Observing System Simulation Experiments: Application to Indian Ocean

1. Observing System Simulation Experiments:Application to Indian Ocean G.A. Vecchi, M. Harrison, Q. Song A. Wittenberg, A. Rosati NOAA/GFDL, Princeton, NJ, USA Gabriel.A.Vecchi@noaa.gov

2. Problem ARGO • Observing system for Indian Ocean being deployed. • Multi-platform, multi-purpose system. • Can models help guide/assess the developing observing system? • First step: ability of O.S. to map temperature. • Part of larger effort: Schiller et al (2005), Ballabrera-Poy(2006), Oke and Schiller (2006), Vecchi and Harrison (2006), Lee (2006) MOORINGS From IOGOOS(2006) XBTs

3. Interannual SSTA Variability • Indian Ocean Dipole/Zonal Mode • ENSO precursive signals and responses • SST relationships to monsoon and African rainfall (subsurface precursors?) • Decadal and longer timescales • Explore…what else is there? • Model development/tuning/evaluation

4. Subseasonal SSTA Variability • MJO • Relationship to ENSO? • Weather in mid- and high-latitudes • Monsoon breaks over India • Connection to subseasonal SST signals. Predictive? • Explore…what else is there? • Model development/tuning/evaluation

5. Observed cooling event - Jan 1999 event Harrison and Vecchi (2001, GRL) - see also Duvel et al (2004), Saji et al (2005)

6. Subsurface temperature preconditioning in coupled model 0-100m anomalous temperature preceding cooling events in CM2.1

7. Sub-sample OGCM • MOM-2, Indian Ocean, 0.33-0.5 deg zonal, 0.33 deg meridional, 27 levels (10 in upper 100m). • ECMWF 12hr winds 1986-2003; QuickScat daily 0.25x0.25 2000-2003. • Bulk sensible and latent HF (NCEP airt) • Radiation from NCEP Reanalysis (daily) • Assess impact of variability not explicitly represented in OGCM. • Subdaily “noise” • Produce monthly temperature analysis for various O.S. configurations. • Use simple “O.I.” technique: 5° zonal, 3° meridional scales. • Evaluate ability of each O.S. configuration to reproduce model temperature anomalies.

8. Potential Sources of Error • Aliasing of: • sub-seasonal variability (also a signal) • sub-daily variability (only to instantaneous profiles, not moorings) • eddies/sub-gridscale variability. • ARGO float spatial aliasing: • surface divergence regions are interesting regions. • We ignore instrument error, drift, biofouling, vandalism, failures, etc.

9. Sub-daily variability observed from TAO Moorings For subsampling experiments add Gaussian noise. Amplitude = 8m * dT/dz Split energy 50/50 between vertically coherent and incoherent noise. Inside mixed layer use 0.2C Gaussian Tsd = T - <T>24hr hsd = Tsd/ <dT/dz>24hr

10. For sub-sampling experiments add Gaussian noise. Amplitude = 8m * dT/dz Split energy 50/50 between vertically coherent and incoherent noise.

12. Impact of 5-day ARGO Sampling RMS Error 100-m monthly Tanom. 5-day sampling gives: • More samples per float per month • Fewer floats per area in divergent flow • Net Effect: reduced quality of monthly and little improvement in subseasonal

13. Impact of various components

14. Subseasonal variability

15. Limitations of Study • We ignore instrument error, drift, biofouling, vandalism, failures, etc. • Model errors and biases….multi-model. • Simple O.I. technique: • Do not use dynamically-based ODA. • Do not include satellite altimetry information. • Do not consider impact on forecasts explicitly.

16. Summary • Models used to assess proposed Indian Ocean Observing System. • Appears adequate for mapping seasonal to interannual temperature. • Except western Arabian Sea. • Proposed moorings spans region of active intraseasonal variability. • Particularly south I.O. Thermocline ridge. • Subdaily variability can cloud subseasonal variability in profiles. • This study and others have fed back into system design: • IX-1 XBT line to be run weekly. • Recommend 10-day ARGO sampling. • Moorings important on equator and t’cline ridge.

17. Ongoing/future work • How effective is proposed observing system for forecast model initialization? • How do satellite data complement in situ components? • Extension of OSSE assessment to global ARGO.

18. Need to account for eddies • Subsample HIM 1/6 degree global run.

19. Eddies add more variability to ARGO trajectories than do variations in forcing.

20. IOD Forecasts Initialized Dec. There are “stochastic” and “deterministic” dipoles.

21. Perfect model/perfect obs forecasts of “Predictable” dipole in coupled run East Pole SST Song et al (2006, in prep.) West Pole SST Find IOD in Control run. Run 10-member ensemble. This IOD preconditioned by ocean state.

22. East Pole SST Perfect model/perfect obs forecasts of “Unpredictable” dipole in coupled run Control run member West Pole SST Find IOD in Control run. Run 10-member ensemble. This IOD driven by stochastic atmospheric variability. Song et al (2006, in prep.)