Regional ocean data assimilation system in the Indian-Pacific Oceans

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1 Regional ocean data assimilation system in the Indian-Pacific Oceans Changxiang Yan, Jiang Zhu and Jiping Xie Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

2 outline Regional ocean data assimilation system model assimilation scheme for T, S assimilation scheme for SLA Evaluation of the assimilation system conclusions

3 Model HYCOM(verision 2.2): hybrid coordinate ocean model evolved from Miami isopycnic coordinate ocean model (MICOM),and upgraded by NERSC. Model domain: including Indian and Pacific oceans with the horizontal resolution of 1/3x1/3 and with vertical 28 layers, as well as a nested region with a fine resolution of 1/5x1/5. Forcing: 6-hr ERA-interim wind stress, atmospheric temperature, cloud, dew temperature. Sea level pressure, precipitation, and climatology fresh river data 1/5x1/5 1/3x1/3

Assimilation method EnOI (Ensemble Optimal Interpolation): A method that uses an ensemble from long-time model results to estimate the background error covariance matrix.

4 Assimilation method EnOI (Ensemble Optimal Interpolation): A method that uses an ensemble from long-time model results to estimate the background error covariance matrix. The analysis equation is given by X a X f b P f H T ( HP f H T R) ( Y HX 1 f b ) X a f : Analysis, X f b : Background, P :backgroud error covariance matrix, R: observation error covariance matrix, H: observed operator, Y: observation To reasonably reflect background error covariance in the monsoondominated ocean, the different ensembles in different seasons are adopted.

5 New assimilation scheme for T/S profiles Obs: obs temp. obs sal. T o (z) S o (z) Innovation: layer thickness layer temp. layer salinity. Updating: Layer temp. Layer sal. Layer thickness Layer velocity T b S b d b V bc V bt P b d o - - d a V bca T o (z) T a - - S o (z) - S a - Seawater state eqn. T a S a d a - - P a V bta V bta V bca P a

6 Temperature and salinity Difference between experiments and WOA along165e T S NO assimilation Straightforward scheme New scheme The new scheme may efficiently improve both temperature and salinity

7 RMSEs of T and S relative to independent observations WOA01:climatology, EXP0(dashed):No assimilation, EXP2T: New scheme, EXP1A:straightforward scheme The new scheme may greatly reduce RMSEs

8 SLA assimilation scheme when the SLA is assimilated, a mean dynamic topography (MDT) is needed to add to the observed SLA for a comparison with the model sea surface height (SSH). The MDT is usually taken as the long-term mean sea surface height from the model free-run. However, it probably induces some problems. RMSE of T RMSE of S SLA assimilation No assimilation SLA assimilation No assimilation After SLA assimilation,both T and S are deteriorated

9 ARGO distribution used in assimilation Difference between SSH derived from the ARGO assimilation experiment and SSH derived from the freerun experiment On one hand, after ARGO assimilation, the SSH is decreased in the ARGO locations. That means the accurate T and S results in a decreased SSH. On the other hand, when the high MDT derived from the model free-run is used, the observed SSH (=SLAo +MDT) is increased. According to the innovation term (SLAo+MDT)-SSHm the SLA assimilation increases the model SSHm to fit the observations. That leads to a worse T and S.

New MDT A new MDT is obtained by assimilating ARGO profiles into the model. Some SLA assimilation experiments using different MDTs are carried out.

10 New MDT A new MDT is obtained by assimilating ARGO profiles into the model. Some SLA assimilation experiments using different MDTs are carried out. 1 use new MDT derived from the ARGO assimilation experiment 2 use MDT derived from the model free-run; 3 NO assimilation experiment RMSE of T RMSE of S Observed MDT MDTTS MDTMOD No assimilation Observed MDT MDTTS MDTMOD No assimilation The use of new MDT may improve greatly T and S

11 Evaluation of the assimilation system assimilation experiment(aipo)is carried out Observations used for assimilation: SST,SLA, Temperature and salinity profiles (ARGO, XBT,CTD,TAO) Assimilation frequency: 7 days Ensemble size: 120 members Period:

12 Annual mean SST AIPO

13 Annual mean SSS AIPO

14 Annual mean S along 65E AIPO

15 Comparison with independent data Time series of SST RMSE West-Pacific Indian ocean

16 Comparison with independent data Independent T/S observations (blue dots) RMSE of T RMSE of S

17 Drifters (red) and sea surface current stream of AIPO in the Indian ocean in June 2010

18 Comparison with other reanalysis products Time series of SST bias averaged over Indian ocean

21 SLA variability

22 SLA along equator OBS AIPO GODAS SODA

23 Comparisons with surface current observations (TAO/RAMA) AIPO(0.07cm, 0.87) GODAS(0.1cm, 0.65) SODA(0.14cm, 0.54) OBS AIPO(0.16cm, 0.92) GODAS(0.22cm, 0.73) SODA(0.35cm, 0.58) OBS

24 Comparisons with surface current observations (TAO/RAMA) AIPO(0.1cm, 0.87) GODAS(0.19cm, 0.67) SODA(0.29cm, 0.26) OBS AIPO(0.15cm, 0.94) GODAS(0.35cm, 0.77) SODA(0.58cm, 0.37) OBS

25 Comparisons with current observations (TAO/RAMA) RMSE of u RMSE of v

26 conclusions The regional ocean data assimilation system is developed in the Indian-Pacific oceans. A different scheme is used for the T and S assimilation. A new MDT derived from the ARGO assimilation is used for the SLA assimilation The regional assimilation system is evaluated by the comparisons with other reanalysis products and independent observations, and shows a good performance.

28 EnOI: Difference between observations and model X a X f b P f H T ( HP f H T R) ( Y HX 1 f b ) obs Y Operator H Model X H is nonlinear. The nonlinearity induces some probablems.

hydrographic variability from Argo, SST and altimeter observations

Monitoring the global ocean hydrographic variability from Argo, SST and altimeter observations S. Guinehut A.-L. Dhomps G. Larnicol P.-Y. Le Traon -1- Our approach : Introduction - Outline Consists of