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Results of the US IOOS Testbed for Comparison of Hydrodynamic and Hypoxia Models of Chesapeake Bay

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

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Results of the US IOOS Testbed for Comparison of Hydrodynamic and Hypoxia Models of Chesapeake Bay

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  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 • JianShen (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 • JianShen (VIMS) – SELFE, FVCOM, EFDC models • John Wilkin, Julia Levin (Rutgers) – ROMS-Espresso + 7 other MAB hydrodynamic models Here today Presented at MABPOM Horn Point, MD, October 12, 2011

  3. Results of the US IOOS Testbed for Comparison of Hydrodynamic (and Hypoxia) Models of Chesapeake Bay OUTLINE • 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 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. Methods (i) Models (cont.): 5 Dissolved Oxygen Models (so far) • ICM: CBP model; complex biology • bgc: NPZD-type biogeochemical model • 1eqn: Simple one equation respiration (includes SOD) • 1term-DD: depth-dependent net respiration • (not a function of x, y, temperature, nutrients…) • 1term: Constant net respiration

  6. Methods (i) Models (cont.): 5 Dissolved Oxygen Models (so far) • ICM: CBP model; complex biology • bgc: NPZD-type biogeochemical model • 1eqn: Simple one equation respiration (includes SOD) • 1term-DD: depth-dependent net respiration • (not a function of x, y, temperature, nutrients…) • 1term: Constant net respiration • CH3D + ICM • EFDC + 1eqn, 1term • CBOFS2 + 1term, 1term+DD • ChesROMS + 1term, 1term+DD, bgc Methods (i) Models (cont.): 8 Multiple combinations (so far)

  7. Methods (ii) observations: S and DO from Up to 40 CBP station locations Data set for model skill assessment: ~ 40 EPA Chesapeake Bay stations Each sampled ~ 20 times in 2004 Temperature, Salinity, Dissolved Oxygen Map of Late July 2004 Observed Dissolved Oxygen [mg/L] (http://earthobservatory.nasa.gov/Features/ChesapeakeBay)

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

  9. Results of the US IOOS Testbed for Comparison of Hydrodynamic (and Hypoxia) Models of Chesapeake Bay OUTLINE • 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 Presented at MABPOM Horn Point, MD, October 12, 2011

  10. Results (i): Hydrodynamic Model Comparison bias [psu] (a) Bottom Temperature bias [°C] (b) Bottom Salinity Inner circle in (a) & (b) = error from CH3D model • All models do very well hind-casting temperature. • All do well hind-casting bottom salinity with CH3D and EFDC doing best. unbiased RMSD [psu] unbiased RMSD [°C] • 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. bias [psu/m] bias [m] (c) Stratification at pycnocline Outer circle in each case = error from simply using mean of all data (d) Depth of pycnocline unbiased RMSD [psu/m] unbiased RMSD [m] (from A. Bever, M. Friedrichs)

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

  12. Results (i) Hydrodynamics (cont.): Sensitivity to model refinement • Used 4 models to test sensitivity of hydrodynamic skill to: • Vertical grid resolution (CBOFS2) • Freshwater inflow (CBOFS2; EFDC) • Vertical advection scheme (CBOFS2) • Choice of wind models (ChesROMS; EFDC) • Horizontal grid resolution (UMCES-ROMS) • Coastal boundary condition (UMCES-ROMS) • 2004 vs. 2005 (UMCES-ROMS)

  13. Results (i) Hydrodynamics (cont.): Sensitivity of pycnocline depth CBOFS2 0.2 -0.6 -0.2 -0.4 -0.8 -0.2 -0.2 (from A. Bever, M. Friedrichs) CBOFS2 model pycnocline depth is insensitive to: vertical grid resolution, vertical advection scheme and freshwater river input

  14. Results (i) Hydrodynamics (cont.): Sensitivity of stratification at pycnocline Stratification (from A. Bever, M. Friedrichs) CH3D, EFDC ROMS 2005 ROMS 2004 Stratification at pycnocline is not sensitive to horizontal grid resolution or changes in atmospheric forcing. (Stratification is still always underestimated)

  15. Results (i) Hydrodynamics (cont.): Sensitivity of bottom salinity Bottom Salinity High horiz res Low horiz res Bottom salinity IS sensitive to horizontal grid resolution (from A. Bever, M. Friedrichs)

  16. Results of the US IOOS Testbed for Comparison of Hydrodynamic and Hypoxia Models of Chesapeake Bay OUTLINE • 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 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. 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)

  19. Results (ii) (cont.): Effect of Physical Forcing on Dissolved Oxygen ChesROMS-1term model 20 10 0 Base Case Hypoxic Volume in km3 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Date in 2004 (by M. Scully) (by M. Scully)

  20. Results (ii) (cont.): Effect of Physical Forcing on Dissolved Oxygen ChesROMS-1term model 20 10 0 Base Case Hypoxic Volume in km3 Freshwater river input constant Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Date in 2004 Seasonal changes in hypoxia are not a function of seasonal changes in freshwater. (by M. Scully) (by M. Scully)

  21. Results (ii) (cont.): Effect of Physical Forcing on Dissolved Oxygen ChesROMS-1term model 20 10 0 July wind year-round Base Case Hypoxic Volume in km3 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Date in 2004 Seasonal changes in hypoxia may be largely due to seasonal changes in wind. (by M. Scully) (by M. Scully)

  22. Results (ii) (cont.): Effect of Physical Forcing on Dissolved Oxygen ChesROMS-1term model 20 10 0 Base Case Hypoxic Volume in km3 January wind year-round Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Date in 2004 Seasonal changes in hypoxia may be largely due to seasonal changes in wind. (by M. Scully) (by M. Scully)

  23. SUMMARY & CONCLUSIONS • Availablemodels 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.

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