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 ESPreSSO Experimental Shelf Predictive Shelf-Slope Optics (ESPreSSO): Overview Oscar Schofield, Katja Fennel, Glen Gawarkiewicz, Scott Glenn, Ruoying.

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Presentation on theme: " ESPreSSO Experimental Shelf Predictive Shelf-Slope Optics (ESPreSSO): Overview Oscar Schofield, Katja Fennel, Glen Gawarkiewicz, Scott Glenn, Ruoying."— Presentation transcript:

1  ESPreSSO Experimental Shelf Predictive Shelf-Slope Optics (ESPreSSO): Overview Oscar Schofield, Katja Fennel, Glen Gawarkiewicz, Scott Glenn, Ruoying He, Dennis McGillicuddy, Mark Moline, John Wilkin, Bronwyn Cahill Collaboration with NRL BioSPACE and NASA Sensor Net NRL – Igor Schulman, Bob Arnone, Bill Teague, ZhongPing Lee NASA JPL - Yi Chao, Steve Chien NSF OOI Cyberinfrastructure

2 Ocean color integrates the water column physics over long time scales (days to months). This results in enhanced spatial complexity in biological fields that are not seen in the physical remote sensing parameters SST CHL CHL

3 Remote Sensing: Bio-optical Why IOP model of the food web?
Overall Goal: Predict 3-D optical properties of coastal ocean on time scales of 1-5 days. Improve our understanding of coupled bio-optical and physical processes in the coastal zone on time scales of 1-5 days. Most accurate representation and prediction of optical properties requires fusion of remote sensing, in-situ observations and data-assimilative modeling using coupled optical-biological-physical models. Remote Sensing: Bio-optical Why IOP model of the food web? - advantages, can be applied in optically complex waters - properties are linear combinations of colored material can be measured from satellites & AUVs - quantum yields can convert energy into organic material IOP Approach Inherent Optical Properties( IOP) 3-d light field Optical Model IOP Bio-Optical Model Data assimilation IOP Phytoplankton Detritus, CDOM In-situ Observations Coupled Bio-physical model

4 Cahill et al GRL In turbid coastal waters, the physics of the water column cannot be accurately modeled without accounting for the water column turbidity Impact of buoyant plume turbidity in the water column stratification. ROMS model run using standard parameterization versus the measured river turbidity. Standard Day 0 Day 15 Day 18 Day 19 Measured River Day 0 Day 15 Day 18 Day 19

5 The “easy” Fasham-Type Biology
NO3 Chlorophyll Large detritus Organic matter N2 NH4 Water column Sediment Phytoplankton Mineralization Uptake Nitrification Grazing Mortality Zooplankton Susp. particles Aerobic mineralization Denitrification Fennel et al., 2006

6 ESPRESSO-IOP model aCDOM aphy bphy bdet + aSW + aNAP = a + bSW + bbg
IOP Model (FOOD WEB models in general) How complex do they need to be? DIN 2 1 aCDOM aphy bphy bdet + aSW + aNAP = a + bSW + bbg = b NO3 SmS ~ O2  + aNAP = cP

7 The “urber-complex” EcoSim Biology
Phytoplankton 4 Groups NO3 SiO PO4 FeO 1 2 3 4 FECAL DIC CDM DOC, DON & DOP Bacteria NH4 Grazing Uptake / Autotrophs Losses Uptake / Heterotrophs Remineralization Carbon Fixation Chlorophyll Pigments IOPs aphy(,z) aCDM(,z) Ed(0,) 1% Ed(0,)

8 Bio-Optically Nested Modeling Domain
Northeast North America Mid-Atlantic Bight New York Bight

9 Collect data over spatial scales relevant to the physical models

10 Lima-Doney model in MABGOM
ROMS (MAB-GOM) HYCOM HYCOM Lima-Doney model in MABGOM

11 Model Forcing + Boundaries Initial Conditions Validation Skill
Assessment Run Period January to July 2006 ROMS Forward + Biomass-Based (Fennel) Model Metrics … TBD forward Chl POC NCEP-NARR (TIDES) MABGOM Espresso Reanalysis ROMS Forward + Biomass-Based (Fennel) Model + Continuous Update Metrics … TBD 3 day update Chl POC ROMS Forward + IOP Model + SIR Metrics … TBD assim sat. aCDOM aCDOM

12 Model Forcing + Boundaries Initial Conditions Validation Skill
Assessment Run Period January to July 2006 ROMS Forward + Biomass-Based (Fennel) Model Metrics … TBD forward Chl POC NCEP-NARR (TIDES) MABGOM Espresso Reanalysis ROMS Forward + Biomass-Based (Fennel) Model + Continuous Update Blend reanalysis, sat. chlorophyll, bio state Metrics … TBD Chl POC ROMS Forward + IOP Model + SIR Assimilate CDOM absorption from satellite Metrics … TBD aCDOM

13 Data Strategies Espresso Reanalysis
Bias corrected ocean estimate by assimilation of climatology, SST and SSH. Dynamically balanced T / S fields. Continuous update Blend reanalysis, satellite chlorophyll and model biological state vectors (1) physics only; (2) physics + chlorophyll. Update every 3 days Validate satellite chlorophyll 10 days forward SIR - Sequential Importance Resampling Resampling of forecast ensemble. Probability assigned to each ensemble member based on agreement with new observation. Ensemble is resampled given these probabilities. Hence, ensemble member close to obs. (high weight) are likely to be picked, ensemble member far from obs. (low weight) is likely to drop out. (Ristic et al. 2004)

14 ROMS Forward + Biomass-Based (Fennel) Model
Satellite Obs. Model - Observations

15 ROMS Forward + Biomass-Based (Fennel) Model + Continuous Update
Satellite Obs. Model - Observations

16 ROMS Forward + IOP Model + SIR
aCDOM ensemble mean aCDOM observation aCDOM model - obs

17 Ensemble assimilation
Ensembles are an approach for dealing with uncertainty in numerical prediction Models of fluid flow coupled with biological processes are highly non-linear Reality: fundamental limits on accuracy/predictability Model ensembles: approximate true state of the system (PDF) by an ensemble which samples uncertain inputs and processes; predictions in form of PDF (probability of different outcomes) deterministic case probability ensemble approximating a PDF model state

18 EnKF SIR

19 The sequential impact on CDOM estimates

20 Spatial tests on the approach for constraining
CDOM degradation

21 Some science results: MAB productivity peaks in winter.
Most recurrent bloom on MAB and is centered on inner shelf Parameter EOF1 EOF2 Mean Chl 1.7 0.7 Max Chl 4.9 2.1 Min Chl 0.6 0.2 Mean Chl 1% light depth 20 33 Max Chl 1% light depth 12 27 Min Chl 1% light depth 36 55 % water column in euphotic zone 49% 17(5)% Yi et al. submitted

22 The dominant factor regulating the timing of color events is the onset and disruption of the shelf stratification

23 The size of the winter bloom is a function of river outflow in warm winters
and most importantly the number of stormy days which presumably set up water column mixing which determines the degree of light limitation Yi et al. submitted Castelao et al. 2008

24 Comparison with historical data suggest that the Fall and
winter bloom activity has changed on the MAB Schofield et al. 2008

25 Comparison with historical data suggest that the Fall and
winter bloom activity has changed on the MAB

26 Fall Bloom decline: Is it taking longer for the shelf stratification to erode?
From ECMWF reanalysis (single grid point)

27 Why winter decline? Since the mid-nineties winter winds have increased

28 In the coming year: A) The continuous blended bio-optical/physical models focused
on the fall transition and light limitation of the winter phytoplankton, B) data collected adaptively by the northeast observatory, C) adjust the network using suite of cyber infrastructure tools providing a sensor net

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