1. Coauthors: Doug Smith 1, Shuhei Masuda 2, Magdalena Balmaseda 3, Kentaro Ando 2, Masafumi Kamachi 4 （ 1 Met Office Hadley Center, 2 JAMSTEC, 3 ECMWF, JMA/MRI ） Ocean Observing System Evaluation for Seasonal/Decadal Prediction GODAE Ocean View-GSOP-CLIVAR Workshop on Observing System Evaluation and Intercomparisons Jun. 14th, 2011, Santa Cruz, USA Yosuke Fujii (JMA/MRI)

2. 1. Observing System Evaluation for the Seasonal Forecasting * Experimental design and the results of OSE in JMA/MRI * Plan and early results of OSE using the new system in ECMWF * Comments on OSEs for the seasonal forecasting 2. OSSE for the Decadal Forecasts in the Hadley Centre 3.Adjoint Sensitivity Study of the Pacific Bottom Water Warming for Observation Planning 4.Summary

3. 1. Observing System Evaluation for the Seasonal Forecasting

4. Increase of Argo floats Jan. 2000 Jan. 2006 ARGO floats TAO/TRITON buoys Others (XBT, CTD) Because of the rapid increase of the Argo floats after 2000, the importance of the TAO/TRITON buoys seems to be relatively decreased.

5. Background in MRI/JMA JAMSTEC Argo Floats TRITON Buoys JMA/MRI Provide the obs. platforms Use obs. data Seasonal Forecasting Ocean weather Japanese Society Request of feedback! Information of the observation impacts Are the TRITON and Argo really useful for the Japanese society? Are the current observing system with TAO/TRITON and Argo effective? (Maybe they are redundant and JAMSTEC can cut the budget for one of them.) How they can improve the efficiency of the observing system? JAMSTEC needs to know …

6. JMA Seasonal Forecasting System Atmos. Data Assim. System. (JRA-25, JCDAS) JMA/MRI-CGCM Ensemble Forecasts Ocean Data Assim. System (MOVE-G) Ocean Obs. Data Atmos. Obs. Data Initial Values NINO34 SST Index Forecasts by the JMA Seasonal Forecasting System ENSO Forecast : Since Mar. 2008 Seasonal Forecast : Since Mar. 2010

7. Experimental Design of SF-OSE in JMA/MRI Assimilation （ MOVE/MRI.COM-G) → Jan. 2000-Dec. 2009 ・ ALL → Use all available data ・ XTT → withholding the TAO/TRITON buoy profiles ・ XAF → withholding the ARGO float profiles Forecast （ JMA/MRI-CGCM)→ 2004-2008 （ 20 cases ） ・ Forecasts from the assimilation results of ALL, XTT, XAF ・ Initial date ： Jan. 31st, Apr. 26th, Jul. 30th, Oct. 28th ・ Forecast length:13 months ・ Number of the ensemble members: 11 (Generated by perturbed SST OBS) ・ Flux Correction: Same as in the JMA operation. ・ Calibration: performed for ALL, XTT, XAF, separately.

8. Differences of OHC in the Assimilation Fields ALL ｰ XTT （ Impacts of Buoys)ALL ｰ XAF (Impacts of floats) The impact of float data is increasing with the increase of the number of Argo floats. In contrast, the impact of TAO/TRITON buoys are decreasing. The type of observation which mostly impacts on the assimilation fields are changed from TAO/TRITON buoys to Argo floats around 2003.

9. Differences in NINO34 Forecasts （ Examples) COBE-SST ALL XTT XAF Ensemble Mean Single Member Forecasts It is difficult to get the statistically significant impacts of ocean observations

10. Impacts on SST Indices Ratio of the Reduction of RMSEs by assimilating Buoys or Floats %

11. Impacts on SST Ratio of the Reduction of RMSEs by assimilating Buoys or Floats for each 2.5˚×2.5 ˚ grid %

12. Impacts on Sea Level Pressure (SLP) Ratio of the Reduction of RMSEs by assimilating Buoys or Floats for each 2.5˚×2.5 ˚ grid %

13. Impacts on 850-200hPa Averaged Temperature Ratio of the Reduction of RMSEs by assimilating Buoys or Floats for each 2.5˚×2.5 ˚ grid %

14. Impacts on Outgoing Longwave Radiation (OLR) Ratio of the Reduction of RMSEs by assimilating Buoys or Floats for each 2.5˚×2.5 ˚ grid %

15. Impacts on Atmospheric Indices Ratio of the Reduction of the RMSEs by assimilating Buoys or Floats %

16. Initial Date: 2006/01/31 ALL XTT XAF Reference (3M moving averaged) Reference Forecasts of Atmospheric Indices (2)

17. Initial: 2005/07/30 ALL.vs. XTT (Impacts of Buoys) ALL What Causes the difference? (1) Initial Difference (Eq. Pac. Temp.) XAF XTT X-T Sections of ensemble mean difference (ALL-XTT)

18. Initial: 2006/01/31 ALL.vs. XAF (Impacts of Floats) ALL What Causes the difference? (3) Initial Difference (Eq. Pac. Temp.) XAF XTT X-T Sections of ensemble mean difference (ALL-XAF)

19. Initial: 2007/10/28 ALL.vs. XAF (Impacts of Floats) ALL What Causes the difference? (4) Initial Difference (Eq. Pac. Temp.) XAF XTT X-T Sections of ensemble mean difference (ALL-XAF)

20. Initial: 2007/10/28 ALL.vs. XAF (Impacts of Floats) ALL What Causes the difference? (4) Initial Difference (Eq. Pac. Temp.) XAF XTT X-T Sections of ensemble mean difference (ALL-XAF)

21. Very different data assimilation system (NEMOVAR). OSEs on the impact of assimilation (all data) is performed. Experiments on impact of individual observing system (withdrawing data) ongoing. (1993 onwards for altimeter/mooring, 2000 onwards for Argo) Measure impact on: * 10days ocean forecast * Robustness of climate signals (ocean reanalysis) * Seasonal forecasting skill Initial results are encouraging, and … OSEs for seasonal forecasts at ECMWF Published results with previous system (ORA-S3) Balmaseda and Anderson 2009, OceanObs’09 No observing system is redundant. Often complementary. Problems in the Equatorial Atlantic. Plan of OSE with the new seasonal forecasting system (ORA-S4, S4)

22. Impacts of Assimilation on the new System First time that ASSIMILATING DATA IMPROVES the skill on the EQUATORIAL ATLANTIC!! (although modestly)

23. T+S+ALTI Western Equatorial PACIFIC SSTEquatorial ATLANTIC SST North Subtropical Atlantic SSTNorth Subtropical PACIFIC SST Impacts of Altimeter in Seasonal Forecast The impact of Altimeter is small but consistency positive in all regions. T+S

24. １. The targets are very stochastic. → Ensemble Forecasts ( More than 5 members are required!? ) Impacts of observations are weaken by stochastic processes. (Difficult to establish statistical significance.) It is difficult to trace impacts of observations. It is also difficult to trace back the cause of the forecast improvements to the observation (e.g., Adjoint sensitivity study is difficult to work.) How we measure the impacts? (How we can evaluate a good ensemble mean forecast with large spread?) 2. System (model and assimilation scheme) dependence Coupled model still have large mode errors and biases. A sophisticated model or data assimilation scheme may reduce the impacts of observations. (It may be able to subtracts information enough from small data.) 3. Using real observation data (not OSSE) Society want to know the impacts of observations in the real world. We have enough(?) historical data for the evaluation (We have no reason to escape to the perfect model experiments.) However, model biases can destroy the observation impacts.

25. Impact of ocean observation data on the seasonal forecasting is an important information for sustaining the observation platform, because the seasonal forecasting is one of the most influential products from the ocean observation data. OSEs for the Seasonal Forecasting (SF-OSE) should be performed using multi-systems, because of its dependence on systems. SF-OSE is included in the activities of the GODAE Ocean View OSEval task teams (as a delayed mode OSE). JMA/MRI and ECMWF will continue SF-OSE according to the recommendation of the team. Other groups can join SF-OSE. It would be great if we can exchange the information of SF-OSE, and can coordinate the plan of a multi-system activity.

26. 2. OSSE for the decadal forecasts in the Hadley Centre Dunstone et al. 2011, Submitted

27. © Crown copyright Met Office Skill in tropical Atlantic atmosphere in idealised experiments Dunstone et al, 2011, submitted Large set of idealised model experiments (>25 start dates) Monthly mean T & S ocean data is assimilated at all model locations (no atmosphere assimilation) Stippled regions are significant at the 5% level Blue box shows the main hurricane development region (MDR) JJASON seasons, Forecast years 2-6: temperature zonal wind shear precipitation MSLP

28. © Crown copyright Met Office Skill originates from sub-polar gyre Dunstone et al, 2011, submitted precipitation wind shear

29. © Crown copyright Met Office AMOC at 26 o N Dunstone et al, 2011, submitted

30. 3. Adjoint Sensitivity Study of the Pacific Bottom Water Warming for Observation Planning Published in Masuda et al. 2010, Science 329, 319-322

31. Difference of Temp. between 1985 – 1999 in the North Pacific (47˚N Line) Fukasawa et al. (2004), Kawano et al. （ 2006) Bottom-water warming: 0.003-0.01 o C Target of the Analysis Depth Longitude The Origin of the warming is traced back using the adjoint code of the OGCM (MOM3) in JAMSTEC. OGCM: GFDL MOM3, quasi-global 75 o S-80 o N horizontal res: 1 o x1 o, vertical res:45 levels Use of optimal parameters: Green’s function method is applied to some physical parameters (Toyoda et al.,20XX). Adjoint Coding: by TAMC with some modifications. 4DVAR sysnthesis (Sugiura et al. 2008) : assimilation window: 50 years (1957-2006) control variables: initial conditions 10-daily surface fluxes assimilated elements: OISST,T,S,AVISO SSH anomaly

32. Results of adjoint sensitivity analysis for a positive temperature anomaly in the abyssal North Pacific After 0-year ---A contour surface shows bottom-water warming rate when a constant change in water temperature is given---

33. Results of adjoint sensitivity analysis for a positive temperature anomaly in the abyssal North Pacific After 5-year

34. ---A contour surface shows bottom-water warming rate when a constant change in water temperature is given--- Results of adjoint sensitivity analysis for a positive temperature anomaly in the abyssal North Pacific After 15-year

35. ---A contour surface shows bottom-water warming rate when a constant change in water temperature is given--- Results of adjoint sensitivity analysis for a positive temperature anomaly in the abyssal North Pacific After 25-year

36. ---A contour surface shows bottom-water warming rate when a constant change in water temperature is given--- Results of adjoint sensitivity analysis for a positive temperature anomaly in the abyssal North Pacific After 35-year

37. ---A contour surface shows bottom-water warming rate when a constant change in water temperature is given--- Results of adjoint sensitivity analysis for a positive temperature anomaly in the abyssal North Pacific After 45-year

38. bottom-water warming rate when a change in surface heat flux is given Observation Planning for R/V Mirai Origin at the surface The adjoint sensitivity study contributes to the planning of an observational cruise in the Southern Ocean by R/V Mirai next year in JAMSTEC.

39. ４. Summary

40. 1. JMA/MRI and ECMWF will continue OSEs for the seasonal forecasting (SF-OSE) according to the recommendation of the GODAE Ocean View OSEval task team. The team recommends that other groups join SF-OSE. It is possible to exchange information of SF-OSE, and to coordinate a plan of a multi-system activity. 2. A couple of groups have started to study the impacts of observation data on the decal forecasts. They usually perform OSSE since they do not have historical data enough for OSEs. Their results show the validity of using ocean data and effectiveness of Argo network for the decadal forecasting. 3. OSE/OSSE activities (including adjoint sensitivity studies) can provide useful information for planning or improving observing system for the climate study. Thus, we can establish positive feedbacks also in this area.

41. Thank You!Thank You!

42. RMSEs for 0-12M Forecasts of SST Indices

43. Impacts on Zonal winds at 200hPa (U200) Ratio of the Reduction by assimilating Buoys or Floats for RMSEs for each 2.5˚×2.5 ˚ grid %

44. Impacts on Velocity Potential at 200hPa Ratio of the Reduction by assimilating Buoys or Floats for RMSEs for each 2.5˚×2.5 ˚ grid %

45. Impacts on Ocean Heat Contents (OHC) Ratio of the Reduction by assimilating Buoys or Floats for RMSEs for each 2.5˚×2.5 ˚ grid %

46. Initial Date: 2005/07/30 ALL XTT XAF Reference (3M moving averaged) Reference Forecasts of the Atmospheric Indices (1)

47. Initial: 2007/07/30 ALL.vs. XAF (Impacts of Floats) ALL What Causes the difference? (2) Initial Difference (Eq. Pac. Temp.) XAF XTT X-T Sections of ensemble mean difference (ALL-XAF)