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The Application of ARGO Data to BCC-GOADS

For BDCP 20 th session in Chennai. The Application of ARGO Data to BCC-GOADS. Yimin Liu *, Renhe Zhang **, Yonghong Yin ** ( * National Climate Center of China, Beijing, 100081, P. R. China, ** Chinese Academy of Meteorological Science, 100081, P. R. China . E-mail: liuym@cma.gov.)

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The Application of ARGO Data to BCC-GOADS

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  1. For BDCP 20th session in Chennai The Application of ARGO Data to BCC-GOADS Yimin Liu *, Renhe Zhang **, Yonghong Yin ** (* National Climate Center of China, Beijing, 100081, P. R. China,** Chinese Academy of Meteorological Science, 100081, P. R. China .E-mail: liuym@cma.gov.) The data sets of output of BCC-GODAS cab be found on the URLs: http://ncc.cma.gov.cn/bcc/ products /products.htm Or http://iridl.ldeo.columbia.edu/SOURCES/.CMA/.BCC/.GODAS/

  2. Outline • Brief description of BCC-GODAS • The application of Argo • Results • Conclusion

  3. 1, About BCC-GODAS BCC-GODAS, as one part of climate dynamic model operational system of NCC, has been employed to offer routinely the ocean initial fields since the October of 2001. This system mainly contains 4 parts as followings: 1.1 Pre-processing data, 1.2 Calculating global real-time wind stress, 1.3 Ocean dynamic model, 1.3 Variational analysis and interpolating.

  4. 1.1 Pre-processing observation data The available data mainly involve VOS, buoy, ARGO data ,etc. The data used in NCC_GODAS are sea surface wind and atmosphere temperature, the profile of sea temperature and salinity. Collecting data : NMC data bank, GTSPP(SS,SN,SO report files) ,Real-time. internet and exchange. Downloading from IFREMER, CDC/NOAA, NODC/NOAA, MEDS.. Decoding and formatting data. Data quality control(QCD):: remove some ineligible data  and feedback QCD information to the system: • The observation date is wrong, for example, the record is on 30th of Feb. • The observation is located on land. • The ship sails too fast, for instance, the speed of some ship nears that of airplane. • The value of observation data is too large or too small. • The gradient of observation data in the depth direction are unreasonable. • The depth of observation is not increasing.

  5. 1.2 Calculating global real-time wind stress Having been calculated in the observation points, the wind stress data are processed by means of optimal interpolation.

  6. 1.3 Ocean dynamic model The dynamic model used in this system is L30T63 OGCM Version 1.0, which is established and developed by State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG). This model has the same horizontal resolution as T63 atmosphere model, and there are 30 layers in the vertical direction, in which the first 10 layers are from 0 to 250m, the second 10 layers span from 250m to 1000m, and the last 10 layers are located deeper till 5600m. There are two vertical mixing parameterization schemes in this model. One is based on Richardson number applied in the tropic region from 30S to 30N. Another is Isopycnal mixing scheme. While using this model, we have made some improvement on the vertical mixing parameterization scheme: a transition zone to connect two areas mentioned above is designed, and the criterion of the stability depending on gradient of density in Isopycnal mixing scheme is redefined as a spatial function rather than a constant.

  7. 1.4 Variational analyses scheme Time-window plus 3DV • Open a 4 week time-window to employ as many ocean observation data as possible. • background error covariance • Described by a unified formula , Gaussian type function. • And the parameter can all be separated by (x,y),z. • A few of main control parameter, amplitude and correlation length. • Observation covariance • f the data spatial distribution factor, which varies with the density of observation data. • tm is the time factor and its value is from 1 to 0 according to the interval between assimilation time and observation time (from 0 day to 15 days). • cnt is the quality control factor.

  8. 2, The application of Argo For the sake of using Agro, some of the relevant improvement in BCC-GODAS have been made : 2.1 QCD, 2.2 Re-estimation of background errors covariance matrixes(BECM) and observation errors covariance matrixes(OECM), 2.3 Modification of variational analyses scheme

  9. 2.1 QCD Some factor have been added: • Distinguishing Argo and buoy data from XBT, The QCD criteria of Argo and buoy data are stricter than that of XBT, e.g. The eligible temperature for the previous is confined in the interval from -2.5 to 34, but, for the later –3 to 35. • The criterion vary from the South to the North. The criterion in the South are looser than in the North. • T-S profile check, if the data available.

  10. 2.2 Re-estimation of BECM and OECM 1, Modifying BECM: • Introducing the synergic assimilation of temperature and salinity. The correlation between temperature and salinity is described in BECM. 2, Modifying OECM: • Distinguishing OECM of Argo and buoy data from that of XBT. The max-value of the previous is 1.0 and The max-value of the later is 1.4. • More ranks of the difference of the temperature between the present and climate are defined. • The priority of Argo and buoy data. If there are different kinds of observation data at the same model point. Argo, buoy and XBT data are considered together, but the previous two is prior to XBT by means of weight. The proportion is design as 1.8:1, and Argo and buoy have the same weight.

  11. 2.3 Modification of variational analyses scheme In present version of BCC-GODAS, it is supposed that c is smaller thana and b. So, I can be approximately divided into two parts, one is zero order approximation involving a and b, another is higher order approximation involving c. Firstly, we solve the zero order approximation. And then we solve the higher order approximation , based on the zero order approximation, to correct the zero order approximation.

  12. 3, Results The observation data used: GTSPP data (from 1981) and Argo data (from 1998). Data source: • The data bank of Meteorological Information Center of CMA (China Meteorological administration). • IFREMER, CDC/NOAA, NODC/NOAA, MEDS(Canada) and China Argo Real-time Data Center. Some of the results of BCC-GODAS are shown as following. We choose oisst_v2, EMC/NCEP and Levitus 94 as the standard for BCC-GODAS to be compared with.

  13. The spatial distribution of observation sea temperature profiles: left one, Argo data from 2001 to the Jul. of 2003; right one, XBT and other buoy data from 1982 to the Jul. of 2003. The number in the figures is accumulated at most one time every day.

  14. Improvement on vertical mixing parameterization: top-left one, original dynamic model; top-right one, improved model; down one, Levitus94

  15. The correlation coefficient of monthly anomalies of wind stress between NCC and FSU ( from 192 to 1999, total 216 months ) Top one: Taux, max value .870, min value .170, averaged value .583. Down one: Tauy, max value .849, min value .178, averaged value .572.

  16. Annual mean climate SST: from the left to the right and from the top down, they are Levitus94, OISST_v.2, dynamic model without assimilation and NCC-GODAS with Argo, respectively.

  17. The difference of Annual mean climate SST : from the left to the right and from the top down, they are between Levitus94 and model, Levitus94 and NCC-GODAS without Argo, Levitus94 and NCC-GODAS with Argo , and NCC-GODAS with Argo and NCC-GODAS without Argo, respectively. Note: the last one has been multiplied by 20.

  18. Nino3 and Nino34 indexes(from 1993 to the Jul. of 2003 ): black line is OISST_v.2, read one is NCC-GODAS with Argo, and blue one NCC-GODAS without Argo.

  19. The time-longitude sector of SSTA (mean among 5N~5S, time from the Apr. of 2001 to the Aug. of 2003, longitude from 120E to 80W): from left to right, they are OISST_v.2, EMC/NCEP, NCC-GODAS without Argo, NCC-GODAS with Argo, respectively.

  20. The time-longitude sector of sea temperature at the depth of 62.5m (mean among 5N~5S, time from the Apr. of 2001 to the Aug. of 2003, longitude from 120E to 80W): from left to right, they are EMC/NCEP, NCC-GODAS without Argo and NCC-GODAS with Argo, respectively

  21. Global SSTA in the Jun. of 2003: top-right is NCC-GODAS with Argo, top-right is NCC-GODAS without Argo and the bottom is OISST_v.2.

  22. Root Mean Square (RMS) of SST between OISST_v.2 and NCC-GODAS (time from 2001 to the Aug. of 2003, averaged in the latitude direction). In the top one, red line is NCC-GODAS with Argo and the black without Argo. The down chart is the difference between NCC-GODAS with Argo and NCC-GODAS without Argo.

  23. Root Mean Square (RMS) of SST between OISST_v.2 and NCC-GODAS (time from 2001 to the Aug. of 2003, averaged in the longitude direction). In the top one, red line is NCC-GODAS with Argo and the black without Argo. The down chart is the difference between NCC-GODAS with Argo and NCC-GODAS without Argo.

  24. The correlation coefficient of monthly anomalies of sea temperature between NCC and Oisst_v2( or EMC) from 1981.1 to 2003.3( total 255 months). Top-left, NCC and Oisst_v2. Top-right, NCC and EMC( depth 62.5m). Bottom NCC and EMC (temperature profile).

  25. 4, Conclusion • Using Argo data to BCC-GODAS, The tendency of improvement of model climate fields is correct. But the adjustment is weak, due to the amount of Argo data is much less than that of other data from 1982 to present. • Comparing with the corresponding results of NCEP, It is illustrated that using Argo data can improve the results of BCC-GODAS in the region of the Middle Pacific, for instances SST, SSTA, Nino index, sub-surface temperature, etc. Furthermore, it is shown that BCC-GODAS benefits from Argo data in the other regions such as Atlantic Ocean, Indian Ocean and extratropical Pacific Ocean much more than in the Middle Pacific. • The above analysis and discussion indicate that not only BCC-GODAS is valid and its results are reasonable, but also the improvement of BCC-GODAS regarding Argo is successful. Therefore, this system’s results can be used as the initial fields of relevant ocean dynamic climate model system as well as a kind of reanalyzed data sets.

  26. Acknowledgment: we wish to thank IFREMER, CDC/NOAA, NODC/NOAA, MEDS(Canada) and China Argo Real-time Data Center for their observation data.

  27. Thank You !

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