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CLIVAR Working Group on Seasonal to Interannual Prediction

CLIVAR Working Group on Seasonal to Interannual Prediction . Who we are: ECMWF,UKMO,Meto-France, SNU,APCC,BMRC,NCEP,GSFC,COLA,IRI, CPTEC,SAWS,CCC What we do: Assess and improve S/I predictions considering various factors including how the observing system affects predictions. Why am I here:

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CLIVAR Working Group on Seasonal to Interannual Prediction

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  1. CLIVAR Working Group on Seasonal to Interannual Prediction Who we are: ECMWF,UKMO,Meto-France, SNU,APCC,BMRC,NCEP,GSFC,COLA,IRI, CPTEC,SAWS,CCC What we do: Assess and improve S/I predictions considering various factors including how the observing system affects predictions. Why am I here: Learn about the observing system. Hopefully, open dialog about some issues.

  2. Talk Outline • Issues from a modelers perspective: Please forgive my ignorance. • 1. Ocean observations • 2. Ocean Data Assimilation: 1 ≠ 2 • 3. Surface Flux Data • 4. Consistency Between Observed Data Sets • 5. Wish List

  3. Observed Data: Messy 1. Missing Data 2. Irregular/Sparse Grid 3. Sparse Coverage 4. Representative Scale 5. Not gridded Model Data: Neat (Wrong) Ocean Observations: How to Increase use by the S/I Community

  4. How to Get Around this Mismatch • 1. Provide gridded, objectively analyzed climatologies (mean annual cycle) with as few missing data as possible. Perhaps multiple versions for different anticipated uses. Doesn’t need to be exhaustive just representative: • Observational oceanographers are much better positioned to perform these tasks (with associated caveats on data) than are individual modelers of varying backgrounds and experience. • Unfortunately, it appears that ocean data assimilation in the presence of sparse observations and imperfect models will not fulfill this need for observational data in the near future (or ever). See next slide.

  5. Comparison of 20 year climatology of January surface zonal current from 3 state of the art ODA systems: Rather large differences especially near the equator. Paucity of data combined with imperfect models and imperfect knowledge of model errors make it unlikely ODA can replace observations. Can Ocean Data Assimilation Fill the Gap?

  6. Observed Solar Flux Estimates Vary Considerably Especially in the Southeastern Tropical Oceans

  7. Consistency of Observed Data Sets

  8. Simulated ENSO More Realistic if Climatology is Prescribed to be Correct and Ocean Thermocline Effects are Treated Correctly

  9. Wish List • 1. Gridded, TAO data climatological annual cycle of dynamical fields including currents at resolutions of 0.5, 1.0 and 1.5° resolution. 2. Consensus surface flux mean annual cycle climatology especially for latent and solar heat fluxes, and zonal and meridional wind stress.

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