Browse > Article
http://dx.doi.org/10.14191/Atmos.2015.25.2.353

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

Ahn, Joong-Bae (Division of Earth Environmental System, Pusan National University)
Lee, Joonlee (Division of Earth Environmental System, Pusan National University)
Publication Information
Atmosphere / v.25, no.2, 2015 , pp. 353-366 More about this Journal
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
AMIP; data assimilation; CGCM; initial condition;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
15 Dommenget, D., and D. Stammer, 2004: Assessing ENSO simulations and predictions using adjoint ocean state estimation. J. Climate, 17, 4301-4315.   DOI
16 Gates, W. L., 1992: AMIP: The atmospheric model intercomparison project. Bull. Amer. Meteor. Soc., 73, 1962-1970.   DOI
17 Ghil, M., and P. Malanotte-Rizzoli, 1991: Data assimilation in meteorology and oceanography. Adv. Geophys., 33, 141-266.   DOI
18 Hendon, H. H., M. C. Wheeler, and C. Zhang, 2007: Seasonal dependence of the MJO-ENSO relationship. J. Climate, 20, 531-543.   DOI
19 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.   DOI
20 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.   DOI
21 Houtekamer, P. L., and J. Derome, 1995 : Methods for ensemble prediction. Mon. Wea. Rev., 123, 2181-2196.   DOI
22 Huang, X. Y., 2000: Variational analysis using spatial filters. Mon. Wea. Rev., 128, 2588-2600.   DOI
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.   DOI
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.   DOI
27 Kiehl, J. T., and P. R. Gent, 2004: The community climate system model, version 2. J. Climate, 17, 3666-3682.   DOI
28 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.   DOI
29 Kessler, W. S., and R. Kleeman, 2000: Rectification of the Madden-Julian oscillation into the ENSO cycle. J. Climate, 13, 3560-3575.   DOI
30 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.   DOI
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.   DOI
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.   DOI
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.   DOI
37 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.   DOI
38 Molteni, F., and Coauthors, 2011: The new ECMWF seasonal forecast system (System 4). ECMWF Technical Memorandum 656.
39 Pacanowski, R. C., and S. M. Griffies, 1998: MOM 3.0 Manual. NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, USA 08542.
40 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.   DOI
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.   DOI
43 Saha, S., and Coauthor, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015-1057.   DOI
44 Saha, S., and and Coauthors, 2014: The NCEP climate forecast system version 2. J Climate, 27, 2185-2208.   DOI
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.   DOI
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.   DOI
47 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.   DOI   ScienceOn
48 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.   DOI
49 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.   DOI
50 Sun, J., and J. B. Ahn, 2015: Dynamical seasonal predictability of the Arctic Oscillation using a CGCM. Int. J. Climatol., 35, 1342-1353.   DOI
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.   DOI
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.   DOI