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Jerome Fiechter, Andy Moore, Gregoire Broquet Ocean Sciences Department University of California, Santa Cruz ROMS Workshop, Sydney, April 2009 Improving.

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Presentation on theme: "Jerome Fiechter, Andy Moore, Gregoire Broquet Ocean Sciences Department University of California, Santa Cruz ROMS Workshop, Sydney, April 2009 Improving."— Presentation transcript:

1 Jerome Fiechter, Andy Moore, Gregoire Broquet Ocean Sciences Department University of California, Santa Cruz ROMS Workshop, Sydney, April 2009 Improving Ecosystem Model Predictions through Data Assimilation

2 Outline Physical/biological properties of Coastal Gulf of Alaska (CGOA) Physical/biological properties of Coastal Gulf of Alaska (CGOA) Ocean circulation, ecosystem, and iron limitation models Simulation results for without data assimilation Simulation results for 2001 with data assimilation

3 CGOA Physical and Biological Properties Physical Variability Downwelling-favorable wind regime (Stabeno et al., 2004) AS intrinsic mesoscale variability (Combes and Di Lorenzo, 2007) Anticyclonic (Yakutat) eddy passages (Okkonen et al., 2003) Biological Variability CGOA: high-productivity shelf, fisheries Subarctic Gyre: HNLC region (Lam et al., 2006) Iron limitation on primary production (Strom et al., 2006) Interannual Variability El Niño; 1999 La Niña 1999 NEP “Cold” Regime Shift (Peterson and Schwing, 2003) 2002 NEP Subsurface Cold Event (Curchitser et al., 2005)

4 Coastal Gulf of Alaska Ocean Circulation Model ROMS: ~10 km horizontal resolution, 42 vertical levels One-way offline nesting with North East Pacific ROMS Monthly mean atmospheric and open boundary forcing Macro Nutrients from monthly WOA01 climatology Dissolved iron from VERTEX (Martin et al., 1989)

5 Lower Trophic Level Ecosystem Models NPZD+Fe (Powell et al., 2006; Fiechter et al., 2009) NPZD+Fe (Powell et al., 2006; Fiechter et al., 2009) NEMURO+Fe (Kishi et al., 2007; Fiechter and Moore, 2009) (from Kishi et al., 2007) Strom et al., 2007 NPZD PS: Nano P PL: Diatoms ZS: Ciliates ZL: Copepods ZP: Krill Fe

6 RESULTS: PART I INTERANNUAL VARIABILITY ( ) ROMS + NEMURO/NPZD + Fe-limitation WITHOUT DATA ASSIMILATION

7 Surface Chlorophyll EOFs: Models vs. Observations NEMURO+FeNPZD+FeSeaWiFS

8 NEMURO/NPZD Surface Chlorophyll, Taylor diagrams with respect to SeaWiFS based on monthly means ( NEMURO+Fe, NPZD+Fe ) 2001 LACK OF VARIABILITY

9 Sea Surface Height: GAK Line, ROMS AVISO GAK Line GAK Stations ROMS vs. AVISO

10 RESULTS: PART II SEASONAL VARIABILITY (2001) ROMS + NPZD + Fe-limitation WITH DATA ASSIMILATION

11 IS4DVAR Data Assimilation CASE NAME 7-DAY SSH (AVISO) 5-DAY SST (Pathfinder) IN SITU T/S (GLOBEC) 8-DAY CHL (SeaWiFS) FREENO SSHTYES NO SSHTP (AD, PASS)YES SSP (AD, PASS)NO YES Configuration: ROMS+NPZD+Fe, adjoint/passive biology Assimilation: 7-day cycle, 1 outer (NL) loop, 10 inner (TL/AD) loops Strong constraint (no model error), adjust IC only, model space search Univariate background error covariance: a) isotropic, homogeneous correlations (50km horiz., 30m vert.) b) std deviations based on 10-year non-assimilated solution Observation std deviations: SSH=2cm; T=0.25C; S=0.1; Chl=0.5mg/m 3

12 Sea Surface Height, 2001 RMS errors and correlations with AVISO based on weekly means FREE SSHTSSHTP-BIOADSSP-BIOAD

13 Sea Surface Temperature, 2001 RMS errors and correlations with Pathfinder based on weekly means FREESSHTSSHTP-BIOAD SSP-BIOAD

14 Sea Surface Height, 2001 Taylor diagrams with respect to AVISO based on weekly means

15 NPZD Surface Chlorophyll, 2001 RMS errors and correlations with SeaWiFS based on monthly means FREESSHTSSHTP-BIOADSSHTP-BIOPASS

16 NPZD Surface Chlorophyll, 2001 RMS errors and correlations with SeaWiFS based on monthly means SSHT-BIOADSSHT-BIOPASSSSP-BIOADSSP-BIOPASS

17 NPZD Surface Chlorophyll, 2001 Taylor diagrams with respect to SeaWiFS based on monthly means NO BIO ASSIM PASSIVE BIO ASSIM ADJOINT BIO ASSIM

18 NPZD Surface Chlorophyll: Seasonal Means, 2001 FREESSHTSSHTP-BIOADSeaWiFS APR-JUN JUN-AUG AUG-OCT

19 Surface Chlorophyll and Nutrients: GAK Stations, 2001 Comparisons between model, SeaWiFS, and in situ chlorophyll

20 RESULTS: PART III SEASONAL VARIABILITY (2001) ROMS + NEMURO + Fe-limitation WITH DATA ASSIMILATION

21 NEMURO Surface Chlorophyll, 2001 RMS errors and correlations with SeaWiFS based on monthly means FREESSHTSSHTP-BIOPASSSSP-BIOPASS

22 NEMURO Surface Chlorophyll: Seasonal Means, 2001 FREESSHTSSHTP-BIOPASSSeaWiFS APR-JUN JUN-AUG AUG-OCT

23 Summary Interannual variability, no data assimilation, Models reproduce spring bloom, underestimate fall bloom Models good on “normal” years, not so good on “abnormal” years Seasonal variability, data assimilation, 2001 Chlorophyll not improved by assimilation of physical data only Chlorophyll improved by assimilation of biological data Chlorophyll assimilation improved by using adjoint biology Assimilation incompatibilities between physics and biology Future work Adjoint vs. passive NPZD solutions (sensitivity studies) Assimilation with NEMURO (Chl to small/large phytoplankton) Forecast skill assessment for physics and biology

24 Collaborators: H. Arango (Rutgers), K. Bruland (UC Santa Cruz), E. Curchitser (Rutgers), E. Di Lorenzo (Georgia Tech), C. Edwards (UC Santa Cruz), K. Hedstrom (ARSRC), A.Hermann (NOAA/PMEL), B. Powell (U. Hawaii),Hermann (NOAA/PMEL), B. Powell (U. Hawaii), T. Powell (UC Berkeley) Funding: National Science Foundation (U.S. GLOBEC)

25 Iron Limitation on Phytoplankton Growth Nitrate-limited phytop. growth rate: Dissolved (available) Iron: Phytop.-associated Iron: Iron uptake:Optimal Fe:C: Realized Fe:C: Iron-limited phytop. growth:

26 Chlorophyll Vertical Profiles: GAK Stations, 2001


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