Arctic Energy Summit 2013
An Integrated Approach to Oil Spill
Research for the Offshore Arctic
Akureyri, Iceland
October 9, 2013
Mark D. Myers, PhD
Vice Chancellor for Research
Key Drivers That Will Effect Future
Oil and Gas Development
1. Future Demand and Price
World economy, non OCED and general growth
2. Energy Endowment
Oil and gas are where you find it!
3. Development Technology
Conventional and Unconventional Resources
4. Access to resource
Challenging environments, infrastructure and geopolitics
5. Environmental risk
Particularly wrt oil spills and water (policy, technology
and investment)
USGS Circum-Arctic Resource Assessment
Circum-Arctic Non-Petroleum Resources
Lawson Brigham
AMSA
12 August 2008
Diverse Sources of Spill Risk Needs Diverse Response
Capacity
Tugboat runs aground on Alaska Peninsula, stranding
crew and barge. ADN : November 14, 2012
Crowley Marine
Anchorage Daily News, 21 Sept 2011/14 Nov 2012
Peeling Back the Onion – Understanding the Ecosystem
Highly complex
interplay of
physical,
chemical,
biological
and
social
processes.
In the Arctic Stationarity - the concept and
practice that natural systems fluctuate
within an unchanging envelope of variability
is Dead
Dramatic Change in Sea Ice Extent
Rapid ecosystem change
Increased access to energy, hard minerals and fishery resources
Increased shipping, tourism, and community access
National Snow and Ice Data Center
Permafrost Thaw
Photo by Mikhail Kanevskiy
photo Cameron Wobus, CIRES
Trends of decreasing sea
ice and increased openwater fetch, combined with
warming air and ground
temperatures, are
expected to result in higher
wave energy, increased
seasonal thaw, and
accelerated coastal retreat
along large parts of
circum-Arctic coast.
photo Susan Flora, BLM
Coastal Erosion National Petroleum Reserve Alaska
USGS
Research Tracks
Targeted Observing System
Research Outcome
Prevention
Integrated Modeling and Data Assimilation
Detection
Data Dissemination (ERMA Framework)
Food Webs
Mitigation
Shoreline Cleanup and Assessment
Communities, Policy and Infrastructure
Impacts
Improving Environmental Security & Oil-Spill Response Through
an Integrated Coastal Observing System
Remote sensing* (km-scale): Coastal
environments & infrastructure, ice
hazards
Coastal radar* (sub-km scale): Vessel &
ice tracking, ice dynamics & potential
disaster response
Aerial surveys (including UAVs), ice &
sub-ice sensor systems*
Local knowledge*: Potentially important
role for disaster response
Integration of data streams, GIS-based
decision support systems
* Leveraged through integration & assimilation of
existing coastal observing system resources
supported by NSF, DHS, and NOAA
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Eicken, Petrich, Mahoney, et al.; www.sizonet.org
Oil Trajectory Modeling in
Ice-Covered Waters
Products
Daily to weekly
Predicting forecasts for spill
Monitoring
Assimilation of
data from the
monitoring
program
into forecast
model
Evaluate the
capability of current
state-of-the-art seaice models to predict
oil-spill trajectories
Identify key research
areas to transition
models to operations
response and planning
Modeled oil-spill
trajectories based on
key spill scenarios for
EIAs and risk
assessments
Regions Covered By High-Frequency
shore-based, surface mapping radars
(Open Water Season)
Coastal AK: NO Power Grid!
UAF built and tested an autonomous
power system (wind, solar, &
biodiesel).
System: proven, flexible, portable &
arctic-proof. Can support other
sensors.
The Future?
8 radar-RPM systems can cover
Beaufort and NE Chukchi (40,000
n.mi.2)
(Supported by: BOEM, DHS and AKDCED)
Coastal Radars map surface
(upper 1 m) currents:
- hourly
- over broad areas (~175 km) at
6-km resolution
- real-time access via the Web
- easily understandable
- cost effective
To guide open-water response to
marine spills (and other
purposes).
~4MHz
Shore-based power available
Funded by:
BOEMRE, Shell, and ConocoPhillips
9/25
9/27
Large temporal
variability too…
9/26
9/28
SFOS/UAF Autonomous Remote Technology Lab
Operates three Webb Slocum Autonomous Underwater Vehicle (AUV) gliders.
Non-propelled, autonomous, quiet, low-power, long-endurance specific AUV 
up to ~ 3-month missions using lithium batteries.
Two-way, real-time Iridium satellite communication  mission change on the fly + relay
data to scientists, numerical models and decision makers.
Unique, high-resolution (vertical and horizontal) surface-to-bottom data coverage.
2010 Mission accomplishments:
 AUVs can successfully navigate large portions of the Chukchi Sea
 65-day total glider missions using two Webb Slocum gliders
 >3,000 vertical hydrographic profiles
 >800 km of glider transects
UAF is the only academic entity operating joint glider/HF radar programs in
the Arctic.
Oil Spill Monitoring
Prince William Sound Technology Evaluation (Conoco Phillips and BP)
• Objective: Understand how small-unmanned aircraft can
contribute to environmental cleanup operations associated with an
oil spill
• Partners:
– Alaska Department of Environmental Conservation
– US Fish and Wildlife
– Environmental Protection Agency
AeroVironment Puma Aircraft
BP’s Aeryon Scout
Prince William Sound
Airspace
Meeting the Arctic Challenge
Unmanned Bering Sea Operation, May 2009
Lack of
infrastructure:
Harsh conditions:
• No runways
• Icing potential
• No cell phones
• Low visibility
•UAF scientists
successfully
overcame these
challenges to obtain
high-resolution
imagery for an ice
seal survey.
Ice Profiling LIDAR
Deployed by NASA in Greenland
and Svalbard Norway
LIDAR
Color Visible
SAR
• Partnership with University of Colorado
Other Payloads
• Ball Experimental Sea
Hyperspectral Imager:
Surface Temperature
(BESST) Radiometer:
 400-900 nm
• Resolution: ±0.5 K
 Up to 240 spectral
(uncooled)
bins
<1 m spatial
resolution
 Push broom
deployment for image
cube generation
Miniature
Meteorological
Dropsondes:


•
•
BP Deepwater Horizon
SST from BESST



Wind speed and
direction
Relative humidity
Pressure
Temperature
as a function of altitude
(~ 5m vertical
resolution)
R/V Sikuliaq
Launched October 2012 - Operational 2014
Seward Based
Length 261, ’ 45 day endurance, ice strengthened
20 Crew, 26 Science
Managed and Operated by UAF - Supported by: NSF
Coupled Atmosphere-Ice-Ocean
Modeling
- High-resolution meteorological model
developed
- Performs well year-round
- Hindcasting studies underway
- Forecasting capability
Wind Speed
Wind Direction
Courtesy of Zhang
and Zhang; UAF
International Arctic
Research Center and
Arctic Supercomputer
Center. Supported by
BOEMRE.
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• Cold lab experiments & numerical modeling
of oil entrainment & movement through ice
• Goal: Inform oil-spill response in coastal ice
settings; improve oil-in-ice detection &
modeling of ecological impacts
0
Porosity
Oil in Ice:
Measurement & Simulation
1
Food and Cultural Security
Toxicity of Dispersed Petroleum to Arctic Species
Copepod (Calanus glacialis)
Arctic cod (Boreogadus)
Sculpin (Myoxocephalus)
Species selected based upon role in the pelagic food web
of the Beaufort and Chukchi Seas
Toxicity tests conducted on ANS crude oil:
Chemically dispersed (COREXIT 9500)
Mechanically dispersed ANS crude oil
 Characterize
food webs prior
to spills
 Use post-spill
assessment to
gage impact and
monitor
recovery
Some Components of an Enhanced Arctic Oil Spill Response Capacity
COMMUNITIES
Engaged Research Community at UAF: Identified 40 Research Scientists
studying topics from Anthropology to Zooplankton
Building a Vibrant and Sustainable Future
Requires Research.
Environment
Human Health
and Other
Societal Values
Economy
•Integrated and authoritative
data and technical means
including long-term integrated
data bases with near real time
data
• Better models and decision
support tools
•An integrated perspective to
make wise decisions
• The best management
techniques including effective
adaptive management
Although you can learn much from the outside
the conditions in the Arctic have important
differences that need to be considered
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Geology
Physical geography
Ecosystems and rate of ecosystem change
Culture
Infrastructure and access
Climate and weather
Data – depth, breath, and length of record
Rapid adaptation of new and emerging technologies, stronger
partnerships and data fusion, and improved modeling,
forecasting and planning can be achieved!
Questions?