Coupled assimilative modeling of ocean color

1. Coupled assimilative modeling of ocean color Bronwyn Cahill John Wilkin Javier Zavala-Garay Julia Levin Katja Fennel Jann Paul Mattern Mike Dowd Susanne Craig

2. Main objective Develop and contrast biological and bio-optical models and data assimilation strategies that improve near-shore predictive capabilities by taking advantage of emerging autonomous data. Implement and contrast alternative approaches and assess their predictive skill.

3. Approaches • Biomass-based models: • ECOSIM • BIO_FENNEL • BIO_LIMADONEY Deriving IOPs from biomass (Fujii et al. approach) IOP-based model: BIO_IOP Sequential updating 4DVAR EnKF (Ensemble Kalman Filter) SIR (Sequential Importance Resampling) models assimilation schemes

4. Approaches • Biomass-based models: • ECOSIM • BIO_FENNEL • BIO_LIMADONEY Deriving IOPs from biomass (Fujii et al. approach) IOP-based model: BIO_IOP Sequential updating 4DVAR EnKF (Ensemble Kalman Filter) SIR (Sequential Importance Resampling) models assimilation schemes

5. EcoSiM - Treatment of Light Water Mass Structures River Plume Attenuation Different parameterizations of downward irradiance (Iz) lead to different upper ocean water mass structures . Formation and transport of freshwater in the plume influences variability in light attenuation. Cahill et al., 2008

6. EcoSiM - Treatment of Light POC Export and Oxygen Depletion Rapid primary production within the re-circulating freshwater bulge. Large quantities of POC available for export to bottom waters Changes in circulation potentially reduces efficiency for bottom export of POC. Available POC advected toward Long Island coast. Cahill in prep.

7. EcoSiM - Treatment of Light Analytical versus accurate calculations for ecosystem models(Mobley et al., submitted Biogeosciences) Improving underwater light field … largest differences occur in upper 20m

8. ROMS Forward + Biomass-Based (Fennel) Model Model Bias RMSE Model Skill Histogram & Taylor diagrams forward NCEP-NARR (TIDES) MABGOM T/S MLD Chl PP Espresso Reanalysis ROMS Forward + Biomass-Based (Fennel) Model + Continuous Update Physics 3 day update Chl 10 day update BIO_Fennel - Sequential Updating January to July 2006 • Espresso Re-analysis • Bias corrected ocean estimate by sequential assimilation of climatology, SST and SSH. Dynamically balanced T / S fields.

9. ROMS Forward + Biomass-Based (Fennel) Model Model Bias RMSE Model Skill Histogram & Taylor diagrams forward NCEP-NARR (TIDES) MABGOM T/S MLD Chl PP Espresso Reanalysis ROMS Forward + Biomass-Based (Fennel) Model + Continuous Update Physics 3 day update Chl 10 day update Initial Conditions Model Forcing + Boundaries Validation Skill Assessment Run Period January to July 2006 • Espresso Re-analysis • Bias corrected ocean estimate by sequential assimilation of climatology, SST and SSH. Dynamically balanced T / S fields.

10. ROMS Forward + Biomass-Based (Fennel) Model Model Bias RMSE Model Skill Histogram & Taylor diagrams forward NCEP-NARR (TIDES) MABGOM T/S MLD Chl PP Espresso Reanalysis ROMS Forward + Biomass-Based (Fennel) Model + Continuous Update Blend reanalysis, sat. chlorophyll, bio state Initial Conditions Model Forcing + Boundaries Validation Skill Assessment Run Period January to July 2006 • Continuous update • Blend re-analysis, satellite chlorophyll and model biological state vectors • (1) physics only; (2) physics + chlorophyll. • Update every 3 days (physics), 10 days (chlorophyll) • Validate satellite SST & chlorophyll 1 month forward

11. Forward & Re-analysis - Temperature July 2006 Forward Model Satellite Obs. Forward Bias Re-analysis Taylor Statistics Re-analysis Bias

12. Forward & Re-analysis (Physics Only) - Chl July 2006 Forward Model SeaWiFs Forward Bias Re-analysis Taylor Statistics Re-analysis Bias

13. Re-analysis Physics vs Physics + Chl - Chl July 2006 RA Physics Only SeaWiFs RA Physics Bias RA Physics + Chl Taylor Statistics RA Physics + Chl Bias

14. Approaches • Biomass-based models: • ECOSIM • BIO_FENNEL • BIO_LIMADONEY Deriving IOPs from biomass (Fujii et al. approach) IOP-based model: BIO_IOP Sequential updating 4DVAR EnKF (Ensemble Kalman Filter) SIR (Sequential Importance Resampling) models assimilation schemes

15. Chl T Chl T Chl T POC Model Observations Lima-Doney model in MABGOM POC MAB shelf MAB slope POC GS

16. Lehmann, Fennel & He Biogeosciences, 2009

17. Lehmann, Fennel & He Biogeosciences, 2009

19. Lima-Doney model in MABGOM Model Observations adg(443) aph(443) a(443) Deriving IOPs from biomass-based model using approach of Fujii, Boss & Chai (2007) bb(443)

20. 2 2 2 2 1 1 1 1 IOP-based model NO3 SmS ~ O2 DIN • + aSW + aNAP = a aCDOM aphy • + bSW + bbg = b bphy bdet • variables/combinations of variables • are directly observable • potential for spectral resolution • potentially improved underwater light field  + aNAP = cP

21. IOP model example for a station on the Scotian Shelf aphy(440) bphy(440) bdet(440)

22. aCDOM(443) from A. Mannino mean standard dev.

23. deterministic case probability ensemble approximating a PDF model state Ensemble assimilation • 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)

24. Ensemble assimilation Model state: X Ensemble: {X(i)}i=1n (n ensemble members) Observations: y1:t = (y1,y2, … , yt), t=1,…,T Transition from t-1 to t: • Prediction step: {X(i)t-1|t-1}  {X(i)t|t-1} • Update step: target {X(i)t|t} (different methods: EnKF, SIR) • EnKF: X(i)t|t = X(i)t|t-1 + Kt(Y(i)t - HtX(i)t|t-1) i Model-data discrepancy is added to the model state weighted by the Kalman gain matrix. (Evensen 2003, 2006) • SIR: resampling of forecast ensemble Probability is assigned to each ensemble member based on its 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)

25. Ensemble Assimilation: In practice Jiatang Hu Paul Mattern

26. SIR weighting

27. Assessment of image comparison measures Mattern et al. (subm.)

28. Adapted Gray Block Distance

29. Ensemble assimilation applied to aCDOM in ESPRESSO Assimilation period: April 28, 2006 to May 25, 2008 aCDOM ensemble mean aCDOM observation aCDOM model - obs

30. Ensemble assimilation applied to aCDOM in ESPRESSO Evolution of CDOM degradation rate

31. What have we learnt? What do we need? • Implementation of feedback from biology to physics; accurate resolution of light field. • Application of sequential updating … improve biological estimates using good physics … and even more including chlorophyll. More work on river inputs and optimizing constraint windows. Data needed for validation. • IOP model; need data for validation/assimilation. • Ensemble assimilation schemes promising (aCDOM with SIR; chl in BIO_FENNEL w/EnKF); both can be combined with parameter optimization.