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Estuarine Hypoxia Component of Testbed 2

Estuarine Hypoxia Component of Testbed 2. Marjorie Friedrichs, VIMS, lead Carl Friedrichs, VIMS, co-lead Wen Long and Raleigh Hood, UMCES Malcolm Scully, ODU. FY12 Testbed 2 Kick-off Telecon. Objectives.

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Estuarine Hypoxia Component of Testbed 2

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  1. Estuarine Hypoxia Component of Testbed 2 Marjorie Friedrichs, VIMS, lead Carl Friedrichs, VIMS, co-lead Wen Long and Raleigh Hood, UMCES Malcolm Scully, ODU FY12 Testbed 2 Kick-off Telecon

  2. Objectives • Compare relative skill of various hydrodynamic and dissolved oxygen models in reproducing observations on seasonal time scales in Chesapeake Bay, by examining: • bottom/surface temperature • bottom/surface salinity • bottom/surface dissolved oxygen • maximum stratification • depth of maximum stratification • hypoxic volume • Provide information to managers such that results of these analyses could be transitioned to operational/scenario models

  3. Five Hydrodynamic Models Configured for the Bay EFDC Shen VIMS CH3D Cerco & Wang USACE UMCES-ROMS Li & Li UMCES CBOFS (ROMS) Lanerolle & Xu NOAA ChesROMS Long & Hood UMCES

  4. Five 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

  5. 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

  6. Stratification (max dS/dz; 2004) bias [psu/m] unbiased RMSD [psu/m] Stratification is a challenge; CH3D, EFDC reproduce seasonal/spatial variability best

  7. Sensitivity Experiments Maximum Stratification CH3D, EFDC ROMS Stratification is insensitive to grid resolution and changes in atmospheric forcing

  8. Stratification (max dS/dz; 2004) bias [psu/m] unbiased RMSD [psu/m] ROMS with new TKE parameter Adjusting the minimum TKE parameter reduces the bias in ChesROMS

  9. Hypoxic Volume bias [km3] unbiased RMSD [km3] Several simple DO models reproduce seasonal variability of hypoxic volume about as well as ICM

  10. Hypoxic Volume bias [km3] unbiased RMSD [km3] 5-model average does better than any single model

  11. Overall Progress from Testbed 1 • Compared 5 different hydrodynamic models • with 5 different DO models (examined 12 different combinations of hydrodynamics+DO for 2004, subset of these for 2005) • Density stratification at pycnocline is a challenge • Simplest DO models reproduce seasonal variability as well as most complex models • Multi-model average for hypoxic volume does better than • any single model • Models do much better in our wet year (2005) than our • dry year (2004) • 2. Began to examine sensitivity experiments with individual models • Strong sensitivities to wind, min TKE, advection scheme • Weak sensitivities to river discharge, coastal BC, grid resolution

  12. Overall Progress from Testbed 1(cont.) • 3. Transitioning information to federal agencies • Simple DO model incorporated into the research version of NOAA CSDL’s Chesapeake Bay Operational • Forecast System • Participated in Eco-Forecasting workshop at NOAA/ • NCEP to further hammer out transition steps for moving to fully operational version of the DO model • Provided advice to the CBP on future estuarine and • hypoxia modeling strategies, in support of federally • mandated environmental restoration, via a STAC • workshop report

  13. Update on Testbed 1 Deliverables • We are on track to provide all deliverables as promised by Dec. 31, 2011

  14. Plans for Testbed 2 Year 1 • Improve modeled density stratification • Examine choice of turbulence closure scheme • and advection scheme • 2. Idealized sensitivity experiments with all models • Concentrating on wind, may also include river discharge • 3. Additional skill metric • Averaged Discrete Frechet Distance • 4. Presentations/publications • Five publications are in preparation • Multiple presentations to managers and • scientific community

  15. “Wish List” for Testbed 2 • Unstructured Grids • SELFE • FVCOM • 2. Potential for < 10 day operational forecasts • Need to examine skill of models in reproducing high • frequency data sets • 3. Interannual/Interdecadal skill • We have simulations from 1991-2005 from multiple models, but we do not yet have the resources to make these comparisons

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