Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

Comparative Study on the Seasonal Predictability Dependency of Boreal Winter 2m Temperature and Sea Surface Temperature on CGCM Initial Conditions

접합대순환모형의 초기조건 생산방법에 따른 북반구 겨울철 기온과 해수면 온도의 계절 예측성 비교 연구

  • Ahn, Joong-Bae (Division of Earth Environmental System, Pusan National University) ;
  • Lee, Joonlee (Division of Earth Environmental System, Pusan National University)
  • 안중배 (부산대학교 지구환경시스템학부) ;
  • 이준리 (부산대학교 지구환경시스템학부)
  • Received : 2015.01.20
  • Accepted : 2015.02.25
  • Published : 2015.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

The impact of land and ocean initial condition on coupled general circulation model seasonal predictability is assessed in this study. The CGCM used here is Pusan National University Couple General Circulation Model (PNU CGCM). The seasonal predictability of the surface air temperature and ocean potential temperature for boreal winter are evaluated with 4 different experiments which are combinations of 2 types of land initial conditions (AMI and CMI) and 2 types of ocean initial conditions (DA and noDA). EXP1 is the experiment using climatological land initial condition and ocean initial condition to which the data assimilation technique is not applied. EXP2 is same with EXP1 but used ocean data assimilation applied ocean initial condition. EXP3 is same with EXP1 but AMIP-type land initial condition is used for this experiment. EXP4 is the experiment using the AMIP-type land initial condition and data assimilated ocean initial condition. By comparing these 4 experiments, it is revealed that the impact of data assimilated ocean initial is dominant compared to AMIP-type land initial condition for seasonal predictability of CGCM. The spatial and temporal patterns of EXP2 and EXP4 to which the data assimilation technique is applied were improved compared to the others (EXP1 and EXP3) in boreal winter 2m temperature and sea surface temperature prediction.

Keywords

References

  1. Ahn, J. B., and J. A. Lee, 2001: Numerical study on the role of sea-ice using ocean gerneral cirulation model. J. Korean Soc. Oceanogr., 6, 225-233.
  2. Ahn, J. B., J. L. Lee, and E. S. Im, 2012(a): The reproducibility of surface air temperature over South Korea using dynamical downscaling and statistical correction. J. Meteor. Soc. Japan, 90, 493-507. https://doi.org/10.2151/jmsj.2012-404
  3. Ahn, J. B., S. B. Lee, and S. B. Ryoo, 2012(b): Development of 12-month ensemble prediction system using PNU CGCM V1.1. Atmos. Korean Meteor. Soc., 22, 455-464.
  4. Ahn, J. B., Y. H. Yoon, E. H. Cho, and H. R. Oh, 2005: A study of global ocean data assimilation using VAF. J. Korean. Soc. Oceanogr., 10, 69-78.
  5. Alves, O., M. A. Balmaseda, D. Anderson, and T. Stockdale, 2004: Sensitivity of dynamical seasonal forecasts to ocean initial conditions. Quart. J. Roy. Meteor. Soc., 130, 647-667. https://doi.org/10.1256/qj.03.25
  6. Anderson, J. L., and J. J. Ploshay, 2000: Impact of initial conditions on seasonal simulations with an atmospheric general circulation model. Quart. J. Roy. Meteor. Soc., 126, 2241-2264. https://doi.org/10.1256/smsqj.56712
  7. Balmaseda, M., and D. Anderson, 2009: Impact of initialization strategies and observations on seasonal forecast skill. Geophys. Res. Lett., 36, L01701, doi:10.1029/2008GL035561.
  8. 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. https://doi.org/10.1175/1520-0493(1998)126<1013:AICMFE>2.0.CO;2
  9. Bennett, A. F., 2002: Inverse modeling of the ocean and atmosphere, Cambridge University Press.
  10. Bonan, G. B., 1998: The land surface climatology of the NCAR Land Surface Model (LSM 1.0) coupled to the NCAR Community Climate Model (CCM3). J. Climate, 11, 1307-1326. https://doi.org/10.1175/1520-0442(1998)011<1307:TLSCOT>2.0.CO;2
  11. Brankovic, C., T. N. Palmer, F. Molteni, S. Tibaldi, and U. Cubasch, 1990 : Extended?range predictions with ECMWF models: Time?lagged ensemble forecasting. Quart. J. Roy. Meteor. Soc., 116, 867-912. https://doi.org/10.1002/qj.49711649405
  12. Charney, J. G., 1951: Dynamical forecasting by numerical process. Compendium of Meteorology, T. F. Malone, Ed., Amer. Meteor. Soc., 470-482.
  13. Chen, M., W. Wang, and A. Kumar, 2010: Prediction of monthly-mean temperature: The roles of atmospheric and land initial conditions and sea surface temperature. J. Climate, 23, 717-725. https://doi.org/10.1175/2009JCLI3090.1
  14. Collins, M., S. F. B. Tett, and C. Cooper, 2001: The internal climate variability of HadCM3, a version of the Hadley Centre coupled model without flux adjustments. Clim. Dynam., 17, 61-81. https://doi.org/10.1007/s003820000094
  15. Dommenget, D., and D. Stammer, 2004: Assessing ENSO simulations and predictions using adjoint ocean state estimation. J. Climate, 17, 4301-4315. https://doi.org/10.1175/3211.1
  16. Gates, W. L., 1992: AMIP: The atmospheric model intercomparison project. Bull. Amer. Meteor. Soc., 73, 1962-1970. https://doi.org/10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2
  17. Ghil, M., and P. Malanotte-Rizzoli, 1991: Data assimilation in meteorology and oceanography. Adv. Geophys., 33, 141-266. https://doi.org/10.1016/S0065-2687(08)60442-2
  18. Gregory, D., and Coauthors, 2000: Revision of convection, radiation and cloud schemes in the ECMWF Integrated Forecasting System. Quart. J. Roy. Meteor. Soc., 126, 1685-1710. https://doi.org/10.1002/qj.49712656607
  19. Guilyardi, E., and Coauthors, 2004: Representing El Nino in coupled ocean-atmosphere GCMs: the dominant role of the atmospheric component. J. Climate, 17, 4623-4629. https://doi.org/10.1175/JCLI-3260.1
  20. Hendon, H. H., M. C. Wheeler, and C. Zhang, 2007: Seasonal dependence of the MJO-ENSO relationship. J. Climate, 20, 531-543. https://doi.org/10.1175/JCLI4003.1
  21. Houtekamer, P. L., and J. Derome, 1995 : Methods for ensemble prediction. Mon. Wea. Rev., 123, 2181-2196. https://doi.org/10.1175/1520-0493(1995)123<2181:MFEP>2.0.CO;2
  22. Huang, X. Y., 2000: Variational analysis using spatial filters. Mon. Wea. Rev., 128, 2588-2600. https://doi.org/10.1175/1520-0493(2000)128<2588:VAUSF>2.0.CO;2
  23. Hurrel, J., J. J. Hack, B. A. Boville, D. Williamson, and J. T. Kiehl, 1998: The dynamical simulation of the NCAR Community Climate Model version 3 (CCM3). J. Climate, 11, 1207-1236. https://doi.org/10.1175/1520-0442(1998)011<1207:TDSOTN>2.0.CO;2
  24. IPCC, 1990: Climate Change: The IPCC Scientific Assessment Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge.
  25. IPCC, 2007: Climate Change 2007-The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B Averyt, M. Tignor, and H. L Miller (eds.)]. Cambridge University Press, Cambridge, United?Kingdomand New York, NY, USA, 634, 647, 793-795.
  26. Ji, M., D. W. Behringer, and A. Leetmaa, 1998 : An improved coupled model for ENSO prediction and implications for ocean initialization. Part II : The coupled model. Mon. Wea. Rev., 126, 1022-1034. https://doi.org/10.1175/1520-0493(1998)126<1022:AICMFE>2.0.CO;2
  27. Kanamitsu, M., W. Ebisuzaki, J. Woollen, S. K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEPDEO AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631-1643. https://doi.org/10.1175/BAMS-83-11-1631
  28. Kessler, W. S., and R. Kleeman, 2000: Rectification of the Madden-Julian oscillation into the ENSO cycle. J. Climate, 13, 3560-3575. https://doi.org/10.1175/1520-0442(2000)013<3560:ROTMJO>2.0.CO;2
  29. Kharin, V. V., F. W. Zwiers, and N. Gagnon, 2001: Skill of seasonal hindcasts as a function of the ensemble size. Clim. Dynam., 17, 835-843. https://doi.org/10.1007/s003820100149
  30. Kiehl, J. T., and P. R. Gent, 2004: The community climate system model, version 2. J. Climate, 17, 3666-3682. https://doi.org/10.1175/1520-0442(2004)017<3666:TCCSMV>2.0.CO;2
  31. Kiehl, J. T., J. J. Hack, G. B. Bonan, B. A. Boville, B. P. Briegleb, D. L. Williamson, and P. J. Rasch, 1996: Description of the NCAR Community Climate Model (CCM3). NCAR Tech. Note. NCAR/TN-420+STR, 152 pp.
  32. Kim, H. M., P. J. Webster, and J. A. Curry, 2012: Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter. Clim. Dynam., 39, 2957-2973. https://doi.org/10.1007/s00382-012-1364-6
  33. Koster, R. D., and Coauthors, 2010: Contribution of land surface initialization to subseasonal forecast skill: First results from a multi-model experiment. Geophys. Res. Lett., 37.
  34. Koster, R. D., and Coauthors, 2011: The second phase of the global land-atmosphere coupling experiment: soil moisture contributions to subseasonal forecast skill. J. Hydro meteor., 12, 805-822.
  35. Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130-148. https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2
  36. Lu, C., H. Yuan, B. E. Schwartz, and S. G. Benjamin, 2007: Short-range numerical weather prediction using time-lagged ensembles. Wea. Forecasting, 22, 580-595. https://doi.org/10.1175/WAF999.1
  37. Molteni, F., and Coauthors, 2011: The new ECMWF seasonal forecast system (System 4). ECMWF Technical Memorandum 656.
  38. Pacanowski, R. C., and S. M. Griffies, 1998: MOM 3.0 Manual. NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, USA 08542.
  39. Palmer, T., and Coauthors, 2004: Development of a European multimodel ensemble system for seasonal-tointerannual prediction (DEME-TER). Bull. Amer. Meteor. Soc., 85, 853-872. https://doi.org/10.1175/BAMS-85-6-853
  40. 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, No. D14, 4407. https://doi.org/10.1029/2002JD002670
  41. Reichler, T. J., and J. O. Roads, 1999: The role of boundary and initial conditions for dynamical seasonal predictability. Nonlinear Proc. Geophys., 10, 211-232.
  42. Saha, S., and Coauthors, 2006: The NCEP Climate Forecast System. J. Climate, 19, 3483-3517. https://doi.org/10.1175/JCLI3812.1
  43. Saha, S., and Coauthor, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015-1057. https://doi.org/10.1175/2010BAMS3001.1
  44. Saha, S., and and Coauthors, 2014: The NCEP climate forecast system version 2. J Climate, 27, 2185-2208. https://doi.org/10.1175/JCLI-D-12-00823.1
  45. Shi, L., O. Alves, H. H. Hendon, G. Wang, and D. Anderson, 2009: The role of stochastic forcing in ensemble forecasts of the 1997/98 El Nino. J. Climate, 22, 2526-2540. https://doi.org/10.1175/2008JCLI2469.1
  46. Stensrud, D. J., H. E. Brooks, J. Dun, M. S. Tracton, and E. Rogers, 1999: Using ensembles for short-range forecasting. Mon. Wea. Rev., 127, 433-446. https://doi.org/10.1175/1520-0493(1999)127<0433:UEFSRF>2.0.CO;2
  47. Stensrud, D. J., J. W. Bao, and T. T. Warner, 2000: Using initial condition and model physics perturbations in shortrange ensemble simulations of mesoscale convective systems. Mon. Wea. Rev., 128, 2077-2107. https://doi.org/10.1175/1520-0493(2000)128<2077:UICAMP>2.0.CO;2
  48. Sun, J. Q., and J. B. Ahn, 2011: A GCM-based forecasting model for the landfall of tropical cyclones in China. Adv. Atmos. Sci., 28, 1049-1055. https://doi.org/10.1007/s00376-011-0122-8
  49. Sun, J., and J. B. Ahn, 2015: Dynamical seasonal predictability of the Arctic Oscillation using a CGCM. Int. J. Climatol., 35, 1342-1353. https://doi.org/10.1002/joc.4060
  50. Wang, B., J. Y. Lee, and I. S. Kang, 2009: Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980-2004). Clim. Dynam., 33, 93-117. https://doi.org/10.1007/s00382-008-0460-0
  51. Wang, G., R. Kleeman, N. Smith, and F. Tseitkin, 2001: The BMRC coupled general circulation model ENSO forecast system. Mon. Wea. Rev., 130, 975-991.
  52. Wunsch, C., 1996: The Ocean Circulation Inverse Problem. Cambridge University Press.
  53. Wikle, C. K., and L. M. Berliner, 2007: A Bayesian tutorial for data assimilation. Physica D., 230, 1-16. https://doi.org/10.1016/j.physd.2006.09.017
  54. Yang, S. C., M. Corazza, A. Carrassi, E. Kalnay, and T. Miyoshi, 2009: Comparison of local ensemble transform Kalman filter, 3DVAR, and 4DVAR in a quasigeostrophic model. Mon. Wea. Rev., 137, 693-709. https://doi.org/10.1175/2008MWR2396.1