ArticlePDF Available

A Test Bed for Coastal and Ocean Modeling

August 2017
Eos Transactions American Geophysical Union 98(11)

DOI:10.1029/2017EO078243

Authors:

Richard A. Luettich, Jr.

University of North Carolina at Chapel Hill

L. Donelson Wright

William & Mary

C. Reid Nichols

Marine Information Resources Corporation

Rebecca Baltes

National Oceanic and Atmospheric Administration

Show all 9 authorsHide

An ocean modeling program is improving our ability to predict circulation along the U.S. West Coast, dead zones and other coastal ecosystem responses, and storm surges in island environments.

Changing levels of dissolved oxygen concentrations were measured at a U.S. Environmental Protection Agency station near Kent Point in the upper Chesapeake Bay. Red dots represent 34 observations made during 2004-2005. Gray curves represent the predictions of several individual models. The dark blue curve represents the model mean, and the turquoise curves give the 95% confidence interval. The model mean does better in matching the observations than any individual model. Source: Irby et al. [2016], CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/legalcode)

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A Test Bed for Coastal and Ocean Modeling

An ocean modeling program is improving our ability to predict circulation along the U.S. West Coast, dead zones and

other coastal ecosystem responses, and storm surges in island environments.

This visualization of a modeled surface wavefield, part of a study of storm surge and inundation prediction in the Caribbean, shows water level changes as

Hurricane Georges moves into the Caribbean Sea in 1998. Colors indicate significant wave height. The Coastal and Ocean Modeling Testbed (COMT)

program conducts targeted research and development to accelerate the transition of scientific and technical advances from the modeling research community

into improved products and services for a wide range of users. Credit: COMT/RPS Applied Science Associates

By Richard A. Luettich Jr., L. Donelson Wright, C. Reid Nichols, Rebecca Baltes, Marjorie A. M. Friedrichs, Alexander Kurapov, Andre van der Westhuysen,

Katja Fennel, and Eoin Howlett  4 August 2017

From forecasting the incidence of oxygen-depleted “dead zones” in the Chesapeake Bay and northern Gulf of Mexico to predicting circulation along the U.S.

West Coast and storm surges in the Caribbean, coastal and ocean modeling offers tools that can help save lives, protect property, and sustain marine

resources.

The Coastal and Ocean Modeling Testbed (https://ioos.us/comt) (COMT) supports this effort by conducting targeted research and development aimed at

speeding up the process by which scientific and technical advances from the coastal and ocean modeling research community are transitioned into improved

operational ocean products and services.

The COMT program, led by the Southeastern Universities Research Association (http://www6.sura.org/) (SURA), is part of the National Oceanic and

Atmospheric Administration’s (NOAA) Integrated Ocean Observing System (https://ioos.noaa.gov/) (IOOS) effort. Projects supported through COMT

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(https://eos.org/opinions/collaboration-to-enhance-coastal-resilience) are designed to assess the performance of existing models, create new model code and tools as

necessary, inform and train users, and build a repository of evaluation data sets to expand and improve the modeling capabilities of operational partners and

the broader coastal and ocean modeling community.

The initial COMT program began in 2010 and was completed in 2013. A description of the program from its inception and a compilation of scientific results

are available in a special issue (http://onlinelibrary.wiley.com/doi/10.1002/2013JC008939/abstract) of the Journal of Geophysical Research: Oceans [Luettich et al.,

2013].

The current COMT program began in 2013 and includes participants from academia, the private sector, and government agencies (Table 1). Here we

highlight progress in five ongoing COMT projects (https://ioos.us/comt).

Table 1. Coastal and Ocean Modeling Testbed Collaborators and

Partners

University

Collaborators

Government

Collaborators

Industry/Nongovernmental

Organization Collaborators Partners

Dalhousie

University

Louisiana State

University

Oregon State

University

Texas A&M

University

University of

California, San

Diego

University of

California,

Santa Cruz

University of

Maryland

University of

Maine

University of

North Carolina

University of

Notre Dame

University of

Puerto Rico

University of

Washington

Virginia

Institute of

Marine Science,

College of

William and

Mary

Woods Hole

Oceanographic

Center for

Operational

Oceanographic

Products and

Services, NOAA

Environmental

Modeling

Center, NOAA

National

Hurricane

Center, NOAA

U.S. Integrated

Ocean

Observing

System, NOAA

Naval Research

Laboratory,

U.S. Navy

Coastal and

Hydraulics

Laboratory,

U.S. Army

Corps of

Engineers

Gulf Ecology

Division, U.S.

Environmental

Protection

Agency

Remote Sensing Solutions

RPS Applied Science Associates

Southeastern Universities

Research Association

IOOS regional

associations for

Southern

California,

central and

Northern

California,

Pacific

Northwest,

Mid-Atlantic,

Gulf of Mexico,

and Caribbean

Chesapeake Bay

Program Office,

U.S.

Environmental

Protection

Agency

National

Environmental

Satellite, Data,

and

Information

Service, NOAA

National Ocean

Service, NOAA

National

Weather

Service, NOAA

U.S. Geological

Survey

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Institution

Chesapeake Bay Oxygen Depletion

Changes in levels of dissolved oxygen have a significant effect on the health of the Chesapeake Bay, an estuary located in the Mid-Atlantic region of the U.S.

East Coast. Excessively low oxygen levels can stress or kill fish and other animals, creating dead zones within the bay.

COMT’s Chesapeake Bay project (http://ioos.us/comt/projects/cb_hypoxia) seeks to improve models of dissolved oxygen concentration in the estuary, taking into

consideration a variety of contributing factors. The study is designed to provide a better understanding of the uncertainty inherent in predictions of such

properties as salinity, temperature, chlorophyll content, and nutrient concentration (https://eos.org/scientific-press/study-finds-hotspots-of-ammonia-over-worlds-major-

farming-areas) and how this uncertainty contributes to the predictability of dissolved oxygen levels.

The project team compared the regulatory model used by Chesapeake Bay program managers with research models currently being used by the scientific

community and found that the regulatory model performed as well as many of the scientific models. This result gives program managers and academic

scientists more confidence in the regulatory model.

Fig. 1. Changing levels of dissolved oxygen concentrations were

measured at a U.S. Environmental Protection Agency station

near Kent Point in the upper Chesapeake Bay. Red dots

represent 34 observations made during 2004–2005. Gray

curves represent the predictions of several individual models.

The dark blue curve represents the model mean, and the

turquoise curves give the 95% confidence interval. The model

mean does better in matching the observations than any

individual model. Source: Irby et al. [2016], CC BY 3.0

(https://creativecommons.org/licenses/by/3.0/legalcode)

The team also found that an ensemble mean of multiple models is better at predicting hypoxia than any individual model (Figure 1), illustrating the potential

value of multimodel ensembles for decision-making concerning the Chesapeake Bay.

Project researchers developed a simple one-term model for hypoxia (oxygen deficiency in the environment) for use in day-to-day operational forecasts

(http://www.vims.edu/newsandevents/topstories/2016/cb_hypoxia_fcst.php). The model is currently providing same-day and 3-day forecasts

(http://www.vims.edu/research/topics/dead_zones/forecasts/cbay/) of Chesapeake Bay hypoxia on the Virginia Institute of Marine Science (http://www.vims.edu/) website.

The team is pursuing incorporation of this tool into NOAA’s Chesapeake Bay Operational Forecast System

(https://tidesandcurrents.noaa.gov/ofs/cbofs/cbofs_info.html).

The Chesapeake Bay project is also running a variety of scenarios using multiple models, including the regulatory program model, to assess responses to

nutrient load reductions and future climate change. These results will help inform coastal resource managers directing the restoration and protection of the

Chesapeake Bay.

Gulf of Mexico Oxygen Depletion

Dissolved oxygen depletion is also a significant problem in the Gulf of Mexico, where a large hypoxic area (https://eos.org/articles/gulf-of-mexico-dead-zone-largest-

since-2002) forms every summer over the Texas-Louisiana shelf in the northern part of the gulf.

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As in the Chesapeake Bay project, COMT’s Gulf of Mexico study (http://ioos.us/comt/projects/gom_hypoxia) is focusing on identifying factors that influence the

prediction of dissolved oxygen concentration. This is done to improve understanding of the controlling processes and to provide guidance on the eventual

implementation of dissolved oxygen forecasts in NOAA’s Northern Gulf of Mexico Operational Forecast System

(https://tidesandcurrents.noaa.gov/ofs/ngofs/ngofs.html).

Scenario-based simulations assess the potential impacts of nutrient management decisions and future climate conditions.

A comparison of three coupled hydrodynamic-ecosystem models (http://www.meece.eu/models.html) has shown that skillful hypoxia predictions in this region

require accurate reproduction of bottom water temperature, bottom boundary layer thickness, and vertical attenuation of short-wave solar radiation (the way

that the water column filters out the blue and ultraviolet wavelengths of sunlight at progressively greater depths) [Fennel et al., 2016].

Project researchers are conducting scenario-based simulations to assess the potential impacts of nutrient management decisions in the Mississippi River

Basin and future climate conditions on hypoxia in the northern Gulf of Mexico. Results are informing the interagency Hypoxia Task Force’s

(https://www.epa.gov/ms-htf) efforts to devise near-term and long-term nutrient management strategies.

West Coast Forecast System

COMT’s West Coast project (https://ioos.us/comt/projects/usw_integration) is part of a larger NOAA effort to develop a new U.S. West Coast basin-wide operational

forecast system (WCOFS). Operational forecast systems (http://oceanservice.noaa.gov/facts/ofs.html) provide near-term and long-term predictions of such factors as

wind, temperature, water levels, salinity, currents, and, eventually, water biogeochemistry.

The project’s 6-year evaluation of the Regional Ocean Modeling System (https://www.myroms.org/) (ROMS) shows that this model can realistically reproduce

ocean variability on temporal scales from a few days to years along the entire U.S. West Coast.

The project team is studying three biogeochemical models (https://eos.org/research-spotlights/uncertainty-evaluations-improve-biogeochemical-simulations) of differing

complexity, already incorporated into ROMS, to identify advantages and limitations of the varying model formulations and to assess associated rate

processes.

Data assimilation is a critical future component of the WCOFS. Thus, we are also evaluating approaches to data assimilation in ROMS using new metrics for

assessing model and assimilation system skill. We have shown that existing observational data are reasonably effective at correcting model representations of

circulation associated with the California Current system.

Close coordination between the COMT team and the WCOFS implementation team is helping to accelerate the development of WCOFS at NOAA.

Caribbean Surge and Wave Modeling

Intense storms bring high waves and surges that can inundate coastal areas and flood rivers. COMT’s Caribbean project (http://ioos.us/comt/projects/pr_inundation)

aims to extend effective forecasting of waves and storm surges from gently sloped areas, such as the Gulf of Mexico’s northern edge, to steep-sloped areas,

like those surrounding Caribbean islands.

Taking advantage of observational data available for Puerto Rico and the U.S. Virgin Islands, the project team evaluated a number of models for their

effectiveness in predicting storm surges (https://eos.org/opinions/climate-changes-pulse-is-in-central-america-and-the-caribbean) and coastal inundation.

Regional-scale model runs for Hurricane Georges (https://www.weather.gov/mob/georges) (1998) and Hurricane Irene

(http://www.weather.gov/mhx/Aug272011EventReview) (2011) elucidated the relative roles of wind forcing and wave forcing on storm surge and inundation in Puerto

Rico. These model runs used the Advanced Circulation model (ADCIRC (http://adcirc.org/)) and NOAA’s Sea, Lake, and Overland Surges from Hurricanes

(SLOSH (http://www.nhc.noaa.gov/surge/slosh.php)) model, coupled to the Simulating Waves Nearshore (SWAN (http://www.swan.tudelft.nl/)) model.

The following animations show a modeled surface wavefield, illustrating water level changes as Hurricane Georges approaches Puerto Rico, with a close-up

showing wave heights as the hurricane makes landfall at Vieques and the eastern end of Puerto Rico. Red and yellow colors indicate significant wave height.

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Hurricane Georges approaches Vieques and Puerto Rico. Credit:

COMT/RPS Applied Science Associates

(http://ioos.us/comt/projects/pr_inundation/georges/2016/05/COMT_CI.gif)

Hurricane Georges makes landfall. Credit: COMT/RPS Applied

Science Associates

(https://ioos.us/comt/projects/pr_inundation/georges/2016/05/COMT_CI2.gif)

Comparisons between the coupled ADCIRC-SWAN model and the SLOSH-SWAN model, developed during the original COMT program, have helped to

document the performance of the latter approach. On the basis of these findings, the National Hurricane Center (http://www.nhc.noaa.gov/) used the coupled

SLOSH-SWAN model to produce the first surge and inundation hazard database for Puerto Rico that includes the effect of waves.

The associated Storm Surge Maximum Envelope of Water (MEOW (http://www.nhc.noaa.gov/surge/meowOverview.php)) and Storm Surge Maximum of the

Maximum (MOM (http://www.nhc.noaa.gov/surge/momDescrip.php)) models are now providing crucial guidance tools for the Puerto Rico Weather Forecast Office

(http://www.weather.gov/sju/) and are being used in the development of evacuation zones for the National Hurricane Program’s

(http://www.ofcm.gov/groups/DIAP/Meetings/2016/01%20NHP%20WG-DIAP%20Brief%20Penney.pdf) hurricane evacuation studies (https://coast.noaa.gov/hes/hes.html).

Model Viewer

The COMT computer infrastructure project (http://ioos.us/comt/projects/cyber_infrastructure) is focused on archiving data for evaluating models, providing tools to

discover and access these data, and creating visualizations of model outputs.

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Although researchers often have in-house visualization and analysis tools tailored for use with their specific numerical models, our new model viewer

(http://ioos.us/comt/model_viewer) allows the simultaneous visualization of results from different models. This facilitates model comparisons and helps extend the

value of COMT results to future modeling research and development activities.

Fostering Collaboration

Since 2010, COMT has facilitated collaboration among more than 20 universities and a range of government agencies.

Since 2010, COMT has facilitated collaboration among more than 20 universities and a range of government agencies, including NOAA, the U.S. Navy, the

Environmental Protection Agency, and the U.S. Army Corps of Engineers. COMT is now 1 of 11 official NOAA test beds (http://www.testbeds.noaa.gov/).

Collaboration has underpinned program success from the outset, within the academic community, and, more important, between academia and operational

users. Such collaboration requires dedicated effort, along with a mutual acceptance of goals, critical assessment of diverse approaches, iterative updates,

promotion of new paradigms, and effective communication.

Scientific understanding, like nature, is impermanent and dynamic. We expect COMT to continue to evolve in concert with the operational use of the coastal

and ocean models that it is intended to advance.

References

Fennel, K., et al. (2016), Effects of model physics on hypoxia simulations for the northern Gulf of Mexico: A model intercomparison, J. Geophys. Res. Oceans, 121, 5731–5750,

https://doi.org/10.1002/2015JC011577 (https://doi.org/10.1002/2015JC011577).

Irby, I. D., et al. (2016), Challenges associated with modeling low-oxygen waters in Chesapeake Bay: A multiple model comparison, Biogeosciences, 13, 2011–2028,

https://doi.org/10.5194/bg-13-2011-2016 (https://doi.org/10.5194/bg-13-2011-2016).

Luettich, R. A., et al. (2013), Introduction to special section on the U.S. IOOS Coastal and Ocean Modeling Testbed, J. Geophys. Res. Oceans, 118, 6319–6328,

https://doi.org/10.1002/2013JC008939 (https://doi.org/10.1002/2013JC008939).

Author Information

Richard A. Luettich Jr. (email: rick_luettich@unc.edu (mailto:rick_luettich@unc.edu)), Institute of Marine Sciences, University of North Carolina at Chapel Hill, Morehead City; L.

Donelson Wright and C. Reid Nichols, Southeastern Universities Research Association, Washington, D. C.; Rebecca Baltes, U.S. Integrated Ocean Observing System Program,

Silver Spring, Md.; Marjorie A. M. Friedrichs, Virginia Institute of Marine Science, College of William and Mary, Gloucester Point; Alexander Kurapov, College of Earth, Ocean

and Atmospheric Sciences, Oregon State University, Corvallis; Andre van der Westhuysen, IMSG at Environmental Modeling Center, NOAA Center for Weather and Climate

Prediction, College Park, Md.; Katja Fennel, Department of Oceanography, Dalhousie University, Halifax, NS, Canada; and Eoin Howlett, RPS Applied Science Associates,

South Kingstown, R.I.

Citation: Luettich, R. A., Jr., L. D. Wright, C. R. Nichols, R. Baltes, M. A. M. Friedrichs, A. Kurapov, A. van der Westhuysen, K. Fennel, and E. Howlett (2017), A test bed for coastal and ocean

modeling, Eos, 98, https://doi.org/10.1029/2017EO078243. Published on 04 August 2017.

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Apr 2016
BG

As three-dimensional (3-D) aquatic ecosystem models are used more frequently for operational water quality forecasts and ecological management decisions, it is important to understand the relative strengths and limitations of existing 3-D models of varying spatial resolution and biogeochemical complexity. To this end, 2-year simulations of the Chesapeake Bay from eight hydrodynamic-oxygen models have been statistically compared to each other and to historical monitoring data. Results show that although models have difficulty resolving the variables typically thought to be the main drivers of dissolved oxygen variability (stratification, nutrients, and chlorophyll), all eight models have significant skill in reproducing the mean and seasonal variability of dissolved oxygen. In addition, models with constant net respiration rates independent of nutrient supply and temperature reproduced observed dissolved oxygen concentrations about as well as much more complex, nutrient-dependent biogeochemical models. This finding has significant ramifications for short-term hypoxia forecasts in the Chesapeake Bay, which may be possible with very simple oxygen parameterizations, in contrast to the more complex full biogeochemical models required for scenario-based forecasting. However, models have difficulty simulating correct density and oxygen mixed layer depths, which are important ecologically in terms of habitat compression. Observations indicate a much stronger correlation between the depths of the top of the pycnocline and oxycline than between their maximum vertical gradients, highlighting the importance of the mixing depth in defining the region of aerobic habitat in the Chesapeake Bay when low-oxygen bottom waters are present. Improvement in hypoxia simulations will thus depend more on the ability of models to reproduce the correct mean and variability of the depth of the physically driven surface mixed layer than the precise magnitude of the vertical density gradient.

Introduction to special section on The U.S. IOOS Coastal and Ocean Modeling Testbed

Article

Dec 2013

[1] Strong and strategic collaborations among experts from academia, federal operational centers, and industry have been forged to create a U.S. IOOS Coastal and Ocean Modeling Testbed (COMT). The COMT mission is to accelerate the transition of scientific and technical advances from the coastal and ocean modeling research community to improved operational ocean products and services. This is achieved via the evaluation of existing technology or the development of new technology depending on the status of technology within the research community. The initial phase of the COMT has addressed three coastal and ocean prediction challenges of great societal importance: estuarine hypoxia, shelf hypoxia, and coastal inundation. A fourth effort concentrated on providing and refining the cyberinfrastructure and cyber tools to support the modeling work and to advance interoperability and community access to the COMT archive. This paper presents an overview of the initiation of the COMT, the findings of each team and a discussion of the role of the COMT in research to operations and its interface with the coastal and ocean modeling community in general. Detailed technical results are presented in the accompanying series of 16 technical papers in this special issue.

Effects of model physics on hypoxia simulations for the northern Gulf of Mexico: A model intercomparison

Jan 2016
5731-5750

K Fennel

Fennel, K., et al. (2016), Effects of model physics on hypoxia simulations for the northern Gulf of Mexico: A model intercomparison, J. Geophys. Res. Oceans, 121, 5731–5750, https://doi.org/10.1002/2015JC011577 (https://doi.org/10.1002/2015JC011577).

Challenges associated with modeling low-oxygen waters in Chesapeake Bay: A multiple model comparison

Jan 2011
BG
2011-2028

I D Irby

Irby, I. D., et al. (2016), Challenges associated with modeling low-oxygen waters in Chesapeake Bay: A multiple model comparison, Biogeosciences, 13, 2011–2028, https://doi.org/10.5194/bg-13-2011-2016 (https://doi.org/10.5194/bg-13-2011-2016).

Introduction to special section on the U.S. IOOS Coastal and Ocean Modeling Testbed

Jan 2013
6319-6328

R A Luettich

Luettich, R. A., et al. (2013), Introduction to special section on the U.S. IOOS Coastal and Ocean Modeling Testbed, J. Geophys. Res. Oceans, 118, 6319–6328, https://doi.org/10.1002/2013JC008939 (https://doi.org/10.1002/2013JC008939).

email: rick_luettich@unc.edu (mailto:rick_luettich@unc

A Richard
Luettich Jr

Richard A. Luettich Jr. (email: rick_luettich@unc.edu (mailto:rick_luettich@unc.edu)), Institute of Marine Sciences, University of North Carolina at Chapel Hill, Morehead City; L.

Integrated Ocean Observing System Program, Silver Spring, Md.; Marjorie A. M. Friedrichs, Virginia Institute of Marine Science, College of William and Mary, Gloucester Point

Donelson Wright
C Reid Nichols Washington
D C Baltes

Donelson Wright and C. Reid Nichols, Southeastern Universities Research Association, Washington, D. C.; Rebecca Baltes, U.S. Integrated Ocean Observing System Program, Silver Spring, Md.; Marjorie A. M. Friedrichs, Virginia Institute of Marine Science, College of William and Mary, Gloucester Point; Alexander Kurapov, College of Earth, Ocean and Atmospheric Sciences, Oregon State University, Corvallis; Andre van der Westhuysen, IMSG at Environmental Modeling Center, NOAA Center for Weather and Climate Prediction, College Park, Md.; Katja Fennel, Department of Oceanography, Dalhousie University, Halifax, NS, Canada; and Eoin Howlett, RPS Applied Science Associates, South Kingstown, R.I.

A test bed for coastal and ocean modeling

Aug 2017
98

R A Luettich
L D Jr
C R Wright
R Nichols
M A M Baltes
A Friedrichs
A Kurapov
K Van Der Westhuysen
E Fennel
Howlett

Citation: Luettich, R. A., Jr., L. D. Wright, C. R. Nichols, R. Baltes, M. A. M. Friedrichs, A. Kurapov, A. van der Westhuysen, K. Fennel, and E. Howlett (2017), A test bed for coastal and ocean modeling, Eos, 98, https://doi.org/10.1029/2017EO078243. Published on 04 August 2017.

Such collaboration requires dedicated effort, along with a mutual acceptance of goals, critical assessment of diverse approaches, iterative updates, promotion of new paradigms, and effective communication

Jan 2016
5731-5750

Collaboration has underpinned program success from the outset, within the academic community, and, more important, between academia and operational users. Such collaboration requires dedicated effort, along with a mutual acceptance of goals, critical assessment of diverse approaches, iterative updates, promotion of new paradigms, and effective communication. Scientific understanding, like nature, is impermanent and dynamic. We expect COMT to continue to evolve in concert with the operational use of the coastal and ocean models that it is intended to advance. References Fennel, K., et al. (2016), Effects of model physics on hypoxia simulations for the northern Gulf of Mexico: A model intercomparison, J. Geophys. Res. Oceans, 121, 5731-5750, https://doi.org/10.1002/2015JC011577 (https://doi.org/10.1002/2015JC011577).