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

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

Estuarine Hypoxia Team: Carl Friedrichs (VIMS) Marjorie Friedrichs (VIMS) Aaron Bever (VIMS) Jian Shen (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) U.S. IOOS Modeling Testbed

To help improve operational and scenario-based modeling of hypoxia in Chesapeake Bay Methods: 1.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 Overarching Goal: U.S. IOOS Modeling Testbed

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

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

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

Biological models: 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 respiration (not a function of x, y, temperature, nutrients…) o 1term: Constant net respiration Multiple combinations: o CH3D + ICM o EFDC + 1eqn, 1term o CBOFS2 + 1term, (1term+DD soon!) o ChesROMS + 1term, 1term+DD, bgc Biological-Hydrodynamic models

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

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

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

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

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 Models do better at hindcasting bottom T & S than stratification Stratification is a challenge for all the models: All underestimate strength and variability of stratification All underestimate variability of pycnocline depth. Hydrodynamic Model Skill

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 Horizontal grid resolution (UMCES-ROMS) o Coastal boundary condition (ChesROMS) o Mixing/turbulence closure (ChesROMS) o 2004 vs (all models; in progress) Sensitivities not yet tested: Bathymetry Vertical grid type: sigma vs. z-grid Sensitivity Experiments

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

- 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. Dissolved Oxygen Model Comparison Bottom DO Hypoxic Volume

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

Hypoxic Volume Time Series 2004 Absolute model-data match 3D modeled hypoxic volume 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!

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

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)

EXTRA SLIDES

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

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

Depth of maximum stratification CBOFS2 CBOFS2 stratification is insensitive to: vertical grid resolution, vertical advection scheme and freshwater river input

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

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

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

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

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

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

(by M. Scully) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Date in 2004 Hypoxic Volume in km Base Case (by M. Scully) Effect of physical forcing on hypoxia ChesROMS+1-term model

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 Base Case Freshwater river input constant (by M. Scully) Effect of physical forcing on hypoxia ChesROMS+1-term model

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 Base Case July wind year-round (by M. Scully) Effect of physical forcing on hypoxia ChesROMS+1-term model

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Date in 2004 Hypoxic Volume in km Base Case January wind year-round (by M. Scully) Seasonal changes in hypoxia may be largely due to seasonal changes in wind. Effect of physical forcing on hypoxia ChesROMS+1-term model

EXTRA SLIDES 2004 simulation vs data 2004 simulation vs data STRATIFICATION