results of the us ioos testbed for comparison of hydrodynamic and hypoxia models of chesapeake bay
DESCRIPTION
Results of the US IOOS Testbed for Comparison of Hydrodynamic and Hypoxia Models of Chesapeake Bay Carl Friedrichs (VIMS) and the Estuarine Hypoxia Team. Federal partners David Green (NOAA-NWS) – Transition to operations at NWS - PowerPoint PPT PresentationTRANSCRIPT
Results of the US IOOS Testbed for Comparison of Hydrodynamic and Hypoxia Models of Chesapeake Bay
Carl Friedrichs (VIMS) and the Estuarine Hypoxia Team
Federal partners• David Green (NOAA-NWS) – Transition to operations at NWS• Lyon Lanerole, Rich Patchen, Frank Aikman (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/IOOS
Non-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 MABPOMHorn Point, MD, October 12, 2011
Results of the US IOOS Testbed for Comparison of Hydrodynamic and Hypoxia Models of Chesapeake Bay
Carl Friedrichs (VIMS) and the Estuarine Hypoxia Team
Federal partners• David Green (NOAA-NWS) – Transition to operations at NWS• Lyon Lanerole, Rich Patchen, Frank Aikman (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/IOOS
Non-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 MABPOMHorn Point, MD, October 12, 2011
Here today
• 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 Conclusions
Results of the US IOOS Testbed for Comparison of Hydrodynamic (and Hypoxia) Models of Chesapeake Bay
OUTLINE
Presented at MABPOMHorn Point, MD, October 12, 2011
Methods (i) Models: 5 Hydrodynamic Models (so far)
(& J. Wiggert/J. Xu, USM/NOAA-CSDL)
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
Methods (iii) Skill Metrics: Target diagram
(modified from M. Friedrichs)
Dimensionless version of plot normalizes by standard deviation of observations
• 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 Conclusions
Results of the US IOOS Testbed for Comparison of Hydrodynamic (and Hypoxia) Models of Chesapeake Bay
OUTLINE
Presented at MABPOMHorn Point, MD, October 12, 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)
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 Choice of wind models (ChesROMS; EFDC)o Horizontal grid resolution (UMCES-ROMS)o Coastal boundary condition (UMCES-ROMS)o 2004 vs. 2005 (UMCES-ROMS)
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 2004
Stratification
Results (i) Hydrodynamics (cont.): Sensitivity of stratification at pycnocline
(from A. Bever, M. Friedrichs)
ROMS 2005
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)
• 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 Conclusions
Results of the US IOOS Testbed for Comparison of Hydrodynamic and Hypoxia Models of Chesapeake Bay
OUTLINE
Presented at MABPOMHorn Point, MD, October 12, 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)
(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
Hyp
oxic
Vol
ume
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
Hyp
oxic
Vol
ume
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
Hyp
oxic
Vol
ume
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
Hyp
oxic
Vol
ume
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
• 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 hindcast than relying on any individual model alone.
SUMMARY & CONCLUSIONS