COMT
Chesapeake Bay Hypoxia Modeling
VIMS: Marjy Friedrichs (lead PI)
Carl Friedrichs (VIMS-PI)
Ike Irby (funded student)
Aaron Bever (consultant)
Jian Shen (collaborator)
UMCES: Raleigh Hood (UMCES-PI)
Hao Wang (funded student)
Jeremy Testa (collaborator)
Wen Long (collaborator)
Meng Xia (collaborator)
WHOI: Malcolm Scully (WHOI-PI)
NOAA-CSDL: Lyon Lanerolle (NOAA-PI)
Frank Aikman (collaborator)
EPA-CBP: Ping Wang, Lewis Linker (collaborators)
NOAA NOS/COOPS Transition Partner: Pat Burke
July 30-31, 2015
COMT Annual Meeting
Chesapeake Hypoxia Objective
Assess the readiness/maturity of a suite of existing estuarine
community models for determining past, present and future hypoxia
events within the Chesapeake Bay, in order to accelerate the
transition of hypoxia model formulations and products from
“academic research” to “operational” centers
Chesapeake Hypoxia COMT “operational” centers include:
• Chesapeake Bay Ecological Prediction System
- Quasi-operational short-term forecasts
• NOAA NOS/CO-OPS
- Truly-operational short-term forecasts
• EPA Chesapeake Bay Program
- Regulatory scenario-based forecasts
Hydrodynamic Models
 3 ROMS-based model variants
• ChesROMS
• ROMS-UMCES
• CBOFS (NOAA operational model)
 3 other types of models
• CH3D (EPA operational/regulatory model)
• EFDC
• FVCOM
All hydrodynamic models are similar, but vary in terms of:
vertical and horizontal resolution (bathymetry)
sigma vs. z-grid
structured vs. unstructured grids
riverine/atmospheric forcing (in some cases)
Dissolved Oxygen (DO) Models
4 Full biogeochemistry models
•
•
•
•
ICM (CH3D, FVCOM)
RCA (ROMS)
ECB (ChesROMS)
BGC (ChesROMS)
1 Constant biology models
• 1term (CBOFS, ChesROMS, EFDC)
 8 model combinations
Outline
1. Model comparisons:
• How well do these 8 models simulate DO?
• How can these model simulations be improved?
2. Lessons learned from model comparisons:
• Uncertainties in computing hypoxic volume by
interpolating DO observations
• Estimating hypoxic volume from a few vertical
profilers
3. Interdecadal model simulations:
• Comparison of two 20-yr simulations:
CH3D-ICM & ChesROMS-1term
• Analysis of 30 year ChesROMS-1term simulation
4. Future Directions: Transitioning to operations
Chesapeake Hypoxia Model Comparisons
Goals:
-Which models best resolve bottom DO in the Chesapeake
Bay?
-How do modelers move forward in improving model skill of
low-oxygen conditions?
Chesapeake Hypoxia Model Comparisons
Compare simulations
to observations at 13
stations for ~34 total
cruises from
Jan 2004 – Dec 2005
Model Skill Assessment
Model Skill Assessment
Target Diagrams
bias
Model skill
same as skill
of mean of
observations
Unbiased RMSD
(STD)
RMSD = Root mean square difference
Taylor Diagrams
Model Skill Assessment
Target Diagrams
bias
Unbiased
RMSD
Taylor Diagrams
Model Skill Assessment
Target Diagrams
bias
Unbiased
RMSD
Taylor Diagrams
Model Skill Assessment
Target Diagrams
bias
Unbiased
RMSD
(STD)
Taylor Diagrams
Model Skill Assessment
Target Diagrams
bias
Taylor Diagrams
0
Unbiased
RMSD
(STD)
1
Model Skill Assessment
Target Diagrams
Taylor Diagrams
0
2
Unbiased
RMSD
(STD)
Standard Deviation
bias
1
0
1
Model Skill Assessment
Target Diagrams
Taylor Diagrams
0
2
Unbiased
RMSD
(STD)
Standard Deviation
bias
1
0
1
2004-2005 Model Comparison
normalized
bias
1
normalized
unbiased
1 RMSD
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ROMS-RCA
FVCOM-ICM
ChesROMS-1term
CBOFS2-1term
EFDC-1term
Model Mean
DO at Surface
normalized standard deviation
2
0.2
0.4
0.6
0.8
1
0
0.95
1
2004-2005 Model Comparison
normalized
bias
1
normalized
unbiased
1 RMSD
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ROMS-RCA
FVCOM-ICM
ChesROMS-1term
CBOFS2-1term
EFDC-1term
Model Mean
DO at Surface
DO at 5m Depth
normalized standard deviation
2
0.2
0.4
0.6
0.8
1
0
0.95
1
2004-2005 Model Comparison
normalized
bias
1
normalized
unbiased
1 RMSD
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ROMS-RCA
FVCOM-ICM
ChesROMS-1term
CBOFS2-1term
EFDC-1term
Model Mean
DO at Surface
DO at 5m Depth
DO at 10m Depth
normalized standard deviation
2
0.2
0.4
0.6
0.8
1
0
0.95
1
2004-2005 Model Comparison
normalized
bias
normalized
unbiased
1 RMSD
DO at Surface
DO at 5m Depth
DO at 10m Depth
DO at Bottom
0.4
0.6
normalized standard deviation
1
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ROMS-RCA
FVCOM-ICM
ChesROMS-1term
CBOFS2-1term
EFDC-1term
Model Mean
0.2
2
0.8
1
0.95
0
Overall skill of all models
(temporal + spatial variability):
• High in terms of DO
*especially bottom DO
1
2004-2005 Model Comparison
normalized
bias
normalized
unbiased
1 RMSD
DO at Surface
DO at 5m Depth
DO at 10m Depth
DO at Bottom
0.4
0.6
normalized standard deviation
1
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ROMS-RCA
FVCOM-ICM
ChesROMS-1term
CBOFS2-1term
EFDC-1term
Model Mean
0.2
2
0.8
1
0.95
0
Overall skill of all models
(temporal + spatial variability):
• High in terms of DO
*especially bottom DO
1
2004-2005 Model Comparison
Observation Station CB4.1C
Model Mean
Observations
12
DO Concentration
10
95%
Confidence
Interval
8
Models
6
4
2
0
-2
0
5
10
15
20
25
30
2004 & 2005 Observation Number
21
2004-2005 Model Comparison
normalized
bias
1
normalized
unbiased
1 RMSD
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ROMS-RCA
FVCOM-ICM
ChesROMS-1term
CBOFS2-1term
EFDC-1term
Model Mean
Temp at Surface
Temp at Bottom
normalized standard deviation
2
0.2
0.4
0.6
0.8
1
0
0.95
1
2004-2005 Model Comparison
normalized
bias
1
normalized
unbiased
1 RMSD
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ROMS-RCA
FVCOM-ICM
ChesROMS-1term
CBOFS2-1term
EFDC-1term
Model Mean
Temp at Surface
Temp at Bottom
Salinity at Surface
Salinity at Bottom
normalized standard deviation
2
0.2
0.4
0.6
0.8
1
0
0.95
1
2004-2005 Model Comparison
normalized
bias
1
normalized
unbiased
1 RMSD
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ROMS-RCA
FVCOM-ICM
ChesROMS-1term
CBOFS2-1term
EFDC-1term
Model Mean
Temp at Surface
Temp at Bottom
Salinity at Surface
Salinity at Bottom
Chlorophyll at Surface
Chlorophyll at Bottom
normalized standard deviation
2
0.2
0.4
0.6
0.8
1
0
0.95
1
2004-2005 Model Comparison
normalized
bias
1
normalized
unbiased
1 RMSD
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ROMS-RCA
FVCOM-ICM
ChesROMS-1term
CBOFS2-1term
EFDC-1term
Model Mean
Temp at Surface
Temp at Bottom
Salinity at Surface
Salinity at Bottom
Chlorophyll at Surface
Chlorophyll at Bottom
Nitrate at Surface
Nitrate at Bottom
normalized standard deviation
2
0.2
0.4
0.6
0.8
1
0.95
0
1
Overall skill of all models
(temporal + spatial variability):
• High in terms of temp and salinity
• Low in terms of chlorophyll and
nitrate
2004-2005 Model Comparison
normalized
bias
1
normalized
unbiased
1 RMSD
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ROMS-RCA
FVCOM-ICM
ChesROMS-1term
CBOFS2-1term
EFDC-1term
Model Mean
Temp at Surface
Temp at Bottom
Salinity at Surface
Salinity at Bottom
Chlorophyll at Surface
Chlorophyll at Bottom
Nitrate at Surface
Nitrate at Bottom
DO at Bottom
normalized standard deviation
2
0.2
0.4
0.6
0.8
1
0.95
0
1
Overall skill of all models
(temporal + spatial variability):
• High in terms of temp and salinity
• Low in terms of chlorophyll and
nitrate
2004-2005 Model Comparison
Maximum Stratification & Mixed Layer Depth
Depth
Surface
Bottom
Low
Salinity
High
2004-2005 Model Comparison
Maximum Stratification & Mixed Layer Depth
Surface
Stratification exists if:
abs(Surface – Bottom) * 0.10
meter
Depth
Maximum
Stratification
Bottom
Low
Salinity
High
2004-2005 Model Comparison
Maximum Stratification & Mixed Layer Depth
Surface
Stratification exists if:
abs(Surface – Bottom) * 0.10
meter
Depth
Maximum
Stratification
MLD
MLD is the depth above the most
shallow existence of stratification
Bottom
Low
Salinity
High
2004-2005 Model Comparison
Maximum Stratification & Mixed Layer Depth
Surface
Stratification exists if:
abs(Surface – Bottom) * 0.10
meter
Depth
Maximum
Stratification
MLD
MLD is the depth above the most
shallow existence of stratification
Halocline & Oxycline
Bottom
Low
Salinity
High
2004-2005 Model Comparison
normalized
bias
1
normalized
unbiased
RMSD
1
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ROMS-RCA
FVCOM-ICM
ChesROMS-1term
CBOFS2-1term
EFDC-1term
Model Mean
Salinity MLD
Maximum dS/dz
normalized standard deviation
2
0.2
0.4
0.6
0.8
1
0
0.95
1
2004-2005 Model Comparison
normalized
bias
1
normalized
unbiased
RMSD
1
too weak
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ROMS-RCA
FVCOM-ICM
ChesROMS-1term
CBOFS2-1term
EFDC-1term
Model Mean
Salinity MLD
Maximum dS/dz
normalized standard deviation
too
shallow
2
0.2
0.4
0.6
0.8
1
0
0.95
1
2004-2005 Model Comparison
normalized
bias
1
normalized
unbiased
RMSD
1
too weak
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ROMS-RCA
FVCOM-ICM
ChesROMS-1term
CBOFS2-1term
EFDC-1term
Model Mean
Salinity MLD
Maximum dS/dz
Oxygen MLD
Maximum dO/dz
normalized standard deviation
too
shallow
2
0.2
0.4
0.6
0.8
1
0.95
0
Overall skill of all models
(temporal + spatial variability):
• Underestimate mean and variability
of max dx/dz
• All but CH3D overestimate mean &
underestimate variability of MLD
1
1998-2006 Observations
0
Salinity MLD (m)
Maximum dS/dz
6
4
2
0
2
4
Maximum dO/dz
6
-10
-20
-30
-30
-20
-10
Oxygen MLD (m)
r2 = 0.15
Stratification defined as > 10%
r2 = 0.47
r2 = 0.21
Stratification defined as > 25%
r2 = 0.81
There is not a strong relationship between dS/dz and dO/dz, but there is a strong
relationship between the depth of the halocline and the depth of the oxycline.
Ramifications for Habitat Compression.
0
1998-2006 Observations
0
Salinity MLD (m)
Maximum dS/dz
6
4
2
0
2
4
Maximum dO/dz
6
-10
-20
-30
-30
-20
-10
Oxygen MLD (m)
r2 = 0.15
Stratification defined as > 10%
r2 = 0.47
r2 = 0.21
Stratification defined as > 25%
r2 = 0.81
There is not a strong relationship between dS/dz and dO/dz, but there is a strong
relationship between the depth of the halocline and the depth of the oxycline.
Ramifications for Habitat Compression.
0
Model vs Observations
0
Salinity MLD (m)
Salinity MLD (m)
0
-10
-20
ChesROMS-ECB
-30
-30
-20
-10
Oxygen MLD (m)
r2 = 0.59
0
-10
-20
-30
-30
-20
-10
Oxygen MLD (m)
Stratification defined as > 10%
r2 = 0.47
The models correctly represent the relationship between the halocline and oxycline.
0
Model vs Observations
0
Salinity MLD (m)
Salinity MLD (m)
0
-10
-20
ChesROMS-ECB
-30
-30
-20
-10
Oxygen MLD (m)
r2 = 0.59
0
-10
-20
-30
-30
-20
-10
0
Oxygen MLD (m)
Stratification defined as > 10%
r2 = 0.47
The models correctly represent the relationship between the halocline and oxycline.
Important the models improve their ability to match the location of the halocline.
Model Comparison Conclusions
• All models do similarly well at reproducing bottom DO
 Simple oxygen parameterization models can be used for short-term
forecasting… BUT, full biogeochemical components will be required
for long-term and scenario-based forecasting.
• The model mean represents the observations better than any
single model across the variables examined
 Multiple model ensemble can be used to provide confidence bounds
of DO forecasts.
• Observations demonstrate a strong relationship between the
salinity MLD and oxygen MLD
 Models correctly represent this relationship
 Models need to better resolve the location, rather than the degree
(as long as stratification exists), of the halocline in order to properly
simulate habitat compression from low-oxygen waters.
Outline
1. Model comparisons:
• How well do these 8 models simulate DO?
• How can these model simulations be improved?
2. Lessons learned from model comparisons:
• Uncertainties in computing hypoxic volume by
interpolating DO observations
• Estimating hypoxic volume from a few vertical
profilers
3. Interdecadal model simulations:
• Comparison of two 20-yr simulations:
CH3D-ICM & ChesROMS-1term
• Analysis of 30 year ChesROMS-1term simulation
4. Future Directions: Transitioning to operations
Lessons Learned from Model Comparisons
Use 3D models to examine uncertainties in EPA observation-based “operational”
estimates of Chesapeake Bay hypoxic volume.
•
DO observed by EPA are not a “snapshot” = temporal error
•
DO observed by EPA have coarse spatial resolution = spatial error
•
Use 3D models to improve EPA interpolated estimates of hypoxic volume
JGR-Oceans, October 2013 issue:
Temporal Uncertainties in Interpolated Hypoxic Volumes
Blue triangles = 13 selected CBP stations
 Reduce Temporal errors:
1. Choose subset of 13 CBP
stations
2. Routinely sampled within
2.3 days of each other
3. Characterized by high DO
variability
 This reduces Temporal errors
from ~ 5 km3 to ~ 2.5 km3
Spatial Uncertainties in Interpolated Hypoxic Volumes
 Reduce Spatial errors:
Before
Scaling
Model 13-station cruises
(4 models x 2 years x 12
cruises/year)
Derive a factor to “correct”
13-station interpolation to
equal the Integrated 3D
hypoxic volume.
 This reduces Spatial errors
from ~ 5 km3 to ~ 2.5 km3
After
Scaling
Spatial Uncertainties in Interpolated Hypoxic Volumes
 Reduce Spatial errors:
Before
Scaling
Model 13-station cruises
(4 models x 2 years x 12
cruises/year)
Derive a factor to “correct”
13-station interpolation to
equal the Integrated 3D
hypoxic volume.
 This reduces Spatial errors
from ~ 5 km3 to ~ 2.5 km3
 Total Temporal + Spatial errors
≈ ~ 2.5 km3 + ~ 2.5 km3 ≈ ~ 5 km3
After
Scaling
Lessons Learned from Model Comparisons
Newest work: Can we use a few continuous observations from a few
profiles to accurately estimate real-time hypoxic volume?
Hypothesis:
For a given thickness of the hypoxic layer in the Chesapeake Bay, the
horizontal extent of hypoxia is constrained by the steep topography of the
Bay’s deep channel.
Therefore, the volume of hypoxic water can be reasonably estimated with
data from a relatively small number of vertical profiles.
If a few automated profilers or well-instrumented moorings can report
oxygen data in real time, then the hypoxic volume (HV) of the Bay can be
reasonably estimated in real time.
Hypoxic Volume Based on a Few Profilers
 Methods: Three ways to estimate hypoxic volume (HV)
1. 3D HV = Sum up volume of each 3D model cell that is hypoxic. This can
be done for model output only, not cruise-based observations or profilers.
2. Interpolated HV = Uses Chesapeake Bay Program’s Interpolator
software. Bever et al. (2013) showed 13 stations are ideal. Can be done
for cruise-based observations (error ≈ +/- ~ 5 km3) but not for profilers.
3. Geometric HV (New method) = Assumes hypoxia is constrained by
steep bathymetry and top of hypoxic zone is relatively flat. Can be done
with a few profilers. Accuracy can be checked by cruise-based
observations and by models.
 Method
s
(cont.):
Calculating
“Geometric HV”:
Hypoxic Volume Based on a Few Profilers
 Method
s
(cont.):
Calculating
“Geometric HV”:
Hypoxic Volume Based on a Few Profilers
 Method
s
(cont.):
Calculating
“Geometric HV”:
 Results
Next:
(1) Geometric HV
compared to
Interpolated HV
(2) Geometric HV
compared to
3D HV
 Results (1): Compare observed “Geometric HV” with 1 site to observed
“Interpolated HV” (13 sites):
Hypoxic Volume (HV) based
on Monitoring Cruise Data
(every 2 to 4 weeks for 28
years)
 Results (1): Compare observed “Geometric HV” with 2 to 3 sites to observed
“Interpolated HV” (13 sites):
Hypoxic Volume (HV) based on
Monitoring Cruise Data (every 2
to 4 weeks for 28 years)
 Results (1): Compare observed “Geometric HV” with 1 to 10 sites to observed
“Interpolated HV” (13 sites):
Target diagram indicates that 3 sites for “Geometric HV” are nearly as good as 10.
3 sites
1 = uncertainty in
Interpolated HV
(± 5 km3)
 Results (2): Compare modeled “Geometric HV” with
2 sites to “3D HV” from model output (Integrated
over 1000s of grid points) daily for 20 years
(Model = ChesROMS + 1-term constant net
respiration)
Best 2 sites
1 = no better
than 3D HV
mean
 Results (2): Compare “Geometric HV” with 2 to 3 sites to “3D HV” (integrated
over 1000s of grid points) daily for 20 years
Based on 3D model output, 2 sites
for “Geometric HV” are as good as 3.
Lessons Learned from Model Comparisons
Summary
 Information from multiple models (2004-2005) has been used to assess
and reduce uncertainties in present CBP interpolated hypoxic volume
estimates
• 13 stations (sample in 2 days) do as well for HV as 40-60 or more
• Temporal and spatial uncertainties: together ~5 km3
 Info from 20+ years based on monitoring and 3D model output suggests
that 2 to 3 well-chosen stations can do almost as well as 13
• Added error due to 2 to 3 stations is less than uncertainty from 13
• 2 to 3 automated stations could provide continuous real-time HV
 Real-time observations of hypoxia would enhance planned model-based
operational DO products and could potentially reduce EPA cruise costs
Lessons Learned from Model Comparisons
Summary
 Information from multiple models (2004-2005) has been used to assess
and reduce uncertainties in present EPA interpolated hypoxic volume
estimates for Chesapeake Bay
• 13 stations (sample in 2 days) do as well for HV as 40-60 or more
• Temporal and spatial uncertainties: together ~5 km3
 Info from 20+ years based on monitoring and 3D model output suggests
that 2 to 3 well-chosen stations can do almost as well as 13
• Added error due to 2 to 3 stations is less than uncertainty from 13
• 2 to 3 automated stations could provide continuous real-time HV
 Real-time observations of hypoxia would enhance planned model-based
operational DO products and could potentially reduce EPA cruise costs
Outline
1. Model comparisons:
• How well do these 8 models simulate DO?
• How can these model simulations be improved?
2. Lessons learned from model comparisons:
• Uncertainties in computing hypoxic volume by
interpolating DO observations
• Estimating hypoxic volume from a few vertical
profilers
3. Interdecadal model simulations:
• Comparison of HV from two 20-yr simulations:
CH3D-ICM & ChesROMS-1term
• Analysis of 30 year ChesROMS-1term simulation
4. Future Directions: Transitioning to operations
Hypoxic Volume [km 3 ]
Hypoxic Volume [km 3 ]
20-year Hypoxic Volume comparison
ChesROMS-1term
20
15
10
5
0
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
observations
CH3D-ICM
20
Interpolated:
ChesROMS-1term
CH3D-ICM
15
based on 13
main stem stations
10
5
0
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
ChesROMS-1term overestimates HV and CH3D-ICM underestimates HV
20-year Hypoxic Volume comparison
complex EPA model
constant
biology model
ChesROMS-1term
CH3D-ICM
20
slope = 0.65
r = 0.73
18
Hypoxic Volume from Model [km 3 ]
Hypoxic Volume from Model [km 3 ]
20
16
14
12
10
8
6
4
2
0
0
5
10
15
20
3
Hypoxic Volume from CBP data [km ]
slope = 1.26
r = 0.87
18
16
14
12
10
8
6
4
2
0
0
5
10
15
20
3
Hypoxic Volume from CBP data [km ]
On interannual time scales, constant biology (1-term) model does
significantly better than the regulatory model in terms of reproducing our
best estimate of hypoxic volume
Model-data DO correlation (r)
Analysis of 30-year ChesROMS-1term simulation
0.6
0.4
< 2.0 mg/L
0.2
< 0.2 mg/L
0
May
June
July
Aug
Sept
On interannual time scales, ChesROMS constant biology (1-term) model:
reproduces hypoxic volume (DO < 2.0 mg/L) better than anoxic volume (DO < 0.2 mg/L)
reproduces hypoxic volume better in Jun-Sept than in May
 Biological variability is more critical where DO < 0.2 mg/L and earlier in the year
Analysis of 30-year ChesROMS-1term simulation
Jan-May River
Discharge
Jan-May
Nitrogen Load
June-Aug
NARR
Wind Speed
< 2.0 mg/L
0.42
0.42
-0.85
< 0.2 mg/L
0.41
0.39
-0.81
Model variability is dominated by wind but also significantly correlated with river discharge.
Thus it captures some of the biological variability with no biology.
Red color denotes p<0.05
Analysis of 30-year ChesROMS-1term simulation
Jan-May River
Discharge
Jan-May
Nitrogen Load
June-Aug
NARR
Wind Speed
< 2.0 mg/L
0.42
0.42
-0.85
< 0.2 mg/L
0.41
0.39
-0.81
Model variability is dominated by wind but also significantly correlated with river discharge.
Thus it captures some of the biological variability with no biology.
Hypoxia/Anoxia from OBSERVATIONS
Jan-May
River
Discharge
Jan-May
Nitrogen
Load
June-Aug
NARR Wind
Speed
June-Aug
TPL Wind
Speed
< 2.0 mg/L
0.69
0.69
-0.31
-0.44
< 0.2 mg/L
0.77
0.81
-0.21
0.08
Observational variability is dominated by nitrogen loading/river discharge. Observations are not
significantly correlated with the NARR wind reanalysis product, but are with observed winds.
Red color denotes p<0.05
Residuals (Model minus Observations)
Jan-May River
Nitrogen Load
June-Aug Wind
Speed
< 2.0 mg/L
-0.31
-0.60
< 0.2 mg/L
-0.46
-0.32
• Anoxia residuals are negatively correlated with nitrogen loading
 lack of biology explains some of the model-data misfit
• Hypoxia residuals are more strongly correlated with summer winds
 the model is not accurately capturing wind response
Is the NARR wind product not good enough?
Is the model not responding correctly to the wind forcing?
Errors in air T and SST are leading to errors in surface stress
Surface flux of O2 is not accurately represented
Red color denotes p<0.05
Outline
1. Model comparisons:
• How well do these 8 models simulate DO?
• How can these model simulations be improved?
2. Lessons learned from model comparisons:
• Uncertainties in computing hypoxic volume by
interpolating DO observations
• Estimating hypoxic volume from a few vertical
profilers
3. Interdecadal model simulations:
• Comparison of HV from two 20-yr simulations:
CH3D-ICM & ChesROMS-1term
• Analysis of 30 year ChesROMS-1term simulation
4. Future Directions: Transitioning to operations
“Operational” Centers/Partners
Chesapeake Hypoxia COMT “operational” centers:
• NOAA NOS/CO-OPS
- Truly-operational short-term forecasts
• Chesapeake Bay Ecological Prediction System (CBEPS)
- “Quasi-operational” short-term forecasts
• EPA Chesapeake Bay Program
- Regulatory scenario-based forecasts
Current CBOFS Forecast
= observation
= nowcast
= forecast
Temperature
Salinity
Baltimore
Temperature
Baltimore
Salinity
8/5
8/6
Time (EDT)
8/7
8/8
Add Dissolved Oxygen!!!!
Future NOAA NOS/CO-OPS CBOFS
Year 3:
• How do the DO models compare when they are run
with operational forcing?
- Research version of CBOFS-1term run using NOAA NOS/CO-OPS
forcing for 2012, compared with hindcast simulations
- Compared with other models also run using operational forcing
Years 4 & 5:
• What is the impact of data assimilation and improved
grid/bathymetry on predictive capability of CBOFS1term?
- Analyze the effect of improvements of CBOFS on DO forecasts
Current CBEPS
 CBEPS runs a parallel research
operational model to CBOFS; run
at UMCES and visualized at VIMS
 Provides “quasi-operational”
nowcasts and short-term (3-day)
forecasts of bottom DO for
research, management and public
uses in Chesapeake Bay.
Nowcast
3day Forecast
Future CBEPS
Year 3:
• Can we quantitatively document a user-base for
pseudo-operational model products in CBEPS?
- Improve bottom DO product available on pseudo-operational website
- Document site usage for various products
Years 4 & 5:
• How will end-users react to improved products?
- Add
additional model products (HV, model mean) and uncertainties to
CBEPS website
- Analyze product usage and feedback from users
Future: EPA - CBP
Year 3:
• Will the application of nutrient reduction scenarios to the
ROMS-ECB model produce similar changes in DO as
those generated by the EPA regulatory model?
- Apply reduction strategies used by the EPA to our “research”
model
- Compare results to those obtained for the EPA model
- Of great interest to EPA managers, as our results will help
establish uncertainty bounds on the official nutrient reduction
requirements
Years 4 & 5:
• Will future climate change inhibit the success of current
nutrient reduction requirements?
- Utilizing EPA’s climate scenarios, we will examine the potential success
of the current nutrient load reduction regulations
Extra Slides