intro: participants, motivation methods: ( i ) models, (ii) observations, (iii) skill metrics
DESCRIPTION
Estuarine Hypoxia Modeling OUTLINE. Carl Friedrichs (VIMS) and the Estuarine Hypoxia Team. Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics Results ( i ): What is the relative hydrodynamic skill of these CB models? - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/1.jpg)
• Intro: Participants, motivation
• Methods: (i) Models, (ii) observations, (iii) skill metrics
• Results (i): What is the relative hydrodynamic skill of these CB models?
• Results (ii): What is the relative dissolved oxygen skill ofthese CB models?
• Summary and ongoing work
Estuarine Hypoxia Modeling
OUTLINE
Carl Friedrichs (VIMS) and the Estuarine Hypoxia Team
Presented at U.S.IOOS/SURA Modeling Testbed All-Hands Annual MeetingWashington, DC, June 22, 2011
![Page 2: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/2.jpg)
Estuarine Hypoxia Modeling
Carl Friedrichs (VIMS) and the Estuarine Hypoxia Team
Federal partners (*unfunded partner)• David Green* (NOAA-NWS) – Transition to operations at NWS• Lyon Lanerole (NOAA-CSDL) – Transition to operations at CSDL; CBOFS2• Lewis Linker* (EPA), Carl Cerco* (USACE) – Transition to operations at EPA; CH3D, CE-ICM• Doug Wilson* (NOAA-NCBO) – Integration w/observing systems at NCBO/IOOSNon-federal partners• Marjorie Friedrichs, Aaron Bever (VIMS) – Metric development and model skill assessment• Ming Li, Yun Li (UMCES) – UMCES-ROMS hydrodynamic model• Wen Long, Raleigh Hood (UMCES) – ChesROMS with NPZD water quality model • Scott Peckham (UC-Boulder) – Running multiple ROMS models on a single HPC cluster• Malcolm Scully (ODU) – ChesROMS with 1 term oxygen respiration model• Kevin Sellner (CRC) – Academic-agency liason; facilitator for model comparison• Jian Shen (VIMS) – SELFE, FVCOM, EFDC models• John Wilkin, Julia Levin (Rutgers) – ROMS-Espresso + 7 other MAB hydrodynamic models
Presented at U.S.IOOS/SURA Modeling Testbed All-Hands Annual MeetingWashington, DC, June 22, 2011
![Page 3: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/3.jpg)
Motivation for Studying Estuarine Hypoxia
(VIMS, ScienceDaily)
1) Help enable operational short-term (≤ weeks) estuarine hypoxia forecasts (NOAA-CSDL, NOAA-NCEP)
(UMCES, Coastal Trends)
2) Help enable long-term restoration scenario (≥ years) estuarine hypoxia forecasts (EPA-CBP)
![Page 4: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/4.jpg)
Mid-summer oxygen differences across the pycnocline mirror the salinity difference across the pycnocline for mid-Chesapeake Bay stations, 1949-1980.
(Flemer et al. 1983)
Motivation (cont.): 3) Evaluate the ability of multiple models to hindcast salinity stratification and dissolved oxygen, given the paradigm that salinity stratification must be modeled well to predict estuarine hypoxia well.
![Page 5: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/5.jpg)
• Intro: Participants, motivation
• Methods: (i) Models, (ii) observations, (iii) skill metrics
• Results (i): What is the relative hydrodynamic skill of these CB models?
• Results (ii): What is the relative dissolved oxygen skill ofthese CB models?
• Summary and ongoing work
Estuarine Hypoxia Modeling
OUTLINE
Presented at U.S.IOOS/SURA Modeling Testbed All-Hands Annual MeetingWashington, DC, June 22, 2011
![Page 6: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/6.jpg)
Methods (i) Models: 5 Hydrodynamic Models (so far)
(& J. Wiggert/J. Xu, USM/NOAA-CSDL)
![Page 7: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/7.jpg)
o ICM: CBP model; complex biologyo bgc: NPZD-type biogeochemical modelo 1eqn: Simple one equation respiration (includes SOD)o 1term-DD: depth-dependent net respiration
(not a function of x, y, temperature, nutrients…)o 1term: Constant net respiration
Methods (i) Models (cont.): 5 Dissolved Oxygen Models (so far)
![Page 8: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/8.jpg)
o ICM: CBP model; complex biologyo bgc: NPZD-type biogeochemical modelo 1eqn: Simple one equation respiration (includes SOD)o 1term-DD: depth-dependent net respiration
(not a function of x, y, temperature, nutrients…)o 1term: Constant net respiration
Methods (i) Models (cont.): 5 Dissolved Oxygen Models (so far)
o CH3D + ICMo EFDC + 1eqn, 1termo CBOFS2 + 1term, 1term+DD o ChesROMS + 1term, 1term+DD, bgc
Methods (i) Models (cont.): 8 Multiple combinations (so far)
![Page 9: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/9.jpg)
Map of Late July 2004
Observed Dissolved Oxygen [mg/L]
~ 40 EPA Chesapeake Bay stationsEach sampled ~ 20 times in 2004
Temperature, Salinity, Dissolved Oxygen
Data set for model skill assessment:
(http://earthobservatory.nasa.gov/Features/ChesapeakeBay)
Methods (ii) observations: S and DO from Up to 40 CBP station locations
![Page 10: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/10.jpg)
- How do we compare these models to data and to each other?- e.g., Is ChesROMS 1-Term (very simple) or CH3D-ICM (very complex) more accurate?- e.g., Which is modeled better, dissolved oxygen (DO) or salinity stratification (dS/dz)?
(from A. Bever)
![Page 11: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/11.jpg)
Methods (iii) Skill Metrics: Target diagram
(modified from M. Friedrichs)
Dimensionless version of plot normalizes by standard deviation of observations
![Page 12: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/12.jpg)
• Intro: Participants, motivation
• Methods: (i) Models, (ii) observations, (iii) skill metrics
• Results (i): What is the relative hydrodynamic skill of these CB models?
• Results (ii): What is the relative dissolved oxygen skill ofthese CB models?
• Summary and ongoing work
Estuarine Hypoxia Modeling
OUTLINE
Presented at U.S.IOOS/SURA Modeling Testbed All-Hands Annual MeetingWashington, DC, June 22, 2011
![Page 13: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/13.jpg)
unbiasedRMSD
[°C]
bias [°C]
unbiasedRMSD[psu]
bias [psu]
unbiasedRMSD
[psu/m]
bias [psu/m]
unbiasedRMSD
[m]
bias [m]
(a) Bottom Temperature (b) Bottom
Salinity
(c) Stratificationat pycnocline (d) Depth of
pycnocline
Outer circle in each case = error from simply using mean of all data
Inner circle in (a) & (b) = errorfrom CH3D model
Results (i): Hydrodynamic Model Comparison
- All models do very well hind-casting temperature.
- All do well hind-casting bottom salinity with CH3D and EFDC doing best.
- Stratification is a challenge for all the models.
- All underestimate strength and variability of stratification with CH3D and EFDC doing slightly better.
- CH3D and ChesROMS do slightly better than others for pycnocline depth, with CH3D too deep, and the others too shallow.
- All underestimate variability of pycnocline depth.(from A. Bever, M. Friedrichs)
![Page 14: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/14.jpg)
ChesROMS
EFDC
UMCES-ROMS
CH3D
CBOFS2
Results (i) Hydrodynamics: Temporal variability of stratification at 40 stations
Mean salinity of individualstations
[psu]
- Model behavior for stratification is similar in terms of temporal variation of error at individual stations
(from A. Bever, M. Friedrichs)
![Page 15: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/15.jpg)
Used 4 models to test sensitivity of hydrodynamic skill to:
o Vertical grid resolution (CBOFS2)o Freshwater inflow (CBOFS2; EFDC)o Vertical advection scheme (CBOFS2)o Atmospheric forcing – winds (ChesROMS; EFDC)o Horizontal grid resolution (UMCES-ROMS)o Coastal boundary condition (UMCES-ROMS)o 2004 vs. 2005 (in progress)
Results (i) Hydrodynamics (cont.): Sensitivity to model refinement
![Page 16: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/16.jpg)
-0.2-0.4-0.6-0.8
0.2
-0.2
-0.2
CBOFS2
CBOFS2 model pycnocline depth is insensitive to: vertical grid resolution, vertical advection scheme and freshwater river input
Results (i) Hydrodynamics (cont.): Sensitivity of pycnocline depth
(from A. Bever, M. Friedrichs)
![Page 17: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/17.jpg)
Stratification at pycnocline is not sensitive to horizontal grid resolution or changes in atmospheric forcing. (Stratification is still always underestimated)
CH3D, EFDC
ROMS
Stratification
Results (i) Hydrodynamics (cont.): Sensitivity of stratification at pycnocline
(from A. Bever, M. Friedrichs)
![Page 18: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/18.jpg)
Bottom salinity IS sensitive to horizontal grid resolution
High horiz res
Low horiz res
Bottom Salinity
Results (i) Hydrodynamics (cont.): Sensitivity of bottom salinity
(from A. Bever, M. Friedrichs)
![Page 19: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/19.jpg)
• Intro: Participants, motivation
• Methods: (i) Models, (ii) observations, (iii) skill metrics
• Results (i): What is the relative hydrodynamic skill of these CB models?
• Results (ii): What is the relative dissolved oxygen skill ofthese CB models?
• Summary and ongoing work
Estuarine Hypoxia Modeling
OUTLINE
Presented at U.S.IOOS/SURA Modeling Testbed All-Hands Annual MeetingWashington, DC, June 22, 2011
![Page 20: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/20.jpg)
Results (ii): Dissolved Oxygen Model Comparison
- Simple models reproduce dissolved oxygen (DO) and hypoxic volume about as well as more complex models.- All models reproduce DO better than they reproduce stratification.- A five-model average does better than any one model alone.
(from A. Bever, M. Friedrichs)
![Page 21: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/21.jpg)
Results (ii): Dissolved Oxygen Model Comparison (cont.)
(from A. Bever, M. Friedrichs)
![Page 22: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/22.jpg)
Average of these 5 models is better than any single model.
Results (ii): Dissolved Oxygen Model Comparison (cont.)Hy
poxi
c Vo
lum
e in
km
3
![Page 23: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/23.jpg)
1-term DO model ICM (most complex model)
1-term DO model underestimates high DO and overestimates low DO: high not high enough, low not low enough
Total RMSD = 0.9 ± 0.1 Total RMSD = 0.9 ± 0.1
ChesROMS-1term (simplest model)
Results (ii) Dissolved Oxygen: Temporal variability of bottom DO at 40 stations
Mean bottom DO
individualstations[mg/L]
(from A. Bever, M. Friedrichs)
![Page 24: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/24.jpg)
(by M. Scully)
Results (ii) Dissolved Oxygen: Top-to-Bottom DS and Bottom DO in Central Chesapeake Bay
ChesROMS-1term model
- All models reproduce DO better than they reproduce stratification.- So if stratification is not controlling DO, what is?
![Page 25: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/25.jpg)
(by M. Scully)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Date in 2004
Hypo
xic
Volu
me
in k
m3
20
10
0
Base Case
(by M. Scully)
ChesROMS-1term model
Results (ii) (cont.): Effect of Physical Forcing on Dissolved Oxygen
![Page 26: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/26.jpg)
Seasonal changes in hypoxia are not a function of seasonal changes in freshwater.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Date in 2004
Hypo
xic
Volu
me
in k
m3
20
10
0
Base Case
Freshwater river input constant
(by M. Scully)(by M. Scully)
ChesROMS-1term model
Results (ii) (cont.): Effect of Physical Forcing on Dissolved Oxygen
![Page 27: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/27.jpg)
Seasonal changes in hypoxia may be largely due to seasonal changes in wind.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Date in 2004
Hypo
xic
Volu
me
in k
m3
20
10
0
Base CaseJuly wind year-round
(by M. Scully)(by M. Scully)
ChesROMS-1term model
Results (ii) (cont.): Effect of Physical Forcing on Dissolved Oxygen
![Page 28: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/28.jpg)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Date in 2004
Hypo
xic
Volu
me
in k
m3
20
10
0
Base Case
January wind year-round
(by M. Scully)(by M. Scully)
Seasonal changes in hypoxia may be largely due to seasonal changes in wind.
ChesROMS-1term model
Results (ii) (cont.): Effect of Physical Forcing on Dissolved Oxygen
![Page 29: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/29.jpg)
• Intro: Participants, motivation
• Methods: (i) Models, (ii) observations, (iii) skill metrics
• Results (i): What is the relative hydrodynamic skill of these CB models?
• Results (ii): What is the relative dissolved oxygen skill ofthese CB models?
• Accomplishments, scientific summary and ongoing work
Estuarine Hypoxia Modeling
OUTLINE
Presented at U.S.IOOS/SURA Modeling Testbed All-Hands Annual MeetingWashington, DC, June 22, 2011
![Page 30: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/30.jpg)
• Model-model and model-data comparisons for 5 hydrodynamic and 8 combined hydrodynamic-dissolved oxygen models (so far).
• Model forcings, grids, output, and observed data sets transferred to Testbed CI Team.• Progress on transitioning of findings to NOAA:
-- Team member L. Lanerolle (NOAA-CSDL) has incorporated our “1term” hypoxia formulation within the research version of NOAA-CSDL’s Chesapeake Bay Operational Forecast System.
-- A meeting between NOAA-NCEP and Estuarine Hypoxia Team PIs (coordinated by Team member D. Green, NOAA-NWS) is scheduled for July 5-6, 2011, to further hammer out transition steps for moving the CBOFS hypoxia model to NCEP.
• Progress on transitioning of findings to EPA:
-- Estuarine Hypoxia Team helped organize a June 9-10, 2011, workshop to provide advice to the EPA Chesapeake Bay Program on future estuarine modeling strategies in support of Federally mandated environmental restoration.
-- At the workshop, it was proposed that the Estuarine Hypoxia Team play a major role in establishing a Chesapeake Modeling Laboratory for developing and testing agency models (as recommended by a new National Academy of Sciences report to EPA).
ACCOMPLISHMENTS
![Page 31: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/31.jpg)
• Available models generally have similar skill in terms of hydrodynamic quantities• All the models underestimate strength and variability of salinity stratification.• No significant improvement in hydrodynamic model skill due to refinements in:
– Horizontal/vertical resolution, atmospheric forcing, freshwater input, ocean forcing.
• In terms of DO/hypoxia, simple constant net respiration rate models reproduce seasonal cycle about as well as complex models.
• Models reproduce the seasonal DO/hypoxia better than seasonal stratification.• Seasonal cycle in DO/hypoxia is due more to wind speed and direction than to
seasonal cycle in freshwater input, stratification, nutrient input or respiration.– Note: This does not mean than inter-annual variation in nutrient input/respiration is unimportant.
• Averaging output from multiple models provides better hypoxia hindcasts than relying on any individual model alone.
SCIENTIFIC SUMMARY
![Page 32: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/32.jpg)
• Available models generally have similar skill in terms of hydrodynamic quantities• All the models underestimate strength and variability of salinity stratification.• No significant improvement in hydrodynamic model skill due to refinements in:
– Horizontal/vertical resolution, atmospheric forcing, freshwater input, ocean forcing.
• In terms of DO/hypoxia, simple constant net respiration rate models reproduce seasonal cycle about as well as complex models.
• Models reproduce the seasonal DO/hypoxia better than seasonal stratification.• Seasonal cycle in DO/hypoxia is due more to wind speed and direction than to
seasonal cycle in freshwater input, stratification, nutrient input or respiration.– Note: This does not mean than inter-annual variation in nutrient input/respiration is unimportant.
• Averaging output from multiple models provides better hypoxia hindcasts than relying on any individual model alone.
• Why are the errors from multiple hydrodynamic models so similar, and why do they all underpredict salinity stratification?
SCIENTIFIC SUMMARY
![Page 33: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/33.jpg)
From Warner et al. 2005, Ocean Modeling
Turbulence Models Have Lots of Coefficients!!They must be selected carefully!!
(slide from M. Scully)
Minimum TKE setting
![Page 34: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/34.jpg)
Kzmin = 5×10-7 TKEmin=7.6×10-6
Kzmin = 5×10-8 TKEmin=7.6×10-6
Kzmin = 5×10-7 TKEmin=7.6×10-8
Importance of setting appropriate minimum value for TKE
Only when only the minimum value of TKE is sufficiently low, can model achieve the specified background diffusivity!!
(slide from M. Scully)
![Page 35: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/35.jpg)
Importance of setting appropriate minimum value for TKE
(slide from M. Scully)
![Page 36: Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics](https://reader036.vdocument.in/reader036/viewer/2022062323/56815b66550346895dc956eb/html5/thumbnails/36.jpg)
Proposed 2nd Year Efforts
1) Continue to work closely with NOAA-CSDL and NOAA-NCEP to transition our findings for use in short-term (≤ weeks) hypoxia forecast models used by NOAA.
2) Continue to work closely with the EPA Chesapeake Bay Program to transition our findings for use in scenario (≥ years) hypoxia forecast models used by EPA.
3) Further explore the model properties (including canonical settings) that lead to the inability of hydrodynamic models to capture the observed intensity of density stratification.
4) More fully include unstructured grid models in the Year 2 estuarine hydrodynamics and hypoxia intercomparison.
5) Expand the parameter space of model runs to include additional degrees of biological model complexity as well as coordinated, idealized sensitivity runs across multiple models.
6) Run hindcasts encompassing entire CB monitoring history (~30 years) to better understand controls on inter-annual variability in hypoxia.
Presented at U.S.IOOS/SURA Modeling Testbed All-Hands Annual MeetingWashington, DC, June 22, 2011