EPANDING THE CONTENT OF AN OUTLINE using notes.pptx
morningkeynote.pdf
1. Decolonizing US-Based Research in Greenland
Future of Greenland Ice Sheet Science (FOGSS) Workshop
22 March 2023
Melody Brown Burkins, PhD
Director, Institute of Arctic Studies
UArctic Chair in Science Diplomacy & Inclusion
Steering & Host Committees, International Conference on Arctic Research Planning (ICARP IV)
Dartmouth
4. Established in 1989, We Are:
● International and Transdisciplinary
● Host to Opportunities for Students in Experiential Education, Research, and Policy
● Host to Opportunities for Faculty to Coordinate, Collaborate, & Engage in Arctic Policy
● Host to Diverse Arctic Knowledge Holders (Fulbright Canada Research Chair in Arctic Studies)
● Hub for Building and Maintaining Trusted Partnerships with US & Global Arctic institutions
and Arctic Indigenous Peoples’ Organizations
● Hub for Developing Informed, Inclusive, Impactful Solutions to Arctic & Global Policy
Challenges, from Climate Change to Governance, Security, Health, and Human Rights
7. Arctic Indigenous Peoples’ Guidance
for Ethical and Equitable Engagement
‘Nothing About Us Without Us’
• Co-Design of Research Questions with Communities
• Recognize Indigenous Knowledge in its Own Right & Respect of Diverse
Ways of Knowing
• Commitment to Knowledge Co-Production
• Commitment to Knowledge Reciprocity
• Uphold Rights to Self-Determination (UNDRIP, FPIC)
• Respect of Language, History, and Culture
• Practice Good Governance
• Exercise Accountability - Build Trust
• Equitably Fund Indigenous Representation & Knowledge
Summary from documents in previous slide, with specific points from the Inuit Circumpolar Council synthesis report (2022) Circumpolar Inuit Protocols for Equitable and Ethical Engagement
(ICC EEEP) https://hh30e7.p3cdn1.secureserver.net/wp-content/uploads/EEE-Protocols-LR-WEB.pdf
9. • Knowledge co-production has been linked to cultivating trust, capacity,
and knowledge flows, which can assist learning within the participating
stakeholder groups, build networks, foster social capital, strengthen
funding for collaborative research, inform policy formation, rally public
acceptance, and develop actions that contribute to sustainability (Arnott
et al. 2020; Norström et al. 2020; Reyers et al. 2015).
• The processes of bridging cultural/epistemological differences force
partners to openly confront histories of colonisation, reflect on their
positions as researchers, and develop decolonising methods to redress
those histories in pursuit of Indigenous data sovereignty, ownership, and
intellectual property rights (CTKW 2014; Hill et al. 2020b; Maclean et al.
2021; Zurba et al. 2019).
from Zurba et al. (2021): https://link.springer.com/article/10.1007/s11625-021-00996-x
12. “Addressing unprecedented Arctic
environmental changes requires listening to
one another, aligning values, and collaborating
across knowledge systems, disciplines, and
sectors of society.”
2022 Arctic Report Card
13. 2018
2021
Bilateral collaboration
between the Greenland
(Kalaallit Nunaat) and
United States Research
Communities – from a
vision to everyday
practice
Jennifer L. Mercer , Josephine
Nymand, Lauren E. Culler , Rebecca
Lynge, Sten Lund, Bo Gregersen,
Brett Makens, Ross A. Virginia and
Kristian G. Moore
14. 2022
The solution lies in climate resilient
development…
This why the choices made in the next
few years will play a critical role in
deciding our future and that of
generations to come.
To be effective, these choices need to
be rooted in our diverse values,
worldviews and knowledges, including
scientific knowledge, Indigenous
Knowledge and local knowledge.
This approach will facilitate climate
resilient development and allow locally
appropriate, socially acceptable
solutions.
16. When it comes to climate change, Greenland
takes center stage, and it holds many of the
answers we’re looking for. This explains why
researchers from all over the world find their way
here. The knowledge gained in Greenland is
valuable and has the potential to drive society
forward – both locally and globally.
For that reason, it is important to have a
comprehensive unit in place that can ensure that
the knowledge gained here is guaranteed the
greatest possible outreach and impact, and that
Greenland is not just a backdrop for
research, but an active participant.
22. What can you do?
• Practice and Teach Ethical & Equitable Engagement in Arctic
Research & Scholarship
• If You Haven’t Yet: Share Your Research with the Arctic Hub of
Greenland (https://arctichub.gl)
• Co-Design Future Research with Greenland’s Researchers
• Consider a FOGSS Contribution to ICARP IV (www.iasc.info) in
2023
25. How physical oceanographers have been
ingesting/assimilating data to get to a
better state estimate
Focus of talk
https://www.ecco-group.org/docs/ss_2019_ECCO_FHL_assim_2.pdf
26. Resulting framework (in ECCO approach):
- is data-constrained & dynamically coherent
- allows observations to be viewed in fullest space-time context
- allows connection & comparison of diverse observations
- exposes observation impact on underlying model
Why data assimilation?
27. It’s all about …
– making optimal use of,
– consistently extracting,
– or combining
information contained in observations and physical laws expressed
through a model, and taking into account all uncertainties.
What is data assimilation?
28. Data Assimilation can mean very(!) different things to different people
Science goal / application à determines the framework
[Stammer et al., 2016]
• I have data from 20XX-YY,
will it help the model before
and after?
• I have observations from
20XX, why can’t the model
match the data?
• ECCO uses … (e.g., SST) to
“constrain” the state
estimate
29. Data Assimilation: Smooth/adjoint vs Filter Approach
Ø Strictly obeys model physics at all time
Ø Bring all observations into a dynamically
consistent description of the past and
recent time-varying ocean circulation.
Ø Bring all observations into a model for
the purpose of prediction / forecasting
Ø Model updates can break conservation law
Ø days to months timescale
Ø Initialization, operational
Ø Ocean dynamics & variability, global &
regional energy, heat & water budgets.
Ø Decadal to multi-decadal timescale.
Ø non-linear inversion, iterative Ø Update: priors ßà data-model misfits
Data Assimilation
(filter, sequential):
Smoothed
trajectory Filter
trajectory
Data
Time
State Estimation
(smoother/adjoint, non-sequential):
30. Combine two incomplete information sources
Observations (“data”):
• incomplete/sparse probing
of the physical system
– spatial sampling
– temporal sampling
– incomplete state
• different physical variables
• heterogeneous data streams
• measurement errors
WHOI database
(hydrography)
Argo
T/P, Jason
GRACE
WOCE
31. Combine two incomplete information sources
Physical model:
• representation of time-evolving state via
equations of motion, conservation laws,
theory, …
• A dynamical interpolator
• uncertainties/errors:
– initial conditions
– boundary conditions (surface, bottom, lateral)
– model parameters (e.g., internal mixing coefficients)
– “model errors” (formulation, discretization, …)
32. ECCO Misfit Function, Part 1
Model -- data misfit:
y(t): data ; x(t): model T,S,U,V etc.
R(t): error covariance
Initial conditions:
x(0): initial guess, e.g., WOA18, PIOMASS
x0: optimized (e.g., data constrained)
P(0): error covariance
Uncertain Parameters ß different from just initial condition DA problem such as in NWP
u(t): input parameter adjustments to
e.g., time-dependent 2D+1D atmospheric forcing (from ECMWF for example)
3D time-mean internal mixing coefficients, horizontal eddy-stirring
Q(t): error covariance
ECCO: Forget el al. 2015
ASTE: Nguyen et al., 2021a
33. Uncertainty
scale of in situ obs.
model grid length-scale
Nguyen et al., 2021b
Fenty, 2010, Ph.D. thesis
Fenty & Heimbach,
JPO, 2013a,b
Data error Model representation error
34. Uncertainty in inputs, e.g. surface forcing
Collow et al., 2020
Graham et al., 2019,
Evaluation of Six Atmospheric Reanalyses
over Arctic Sea Ice from Winter to Early
Summer
Collow et al., 2020, Recent
Arctic Ocean Surface Air Temperatures in
Atmospheric Reanalyses and Numerical
Simulations
~6ºC
35. Where to map this error/bias
in atmospheric reanalysis to?
Data Assimilation, an example
Forcing from Reanalyses
∂h
∂t
= S Fi + dF
i
source?
Conservation equation
for thickness h
ha(t1) = hp(t1) + dh(t1)
dh(t1) = dh(t1)
Based on statistics?
Artificial jumps
(e.g., most of reanalyses)
dh(t1) = f(dT(t1))
Propagated through
all physics? (ECCO)
36. ECCO Misfit Function, Part 2: Physical consistency
• State estimates integrate very diverse sets of data using models
under strict constraint of physical consistency
https://www.ecco-group.org/docs/ss_2019_ECCO_FHL_assim_2.pdf
Model physics
Part 1
37. Example: ASTE R1, subpolar gyre + N. Atlantic
Compare to Argo data, 2002-2015, 1600-2000m
qArgo – qi46
sq
0
10
-10
qArgo – qR1
sq
Nguyen et al., 2021 kredi,450-500m
kz,450-500m
100
104
10-3.8
10-5.2
m2/s
m2/s
39. Summary
State estimates:
- allows observations to be viewed in fullest space-time context
- data-constrained & is dynamically coherent
- Long term variability can be meaningfully investigated
Thank you!
Questions?
40. Carton et al., 2019
Xie et al., 2017
Investigation of changes, caution
60. RACMO and MAR calibration/evaluation
PROMICE AWS
Fausto et al. (2021)
SUMup firn cores
Alexander et al. (2019)
61. PROMICE AWS
Fausto et al. (2021)
SUMup firn cores
Alexander et al. (2019) J. Miller et al. (2022)
RACMO and MAR calibration ice slabs and firn aquifers
62. Water in Greenland
— Non-negligible component of the mass balance
— Has direct and indirect effects on
• other ice-sheet components (snow, firn and ice)
• climate components (atmospher/ocean/biosphere)
— Present at the surface, bed, in the interior and
easily moves
— Affects altimetry observations
63. Wish list
— Close the water budget
— Improve understanding and quantify its effects
— Estimate the residence time
64. Pathways
— Close the water budget
— Improve understanding of its effects and quantify them
— Estimate the residence time
Systematic monitoring (quantitative measurements)
Systematic monitoring (quantitative measurements)
Development of multiphase snow/firn/ice models
66. 2023: Federal Year of Open Science
• “Open Science is the principle and practice of making research products
and processes available to all, while respecting diverse cultures,
maintaining security and privacy, and fostering collaborations,
reproducibility, and equity.”
• Multi-agency initiative to spark change and inspire open science
engagement and adoption.
• https://open.science.gov/
oNASA’s Transformation to OPen Science & Open Source Science Inititative
oNSF FAIROS RCNs, GEO OSE, and more
67. “Nelson” Memo on Public Access
• Goal: Ensuring Free, Immediate, and Equitable Access to Federally
Funded Research
• Tells agencies to, among other things, update public access policies as
soon as possible (and no later than December 31st, 2025) to make
publications and their supporting data resulting from federally funded
research publicly accessible without an embargo on their free and public
release.
• NSF Public Access Repository: https://par.nsf.gov/
oCurrently publications are uploaded (with persistent identifiers) to PAR via
reporting, but with a 1-year embargo
68. What is Open Science?
• An Open Science Ecosystem
• Open science aims to ensure the free availability and usability of
scholarly publications, the data that result from scholarly research, and
the methodologies, including code or algorithms, that were used to
generate those data.
This & subsequent slides draw from,
“Open Science by Design: Realizing a Vision for 21st Century Research” (NASEM, 2018)
69. Why Open Science?
• Rigor and reliability
• Ability to address new questions
• Faster and more inclusive dissemination of knowledge
• Broader participation in research
• Effective use of resources
• Improved performance of research tasks
• Open publication for public benefit
70. Limitations
• Costs & infrastructure
• Structure of scholarly communications
• Lack of supportive culture, incentives, and training
• Privacy, security, and proprietary barriers to sharing
• Disciplinary differences
71. Limitations – that NSF is hoping to address
• Costs & infrastructure
• Structure of scholarly communications
• Lack of supportive culture, incentives, and training
• Privacy, security, and proprietary barriers to sharing
• Disciplinary differences
72. OPP Data Policy
• Leapfrogging with the polar research community
• 16-055 was updated by 22-106
o “Dear Colleague Letter: Office of Polar Programs Data, Code, and Sample Management
Policy”
o Like the title says – all data, code, and samples
o Advancing FAIR & CARE
o Everything to be put in long-lived, publicly-accessible repository
o Timeline: within 2 years of collection or by end of the award, whichever is first
o Upload (meta)data to Arctic Data Center and/or US Antarctic Program Data Center
o Reporting guidance
o Complementary resources for DMPs, choosing repositories, etc.
• Default to open – aligns with the statement, “as open as possible, as closed as
necessary.”
73. Data Management Principles
FAIR Principles
for Data Management
• Findability
• Accessibility
• Interoperability
• Reusability
• https://www.go-fair.org/
fair-principles/
CARE Principles
for Indigenous Data Governance
• Collective Benefit
• Authority to Control
• Responsibility
• Ethics
• https://www.gida-global.org/care
74. Open Polar Science Resources
• Arctic Data Center - https://arcticdata.io/
o(Meta)data repository, DMP tools, trainings, portals, resources & more!
• US Antarctic Program Data Center - https://www.usap-dc.org/
oIncludes former Antarctic Glaciological Data Center content
o(Meta)data repository, Antarctic Treaty compliance, trainings, DMP tools, &
more!
• Polar Geospatial Center - http://pgc.umn.edu/
oDifferent services depending on current funding, but something for everybody
oOpen data products like DEMs, commercial high-rez satellite data,
outreach/training, geospatial services
• Physical Repositories: Ice Core Facility, Polar Rock Repository, Marine
Core Repository
75. Polar Cyberinfrastructure
• The Polar Cyberinfrastructure program considers proposals that
promote effective collaboration between Polar and cyberinfrastructure
researchers (in both the Arctic & Antarctic solicitations).
• …and/or will support proposals that provide significant benefit to the
Polar research community including
o cost-effective transfer of data from remote field locations;
o long-term sustainable curatorship, standardization, management and discovery
of data and metadata;
o visualization, manipulation, and analysis, particularly for understanding
complexity; [including AI & ML]
o access and interoperability across scientific disciplines;
o promotion of effective use of High Performance Computing (HPC) for direct and
sustainable advances in current Arctic research; and
o e-learning and educational tools based on cyberinfrastructure components.
76. Supporting Open Polar Research Software
• Builds on Supporting Data and Sample Reuse in Polar Research (DCL 21-041)
• Encouraging:
o Translating / modernizing code into open languages/licenses
o Upgrading, refining, and/or documenting code to enable broad use
o Training, workshops, hackathons, cohort-building activities, etc.
o Broadening participation and building open cyberinfrastructure skills and capacity in the
polar research community across career stages and career paths.
• BUT…
o Make sure to align with other policies described earlier, and
o Make sure it doesn’t fit in another NSF program
• Both supplements and new proposals are welcome
• Dear Colleague Letter 23-053
https://beta.nsf.gov/funding/opportunities/supporting-open-polar-research-software
77. GEO-wide & NSF-wide Collaboration
• Works across GEO and with the Office of Advanced Cyberinfrastructure
o https://www.nsf.gov/geo/geo-ci/index.jsp
• Opportunities like:
o Geosciences Open Science Ecosystem (GEO OSE; follow-on from EarthCube)
o Advancing Geosciences using AI & ML (DCL, NSF 23-046)
o Cyberinfrastructure for Sustained Science Innovation
o CyberTraining
o Strengthening the Cyberinfrastructure Professionals Ecosystem
o CloudBank & PATh
• This includes open, flexible, scalable, and interoperable cyberinfrastruture that will enable a broad
and diverse community of geoscientists to integrate data, models, software, and knowledge.
• GEO is looking to building on existing efforts by promoting an ecosystem for geoscience research
that:
o Links data & models, big data & small data, networking knowledge from diverse perspectives,
o Builds on EarthCube experience and investments to move towards flexible, scalable workflows and tools, and
o Provides exemplars for advancing NSF priority areas for open & inclusive science, FAIR data, reproducibility &
replicability, CARE & TRUST principles, and BA-JEDI.
78. NSF Support of Open Science
• Open science is a part of existing & likely upcoming solicitations –
Full range of proposal types!
oStandard/Collaborative, Supplements, Planning Proposals, RCNs,
Conference/Workshop, CAREER, Mid-Career Advancement, Career-Life Balance,
REUs, RAPID, EAGER, TCUP, RUI/ROA, FASED, HBCU-EiR, etc.
• Takeaway: Do your prep, write a one-pager, and reach out to POs!
Don’t be afraid to ask - NSF supports all sorts of (well-reviewed) great
ideas!
79. Alexander Robel | 2023 FOGSS Workshop | Georgia Tech
From Greenland to Georgia
A Perspective on Sea Level Projections and What Ice Sheet
Scientists Can Do For Coastal Communities
2015 US Hwy 80 Drone video courtesy of Sean Compton
80.
81. 0 20 40 60 80
High Tide (inches above NADV88 datum)
0
5
10
15
20
25
30
Occurences
per
year
High tides on Highway 80 (Fort Pulaski Tide Gauge)
1935-1940
2014-2019
Flooding on Highway 80
~1 ft local
sea level
rise
82. Starting in 2019, GA
DOT spent first
several million $ to
raise lowest parts of
Highway 80 by 8
inches
83. 0 20 40 60 80
High Tide (inches above NADV88 datum)
0
5
10
15
20
25
30
Occurences
per
year
High tides on Highway 80 (Fort Pulaski Tide Gauge)
1935-1940
2014-2019
Flooding on Highway 80
0 10 20 30 40 50 60 70 80
High Tide (inches above NADV88 datum)
0
5
10
15
20
25
30
Occurences
per
year
High tides on Highway 80 (Fort Pulaski Tide Gauge)
1935-1940
2014-2019
Flooding on Highway 80
Flooding on Raised Highway 80
84. A
Chatham County, GA SLR Projections (NOAA)
•“Typical” design life for a
residential building is 30 years:
~1 ft of uncertainty in SLR
•Multifamily building design life
closer to 50 years: 2-3 ft
uncertainty in SLR
•Critical infrastructure (bridges,
physical plant, etc) design life
75 years: 4 ft uncertainty in SLR
85. A
Chatham County, GA SLR Projections (NOAA)
Cost of a mistake:
Homeowner:
•Seawall install: $1-2k/foot
•Seawall cap: $100-200/foot
•Chronic flooding: loss of home
value
Government:
• Beach nourishment:
~$million/mile/year
• NFIP premium subsidy:
~$billion/year
86. A problem: most communities use little or no information
from state-of-the-art ice sheet/sea level projections when
planning for sea level rise
Hirschfeld et al. 2022
North American survey respondents
87. So…how can ice sheet scientists help
facilitate effective adaptation with the best
possible information?
88. 1. Build relationships with boundary organizations or directly with
practitioners to address community needs (Ultee et al., Earth’s
Future, 2018)
2. Recruit students from frontline communities into ice
sheet science to internalize on-the-ground expertise that
can work parallel to co-production (Robel, Ultee,
Ranganathan, Nash, In Prep)
90. Tell communities and practitioners what we know,
what we don’t know, and what we need
• Advocacy recognizing the immense
financial implications tied to sea level
projections
• $12-71 billion/year globally ($3-4
billion/year in US) for coastal
adaptation/armoring which uses sea
level projections from ice sheet models
(Hinkel et al. 2014, Neumann et al. 2014)
• NSF funding for ice sheet modeling is
less than $3 million/year, NASA/
DOE+other agencies likely less than
$10 million total
91. Need: short-term sea level projections (<50 years)
Potential users: homeowners, engineers, planning professionals
Need better methods to transiently assimilate observations of
recent ice sheet change to initialize future projections
Aschwanden et al. 2021
V2015 − V2000 ∝ V2060 − V2015
92. Need: short-term sea level projections (<50 years)
Potential users: homeowners, engineers, planning professionals
Certain ice sheet processes are more important for short-term
projections (also more and varied uncertainty quantification)
Aschwanden et al. 2019
93. Need: long-term sea level projections (>50 years)
Where do/should we build new communities in coastal areas?
The most important processes are… 🤐 Not going to catch me in this trap!
The reality is that more and better uncertainty quantification and optimal
experimental design are needed to formally answer these questions for
processes that already have parameterizations in models. Most UQ and
Bayesian calibration studies have focused on Antarctica.
Greenland Examples:
94. Need: long-term sea level projections (>50 years)
Where do/should we build new communities in coastal areas?
But, this isn’t enough because:
(1) We don’t have well-informed priors on
all the parameters we do have in
models.
(2) not at all processes are parameterized
in any way in most models (aka
structural model uncertainty) implying
perfect confidence of zero importance
We need structural change in our funding and educational model to:
“Grow the modeler pipeline and treat models as instruments that
require maintenance to continue operation.” (2022 FOGSS Report)
Frequency
Parameter Value
96. Need: translating knowledge across disciplines
Extending knowledge beyond ice sheets
InSAR/GNSS for estimating vertical land motion
Shirzaei et al. 2021
97. Need: translating knowledge across disciplines
Extending knowledge beyond ice sheets
Local
inundation/
flood modeling
using similar
systems to
those used for
regional ice
sheet modeling
Park et al., Coastal Engineering, 2022
98. Need: building interdisciplinary teams
Team science ensures that ice sheet knowledge isn’t siloed within our community and that we
can address cross-cutting issues beyond ice sheets
99. • Fundamental science is important for the long-term enterprise of understanding
and predicting ice sheet change
• But…producing the most usable science requires:
1. Building relationships with communities that we want to use the scientific
knowledge that we produce through intermediaries or by leveraging local
connections to our institutions/groups
2. Asking communities what they actually want/need for effective adaptation
3. Working effectively to build capacity within our discipline to target those needs
4. Translating our skills to problems outside of ice sheet science
5. Building interdisciplinary teams when our skills or capacity fall short
Takeaways
