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Jerome Fiechter, Andy Moore, Gregoire Broquet Ocean Sciences Department

Improving Ecosystem Model Predictions through Data Assimilation. Jerome Fiechter, Andy Moore, Gregoire Broquet Ocean Sciences Department University of California, Santa Cruz ROMS Workshop, Sydney, April 2009. Outline. Physical/biological properties of Coastal Gulf of Alaska (CGOA)

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Jerome Fiechter, Andy Moore, Gregoire Broquet Ocean Sciences Department

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

  2. Outline • Physical/biological properties of Coastal Gulf of Alaska (CGOA) • Ocean circulation, ecosystem, and iron limitation models • Simulation results for 1998-2002 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 • 1997-1998 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) • NEMURO+Fe (Kishi et al., 2007; Fiechter and Moore, 2009) PS: Nano P PL: Diatoms ZS: Ciliates ZL: Copepods ZP: Krill NPZD Fe Strom et al., 2007 (from Kishi et al., 2007)

  6. RESULTS: PART I INTERANNUAL VARIABILITY (1998-2002) ROMS + NEMURO/NPZD + Fe-limitation WITHOUT DATA ASSIMILATION

  7. Surface Chlorophyll EOFs: Models vs. Observations NEMURO+Fe NPZD+Fe SeaWiFS

  8. NEMURO/NPZD Surface Chlorophyll, 1998-2002 Taylor diagrams with respect to SeaWiFS based on monthly means (NEMURO+Fe,NPZD+Fe) 2001 2001 LACK OF VARIABILITY

  9. Sea Surface Height: GAK Line, 1995-2004 GAK Stations GAK Line GAK Line ROMS vs. AVISO AVISO ROMS

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

  11. IS4DVAR Data Assimilation • 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/m3

  12. Sea Surface Height, 2001 RMS errors and correlations with AVISO based on weekly means FREE SSHT SSHTP-BIOAD SSP-BIOAD

  13. Sea Surface Temperature, 2001 RMS errors and correlations with Pathfinder based on weekly means SSP-BIOAD FREE SSHT SSHTP-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 FREE SSHT SSHTP-BIOAD SSHTP-BIOPASS

  16. NPZD Surface Chlorophyll, 2001 RMS errors and correlations with SeaWiFS based on monthly means SSHT-BIOAD SSHT-BIOPASS SSP-BIOAD SSP-BIOPASS

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

  18. NPZD Surface Chlorophyll: Seasonal Means, 2001 FREE SSHT SSHTP-BIOAD SeaWiFS 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 FREE SSHT SSHTP-BIOPASS SSP-BIOPASS

  22. NEMURO Surface Chlorophyll: Seasonal Means, 2001 FREE SSHT SSHTP-BIOPASS SeaWiFS APR-JUN JUN-AUG AUG-OCT

  23. Summary • Interannual variability, no data assimilation, 1998-2002 • 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), • 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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