-1). In all cases, the annual cycle follows a characteristically asymmetric 

pattern with a gradual advance of the SLIE over winter followed by a rapid retreat, but there are some 

notable differences between the 9-year periods and between regions. 

Both regions show a marked decline in median landfast ice width from the first period to the last. In the 

Chukchi region, this retreat of landfast ice appears to take place primarily between the second and third 

periods, with relatively little difference between the first and second (Figure 4-1a). In contrast, in the 

Beaufort region, the most substantial change in landfast ice width appears to occur between the first and 

second 9-year period (Figure 4-1b). Moreover, whereas the Chukchi region exhibits a relatively consistent 

decrease in landfast ice width in all months, there is a markedly larger change in the Beaufort region 

between February and April than in other months. It is therefore likely that this pronounced drop in 

landfast ice width is related to the absence of stable extensions since 2004 (Figure 3-4). These features are 

discussed in some detail by Mahoney et al., (2014; 2007) and they meet the definition of landfast ice used 

in the M2014 dataset (Section 2.1.2). However, we believe it is unlikely they would be classified as 

landfast ice by NIC and ASIP analysts due to their unusually broad extent. We therefore avoid further 

interpretation of any apparent changes in the landfast ice extent in the Beaufort Sea during these months.

Figure 4-1: Daily median landfast ice width in a) the Chukchi region and b) the Beaufort region computed for the 

three periods 1996–2005, 2005–2014, and 2014–2023. Shaded regions represent the upper and lower 20th

percentile for 1996–2005 and 2014–2023.
79

Figure 4-2: Monthly median landfast ice width from 1996–2023 in a) the Chukchi region and b) the Beaufort 

region.
80

Landfast ice in the Chukchi region exhibits significant (p <0.05) negative linear trends in width in all 

months from January to May (Figure 4-2). These trends vary between -0.09 and -0.28 km yr-1, which is 

consistent with a 3.4 km reduction in end-of-April median landfast ice between the 1996–2005 and 2014–

2023 periods (Figure 4-1a). Landfast ice in the Beaufort region also exhibits a similar change in end-ofApril median landfast ice width and there are significant negative trends for the months between February 

and April. However, we omit consideration of trends during these months for the reasons just discussed 

above. Instead, we note that there is trend of -0.09 km yr-1 in November (Figure 4-2b), when the median 

landfast ice width in the Beaufort region has been zero since 2015. This represents a potentially important 

transition to a state in which landfast sea ice is more often absent than present. A similar transition 

appears to have taken place for the month of December in the Chukchi region in 2013 (Figure 4-2c). 

4.1.2 Changes in timing of landfast ice season

To evaluate interannual variability in the timing of the landfast sea ice season, we computed regional 

mean values for each of the 3 key events described in Section 2.2.5 for both the Chukchi and Beaufort 

regions (Figure 4-3). These results indicate significant linear trends (p < 0.05) for all events in the 

Chukchi region (Figure 4-3a) and in the date of landfast ice formation (first ice) in the Beaufort region 

(Figure 4-3b). The rates of these trends and their associated RR2 values are given in Table 4-1. With 

formation getting later by 1.6 d yr-1 and ice free conditions occurring 0.8 d yr-1 earlier, the average 

landfast sea ice season in the Chukchi region is getting shorter by around 2.4 d yr-1, amounting to a total 

shortening of over two months over the 27-year duration of the dataset. In the Beaufort region, the trend 

toward later formation is weaker than in the Chukchi region (1.1 d yr-1) and we see no significant trends 

in the timing of breakup or the onset of ice free conditions. 

The year-to-year differences in the occurrence dates of key events within each region are considerable 

and, as a result linear trends describe 34% of the variability, at best (Table 4-1). Notably, formation of 

landfast ice was anomalously late in the 2017–18 season, with 80% of the coastline reportedly remaining 

free of landfast ice until March in the Chukchi region (Figure 4-3a) and January in the Beaufort region 

(Figure 4-3b). Also, breakup was anomalously early in 2018–19, commencing in some areas of the 

Chukchi region in January and in March in the Beaufort region. If observed trends toward later formation 

and earlier breakup continue, the likelihood of two such anomalies overlapping in the same season will 

increase, resulting in an effective absence of landfast ice in such areas. 

Table 4-1: Linear regression values for regional mean occurrence dates of key landfast ice events

Region First ice Breakup Ice free

Chukchi 1.6 d yr-1, RR2 = 0.30 -0.6 d yr-1, RR2 = 0.15 -0.8 d yr-1, RR2 = 0.34

Beaufort 1.1 d yr-1, RR2 = 0.21 No significant trend No significant trend
81

Figure 4-3: Timeseries of mean occurrence date for each key event in the landfast ice cycle over the period of 1996–2023 calculated for a) the Chukchi region 

and b) the Beaufort region. Dashed lines indicated a statistically significant (P-value < 0.05) trend in the event date.
82

4.1.3 Evidence of changes in landfast ice breakouts

The catalog of breakout events derived from the EM2024 dataset (Appendix A) shows significant 

interannual variability and even some evidence of periodicity at timescales of 7–8 years (Figure 3-12). 

However, there is no indication of a trend or persistent change in the number of breakouts in either region. 

Moreover, we found that the number of breakout events detected was sensitive to the application of the 

running minimum that was applied to the interpolated ice chart data products (Section 2.3). Specifically, 

we found that the number of breakout events cataloged in the ice chart era (i.e., after the 2007–08 season) 

was approximately 50% higher if the running minimum step was applied after averaging between the 

NIC- and ASIP-derived data products, instead of being applied to results of each ice chart before 

averaging as illustrated in Figure 3-12. We believe this sensitivity is caused by the mismatch in the 

reporting interval of the two charts, which can lead to inconsistent detection of events if a breakout is 

recorded by charts published on the different days. Applying the 20-day minimum to results from each 

chart source before averaging results in more consistent counts before and after the 2007–08 season, but 

due to this sensitivity we avoid making any conclusions about long term changes in breakout frequency. 

However, we note that 7–8 year fluctuations in breakout count appear robust features of the data such that

they were evident even when the running minimum was applied after averaging the NIC and ASIP 

landfast ice width measurements.

4.2 InSAR detection of landfast ice extent and stability

4.2.1 Suitability of InSAR for routine identification of landfast sea ice

Landfast ice extent is captured well during the middle months of the landfast ice season using InSAR but 

does not capture any landfast ice at the beginning or end of the season. During the early months of 

December and January the average the monthly landfast ice width measured by the InSAR based method 

is slightly less than the average monthly landfast ice width from the EM2024 dataset from 2017–2021 

(Figure 3-16a-b). The majority of the difference in the Beaufort region occurs, between Point Barrow and 

Kaktovik where our InSAR-derived results show the landfast ice is consistently 6–10 km narrower. In the 

Chukchi region, differences between InSAR-derived width and EM2024 monthly mean width exist in 

Kotzebue Sound. Similar to the area between Point Barrow and Kaktovik in the Beaufort region, The 

InSAR-derived width was on average 7.7 km less than the EM2024 width in Kotzebue Sound during 

January. Early season deviation is caused by thin newly formed ice whose dielectric properties change 

rapidly, causing the reduction in coherence.

Agreement between InSAR-derived landfast ice extent and the EM2024 dataset is best from February 

through April. In February and March, our InSAR-based method underestimated landfast ice width 

compared to the EM2024 dataset by an average of 2.1 km and 1.1 km, respectively for the combined 

Chukchi and Beaufort regions. In April the InSAR-derived landfast ice width exceeded EM2024 width by 

an average of 0.7 km across both study regions. The main areas where differences persisted from 

February through April were Kotzebue Sound and the Colville Delta. Finally, in May, the InSAR based 

method under measured the landfast ice width consistently across the study region. The consistent under 

measuring of landfast ice in May is attributed to surface melting causing a loss of coherence between 

acquisitions. Overall, the InSAR based identification of landfast ice measures the landfast ice width well 

however the there are certain areas where the 12 days between acquisitions prevent the methods from 

identifying landfast ice. On this basis, we find that 12-day repeat Sentinel-1 InSAR may be a useful tool 

for helping discriminate landfast ice sea ice extent during the coldest months of the year when the 

dielectric properties of the ice surface are most stable. With the imminent launch of the NISAR satellite, 

12-day repeat L-band InSAR will be possible for all but the most northern regions of landfast ice in the 

Arctic. Meyer et al., (2011) showed that L-band coherence could be maintained for 45 days over landfast 
83

ice and so we anticipate that NISAR may allow us to extend the useful season of InSAR for this landfast 

ice detection.

4.2.2 Regional variability and annual evolution of landfast ice stability

In Figure 3-19 we showed how the apparent strain distribution for the whole study area varied month-bymonth from December to May. By partitioning this analysis between the Beaufort and Chukchi regions

and each of the 11 subregions (see Figure 2-1) we can see landfast ice stability varies around the coastline 

and over the course of a year. The results of this analysis are shown in Figure 4-4 with shaded regions 

representing the stability categories based on apparent strain thresholds derived in Section 3.2.3. The 

mode of the apparent strain distribution decreases monotonically over time in most subregions, achieving 

the greatest proportion of ice in the stabilized category in April or May. Subregions where the distribution 

does not decrease throughout the season were subregions 3, 5, and 7. Subregion 3, Outer Kotzebue 

Sound, did not have any landfast ice present during the month of May. In addition, subregion 3 is 

dissimilar to the majority of the other subregions. Subregion 3 does not contain much coastline and 

landfast ice typically ends this region via extending out of Kotzebue Sound rather than found within the 

region. Subregion 5, Cape Kruzenstern to Point Hope, is the only south facing subregion. While the exact 

reason the distribution is abnormal is not known, we suspect the small amount of landfast ice in this 

subregion resulted in the dispersed monthly distributions with poorly define modes. Subregion 7, Cape 

Lisburne to Wainwright, had the lowest distribution of landfast ice in December. The only landfast ice in 

this subregion during December occurred within lagoons. The lack of landfast ice outside the lagoon 

presented an artificially low distribution of apparent strain during December. The modal apparent strain 

values in the remaining subregions, Chukchi region, Beaufort region, and the entire study region all 

decreased from December through April or May. Further investigation is needed, but these results 

indicate landfast ice becomes more stable the longer a pixel is classified as landfast ice. The increase in 

stability over time could be attributed to the thickening observed by the SIMBs discussed in Section 3.3.
84

Figure 4-4: Monthly distribution of interferometric phase gradient in each of the 2 regions, 11 subregions, and 

entire study region. Shaded regions indicated the stability categories discussed in Section 3.2.3.
85

4.3 In situ observations of landfast ice

Our ability to discuss the oceanographic and thermodynamic processes that drive the breakup of landfast 

ice is limited by the challenges we faced deploying in-situ instruments in and beneath landfast ice. These 

challenges included travel restrictions and supply-chain delays during the COVID-19 pandemic as well as 

damage caused by polar bears, who commonly take an interest in items protruding from the ice. As a 

result, there is only a short period in early winter of 2022, when we have coincident data from both a 

SIMB and ADCP. Nonetheless, there are some relevant observations we can make from SIMB and ADCP 

data that we were able to obtain in Point Hope and Utqiagvik. First, it is clear from the SIMB data from 

Point Hope in 2020 (Figure 3-22a) and from Utqiagvik in 2024 (Figure 3-24c) that comparatively warm, 

above-freezing water can make its way under landfast ice even in shallow water and this can pause ice 

growth or cause bottom melt. However, closer examination of the water temperature before and after 

breakup of landfast near Point Hope in 2020 (Figure 4-5) suggests that landfast ice in shallow water (i.e.,

≲10 m) may be less susceptible to bottom melt than adjacent pack ice. Specifically, the temperature of the 

water immediately below the ice (red line) started warming around May 22, which may have been related 

to pooling of fresher meltwater with higher freezing point (Polashenski et al., 2012). However, the water 

temperature more than 0.5 m below ice (blue line) did not increase substantially until the SIMB began to 

move (black line). The inset in Figure 4-5 shows a destratification at the moment of detachment (June 8, 

08:00 UTC) resulting in a cooling immediately below ice the before the whole water column abruptly 

warmed. Although we do not know at what point the SIMB was released from its surrounding ice, it is 

clear that the temperature profile of the water changed abruptly once it ceased to be landfast.

Figure 4-5: Timeseries of water temperature below the ice at different depths and SIMB position in the final 

50 days of the data from the SIMB deployed near Point Hope in 2020. The inset shows a 36-hour period 

starting 12 hours before the detachment.

If the fastness of landfast ice bestows any protection from ocean heat transport, this is likely to depend 

upon the speed and direction of the currents. The loss of the ADCP deployed alongside the SIMB near 

Point Hope in 2020 prevents us from knowing what role currents played in either the breakup of landfast 

ice or the arrival of warm water beforehand. However, the data from the ADCP deployed in similar water 

depths near Utqiagvik in 2022 (Section 3.3.3) demonstrates that currents below landfast ice can be of 
86

sufficient magnitude and persistence to deliver water masses from substantial distances. In particular, the 

last 4 weeks of ADCP data show consistent northward flow beneath the ice (Figure 3-26h-i). Although 

the ADCP’s temperature sensor does not indicate any pronounced warming and the instrument was 

recovered before detachment of the landfast ice, it is conceivable that this regime of circulation could 

effectively introduce warm water from more southerly latitudes to promote bottom melting like observed 

at Point Hope.

4.4 Impact of storms on landfast ice extent

The results showed that winds and storms account for many, but not all, landfast breakout events along 

the Beaufort and Chukchi Sea coasts. While the composite analysis showed the signal of Bering Sea 

storms being associated with these events, the statistical significance was limited in geographic extent. A 

daily synoptic map typing analysis of sea level pressure using SOM revealed that storms in the southern 

Bering Sea is the most common pattern associated with these events. This pattern leads to enhanced 

easterly winds along the northern coasts of Alaska. However, no type of SOM pattern clearly dominated 

the results. The patterns shown in Figure 3-28 and the Node 19 SOM results (Figure 3-29) suggest that 

remote wind forcing in the southeast Bering Sea may be a key component of some landfast ice breakout 

events. Together, these analyses are consistent with the suggestions of Danielson et al., (2020b; 2014) that 

continental shelf waves triggered by storms in the Bering Sea could be the driving mechanism, although 

these patterns also show local winds that could also contribute to landfast ice breakout events.

The findings of our analysis share key similarities with existing literature on atmospheric forcing of 

landfast breakout events in Alaska. Jones et al., (2016) noted enhanced wind stress occurred prior to two 

breakout case studies in 2009 and 2010 at Utqiagvik but with differences in wind direction. However, the 

2010 case had easterly winds consistent with main SOM node noted in our results for the Beaufort Sea.

Bogardus et al., (2020) found that strong southerly winds played an important role in a breakout event 

near Kotzebue in December 2016 in the Chukchi Sea. Southerly wind conditions were also noted to be 

important for some breakout events at St. Lawrence Island in northern Bering Sea as well (Jensen et al.,

2023). Based on our SOM analysis these wind directions would typically fall under Nodes 1–2, which 

occurred more rarely in conjunction with the breakout events in the database. However, Nodes 1–2 

occurred more frequently for the Chukchi than the Beaufort region.

4.5 Comparison between simulated and observed landfast sea ice extent

By reprojecting and rescaling the 12-km PAMS gridded landfast ice fields into the grid used for the 

EM2024 dataset, we were able to apply the SLIEalyzer tools (Section 2.2) to make directly comparable 

measurements of landfast width and related metrics for the period 1996–2017 when the model output 

overlap temporally with the EM2024 dataset. A comparison of the regional mean daily landfast ice width 

(Figure 4-6) shows that the model tends to underestimate the median landfast ice width in most months, 

resulting in a substantial underestimation in the duration of the landfast ice season. In the Chukchi region 

as a whole, the median daily landfast ice width remains zero throughout the year in all simulations except 

model run 51 for thresholds 1 and 2, when it is non-zero from early March to mid-May. In the Beaufort 

Region, all simulations achieve a non-zero median daily landfast ice width at some point during the year, 

but for a substantially shorter duration than shown by the EM2024 data.

Overall, the simulated landfast ice widths are in closest agreement with observations during April, when 

the curves of median landfast ice width from the PAMS model results often intersect with those derived 

from the observations in the EM2024 dataset. In months prior to April, landfast ice is consistently 

underestimated while May is the only month when there is a substantial overestimate in landfast ice 

width. This reflects another notable aspect of the model results, which is that the annual maximum extent 
87

of landfast ice in the Beaufort Region occurs consistently later than observed. This result is somewhat 

surprising and possibly indicates the model lacks a way represent processes associate with destabilization 

of landfast ice, such as thermal degradation of grounded ridge keels reducing the amount of basal shear 

stress they can exert on the ice. Discharge of terrestrial meltwater is another process strongly associated 

with breakup of landfast ice (e.g., Bareiss et al., 1999), which maybe underrepresented by the PAMS 

model due to the lack of reporting point data included in the GloFAS forcing data (Section 2.7.2). 

Finally, when the PAMS model shows landfast ice is present, it tends to overestimate its extent. This 

results in the substantially greater variance in the model results, compared with the observations. The 

width of the shaded areas in Figure 4-6 represents both spatial and temporal variability. The 

overestimation of spatial variability in landfast ice extent results from a tendency to consistently 

overestimate landfast ice in some areas while underestimating in others, as illustrated for the month of 

April in Figures 3-42 and 3-43. At the same time, inspection of results from individual years shows 

considerable short-term variability where the landfast ice appears to completely breakout and reform 

multiple times in a single season. This type of behavior suggests that much of what is being identified as 

landfast ice is not actually securely anchored and may only exhibit sub-threshold velocity during periods 

of low wind and current. However, the exclusion of such ice is the principal reason we adopt a 20-day 

duration in our definition of landfast ice (Section 2.1.2). An alternative explanation might therefore be 

that the basal shear stress is varying over time, perhaps due to sensitivity in the mean thickness or 

thickness distribution within a grid cell. In reality, the thickness distribution of landfast ice should only 

evolve slowly through thermodynamic processes, but the non-zero velocity of the simulated ice may lead 

to internal deformation that should require some change to the thickness distribution. 
88

Figure 4-6: Regional mean landfast ice width from the PAMS model for (a-f) runs 51 and (g-i) runs 56 with the 

three velocity thresholds described in Section 2.7.4. Lefthand panels (a,c,e,g,i,k) show results for the Chukchi 

Region, while righthand panels (b,d,f,h,j,l) show results for the Beaufort Region.
89

5 Conclusions

5.1 Lessons from an updated climatology of landfast sea ice in the Arctic 

OCS

By extending the prior work of Mahoney et al., (2014), we have developed a 27-year climatology of daily 

landfast ice width in the Alaska Arctic OCS, spanning the Chukchi and Beaufort Seas. These data show 

that the duration and extent of landfast ice is declining throughout the region. During the 9-year period 

from 1996–2005, the median landfast ice extent in April occupied 3.8% of the area of the U.S. Arctic 

OCS, whereas for the period 2014–2023 this percentage had reduced to 2.1%, representing a total 

decrease by 9,950 km2

. However, we remind the reader that our study area extends outside the Arctic 

OCS (Figure 1-2) and so these numbers exclude landfast within 3 nautical miles of the coast or adjacent 

to the Russian and Canadian coastlines.

The changes in landfast ice appear most pronounced in the Chukchi Sea, where landfast sea ice is forming 

later and breakup earlier (Figure 4-3) and shows a significant decrease in extent during every month in 

between January and May (Figure 4-2). In the Beaufort Sea, there is a significant trend toward later 

formation (Figure 4-3b) and negative trend in region-wide median landfast ice width in the month of 

November. However, there is no evidence of a trend in the dates of breakup and ice-free conditions in the 

Beaufort Sea and we have reason to be cautious about interpreting negative trends in landfast ice width 

during the months of January–March due to possible discrepancies in the treatment of stable extensions 

between the M2014 dataset and the later NIC and ASIP ice chart-derived results. 

Comparing landfast ice width measurements during the 2007–08 landfast ice season, when data from 

M2014, NIC, and ASIP datasets are available, we find good overall agreement in the timing of formation 

and breakup between all three parent data sources (Figure 3-1). In the Beaufort region, there is good 

agreement in landfast ice width between the M2014 and NIC datasets throughout the season, though in 

the Chukchi region year the NIC data underestimates the maximum annual width compared with the 

M2014 dataset. The ASIP datasets shows greater landfast ice width than either of the other two datasets in 

both the Chukchi and Beaufort regions, though this discrepancy is related to a few specific polygons in 

individual ice charts. For the remainder of the EM2024 period, we find no consistent bias between the 

NIC and ASIP datasets and we believe that an average of both data sources, when available, is likely to 

provide more consistent data for a climatology than data from a single chart-based source, since it will be 

less sensitive to missing or misidentified polygons. However, the existence of such irregularities in the ice 

charts highlights the value of a more consistent approach such as that used in the production of the M2014 

dataset.

5.2 Application of InSAR for landfast ice mapping

Although prior work by Meyer et al., (2011) demonstrated the potential for using interferometric 

coherence as a means of automating landfast ice detection, our evaluation of Sentinel-1 InSAR products 

suggests that this is only likely to be practicable with currently available C-band SAR data during the 

coldest months of the year, when the landfast ice and its dielectric properties are most stable. The longer 

wavelength of L-band SAR is expected to be less sensitive to dielectric changes that take place during the 

earlier and later stages of the landfast ice, and we therefore anticipate this topic will be worth revisiting 

after the launch of the National Aeronautics and Space Administration (NASA)-Indian Space Research 

Organisation (ISRO) synthetic aperture radar (NISAR) satellite, which is expected later in 2024. 

Nonetheless, our quantitative analysis of phase gradients within landfast ice shows that we can use InSAR 

to classify the stability of landfast ice. Comparison between the location of SLIE nodes (Figure3-5) and 

the InSAR-derived extent of stabilized ice (Figure 3-21) demonstrates InSAR may be useful for 
90

identifying the location of grounded ridges. Moreover, our results show that landfast ice tends to become 

more stable the longer it remains in place. This suggests that a later onset of landfast ice formation, may 

contribute to a reduced stability and greater susceptibility to breakout.

5.3 Impact of the ocean on landfast ice mass balance and stability

Although our program of in-situ observations was curtailed by the COVID-19 pandemic, we obtained 

some valuable data on the sub-ice temperature profile immediately before and after detachment of 

landfast sea ice near Point Hope. The abrupt warming of water below the ice within hours of breakup 

suggests that the ice was buffered from the associated heat flux while it was landfast and that the mass 

balance of landfast ice may be markedly different from that of adjacent pack ice once sea surface 

temperatures rise above the freezing point. Had the SIMB deployed in February 2024 (Table 2-3) not 

been damaged by a polar bear in early May, we had been planning to leave it in place to obtain more data 

during breakup and transition to mobile pack ice. Nonetheless, more such SIMB deployments could 

provide valuable insights into the interrelation between ocean heat and landfast ice detachment. Ocean 

current data would also add significant value to such deployments, but ADCPs are unlikely to be suitable 

in this application. Instead, a less expensive instrument with satellite telemetry capabilities would be 

preferable.

5.4 Forecastability of breakout events

We find no evidence of long-term change in the occurrence of breakouts, though there is substantial

interannual variability and possible evidence of sub-decadal periodicity in the frequency of such events 

(Figure 3-12). We also found that some regions are more prone to breakout than others (Figure 3-14) and 

a visual comparison suggests these regions may exhibit higher average interferometric phase gradients 

(Figure 3-17 and Figure 3-18). However, we found no evidence that the phase gradient in individual 12-

day interferograms can be used to predict breakout events. However, analysis of ERA-5 SLP data 

suggests that breakout events are commonly associated with anomalously low pressure in the southern 

Bering Sea (Figure 3-28) related to storms in this region (Figure 3-29 and Figure 3-30). Together, these 

results suggest that storm predictions might be combined with the spatial distribution of breakout 

frequency to provide warnings of increased likelihood of landfast ice breakout.

5.5 Numerical simulation of landfast ice in the Arctic OCS

As of July 2024, we have completed numerous different runs of the PAMS coupled ice-ocean model and 

have analyzed landfast ice extent in two of these that reproduced acceptable results in terms of total ice 

concentration and temperature and salinity at depth. Although these results cannot be described as 

realistic simulations of landfast ice behavior, they provide valuable insights for ongoing model 

development. For example, each simulation achieves a characteristic annual of landfast ice extent with a 

gradual advance and rapid retreat (Figure 4-6), but in comparison with the EM2024 dataset, the modeled 

landfast ice season is consistently too short, and the short-term and regional variance is too great. In both 

model runs the timing of maximum landfast ice extent also occurs notably later than observed, 

particularly in the Beaufort Sea. This may suggest the basal shear stress parameterization fails to capture a 

seasonal process that reduces the coupling with the seafloor, such as preferential melting of ridge keels 

(e.g., Salganik et al., 2023). We also note that model run 56 produces considerably less landfast in the 

Arctic OCS than model run 51, which we attribute to the use of a new, experimental coupled ice-ocean 

driver (CIOD) in run 56 (see Section 2.7.5). A closer inspection of run 56 shows that the CIOD is driving 

some noise in the surface ocean velocity fields, which likely generates greater variance in the ice velocity 

field, thereby reducing the extent of ice that meets the 20-day criteria for being landfast.
91

The differences between the simulation and the EM2024 dataset notwithstanding, our results represent a 

useful testbed for further PAMS model development. Not only is the EM2024 dataset a benchmark 

against which we can assess model performance, the study region contains geographic features that 

should facilitate diagnosis of model behavior. For example, the occurrence of stationary ice over shallow 

features such as Hanna Shoal (Figures 3-42 and 3-43) allows us to assess the effects of changes to the 

basal shear stress parameterization (Section 2.7.3) largely independently from other model parameters. A 

realistic simulation in agreement with observations (i.e., Mahoney et al., 2012) would reproduce basal 

shear stress in a local area on top of the shoal without creating a large area of surrounding stationary ice. 

Conversely, the model should be able to reproduce short-lived regions of stationary ice across features 

like Bering Strait in accordance with observed ice-arching behavior (Torgerson and Stringer 1985), 

without creating persistent ice bridges spanning the southern Chukchi Sea.

5.6 Recommendation for future studies

5.6.1 Development of Pan-Arctic landfast ice dataset

Here we have demonstrated the feasibility of using existing ice charts to develop a climatology of landfast 

sea ice extent. We recommend extending this approach to provide pan-Arctic coverage using all available 

ice chart data. For example, the Canadian Ice Service, the Danish Meteorological Institute, Finnish 

Meteorological Institute, and Russia’s Arctic and Antarctic Research Institute all routinely produce digital 

vector ice charts that identify the extent of landfast ice. With the definition of a new set of pan-Arctic 

coast vectors (segregated into as many subregions as necessary), it would be relatively straightforward to

apply the SLIEalyzer to replicate our analysis to the whole Arctic.

5.6.2 Targeted post-detachment in-situ observations

The SIMB data from the Point Hope provides valuable insight into the mass balance of sea ice before and 

after detachment from the coast. Once the ice ceased to be landfast, it appears that it was exposed to a 

substantially warmer ocean. This raises the possibility that detachment of the ice allowed the incursion of 

warm water into the region formerly occupied by landfast ice. Additionally, it has been speculated that 

since landfast ice commonly persists after the retreat of pack ice from a region, it may serve as valuable 

habitat for walrus after it detaches (Mahoney 2018). We therefore recommend continued deployments of 

SIMBs with the goal of letting them detach with the ice to obtain valuable mass balance data from this 

under-observed period of the landfast and post-landfast ice season.

5.6.3 Application of NISAR data to landfast ice

The expected 2024 launch of NASA and ISRO’s NISAR satellite should provide further opportunities for 

the application InSAR techniques to the study of landfast ice. The 12-day repeat interval will the same as 

that of the Sentinel-1 data we have used here, but there is reason to expect the data from NISAR’s L-band 

radar will be less susceptible to decorrelation during the melt season. Once NISAR data become 

available, we therefore recommend an immediate assessment of its value for automated detection of 

landfast ice extent or categorization of stability through calculation of apparent strain.

5.6.4 Continued development and evaluation of simulated landfast ice in PAMS model

Development of the PAMS model is anticipated to continue and, as discussed in Section 5.5, the EM2024 

dataset represents ideal product against which to validate its simulation of landfast ice. In conjunction 

with our recommendation described in Section 5.6.1, we therefore recommend continued assessment of 

simulated landfast ice extent, with particular attention paid to the timing of formation and breakup, the 

characteristics of short term variability in the location of the SLIE, and the potential for unrealistic ice 
92

bridges to form across channels and straights, or even between the coastline and shallow water regions 

offshore like Hanna Shoal.
93

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100

Appendix A Catalog of Breakout Events

A.1 Chukchi region breakout events

The 161 breakout events identified the Chukchi region from the EM2024 according to the 

criteria described in Section 2.2.7 are listed in Table A-1 below. The columns in this table are 

explained below: 

ID: Each breakout is assigned a number based on its chronological sequence in the dataset. 

SLIE file: This the image in which the reduced landfast ice extent can be seen. 

SLIE date: This is the date corresponding to this image, as discussed in Section 2.2.2. 

V0: The first coast vector bounding the breakup region.

V1: The last coast vector bounding the breakup region.

W: the mean width reduction (km) measured across all coast vectors between V0 and V1. 

L: The length (km) along the coast between the origin points of V0 and V1. 

A: The area (km2)of landfast ice involved in the breakout.

Latitude: The latitude (in decimal degrees) of the centroid of the breakout area. 

Longitude: The longitude (in decimal degrees) of the centroid of the breakout area. 

Table A-1: Catalog of breakout events identified in the Chukchi region. 

ID Season SLIE file SLIE date v0 v1 W (km) L (km) A (km2) Latitude Longitude

1 1996-97 r19970108_dailyslie.tif 1997-01-08 5064 5190 2.9 52.4 126.9 69.419 -163.178

2 1996-97 r19970329_dailyslie.tif 1997-03-29 949 1195 3.2 72.3 154.8 66.236 -169.980

3 1996-97 r19970407_dailyslie.tif 1997-04-07 773 865 0.6 25.4 18.6 66.540 -170.984

4 1997-98 r19980103_dailyslie.tif 1998-01-03 6159 6331 1.2 12 9.5 70.531 -160.318

5 1997-98 r19980413_dailyslie.tif 1998-04-13 6009 6331 6.7 78.9 402.7 70.458 -160.893

6 1998-99 r19990122_dailyslie.tif 1999-01-22 6900 6956 1.8 36.5 24 66.519 -163.566

7 1998-99 r19990130_dailyslie.tif 1999-01-30 844 1349 3.2 108.8 318.4 66.334 -170.276

8 1999-00 r20000113_dailyslie.tif 2000-01-13 550 638 4 26.7 114.8 66.854 -171.533

9 2000-01 r20010107_dailyslie.tif 2001-01-07 5185 5244 1.5 7.6 9.2 69.628 -163.111

10 2000-01 r20010107_dailyslie.tif 2001-01-07 5769 6026 1.9 31.9 45.8 70.326 -161.747

11 2000-01 r20010126_dailyslie.tif 2001-01-26 2 365 8.2 86.2 786.6 67.102 -172.882

12 2000-01 r20010204_dailyslie.tif 2001-02-04 587 624 2 13.5 25.9 66.849 -171.592

13 2000-01 r20010204_dailyslie.tif 2001-02-04 782 1111 7 111.6 521.7 66.391 -170.401

14 2000-01 r20010204_dailyslie.tif 2001-02-04 1628 2052 11.8 98.6 727.1 65.866 -167.719

15 2000-01 r20010204_dailyslie.tif 2001-02-04 2629 2812 4.2 167.9 209.1 66.543 -164.490

16 2000-01 r20010204_dailyslie.tif 2001-02-04 4800 5072 4.8 90.5 436.4 69.042 -164.036

17 2000-01 r20010204_dailyslie.tif 2001-02-04 5701 6010 2.2 40 84 70.285 -161.995

18 2000-01 r20010204_dailyslie.tif 2001-02-04 6064 6096 1.8 13.6 21 70.334 -161.055

19 2000-01 r20010219_dailyslie.tif 2001-02-19 6673 6711 1.3 23.9 23.9 66.046 -162.417

20 2001-02 r20020303_dailyslie.tif 2002-03-03 2 39 2.4 13.6 29.5 67.116 -173.452

21 2001-02 r20020421_dailyslie.tif 2002-04-21 550 1343 6.4 179.5 1072.2 66.513 -170.693
101

ID Season SLIE file SLIE date v0 v1 W (km) L (km) A (km2) Latitude Longitude

22 2001-02 r20020428_dailyslie.tif 2002-04-28 747 782 0.4 12.7 5.2 66.617 -171.203

23 2001-02 r20020428_dailyslie.tif 2002-04-28 1832 1988 0.6 40.3 16.2 65.849 -167.460

24 2002-03 r20030219_dailyslie.tif 2003-02-19 1787 2262 11.3 121.6 874.8 66.033 -167.347

25 2002-03 r20030301_dailyslie.tif 2003-03-01 512 638 0.7 27 25.8 66.842 -171.548

26 2002-03 r20030301_dailyslie.tif 2003-03-01 789 1055 6.2 71.9 381.9 66.425 -170.488

27 2002-03 r20030403_dailyslie.tif 2003-04-03 490 869 3.8 80.5 371.1 66.670 -171.156

28 2002-03 r20030403_dailyslie.tif 2003-04-03 1690 2748 25.4 264.5 4464.8 66.274 -166.727

29 2004-05 r20050102_dailyslie.tif 2005-01-02 980 1044 0.3 3.9 1.5 66.287 -170.221

30 2004-05 r20050102_dailyslie.tif 2005-01-02 2656 2765 1 30.8 29 66.541 -164.700

31 2004-05 r20050102_dailyslie.tif 2005-01-02 5861 6077 3.2 49.7 169.5 70.334 -161.474

32 2004-05 r20050102_dailyslie.tif 2005-01-02 6664 6708 1.8 25.5 40.2 66.054 -162.344

33 2004-05 r20050102_dailyslie.tif 2005-01-02 6849 6952 3.4 71.3 99 66.408 -163.665

34 2004-05 r20050111_dailyslie.tif 2005-01-11 5051 5107 0.4 22.1 9.1 69.293 -163.273

35 2004-05 r20050203_dailyslie.tif 2005-02-03 947 1064 1.6 35.9 22.6 66.306 -170.219

36 2005-06 r20060204_dailyslie.tif 2006-02-04 430 1329 7.9 179.5 1582.8 66.509 -170.633

37 2005-06 r20060204_dailyslie.tif 2006-02-04 1502 2929 30.6 582.3 10487 66.420 -165.160

38 2005-06 r20060204_dailyslie.tif 2006-02-04 4765 5218 6 149.1 834.6 69.158 -163.873

39 2005-06 r20060204_dailyslie.tif 2006-02-04 5678 6097 9.9 90.7 595.3 70.354 -161.712

40 2005-06 r20060204_dailyslie.tif 2006-02-04 6140 6331 0.6 19 17.6 70.492 -160.424

41 2005-06 r20060204_dailyslie.tif 2006-02-04 6346 6956 39 384.9 4256.9 66.393 -162.859

42 2005-06 r20060214_dailyslie.tif 2006-02-14 2 236 6.4 67.5 333.9 67.090 -172.851

43 2006-07 r20070124_dailyslie.tif 2007-01-24 5056 5084 0.6 11.4 5.8 69.262 -163.342

44 2006-07 r20070203_dailyslie.tif 2007-02-03 5752 6003 2 17.6 20.2 70.329 -161.919

45 2007-08 r20080114_dailyslie.tif 2008-01-14 2376 2496 1.3 14.7 21.5 66.376 -165.615

46 2007-08 r20080114_dailyslie.tif 2008-01-14 2673 2838 1.8 197.3 143.1 66.433 -163.261

47 2007-08 r20080114_dailyslie.tif 2008-01-14 4331 5090 9.5 145.2 1327 68.977 -164.793

48 2007-08 r20080114_dailyslie.tif 2008-01-14 5154 5274 1.6 37.5 40.9 69.625 -163.096

49 2007-08 r20080114_dailyslie.tif 2008-01-14 5282 5548 3.1 43.8 234.5 69.943 -162.692

50 2007-08 r20080114_dailyslie.tif 2008-01-14 5737 6151 6 89.6 419.4 70.338 -161.474

51 2007-08 r20080114_dailyslie.tif 2008-01-14 6721 6941 6.8 118.9 438.8 66.269 -163.445

52 2007-08 r20080305_dailyslie.tif 2008-03-05 357 576 1.4 16.5 45.8 66.977 -171.795

53 2007-08 r20080324_dailyslie.tif 2008-03-24 2137 2939 13.1 480.5 2180.7 66.581 -165.245

54 2007-08 r20080403_dailyslie.tif 2008-04-03 2 970 25.9 220.7 4471.9 67.094 -171.891

55 2007-08 r20080413_dailyslie.tif 2008-04-13 717 1323 9.4 144.6 1258.1 66.461 -170.510

56 2007-08 e20080430_dailyslie.tif 2008-04-30 1019 1047 0.2 2.6 0.9 66.280 -170.195

57 2007-08 e20080430_dailyslie.tif 2008-04-30 2483 2561 0.8 7.9 6.1 66.401 -165.488

58 2007-08 e20080430_dailyslie.tif 2008-04-30 4986 5101 5.7 43.6 220.2 69.233 -163.530

59 2008-09 c20090104_dailyslie.tif 2009-01-04 1987 2848 12.9 386.9 3363.3 66.453 -164.123

60 2008-09 c20090104_dailyslie.tif 2009-01-04 3117 3731 2.2 154.8 332.1 68.088 -165.875
102

ID Season SLIE file SLIE date v0 v1 W (km) L (km) A (km2) Latitude Longitude

61 2008-09 c20090104_dailyslie.tif 2009-01-04 6892 6956 2.1 39.9 32.1 66.506 -163.580

62 2008-09 c20090125_dailyslie.tif 2009-01-25 3549 4351 5.1 136.8 645.6 68.458 -166.417

63 2008-09 c20090308_dailyslie.tif 2009-03-08 764 1190 2.6 121.5 282 66.388 -170.495

64 2008-09 c20090419_dailyslie.tif 2009-04-19 104 378 3 54.6 188 67.011 -172.224

65 2008-09 c20090419_dailyslie.tif 2009-04-19 582 636 1.5 21 28.1 66.832 -171.584

66 2008-09 c20090419_dailyslie.tif 2009-04-19 718 856 1.4 44.5 64.2 66.629 -171.202

67 2009-10 c20100207_dailyslie.tif 2010-02-07 1 767 1.6 149.6 247.9 67.005 -172.419

68 2009-10 c20100311_dailyslie.tif 2010-03-11 640 1115 3.5 142 403 66.394 -170.502

69 2009-10 c20100401_dailyslie.tif 2010-04-01 264 377 0.4 12.5 7.2 66.962 -171.862

70 2010-11 c20110104_dailyslie.tif 2011-01-04 1765 2830 2.5 414.2 715 66.365 -164.616

71 2010-11 c20110224_dailyslie.tif 2011-02-24 6774 6956 1.4 115.6 61 66.376 -163.466

72 2012-13 c20130221_dailyslie.tif 2013-02-21 261 1112 4 190 698.5 66.517 -170.773

73 2012-13 c20130221_dailyslie.tif 2013-02-21 5998 6162 1.6 74.7 91.3 70.358 -161.241

74 2012-13 c20130331_dailyslie.tif 2013-03-31 6124 6183 0.6 22.1 11.5 70.468 -160.488

75 2013-14 c20140115_dailyslie.tif 2014-01-15 250 372 0.8 16.1 14.3 66.969 -171.920

76 2013-14 c20140122_dailyslie.tif 2014-01-22 6152 6183 0.6 11 6.5 70.496 -160.411

77 2013-14 c20140216_dailyslie.tif 2014-02-16 1799 2245 6.2 109.9 453.9 65.995 -167.238

78 2013-14 c20140406_dailyslie.tif 2014-04-06 485 605 0.2 16.5 4.6 66.870 -171.657

79 2013-14 c20140427_dailyslie.tif 2014-04-27 3158 3240 1 27.4 22.1 67.779 -164.637

80 2014-15 c20150128_dailyslie.tif 2015-01-28 264 427 0.7 16 12.5 66.961 -171.827

81 2014-15 c20150204_dailyslie.tif 2015-02-04 6386 6452 3.9 54.8 86.8 66.822 -162.798

82 2014-15 c20150311_dailyslie.tif 2015-03-11 14 374 2.5 85.4 203.8 67.029 -172.478

83 2014-15 c20150311_dailyslie.tif 2015-03-11 3618 3701 1.4 29.7 40.1 68.309 -166.614

84 2014-15 c20150311_dailyslie.tif 2015-03-11 6749 6797 0.7 22.3 14.4 66.075 -163.252

85 2014-15 c20150318_dailyslie.tif 2015-03-18 6607 6723 0.9 99.6 46.1 66.169 -162.282

86 2014-15 c20150401_dailyslie.tif 2015-04-01 3629 3685 1.1 19.9 20.4 68.303 -166.499

87 2015-16 c20160104_dailyslie.tif 2016-01-04 6785 6941 3.2 89.8 170.3 66.264 -163.626

88 2015-16 c20160106_dailyslie.tif 2016-01-06 2904 2930 1.7 38.6 24.7 66.964 -162.771

89 2015-16 c20160106_dailyslie.tif 2016-01-06 5788 6139 1.2 77.6 76.8 70.331 -161.309

90 2015-16 c20160124_dailyslie.tif 2016-01-24 6846 6956 3.5 75.4 121.2 66.403 -163.725

91 2015-16 c20160210_dailyslie.tif 2016-02-10 2421 2606 1.1 23 27.6 66.412 -165.467

92 2015-16 c20160210_dailyslie.tif 2016-02-10 6332 6471 12.8 99.8 772.3 66.934 -163.215

93 2015-16 c20160221_dailyslie.tif 2016-02-21 5065 5096 1.1 12.7 12.2 69.290 -163.280

94 2015-16 c20160323_dailyslie.tif 2016-03-23 3094 3377 1.3 79 88.3 67.782 -164.659

95 2015-16 c20160323_dailyslie.tif 2016-03-23 5043 5177 1.3 56 62.1 69.381 -163.239

96 2015-16 c20160324_dailyslie.tif 2016-03-24 4297 5070 6.8 138.4 730.3 68.955 -164.974

97 2015-16 c20160324_dailyslie.tif 2016-03-24 5079 6143 2.5 231 534.9 70.140 -162.085

98 2015-16 c20160329_dailyslie.tif 2016-03-29 2798 2928 14.1 273.1 1205.7 66.512 -162.633

99 2015-16 c20160329_dailyslie.tif 2016-03-29 6385 6795 16.8 254.9 1094.6 66.467 -162.660
103

ID Season SLIE file SLIE date v0 v1 W (km) L (km) A (km2) Latitude Longitude

100 2015-16 c20160329_dailyslie.tif 2016-03-29 6385 6805 16.8 261.8 1123.4 66.464 -162.673

101 2015-16 c20160329_dailyslie.tif 2016-03-29 6819 6854 23.3 22.4 100.1 66.372 -163.250

102 2015-16 c20160330_dailyslie.tif 2016-03-30 5249 5440 0.6 22.5 12.5 69.737 -163.037

103 2015-16 c20160330_dailyslie.tif 2016-03-30 5712 6331 1.9 113.7 161.7 70.365 -161.204

104 2015-16 c20160427_dailyslie.tif 2016-04-27 1526 3116 8.6 680 4489.5 66.616 -164.130

105 2016-17 c20170111_dailyslie.tif 2017-01-11 1071 1108 1.2 25.8 16.3 66.161 -169.980

106 2016-17 c20170125_dailyslie.tif 2017-01-25 5870 6110 4.6 62.7 190.3 70.341 -161.427

107 2016-17 c20170208_dailyslie.tif 2017-02-08 925 965 1.4 27.8 20.5 66.319 -170.456

108 2016-17 c20170208_dailyslie.tif 2017-02-08 1053 1108 1.4 34 28.3 66.179 -170.026

109 2016-17 c20170222_dailyslie.tif 2017-02-22 6717 6882 2 97 129.5 66.152 -163.423

110 2016-17 c20170412_dailyslie.tif 2017-04-12 251 595 1.1 36.2 30.6 66.945 -171.713

111 2017-18 a20180403_dailyslie.tif 2018-04-03 1 382 3.7 92.9 360.7 67.052 -172.831

112 2017-18 a20180410_dailyslie.tif 2018-04-10 2823 2938 16.7 172.1 1211.5 66.651 -162.468

113 2017-18 a20180410_dailyslie.tif 2018-04-10 6340 6715 10.7 245 1778.3 66.493 -162.396

114 2017-18 a20180418_dailyslie.tif 2018-04-18 920 957 1.9 14.1 25.3 66.346 -170.494

115 2017-18 a20180418_dailyslie.tif 2018-04-18 2290 2556 1.9 75.4 76.1 66.329 -165.811

116 2017-18 a20180418_dailyslie.tif 2018-04-18 2663 2724 0.9 12 15 66.524 -164.810

117 2017-18 a20180422_dailyslie.tif 2018-04-22 1816 1892 2 27.5 31.2 65.818 -167.573

118 2017-18 a20180423_dailyslie.tif 2018-04-23 958 1198 1.9 62.5 132.7 66.209 -170.036

119 2017-18 a20180429_dailyslie.tif 2018-04-29 2778 2811 1.2 24.7 17.4 66.599 -163.967

120 2018-19 a20190109_dailyslie.tif 2019-01-09 199 382 8 38.2 224.1 67.062 -172.048

121 2018-19 a20190119_dailyslie.tif 2019-01-19 1670 2811 10 304 2027 66.162 -166.657

122 2018-19 a20190120_dailyslie.tif 2019-01-20 719 1117 5.3 137.3 571.3 66.448 -170.640

123 2018-19 a20190201_dailyslie.tif 2019-02-01 6609 6643 4.2 35.9 68.5 66.229 -161.934

124 2018-19 a20190204_dailyslie.tif 2019-02-04 2101 2232 3.9 15.2 70.8 66.125 -166.628

125 2018-19 a20190216_dailyslie.tif 2019-02-16 2472 2561 1.7 11.5 22.3 66.400 -165.518

126 2018-19 a20190216_dailyslie.tif 2019-02-16 2643 2685 2.3 17.7 32.5 66.509 -164.903

127 2018-19 a20190216_dailyslie.tif 2019-02-16 5947 6153 5.2 79.3 296.5 70.353 -161.341

128 2018-19 a20190220_dailyslie.tif 2019-02-20 6578 6608 2.3 14.8 29.2 66.366 -161.934

129 2018-19 a20190224_dailyslie.tif 2019-02-24 1796 1950 1.6 43.6 50 65.831 -167.557

130 2018-19 a20190309_dailyslie.tif 2019-03-09 2880 2931 21.4 76.2 622.7 66.878 -162.769

131 2018-19 a20190309_dailyslie.tif 2019-03-09 6367 6507 11.8 87.5 660.3 66.887 -162.767

132 2018-19 a20190312_dailyslie.tif 2019-03-12 5935 5965 4.2 0.8 6.6 70.362 -161.878

133 2018-19 a20190318_dailyslie.tif 2019-03-18 6622 6688 1.2 50 37.5 66.112 -162.109

134 2018-19 a20190322_dailyslie.tif 2019-03-22 1831 1946 0.9 31.1 22 65.849 -167.466

135 2018-19 a20190323_dailyslie.tif 2019-03-23 2378 2472 1.7 6 10.6 66.357 -165.683

136 2018-19 a20190326_dailyslie.tif 2019-03-26 1 269 2.5 80.5 192.4 67.031 -172.777

137 2019-20 c20200306_dailyslie.tif 2020-03-06 893 1106 1.4 82.3 79.1 66.270 -170.205

138 2019-20 c20200306_dailyslie.tif 2020-03-06 1655 1990 1.8 72.4 82.4 65.800 -167.736
104

ID Season SLIE file SLIE date v0 v1 W (km) L (km) A (km2) Latitude Longitude

139 2019-20 c20200311_dailyslie.tif 2020-03-11 154 196 0.8 11.2 10 67.016 -172.595

140 2019-20 c20200315_dailyslie.tif 2020-03-15 581 892 0.6 78.5 52 66.639 -171.162

141 2019-20 c20200316_dailyslie.tif 2020-03-16 1870 2011 0.7 32.5 22.4 65.913 -167.253

142 2019-20 c20200325_dailyslie.tif 2020-03-25 65 119 1.2 14.3 17.6 67.062 -172.932

143 2019-20 c20200325_dailyslie.tif 2020-03-25 1071 1108 1.2 25.8 16 66.172 -169.965

144 2019-20 c20200419_dailyslie.tif 2020-04-19 1 70 1.7 24.7 39.7 67.113 -173.293

145 2020-21 c20210114_dailyslie.tif 2021-01-14 6688 6749 1.2 37.6 30.8 66.064 -162.643

146 2020-21 c20210218_dailyslie.tif 2021-02-18 5747 6056 1.3 47.4 39.9 70.311 -161.678

147 2020-21 c20210224_dailyslie.tif 2021-02-24 5465 6331 1.1 163.4 180.6 70.246 -161.769

148 2020-21 c20210228_dailyslie.tif 2021-02-28 5540 5689 1.3 12.4 18 70.106 -162.470

149 2020-21 c20210228_dailyslie.tif 2021-02-28 5708 5798 1.4 18.5 29.5 70.243 -162.125

150 2020-21 c20210303_dailyslie.tif 2021-03-03 3893 4172 1.8 24 28.9 68.412 -166.489

151 2021-22 c20220225_dailyslie.tif 2022-02-25 6332 6456 6.8 94.1 281.5 66.861 -162.971

152 2022-23 a20230120_dailyslie.tif 2023-01-20 37 373 2.1 76.7 165.6 67.056 -172.524

153 2022-23 a20230120_dailyslie.tif 2023-01-20 6659 6913 19.9 151.6 1255.2 66.238 -163.040

154 2022-23 a20230215_dailyslie.tif 2023-02-15 1548 2420 11.8 203.4 1605.7 65.988 -167.376

155 2022-23 a20230215_dailyslie.tif 2023-02-15 6661 6773 3.4 64.1 145.6 66.123 -162.714

156 2022-23 a20230217_dailyslie.tif 2023-02-17 1880 1989 0.8 20.9 12.6 65.884 -167.335

157 2022-23 a20230217_dailyslie.tif 2023-02-17 2421 2485 2.4 9.3 22 66.371 -165.640

158 2022-23 a20230217_dailyslie.tif 2023-02-17 2491 2557 1.7 4 6.1 66.410 -165.468

159 2022-23 a20230220_dailyslie.tif 2023-02-20 2812 2842 3.4 55.1 84.9 66.227 -162.069

160 2022-23 a20230220_dailyslie.tif 2023-02-20 6605 6676 3 68.7 91.6 66.222 -162.087

161 2022-23 a20230329_dailyslie.tif 2023-03-29 5012 5066 1.1 19.7 20.6 69.168 -163.517

A.2 Beaufort region breakout events

The 202 breakout events identified the Beaufort region from the EM2024 according to the criteria 

described in Section 2.2.7 are listed in Table A-2 below. The columns in this table are explained above in 

Section A.1. 

Table A-2: Catalog of breakout events identified in the Beaufort region 

ID Season SLIE file SLIE date v0 v1 W (km) L (km) A (km2) Latitude Longitude

1 1996-97 r19970306_dailyslie.tif 1997-03-06 333 373 1.9 20 37.6 71.340 -156.791

2 1997-98 r19980105_dailyslie.tif 1998-01-05 2 78 0.7 25.8 17.1 70.630 -160.077

3 1997-98 r19980105_dailyslie.tif 1998-01-05 2 207 1 50.8 48.5 70.751 -159.736

4 1997-98 r19980105_dailyslie.tif 1998-01-05 1246 1272 1.2 32.6 32.5 70.052 -144.803

5 1997-98 r19980105_dailyslie.tif 1998-01-05 1407 1439 0.6 16 13.6 70.079 -142.885

6 1997-98 r19980116_dailyslie.tif 1998-01-16 216 253 0.7 19.3 9.7 70.894 -159.023

7 1997-98 r19980207_dailyslie.tif 1998-02-07 369 644 12.9 142.7 1782.3 71.383 -155.288

8 1997-98 r19980428_dailyslie.tif 1998-04-28 327 357 0.6 15.2 12 71.264 -156.889

9 1998-99 r19990113_dailyslie.tif 1999-01-13 271 329 3.3 67.9 215.2 70.999 -157.571
105

ID Season SLIE file SLIE date v0 v1 W (km) L (km) A (km2) Latitude Longitude

10 1998-99 r19990113_dailyslie.tif 1999-01-13 552 658 10.9 98.9 704.1 71.147 -154.563

11 1998-99 r19990113_dailyslie.tif 1999-01-13 730 1028 17 232.9 3075.8 70.796 -150.544

12 1998-99 r19990113_dailyslie.tif 1999-01-13 1074 1267 6.3 184.4 879.8 70.297 -146.456

13 1998-99 r19990113_dailyslie.tif 1999-01-13 1453 1565 3.4 111.2 359.2 69.846 -141.846

14 1998-99 r19990113_dailyslie.tif 1999-01-13 1754 1799 11.4 78.8 654.9 69.226 -137.890

15 1998-99 r19990312_dailyslie.tif 1999-03-12 1212 1274 11.3 77.9 647.4 70.164 -145.120

16 1998-99 r19990312_dailyslie.tif 1999-03-12 1363 1524 8.6 87.4 807.7 70.073 -142.186

17 1998-99 r19990421_dailyslie.tif 1999-04-21 878 1936 81.2 885.4 49353 70.349 -140.979

18 1999-00 r20000106_dailyslie.tif 2000-01-06 216 248 1.5 13.3 9 70.891 -159.114

19 1999-00 r20000106_dailyslie.tif 2000-01-06 569 666 6.1 100.5 500.8 71.105 -154.421

20 1999-00 r20000402_dailyslie.tif 2000-04-02 533 1936 77.4 1215.9 61395 70.660 -143.156

21 2000-01 r20010110_dailyslie.tif 2001-01-10 551 780 16.6 183.7 2315.7 71.175 -153.949

22 2000-01 r20010110_dailyslie.tif 2001-01-10 1172 1248 3.8 51.1 253.7 70.112 -145.347

23 2000-01 r20010110_dailyslie.tif 2001-01-10 1309 1360 1.8 20.7 34 70.160 -143.475

24 2000-01 r20010121_dailyslie.tif 2001-01-21 1295 1323 2.2 8 11.2 70.151 -143.717

25 2000-01 r20010201_dailyslie.tif 2001-02-01 1174 1218 1.5 7.9 12.8 70.164 -145.783

26 2000-01 r20010201_dailyslie.tif 2001-02-01 1393 1540 10.8 104.3 1011.6 69.978 -142.173

27 2000-01 r20010306_dailyslie.tif 2001-03-06 164 252 1.1 33.5 26.7 70.876 -159.234

28 2000-01 r20010306_dailyslie.tif 2001-03-06 295 321 1.7 30.6 48.9 71.027 -157.381

29 2000-01 r20010316_dailyslie.tif 2001-03-16 199 253 0.4 27.6 8.2 70.889 -159.098

30 2000-01 r20010327_dailyslie.tif 2001-03-27 2 96 1.5 32.1 46.3 70.646 -160.061

31 2000-01 r20010327_dailyslie.tif 2001-03-27 109 174 0.8 4.9 4.6 70.813 -159.610

32 2000-01 r20010327_dailyslie.tif 2001-03-27 109 179 0.7 9 5.4 70.815 -159.595

33 2000-01 r20010327_dailyslie.tif 2001-03-27 207 233 0.2 7.2 1.6 70.870 -159.220

34 2000-01 r20010327_dailyslie.tif 2001-03-27 314 439 1.1 45.1 66 71.259 -156.886

35 2001-02 r20020104_dailyslie.tif 2002-01-04 1 62 1.5 20.8 30.2 70.628 -160.130

36 2001-02 r20020104_dailyslie.tif 2002-01-04 255 282 3 33.3 92.2 70.860 -158.464

37 2001-02 r20020111_dailyslie.tif 2002-01-11 1908 1936 1.6 0 7.4 69.821 -135.413

38 2001-02 r20020119_dailyslie.tif 2002-01-19 1487 1524 2.3 29.7 65.8 69.859 -141.901

39 2001-02 r20020127_dailyslie.tif 2002-01-27 16 54 0.9 14 10.9 70.643 -160.063

40 2001-02 r20020219_dailyslie.tif 2002-02-19 28 60 0.5 10 5.3 70.667 -159.994

41 2001-02 r20020219_dailyslie.tif 2002-02-19 1889 1936 5.5 0 67.4 69.848 -135.480

42 2001-02 r20020301_dailyslie.tif 2002-03-01 1525 1584 8.4 70.6 478.8 69.738 -140.882

43 2001-02 r20020311_dailyslie.tif 2002-03-11 1 97 0.7 33.1 22.9 70.643 -160.079

44 2001-02 r20020311_dailyslie.tif 2002-03-11 1849 1936 2.6 13.3 68.2 69.826 -135.807

45 2001-02 r20020319_dailyslie.tif 2002-03-19 12 82 0.6 24 13.9 70.663 -160.025

46 2001-02 r20020420_dailyslie.tif 2002-04-20 517 1076 18 481.2 5995.9 71.061 -152.683

47 2001-02 r20020420_dailyslie.tif 2002-04-20 1631 1720 0.4 14.1 3.2 69.646 -139.137

48 2002-03 r20030118_dailyslie.tif 2003-01-18 280 306 0.7 30.7 22.9 70.912 -157.680
106

ID Season SLIE file SLIE date v0 v1 W (km) L (km) A (km2) Latitude Longitude

49 2002-03 r20030118_dailyslie.tif 2003-01-18 1530 1569 1.8 49.4 70.8 69.674 -140.962

50 2002-03 r20030118_dailyslie.tif 2003-01-18 1626 1730 0.4 24.9 9.2 69.618 -139.167

51 2002-03 r20030127_dailyslie.tif 2003-01-27 331 516 3 27.1 132.7 71.355 -156.738

52 2002-03 r20030214_dailyslie.tif 2003-02-14 1682 1734 0.4 11.8 6.1 69.614 -138.922

53 2002-03 r20030222_dailyslie.tif 2003-02-22 1607 1714 0.7 43.7 26.9 69.598 -139.425

54 2002-03 r20030303_dailyslie.tif 2003-03-03 1733 1763 1.5 60.6 52.8 69.417 -138.496

55 2002-03 r20030313_dailyslie.tif 2003-03-13 1631 1690 0.5 10.2 5.1 69.631 -139.177

56 2002-03 r20030401_dailyslie.tif 2003-04-01 303 336 1.2 37.5 45 71.113 -157.205

57 2003-04 r20040202_dailyslie.tif 2004-02-02 251 289 5 46.3 214.4 70.886 -158.309

58 2003-04 r20040212_dailyslie.tif 2004-02-12 80 162 0.5 11.4 4.6 70.781 -159.700

59 2003-04 r20040313_dailyslie.tif 2004-03-13 61 179 0.5 23.8 12.7 70.769 -159.741

60 2004-05 r20050101_dailyslie.tif 2005-01-01 1128 1212 2.7 41.9 112 70.231 -146.240

61 2005-06 r20060104_dailyslie.tif 2006-01-04 1543 1575 7.4 39.4 241 69.692 -140.840

62 2005-06 r20060104_dailyslie.tif 2006-01-04 1587 1691 0.7 51.4 37.1 69.606 -139.528

63 2005-06 r20060114_dailyslie.tif 2006-01-14 315 368 0.7 34.9 19.5 71.250 -156.916

64 2005-06 r20060114_dailyslie.tif 2006-01-14 498 672 31.4 182.7 3317.5 71.355 -154.687

65 2005-06 r20060114_dailyslie.tif 2006-01-14 1425 1492 0.7 41.3 22.2 69.966 -142.429

66 2005-06 r20060203_dailyslie.tif 2006-02-03 2 177 1.9 41.9 89.4 70.708 -159.937

67 2005-06 r20060203_dailyslie.tif 2006-02-03 731 845 11.3 73.8 637.7 70.945 -151.876

68 2005-06 r20060308_dailyslie.tif 2006-03-08 1907 1935 3.2 0 26.5 69.898 -135.399

69 2006-07 r20070101_dailyslie.tif 2007-01-01 1626 1691 0.6 19.6 25.5 69.621 -139.253

70 2006-07 r20070113_dailyslie.tif 2007-01-13 1173 1232 1.5 27.2 41.6 70.150 -145.606

71 2006-07 r20070123_dailyslie.tif 2007-01-23 223 296 4.9 70.2 389.5 70.886 -158.209

72 2006-07 r20070123_dailyslie.tif 2007-01-23 223 300 4.6 74.9 391.3 70.886 -158.206

73 2006-07 r20070221_dailyslie.tif 2007-02-21 517 701 14.3 194.9 1705.1 71.213 -154.341

74 2006-07 r20070221_dailyslie.tif 2007-02-21 1361 1579 8.6 153.6 1275.1 69.888 -141.691

75 2006-07 r20070221_dailyslie.tif 2007-02-21 1743 1794 11.7 83 740.1 69.274 -138.065

76 2006-07 r20070303_dailyslie.tif 2007-03-03 664 723 4.3 39.2 78.5 71.029 -153.165

77 2006-07 r20070323_dailyslie.tif 2007-03-23 304 358 2.1 42.9 96.1 71.200 -157.053

78 2006-07 r20070323_dailyslie.tif 2007-03-23 1420 1590 13.2 143.1 1491.7 69.921 -141.634

79 2006-07 r20070410_dailyslie.tif 2007-04-10 1619 1690 1.2 27.3 51.2 69.639 -139.313

80 2007-08 r20080114_dailyslie.tif 2008-01-14 54 80 0.2 7.8 1.6 70.713 -159.873

81 2007-08 r20080114_dailyslie.tif 2008-01-14 292 364 1.1 61 67.4 71.105 -157.202

82 2007-08 r20080114_dailyslie.tif 2008-01-14 550 733 8.5 168.8 1048.4 71.090 -154.168

83 2007-08 r20080114_dailyslie.tif 2008-01-14 968 1106 5.6 110.8 490.4 70.488 -148.249

84 2007-08 r20080114_dailyslie.tif 2008-01-14 1202 1251 2.2 50.6 108.4 70.071 -145.318

85 2007-08 r20080114_dailyslie.tif 2008-01-14 1415 1533 4.8 80.4 318.8 69.954 -142.247

86 2007-08 r20080114_dailyslie.tif 2008-01-14 1542 1576 1.6 41.7 58.2 69.653 -140.799

87 2007-08 r20080114_dailyslie.tif 2008-01-14 1763 1933 9.7 225.6 1058.4 69.381 -136.742
107

ID Season SLIE file SLIE date v0 v1 W (km) L (km) A (km2) Latitude Longitude

88 2007-08 r20080124_dailyslie.tif 2008-01-24 11 37 0.6 9 4.7 70.618 -160.107

89 2007-08 r20080124_dailyslie.tif 2008-01-24 48 84 0.4 12.9 5.2 70.712 -159.862

90 2007-08 r20080124_dailyslie.tif 2008-01-24 216 254 0.7 20.5 10.7 70.897 -159.024

91 2007-08 r20080124_dailyslie.tif 2008-01-24 1290 1327 1.1 15.6 10.6 70.141 -143.759

92 2007-08 r20080124_dailyslie.tif 2008-01-24 1496 1588 1.9 94.9 154.3 69.707 -141.214

93 2007-08 r20080124_dailyslie.tif 2008-01-24 1682 1731 0.4 8.5 3.6 69.630 -139.003

94 2008-09 c20090113_dailyslie.tif 2009-01-13 252 358 2.8 105.7 303.1 71.005 -157.757

95 2008-09 c20090208_dailyslie.tif 2009-02-08 1759 1791 3.5 53.8 146.9 69.198 -138.059

96 2008-09 c20090222_dailyslie.tif 2009-02-22 1769 1796 3.2 43.2 119.3 69.127 -137.774

97 2008-09 c20090308_dailyslie.tif 2009-03-08 548 1133 4.1 512.7 1441.2 70.844 -150.951

98 2008-09 c20090308_dailyslie.tif 2009-03-08 1637 1744 0.5 40.7 16.7 69.557 -138.897

99 2008-09 c20090329_dailyslie.tif 2009-03-29 1361 1539 4.1 105.1 434.1 69.971 -142.257

100 2008-09 c20090329_dailyslie.tif 2009-03-29 1585 1691 1 53.6 57 69.643 -139.564

101 2008-09 c20090416_dailyslie.tif 2009-04-16 1129 1246 2.5 83.9 165.5 70.161 -145.717

102 2009-10 c20100103_dailyslie.tif 2010-01-03 1633 1735 0.9 34 17.9 69.627 -139.011

103 2009-10 c20100317_dailyslie.tif 2010-03-17 1631 1733 0.5 19.8 8.8 69.634 -139.051

104 2010-11 c20110123_dailyslie.tif 2011-01-23 335 649 5.8 165.8 897.6 71.328 -155.111

105 2010-11 c20110127_dailyslie.tif 2011-01-27 520 708 6.9 191.1 870.9 71.187 -154.722

106 2012-13 c20130101_dailyslie.tif 2013-01-01 360 511 0.6 12.8 8.7 71.374 -156.561

107 2012-13 c20130106_dailyslie.tif 2013-01-06 7 89 0.9 29.4 26.7 70.700 -159.914

108 2012-13 c20130106_dailyslie.tif 2013-01-06 136 202 0.5 13 5.9 70.825 -159.517

109 2012-13 c20130113_dailyslie.tif 2013-01-13 1799 1936 11.6 164.2 1096.8 69.567 -136.285

110 2012-13 c20130120_dailyslie.tif 2013-01-20 179 258 0.7 36.5 17.1 70.885 -159.126

111 2012-13 c20130217_dailyslie.tif 2013-02-17 260 535 2.8 140.9 609.1 71.069 -157.699

112 2012-13 c20130217_dailyslie.tif 2013-02-17 1610 1806 1.6 180.7 258.8 69.405 -138.282

113 2012-13 c20130221_dailyslie.tif 2013-02-21 251 332 5.7 96.1 513.4 70.958 -157.801

114 2012-13 c20130414_dailyslie.tif 2013-04-14 2 94 1.7 32.1 56.1 70.669 -159.999

115 2013-14 c20140107_dailyslie.tif 2014-01-07 216 299 0.9 75.1 82.1 70.893 -158.125

116 2013-14 c20140115_dailyslie.tif 2014-01-15 54 108 0.4 14.9 7.3 70.740 -159.798

117 2013-14 c20140122_dailyslie.tif 2014-01-22 50 76 0.2 8 1.8 70.699 -159.902

118 2013-14 c20140122_dailyslie.tif 2014-01-22 50 79 0.2 9.5 1.9 70.701 -159.894

119 2013-14 c20140122_dailyslie.tif 2014-01-22 85 114 0.2 5.6 1 70.778 -159.701

120 2013-14 c20140129_dailyslie.tif 2014-01-29 23 56 0.5 12.1 5.6 70.654 -160.021

121 2013-14 c20140129_dailyslie.tif 2014-01-29 109 161 0.5 2.6 1 70.805 -159.633

122 2013-14 c20140205_dailyslie.tif 2014-02-05 1626 1783 0.9 122.2 130.2 69.343 -138.341

123 2013-14 c20140212_dailyslie.tif 2014-02-12 178 248 0.6 25.3 9.8 70.863 -159.304

124 2013-14 c20140216_dailyslie.tif 2014-02-16 1594 1636 1.1 38.5 47 69.607 -139.409

125 2013-14 c20140305_dailyslie.tif 2014-03-05 1636 1777 0.9 98.3 96.4 69.421 -138.464

126 2013-14 c20140316_dailyslie.tif 2014-03-16 90 199 0.7 15.1 11.1 70.817 -159.558
108

ID Season SLIE file SLIE date v0 v1 W (km) L (km) A (km2) Latitude Longitude

127 2013-14 c20140326_dailyslie.tif 2014-03-26 135 269 1 58.1 60.7 70.897 -159.057

128 2013-14 c20140326_dailyslie.tif 2014-03-26 319 464 0.5 39.5 29.8 71.292 -156.864

129 2013-14 c20140327_dailyslie.tif 2014-03-27 42 98 0.7 19 14.7 70.733 -159.885

130 2013-14 c20140402_dailyslie.tif 2014-04-02 1636 1731 0.2 10.9 2.7 69.628 -139.007

131 2013-14 c20140423_dailyslie.tif 2014-04-23 25 96 0.9 24 23.1 70.707 -159.899

132 2013-14 c20140423_dailyslie.tif 2014-04-23 247 369 1.9 118.2 237.5 70.966 -158.030

133 2013-14 c20140427_dailyslie.tif 2014-04-27 52 88 0.5 12.8 5.9 70.719 -159.847

134 2013-14 c20140427_dailyslie.tif 2014-04-27 201 281 0.9 60.2 50.6 70.854 -158.742

135 2013-14 c20140427_dailyslie.tif 2014-04-27 525 939 23.3 399.2 5260.7 71.197 -152.579

136 2013-14 c20140430_dailyslie.tif 2014-04-30 1614 1741 0.7 67.7 50.4 69.609 -139.244

137 2014-15 c20150102_dailyslie.tif 2015-01-02 1417 1560 5.6 114.8 591.1 69.867 -141.797

138 2014-15 c20150115_dailyslie.tif 2015-01-15 1108 1292 5.6 168.7 763.7 70.161 -145.514

139 2014-15 c20150128_dailyslie.tif 2015-01-28 1472 1620 1.5 148.8 187.7 69.748 -141.229

140 2014-15 c20150217_dailyslie.tif 2015-02-17 1137 1173 1.9 26.7 55.4 70.207 -146.179

141 2014-15 c20150218_dailyslie.tif 2015-02-18 1682 1749 1.5 46 64.5 69.555 -138.782

142 2014-15 c20150414_dailyslie.tif 2015-04-14 544 704 10.1 155.2 875.4 71.249 -154.509

143 2014-15 c20150429_dailyslie.tif 2015-04-29 1682 1750 1.2 48.1 56.8 69.543 -138.717

144 2015-16 c20160106_dailyslie.tif 2016-01-06 2 48 0.4 15.8 6.3 70.614 -160.105

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109

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