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Estuarine Hypoxia Component of Testbed 2 Marjorie Friedrichs, VIMS, lead Carl Friedrichs, VIMS, co-lead Wen Long and Raleigh Hood, UMCES Malcolm Scully,

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Presentation on theme: "Estuarine Hypoxia Component of Testbed 2 Marjorie Friedrichs, VIMS, lead Carl Friedrichs, VIMS, co-lead Wen Long and Raleigh Hood, UMCES Malcolm Scully,"— Presentation transcript:

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 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 Objectives

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

4 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 Five biological models

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

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

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

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

9 Hypoxic Volume unbiased RMSD [km 3 ] bias [km 3 ] Several simple DO models reproduce seasonal variability of hypoxic volume about as well as ICM

10 Hypoxic Volume unbiased RMSD [km 3 ] bias [km 3 ] 5-model average does better than any single model

11 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 Overall Progress from Testbed 1

12 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 Overall Progress from Testbed 1 (cont.)

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

14 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 Plans for Testbed 2 Year 1

15 1.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 “Wish List” for Testbed 2


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