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Damage accelerates ice shelf instability and mass loss in
Antarctica’s Amundsen Sea EmbaymentStef Lhermittea , Sainan Sunb ,
Christopher Shumanc, Bert Woutersa,d, Frank Pattynb , Jan Wuitee ,
Etienne Berthierf , and Thomas Naglere
aDepartment of Geoscience & Remote Sensing, Delft University
of Technology, 2600GA Delft, Netherlands; bLaboratoire de
Glaciologie, Université Libre de Bruxelles, B-1050 Bruxelles,
Belgium; cUniversity of Maryland, Baltimore County, Joint Center
for Earth System Technology, NASA Goddard Space Flight Center,
Greenbelt, MD 20771; dInstitute for Marine and Atmospheric
Research
Utrecht, Utrecht University, 3584 CC Utrecht, The Netherlands;
eENVEO IT GmbH, 6020 Innsbruck, Austria; and fObservatoire
Midi-Pyrénées/Laboratoire d’Etudes en Géophysique et
OcéanographieSpatiales (OMP/LEGOS), Centre national d’études
spatiales (CNES)/CNRS/Institut de recherche pourle développement
(IRD)/Université Paul-Sabatier (UPS), 31000 Toulouse, France
Damage evolution of Pine Island (PIG) and Thwaites (TG) glaciers
from Sentinel-1, -2 (a) and Landsat 5 and 8 (b, d) where texture
changes indicate increased crevassing and extension of shear
margins.
Contains modified Copernicus Sentinel data (2015-2018),
processed by ESA.
Rignot etal. (2014)
GroundingLines (GL)1992 GL1994 GL2000 GL2011 GL
Earth Sciences Division – Hydrosphere, Biosphere, and
GeophysicsThwaites Eastern Ice Shelf (E-shelf)
Location of panel a
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Earth Sciences Division – Hydrosphere, Biosphere, and
Geophysics
Name: Christopher A. Shuman, Code 615, Cryospheric Sciences
Laboratory, UMBC JCET at NASA GSFCE-mail:
[email protected], [email protected]
([email protected] and @steflhermitte)Phone: 301-614-5706
References:S. Lhermitte, S. Sun, C. Shuman, B. Wouters, F.
Pattyn, J. Wuite, E. Berthier, and T. Nagler, Damage accelerates
ice shelf instability and mass loss in Amundsen Sea Embayment.
Proc. Natl. Acad. Sci. U.S.A. 201912890, 7 pgs (2020).
https://doi.org/10.1073/pnas.1912890117
E. Rignot, J. Mouginot, M. Morlighem, H. Seroussi, B. Scheuchl,
Widespread, rapid grounding line retreat of Pine Island, Thwaites,
Smith, and Kohler glaciers, West Antarctica, from 1992 to 2011.
Geophys. Res. Lett. 41, 3502–3509 (2014).
https://doi.org/10.1002/2014GL060140
Data Sources: NASA/USGS Landsat 5, 7, and 8; Copernicus
Sentinel-1 and -2, Advanced Spaceborne Thermal Emission and
Reflection Radiometer (ASTER); and CryoSat-2 sensors; plus Making
Earth System Data Records for Use in Research Environments
(MEaSUREs) Annual Antarctic Ice Velocity Maps between 2005 and
2017; InSAR-Based Ice Velocity of the Amundsen Sea Embayment;
Antarctic Grounding Line from Differential Satellite Radar
Interferometry, Version 2; and modeling outputs from
Berkeley-ISICLES (BISICLES) with a continuum damage model (CDM)
component.
Technical Description of Figures:Figure 1: Damage evolution to
Pine Island (PIG) and Thwaites glaciers (TG) in Antarctica’s
Amundsen Sea Embayment. Image (a) shows a Sentinel-2 mosaic
overview of the Amundsen Sea Embayment area and PIG and TG glaciers
with the maximum strain rate overlaid in purple-green since 2015
derived from a Sentinel-1 velocity time series. Zoom boxes,
transects [P1, T1, not shown here], and grounding line evolution
from Rignot et al (2014) are shown in black and spectral colors
(1992, purple; 1994, blue; 2000, green; 2011, orange),
respectively. Inset areas (b–d) show the evolution of damage areas
in selected Landsat (1997 to 2018) satellite images through time.
Slide 1 adapted from from the PNAS paper’s Figure 1 (inset c not
shown).
Scientific significance, societal relevance, and relationships
to future missions: Utilization of NASA and ESA data sets,
specifically from the Sentinel and Landsat missions, as well as
remote sensing data and other NASA-derived data sets combined with
numerical modeling, has allowed a long-term understanding of how
damage processes and mechanical weakening in glacial ice shear
zones has consequences for predictions of ice shelf stability and
thereby future sea level contributions from major Antarctic
glaciers.
ACKNOWLEDGMENTS:S.L. was funded by the Dutch Research Council
(NWO)/Netherlands Space Office Grant. C.S. was funded by the NASA
Cryospheric Science Program. E.B. acknowledges support from the
French Space Agency (CNES). B.W. was funded by NWO VIDI. T.N. and
J.W. acknowledges support from ESA.
This article contains supporting information online at
https://www.pnas.org/lookup/suppl/
doi:10.1073/pnas.1912890117/-/DCSupplemental.
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Land surface albedo assimilation improves snow estimationSujay
Kumar1, David Mocko2,1, Carrie Vuyovich1, Christa
Peters-Lidard3
1Hydrological Sciences Lab, NASA GSFC, 2SAIC, 3Earth Sciences
Division, NASA GSFC
Surface albedo, the fraction of sunlight reflected by the land
surface, can be measured remotely from satellites. Inthis study,
remotely-sensed surface albedo measurements are combined with other
information in a numericalmodel to produce better maps of the
snowpack, which are valuable for water resources applications. The
use ofalbedo significantly improves snow depth estimates over the
High Plains and parts of the Rocky Mountains, whileit degrades the
estimates over the Northeast U.S.
Earth Sciences Division – Hydrosphere, Biosphere, and
Geophysics
Change in RMSE of simulated snow depth betterworse
vs Global Historical Climate Network vs NOAA Snow Data
Assimilation System (SNODAS) vs University of Arizona snow
product
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Name: Sujay V. Kumar, 617, NASA GSFCE-mail:
[email protected]: 301-286-8663
References:Kumar, S.V., D. Mocko, C. Vuyovich, and C.D.
Peters-Lidard, 2020: Impact of surface albedo assimilation on snow
estimation, Remote Sensing, 12, 645, doi:10.3390/rs12040645.
Data Sources: MODIS-based white sky and black sky albedo
retrievals from the University of Maryland Global Land Cover
Facility (GLCF) Global Land Surface Satellites (GLASS), MODIS
collection 5 fractional snow cover product MOD10A1, surface
meteorological data from the North American Land Data Assimilation
System (NLDAS2) obtained from NASA GES-DISC.
Technical Description of Figures:Figure 1: shows the differences
in root mean square error (RMSE) in snow depth fields (computed as
RMSE (no data assimilation) – RMSE (data assimilation)) in units of
mm from the assimilation of MODIS albedo and fractional snow cover.
RMSE values are computed using snow depth measurements from the
Global Historical Climate Network (GHCN), snow depth estimates from
the NOAA National Weather Service’s National Operational
Hydrological Remote Sensing Center (NOHRSC) Snow Data Assimilation
System (SNODAS) and the gridded snow depth estimates developed by
University of Arizona (UA). Warm colors indicate locations of
improvement and cool colors denote areas of degradation from data
assimilation. Strong patterns of improvements are noted over the
High Plains and parts of the Rocky mountains. Some degradations in
simulated snow depth from assimilation are also observed over the
Northeast U.S.
Scientific significance, societal relevance, and relationships
to future missions: Developing improved estimates of snowpack from
optical remote sensing measurements is challenging due to the
issues of translating snow cover estimates into quantitative
information about the snowpack. On the other hand, surface albedo
measurements from similar sensors can be used to improve the
surface energy partitioning and snow estimates. In this study,
surface albedo and fractional snow cover retrievals from MODIS
(Moderate Resolution Imaging Spectroradiometer) are assimilated
into the NoahMP land surface model, during the time period of
2000-2017. The results of this study indicate that optical
sensor-based albedo measurements along with snow cover can be used
to provide larger quantitative improvements in snow simulation.
These improvements in snow states are also useful in improving
hydrology simulations and estimates of streamflow. The results of
this study are highly relevant given the anticipated measurements
of albedo from a future Surface Biology and Geology (SBG)
designated observable mission.
Earth Sciences Division – Hydrosphere, Biosphere, and
Geophysics
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Global Distribution of Anthropogenic and Natural Mangrove Loss
Drivers 2000-2016Liza Goldberg1,2, David Lagomasino2,3, Nathan
Thomas1,2, Temilola Fatoyinbo2
1University of Maryland Earth Systems Science Interdisciplinary
Center, 2NASA Goddard Biospheric Sciences Laboratory,3East Carolina
University Coastal Studies Institute
Humans have contributed most to mangrove loss globally. However,
we show that these anthropogenic hotspots areconcentrated in few
regions around the world regardless of national mangrove inventory.
The increasing influence ofnatural drivers suggest that future
mangrove resilience must account for sea level rise and extreme
events, particularly inhighly populated coastal regions. Our
results can both inform restoration efforts and improve blue carbon
emissionsestimation.
Earth Sciences Division – Hydrosphere, Biosphere, and
Geophysics
-
Name: Liza Goldberg, Earth Sciences, NASA GSFC, University of
Maryland Earth Systems Science Interdisciplinary CenterE-mail:
[email protected]: 443-832-0665
References:Goldberg, L, Lagomasino, D, Thomas, N, Fatoyinbo, T.
Global declines in human-driven mangrove loss. Glob Change Biol.
2020; 00: 1– 12. https://doi.org/10.1111/gcb.15275
Data Sources:• Loss extent maps and Random Forest
classifications relied upon NDVI anomalies using Landsat 5 TM and
Landsat 7 ETM+ imagery from January 1998 through
December 2016. • Mangrove extent maps were derived from the Giri
et al. Global Mangrove Forest layer for the year 2000. • Land use
maps were derived from a series of decision trees that used the JRC
Global Surface Water 2016 occurrence layer, the Hansen et al.
Global Forest Change
layer, the Global Human Settlements Layer (GHSL), and the Global
Food Security-support Analysis Data Cropland Extent 30-m (GFSAD-30)
layer.
Technical Description of Figures:Figure 1: Global distribution
of mangrove loss and its drivers. (a) The longitudinal distribution
of total mangrove loss and the relative contribution of its primary
drivers. Different colors represent unique drivers of mangrove
loss. (b) The latitudinal distribution of total mangrove loss and
the relative contribution of its primary drivers. (c-g) Global
distribution of mangrove loss and associated drivers from 2000 to
2016 at 1°×1° resolution, with the relative contribution
(percentage) of primary drivers per continent: (c) North America,
(d) South America, (e) Africa, (f) Asia, (g) Australia together
with Oceania.
Scientific significance, societal relevance, and relationships
to future missions:Here we provide the first global high-resolution
analysis of the anthropogenic and natural threats to mangrove
forests, enabling conservation and restoration activities to
account for past local ecosystem vulnerabilities in planning for
more sustainable forest management. This work falls under the
Ecosystem Change and Sea Level Rise focuses of the Decadal Survey,
as we analyze the implications of long-term natural disturbances
such as sea level-rise induced erosion and extreme weather events
on ecosystem health at the local to global level. In mapping the
intersection of anthropogenic and natural losses, we provide a more
complete understanding of the most significant hotspots for future
conservation efforts.
Earth Sciences Division – Hydrosphere, Biosphere, and
Geophysics
https://doi.org/10.1111/gcb.15275
