Download - Intro: Participants, motivation Methods: ( i ) Models, (ii) observations, (iii) skill metrics
• 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
Review of the IOOS Super-Regional Modeling Testbed Progress:Estuarine Hypoxia Modeling
OUTLINE
Presented at SURA Coastal & Environmental Research Committee MeetingWashington, DC, June 21, 2011
Review of the IOOS Super-Regional Modeling Testbed Progress:Estuarine Hypoxia Modeling
Carl Friedrichs (VIMS) and the Estuarine Hypoxia Testbed Team
Presented at SURA Coastal & Environmental Research Committee MeetingWashington, DC, June 21, 2011
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
(VIMS, ScienceDaily)(UMCES, Coastal Trends)
Motivation for Studying Estuarine Hypoxia
Motivation (cont.): Existing paradigm is that salinity stratification must bemodeled well to predict hypoxia well.
Mid-summer oxygen differences across the pycnocline versus salinity difference for mid-Cheseapeake Bay stations, 1949-1980 (Flemer et al. 1983; Boicourt et al. 1992).
• 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
Review of the IOOS Super-Regional Modeling Testbed Progress:Estuarine Hypoxia Modeling in Chesapeake Bay
OUTLINE
Presented at SURA Coastal & Environmental Research Committee MeetingWashington, DC, June 21, 2011
Methods (i) Models: 5 Hydrodynamic Models (so far)
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 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)
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
- 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)
Methods (iii) Skill Metrics: Target diagram
(modified from M. Friedrichs)
• 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
Review of the IOOS Super-Regional Modeling Testbed Progress:Estuarine Hypoxia Modeling in Chesapeake Bay
OUTLINE
Presented at SURA Coastal & Environmental Research Committee MeetingWashington, DC, June 21, 2011
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)
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)
ChesROMS
EFDC
UMCES-ROMS
CH3D
CBOFS2
Results (i) Hydrodynamics: Temporal variability of depth of pycnocline at 40 stations
Mean salinity of individualstations
[psu]
- Model behavior for pycnocline depth is also similar in terms of temporal variation of error at individual stations
(from A. Bever, M. Friedrichs)
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
-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)
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)
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)
• 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
Review of the IOOS Super-Regional Modeling Testbed Progress:Estuarine Hypoxia Modeling in Chesapeake Bay
OUTLINE
Presented at SURA Coastal & Environmental Research Committee MeetingWashington, DC, June 21, 2011
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)
Results (ii): Dissolved Oxygen Model Comparison (cont.)
(from A. Bever, M. Friedrichs)
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
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)
(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?
(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
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
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
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
• 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
Review of the IOOS Super-Regional Modeling Testbed Progress:Estuarine Hypoxia Modeling
OUTLINE
Presented at SURA Coastal & Environmental Research Committee MeetingWashington, DC, June 21, 2011
• Model-model and model-data comparisons for 5 hydrodynamic and 8 combined hydrodynamic-dissolved oxygen models (so far).
• 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 is scheduled for July 5-6, 2011, at NCEP to further hammer out transition stems for moving a fully operational version of 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
• 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.• Averaging output from multiple models provides better hypoxia hindcasts than
relying on any individual model alone.
SCIENTIFIC SUMMARY
• 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.• Averaging output from multiple models provides better hypoxia hindcasts than
relying on any individual model alone.• Why are the models so similar, and why to they all underpredict stratification?
SCIENTIFIC SUMMARY
From Warner et al. 2005, Ocean Modeling
Turbulence Models Have Lots of Coefficients!!They must be selected carefully!!
(slide from M. Scully)
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)
Importance of setting appropriate minimum value for TKE
(slide from M. Scully)
Proposed 2nd Year Efforts
1) Continue to work closely with NOAA-CSDL and NOAA-NCEP to transition our findings for use in short-term (≤ ~15 day) hypoxia forecast tools at NOAA.
2) Continue to work closely with the EPA Chesapeake Bay Program to incorporate our findings into the future evolution of CBP scenario hypoxia forecast models.
3) Further explore the model properties 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.