U.S. IOOS Testbed Comparisons:Hydrodynamics and Hypoxia

Marjy FriedrichsVirginia Institute of Marine Science

Including contributions from the entire

Estuarine Hypoxia Testbed team

With special thanks to Aaron Bever

Four Teams:

Cyberinfrastructure TeamCoastal 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 partnersCarl 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 scalesHydrodynamics & 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 biologyo bgc: NPZD-type biogeochemical modelo 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 + ICMo EFDC + 1eqn, 1termo 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 2004some 2005 results

bottom T, bottom S, stratification = max dS/dz,

depth of max dS/dz

bottom DO, hypoxic volume

= ~40 CBP stations used inthis model-data comparison

o bottom temperatureo bottom salinityo 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)

unbiasedRMSD [°C]

variability

bias [°C]

mean

outer circle: mean of data

unbiasedRMSD

[°C]

bias [°C]

unbiasedRMSD[psu]

bias [psu]

unbiasedRMSD[psu/m]

bias [psu/m]

unbiasedRMSD

[m]

bias [m]

(a) Bottom Temperature (b) Bottom

Salinity

(c) Stratificationat 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. 2005 (all models; in progress)

Sensitivities not yet tested: BathymetryVertical 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

HypoxicVolume

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 2004

Absolute model-data match3D 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 ± 0.1 Total RMSD = 0.9 ± 0.1

1-term DO model

Bottom DO – spatial variability

Overall model-data fit to CBP station bottom DO data is similar

1-term DO modelICM

(CBP model)

Total RMSD = 1.0 ± 0.1 Total RMSD = 1.1 ± 0.1

Depth of maximum stratification

-0.2-0.4-0.6-0.8

0.2

-0.2

-0.2

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!

2005

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

Hyp

oxic

Vol

ume

in k

m3

20

10

0

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

Hyp

oxic

Vol

ume

in k

m3

20

10

0

Base Case

Freshwater river input constant

(by M. Scully)(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

Hyp

oxic

Vol

ume

in k

m3

20

10

0

Base CaseJuly wind year-round

(by M. Scully)(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

Hyp

oxic

Vol

ume

in k

m3

20

10

0

Base Case

January wind year-round

(by M. Scully)(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. 2004 data 2004 simulation vs. 2005 data

STRATIFICATION