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Results of the US IOOS Testbed for Comparison of Hydrodynamic and Hypoxia Models of Chesapeake Bay Carl Friedrichs (VIMS) and the Estuarine Hypoxia Team.

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Presentation on theme: "Results of the US IOOS Testbed for Comparison of Hydrodynamic and Hypoxia Models of Chesapeake Bay Carl Friedrichs (VIMS) and the Estuarine Hypoxia Team."— Presentation transcript:

1 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 MABPOM Horn Point, MD, October 12, 2011

2 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 MABPOM Horn Point, MD, October 12, 2011 Here today

3 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 of these CB models? Summary and Conclusions Results of the US IOOS Testbed for Comparison of Hydrodynamic (and Hypoxia) Models of Chesapeake Bay OUTLINE Presented at MABPOM Horn Point, MD, October 12, 2011

4 Methods (i) Models: 5 Hydrodynamic Models (so far) (& J. Wiggert/J. Xu, USM/NOAA-CSDL)

5 o ICM: CBP model; complex biology o bgc: NPZD-type biogeochemical model o 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)

6 o ICM: CBP model; complex biology o bgc: NPZD-type biogeochemical model o 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 + ICM o EFDC + 1eqn, 1term o CBOFS2 + 1term, 1term+DD o ChesROMS + 1term, 1term+DD, bgc Methods (i) Models (cont.): 8 Multiple combinations (so far)

7 Map of Late July 2004 Observed Dissolved Oxygen [mg/L] ~ 40 EPA Chesapeake Bay stations Each 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

8 Methods (iii) Skill Metrics: Target diagram (modified from M. Friedrichs) Dimensionless version of plot normalizes by standard deviation of observations

9 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 of these CB models? Summary and Conclusions Results of the US IOOS Testbed for Comparison of Hydrodynamic (and Hypoxia) Models of Chesapeake Bay OUTLINE Presented at MABPOM Horn Point, MD, October 12, 2011

10 unbiased RMSD [°C] bias [°C] unbiased RMSD [psu] bias [psu] unbiased RMSD [psu/m] bias [psu/m] unbiased RMSD [m] bias [m] (a) Bottom Temperature (b) Bottom Salinity (c) Stratification at pycnocline (d) Depth of pycnocline Outer circle in each case = error from simply using mean of all data Inner circle in (a) & (b) = error from 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)

11 ChesROMS EFDC UMCES-ROMS CH3D CBOFS2 Results (i) Hydrodynamics: Temporal variability of stratification at 40 stations Mean salinity of individual stations [psu] - Model behavior for stratification is similar in terms of temporal variation of error at individual stations (from A. Bever, M. Friedrichs)

12 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

13 -0.2 -0.4 -0.6 -0.8 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)

14 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

15 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)

16 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 of these CB models? Summary and Conclusions Results of the US IOOS Testbed for Comparison of Hydrodynamic and Hypoxia Models of Chesapeake Bay OUTLINE Presented at MABPOM Horn Point, MD, October 12, 2011

17 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)

18 (by M. Scully) Results (ii) Dissolved Oxygen: Top-to-Bottom  S 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?

19 (by M. Scully) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Date in 2004 Hypoxic Volume in km 3 20 10 0 Base Case (by M. Scully) ChesROMS-1term model Results (ii) (cont.): Effect of Physical Forcing on Dissolved Oxygen

20 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 Hypoxic Volume in km 3 20 10 0 Base Case Freshwater river input constant (by M. Scully) ChesROMS-1term model Results (ii) (cont.): Effect of Physical Forcing on Dissolved Oxygen

21 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 Hypoxic Volume in km 3 20 10 0 Base Case July wind year-round (by M. Scully) ChesROMS-1term model Results (ii) (cont.): Effect of Physical Forcing on Dissolved Oxygen

22 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Date in 2004 Hypoxic Volume in km 3 20 10 0 Base Case January wind year-round (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

23 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


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