Mission Updates
Denis Felikson
03/22/2023
Future of Greenland ice Sheet Science (FOGSS)

2
Measurement concept
§ 3-km spacing between pairs provides spatial coverage
§ 90-m pair spacing for slope determination (2° yaw)
§ high-energy beams (3x) for better performance over low-reflectivity targets
§ Single pulse of 532 nm laser light
§ Split into six beams
§ Single-photon sensitive detection
From Smith et al. (2019)

500 km altitude
88S to 88N
15 revs/day
1387 tracks
91-day revisit
Ground tracks at
icesat-2.gsfc.nasa.gov
Orbit and repeat track design

4
~1,700 days on-orbit since launch
ATLAS: transmitting laser light since 01 October 2018
1.3 trillion (and counting) laser pulses
Performance metrics remain nominal,
and within requirements
Current Status
Observatory and instrument status

Outlook on mission lifetime
• Instrument components performing very well
• Fuel is main known limiting factor, projected to until year ~2037

Data products

7
Data access
Several (hopefully) easy ways to get at ICESat-2 lidar data
National Snow and Ice Data Center
http://nsidc.org
OpenAltimetry
http://openaltimetry.org
SlideRule
http://voila.icesat2sliderule.org/
CryoHub
http://cryointhecloud.com
icepyx
https://icepyx.readthedocs.io

https://icesat-2.hackweek.io/
ICESat-2 Hackweek

Greenland Ice Sheet science
More at https://icesat-2.gsfc.nasa.gov/publications
Medley, B., et al. (2022),
https://doi.org/10.5194/tc-16-3971-2022
Smith, B.E., et al. (2020),
https://doi.org/10.1126/science.aaz5845
Pervasive ice sheet mass loss
reflects competing ocean and
atmosphere processes
Khan, S.A., et al. (2022),
https://doi.org/10.1038/s41586-022-05301-z
Extensive inland thinning and speed-up of
Northeast Greenland Ice Stream
Simulations of firn processes
over the Greenland and
Antarctic ice sheets: 1980–2021
On-going work:
1. Release 006 for along-track data products
2. New data products in development:
• Gridded quarterly mass change product (based on ATL15) using Community Firn Model
• Global shallow bathymetry product

Backup slides

11
Measurement Concept
We want to measure elevation
Lidar measures range (time of flight)
Spacecraft measures absolute pointing angle
GPS measures position in orbit
Ground processing puts the pieces together

Josh Willis, Principal Investigator
Eric Rignot, Deputy PI
Ian Fenty, Project Scientist
Oceans Melting Greenland
NASA EVS-2 mission
2015-2021

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GRACE
mass
loss
ocean
warming
Hypothesis:
Ocean warming drives a
large part of Greenland’s
ice loss

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GLISTIN-A
AXCTDs
The OMG Experiment
1) measure how
the ocean
changes
2) observe how
glaciers reacts
3) ask: how do glaciers
react to ocean
changes?

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Free-Air Gravity
Sea Floor Depth
from SONAR
To understand how
they are related, we
also need to know the
shape and depth of the
sea floor…
The OMG Experiment

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OMG revolutionized
our knowledge of
the depth of
Greenland’s
continental shelf
This fundamentally
changes our
understanding of
how the Oceans are
Melting Greenland
Warm
water
Ocean Depth

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OMG science impact: 84 pubs to date
AXCTDs & ship CTDs
GLISTIN
Airborne Gravity
Ship based bathymetry
Willis et al., Oceanography, 2018
An et al., Remote Sensing, 2019 Morlighem et al., Geophys.
Res. Lett., 2017
Data from every survey has been
used in peer reviewed publications

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OMG, Jakobshavn is Growing!
OMG showed that short-term growth of
Greenland’s largest glacier was due to a
cyclic change in ocean temperature
https://apnews.com/b19abfb0a0534b51925aa121806255a8
Growth in 2016
Growth in 2017
Growth in 2018

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Ocean cooling à glacier thickening
30 m rise in
elevation
between 2016
and 2017
OMG observed thickening of Jakboshavn
for the first time in almost 20 years.
Caused by arrival of cold water in Disko
Bay, which saw the coldest summer
water temperature since the 1980s.
2°C
cooling!
Khazendar, Fenty, Carroll, et al., Two decades of Jakobshavn Isbrae Acceleration and Thinning Interrupted as Ocean Cools,
Nature Geoscience, 2019.

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Undercutting of northern ice shelves by a warmer ocean
- Zachariae (ZI) and 79North (79N) hold an ice volume above flotation equivalent to a 1.1 m SLR
- OMG survey reveals a deep channel in front of ZI and a shallow sill in front of 79N
- Modeling of glacier evolution since 1960’s supports retreat of ZI controlled by warmer ocean waters.
An, Rignot, Wood, et al., Ocean melting of the Zachariae Isstrøm and Nioghalvfjerdsfjorden glaciers, northeast Greenland
Proc. Nat. Acad. Sci., 2021
à Glacier undercutting, Qm, controls 1/3 of the retreat, Qgl, vs glacier thinning by melt and speed, Qs.
à OMG data help explain the contrasting behavior of the two glaciers and explain the retreat (within 12%).
Deep channel at Zachariae
Gravity survey Warm waters blocked by sill at 79N

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Hypothesis confirmed:
Ocean Plays a Large Role in
Greenland Ice Loss
Nearby ocean warming and cooling
explains half of Greenland’s ice loss.
If ocean is ignored, predictions
underestimate melt by factor of 2
Wood, Rignot, Fenty, et al., Ocean melting of the Zachariae Isstrøm and Nioghalvfjerdsfjorden glaciers, northeast Greenland
Proc. Nat. Acad. Sci., 2021

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Y. Choi
simulated the
evolution of
200+ glaciers
using OMG
data
Looking to the future: Ocean Will Continue to Play Large Role
in Greenland Ice Loss through 2100
Projections using JPL’s Ice Sheet and Sea
Level System (ISSM) Model
With ocean forcing, ice discharge is 2x higher than in previous projections
Ice loss
from
increased
discharge
continues
through
2100
Choi, Morlighem, Rignot, Wood, Ice dynamics will remain a primary driver of Greenland ice sheet mass loss over the next
century. Nature Communications: Earth and Environment (2021)

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OMG-Narwhals
Hypothesis
Narwhal abundance and behavior is sensitive to seasonal
and interannual changes in ocean physical properties,
including temperature and salinity
Approach
Simultaneously record ocean acoustic signals at multiple
frequencies and ocean physical properties at multiple
depths at sub-daily frequencies three years (reduced to two
years because of Covid-19)
Status
Detection of narwhal acoustic signals in recordings
complete. Ocean physical data analysis ongoing by U.
Washington Ph.D. candidate Marie Zahn (K. Laidre,
advisor)
Moorings
GINR Vessel R/V Sanna
A collaboration between the NASA Phys. Ocean
Program, ONR Marine Mammal Program, and GINR

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Argo-like Floats
MRV
Alamo
Teledyne
APEX
MRV –
small, easy
to handle but
~50% failure
Teledyne -
bulky, but
excellent
reliability
(similar cost)
~$25k

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The APEX floats
not only survived
the winter, they
remained on the
shelf collecting
data all winter
APEX floats can
easily monitor
changes in ocean
temperature and
salinity on the shelf

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OMG About Page
Community Outreach
via OMG Website

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A program called “Greenland Rising” developed an app to deliver
OMG and other bathymetry data to local fisherman.
Halibut provides
primary income
for fisherman
like Jonas,
pictured here
With the help of the app, Jonas
moved his line by 600 m, and
substantially improved his catch

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2021 Public Outreach
Several hundred local
students received lectures
and/or visited the airplane in
Kangerlussuaq, Ilulissat, and
Nuuk
Despite previous NASA
& NSF missions, few
prior talks like these

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https://github.com/nasa/resample_GLISTIN_DEMs

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OMG was successful because NASA
allowed us to be flexibility with how we
achieved the scientific objectives
• aircraft changes
• university partnerships (e.g., U. Washington)
• Instrument changes (e.g., APEX)
• changing survey areas
A robust university science Co-Investigator
team was also critical to the mission
success
Food for Thought: EVS

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Petermann Glacier

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Umiamako Glacier

Eric Rignot, University of California Irvine and JPL
NASA / ISRO SAR Mission “NISAR”
to be launched in early 2024

Science Team at the service of CRYO:
Ian Joughin (lead), Eric Rignot, Ben Holt, Sean Helfrich, Rick Forster, Bernd Scheuchl, Alex Gardner

NISAR Science Observation Overview
Cryosphere, Solid Earth, Ecosystems
NISAR Uniquely Captures the Earth in Motion
NISAR Characteristic: Would Enable:
L-band (24 cm wavelength) Low temporal decorrelation
and foliage penetration
S-band (9 cm wavelength) Sensitivity to light vegetation
SweepSAR technique with
Imaging Swath > 240 km
Global data collection
Polarimetry
(Single/Dual/Quad)
Surface characterization and
biomass estimation
12-day exact repeat Rapid Sampling
3 – 10 meters mode-
dependent SAR resolution
Small-scale observations
3 years science operations
(5 years consumables)
Time-series analysis
Pointing control < 273
arcseconds
Deformation interferometry
Orbit control < 500 meters Deformation interferometry
> 30% observation duty
cycle
Complete land/ice coverage
Left/Right pointing
capability
Polar coverage, north and
south
NISAR Will Uniquely Capture the Earth in Motion
747
km
Earth
surface
Observation Geometry
>240 km
33o
47o
6 AM / 6 PM
NISAR Science pre-CDR Review

• First SAR mission dedicated to SAR Interferometry.
• L-band (US) and S-band (ISRO) radars.
• Left looking mode (unique) to image all of Antarctica,
Greenland south of 78oN, and mountain glaciers south of
78oN.
• Products: L0/L1 (non-geocoded), L2 (geocoded,
unwrapped phases, corrections) in HDF/geotif on AWS
• L3 (grid) products to be made online (AWS tools) or via
MEaSUREs projects (3 projects pending approval)
• 12-day repeat, all the time, ascending and descending to
map velocity and grounding lines with the phase (10 x
more accurate that speckle tracking)
What will we get from it?

• Continuous access to velocity/grounding line data, with
low latency on the Cloud at weekly to monthly time scales,
day and night, independent of clouds.
• Information on ice dynamics, grounding lines, subsidence.
• Also sea ice extent, motion and type; potential for snow
thickness.
• NISAR to be followed by Tier1 Decadal Survey Mission
Surface and Deformation Change (SDC) which will likely
be a constellation for faster repeat and higher resolution.
• 44+ years into the making (Seasat 1978), this is the
InSAR revolution with a long legacy!
• There will be opportunities to join the Science Team or to
get funding for NISAR research for all of you in 2024/25.
What do this mean for
you?

MEaSUREs Phase-Based Antarctica Ice Velocity Map
Mouginot, J., Rignot, E., & Scheuchl, B (2019) Continent-Wide, Interferometric SAR
Phase, Mapping of Antarctic Ice Velocity. Geophys. Res. Lett., 46(16), 9710-9718.
https://nsidc.org/data/NSIDC-0754/
• InSAR phase: 71% coverage with an error 10 x
smaller (10-20 cm/yr) than with speckle tracking
(1-2 m/yr). Product needed 25-years of
acquisitions in ascending and descending orbits.
• Phase + speckle tracking: 99.8% coverage.
à NISAR will provide ascending &
descending coverage for InSAR phase
analysis every 12 days instead of every 25
years J
Monthly ice velocities (red) since 2015 versus
historical annual velocities (orange).
Antarctic examples

MEaSUREs Antarctic Grounding Line and Grounding Zone
https://nsidc.org/data/NSIDC-0498/
https://nsidc.org/data/NSIDC-0778/
• Differential Satellite Radar Interferometry reveals
grounding lines, the transition boundary between
floating and grounded ice.
• Multiple grounding line retrieval reveals the
grounding zone, which is 10 x wider than
expected.
• Data sources: Sentinel-1 (12d/6d), RADARSAT-2
(24d), COSMO SkyMED (1d)
à NISAR will provide monthly coverage of grounding lines/
grounding zones evolution.
MEaSUREs
Grounding zone
Antarctic examples

Greenland
80MHz SP
LSAR
Greenland
25MHz CP &
37.5MHz HH
SSAR
Sea Ice 5MHz
Greenland
80MHz SP
LSAR
Antarctica
40&80MHz SP
LSAR
Global
Urban Areas
40MHz DP
Coverage of India
Region with
LSAR & SSAR
Sea Ice Quadrant
with LSAR & SSAR
Background Land
20MHz SP
Descending direction
Alternating each 12 days with
Africa and South America
Antarctica
25MHz CP &
37.5MHz HH
SSAR
Sahara
5 MHz QD HH/VV
North America
40MHz QQP & QP
High resolution (80MHz) over Greenland and
Antarctic coast;
Lower resolution (40MHz) over interior Antarctica.

Bending the Curve - ICE
80 Mhz HH L-band
Asc, Cycle 0
Yes, we will miss North Greenland but this is the price to pay to
cover all Antarctica, which no other SAR mission does/will do.
North Greenland to be
covered by ESAS
Sentinel1, JAXA ALOS
PALSAR-4, CSA RCM,
and future ESA
Harmony

Thank you!
NASA / ISRO SAR Mission
“NISAR”

CONCEPTUAL CORE STATION ELEVATION VIEW (circa 2014)
Based Upon Model 5 (even earlier)– Depicts Partial Deployment of Elevated Modules 3
• Power
• Winter Berthing
• Living
• Science
• AWO
CORE STATION ELEVATED MODULES CORE STATION SURFACE MODULES
• Operations Garage
• Summer Berthing
• Science Garage
• Renewable Energy

HSF Helheim Glacier mega-site:
collaborative project to quantify ice-ocean-atmosphere interactions
Chris Bickel, AAAS

Beverly, MA (2013) “Workshop on understanding”
San Francisco, CA (2015) “Establishing an observing system”
San Francisco, CA (2018) “Quantifying freshwater fluxes”
1. Long-term observations at a number of sites
(GrIOOS: Greenland Ice-Ocean Observing System).
2. Process studies to address identified dynamical
processes in isolation.
3. Megasite experiments to study inter-linkages of
process.
Workshops -> Community-driven priorities

GOAL:
improve understanding of processes occurring
at the marine margin of Helheim Glacier
Chris Bickel, AAAS

bed topography at the terminus
(Carl Leuschen, KU/CReSIS)
GEOMETRY:
bathymetry beneath the mélange
(Mathieu Morlighem, Dartmouth)
Project Goals:
• Fill in bed topography data gaps by acquiring dense
grids across Helheim’s terminus.
• VTOL/UAS platform can operate from lower altitudes
and slower speeds than crewed aircraft.
• HF radar less susceptible to attenuation in warm ice.
Project Goals:
• Map bathymetry under Helheim’s permanent pro-
glacial mélange;
• Some point measurements from CTDs and gravity
inversion, but largely unknown geometry.
• Integrate in BedMachine Greenland

OCEAN VARIABILITY:
mélange properties
(Luca Centurioni, UCSD-Scripps)
fjord properties
(Fiamma Straneo, UCSD-Scripps)
fjord-mélange modeling
(Kenneth Hughes, OSU)
(Emily Shroyer, ONR)
Project Goals:
• Collect ocean properties (salinity,
temperature, depth) at various depths
in the melange.
• Deploy drifters throughout the year.
• Successful pilot test in 2019.
Project Goals:
• Multi-year simulations of Sermilik Fjord
with realistic forcing and boundary
conditions.
• Idealized, high-res modeling of ocean
flows in, around, and under the melange
Project Goals:
• Maintain long-term acquisition of fjord
properties with moorings.
• Quantify seasonal and decadal fjord
variability in Sermilik Fjord.
• Investigate drivers of the variability.

present & recent SMB
(Marco Tedesco, LDEO)
ATMOSPHERE VARIABILITY:
Project Goals:
• Generate model simulations at 100m of surface
mass balance and energy balance components
• Close the water budget from the surface to the
bottom of the ice sheet
• Produce maps of surface and shallow-surface
evolution of the hydrological system
• Develop ML tools for downscaling and attribution
analysis (NEW !)
historic SMB
(Sarah Das, WHOI)
Project Goals:
• SMB history (seasonal to annual) over decades to ~century
• Identify mean state, trends and extreme years for ~century
• Identify horizons/age-depth for radar layer mapping
• Ground truth for modelled & remotely-sensed firn variables
• Connect history to downstream catchment (esp surface
hydrologic features and outlet glacier behavior)
Variables
-Accumulation
-Refrozen melt
-Density
-Temperature
-Chemistry
These variables are
poorly constrained by
models and reanalyses in
this region due to high
accumulation rates and
steep gradients
• 100 m downscaled product
improves SMB estimates from
MAR
• ML-based modeling and
attribution analysis

GLACIER VARIABILITY:
velocity & calving
(Leigh Stearns, KU)
(Dave Finnegan, CRREL)
coupled models
(Mathieu Morlighem, Dartmouth)
data fusion & models
(Doug Brinkerhoff, UM)
Project Goals:
• Determine the mechanisms that drive
variability in glacier flux at Helheim;
• Contribute to the development of a
robust glacier-ocean observing
system (GrIOOS)
Project Goals:
• Model the impact that ocean and
atmosphere variability has on glacier flow;
• Connect MAR subglacial discharge, MITgcm
ocean thermal forcing to higher resolution
model (100m - 1.5 km) of Helheim.
• Reproduce Helheim’s variability since 2007
• Possibly derive new calving law
Project Goals:
• Better constraining model parameters by
incorporating uncertainties in
observations and model physics.
• Simultaneous data assimilation over all
unknown model parameters given all
available datasets informed by this
project.

ADDITIONAL HSF PROJECTS:
PIs: Winnie Chu (Georgia Tech), Colin Meyer (Dartmouth), Kristin Poinar (SUNY-Buffalo)
Title: Follow the water
Funding: HSF
PIs: Sridhar Anandakrishnan (Penn State)
Title: Seismic monitoring with geoPebbles
Funding: HSF
PI: Alex Robel (Georgia Tech)
Title: Stochastic Ice Sheet Project
Funding: HSF
PI: Emily Arnold (KU/CReSIS)
Title: CAREER: UAS-Based Radar Suite for Sounding and Mapping Glaciers
Funding: NSF-CAREER
Volume
anomaly
(%)
Years

We love to collaborate - be in touch if any
of this data is useful for your projects!
Discussion prompts:
• This “megasite” style project is hard to find funding
for in Greenland (WAIS, ITGC in Antarctica). How
can we support them outside of foundations?
Adam LeWinter, CRREL