Annual Report 23

Calendar Year 2016

Submitted by:

Dr. Brenda Konar, Director

University of Alaska Coastal Marine Institute

To:

U.S. Department of the Interior

Bureau of Ocean Energy Management

Alaska OCS Region

February 2017

OCS Study BOEM 2017-015

 
Contact

Information: email:

CMI@alaska.edu

phone:907.474.6782

fax: 907.474.7204

Coastal Marine Institute

College of Fisheries and Ocean Sciences

University of Alaska Fairbanks

P. O. Box 757220

Fairbanks, AK 99775-7220

Administration of the University of Alaska Coastal Marine Institute is funded in part by the U.S. Department of the Interior, Bureau of

Ocean Energy Management (BOEM) through Cooperative Agreement M13AC00003 between BOEM, Alaska Outer Continental Shelf

Region, and the University of Alaska Fairbanks. This report, CMI Annual Report 23, BOEM 2017-015 is available through the Coastal

Marine Institute, select federal depository libraries or from https://www.boem.gov/BOEM-Newsroom/Library/Publications/Alaska-Scientificand-Technical-Publications.aspx

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 the U.S. Government. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Government.
ii

TABLE OF CONTENTS

Introduction....................................................................................................................................1

CMI Funding and Cost Share Partners.......................................................................................1

CMI Funded Student Support......................................................................................................2

CMI Publications under M13AC00005 .......................................................................................3

Current Projects.............................................................................................................................5

Coastal Marine Institute Program Administration 2013 – 017..................................................5

 Special Outreach Project................................................................................................7

Crude Oil Infiltration and Movement in First-year Sea Ice: Impacts on Ice-associated Biota 

and Physical Constraints ...........................................................................................................9

Development and Testing of a Low-cost Satellite-tracked Ice Drifter for Arctic Alaska .......15

Alaska Monitoring and Assessment Program (AKMAP) Survey of Estuaries within the

 National Petroleum Reserve-Alaska ........................................................................................17

Development of an Autonomous.Carbon Glider to Monitor Sea-Air CO2 Fluxes in the 

Chukchi Sea..... .......................................................................................................................21

Fate and Persistence of Oil Spill Response Chemicals in Arctic Seawater.............................29

Migration Trends for King and Common Eiders and Yellow-billed Loons past Point Barrow 

in a Rapidly Changing Environment........................................................................................33

Identifying Sources of Organic Matter to Benthic Organisms in the Beaufort and Chukchi 

Outer Continental Shelves.......................................................................................................39

Northern Alaska Sea Ice Project Jukebox................................................................................43

Measuring Wave Forces along Alaska's Coastal Sea Ice ........................................................45

OCS Study BOEM 2016-064 (Addendum): Sensitivity to Hydrocarbons and Baselines of 

Exposure tin Marine Birds on the Chukchi and Beaufort Seas ...............................................51

Introduction

The University of Alaska Coastal Marine Institute (CMI) was established through a cooperative

agreement between the University of Alaska and the U. S. Department of the Interior Bureau of

Ocean Energy Management (BOEM) to study coastal topics associated with the development of 

natural resources in Alaska's outer continental shelf. Under this cooperative program, BOEM 

supports highly qualified scientific expertise at the University of Alaska to conduct research used 

to inform management of oil, gas, and marine mineral resources. The initial agreement began in 

June 1993. CMI is pleased to present this annual report for the calendar year 2016, the fourth 

annual report under BOEM Cooperative Agreement M13AC00003. 

Under BOEM, the Environmental Studies Program (ESP) is formally directed to provide 

information in support of the decisions involved in the planning, leasing, and management of 

exploration, development, and production activities. The ESP research agenda is driven by

the identification of specific issues, concerns, or information needs by federal decision

makers and the state and local governments that participate in the process. Within that

framework, the University of Alaska Coastal Marine Institute partners with BOEM and the 

State of Alaska to develop regional research goals and execute a funding cycle to initiate up

to two million dollars of research every year.

CMI Funding and Cost Share Partners

In 2016, BOEM committed $730,000 to fund research projects under the Coastal Marine

Institute partnership, bringing the total BOEM funding committed to CMI since inception in

1993 approximately $22 million. All CMI funded projects require a one-to-one cost share with

non-federal partners. Cost sharing ensures stakeholders are participating in the CMI research

program. The following list of 2016 cost share partners demonstrates the range of support for

CMI-funded programs: 

Alaska Department of Environmental Conservation

Institute of Northern Engineering, UAF 

College of Fisheries and Ocean Sciences, UAF

Rasmuson Library, UAF 

College of Natural Science and Mathematics, UAF

International Arctic Research Center, UAF 

Institute of Arctic Biology, UAF

Geophysical Institute, UAF

The Arctic University of Norway 

Fairweather, Inc.

North Slope Borough 

Oil Spill Response Institute 

Oregon State University 

Pacific Gyre 

Alaska Sea Life Center

Naidu Consulting 

Bigelow Laboratories

Wildlife Conservation Society 

1
CMI Funded Student Support

Projects funded as part of the CMI research program often support the primary dissertation or 

thesis work for graduate students and occasionally provide learning opportunities for other 

students assisting in field or laboratory work. Twenty-two students have participated on regular

CMI funded research projects during the current five-year administrative cycle, fourteen in 

2016. 

CMI Projects Involving Students Students by degree

PhD Master Bachelor

Biogeochemical assessment of the OCS arctic waters: current status and 

vulnerability to climate change (FY08) 1 

Population assessment of snow crab, Chionoecetes opilio, in the Chukchi and 

Beaufort Seas including oil and gas lease areas (FY11) 3 

Dispersal patterns and summer ocean distribution of adult Dolly Varden from the 

Wulik River, Alaska (FY12) 1 

Evaluating Chukchi Sea trace metals and hydrocarbons sourced from nearby

coastal rivers (FY12) 1 

A year in the life of the bowhead whale (FY12) 1

Analysis of benthic communities on weathervane scallop beds in Shelikof Strait 

(FY14) 1 

Sensitivity to hydrocarbons and baselines of exposure in marine birds on the 

Chukchi and Beaufort Seas (FY14) 1 

Biodegradation and transport of crude oil in sand and gravel beaches of arctic

Alaska (FY14) 2 

Crude oil infiltration and movement in first-year sea ice: impacts on iceassociated biota and physical constraints (FY14)** 1 1 

Fate and persistence of oil spill response chemicals in arctic seawater (FY15) ** 1, +1 1 2

Identifying sources of organic matter to benthic organisms in the Beaufort and 

Chukchi OCS (FY16)** 1 

Changes in Beaufort-Chukchi Seas intense storm activity and impacts on surface 

climate and ocean properties (FY16)** 1 

Characterizing bacterial communities in Beaufort Sea Sediments in a changing 

Arctic (FY16)** 1 

Using genotyping by sequencing population genetics approaches to determine the 

population structure of tanner crab in Alaska (FY16)** 1 

** project active into 2017

Bold number denotes project is primary dissertation or thesis effort

2
CMI Publications under M13AC00003 

Bluhm, B., K. Iken and L. Divine. 2015. Population Assessment of Snow Crab, Chionoecetes

opilio, in the Chukchi and Beaufort Seas, Including Oil and Gas Lease Areas. Final

 Report. OCS Study BOEM 2015-029, University of Alaska Coastal Marine Institute and

USDOI, BOEM Alaska OCS Region. 

Carothers, C., S. Cotton and K. Moerlein. 2013. Subsistence Use and Knowledge of Salmon in

Barrow and Nuiqsut, Alaska. Final Report. OCS Study BOEM 2013-0015, University of

Alaska Coastal Marine Institute and USDOI, BOEM Alaska OCS Region. 

Gradinger, R. (Director). 2014. University of Alaska Coastal Marine Institute Annual Report 

No. 20. OCS Study BOEM 2014-0052. University of Alaska Coastal Marine Institute

and USDOI, BOEM Alaska OCS Region. 

Gradinger, R. (Director). 2013. University of Alaska Coastal Marine Institute Annual Report No. 

 19. OCS Study BOEM 2014-0112. University of Alaska Coastal Marine Institute and

USDOI, BOEM Alaska OCS Region. 

Gradinger, R. (Director). 2013. University of Alaska Coastal Marine Institute Program

Administration 2008-2012 Final Report. OCS Study BOEM 2014-0128. University of 

 Alaska Fairbanks and USDOI, BOEM Alaska OCS Region. 

Hollmen T. and A. Riddle. 2016. Sensitivity to Hydrocarbons and Baselines of Exposure in 

Marine Birds on the Chukchi and Beaufort Seas. Final Report. OCS Study BOEM 2016-

 064, University of Alaska Coastal Marine Institute and USDOI, BOEM Alaska OCS 

Region. 

Konar, B. (Director). 2016. University of Alaska Coastal Marine Institute Annual Report No. 22. 

OCS Study BOEM 2016-013. University of Alaska Coastal Marine Institute and

USDOI, BOEM Alaska OCS Region. 

Konar, B. (Director). 2015. University of Alaska Coastal Marine Institute Annual Report No. 21. 

OCS Study BOEM 2015-015. University of Alaska Coastal Marine Institute and

USDOI, BOEM Alaska OCS Region. 

Konar, B. and K. Iken. 2016. Testing the Use of Unmanned Aircraft Systems for Intertidal 

 Surveys. Final Report. OCS Study BOEM 2016-051, University of Alaska Coastal 

Marine Institute and USDOI, BOEM Alaska OCS Region. 

Konar, B. and A. Ravelo. 2013. Epibenthic Community Variability on the Alaskan Beaufort

Sea Continental Shelf. Final Report. OCS Study BOEM 2014-01148, University of

Alaska Coastal Marine Institute and USDOI, BOEM Alaska OCS Region. 

Kruse, G. and J. Glass. 2014. Analysis of Benthic Communities on Weathervane Scallop Beds in

 Shelikof Strait. Final Report. OCS Study BOEM 2014-659, University of Alaska Coastal

 Marine Institute and USDOI, BOEM Alaska OCS Region. 

3
Mathis, J. and J. Cross. 2014. Biogeochemical Assessment of the OCS Arctic Waters: Current 

Status and Vulnerability to Climate Change. Final Report. OCS Study BOEM 2014-668, 

 University of Alaska Coastal Marine Institute and USDOI, BOEM Alaska OCS Region. 

McCarthy, P. and J. Perreault. 2016. Evaluating Chukchi Sea Trace Metals and Hydrocarbons in

the Yukon River Delta, Alaska. Final Report. OCS Study BOEM 2016-078, University

 of Alaska Coastal Marine Institute and USDOI, BOEM Alaska OCS Region. 

Norcross, B., B. Holladay and C. Mecklenburg. 2012. Recent and Historical Distribution and

 Ecology of Demersal Fishes in the Chukchi Sea Planning Area. Final Report. OCS Study 

BOEM 2012-073, University of Alaska Coastal Marine Institute and USDOI, BOEM

Alaska OCS Region. 

Norcross, B., L. Horstmann-Dehn, B. Holladay, L. Edenfield and S. Carroll. 2015. Trophic 

 Links: Forage Fish, Their Prey, and Ice Seals in the Northeast Chukchi Sea. Final Report. 

OCS Study BOEM 2013-00118, University of Alaska Coastal Marine Institute and

USDOI, BOEM Alaska OCS Region. 

Okonnen, S. 2016. Sea Level Measurements along the Alaskan Chukchi and Beaufort Coasts.

Final Report. OCS Study BOEM 2016-075, University of Alaska Coastal Marine

Institute and USDOI, BOEM Alaska OCS Region. 

Okonnen, S., R. Topp and H. Foss. 2015. Arctic Currents: A Year in the Life of a 

 Bowhead Whale. Final Report. OCS Study BOEM 2015-039, University of Alaska

Coastal Marine Institute and USDOI, BOEM Alaska OCS Region. 

Rember, R.D., A.M. Aguilar-Islas and V. Domena. 2016. Distribution and Abundance of Select

Trace Metals in Chukchi and Beaufort Sea Ice. Final Report. OCS Study BOEM 2016-

079, University of Alaska Coastal Marine Institute and USDOI, BOEM Alaska OCS 

Region. 

Seitz, A., M. Courtney and B. Scanlon. 2014. Dispersal Patterns and Summer Ocean

Distribution of Adult Dolly Varden from the Wulik River, Alaska, Evaluated Using

Satellite Telemetry. Final Report. OCS Study BOEM 2014-663, University of Alaska

Coastal Marine Institute and USDOI, BOEM Alaska OCS Region. 

Schiewer, S., A. Iverson and P. Sharma. 2015. Biodegradation and Transport of Crude Oil in

Sand and Gravel Beaches of Arctic Alaska. Final Report. OCS Study BOEM 2015-041, 

 University of Alaska Coastal Marine Institute and USDOI, BOEM Alaska OCS Region. 

Weingartner, T., C. Irvine, L. DeSousa, E. Dobbins, B. Adams, S. Danielson, R. Suydam

and W. Neatok. 2015. Satellite-tracked drifter measurements in the Chukchi and

Beaufort Seas. Final Report. OCS Study BOEM 2015-022, University of Alaska

Coastal Marine Institute and USDOI, BOEM Alaska OCS Region. 

4
Coastal Marine Institute Program Administration 2014 - 2017

Principal Investigator: Brenda Konar

College of Fisheries and Ocean Sciences

University of Alaska Fairbanks

Cooperative Agreement Number: M13AC00003

Period of Performance: 04/09/13 – 05/31/18

PROJECT OVERVIEW

The CMI is guided by a Technical Steering Committee (TSC) with members from the 

University, BOEM and the State of Alaska. With TSC input, the University of Alaska Fairbanks

College of Fisheries and Ocean Sciences provides administrative management of the CMI

program as a special project under the Office of the Dean. The CMI administrative

responsibilities include the following: 

 Facilitating the annual research funding cycle from initial call to award

 Facilitating Technical Steering Committee activities

 Providing program support for researchers 

 Coordinating as liaison between BOEM and awardees

 Monitoring cooperative agreements and assisting with administrative and funding issues 

 Editing and publishing and submitting project final reports and project deliverables

 Event planning for the CMI Annual Research Review 

2016 ADMINISTRATIVE UPDATE

The Coastal Marine Institute Annual Research Review was held in January 2016 in

Anchorage, Alaska. The CMI Annual Research Reviews follow a seminar format and the public

is encouraged to participate. Ten CMI projects presented research updates in 2016. 

The TSC met by teleconference in January 2016 to select projects for the FY16 CMI research 

program. Three new projects were awarded and funded in FY16: 

 Identifying sources of organic matter to benthic organisms in the Beaufort and Chukchi 

Outer Continental Shelves 

PI: Dr. Mathew Wooller and Dr. Katrin Iken, UAF 

 Northern Alaska sea ice: Project Jukebox - Phase III 

PI: Ms. Leslie McCartney, UAF

 Measuring wave forces along Alaska’s coastal sea ice 

PI: Dr. Mark Johnson, UAF 

In 2016, CMI piloted an initiative to fund one-year awards (up to $25,000) for a maximum of 

three current graduate students. Student projects must align with the CMI framework issues and 

provide 1:1 cost-share. The student initiative is intended to leverage BOEM investment and 

5
collaborative opportunities by funding a component of the student’s larger research project. 

Three student projects were funded for FY16: 

 Changes in Beaufort-Chukchi Seas intense storm activity and impacts on surface climate 

and ocean properties 

Yang Yang, Ph.D. student, UAF IARC 

 Characterizing bacterial communities in Beaufort Sea sediments in a changing Arctic

Alexis Walker, Ph.D. Student, UAF CFOS 

Using genotyping by sequencing population genetics approaches to determine the 

population structure of tanner crab in Alaska 

 Genevieve Johnson, MS Student, UAF CFOS 

Final reports for student projects will be published as a compilation each September. Students 

must present at the CMI Annual Review mid-way through their project year, and they are 

encouraged to present again after project completion. The first year of student awards proved 

successful, so the initiative was continued for the FY17 funding cycle. 

The CMI administrative office initiated the regular FY17 funding cycle with a call for letters

of intent in April 2016. This notice was publicized and sent to researchers and stakeholders at 

state agencies, profit and non-profit organizations, and broadly across the University of Alaska 

system. Seven project concepts were received in response to the call, and the TSC selected five

for full proposal development. The graduate funding initiative drew seven student project 

proposals for FY17. All final proposals were received in November and sent for external peer

review and assessment by the TSC and BOEM staff in preparation for funding decisions in

January 2017. 

2016 PUBLICATIONS

• OCS Study BOEM 2016-013: University of Alaska Coastal Marine Institute Annual Report 

22: Calendar Year 2015 

• OCS Study BOEM 2016-051: Testing the Use of Unmanned Aircraft Systems for Intertidal 

Surveys 

• OCS Study BOEM 2016-064: Sensitivity to Hydrocarbons and Baselines of Exposure in 

Marine Birds on the Chukchi and Beaufort Seas 

• OCS Study BOEM 2016-075: Sea Level Measurements along the Alaskan Chukchi and 

Beaufort Coasts

• OCS Study BOEM 2016-078: Evaluating Chukchi Sea Trace Metals and Hydrocarbons in 

the Yukon River Delta, Alaska 

• OCS Study BOEM 2016-079: Distribution and Abundance of Select Trace Metals in 

Chukchi and Beaufort Sea Ice

6
SPECIAL OUTREACH PROJECT: Pop-up Children’s Book

As part of its mission, BOEM has an interest to increase broad science literacy and public 

awareness through outreach and educational programming. In 2011, the Environmental Studies 

Program (ESP) of BOEM initiated a new policy to assign five percent of its annual program 

budget to promote public education. The promotion of public awareness of ongoing 

environmental changes in the Arctic is one area of particular interest, and the objective of this 

project is to develop a unique children’s pop-up book to encourage knowledge of the Alaskan 

Arctic through a popular literary medium. 

The proposed book, The Adventures of APUN the Arctic Fox, is being designed and created 

through collaboration between WonderU, the Anchorage Museum, and Alaskan artists. Topics 

covered in the book may include permafrost, volcanoes, polar bears, and ice dynamics. The book 

will reference University of Alaska scientific research and may directly feature BOEM-funded 

researchers and projects. The goal is to promote arctic science education, in novel ways, to a 

broad public audience including domestic and international tourists. The book will employ a 

blend of art, science, and web-linked videos to target young audiences while appealing to adults. 

2016 PROJECT UPDATE 

In 2016, the pop-up book project accomplished the following objectives: 

 The storyline was completed and tested in the Anchorage Museum Spark Lab, a 

gathering space for young children (ages 0–6) and their parents. Liz O'Connell

(WonderU) read the book (without illustrations) to children who were then asked to draw 

images from the story. Parents provided feedback and suggestions. 

 Draft versions of illustrations were completed (Figure 1). 

Figure 1. Samples of draft illustrations. 

Remaining tasks:

1. Complete illustrations; modify color palette will be modified in some of the page spreads 

2. Complete modifications to accommodate the pop-up engineering 

3. Create mock-up with completed pop-up pages 

4. Incorporate suggestions from the Spark Lab pilot 

5. Test with children; seek Iñupiat feedback

6. Present final product the 2018 Alaska Marine Science Symposium (Anchorage)

7

Crude Oil Infiltration and Movement in First-year Sea Ice: Impacts on Iceassociated Biota and Physical Constraints

Principal Investigator: Eric Collins

College of Fisheries and Ocean Sciences

University of Alaska Fairbanks

Cooperative Agreement Number: M14AC00015

Period of Performance: 04/21/14 – 09/30/17

PROJECT OVERVIEW

The Arctic is in a state of rapid change – environmentally, politically, and economically. The 

recent openings of the Chukchi Sea for shipping and oil exploration has raised the prospect of an 

oil spill in this ecologically sensitive region, but we have very little data with which to predict 

the possible impacts of a spill. The presence of seasonal sea ice increases the risks, and 

potentially the consequences, of such an event. This project addresses the potential impacts on 

sea ice biology of a spill in an oil-under-ice scenario, as well as investigating the physical 

incorporation of oil into ice, which has bearing on various proposed cleanup scenarios. 

The experiment proposed was to grow artificial sea ice in custom built tanks, to inoculate the ice 

with natural sea ice biota, and to simulate an oil spill under the ice. The outcomes we tracked 

included the distribution of oil within the ice sheet and the mortality rates of the biota. We 

predicted that the presence of biota would influence the transport of oil through the ice and 

designed our experimental setup to test this hypothesis. 

The experimental work was designed to investigate the following hypotheses: 

1. Sea ice microstructure predicts and constrains the mobility and dispersion of oil. 

2. The presence of biota decreases oil migration through sea ice. 

3. Oil increases the mortality of sea ice meiofauna and reduces algal biomass. 

Hypothesis 1 follows upon pre-existing knowledge about the microstructure of the sea ice brine 

network, which informs models that can be used to predict the transport of fluid through ice. 

These models have not been tested with oil as a fluid, and there is no existing data against which 

to test such a modified model. A goal of this project is to collect sufficient data to modify and 

test these models in an oil spill scenario.

Hypothesis 2 is an extension of Hypothesis 1 to account for the biological activity within sea ice. 

Extensive observations show that Arctic sea ice is full of life, ranging taxonomically from 

bacteria to algae to worms that can alter the very structure of the ice through the production of 

extracellular polymeric substances. A goal of this project is to describe the impact of these biotic 

influences on the flow of oil through the ice and to incorporate these changes into conceptual 

models of fluid flow through the ice. 

Hypothesis 3 builds upon previous research that indicates some chemical components of crude 

oil are toxic to life. However, not all species are affected equally by these components – some 

are resilient, others are more sensitive. We selected several organisms of particular interest from 

the natural sea ice meiofauna with which to test this hypothesis during in vitro experiments in the 

9
artificial ice tanks. We tracked the change in abundance of these organisms in treatments with 

and without oil to test the hypothesis that oil is harmful to these organisms.

In order to test the three hypotheses, we identified the following tangible objectives:

 Grow artificial sea ice in a cold room laboratory

 Collect microbiota from natural sea ice and inoculate it into artificial ice

 Observe changes in sea ice meiofauna and algal community composition after exposure

to oil

 Measure the distribution of oil in sea ice by X–ray tomography and use them to design

models to predict the small-scale movement of oil in sea ice

During the first year of this project, we successfully built the experimental tanks and tested them. 

During the second year, we revised the tanks and the experimental design based on results from 

the first year. During the third year, we conducted laboratory work and statistical analyses on the 

data from the previous years in an effort to generalize the results. 

2016 PROJECT UPDATE 

The past year was spent primarily on sample processing, statistical analysis, and writing up of 

results of previous experiments, especially related to Objective 3 and Objective 4, which we will 

discuss in more detail below. We chose a replicated experimental design to investigate our 

hypotheses regarding (1) the influence of microbiota on oil migration through ice, and (2) the 

impacts of oil on ice microbiota. For each of two years, and for each of three treatments per year, 

we set up two replicate tanks (Figure 1). The treatments either included or excluded (1) biota and 

(2) oil. Sampling was performed two days prior to oil release (Pre-Oil) to establish a baseline, 

and then oil was released into the experimental tanks. The primary sampling (Post-Oil) followed 

about 10 days later. 

Figure 1. Ice tank schematic.

When we put our work into the context of other ice biota mesocosm experiments (Table 1), we 

found that on the whole, our ice tanks performed well, with similarities both to other experiments 

and also to natural sea ice environments. The greatest difference was perhaps in the abundance of 

bacteria, which was quite low in our tanks compared to previous tank experiments, but actually 

quite similar to natural abundances of bacteria in sea ice. Overall our tanks most closely reflected 

a natural fast ice environment during the early spring. 

10
Table 1. Characteristics from similar experiments.

Study Type

Ice

thickness

(cm)

Incubation

(days)

Chl a 

(μg l ice-1)

algae 

(cells l ice-1)

Bacteria

(cells l ice-1)

Krembs et al. 2002 fast ice 165 [March] 16 na 0.7 x 108

Riedel et al. 2006 fast ice 200 [July] 711 na 4.3 x 109

Weissenberger 1998 tank 15 35 106 37 x 106 0.6 x 1012

Weissenberger 1998 tank 29 42 49 24 x 106 0.6 x 1012

Weissenberger 1998 tank 22 91 80 14 x 106 0.7 x 1012

Krembs et al. 2001 tank 21 17 0.1-2 0.8 x 106 na

Mock et al. 2002 tank 15 10 3-4 na na

Aslam et al. 2012 tank 10 5 na na 0.8 x 109

This study 2015 (no oil) tank 34 12 80 31.1 x 106 0.9 x 108

This study 2015 (oil lens) tank 36 12 8 5.6 x 106 0.2 x 108

This study 2015 (emulsified oil) tank 29 12 10 13.6 x 106 0.5 x 108

Objective 3: Observe changes in sea ice meiofauna and algal community composition after 

exposure to oil. 

Algae – abundance and health

We previously found that the presence of oil significantly decreased the growth of ice algae in 

bottom ice in both years, inhibiting a bottom-ice algal bloom in the oiled tanks compared to the 

un-oiled tanks (Figure 2). 

 Figure 2. Chlorophyll-a concentrations in control (Bio-Cont) and treated tanks

. 

A new microscopic analysis now suggests that the presence of oil also significantly impacts the 

health of the algae by interfering with their membranes. Microscope images (Figure 3) show 

11
healthy cells (a and b) and unhealthy or damaged cells (c) under magnification. When we count 

the relative abundance of unhealthy cells to healthy cells, we find that in the oiled treatments, up 

to 80% of the cells appear damaged, whereas only about 5% of cells in un-oiled controls show 

cell damage (Figure 4). 

Figure 3. Microscopic images of healthy (a. b) and damaged (c) cells.

Figure 4. Relative abundance of unhealthy cells to healthy cells, by treatment.

The observation that contact with oil inhibited and likely damaged the ice algae in our 

experimental system has ecological repercussions for an oil spill scenario under sea ice. In 

contrast to the pelagic realm of the Chukchi Sea, where a large fraction of phytoplankton 

biomass is present in a subsurface chlorophyll layer below 20 m, most of the biomass of sea ice 

algae is present at the ice-water interface. This means that inhibition of algal growth due to oiling 

could have long-lasting impacts on primary production in oiled sea ice, upon which many higher 

12
trophic levels feed, and which accounts for up to 25% of the total Arctic primary production. Our 

observation that chlorophyll-a remains present after oiling suggests the possibility that these 

oiled algae could be released during sea ice melt, passing toxic compounds up the food web. 

Objective 4: Measure the distribution of oil in sea ice by X–ray tomography and use models to 

predict the small-scale movement of oil in sea ice

We experimented with various volumes of crude oil released below the ice, and, as expected, a 

larger amount of crude oil led to deeper oil penetration within the ice. In the two tanks in which a 

small amount of oil was dispersed mechanically in the water column (V=0.5 L), oil droplets 

populated the skeleton layers. For the tank in which 2 L of crude oil was released at the ice/water 

interface, the skeletal layer was fully filled with crude oil. Upward percolation was limited to a 

small number of brine channels over 4 to 5 cm depth. In one tank, we observed one brine channel 

filled with oil all the way to the surface. This explained the presence of crude oil over the whole 

ice surface and could be a major conduit for oil in natural ice as well.

We were surprised to find the possibility of crude oil percolating through cold ice (temperature < 

-5°C). Despite the traditional idea that crude oil is encapsulated at the ice/water interface during 

the ice growth, crude oil released below 20 cm of ice was observed reaching the surface through 

virgin sea ice in multiple areas. A week later, crude oil pooling completely blackened the ice 

surface. A threshold of 5% brine volume fraction corresponds to the minimum porosity required 

before brine movement is possible within the brine channels (Golden et al. 1998). Due to the 

higher viscosity of the oil, a calculated brine volume fraction threshold between 10 and 15% is 

expected to be necessary before oil is mobilized in the brine channel. A few centimeters above 

the ice/water interface, the brine volume fraction decreases exponentially. In our ice, at 15 cm 

depth, the porosity value was lower than 5%. In most cases, already it fell below the 10% 

threshold at 10 cm. According to our current understanding, under these circumstances, oil 

should not have percolated to the surface. Even if some oil migrated through previous core holes, 

it does not explain the percolation of oil throughout previously untouched and un-sampled ice 

area.

One goal of the project was to process imagery from X-ray tomography made at CRREL 

(Hanover, NH). Despite several attempts with different image processing methods, it remained 

very difficult to fully automate the segregation between the three different phases (oil, ice, and 

brine). Manual analysis of the whole dataset would require a large amount of time. Results to 

date for relating specific volume of oil to ice have produced non-significant results. With smaller 

oil inclusions, the blurriness at the interface due to the differences in absorption coefficient 

becomes increasingly problematic. Estimation of the oil volume is highly dependent on the 

chosen threshold and can lead to estimation errors of up to 50%. 

Despite our best efforts, the oil volume in each sample was difficult to estimate. During the 

previous year, ice samples were melted to measure salinity and brine volume fraction. Before 

melting, horizontal and vertical pictures of each sample were taken as a reference (Figure 5). 

Samples were melted in a funnel and liquids collected in a graduated cylinder, allowing a direct 

13
measurement of the oil quantity. Due to the small amount of oil contained in the samples and the 

tendency of the oil to spread into a thin film over plastic and glass surfaces, this method did not 

work as expected. For a few samples, enough oil was present to be measured in volumes of a few 

milliliters. However, for most samples, the volume of oil was not sufficient to cover the water;

hence, thickness and distribution of the oil sheen were non-uniform at the water surface. Shape 

and length of the oil meniscus at the water/oil/glass and water/air/glass interface also varied 

between samples. These problems led to difficulties in downstream analyses and modeling of the 

oil content in ice.

Figure 5. Image taken of a sample before melting to assess volume.

References

Golden, K. M., Ackley, S. F., and Lytle, V. I. (1998). The percolation phase transition in sea ice. 

Science, 282(5397), 2238-2241. 

2016 PRESENTATIONS/PUBLICATIONS

Dilliplaine K, Oggier M, Eicken H, Bluhm B, Gradinger G, Collins RE (2016). Biological 

Influence and Impact of Sea Ice Biota in Ice Covered Waters. (Oral) Alaska Marine Science 

Symposium, 26 January 2016. Anchorage, AK. 

Oggier M, Dilliplaine K, Eicken H, Bluhm B, Gradinger G, Collins RE (2016). Oil in ice: 

Evolution of pore space geometry occupied by crude oil. (Poster) Alaska Marine Science 

Symposium, 26 January 2016. Anchorage, AK. 

14
Development and Testing of a Low-cost Satellite-tracked Ice Drifter for Arctic 

Alaska

Principal Investigator: Jeremy Kasper

Institute of Northern Engineering

University of Alaska Fairbanks

Cooperative Agreement Number: M14AC00019

Period of Performance: 06/02/14 – 08/31/17

PROJECT OVERVIEW

The IceTracker project has successfully demonstrated the durability, longevity, and utility of 

simple, low-cost IceTracker buoys for tracking ice movement. The IceTrackers are easily 

deployed by snowmobile and aircraft and are equipped with a GPS sensor for accurate 

positioning and an Iridium communications chip for data transmission. Of the 20 trackers 

deployed over the period between March and June 2015 one tracker continues to operate and 

several additional IceTrackers continued to transmit data into 2016. The longest surviving 

tracker has now been continuously transmitting its position since its initial deployment in ~April 

2015. This tracker is currently located at ~87.6°N, ~1700 km from its starting point north of Pt. 

Barrow (Figure 1). The second longest surviving tracker lasted ~15 months until June 2016. 

Many of the other trackers deployed during the course of the project were beached and 

subsequently deactivated to limit Iridium data transmission costs. Other trackers stopped 

transmitting data, presumably because they were crushed by ice. As evidenced by the longevity 

of the tracker that is still transmitting, the trackers have sufficient batteries to last for periods 

longer than two years at low data transmission rates. 

Figure 1. Approximate starting point and current location of IceTracker 03.

15
Two trackers have been recovered during the course of the project. One of the trackers was 

returned to Pacific Gyre where it was determined that the batteries were depleted. This tracker 

was set to transmit its positions every half hour. Even after the trackers are deactivated from the 

Iridium network, they continue to transmit data at the interval set before the units are 

disconnected. Thus from now on, trackers will be set to transmit their position every twelve 

hours (the minimum data transmission rate) before the command to deactivate the trackers' 

Iridium communications is sent. In the event that such units are recovered by vessels of 

opportunity, this may allow them to be reused. 

2016 PROJECT UPDATE

Over the course of 2016, we continued monitoring the status of the deployed trackers. No 

additional field work was undertaken during this time period. Efforts to deploy the final set of 

five trackers continued throughout the year. In early 2016, a researcher with the North Slope 

Borough Department of Wildlife requested to use the remaining trackers as part of a project to 

track polar bears using hair traps. We enthusiastically accepted this offer to collaborate. 

However, after several phone calls to discuss deployment plans, the deployment was abruptly 

canceled. UAF learned of cancellation after the breakup of the nearshore ice cover in spring 

2016. Following this, at the suggestion of BOEM, we reached out to Hilcorp in summer 2016 to 

inquire whether they would support the deployment of the remaining trackers near its Liberty 

project in Stefansson Sound. However, Hilcorp expressed little interest in supporting the 

deployment. In December 2016, we arranged for Ukpeaġvik Iñupiat Corporation Science

Division to deploy the remaining trackers by snowmobile in 2017 at Elson lagoon where 

researchers Kasper and Mahoney are beginning a National Science Foundation LTER project to 

study exchange between lagoons and the nearshore Beaufort Sea. 

2016 PRESENTATIONS/PUBLICATIONS

Kasper, J., Trefry, J., Fox, A., Savoie, M., Fox, S., Lawley, G., Trocine, R., and Shipton, P.

2016. The 2015 Spring Freshet and Its Effects on the Alaskan Beaufort Sea. Poster, Ocean 

Sciences Abstracts.

Mahoney, A., Kasper, J., and Winsor, P. 2016. Multiyear ice transport and small-scale sea ice 

deformation near the Alaska coast measured by air-deployable Ice Trackers. Poster, Alaska 

Marine Science Symposium Abstracts. 

Kasper, J. IceTrackers: low-cost tracking of sea ice in remote environments. Oral presentation, 

CMI Annual Review, Anchorage, AK, 2016. 

A draft was manuscript prepared for submission to the Marine Technology Society Journal. 

16
PROJECT OVERVIEW

In August 2015, Chukchi and Beaufort Sea estuaries in the National Petroleum Reserve – Alaska 

(NPR-A) were surveyed by the Alaska Monitoring and Assessment Program (AKMAP). This 

survey supported multiple agency programmatic needs, for example:

 Alaska Department of Environmental Conservation: AKMAP Water Quality Assessment 

 National Oceanic and Atmospheric Administration: National Status and Trends Program 

 Bureau of Ocean Energy Management: Alaska OCS, Environmental Studies Program 

 Environmental Protection Agency: National Coastal Condition Assessment survey 

The AKMAP NPR-A estuary survey utilized a probabilistic sampling design to estimate the 

spatial extent of ecological condition based on stressors and indicators. The design used two 

strata, the Chukchi and Beaufort estuaries, with 20 base and 20 over-sample stations in each 

stratum. Figure 1 shows the AKMAP NPR-A stations sampled and stations not sampled. 

Figure 1. NPR-A Estuaries Survey stations sampled (S) and not sampled (NS).

Stressors, such as chemical contaminants and water quality parameters, and indicators, such as 

macroinvertebrate biodiversity, were used at each station to relate biological response, 

contaminant exposure, and habitat condition. Uniform sampling methods and analytical 

Alaska Monitoring and Assessment Program (AKMAP) Survey of Estuaries 

within the National Petroleum Reserve-Alaska

Principal Investigator: Doug Dasher

College of Natural Science and Mathematics

University of Alaska Fairbanks

Cooperative Agreement Number: M15AC00009

Period of Performance: 07/01/15 – 12/31/17

17
procedures allow for nationwide comparisons of condition, a critical part of the NCCA survey 

methodology. The survey had the following general objectives: 

1. Provide a statistically unbiased assessment to characterize the condition of aquatic 

resources within the NPR-A estuaries

2. Provide estimates of the NPR-A population areas that meet or do not meet water quality 

criteria

3. Allow resource managers to more accurately evaluate data from targeted monitoring 

within the broader regional patterns of contamination and impact provided by the 

AKMAP NPR-A survey 

4. Integrate multiple indicators sampled at each station providing managers with a more 

robust comprehensive assessment of overall regional status

2016 PROJECT UPDATE

The following program activities were accomplished in 2016: 

 Completion of all analytical analysis (July 2016) 

 Completion of QA/QC on all datasets (December 2016) 

 QA/QC dataset entry began into the Alaska Department of Environmental Conservation 

(ADEC) water quality database for eventual transfer to the US EPA STORET national 

database (December 2016) 

In the fall of 2016, the ADEC provided support to combine its NPR-A estuaries dataset into an 

Arctic Synthesis project. The Arctic Synthesis project is an effort to consider the connections 

between the coastal watersheds in NPR-A estuaries and the Chukchi and Beaufort Seas. The 

NPR-A estuaries assessment results and interpretations will be evaluated with consideration of 

the AKMAP Chukchi Sea, Simpson Lagoon, NPR-A wetlands, lakes, and rivers assessments 

conducted in 2010–13. Figure 2 provides an idea of the AKMAP stations included in the NPR-A 

coastal watershed sampling area in the Arctic Synthesis project.

A project extension and task modification were requested for the project because

1. participating agencies took longer than expected in contracting for sample analytical 

parameters, analysis, and QA/QC; however, the cost-effectiveness of this approach made the 

NPR-A estuaries survey viable; 

2. unanticipated leave time was required for a PI; and 

3. sediment trace elements results are still undergoing QA/QC review at NOAA.

The no-cost extension will allow for a more informative presentation of results in 2018. Salary 

and travel adjustments were requested and funded by the reallocation of unspent laboratory 

analysis contract funds. No beam trawl samples were collected, so funds originally targeted for 

biological tissue analysis and biomarker sediment analysis were also reallocated.

18
 

Figure 2. AKMAP stations included in the NPR-A coastal watershed sampling area in the Arctic Synthesis project.

2016 PRESENTATIONS/PUBLICATIONS

• Alaska Marine Science Symposium (Poster) January 2016 

• Alaska Forum on the Environment (Oral presentation) February 2016 

• Abstract accepted for the 2017 International Conference on Arctic Science:

Hartwell, I., D. Dasher and T. Lomax: Alaska Monitoring and Assessment Program 

(AKMAP): Ecological Condition Surveys across Coastal Watersheds, Estuaries and 

 Coastal waters in Alaska’s Arctic 2010 – 2015: A Summary of Findings.

19

PROJECT OVERVIEW

The main goal of this project is to integrate, test, and improve a novel carbon dioxide sensor with 

an autonomous underwater Slocum glider. This unique Autonomous Carbon Glider (ACG) is the 

first to enable the autonomous mapping of CO2 levels throughout the water column at high 

resolution. New autonomous observational capabilities are needed because traditional ship-based 

observations are operationally expensive and unable to provide the type of spatial and temporal 

coverage of seawater CO2 measurements required for an improved conceptual understanding 

and quantitative assessment of the carbon cycle in high latitude oceans. This is especially 

important because these regions are experiencing unprecedented changes including warming 

temperatures, extensive sea-ice losses, increases in storm frequency/magnitude, enhanced winddriven mixing, elevated rates of coastal erosion, increasing inputs of terrestrial organic matter, 

and ocean acidification, all of which could impact cycling of carbon. Exploration and 

development of offshore energy resources may also impact biological communities and/or the 

stability of the water column, both of which influence the carbon cycle. 

This project builds on our experience working with in situ autonomous sensing platforms and 

investigating carbon cycle dynamics in high latitude oceanic regions. The success of this pilot 

study will enable the technology to be extended to a larger fleet of carbon gliders, thereby greatly 

improving our ability to monitor and understand carbon dynamics in this remote but important 

region. 

We have already reached several major milestones in this project including: 

 Integration of the physical, power, communication, and software systems of the ProOceanus Mini-Pro with the Teledyne Webb Slocum Glider

 Collection of the first ever glider-based profiles of pCO2

 Development of glider hover missions that allow for full sensor equilibration and the use

of sensors with slower response times

 Modifications and improvements to the pCO2 sensor to make it less vulnerable to

clogging and flooding

2016 PROJECT UPDATE 

Three sea trials were run in Seward in summer 2016, and an ACG mission was attempted in the 

Chukchi Sea in September 2016. Progress, issues, and corrective actions for each of these sea 

trials are described below.

Development of an Autonomous Carbon Glider to Monitor Sea-Air CO2 

Fluxes in the Chukchi Sea

Principal Investigator: Claudine Hauri

International Arctic Research Center

University of Alaska Fairbanks

Cooperative Agreement Number: M15AC00005

Period of Performance: 04/03/15 – 09/30/18

21
First Seward Sea Trials (May 2016) 

The first field-testing of the ACG showed that the integration of the Teledyne Webb Slocum 

Glider, the Pro-Oceanus Mini-Pro pCO2 sensor (Prototype 1), and a Seabird 5P submersible 

pump was successful (Figure 1, upper panel). The ACG operated as expected during the course 

of several dive and climb cycles and successfully communicated to record pressure, temperature, 

salinity, backscatter, Chlorophyll-a, CDOM concentrations, and the partial pressure of carbon 

dioxide (pCO2) data throughout the water column (Figure 2). We were able to verify that the 

addition of the pCO2 sensor and pump, which directed seawater through the hollow fiber 

module, did not significantly alter the flight characteristics of the glider. In addition, we were 

able to construct glider flight control missions to hover the vehicle at various prescribed depths 

for a preset time (Figure 3). 

Figure 1. Photos of the Autonomous Carbon Glider with two different pCO2 sensor prototypes: Prototype 1 

includes a hollow fiber module (upper picture) and Prototype 2 has a pressure head (lower picture). Prototype 1 

was used during the first Seward sea trials in May, and Prototype 2 was used thereafter.

Figure 2. Time series of temperature, salinity, and pCO2 (backscatter, CDOM, and chlorophyll-a are not shown).

The initial dives to 40 meters continuously sampled temperature, salinity, backscatter, CDOM, and chlorophyll-a 

throughout the water column; whereas, the Mini-pro pCO2 sensor was set to sample only during the down cast in

order to conserve battery power, a requirement for the Chukchi Sea missions.

22
Figure 3. Glider flight data showing the first successful glider hover mission on May 23 with controlled hover at the

prescribed flight depths. Depth (top), ballast pumped (second from top), pitch (third from top) and glider battery

position (bottom) are plotted as a function of time. Legend “m” designates measured value, whereas “c” designates

commanded value.

The testing and refinement of the hover missions was, itself, a success because it is not a 

common glider mission profile so hover mission files are not readily available. It took several 

attempts to refine the technique. The hovering behavior of the glider is a powerful new tool when 

it is applied to sensors that require an equilibration period, such as the Pro-Oceanus Mini-Pro. 

The hover missions enable periodic stops at discrete depths for accurate measurements of pCO2, 

even with sensors that have extended response times. 

During these first sea trials, we encountered a leakage issue with the hollow fiber module that 

was added to the MiniPro in order to accelerate its equilibration timescale. Data suggested that 

the hollow fiber module failed on the second mission at pressures within our intended working 

depths. Informed by findings from our laboratory testing, which indicated the hollow fiber 

module is susceptible to clogging and requires a high-powered pump (both of which shorten 

mission duration), we determined that the hollow fiber module is not suitable or ideal for the 

intended glider application. In collaboration with the pCO2 sensor manufacturer, Pro-Oceanus, 

we modified the sensor configuration to work without the hollow fiber module. Prototype 2 

features a modified pressure housing designed to accommodate the large (4”) diameter disc 

23
membrane head that is commonly used in their workhorse CO2 sensor, the Pro-CV. With the 

conventional flat membrane, the new sensor configuration allows us to fly longer missions 

because it is much less power hungry and less susceptible to clogging. Furthermore, with the 

new sensor, the depth range of ACG is extended to 200 m, which will be important for the wider 

application of this novel technology beyond the Chukchi Sea. 

Second Seward Sea Trials (July 2016) 

Testing of the new sensor (Prototype 2) integrated with the glider (Figure 1, lower panel) 

produced reasonable pCO2 data strings in the lab. However, running the actual glider mission at 

sea produced zero and unknown readings. Troubleshooting suggested that the new sensor 

required longer start-up time than the glider software accommodated, which led to 

communication issues. Pro-Oceanus confirmed that they had made small changes to the 

logger/controller board, despite our agreement to use the firmware of the previous sensor. As a 

corrective measure, Pro-Oceanus shipped a new logger/controller board containing the correct 

firmware. The board was installed and tested in the laboratory in combination with the glider. 

During these tests, we had the glider actively log the data (rather than simply live streaming to 

the computer) to ensure that the tests were representative of a true glider mission at sea.

Third Seward Sea Trials (August 2016) 

Conditions in Resurrection Bay were not favorable for glider missions. Unexpectedly low 

surface salinities (18 salinity units) in the upper 40 m of the water column at our regular field test 

sites created a density gradient twice as large as the glider could handle. We spent the first six 

hours of the day steaming on board the small vessel Dora in search of a suitable deployment 

environment. We were able to deploy the glider in the Gulf of Alaska, about 25 nm offshore 

from Seward, where we encountered surface salinities of 26 and a manageable density gradient 

of 6 sigma units. However, the location put us into the Alaska Coastal current, exposing our 

glider and boat to a large swell and strong drift. These difficult oceanographic conditions caused 

a delay in glider operations and limited the number of deployments we were able to accomplish 

that day. We were able to conduct tethered-glider safety missions (making sure the glider can 

operate in the conditions), two saw-tooth dives, and one hovering mission accompanied by a 

CTD cast (CTD analysis in progress at Oregon State University, OSU). The glider successfully 

logged pCO2 data from the redesigned MiniPro Sensor during these missions. 

The first double sawtooth mission to 40 m demonstrated that the glider was able to dive and 

climb at a steady rate while maintaining course and logging pCO2 data and other ancillary data. 

The resulting pCO2 profile nicely reflected the low-to-high pCO2 gradient expected due to lower 

salinities and higher primary production at the surface and less-fresh water, lower primary 

production, and increased respiration of organic matter at 40 m depth. As expected from the 

response time of the pCO2 instrument, there was some hysteresis observed in the profiles, with 

the maximum pCO2 values observed 67 seconds after the maximum depths were reached. 

24
Figure 4. Image of the ACG at the surface during the August sea trials a) and profiles of temperature b),

salinity c) and pCO2 d) from a hovering mission to 10 m, 15 m, 20 m, 25 m, 30 m and 40 m.

We then conducted a hover mission with programmed hover depths every 5 m down to 40 m 

(Figure 4b, c, d). The glider executed this hovering by dynamically adjusting the battery position 

such that the glider maintained a level pitch. Meanwhile, the buoyancy was dynamically adjusted 

to bring the glider to the desired depth. At the 15 and 20 m hover depths, the glider was 

essentially neutrally buoyant and required very few adjustments. At other depths, significant 

buoyancy adjustments were required to reach the desired depths. These difficult conditions 

pointed out the need to continue improving the glider's hovering capabilities by refining the 

flight control in response to the local conditions. During the hover missions, pCO2 stabilized 

within a few ppm, demonstrating that short hover missions can achieve equilibration of the 

sensor to allow construction of pCO2 profiles in the water column, a first for an autonomous 

glider.

25
This set of tests also demonstrated that the water-pumped head, designed by Pro-Oceanus for our 

second generation MiniPro pCO2 sensor, had the potential to damage the flat, semi-permeable 

membrane used as an interface for the equilibration of headspace gas with the surrounding water. 

The semi-permeable membrane provides the only barrier preventing water from penetrating the 

sensor housing and electronics. As the glider dives and climbs, the repeated pressure cycling 

pushed the membrane against the water-pumped head, slightly wrinkling it and potentially 

flooding the sensor. Leading up to the Arctic cruise, we worked with Pro-Oceanus to better 

accommodate the membrane stability during quick pressure changes. Pro-Oceanus sent a new 

water-pumped head with a waterside protective screen along the membrane. The new waterpumped head and a new semi-permeable membrane were installed for the Chukchi Sea 

deployment. 

Prior to the Arctic deployment, we conducted lengthier tests in the laboratory to ensure the 

sensor functioned over the course of days and not just hours as tested on the glider. These tests 

showed that the sensor would occasionally produce faulty data, which would lead to a time out 

of the data stream, prompt termination of the communication between the sensor and the glider, 

and essentially abort the mission. In response, a Pro-Oceanus engineer joined our glider 

technician, Brita Irving, on the RV Sikuliaq in Nome. During the short visit, the engineer 

installed a new tray and firmware board (v33) in our pCO2 sensor, re-secured and improved 

various connections in the housing, and conducted a final inspection of the sensor and 

membrane. The new firmware board was further tested and sampled in the ship laboratory for 

several days, without error. 

Arctic Deployment (September 2016) 

The glider was deployed and recovered twice during the cruise on the R/V Sikuliaq. The first 

deployment attempt on 11 September was aborted after a failure in the glider’s science bay. 

Following an initial dive to 5 m, the glider lost communication with its science computer and 

began aborting the mission. Investigation showed that a fuse had blown on the science board. 

The next deployment was on 20 September at 71.61°N, 163.77°W. Prior to this deployment, the 

glider sat outside for over 12 hours in 0°C weather. Tests run the night before and an hour before 

deployment returned pCO2 values ranging from -2000 to 0 ppm. After consulting with a ProOceanus engineer, we proceeded with the deployment. The glider was checked-out following the 

standard deployment procedure and then sent on an extra check-out mission that dove to 20 m 

with 2-minute safety stop (at 5 m) at both start and end. On this initial hover mission, the glider 

sank through the water column during its hover attempt at 5 m. Sea trials earlier in the summer

had established that ballasting plays a large role in the ability of the glider to hover at depth. 

Therefore, the poor hover behavior was attributed to the glider being ballasted too heavily for the 

surface density of 1022.7 kg/m3. 

After reviewing science and engineering data files from the test dives, mission plans were 

developed for the glider’s next trials. To support the response times required by the Pro-Oceanus 

MiniPro, we developed novel missions to allow the glider to hover at various depths through the 

26
water column in order to get accurate pCO2 data. Since the glider can only make headway 

during its standard “yo” or “sawtooth” missions, we had to develop a secondary mission that 

would allow us to make forward movement as well as perform the required safety stops. The 

resulting mission plan called for the glider to do a safety stop, perform ten dives to 35 m, do 

another safety stop, and then surface after approximately two hours. 

The glider began its 20 September mission at 21:45 UTC and did not resurface until 21 

September 03:37 UTC, 5.86 hours after diving. The sensor must have been partially flooded 

when the mission started because the glider was not able to do the safety stop or yo properly 

(Figure 5). During each of the ten programmed dives, the glider attempted to climb but would 

complete early and sink back to 37 m. The safety stop was the last behavior the glider was 

supposed to run; instead, it sank to the seafloor at depth of 42.3 m. The glider aborted after 30 

minutes because it was not able to change depth. After four hours of attempting to surface while 

sitting on the seafloor, the glider began its emergency procedure to drop a 1 kg weight and within 

nine minutes of dropping the weight surfaced. 

Upon recovery, the pCO2 sensor was full of seawater. The O-ring between the pressure head and 

the housing was in good condition, the housing had no visible cracks, and the bulkhead 

connectors at the back of the device were tightly secured. After removing the water-pumped 

head, small indentations were visible on the semi-permeable membrane. Science data was not 

collected during the mission because the MiniPro caused a fatal error at the start of the mission.

Inspection of the pCO2 sensor by Pro-Oceanus confirmed that the sensor flooded as a result of 

the perforated membrane. The failure was due to a missing O-ring between the membrane and 

mesh in the pressure head, possibly lost during shipment.

Figure 5. Depth profile of the carbon glider on September 20, 2016.

Outreach

A movie and blog produced by an Oregon-based science teacher featured our carbon glider and 

the work of our glider technician, Brita Irving, during the Arctic sea trials. The story is posted at

27
https://thedynamicarctic.wordpress.com, the outreach website for the OSU-led, NSF-funded 

Arctic cruise on the R/V Sikuliaq.

Continuing Work

Pro-Oceanus is currently testing the sensor's durability during continuous hydrostatic pressure 

cycling at cold temperatures in a pressure chamber and during field-testing. The pCO2 sensor 

will be attached to a profiling CTD during upcoming fieldwork. The response time and how well 

the membrane handles the pressure cycling will be monitored during these tests. We will again 

test using a water-side protective screen along the membrane with the two-fold purpose of 

protecting the membrane from damage and creating an environment where more turbulence can 

be generated immediately next to the membrane. 

The ACG will then be re-ballasted for the oceanic conditions expected in Resurrection Bay 

during the final Seward sea trials in March and April. At that time, we will continue to test the 

pCO2 sensor, improve the glider hovering capabilities, and adjust the hovering controls and 

duration to the equilibration time of the sensor. The overall goals of the final Seward Sea trials 

are to fly the ACG for a few consecutive days, ground-truth the pCO2, salinity and temperature 

data with CTD casts, and test the overall concept of our newly developed ACG. 

2016 PRESENTATIONS/PUBLICATIONS

Results of the laboratory tests were presented at the CMI Annual Research Review in Anchorage 

in January 2016. 

28
PROJECT OVERVIEW

In the event of an oil spill in the Arctic Ocean, the use of chemical dispersants is one potential 

option for oil spill response. Corexit 9500A is currently the principal chemical dispersant 

stockpiled and is likely to be applied to an oil spill in the Arctic, should it receive regulatory 

approval. The objectives of this study are to assess the biodegradation potential of Corexit 

9500A and its effects on crude oil biodegradation in Arctic seawater and to identify bacteria and 

genes involved in biodegradation. We are addressing these goals using laboratory incubation 

tests of oil, Corexit 9500A, and both combined in seawater collected from the Chukchi Sea. We

are applying chemical analyses of oil and Corexit components and molecular characterizations of 

the microbial community using next-generation DNA sequencing to assess biodegradation rates, 

identify oil and dispersant degraders, and examine the response of microbes to oil and 

dispersants. This study will provide novel and timely information valuable for predicting the fate 

of oil, dispersed oil, and dispersants in the Arctic Ocean. 

2016 PROJECT UPDATE 

Research Activities

Analyses of Preliminary Biodegradation Incubation Tests 

Results from two incubation tests were completed prior to this project. The first, Incubation A,

used Fall 2013 offshore Chukchi Sea water in microcosms amended with either Corexit 9500 or 

Alaska North Slope crude oil. Abiotic controls were conducted in parallel to biotic mesocosms. 

This incubation series was analyzed for biodegradation of Corexit and petroleum, and molecular 

microbial analyses were also completed. A second biodegradation incubation test used Summer 

2014 nearshore Chukchi Sea water and Corexit 9500. Abiotic controls were conducted in 

triplicate, alongside live mesocosms. These prior analyses have been completed and were used to 

inform the 2016 work on Incubation B.

New incubation test: Summer 2016 near-shore seawater +/- Corexit (Incubation B). 

While data were being analyzed from the previous incubations, we developed and optimized inhouse petroleum analysis methods at UAF (detailed below), and worked with Oregon State 

University (OSU) and Duke University to design a much larger-scale incubation test in order to 

comprehensively examine the biodegradation of Corexit components, oil, and their interaction. 

We ordered additional glassware and stir plates to accommodate the larger experiment and fullyequipped a cold room at UAF, including modifications to increase the room’s electrical capacity. 

We also worked closely with Ukpeagvik Iñupiat Corporation Science Division (UIC) in Barrow 

on logistical planning for the Arctic field work required in collecting seawater. 

Fate and Persistence of Oil Spill Response Chemicals in Arctic Seawater

Principal Investigator: Mary Beth Leigh

Institute of Arctic Biology

University of Alaska Fairbanks

Cooperative Agreement Number: M15AC00008

Period of Performance: 05/19/15 – 05/14/18

29
Dr. Leigh and Ph.D. student Taylor Gofstein performed fieldwork with support from UIC in 

August 2016 and, with assistance from visiting Ph.D. student Sarah Choyke (Duke University) 

and additional UAF staff and students, we immediately set up and performed the large, 30-day 

incubation experiment. Samples were shipped to OSU and Duke for Corexit analyses, and others 

were preserved at UAF for petroleum and microbial work. Petroleum, Corexit, and microbial 

community analyses are now underway. 

Petroleum Analytical Chemical Methods Development 

We worked to establish and optimize extraction and chemical analysis methods for crude oil in 

seawater. A protocol has been developed for extraction of hydrocarbons from seawater 

incubations using a methylene chloride liquid-liquid extraction, based on the methods of Prince 

and Douglas (2005). Sample cleanup was achieved using solid-phase extraction cartridges to 

remove any contaminants and any extremely polar compounds present from the extraction that 

may damage the GC column and produce variable, noisy chromatograms. We experienced 

instrumentation problems with UAF gas chromatographs used to analyze petroleum, which 

caused additional delays in methods development and analyses. Methods development for 

GC/MS analysis was completed in the fall of 2016, and samples are currently being analyzed.

Chemical Analyses of Dispersant 

Oregon State University (OSU) team members analyzed aqueous microcosm samples collected 

from the two preliminary incubation tests to quantify the individual surfactants 

(dioctylsulfosuccinate or DOSS, Tween 80, Tween 5, and Span 80) in Corexit formulations and 

one degradation product (ethyhexylsulfosuccinate or EHSS). EHSS is also present as a synthetic 

impurity in Corexit. The analysis was carried out by direct aqueous injection of the saline 

microcosm samples into a liquid chromatograph tandem mass spectrometer (LC-MS/MS). DOSS 

and EHSS were quantified by internal standard calibration using stable-isotope-labeled 

standards. The Tweens and Span 80 were quantified by external calibration because isotopically 

labeled internal standards are not commercially available. The Corexit analyses at OSU were 

delayed in 2016 by instrument problems. Corexit requires advanced liquid chromatography 

coupled to tandem mass spectrometry (LC-MS-MS), and these instruments frequently require 

repair. To ensure sound methodology, we felt it was critical to see the data regarding Corexit 

9500 biodegradation from previous tests before initiating Incubation B. Therefore, we waited to 

initiate Incubation B until we had Corexit data from Incubation A. 

Microbial Community Analyses 

We applied next-generation DNA sequencing (Illumina MiSeq) to determine the composition 

and structure of the bacterial community over the course of prior incubation tests (2013/2014) 

amended with oil or Corexit. Increases in the relative abundance of organisms in response to oil 

or Corexit 9500A will implicate them as putative degraders of those substrates. Total DNA was 

extracted from filters containing microorganisms from each mesocosm and then sequenced on 

Illumina’s MiSeq platform using F515 and R806 primers that targeted the V6 region on the 16S 

30
rRNA gene. DNA sequences were then analyzed with mothur open source software. The 

taxonomic identity (to the genus level when possible) and relative abundance of bacteria present 

were determined by comparing 16S rRNA gene sequences to a publicly available online database 

(Ribosomal Database Project; Wang et al., 2007). Operational taxonomic units (OTUs), a rough 

equivalent to bacterial species, were defined at a 97% similarity and abundances were 

normalized to total abundance per sample. 

Results

Petroleum, Corexit, and microbial analyses are all in progress for the 2016 incubation series 

(Incubation B). The results of the first major incubation (A) were presented in the 2015 CMI 

Annual Report. 

Students

This project has supported four graduate students: 

 Kelly McFarlin - Ph.D. student in Biological Sciences in the Leigh lab at UAF 

 Taylor Gofstein - Ph.D. student in Environmental Chemistry in the Leigh lab at UAF 

 Matt Perkins - Ph.D. student in the Field lab at OSU 

 Shannon Davis - senior undergraduate Chemistry major in the Field lab at OSU

References

Prince, RC, and GS Douglas (2005). Quantification of hydrocarbon biodegradation using internal

markers. In: Monitoring and Assessing Soil Bioremediation (pp.179–188). Springer,

Berlin, Germany.

Wang, Q, GM Garrity, JM Tiedje, and JR Cole (2007). Naive Bayesian classifier for rapid

assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ.

 Microbiol. 73:5261–5267. 

2016 PRESENTATIONS/PUBLICATIONS

We submitted a 2015 CMI Annual Report and presented our results to date at scientific meetings, 

including the 2016 CMI Annual Research Review, AMSS, Gulf of Mexico Research InitiativeGOMRI, the American Society for Microbiology Alaska Branch, and the Society for 

Environmental Toxicology and Chemistry. 

A manuscript on Incubation A data and methodology has been drafted for submission to the 

journal, Environmental Science and Technology. It will also serve as a chapter in McFarlin’s 

Ph.D. dissertation (defending March 10, 2017). 

31

PROJECT OVERVIEW

Most of the king (Somateria spectabilis) and common eiders (S. mollissima v-nigra) and yellowbilled loons (Gavia adamsii) nesting in northern Alaska and northwestern Canada migrate twiceannually past Point Barrow, Alaska during their northward spring migration and their southward 

fall migration. At Point Barrow, the migration passes very close to shore and migration counts 

have been conducted periodically since 1976. In 1996, spring and fall counts indicated both eider 

species experienced population declines of approximately 50% between 1976 and 1996. The 

counts were repeated in 2002–2004, at which time it appeared that the number of common eiders 

passing Point Barrow had increased since 1996, but only slightly, and that the number of king 

eiders had remained stable but had not come back to the 1970 levels. The Wildlife Conservation 

Society (WCS), University of Alaska Fairbanks, and the North Slope Borough (NSB) conducted 

an additional set of spring counts in 2015 and 2016, the latter funded primarily by the Coastal 

Marine Institute. In this report, we present the results of the 2016 migration count and compare 

these results to the counts in 2003, 2004, and 2015. 

2016 PROJECT UPDATE 

Field Work

We arrived in Barrow in early April to monitor ice conditions and find a suitable location for our 

counting blind, “the Perch,” on the sea ice lead edge. Counting effort was organized into two 8–

hour shifts, from 0500–1300 for the morning shift and 1700–0100 for the night shift. Each shift 

was composed of three 2–hour counts of recorded observation, with each count separated by a 1–

hour break. 

Counts were conducted from the Perch from 24 April–11 May 2016 (Figure 1). Strong southwest 

winds had been forecasted for late in the day on 11 May and the lead edge was predicted to 

become very unsafe. The morning shift was able to conduct all three counts from the Perch. The 

crew removed the most important things from the Perch when they left (radio, podium box, 

warm–up tent, parts of the bear fence, and parts of the windbreak), but everything else was lost 

that night when the pack ice moved in and crushed the blind. Counts were then conducted from 

land at the Gravel Pit south of Barrow (11–17 May). However, conditions for observing eiders 

were not ideal at the Gravel Pit as the lead was far away, so the crew moved to point farther 

south that provided better counting opportunities. 

Migration Trends for King and Common Eiders and Yellow-billed Loons past 

Point Barrow in a Rapidly Changing Environment 

Principal Investigator: Abby Powell 

Institute of Arctic Biology

University of Alaska Fairbanks

Cooperative Agreement Number: M16AC00002

Period of Performance: 03/15/16 – 04/19/18

33
Figure 1. Eider migration count locations, 2016, near Pt. Barrow, Alaska.

The 2016 spring migration count was conducted over 38 days, with a total of 405.75 hours of 

recorded observation effort. A total of 55,667 eiders counted were identified to species (Table 1), 

and another 208,618 eiders counted were unidentified to species (294 identified as male, 7590 

identified as female). Most of the eiders counted were kings, followed by common, spectacled 

(S. fischeri), and Steller’s (Polysticta stelleri; Table 1). We also counted 616 yellow-billed loons. 

It is important to note that Table 1 presents the number of eiders with positive species and sex 

identification; it is not a population estimate. 

Table 1. Number of birds counted with positive species and sex identification during spring migration, Point 

Barrow, Alaska, 2016. 

Date Analysis 

To account for daily variation in our estimate of total population size, we followed Quakenbush 

et al. (2009) and treated our sample as coming from a stratified design, where each day 

represents a separate stratum. Within each day, we used the observed ratio of king to common 

eiders to assign unidentified eiders to one or the other species. We assumed a 50–50 sex ratio for 

flocks where sex ratio was not determined. Within each day (d), the average number of eiders 

passing (ȳd) was estimated using all 2–hour sample periods (the standard observation 

increment). This average was then multiplied by the total number of sampling periods that are 

Species Total Counted Males Females

King Eider 42,513 21,944 11,987

Common Eider 12,896 6,210 3,009

Spectacled Eider 174 88 80

Steller’s Eider 84 23 17

34
possible within each day (Nd=12). Following Thompson (2002; page 119), the index population 

total thus is defined as the sum of the daily totals:

where L is the total number of days sampled. The variance estimator for the population total

accounts for the number of 2–hour periods sampled within each day (nd) and the sampling 

variance within each day (s2d) and is defined as: 

For the rest of the results, we compare the 2016 data to the data collected in 2015. Eider 

migration peaked May 7–8 in both years, and most birds passed Point Barrow between April 30 

and May 13 (Figure 2). 

Figure 2. Number of king and common eiders observed passing Pt. Barrow, AK, on spring migration, 2015–2016.

We estimated (estimates + 95% confidence intervals) that 787,277 (737,527–837,026) king 

(Figure 3) and 98,121 (91,136–105,107) common (Figure 4) eiders passed Point Barrow in 2015 

and 322,292 (298,635–345,950) king and 130,027 (124,425–135,628) common eiders passed 

Point Barrow in 2016. 

35
Figure 3. Estimated number of king eiders migrating past Pt. Barrow, AK on spring migration, 2015–2016.

Figure 4. Estimated numbers of common eiders migrating past Pt. Barrow, AK on spring migration, 2015–2016

Our estimates of the population of king eiders are very different (>50% difference) for 2015 and 

2016 (Figure 5), which is likely due to the non–normal distribution of the data. Eiders pass by in 

big pulses, so there can be zero count days followed by a day with tens of thousands of birds 

passing. It can be difficult to adequately capture this variability. However, it is important to note 

that the difference in count estimates between the two years does not necessarily indicate that the 

population actually declined by 50% between the two years. Previous counts have also found 

very high inter–annual variability. The estimates of the numbers of king eiders in 2003 and 2004 

(Quakenbush et al. 2009) varied between years (304,966 ±76,254 in 2003; 591,961 ± 172,011 in 

2004) but within the same ranges as we found 12 years later (Figure 5). 

36
Unlike king eiders, the estimated number of common eiders passing Point Barrow in spring was 

similar, both between the two years of this study and to the estimates derived in 2003 and 2004 

(Figure 5). Compared to our estimates, Quakenbush et al. (2009) estimated the population on 

spring migration at 114,998 ± 28,566 in 2003 and 110,561 ± 32,087 in 2004. 

Figure 5. Estimated population size ± 95% confidence intervals for king (blue) and common (red) eiders in 2003–

2004 (Quakenbush et al. 2009) and 2015–2016 (this study) passing Pt. Barrow, AK on spring migration.

References

Quakenbush, L., R.S. Suydam, R. Acker, M. Knoche and J. Citta. 2009. Migration of King and 

Common Eiders Past Point Barrow, Alaska, during Summer/Fall 2002 through Spring 

2004: Population Trends and Effects of Wind. Final Report, OCS Study MMS 2009-036, 

University of Alaska Coastal Marine Institute, University of Alaska Fairbanks and 

 USDOI, MMS, Alaska OCS Region, 42 pp. 

Thompson, SK. 2002. Sampling. John Wiley & Sons, Inc. New York. 

2016 PRESENTATIONS/PUBLICATIONS

We did not have any presentations or publications of our work in 2016. 

37
 

King (left) and common eiders (right), Pt. Barrow, AK, 2016. Photo credit: Mark Dodds. 

King and common eiders in flight, Pt. Barrow, AK, 2016. Photo credit: Mark Dodds. 

The Perch, Pt. Barrow, AK, 2016. WCS staff photo.

38
PROJECT OVERVIEW

This project will provide a better understanding of the organic matter sources consumed by 

benthic organisms using a state-of-the-art essential “amino acid fingerprinting” approach. When 

compared with future samples, this important benchmark data may show changes in proportional 

contributions of organic carbon sources. Such proportional changes could signal impacts linked 

to offshore oil and gas exploration and extraction on the Outer Continental Shelf (OCS), coastal 

erosion, and other climate change related ecosystem effects. Although previously tested in 

marine systems, our project is the first to apply this fingerprinting approach to the Arctic. The 

method specifically allows the separation of microbial and terrestrial carbon sources from marine 

production, filling a gap identified in previous work of benthic food webs on these Arctic OCS 

systems. Our project complements much of the bulk isotope research that has already been 

conducted in the OCS regions by providing a finer ability to discern primary production sources 

of organic matter. Essential amino acids within a consumer create a pattern, or “stable isotope 

fingerprint,” which can be statistically compared with the fingerprints from primary producers. 

We are using existing and archived samples from previous BOEM-funded projects, which is 

highly cost effective and will provide novel, quantitative baseline food web information that can 

inform management decisions related to OCS oil and gas activities. 

2016 PROJECT UPDATE

Major activities during this first year included identification and prioritization samples aligned 

with project objectives and the subsequent laboratory preparation and analyses of those samples. 

A graduate student (Audrey Rowe) was recruited for the project, and her training in preparing 

samples for compound-specific stable isotope analyses of amino acids in the Alaska Stable 

Isotope Facility has been ongoing. The Chukchi samples associated with this project continue to 

be analyzed by graduate student, Ann-Christine Zinkann, whose research project is dedicated to 

investigating benthic organisms in the Chukchi region. 

Analyses have been focused towards the priority species outlined in the proposal, and some 

samples from both the Beaufort and Chukchi regions have been completed. For the Chukchi, a 

total of 35 individual specimens have been analyzed. Each sample has currently been run in 

triplicate, resulting in a total of ~ 735 individual instrumental GC-IRMS analyses of samples 

conducted for the Chukchi. In almost all cases, the carbon isotope compositions of 11 amino 

acids were measured from each specimen, which equates to a net total of ~1155 individual stable 

Identifying Sources of Organic Matter to Benthic Organisms in the Beaufort 

and Chukchi Outer Continental Shelves

Principal Investigator: Mark Wooller

Institute of Northern Engineering

University of Alaska Fairbanks

Cooperative Agreement Number: M16AC00005

Period of Performance: 05/15/2016 – 05/14/19

39
carbon isotope measurements. Additionally, initial replicate analyses of samples from the 

Chukchi have been completed for seven species.

With regard to the Beaufort, a total of ~80 individual specimens have been analyzed. Each 

sample has currently been run in triplicate, resulting in a total of ~240 individual instrumental 

GC-IRMS analyses of samples conducted for the Beaufort. In almost all cases, the stable carbon 

isotope composition of 11 amino acids was measured from each specimen, which equates to a 

net total of ~2640 individual stable carbon isotope analyses from the Beaufort. Notably, this is an 

unprecedented number of analyses of this type from anywhere in the Arctic. All QA/QC has 

been completed for these samples and data analysis has begun for both regions. 

Project progress is in agreement with proposed timeline (Figure 1), with the Chukchi sample 

analyses ahead of schedule. A minor setback occurred in the analyses of species of echinoderms 

(e.g., Ophiocten sericeum). In both regions, we found echinoderms possess a very low yield of 

measurable amino acids. This was an unexpected result and we speculate it might result from an 

unknown taxon-specific biochemical issue. In response to this minor issue, we substituted other 

archived samples of other species from the Beaufort and Chukchi regions (e.g., the shrimp 

Eualus sp., the bivalve Bathyarca sp.). 

Figure 1. Project timeline with activities marked that are underway or completed (red tick mark).

Results

Preliminary data analyses indicate that different organisms from the OCS have distinctly 

different essential amino acid fingerprints (Figure 2), indicating that they likely derive their 

essential amino acids from different proportions of terrestrial versus microbial versus marine 

photosynthetic sources of essential amino acids. 

40
Figure 2. Normalized essential amino acid stable carbon isotope data of five essential amino acids from two benthic 

species from the Chukchi Sea.

2016 PRESENTATIONS/PUBLICATIONS

A poster was being prepared at the end of Year 1 and the abstract was accepted for presentation 

at the AMSS meeting in Anchorage in January 2017. 

41

Northern Alaska Sea Ice Project Jukebox

Principal Investigator: Leslie McCartney

Rasmuson Library

University of Alaska Fairbanks

Cooperative Agreement Number: M16AC00017

Period of Performance: 09/01/16 – 06/30/18

PROJECT OVERVIEW

As arctic Alaska has become a focal point for oil and gas development, an increased rate of sea 

ice retreat has become of greater significance to commercial activities. The presence or stability 

of shorefast sea ice in Alaska is of considerable economic importance as it can be a platform for 

offshore oil and gas activities (Eicken et al., 2009), can affect the development and spill response 

methods to be utilized, or can be a place of safe refuge for maritime activities (CRRC, 2009). 

This increased activity in the Arctic, as well as safety and oil spill concerns, has demanded 

development of management regimes based on better understanding of the sea ice environment. 

Scientific study of sea ice has focused on understanding the dynamics of the ecosystem, energy 

flow, special and temporal changes, and climate effects.

For generations, Iñupiat subsistence hunters have relied upon shorefast sea ice as a platform from 

which to hunt whales, seals, and walrus in order to feed their families and communities. Iñupiat 

experience and traditional knowledge can provide a rich source of information regarding this

dynamic environment, including insights on identifying and avoiding hazards, predicting ice 

behavior to ensure safe travel, and understanding seasonal and cyclical processes. Observations 

from residents in local communities such as Utqiaġvik (Barrow) and Kotzebue, Alaska, (Figure 

1) highlight the relationship between people and their environment, demonstrate the effect of 

changing conditions on their behavior and adaptability, and inform understanding of current 

nearshore and shorefast sea ice conditions in arctic Alaska. 

Figure 1. Participating community locations.

43
This project will support ten new audio/visual recordings with sea ice scientists and local 

residents of Utqiaġvik and Kotzebue for archival and public dissemination through the project

website (Figure 2). Participant recordings of their observations of changing sea ice conditions 

will become part of a larger longitudinal qualitative project currently chronicling 1978 to 2013 in 

the Barrow area. The inclusion of Kotzebue will serve as a baseline of observations in that 

community. In addition, a photo gallery will be added to the website. These photographs, of 

various ice conditions and features, will be useful to views to understand the various terms, 

conditions and ice features that are discussed in the interviews. 

Figure 2. Image of Project Jukebox website home page.

References

Coastal Response Research Center. 2009. Opening the Arctic seas: envisioning disasters and 

framing solutions, Final Report. Opening the Arctic Seas Workshop, CRRC, University 

 of New Hampshire, 18-20 March 2008. 

Eicken, H., Gradinger, R, Salganek, M.,Shiraswana, K.,Perovich, D., and Lepparanta, M., 

 editors. 2009. Field techniques for sea ice research. University of Alaska Press, 

Fairbanks, Alaska. 

2016 PROJECT UPDATE 

Initial planning was done to lay the groundwork for field efforts in spring of 2017. Contacts have 

been initiated with scientists and communities and some responses have been received. 

2016 PRESENTATIONS/PUBLICATIONS 

There were no presentations or publications in 2016. . 

44
Measuring Wave Forces along Alaska’s Coastal Sea Ice

Principal Investigator: Mark Johnson

College of Fisheries and Ocean Sciences

University of Alaska Fairbanks

Cooperative Agreement Number: M16AC00006

Period of Performance: 05/15/16 – 12/15/19

PROJECT OVERVIEW

Wave propagation into sea-ice is important to breaking the ice and controlling the characteristics 

of the marginal ice zone (Mahoney et al., 2016). Surface wave heights in the Arctic are 

increasing (Francis et al., 2011), and bigger waves may lead to more ice break, more open water, 

and increasingly bigger waves via a positive feedback loop. With reduced Arctic ice cover and 

larger waves, swells may become more common. Swells carry more energy and have longer 

attenuation scales (Squire et al. 1995; Squire, 2007) and can further increase ice break and 

enhance ice retreat (Thomson and Rogers, 2014), potentially becoming another driver towards 

reduced or ice-free Arctic summers.

Shifts in the sea-ice seasonality impact fetch, the contact area between wind and ocean. In the 

Arctic, fetch essentially vanishes in winter but increases dramatically during the summer sea-ice 

minimum. The duration of the reduced-ice season is, therefore, important to wave generation. 

Satellite data from 1979–2013 show that freeze-up, the time of ice onset, is delayed by two 

weeks per decade for the Chukchi coast and one week per decade for the Beaufort coast. For 

both regions, break-up start, the beginning of the open water season, is arriving earlier by 5–7 

days per decade and break-up end is arriving earlier by 10–12 days per decade (Johnson and 

Eicken, 2016). This trend toward a shorter ice season and longer open water points to substantial 

changes in the winter ice regime by mid-century with the potential for overlap of the end of the 

freeze-up and start of the break-up season as defined by coastal ice users (Figure 1). In the 

Beaufort Sea, the amount of open water ranges from near zero in April, during the sea ice 

maximum, to more than 1000 km during the September sea ice minimum. 

Fetch controls wave height and, although wave height is generally small in the Arctic Ocean due 

to the ice cover, there is a trend for increasing surface wave heights in the region (Francis et al., 

2011). For example, in September 2012, when the Beaufort Sea was largely free of ice, the 

significant open water area allowed wind waves to evolve into sea swell, with the long waves 

propagating away from the generating winds. Waves of 5 m were forced by winds up to 18 m/s

(Thomson and Rogers, 2014). 

In the open ocean, a typical wave spectrum has an amplitude peak in the period range of 5 to 20 

seconds. Large-scale atmospheric storms can excite waves outside of sea-ice cover having 

periods generally exceeding 25 seconds (Hanafin et al., 2012 in Ardhuin et al., 2016). The 

WAVEWATCH II model, for example, includes wave frequencies from 0.0338 to 0.7 Hz, or 

periods from 1.4 to 29.6 seconds (from Ardhuin et al. 2016). In Mahoney et al. (2016), we 

showed the existence of infra-gravity waves with periods estimated to be between 30 and 50 s, 

and probably closer to 50 s, based on InSAR remote sensing. 

45
Figure 1. Convergence of sea-ice freeze-up and break-up in 2095 associated with increased open water or fetch.

When surface waves reach the sea-ice boundary, their energy is attenuated due to scattering and 

dissipation. Scattering and dissipation have different effects on wave energy, so analysis of swell 

directional spreading and arrival times can help determine the relative importance of scattering 

versus dissipation. Measurements in pack ice show that scattering has a negligible role in 

attenuating long swells but reduces the passage of shorter waves. 

Away from the ice edge and into the ice, flexing and weakening may be relevant to fast-ice 

“break out” events along the Chukchi coast (Mahoney et al., 2007) where ice was observed to 

“flicker” in sequences of radar imagery before break-out events, suggesting wave propagation 

into the ice. A reasonable expectation is that wave propagation destabilizes the landfast ice either 

by fracturing the ice cover or reducing the shear strength of grounded ridge keels, creating the 

potential for offshore winds to displace the ice. The rarity of mid-winter break-out events prior to 

the 1990s suggests that landfast ice is responding to ongoing climate change. 

Robin (1963) conducted one of the first systematic comparisons of wave amplitude and wave 

period in the Weddell Sea and found ocean swells with periods greater than 10 s propagate 

through large floes by bending them. Ice flex can generate turbulence, weaken ice, and cause ice 

fracture. Smaller floes, those less than ~40 m diameter and 1.5 m thick, respond as rigid bodies. 

46
Most of the wave energy passes when the floes are less than 1/6 the wavelength, but ice floes 

larger than half of the wavelength block wave penetration. In this regard, the ice acts as a lowpass filter by removing shorter periods while longer swells are less attenuated. The most intense 

energy near the ice edge flexes the ice and breaks it into fragments resulting in a floe size 

distribution where the smallest pieces are nearest to open water. Wave energy decreases in 

intensity and the peak period increases as waves encounter and propagate into the marginal ice 

zone. For continuous sea-ice, waves and swell can cause cracks that may open into leads when 

winds or currents diverge the ice. 

Our goal in this three-year project of sensor development and fieldwork is to directly measure 

infra-gravity waves (e.g., the 30- to 50-second range as found by Mahoney et al. 2016) and other 

waves having periods greater than ~1 s. The Year 1 objective was to produce and cold test a 

prototype instrument for wave measurements. In Year 2, we plan to acquire data on coastal sea 

ice. By Year 3, we should be continuing successful measurements and acquiring data over 

several weeks or perhaps months. 

2016 PROJECT UPDATE 

Objective: Produce and cold test a prototype instrument for wave measurements. 

We accomplished our first year objectives. Two prototype instruments, “Ice Wave Riders” 

(IWR) were built to power a high-performance Inertial Measurement Unit, the VectorNav VN100. The VN-100 is a micro-electronic mechanical system that contains 3-axis accelerometers, 3-

axis gyroscopes, 3-axis magnetometers, a barometric pressure sensor, a temperature sensor, and a 

32-bit processor. It is also an Attitude Heading Reference System (AHRS) because it can output 

filtered attitude estimates in a local coordinate frame (NED, North-East-Down). Digital output 

collected at a sampling rate of 10 Hz is stored on a removable flash drive. 

The prototype IWR units were constructed in fall of 2016 by Pacific Gyre, Inc. (Figure 2). The 

units weigh about 50 pounds and use three 12V battery packs to supply 56 amp hours of power

to the VN-100, enough for several weeks of measurements. Iridium telemetry sends selected data 

(time, location) at programmable intervals to the Pacific Gyre, Inc. website for user download.

Figure 2. The Ice Wave Rider prototype with a case, battery, and Iridium and GPS antennas.

47
One IWR was shipped to Barrow and tested on sea-ice in Elson Lagoon (71.4°N, -156.3°W) 

between December 21, 2016, and January 5, 2017 (Figures 3 and 4). The deployment and 

recovery of the IWR were completed by Nagruk Harcharek and Michael Donovan with UIC 

Science in Barrow. The deployment site, in ~1.5 m of water and 1.5 km inside of Plover Pass, 

was selected because it is on stable ice but potentially susceptible to offshore ocean-ice 

dynamics. 

 

Figure 3. IWR deployment site in Elson Lagoon, Barrow, Alaska.

Figure 4. Photo of IWR deployment on the Elson Lagoon ice during the December 22, 2016, deployment. Photo by 

Mike Donovan, UIC, Barrow.

Sampling for fifteen days at 10 Hz collected 367 hour-long files, totaling 3.31GB of compressed, 

binary data. We are now building software to analyze the significant data return. Preliminary 

results show clear spectral peaks in the vertical acceleration having periods between 1 and 30 

seconds and longer (Figure 5). Waves are present at only specific times in the nearly two-week 

record. Figure 6 shows the VN-100 yaw, pitch, and roll data from the two-week data set.

48
Figure 5. Spectral analysis of the vertical acceleration showing selected days and periods. 

Figure 6. Yaw, pitch, and roll from the two-week VN-100 data set.

The IWR unit worked well, was easily deployed and recovered, and meets our Year 2 goal for 

field deployment, data collection, and power supply. We have an opportunity to deploy both 

units from the RV Lance cruise to the Barents Sea, which will be led by colleagues at University 

Centre in Svalbard in spring of 2017. We are assessing cost and risk, and comparing those with 

additional opportunities in Barrow. 

A second and parallel approach to wave measurement is under design at the College of Fisheries 

and Ocean Sciences using the SeaView Systems SVS-603 Wave Sensor (Figure 7). This sensor, 

which is used by the National Data Buoy Center program, reports significant wave height and 

other wave data. We are currently running laboratory evaluations on sensor performance and 

options for data capture. If results confirm that the sensor is sufficiently sensitive for wave 

49
measurements, we will build this sensor into the lightest, smallest, and the least expensive 

deployable unit we can design so that multiple units can be placed on sea-ice.

Figure 7. SVS-603 sensor from SeaView Systems is undergoing laboratory testing in Fairbanks.

References

Ardhuin F., P. Sutherland, M. Doble, and P. Wadhams. 2016. Ocean waves across the Arctic:

Attenuation due to dissipation dominates over scattering for periods longer than 19s.

 Geophys. Res. Lett., 43, 5775–5783, doi:10.1002/2016GL068204. 

Francis, O. P., G.G. Panteleev, and D.E. Atkinson. 2011. Ocean wave conditions in the Chukchi 

 Sea from satellite and in situ observations. Geophys. Res. Lett., 38, L24610. 

Hanafin, J., et al. 2012. Phenomenal sea states and swell radiation: a comprehensive analysis of

the 12–16 February 2011North Atlantic storms. Bull. Amer. Meterol. Soc., 93, 1825–

 1832. 

Johnson, M., and H. Eicken. 2016. Estimating Arctic sea-ice freeze-up and break-up from the

satellite record: A comparison of different approaches in the Chukchi and Beaufort Seas. 

 Elem. Sci. Anth., 4:124, doi:10.12952/journal.elementa.000124. 

Mahoney, A.R., D.O. Dammann, M.A. Johnson, H. Eicken, and F.J. Meyer 2016. Measurement

and imaging of infragravity waves in sea ice using InSAR. Geophys. Res. Lett., 43(12),

 6383–6392, doi:10.1002/2016GL069583. 

Mahoney, A., H. Eicken, and L. Shapiro. 2007. How fast is landfast sea ice? A study of the

attachment and detachment of nearshore ice at Barrow, Alaska. 2007. Cold Reg. Sci. 

 Technol., 47, 233–255. 

Robin, G. de Q. 1963. Wave Propagation through Fields of Pack Ice. Phil. Trans. R. Soc. Lond.

 Ser. A, 225, 313–339. 

Squire, V.A., 2007. Of ocean waves and sea ice revisited. 2007. Cold Reg. Sci. Technol., 49,

 110–133. 

Squire, V.A., J.P. Dugan, P. Wadhams, P.J. Rottier and A.K. Liu. 1995. Of ocean waves and seaice. Ann. Rev. Fluid Mech., 27, 115–168. 

Thomson, J., and W.E. Rogers. 2014. Swell and sea in the emerging Arctic Ocean. Geophys.

 Res. Lett., 41, 3136–3140, doi:10.1002/2014GL059983. 

2016 PRESENTATIONS/PUBLICATIONS

There were no project presentations or publications in 2016. 

50
OCS Study BOEM 2016-064: Addendum

Sensitivity to Hydrocarbons and Baselines of Exposure in Marine Birds on the Chukchi and 

Beaufort Seas

Principal Investigators: Tuula Hollmén, Ann Riddle

College of Fisheries and Ocean Sciences

University of Alaska Fairbanks

Cooperative Agreement Number: M13AC00005 

Period of Performance: Ended 10/31/16

Introduction

The Beaufort and Chukchi Seas and nearby coastal areas are important habitats for many marine 

and waterfowl bird species. Among these birds are species of subsistence importance (e.g., 

greater white-fronted geese; Anser albifrons) and conservation concern (e.g., Steller’s eider; 

Polysticta stelleri) which rely on this habitat for breeding, nesting, brood rearing, and migration 

staging. With prospects of oil and gas resource development and increases in commercial 

shipping in the Chukchi and Beaufort seas, establishing baselines and assessing the sensitivity of 

Arctic birds to hydrocarbon exposure will provide essential information needed for future 

population monitoring, identifying risks to populations, and in the event of exposure, recovery 

and restoration efforts. 

We used cytochrome P450 (CYP1A) enzyme induction measured by liver 7-ethoxyresorufin-Odeethylase (EROD) activity to evaluate sensitivity and establish exposure baselines. For wildlife, 

including waterfowl, CYP1A has been widely used as a biomarker of exposure to polycyclic 

aromatic hydrocarbons (PAHs) found in crude oil. For example, increased EROD activity has 

been linked to oil exposure in sea ducks (Trust et al. 2000; Miles et al. 2007; Esler 2008; Esler et 

al. 2010). In our final report for this project (BOEM 2016-064; Hollmen and Riddle, 2016), we 

reported our results for current CYP1A activity in livers collected from king eider (Somateria 

spectabilis), common eider (Somateria mollissima), and greater white-fronted goose. Based on 

EROD responses in hydrocarbon dosed liver cell cultures, we also identified Arctic birds 

sensitive to P450 induction. To further assess potential hydrocarbon sensitivity, we proposed to 

sequence the hydrocarbon receptor gene in several of our target species. Recently, analysis of the 

aryl hydrocarbon receptor 1 ligand binding domain (AHR 1 LBD) has been used to assess 

sensitivity in avian species, including common eider, to dioxin-like compounds (Head et al. 

2010), but many Arctic species have not been investigated. 

Methods

We used archived whole blood or muscle tissue for genetic analysis of the AHR 1 LBD sequence 

from five individuals of species also used in cell culture sensitivity analysis: greater whitefronted goose, king eider, Steller’s eider, spectacled eider (Somateria fischeri), and long-tailed 

duck (Clangula hyemali). Additionally, we acquired tissue from five black guillemots (Cepphus 

grylle). DNA was extracted using the Qiagen DNeasy blood and tissue kit following 

manufacturer protocols. Samples were then amplified by PCR using four primer sets (1 forward 

51
(F) & reverse (R), 2F&R, 3F&R, 4F&R) designed from the alignment of several avian AHR 1 

LBD coding sequences and the full-length chicken genome (Head et al. 2010). Fragments 1, 2, 

and 4 were amplified using 0.4 μM of each primer (F&R), 1x PCR buffer, 15 mM MgCl2, 0.4 

mM dNTPs, and 0.5U Taq DNA polymerase in a 25 or 50 μL reaction. The thermal profile was 

95°C for 2 min, 35 cycles of 95°C for 45 seconds and 56°C for 45 seconds, and lastly 72°C for 5 

mins. We were unable to amplify product for fragment 3 with this protocol and optimized the 

thermal program as follows: 95°C for 3 mins, and 30 cycles of 95°C for 30 seconds and 58°C for 

45 seconds and 72°C for 1 min, 72°C for 10 mins. PCR product was visualized on a 1% agarose 

gel and fragment length was estimated using a 100 bp DNA ladder. Next, PCR product was 

cleaned using Exo-Sap and packaged for shipment to the University of California-Davis DNA 

sequencing facility or to Functional Biosciences in Madison, Wisconsin. Resulting sequence data 

were imported into Sequencher for editing and aligning to other AHR 1LBD published avian 

sequences.

Results 

Fragment lengths varied by species (Table 1). We were unable to amplify product from longtailed duck for fragment 3 and black guillemot for fragment 4. Head et al. (2010) reported 

fragment length in chicken and herring gull as fragment 1 ~1100 bp, fragment 2 ~1800 bp, 

fragment 3 ~1100–1600 bp, and fragment 4 ~1300 bp. 

Table 1: Tested species resulting PCR product length for each of the four primer sets.

Species Fragment 1 Fragment 2 Fragment 3 Fragment 4

Spectacled eider ~1100 bp ~600 bp ~1800 bp ~800 bp

Steller’s eider ~2500 bp ~600 bp ~1800 bp ~800 bp

Long-tailed duck ~1100 bp ~600 bp n/a ~800 bp

King eider ~1100 bp ~600 bp ~1800 bp ~800 bp

Greater white-fronted goose ~1300 bp ~600 bp ~1800 bp ~1000 bp

Black guillemot ~1300 bp ~1800 bp ~1800 bp n/a

King eider was the only species we were able to get clean sequences from for all 4 fragments. 

This was likely due to a long fragment length, co-amplification of a similar gene, or quality of 

PCR product in the remaining species. Successful sequences were searched in GenBank with all 

species most closely matching herring gull (Larus argentatus) aryl hydrocarbon receptor gene 

sequence (HM239650.1) from Head et al. 2010. We attempted to align the fragment sequence 

results from our tested species with the herring gull sequence but were unable to obtain a 

complete, or near complete, sequence. Greater white-fronted goose sequence data (Figure 1) 

resulted in little overlap between fragment 1 forward and reverse sequences, only forward 

sequence for fragment 2, no sequence data for fragment 3, good forward and reverse overlap in 

fragment 4, and large areas of no sequence overlap with the herring gull. King eider sequence 

data (Figure 2) produced little overlap between fragment 1 forward and reverse, no sequence 

data for fragment 2, one forward sequence for fragment 3, good sequence overlap for fragment 4, 

52
and large areas of no sequence overlap with the herring gull. Steller’s eider (Figure 3) had no 

sequence results for fragment 1 and 2, little forward and reverse sequence overlap for fragment 

3, good overlap for fragment 4, and large areas of no sequence overlap with the herring gull. 

Long-tailed duck (Figure 4) had good forward and reverse overlap with fragments 1, 2, and 4, no 

data for fragment 3, and limited overlap with the herring gull. Spectacled eider sequence data 

(Figure 5) had good forward and reverse sequence alignment in fragment 2 and 4, only forward 

primer results for fragment 1, no data for fragment 3, and large areas of no sequence overlap 

with the herring gull. Lastly, there was very little overlap between forward and reverse black 

guillemot sequences (Figure 6) for fragment 1, no data for fragments 2 and 4, only reverse 

primer sequence data for fragment 3, and again, very little alignment with the herring gull 

sequence. While we likely would not need complete overlap with the herring gull sequences to 

compare sensitivity haplotypes, we need more overlap than we were able to obtain. Additionally, 

more optimization would need to occur to obtain forward and reverse sequences (with good 

overlap) for each fragment which aids in alignment and confirmation of sequence results. 

Recommendation 

Based on the findings, the method is promising. Using the described techniques, we were unable 

to obtain enough sequence data to assess hydrocarbon sensitivity using the hydrocarbon receptor 

gene and suggest future work to use next-generation sequencing. Next-generation sequencing is 

not limited by fragment length and removes the potential for co-amplification by creating 

sequence libraries. 

Acknowledgments

We thank George Divoky for black guillemot tissue and Sarah Sonsthagen for assistance with 

sequencing and data analysis. 

References

Esler, D. 2008. Quantifying temporal variation in harlequin duck cytochrome P4501A induction.

Exxon Valdez Oil Spill Trustee Council Gulf Ecosystem Monitoring and Research

Project. Final Report GEM Project 050777. 

Esler, D., KA Trust, BE Ballachey, SA Iverson, TL Lewis, DJ Rizzolo, DM Mulcahy, AK Miles,

BR Woodin, JJ Stegeman, JD Henderson, and BW Wilson. 2010. Cytochrome P4501A

biomarker indication of oil exposure in harlequin ducks up to 20 years after the Exxon

 Valdez oil spill. Environ. Toxicol. Chem. 29(5):1138–1145. 

Head, JA, R Farmahin, AS Kehoe, JM O’Brien, JL Shutt, and SW Kennedy. 2010.

Characterization of the avian aryl hydrocarbon receptor 1 from blood using non-lethal

 sampling methods. Ecotoxicology 19:1560–1566. 

Hollmén, T., and A. Riddle. 2016. Sensitivity to Hydrocarbons and Baselines of Exposure in 

Marine Birds on the Chukchi and Beaufort Seas. Final Report. OCS Study BOEM 2016-

 064, University of Alaska Coastal Marine Institute and USDOI, BOEM Alaska OCS 

Region. 

53
Miles, AK, PL Flint, KA Trust, MA Ricca, SE Spring, DE Arietta, T Hollmén, and BW Wilson.

2007. Polycyclic aromatic hydrocarbon exposure in Steller’s eiders (Polysticta stelleri)

and harlequin ducks (Histrionicus histrionicus) in the eastern Aleutian Islands, Alaska,

 USA. Environ. Toxicol. Chem. 26(12):2694–2703. 

Trust, KA, D Esler, BR Woodin, and JJ Stegeman. 2000. Cytochrome P450 1A induction in sea

 ducks inhabiting nearshore areas of Prince William Sound, Alaska. Marine Poll. Bull.

 40(5):397–403. 

54
Figure 1: Greater white-fronted goose sequence results: alignment of fragment forward and reverse sequences to the complete h

sequence. No data for fragment 3.

Figure 2: King eider sequence results: alignment of fragment forward and reverse sequences to the complete herring gull AHR 1 LBD s

55
Figure 3: Steller’s eider sequence results: alignment of fragment forward and reverse sequences to the complete herring gull AHR 1 L

fragments 1 and 2.

Figure 4: Long-tailed duck sequence results: alignment of fragment forward and reverse sequences to the complete herring gull AHR 

for fragment 3.

56
Figure 5: Spectacled eider sequence results: alignment of fragment forward and reverse sequences to the complete herring gull AHR 

for fragment 3. 

Figure 6: Black guillemot sequence results: alignment of fragment forward and reverse sequences to the complete herring gull AHR 1 L

fragment 2 and 4.

57
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