1. Ocean State Estimation by 4D-VAR Data Assimilation using ARGO Data S
Ocean State Estimation by 4D-VAR Data Assimilation using ARGO Data S. Masuda, T.Awaji,N.Sugiura,H.Igarashi,Y.Ishikawa,Q.Jiang,K.Takeuchi
Four-dimensional Variational (4D-VAR) ocean data assimilation system is used to produce time-varying model/data syntheses.
The result is a dynamically self-consistent estimate of ocean state.
It has greater information and forecast potential than do models or data alone (e.g., Stammer et al., 2002; Masuda et al., 2003).

2. Data Assimilation System
Masuda et al. (2003)

3. Cost Function

4. Assimilated Data Surface data Subsurface data ・Reynolds SST (10daily)
・FNMOC; T, S (monthly)
・TAO/TRITON/PIRATA; T, S (monthly)
from NOAA
・ARGO buoy; T, S (monthly)
from Coriolis Data Centre
・WOD98 climatologies; T, S (monthly)

5. Subsurface Data without ARGO Buoys T S

6. Subsurface Data with ARGO Buoys T S

7. Subsurface Data with ARGO Buoys T S

8. Data Assimilation Experiment
Assimilation period;
EXP.1
Simulation
EXP.2
Assimilation with whole the data
(FNMOC,ARGO,…)
EXP.3
Assimilation with only Reynolds SST and
WOD98

9. Salinity Distribution at 26.8sq (Jan;2002)
Exp.1:Simulation
Exp.2:Assimilation
Exp.2-Exp.1:Difference

10. Salinity Distribution at 26.8sq (Jan;2002)
The NPIW water extends southward
Exp.1:Simulation
Exp.2:Assimilation
～O(0.1psu)
Exp.2-Exp.1:Difference

11. Subarctic Water Mass Distribution at 47oN (Jul;2002)
Exp.1:Simulation
Exp.2:Assimilation

12. Subarctic Water Mass Distribution at 47oN (Jul;2002)
Dichothermal
structure
Mesothermal
structure
Exp.1:Simulation
Exp.2:Assimilation
Oyashio
Recirculation

13. WOCE Revisit Data at 47oN q 47ºN P1 Revisit（1999） Suga et al. (2002)26 sq 27
160ºE
160ºW
180ºE
140ºW
Dichothermal
structure
Mesothermal
structure
Suga et al. (2002)

14. Estimated Subsurface Circulation in the North Pacific Ocean (Jul; 2002)
26.2sq
26.6sq

15. Estimated Subsurface Circulation in the North Pacific Ocean (Jul; 2002)
26.2sq
26.6sq

16. HydroBase Climatological Map at 26.6sq
HydroBase po.temp & circulation
Suga et al. (2003)

17. Estimated Northward Heat Transport (2000-2002 annual mean)
Exp.1
Exp.1
Exp.2
Exp.2

18. Impact of Interannual Subsurface Observations (ARGO,FNMOC…)
Exp.2(assimilation) – Exp.3(assimilation without
ARGO, FNMOC…)

19. Impact of Interannual Subsurface Observations (ARGO,FNMOC…)
Exp.2 – Exp.3

20. Impact of Interannual Subsurface Observations (vertical section 30oW)
26.8sq
Exp.2 – Exp.3

21. Concluding Remarks Estimates of the dynamical state for 2000-2002 .
Dynamically consistent time-varying data set with realistic features (e.g. NPIW, subarctic water masses) is obtained.
The impacts of ARGO, FNMOC…data are striking particularly in the North Atlantic region (and the Bering Sea) at 26.8sq in this assimilation experiment.
The ARGO buoy is promising. (We hope data coverage for all over the world ocean like for the North Atlantic.)

22. Future Work Long-term assimilation experiment
Information of recent observations can improve the quality of reanalysis data for the past.

24. Future Work 2
Is velocity information from ARGO buoys available as an assimilation element?
Iwao et al. (2003)

25. Time Change of the Costs

26. Computational Efficiency
31CPU, 57.4GFOPS on the Earth Simulator.
1 iteration (3.5yr) ～４hours　　　　

27. Longitude-time Distribution of T at 300m (40oN)
Observed data Simulation Estimated result 
2002
2000
Pacific Atlantic
year

28. SST Distribution (Jul;2002)

29. Net Heat Flux Distribution (Jan;2002)
Difference

30. Net Heat Flux Distribution (Jul;2002)
Difference

31. Subarctic Water Mass Distribution at 47oN (Jul;2002)
Dichothermal
structure
Exp.2:Assimilation
Mesothermal

32. Estimated Subsurface Circulation at 26.6sq (North Pacific Ocean; 2002)
Exp.2:January
Exp.2July

33. Estimated Subsurface Circulation at 26.8sq (North Pacific Ocean; 2002)
Exp.2:January
Exp.2July

34. Estimated Northward Heat Transport (24oN; 2000-2002)
Exp.1
Exp.1
Exp.2
Exp.2

35. Previous study using our 4D-VAR ocean data assimilation system
Target: Estimates of the dynamical state for
Climatological Seasonal Cycle
　 (a)Dynamically consistent time-varying data set
with realistic features of the global ocean
circulation.
(b)Sensitivity experiment for the NPIW (water
mass analysis).
(c)Manifestation of the advantage of a 4D-VAR
method ( a nudging method).
 Sugiura et al. (PCFD,2003), Masuda et al. (GRL,2003),
Awaji et al. (JO,2003)

36. Results of ODA experiment for climatological seasonal cycle
(b)Sensitivity experiment
(a)Reanalysis data set
Masuda et al. (2003)

37. Experment  
WOD01
Reanalysis
MOM3

38. SST distribution in the Pacific region

39. Nino3 SST
Assimilation
rmsd:0.69K
Simulation
rmsd:1.60K

40. Net Heat Flux distribution in the Pacific region

41. Wind stress curl distribution in the Pacific region

42. Longitude-time distribution of SST
Simulation Observation Assimilation

43. Longitude-time distribution of the various variables (Assimilation)

44. Latitude-time distribution of the SSH anomaly
Averaged 120E-170E; as heat content of off-equatorial region

45. Latitude-time distribution of the wind curl
Averaged 120E-170E; Ekman pumping of off-equatorial region
　Sensitivity experiment !!

46. Longitude-time distribution of the equatorial Kelvin waves and of the 1st mode equatorial Rossby waves

47. Velocity & Temperature distribution (onset period)

48. Coupled Data assimilation （RR２００２）
Atmospheric data assimilation system
(now constructing)
Ocean data assimilation system + ice model
CFES coupling
system

49. High resolution data assimilation (Assimilation)
Fwd high resolution
Bkw low resolution
(e.g., Kohl and Willebrand, 2003) ?
Bkw high resolution
Cost averaging

50. Longitude-time distribution of the equatorial Kelvin waves and of the 1st mode equatorial Rossby waves
Eq 　5S(inverted) Eq