Transcript ppt
Combining Observations & Numerical Model Results to Improve
Estimates of Hypoxic Volume within the Chesapeake Bay
JGR-Oceans, October 2013 issue
Aaron Bever, Marjy Friedrichs, Carl Friedrichs, Malcolm Scully, Lyon Lanerolle
OUTLINE / SUMMARY
1.
Relation to US-IOOS Modeling Testbed program and general methods.
2.
Use 3D models to examine uncertainties in interpolating hypoxic volume.
3.
•
Observed DO have coarse spatial resolution = spatial error
•
Observed DO are not a “snapshot” = temporal error
Use 3D models to improve EPA-CBP interpolations of hypoxic volume.
p.1 of 21
Combining Observations & Numerical Model Results to Improve
Estimates of Hypoxic Volume within the Chesapeake Bay
Relationship to US-IOOS Modeling Testbed:
Part of Coastal & Ocean Modeling Testbed (COMT) Project headed by
Rick Luettich (UNC), funded by NOAA US-IOOS Office
COMT Mission: Accelerate the transition of scientific and technical
advances from the modeling research community to improve federal
agencies’ operational ocean products and services
Initial Phase: Estuarine Hypoxia, Shelf Hypoxia and Coastal
Inundation Modeling Testbeds; Cyber-infrastructure to advance
interoperability and archiving
p.2 of 21
Combining Observations & Numerical Model Results to Improve
Estimates of Hypoxic Volume within the Chesapeake Bay
General COMT Estuarine Hypoxia modeling methods:
• Compare relative skill and strengths/weaknesses of
various Chesapeake Bay models
• Assess how model differences affect water quality
simulations
• Recommend improvements to agency operational
products associated with managing hypoxia
p.3 of 21
Five hydrodynamic models configured for the Bay
p.4 of 21
Five hydrodynamic models configured for the Bay
TODAY’S TALK
p.4 of 21
Five dissolved oxygen (DO) models configured for the Bay
o ICM: EPA-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
(not a function of x, y, temperature,
nutrients OR depth…)
p.5 of 21
Five dissolved oxygen (DO) models configured for the Bay
o ICM: EPA-CBP model; complex biology
o BGC: NPZD-type biogeochemical model
o 1eqn: Simple one equation respiration
(includes SOD)
TODAY’S TALK
o 1term-DD: depth-dependent respiration
(not a function of x, y, temperature,
nutrients…)
o 1term: Constant net respiration
(not a function of x, y, temperature,
nutrients OR depth…)
p.5 of 21
Coupled hydrodynamic-DO models
Today’s talk = Four combinations:
o
o
o
o
CH3D
CBOFS
ChesROMS
ChesROMS
+
+
+
+
ICM EPA-CBP model
1term
1term
1term+DD
-- Physical models are similar, but grid resolution differs
-- Biological/DO models differ dramatically
-- All models run for 2004 and 2005 and compared to EPA Chesapeake
Bay Program DO observations
p.6 of 21
Model skill: Bottom DO
Total RMSD2 = Bias2 + unbiased RMSD2
-- The models all have significant skill (normalized RSMD < 1) in reproducing
observed bottom dissolved oxygen (DO).
-- The four models all reproduce observations of bottom DO about equally well.
-- Unlike observations, model output is continuous in space and time.
-- So use the continuous model output to estimate uncertainties caused by CBP
interpolations of discontinous observed data.
p.7 of 21
Combining Observations & Numerical Model Results to Improve
Estimates of Hypoxic Volume within the Chesapeake Bay
JGR-Oceans, October 2013 issue
Aaron Bever, Marjy Friedrichs, Carl Friedrichs, Malcolm Scully, Lyon Lanerolle
OUTLINE
1.
Relation to US-IOOS Modeling Testbed program and general methods.
2.
Use 3D models to examine uncertainties in interpolating hypoxic volume.
3.
•
Observed DO have coarse spatial resolution = spatial error
•
Observed DO are not a “snapshot” = temporal error
Use 3D models to improve EPA-CBP interpolations of hypoxic volume.
p.8 of 21
Four Types of Hypoxic Volume Estimates
Interpolation Method used for #1 - #3:
CBP Interpolator Tool
HV = DO < 2 mg/L
#1) Observations
Of 99 CBP stations (red dots), 30-65
are sampled each “cruise”
Each cruise takes 1 to 2 weeks
#2) Modeled Absolute Match:
Same 30-65 stations are “sampled” at
same time/place as observations are
available
#3) Modeled Spatial Match:
Same stations are “sampled” in
space, but samples are taken
synoptically (i.e., all at once in time)
#4) Integrated 3D Model:
Hypoxic volume is computed from
integrating over all model grid cells
p.9 of 21
(“CBP” = EPA Chesapeake Bay Program)
Hypoxic Volume Estimates
• When observations
and model are
interpolated in
same way, the
match is
reasonably good
20
CH3D-ICM
= Absolute Match
Hypoxic Volume, km
3
10
0
20
ChesROMS+1term
10
0
20
Observations-derived
10
0
p.10 of 21
05/01
06/01
07/01
08/01
09/01
Date in 2004, Month/Day
10/01
11/01
Integrated 3D HV
• But interpolated
HV underestimates
actual HV for every
cruise
06/01
07/01
08/01
09/01
Date in 2004, Month/Day
10/01
11/01
Integrated 3D HV
Absolute Match
20 CH3D-ICM
CH3D-ICM
= Absolute Match
10
3
0
• When observations
05/01
and model are
interpolated in
same way, the
match is
reasonably good
Hypoxic Volume Estimates
Hypoxic Volume, km
10
Cruise Date Range
0
20
ChesROMS+1term
ChesROMS+1term
10
0
20
Observations-derived
Data-derived
10
0
p.11 of 21
05/01
06/01
09/01
08/01
07/01
Date in 2004, Month/Day
10/01
11/01
Integrated 3D HV
Absolute Match
06/01
• When observations
and model are
interpolated in
same way, the
match is
reasonably good
• But interpolated
HV underestimates
actual HV for every
cruise
• Much of this
disparity could be
due to temporal
errors (red bars)
07/01
08/01
09/01
Date in 2004, Month/Day
20
10/01
11/01
Integrated 3D HV
Absolute Match
Spatial Match
Spatial Match Range
Cruise Date Range
CH3D-ICM
10
3
05/01
Hypoxic Volume Estimates
Hypoxic Volume, km
0
0
20
ChesROMS+1term
10
0
20
=
Observations-derived
10
0
p.12 of 21
05/01
06/01
07/01
08/01
09/01
Date in 2004, Month/Day
10/01
11/01
Integrated 3D HV
Absolute Match
Spatial Match
Spatial Match Range
Date in 2004, Month/Day
• When observations
and model are
interpolated in
same way, the
match is
reasonably good
• But interpolated
HV underestimates
actual HV for every
cruise
• Much of this
disparity could be
due to temporal
errors (red bars)
• Same pattern
across all 4 models
for both 2004 &
2005
p.13 of 21
Integrated 3D HV
Absolute Match
Spatial Match
Spatial Match Range
Cruise Date Range
Spatial errors show
interpolated HV is almost
always too low (up to 5 km3)
The temporal errors from
non-synoptic sampling can
be as large as spatial errors
(~5 km3)
Similar patterns across all
4 models for both 2004 &
2005
p.14 of 21
Combining Observations & Numerical Model Results to Improve
Estimates of Hypoxic Volume within the Chesapeake Bay
JGR-Oceans, October 2013 issue
Aaron Bever, Marjy Friedrichs, Carl Friedrichs, Malcolm Scully, Lyon Lanerolle
OUTLINE
1.
Relation to US-IOOS Modeling Testbed program and general methods.
2.
Use 3D models to examine uncertainties in interpolating hypoxic volume.
3.
•
Observed DO have coarse spatial resolution = spatial error
•
Observed DO are not a “snapshot” = temporal error
Use 3D models to improve EPA-CBP interpolations of hypoxic volume.
p.15 of 21
Improving observation-derived 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
p.16 of 21
Improving observation-derived 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
But why 13 stations?
p.16 of 21
Improving observation-derived hypoxic volumes
Modeled
Integrated 3D
vs.
Spatial Match for
Different Station Sets
p.17 of 21
Improving observation-derived hypoxic volumes
Reduce Spatial errors:
1. For each model and
each cruise, derive a
correction factor as a
function of interpolated
HV that “corrects” this
13-station Spatial Match
HV to equal the
Integrated 3D HV.
p.18 of 21
Improving observation-derived hypoxic volumes
Reduce Spatial errors:
Before
Scaling
1. For each model and
each cruise, derive a
correction factor as a
function of interpolated
HV that “corrects” this
13-station Spatial Match
HV to equal the
Integrated 3D HV.
2. Apply correction factor
to HV time-series
3. Scaling-corrected
“interpolated” HV more
accurately represents
true HV
p.19 of 21
After
Scaling
Interannual (1984-2012) corrected (i.e., scaled) time
series of observed Hypoxic Volume
Time-series of corrected hypoxic volume for 1984-2012 are provided within JGR
article (annual maximum HV, annual duration of HV, annual cumulative HV), and
corrected HV for every CBP cruise is provided in JGR electronic supplement.
p.20 of 21
Combining Observations & Numerical Model Results to Improve
Estimates of Hypoxic Volume within the Chesapeake Bay
Summary/Conclusions
Information from multiple models (2004-2005) has been used to assess
uncertainties in present CBP interpolated hypoxic volume estimates
• Temporal uncertainties: up to ~5 km3
• Spatial uncertainties: up to ~5 km3
These are significant, given maximum HV is ~10-15 km3
A method for correcting interpolated HV time series for temporal and
spatial errors has been presented, based on the 3D structure of multiple
model DO results
• 13 stations (sample in 2 days) do as well for HV as 40-60 or more
• Corrected HV for 1984-2012 are downloadable from JGR website
p.21 of 21