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EDDY-DRIVEN DISPERSION IN COASTAL UPWELLING SYSTEMS

EDDY-DRIVEN DISPERSION IN COASTAL UPWELLING SYSTEMS. Patrick Marchesiello. COLLABORATORS: P. Estrade, S. Herbette, C. Lett, A. Peliz, C. Roy, B. Sow, C. Roy. ROMS Meeting, VENEZIA October 19 2004. California. Canary. Benguela. Humbolt. Coastal Upwelling?.

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EDDY-DRIVEN DISPERSION IN COASTAL UPWELLING SYSTEMS

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  1. EDDY-DRIVEN DISPERSION IN COASTAL UPWELLING SYSTEMS Patrick Marchesiello COLLABORATORS: P. Estrade, S. Herbette, C. Lett, A. Peliz, C. Roy, B. Sow, C. Roy ROMS Meeting, VENEZIA October 19 2004 California Canary Benguela Humbolt

  2. Coastal Upwelling?

  3. California Senegal

  4. Coastal upwelling Retention zone Eddy mixing zone Divergence zone Marchesiello et al., 2003 Mitchum & Clark, 1978 Lentz & Austin, 2002

  5. ROMS_AGRIF http://www.ird.brest.fr/Roms_tools • ROMS: HYDRODYNAMIC MODEL optimized for regional and coastal high resolution, multi-scale, multidisciplinary applications • AGRIF: Online, synchronous nesting method (L. Debreu) • ROMS_TOOL: Pre- and post-processing package (P. Penven) • DIAGNOSTIC TOOLS:Lagrangian tracers, budgets … • APPLICATION MODELS:Ecosystem dynamics, Water quality, Sediment transport

  6. Note on Regional Models POG - 0.25 deg ROMS – 0.25 deg

  7. CALIFORNIA

  8. APPLICATION TO THE CALIFORNIA CURRENT SYSTEM: CONFIGURATION AND STRATEGY Volume Averaged KE (cm2/s2) 20km, 10km, 5km 20km, 10km, 5km, 2.5km Surface Averaged KE (cm2/s2) • Nesting of the inner domain: on-line or off-line. • Model integration: 10 years. • Surface and lateral boundary forcing: Monthly climatologies.

  9. SST - Model SST - AVHRR Mesoscale Variability in the CCS • Marchesiello et al. (JPO, 2003) • Realistic simulation of the Coastal Transition Zone • More than 2/3 of the mesoscale variability is intrinsic, and produced through instabilities (baroclinic and barotropic) of the coastal currents generated in the upwelling process.

  10. Drifter Estimation [180] 100 Eddy Kinetic Energy [cm2/s2] Model Resolution [km] 10 1 5 10 20 Model Convergence

  11. CANARY - COMPARISON

  12. Canary Current System Configuration Mercator ROMS – Sahara 5 km ROMS – Canary 25 km Levitus C. Blanc C. Blanc Clipper C. Vert

  13. Sahara California 20 17 26 8

  14. Mesoscale Activity In California and Canary Systems California Model Sahara SSH Standard Deviation [cm] For non-seasonal variability

  15. Mesoscale Activity In California and Canary Systems California Model Sahara SSH Standard Deviation [cm] For non-seasonal variability California Altimetry Sahara Topex/ERS from AVISO

  16. Wind Forcing California Morocco Units: Pascal

  17. BAROCLINICITY: Two layer approach • The upwelling front • results from upwelling of the thermocline (Mooers et al., 1976) • Baroclinic instability: • energy conversion from available potential energy to eddy kinetic energy • varies with vertical shear of velocity (Pedlosky, 1986; Barth, 1989) • U=(g’H0)1/2 • where g’=g(ρ2-ρ1)/ ρ2

  18. JOINT I cruise, after Huyer(1976) Salinity profiles & Reduced Gravity Salinity relative to surface Temperature relative to surface Potential density • California • g’=0.019 • Canary • g’=0.008 California California Canary Canary

  19. IMPACT

  20. T’u’ = -Kx dT/dx MESOSCALE CROSS-SHORE DIFFUSION T Erosion of coastal properties Mixing 500km 100km Offshore distance X100 m2/s • Swenson and Niiler (1996)from drifting-buoy trajectories, 1985-1988: K = 1.1 - 4.6 103 m2/s with higher values for Kx compared to Ky • Model: Kx = 2.3 103 m2/s and Ky = 1.3 103 m2/s

  21. Ammonium Nitrate Chlorophyll A Phytoplankton Zooplankton Large Detritus Small Detritus THE ECOSYSTEM MODEL HYDRODYNAMICS Upwelling Nitrification Light Transport New Prod. Reg. Prod. Grazing Breakdown Mortality Excretion Aggregation Sink

  22. New Production NO3 transport Spring-time biology fluxes Units: mmol N cm-2 a-1 LINEAR MODEL (advection terms turned off in the momentum equation) NON-LINEAR MODEL

  23. Seawifs Annual Chl Retention Map From Lagrangian Study SSH Standard Deviation

  24. BIOLOGICALLY ACTIVE AREA IN UPWELLING SYSTEMS CANARY PERU-CHILI BENGUELA CALIFORNIA What drives the observeddifferences in cross-shore distribution of physical and biogeochemical properties? • Latitude (solar flux) • Fe depositions from Sahara (Lene et al., 2001) • Shelf width & nutrients (Johnson et al., 1997) • Mesoscale physics(Marchesiello et al., 2003)

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