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Evaluation of the tropical variability from the Beijing Climate Center’s real-time operational global Ocean Data Assimilation System

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Abstract

The second-generation Global Ocean Data Assimilation System of the Beijing Climate Center (BCC GODAS2.0) has been run daily in a pre-operational mode. It spans the period 1990 to the present day. The goal of this paper is to introduce the main components and to evaluate BCC GODAS2.0 for the user community. BCC GODAS2.0 consists of an observational data preprocess, ocean data quality control system, a three-dimensional variational (3DVAR) data assimilation, and global ocean circulation model [Modular Ocean Model 4 (MOM4)]. MOM4 is driven by six-hourly fluxes from the National Centers for Environmental Prediction. Satellite altimetry data, SST, and in-situ temperature and salinity data are assimilated in real time. The monthly results from the BCC GODAS2.0 reanalysis are compared and assessed with observations for 1990–2011. The climatology of the mixed layer depth of BCC GODAS2.0 is generally in agreement with that ofWorld Ocean Atlas 2001. The modeled sea level variations in the tropical Pacific are consistent with observations from satellite altimetry on interannual to decadal time scales. Performances in predicting variations in the SST using BCC GODAS2.0 are evaluated. The standard deviation of the SST in BCC GODAS2.0 agrees well with observations in the tropical Pacific. BCC GODAS2.0 is able to capture the main features of El Ni˜no Modoki I and Modoki II, which have different impacts on rainfall in southern China. In addition, the relationships between the Indian Ocean and the two types of El Ni˜no Modoki are also reproduced.

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References

  • Alves, O., D. Hudson, M. Balmaseda, and L. Shi, 2011: Seasonal and decadal prediction. Operational Oceanography in the 21st Century, A. Schiller and G. B. Brassington, Eds., Springer, Netherlands, 513–542.

    Chapter  Google Scholar 

  • Ashok, K., S. K. Behera, S. A. Rao, H. Y. Weng, and T. Yamagata, 2007: El Ni˜no Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, doi: 10.1029/2006JC003798.

    Article  Google Scholar 

  • Balmaseda, M. A., K. Mogensen, and A. T. Weaver, 2013: Evaluation of the ECMWF ocean reanalysis system ORAS4. Quart. J. Roy. Meteor. Soc., 139, 1132–1161.

    Article  Google Scholar 

  • Behringer, D. W., M. Ji, and A. Leetmaa, 1998: An improved coupled model for ENSO prediction and implications for ocean initialization. Part I: The ocean data assimilation system. Mon. Wea. Rev., 126, 1013–1021.

    Article  Google Scholar 

  • Counillon, F., I. Bethke, N. Keenlyside, M. Bentsen, L. Bertino, and F. Zheng, 2014: Seasonal-to-decadal predictions with the ensemble Kalman filter and the Norwegian Earth System Model: A twin experiment. Tellus A, 66, 21074.

    Article  Google Scholar 

  • de Boyer Montégut., C., G. Madec, A. S. Fischer, A. Lazar, and D. Iudicone, 2004: Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology. J. Geophys. Res., 109, C12003, doi: 10.1029/2004JC002378.

    Article  Google Scholar 

  • Dong, S. F., S. T. Gille, and J. Sprintall, 2007: An assessment of the Southern Ocean mixed layer heat budget. J. Climate, 20, 4425–4442, doi: 10.1175/JCLI4259.1.

    Article  Google Scholar 

  • Dong, S. F., S. L. Garzoli, and M. Baringer, 2009: An assessment of the seasonal mixed layer salinity budget in the Southern Ocean. J. Geophys. Res., 114, C12001, doi: 10.1029/2008JC005258.

    Article  Google Scholar 

  • D’Ortenzio, F., D. Iudicone, C. de Boyer Montegut, P. Testor, D. Antoine, S. Marullo, R. Santoleri, and G. Madec, 2005: Seasonal variability of the mixed layer depth in the Mediterranean Sea as derived from in situ profiles. Geophys. Res. Lett., 32, L12605, doi: 10.1029/2005GL022463.

    Article  Google Scholar 

  • Fu, W. W., J. Zhu, and C. X. Yan, 2009a: A comparison between 3DVAR and EnOI techniques for satellite altimetry data assimilation. Ocean Modelling, 26, 206–216.

    Article  Google Scholar 

  • Fu, W. W., J. Zhu, C. X. Yan, and H. L. Liu, 2009b: Toward a global ocean data assimilation system based on ensemble optimum interpolation: Altimetry data assimilation experiment. Ocean Dynamics, 59, 587–602.

    Article  Google Scholar 

  • Griffies, S. M., M. J. Harrison, R. C. Pacanowski, and A. Rosati, 2003: A technical guide to MOM4. NOAA/Geophysical Fluid Dynamics Laboratory, GFDL Ocean Group Tech. Rep. No. 5, 371 pp.

    Google Scholar 

  • Griffies, S. M., and Coauthors, 2005: Formulation of an ocean model for global climate simulations. Ocean Science, 1, 45–79.

    Article  Google Scholar 

  • Han, G. J., H. L. Fu, X. F. Zhang, W. Li, X. R. Wu, X. D. Wang, and L. X. Zhang, 2013: A global ocean reanalysis product in the China Ocean Reanalysis (CORA) project. Adv. Atmos. Sci., 30, 1621–1631, doi: 10.1007/s00376-013-2198-9.

    Article  Google Scholar 

  • Huang, B. Y., Y. Xue, and D. W. Behringer, 2008: Impacts of Argo salinity in NCEP Global Ocean Data Assimilation System: The tropical Indian Ocean. J. Geophys. Res., 113, C08002, doi: 10.1029/2007JC004388.

    Google Scholar 

  • Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D: Nonlinear Phenomena, 230, 112–126.

    Article  Google Scholar 

  • Kao, H.-Y., and J.-Y. Yu, 2009: Contrasting Eastern-Pacific and Central-Pacific types of ENSO. J. Climate, 22, 615–632.

    Article  Google Scholar 

  • Kug, J.-S., F.-F. Jin, and S.-I. An, 2009: Two types of El Ni˜no events: Cold tongue El Ni˜no and warm pool El Ni˜no. J. Climate, 22, 1499–1515.

    Article  Google Scholar 

  • Larkin, N. K., and D. E. Harrison, 2005: Global seasonal temperature and precipitation anomalies during El Ni˜no autumn and winter. Geophys. Res. Lett., 32, L16705, doi: 10.1029/2005GL022860.

    Article  Google Scholar 

  • Levitus, S., 1982: Climatological atlas of the world ocean. NOAA/ERL GFDL Professional Paper 13, Princeton, N. J., 173 pp.

    Google Scholar 

  • Liu, Y. M., R. H. Zhang, Y. H. Yin, and T. Niu, 2005: The application of ARGO data to the global ocean data assimilation operational system of NCC. Acta Meteorologica Sinica, 19, 355–365.

    Google Scholar 

  • Merrifield, M. A., and M. E. Maltrud, 2011: Regional sea level trends due to a Pacific trade wind intensification. Geophys. Res. Lett., 38, L21605, doi: 10.1029/2011GL049576.

    Article  Google Scholar 

  • Merrifield, M. A., P. R. Thompson, and M. Lander, 2012: Multidecadal sea level anomalies and trends in the western tropical Pacific. Geophys. Res. Lett., 39, L13602, doi: 10.1029/2012GL052032.

    Article  Google Scholar 

  • Moore, J. K., K. Lindsay, S. C. Doney, M. C. Long, and K. Misumi, 2013: Marine ecosystem dynamics and biogeochemical cycling in the community earth system model [CESM1(BGC)]: Comparison of the 1990s with the 2090s under the RCP4. 5 and RCP8. 5 scenarios. J. Climate, 26, 9291–9312.

    Article  Google Scholar 

  • Nidheesh, A. G., M. Lengaigne, J. Vialard, A. S. Unnikrishnan, and H. Dayan, 2013: Decadal and long-term sea level variability in the tropical Indo-Pacific Ocean. Climate Dyn., 41, 381–402, doi: 10.1007/s00382-012-1463-4.

    Article  Google Scholar 

  • Qiu, B., and S. M. Chen, 2012: Multidecadal sea level and gyre circulation variability in the Northwestern Tropical Pacific Ocean. J. Phys. Oceanogr., 42, 193–206.

    Article  Google Scholar 

  • Ratheesh, S., R. Sharma, and S. Basu, 2014: An EnOI assimilation of satellite data in an Indian Ocean circulation model. IEEE Transactions on Geoscience and Remote Sensing, 52, 4106–4111.

    Article  Google Scholar 

  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, doi: 10.1029/2002JD002670.

    Article  Google Scholar 

  • Ren, L., K. Speer, and E. P. Chassignet, 2011: The mixed layer salinity budget and sea ice in the Southern Ocean. J. Geophys. Res., 116, C08031, doi: 10.1029/2010JC006634.

  • Reynolds, R. W., and D. C. Marsico, 1993: An improved real-time global sea surface temperature analysis. J. Climate, 6, 114–119.

    Article  Google Scholar 

  • Reynolds, R. W., T. M. Smith, C. Y. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolutionblended analyses for sea surface temperature. J. Climate, 20, 5473–5496.

    Article  Google Scholar 

  • Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole mode in the tropical Indian Ocean. Nature, 401, 360–363.

    Google Scholar 

  • Sakov, P., F. Counillon, L. Bertino, K. A. Lisæter, P. R. Oke, and A. Korablev, 2012: TOPAZ4: An ocean-sea ice data assimilation system for the North Atlantic and Arctic. Ocean Science Discussions, 9, 1519–1575.

    Article  Google Scholar 

  • Sallée, J. B., K. Speer, R. Morrow, and R. Lumpkin, 2008: An estimate of Lagrangian eddy statistics and diffusion in the mixed layer of the Southern Ocean. J. Mar. Res., 66(4), 441–463.

    Article  Google Scholar 

  • Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA’s historical merged land-ocean surface temperature analysis (1880–2006). J. Climate, 21, 2283–2296.

    Article  Google Scholar 

  • Talley, L. D., 1993: Distribution and formation of North Pacific intermediate water. J. Phys. Oceanogr., 23, 517–537.

    Article  Google Scholar 

  • Wang, C. Z., and X. Wang, 2013: Classifying El Ni˜no Modoki I and II by different impacts on rainfall in Southern China and typhoon tracks. J. Climate, 26, 1322–1338.

    Article  Google Scholar 

  • Wang, D. X., Y. H. Qin, X. J. Xiao, Z. Q. Zhang, and X. Y. Wu, 2012a: El Ni˜no and El Ni˜no Modoki variability based on a new ocean reanalysis. Ocean Dynamics, 62, 1311–1322.

    Article  Google Scholar 

  • Wang, D. X., Y. H. Qin, X. J. Xiao, Z. Q. Zhang, and F. M. Wu, 2012b: Preliminary results of a new global ocean reanalysis. Chinese Science Bulletin, 57, 3509–3517.

    Article  Google Scholar 

  • Wang, X., D. Wang, and W. Zhou, 2009: Decadal variability of twentieth-century El Ni˜no and La Ni˜na occurrence from observations and IPCC AR4 coupled models. Geophysical research letters, 36, L11701.

    Article  Google Scholar 

  • Wang, X., and C. Z. Wang, 2014: Different impacts of various El Ni˜no events on the Indian Ocean Dipole. Climate Dyn., 42, 991–1005.

    Article  Google Scholar 

  • Wu, T.W., and Coauthors, 2013: Progress in developing the shortrange operational climate prediction system of China national climate center. Journal of Applied Meteorological Science, 24, 533–543. (in Chinese)

    Google Scholar 

  • Wu, T.W., and Coauthors, 2014: An overview of BCC climate system model development and application for climate change studies. Journal of Meteorological Research, 28, 34–56.

    Google Scholar 

  • Xiao, X. J., D. X. Wang, C. X. Yan, and J. Zhu, 2008: Evaluation of a 3dVAR system for the South China Sea. Progress in Natural Science, 18, 547–554.

    Article  Google Scholar 

  • Xue, Y., B. Y. Huang, Z.-Z. Hu, A. Kumar, C. H. Wen, D. Behringer, and S. Nadiga, 2011: An assessment of oceanic variability in the NCEP climate forecast system reanalysis. Climate Dyn., 37, 2511–2539.

    Article  Google Scholar 

  • Yan, C. X., J. Zhu, R. F. Li, and G. Q. Zhou, 2004: Roles of vertical correlations of background error and T–S relations in estimation of temperature and salinity profiles from sea surface dynamic height. J. Geophys. Res., 109, C08010, doi: 10.1029/2003JC002224.

    Google Scholar 

  • Yu, J.-Y., and H.-Y. Kao, 2007: Decadal changes of ENSO persistence barrier in SST and ocean heat content indices: 1958–2001. J. Geophys. Res., 112, D13106, doi: 10.1029/2006JD007654.

    Google Scholar 

  • Zhang, Q., and Y.-H. Ding, 2001: Decadal climate change and ENSO cycle. Acta Meteorologica Sinica, 59, 157–172. (in Chinese)

    Google Scholar 

  • Zhang, Q., Y. Guan, and H.-J. Yang, 2008: ENSO amplitude change in observation and coupled models. Adv. Atmos. Sci., 25, 361–366, doi: 10.1007/s00376-008-0361-5.

    Article  Google Scholar 

  • Zhang, X. B., and J. A. Church, 2012: Sea level trends, interannual and decadal variability in the Pacific Ocean. Geophys. Res. Lett., 39, L21701, doi: 10.1029/2012GL053240.

    Google Scholar 

  • Zheng, F., and J. Zhu, 2015: Roles of initial ocean surface and subsurface states on successfully predicting 2006–2007 El Ni˜no with an intermediate coupled model. Ocean Science, 11, 187–194, doi: 10.5194/os-11-187-2015.

    Article  Google Scholar 

  • Zheng, F., J. Zhu, H. Wang, and R.-H. Zhang, 2009: Ensemble hindcasts of ENSO events over the past 120 years using a large number of ensembles. Adv. Atmos. Sci., 26(2), 359–372, doi: 10.1007/s00376-009-0359-7.

    Article  Google Scholar 

  • Zhou, G. Q., W. W. Fu, J. Zhu, and H. J. Wang, 2004: The impact of location-dependent correlation scales in ocean data assimilation. Geophys. Res. Lett., 31, L21306, doi: 10.1029/2004GL020579.

    Article  Google Scholar 

  • Zhuang, W., B. Qiu, and Y. Du, 2013: Low-frequency western Pacific Ocean sea level and circulation changes due to the connectivity of the Philippine Archipelago. J. Geophys. Res., 118, 6759–6773.

    Article  Google Scholar 

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Zhou, W., Chen, M., Zhuang, W. et al. Evaluation of the tropical variability from the Beijing Climate Center’s real-time operational global Ocean Data Assimilation System. Adv. Atmos. Sci. 33, 208–220 (2016). https://doi.org/10.1007/s00376-015-4282-9

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