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U.S. IOOS Testbed Comparisons: Hydrodynamics and Hypoxia

U.S. IOOS Testbed Comparisons: Hydrodynamics and Hypoxia. Marjy Friedrichs Virginia Institute of Marine Science Including contributions from the entire Estuarine Hypoxia Testbed team With special thanks to Aaron Bever. U.S. IOOS Modeling Testbed. Four Teams: Cyberinfrastructure Team

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U.S. IOOS Testbed Comparisons: Hydrodynamics and Hypoxia

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  1. U.S. IOOS Testbed Comparisons:Hydrodynamics and Hypoxia Marjy Friedrichs Virginia Institute of Marine Science Including contributions from the entire Estuarine Hypoxia Testbed team With special thanks to Aaron Bever

  2. U.S. IOOS Modeling Testbed Four Teams: Cyberinfrastructure Team Coastal Inundation Team Shelf Hypoxia Team (Gulf of Mexico) Estuarine Hypoxia Team (Chesapeake Bay)

  3. U.S. IOOS Modeling Testbed Estuarine Hypoxia Team: Carl Friedrichs (VIMS) Marjorie Friedrichs (VIMS) Aaron Bever (VIMS) JianShen (VIMS) Malcolm Scully (ODU) Raleigh Hood/Wen Long (UMCES, U. Md.) Ming Li (UMCES, U. Md.) John Wilkin/Julia Levin (Rutgers U.) Kevin Sellner (CRC) Federal partners Carl Cerco (USACE) David Green (NOAA-NWS) Lyon Lanerolle (NOAA-CSDL) Lew Linker (EPA) Doug Wilson (NOAA-NCBO)

  4. U.S. IOOS Modeling Testbed Overarching Goal: To help improve operational and scenario-based modeling of hypoxia in Chesapeake Bay Methods: Compare hindcast skill of multiple CB models on seasonal time scales Hydrodynamics & dissolved oxygen (2004 and 2005) 2. Generate metrics by which future models can be tested

  5. Outline • What CB models and metrics are we using? • 5 Hydrodynamic models and 5 Biological (DO) models • RMSD; target diagrams • What is the relative hydrodynamic skill of these CB models? • Is this a function of resolution? Forcing? • What is the relative DO skill of these CB models? • Is this a function of model complexity? • Summary and outlook for forecasting

  6. Outline • What CB models and metrics are we using? • 5 Hydrodynamic models and 5 Biological (DO) models • RMSD; target diagrams • What is the relative hydrodynamic skill of these CB models? • Is this a function of resolution? Forcing? • What is the relative DO skill of these CB models? • Is this a function of model complexity? • Summary and outlook for forecasting

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

  8. Biological-Hydrodynamic models • Biological models: • ICM: CBP model; complex biology • bgc: NPZD-type biogeochemical model • 1eqn: Simple one equation respiration • (includes SOD) • 1term-DD: depth-dependent respiration • (not a function of x, y, temperature, nutrients…) • 1term: Constant net respiration • Multiple combinations: • CH3D + ICM • EFDC + 1eqn, 1term • CBOFS2 + 1term, (1term+DD soon!) • ChesROMS + 1term, 1term+DD, bgc

  9. Outline • What CB models and metrics are we using? • 5 Hydrodynamic models and 5 Biological (DO) models • RMSD; target diagrams • What is the relative hydrodynamic skill of these CB models? • Is this a function of resolution? Forcing? • What is the relative DO skill of these CB models? • Is this a function of model complexity? • Summary and outlook for forecasting

  10. Data from 40 CBP stations = ~40 CBP stations used in this model-data comparison mostly 2004 some 2005 results bottom T, bottom S, stratification = max dS/dz, depth of max dS/dz bottom DO, hypoxic volume

  11. Hydrodynamic Model Comparisons Use consistent forcing for each model to examine model skill in hindcasting spatial & temporal variability of: • bottom temperature • bottom salinity • maximum stratification (dS/dz) • depth of maximum stratification

  12. Bottom Temperature (2004) bias [°C] mean unbiased RMSD [°C] variability outer circle: mean of data Models all successfully reproduce seasonal/spatial variability of bottom temperature (ROMS models do best)

  13. Hydrodynamic Model Skill bias [psu] (a) Bottom Temperature bias [°C] (b) Bottom Salinity unbiased RMSD [psu] unbiased RMSD [°C] Models do better at hindcasting bottom T & S than stratification bias [psu/m] bias [m] (c) Stratification at pycnocline • Stratification is a challenge for all the models: • All underestimate strength and variability of stratification • All underestimate variability of pycnocline depth. (d) Depth of pycnocline unbiased RMSD [psu/m] unbiased RMSD [m]

  14. Sensitivity Experiments • Used 4 models to test sensitivity of hydrodynamic skill to: • Vertical grid resolution (CBOFS2) • Freshwater inflow (CBOFS2; EFDC) • Vertical advection scheme (CBOFS2) • Horizontal grid resolution (UMCES-ROMS) • Coastal boundary condition (ChesROMS) • Mixing/turbulence closure (ChesROMS) • 2004 vs. 2005 (all models; in progress) • Sensitivities not yet tested: • Bathymetry • Vertical grid type: sigma vs. z-grid

  15. Outline • What CB models and metrics are we using? • 5 Hydrodynamic models and 5 Biological (DO) models • RMSD; target diagrams • What is the relative hydrodynamic skill of these CB models? • Is this a function of resolution? Forcing? • What is the relative DO skill of these CB models? • Is this a function of model complexity? • Summary and outlook for forecasting

  16. Dissolved Oxygen Model Comparison Hypoxic Volume Bottom DO • 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.

  17. Hypoxic Volume Time Series 2004 Models generally overestimate hypoxic volumes computed from station data – but what about uncertainties in these interpolated estimates?

  18. Hypoxic Volume Time Series 3D modeled hypoxic volume Absolute model-data match 2004 2004 Interpolated hypoxic volumes contain large uncertainties (factor of two) How can the simple 1-term models resolve seasonal cycle of HV without nutrient or temperature dependent net respiration? Wind & Solubility!

  19. Summary • There currently exist multiple hydrodynamic and DO models for Chesapeake Bay • Hydrodynamic skill is similar in all models • Simple constant net respiration rate models reproduce seasonal DO cycle as well as complex models • But can they reproduce interannual variability? • The simpler models cannot be used to test impacts of decreasing nutrient inputs to the Bay • Models reproduce DO better than stratification • Averaging output from multiple models provides better hypoxia hindcasts than relying on any individual model alone

  20. Outlook for DO forecasting • Strong dependence on solubility (temperature) and winds is a good thing for forecasting, since these are variables we know relatively well! (At least better than respiration rates) • Particularly easy to implement 1-term DO model into the CBOFS hydrodynamic model presently being run operationally at NCEP • Caveat: To date we have tested these models only on seasonal time scales (i.e. not daily and interannual scales)

  21. EXTRA SLIDES

  22. Bottom DO – temporal variability ICM (complex CBP model) 1-term DO model 1-term DO model Total RMSD = 0.9 ± 0.1 Total RMSD = 0.9 ± 0.1 1-term DO model underestimates high DO and overestimates low DO: high not high enough, low not low enough

  23. Bottom DO – spatial variability ICM (CBP model) 1-term DO model Total RMSD = 1.1 ± 0.1 Total RMSD = 1.0 ± 0.1 Overall model-data fit to CBP station bottom DO data is similar

  24. Depth of maximum stratification CBOFS2 0.2 -0.6 -0.2 -0.4 -0.8 -0.2 -0.2 CBOFS2 stratification is insensitive to: vertical grid resolution, vertical advection scheme and freshwater river input

  25. Spatial variability of stratification for each month CH3D ChesROMS EFDC CBOFS2 month UMCES-ROMS CH3D/EFDC slightly better in terms of spatial variability

  26. Temporal variability of depth of max strat. at 40 stations CH3D ChesROMS EFDC CBOFS2 Salinity [psu] UMCES-ROMS Model skill is similar in terms of temporal variability

  27. Spatial variability of depth of max strat. for each month CH3D ChesROMS EFDC CBOFS2 month UMCES-ROMS CH3D slightly better in terms of spatial variability

  28. Atm forcing; Horiz grid resolution Stratification CH3D, EFDC ROMS Max. stratification is not sensitive to horizontal grid resolution or changes in atmospheric forcing

  29. Atm forcing; Horiz grid resolution Stratification 2005 2004 Models do better in 2005 than 2004!

  30. Atm forcing; Horiz grid resolution Bottom Salinity High horiz res Low horiz res Bottom salinity IS sensitive to horizontal grid resolution

  31. Effect of physical forcingon hypoxia ChesROMS+1-term 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)

  32. Effect of physical forcingon hypoxia ChesROMS+1-term 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)

  33. Effect of physical forcingon hypoxia ChesROMS+1-term 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)

  34. Effect of physical forcingon hypoxia ChesROMS+1-term 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)

  35. STRATIFICATION 2004 simulation vs. 2005 data 2004 simulation vs. 2004 data • EXTRA SLIDES

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