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Joint Proposal to WGOMD for a community ocean model experiment

Joint Proposal to WGOMD for a community ocean model experiment. Background. Seasonal Forecasts is an ideal test bed to asses the quality of the GCMS used in future climate projections: Need ocean initial conditions to carry out experiments

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Joint Proposal to WGOMD for a community ocean model experiment

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  1. Joint Proposal to WGOMD for a community ocean model experiment

  2. Background • Seasonal Forecasts is an ideal test bed to asses the quality of the GCMS used in future climate projections: • Need ocean initial conditions to carry out experiments • Not all the groups have ocean data assimilation systems in place. • WGOMD is about to discuss experimental setup to asses ocean model performance in the representation of the inter-annual variability. Possibility of X-Panel interaction and feedback. • The proposed experiment could answer some outstanding questions about the key elements needed to improve the representation of the Pacific basin and ENSO.

  3. Questions from the Pacific Panel • There are known model deficiencies in the representation of the Pacific climate: • Upwelling off the South American Coast • Cold tongue penetrating too far west • Too weak/too strong east-west slope of the thermocline • Equatorial heat content (IT/meridional transport, vertical mixing) • … • Is it possible to say which are the dominant source of error? • Forcing fields? • Model parameterizations/configuration? • Model resolution? • Parameterization of air-sea interaction?

  4. Proposal • Conduct a long ocean model simulation (20-40 years) with a variety of models, using inter-annually varying atmospheric reanalysis (ERA40-ENACT, NCEP-CORE, JMR-25…). This long integration can be used as: • a CNTL experiment to answer some of the above questions (see later) • To provide initial conditions for concerted seasonal forecast experiments. • Conduct additional integrations with each model (optional), to test sensitivities: • Different forcing fields • Different resolution • Different parameterization, bulk formulae, …. • Need to agree the specifications for the CNTL integration.

  5. Sensitivities: Compute and Compare • Sensitivity to Forcing fluxes: • Sensitivity to Resolution: • Sensitivity to Model: • Sensitivity to Bulk Formula: • Sensitivity to P-parameterization:

  6. Sources of Uncertainty All ERA40 ERA40-No data ERA40-Assim

  7. EQPAC EQATL EQIND TRPAC TRATL NPAC NATL GLOBAL Assimilation and Uncertainty T300 Long period 1956-2005: Large part of uncertainty comes from forcing fluxes 1956-2005 ALL SAME FORCING (ERA40) OCEAN MODEL ASSIMILATION SCHEME

  8. Mean Assimation Temperature Increment Sensitivities: preliminary results from ENACT/GSOP MEAN STATE: • Equatorial Pacific heat content is model dependent • Slope of Pacific Eq. thermocline controlled by winds • ERA40 produces weak thermocline slope in most models • Too diffuse thermocline in Indian Ocean? Analysis minus Observations Western Pacific Equatorial Indian DATA ASSIM NO DATA ASSIM

  9. Strawman for the CNTL integration • From spin up, launch the integrations, using daily atmospheric forcing fluxes plus some restoring terms: • 3D relaxation to climatology of T and S (~10 year time scale) • Strong relaxation to time-varying values of SST (~2-3 days t.s) • Relaxation to climatological Surface Salinity (1 year t.s) • If free surface, volume preserving constrains. • The restoring terms are used to diagnose errors. • Atmospheric forcing (daily values): • Momentum flux: ERA40 0-24 wind stress • Heat flux: • ERA40 0-24 solar, latent and sensible heat fluxes. PLUS • Strong relaxation to daily values of SST (~2 days time scale) (Reynolds from 1982) • Fresh Water flux: • ERA40 0-24 PME + ENACT correction • Weak Relaxation to climatological SSS (~12 month time scale) • If free surface, some volume preserving constrains.

  10. CNTL integration at ECWMF • Decent Inter-annual ENSO variability Correlation with altimeter data (1993-2003)

  11. CNTL integration at ECWMF • Some MOC variability

  12. CNTL integration at ECWMF • As initial conditions, decent forecast skill. S3 NodataS3 Assim

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