combining observations & models to improve estimates of chesapeake bay hypoxic volume*
DESCRIPTION
Combining Observations & Models to Improve Estimates of Chesapeake Bay Hypoxic Volume*. Aaron Bever , Marjorie Friedrichs , Carl Friedrichs, Malcolm Scully, Lyon Lanerolle. *Submitted to JGR-Oceans. TMAW DO Seminar May 7, 2013. What method(s) do you use for assessing DO and why? - PowerPoint PPT PresentationTRANSCRIPT
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Combining Observations & Models to Improve Estimates
of Chesapeake BayHypoxic Volume*
Aaron Bever, Marjorie Friedrichs, Carl Friedrichs, Malcolm Scully, Lyon Lanerolle
*Submitted to JGR-Oceans
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• What method(s) do you use for assessing DO and why?• What have you found drives DO patterns in the Bay?• What lessons have you learned?
TMAW DO SeminarMay 7, 2013
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• What method(s) do you use for assessing DO and why?• What have you found drives DO patterns in the Bay?• What lessons have you learned?
TMAW DO SeminarMay 7, 2013
![Page 4: Combining Observations & Models to Improve Estimates of Chesapeake Bay Hypoxic Volume*](https://reader036.vdocuments.mx/reader036/viewer/2022081604/56816735550346895ddbe333/html5/thumbnails/4.jpg)
Objectives: 1. Use multiple models to examine uncertainties
caused by interpolating hypoxic volumes, due to: • Data are not a “snapshot” (collected over ~2 weeks)• Data have coarse spatial resolution
2. Use these multiple models to correct the CBP interpolated hypoxic volumes
3. Use these corrected time series to assess different metrics for estimating interannual variability in hypoxic volume
• Average Summer Hypoxic Volume• Cumulative Hypoxic Volume
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Estuarine Hypoxia Team:Marjorie Friedrichs (VIMS)
Carl Friedrichs (VIMS)Aaron Bever (VIMS)
Jian Shen (VIMS)Malcolm Scully (ODU)
Raleigh Hood/Wen Long (UMCES)Ming Li (UMCES)
Kevin Sellner (CRC)
Federal partnersCarl Cerco (USACE)
David Green (NOAA-NWS)Lyon Lanerolle (NOAA-CSDL)
Lewis Linker (EPA)Doug Wilson (NOAA-NCBO)
Background: The U.S. IOOS Testbed Project
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Methods:• Compare relative skill of various Bay models
• Compare strengths/weaknesses of various models
• Assess how model differences affect water quality simulations
What should a “Next Generation Bay Model” entail?
Background: The U.S. IOOS Testbed Project
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Five Hydrodynamic Models Configured for the Bay
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Five Hydrodynamic Models Configured for the Bay
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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(not a function of x, y, temperature, nutrients OR depth…)
Five Biological (DO) Models Configured for the Bay
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Four combinations:
o CH3D + ICM CBP modelo CBOFS2 + 1termo ChesROMS + 1termo ChesROMS + 1term+DD
Coupled hydrodynamic-DO models
Physical models are similar, but grid resolution differsBiological/DO models differ dramaticallyAll models (except CH3D) run using same forcing/boundary
conditions, etc…
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Model Skill
How well do the models represent the mean and
variability of dissolved oxygen at
~40 CBP stations in 2004 and 2005?
= ~40 CBP stations used inthis model-data comparison
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1
1
-1
-1 variability
bias
Model skill (RMSD) = Distance from Originsymbol at origin model fits observations perfectly
x > 0overestimates
variability
y > 0: overestimates
mean
1
-1
-1
Jolliff et al., 2009
Relative model skill: Target diagrams
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1
1
-1
-1 variability
bias
Model skill (RMSD) = Distance from Originsymbol at origin model fits observations perfectly
x > 0overestimates
variability
y > 0: overestimates
mean
1
-1
-1
Jolliff et al., 2009
Relative model skill: Target diagrams
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Model Skill: Bottom DO (2004)Spatial variability
Bias
unbi
ased
RM
SD
unbi
ased
RM
SD
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CH3D-ICM and ChesROMS reproduce DO patterns similarly well
Spatial variability Temporal variability
Mea
n st
atio
n sa
linity
(psu
)Bias Bias
unbi
ased
RM
SD
unbi
ased
RM
SD
unbiasedR
MS
Dunbiased
RM
SD
Model Skill: Bottom DO (2004)
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unbiasedRMSD[mg/L]
bias [mg/L]
Model Skill: Bottom DO (2004)
Overall, models performed similarly well. Can we use information from the models to assess uncertainties in these data-derived HVs?
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Objectives: 1. Use multiple models to examine uncertainties
caused by interpolating hypoxic volumes, due to: • Data are not a “snapshot” (collected over ~2 weeks)• Data have coarse spatial resolution
2. Use these multiple models to correct the CBP interpolated hypoxic volumes
3. Use these corrected time series to assess different metrics for estimating interannual variability in hypoxic volume
• Average Summer Hypoxic Volume• Cumulative Hypoxic Volume
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Data-derived HV estimatesData: Of 99 CBP stations (red dots),
30-65 are sampled each “cruise”
Note: Cruises use 2 boats from 2 institutions to collect vertical profiles; last for up to 2 weeks
Interpolation Method: CBP Interpolator Tool
Uncertainties arise from: Temporal errors: data are not a
snapshot Spatial errors: portions of Bay
are unsampled
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Model Skill: Hypoxic VolumeData-derived HV vs. Integrated 3D Modeled HV
However… Interpolated HV vs. Integrated HV is an apples vs. oranges comparison
2004 2005
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Model-derived HV estimates
Integrated 3D: Hypoxic volume is computed
from integrating over all grid cells
Interpolated Absolute Match: Same 30-65 stations are
“sampled” at same time/place as data are available
Interpolated Spatial Match: Same stations are “sampled”,
but samples are taken synoptically
Interpolation Method: CBP Interpolator Tool
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Model Skill Assessment for HV2004
Skill of Modeled Absolute Match is higher!Comparison of Absolute Match and Integrated 3D gives estimate of
uncertainty in data-derived HV
Data-derived vs.
Absolute Match
Data-derived vs.
Integrated 3D
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Hypoxic Volume Estimates
CH3D-ICM
ChesROMS+1term
Data-derived
= Absolute Match• Good comparison for Absolute Match
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Hypoxic Volume Estimates
CH3D-ICM
ChesROMS+1term
Data-derived
= Absolute Match• When data and model are interpolated in same way, good match
• Interpolated HV underestimates actual HV for every cruise
CH3D-ICM
ChesROMS+1term
Data-derived
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Hypoxic Volume Estimates
• When data and model are interpolated in same way, good match
• Interpolated HV underestimates actual HV for every cruise
• Much of this disparity could be due to temporal errors (red bars)
CH3D-ICM
ChesROMS+1term
Data-derived
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Uncertainties in data-derived hypoxic volumes
The temporal errors from non-synoptic sampling can be as large as spatial errors (~5 km3)
• Need to limit stations used in interpolation to stations sampled close in time
Spatial errors show interpolated HV is always too low (~2.5 km3)
• Need to increase the interpolated HV
Range of Spatial match over the cruise; range of interpolated HV over the cruise
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Objectives: 1. Use multiple models to examine uncertainties
caused by interpolating hypoxic volumes, due to: • Data are not a “snapshot” (collected over ~2 weeks)• Data have coarse spatial resolution
2. Use these multiple models to correct the CBP interpolated hypoxic volumes
3. Use these corrected time series to assess different metrics for estimating interannual variability in hypoxic volume
• Average Summer Hypoxic Volume• Cumulative Hypoxic Volume
![Page 27: Combining Observations & Models to Improve Estimates of Chesapeake Bay Hypoxic Volume*](https://reader036.vdocuments.mx/reader036/viewer/2022081604/56816735550346895ddbe333/html5/thumbnails/27.jpg)
Blue triangles = 13 selected CBP stations
Correcting data-derived hypoxic volumes
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
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Reduce Spatial errors:
1. For each model and each cruise, derive a correction factor as a function of interpolated HV that “corrects” this data-derived HV.
Correcting data-derived hypoxic volumes
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Reduce Spatial errors:
1. For each model and each cruise, derive a correction factor as a function of interpolated HV that “corrects” this data-derived HV.
2. Apply correction factor to data-derived HV
3. Data-corrected HV more accuratelyrepresents true HV
Correcting data-derived hypoxic volumes
Before Scaling
AfterScaling
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Interannual (1984-2012) data-corrected time series of Hypoxic Volume
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Objectives: 1. Use multiple models to examine uncertainties
caused by interpolating hypoxic volumes, due to: • Data are not a “snapshot” (collected over ~2 weeks)• Data have coarse spatial resolution
2. Use these multiple models to correct the CBP interpolated hypoxic volumes
3. Use these corrected time series to assess different metrics for estimating interannual variability in hypoxic volume
• Average Summer Hypoxic Volume• Cumulative Hypoxic Volume
![Page 32: Combining Observations & Models to Improve Estimates of Chesapeake Bay Hypoxic Volume*](https://reader036.vdocuments.mx/reader036/viewer/2022081604/56816735550346895ddbe333/html5/thumbnails/32.jpg)
How do we determine which years are good/bad? Or whether we’re seeing a recent reduction in hypoxia?
• Length of time waters are hypoxic• Percent of Bay (volume) that is hypoxic
Choose metrics dependent on ecological function of interest:• Prolonged low HV could be worse for some species than an
extensive short duration HV event, and vice versa.
Interannual DO Assessment
Different HV metrics can give different results for which years are “worst”
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Interannual DO Assessment1995 - 1997
Of these three years, 1996 appears to have the least hypoxia
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In 1996 Maximum HV is relatively low BUT Average Summer HV is relatively high;Maximum Annual HV is probably not the best DO metric
Interannual DO Assessment1995 - 1997
Annual HV Time-Series Average Summer HV
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Red dashed lines denote period of
“summer averaging”
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Red dashed lines denote period of
“summer averaging”
2011 looks “good”, because much hypoxia occurs outside of “summer” time period
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Average Summer HV vs.
Cumulative HV
• Performance of relative years changes
• Average Summer HV doesn’t taken into account long HV duration
• If climate change affects time of onset, this will not be seen when using Avg Summer HV
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Information from multiple models (2004-2005) have been used to assess uncertainties in data-derived interpolated hypoxic volume estimates
• Temporal uncertainties: ~5 km3
• Spatial uncertainties: ~2.4 km3
These are significant, given maximum HV is ~10-15 km3
A method for correcting HV time series has been presented, using the model results
Different HV metrics can give different results in terms of assessing DO improvement
• Cumulative HV is a good way to take into account shifts in onset of hypoxia that could occur with climate change
Summary
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Extra Slides
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CH3D, EFDC reproduce bottom salinity best
unbiasedRMSD[psu]
bias [psu]
2004 Bottom Salinity
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Cumulative hypoxic volume.
CBP13 scaled is now much more inline with the model estimates of 3D HV.
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As in previous slide, without HV correction
This demonstrates that the correction of HVs does not significantly affect the Average Summer HV vs. Cumulative HV conclusions
Average Summer HV vs.
Cumulative HV
• Performance of relative years changes
• Average Summer HV doesn’t taken into account long HV duration
• If climate change affects time of onset, this will not be seen when using Avg Summer HV
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• We like the cumulative hypoxic volume because it incorporates both prolonged periods of low hypoxia and any large shorter duration hypoxia.
• We also like coupling the cumulative hypoxic volume with the duration of hypoxia and the maximum hypoxic volume.
• Simply knowing one of the metrics is not sufficient to even get the relative magnitudes of the others.