• Chen, T.-C., and J. C. Alpert, 1990: Systematic errors in the annual and intraseasonal variations of the planetary-scale divergent circulation in NMC medium-range forecasts. Mon. Wea. Rev.,118, 2607–2623.

  • Ferranti, L., T. N. Palmer, F. Molteni, and E. Klinker, 1990: Tropical–extratropical interaction associated with the 30–60 day oscillation and its impact on medium and extended-range prediction. J. Atmos. Sci.,47, 2177–2199.

  • Hendon, H. H., and B. Liebmann, 1990: A composite study of onset of the Australian summer monsoon. J. Atmos. Sci.,47, 2227–2240.

  • ——, and M. L. Salby, 1994: The life cycle of the Madden–Julian oscillation. J. Atmos. Sci.,51, 2225–2237.

  • ——, B. Liebmann, M. Newman, J. D. Glick, and J. E. Schemm, 2000: Medium-range forecast errors associated with active episodes of the Madden–Julian oscillation. Mon. Wea. Rev.,128, 69–86.

  • Higgins, R. W., and K. C. Mo, 1997: Persistent North Pacific circulation anomalies and the tropical intraseasonal oscillation. J. Climate,10, 223–244.

  • Hsu, H.-H., 1996: Global view of the intraseasonal oscillation during northern winter. J. Climate,9, 2386–2406.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc.,77, 437–471.

  • Kang, I.-S., and K. M. Lau, 1994: Principal modes of atmospheric circulation anomalies associated with global angular momentum fluctuations. J. Atmos. Sci.,51, 1194–1205.

  • Lau, K. M., and P. H. Chan, 1985: Aspects of the 40–50 day oscillation during northern winter as inferred from outgoing longwave radiation. Mon. Wea. Rev.,113, 1889–1909.

  • ——, and T. J. Phillips, 1986: Coherent fluctuations of extratropical geopotential height and tropical convection in intraseasonal time scales. J. Atmos. Sci.,43, 1164–1181.

  • ——, and P. H. Chan, 1988: Intraseasonal and interannual variations of tropical convection: A possible link between the 40–50 day oscillation and ENSO? J. Atmos. Sci.,45, 506–521.

  • ——, and F. C. Chang, 1992: Tropical intraseasonal oscillation and its prediction by the NMC operational model. J. Climate,5, 1365–1378.

  • ——, P.-J. Sheu, and I.-S. Kang, 1994: Multiscale low-frequency circulation modes in the global atmosphere. J. Atmos. Sci.,51, 1169–1193.

  • Liebmann, B., and C. A. Smith 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc.,77, 1275–1277.

  • ——, H. H. Hendon, and J. D. Glick, 1994: The relationship between tropical cyclones of the western Pacific and Indian Oceans and the Madden–Julian oscillation. J. Meteor. Soc. Japan,72, 401–412.

  • Lorenc, A. C., 1984: The evolution of planetary-scale 200 mb divergent flow during the FGGE year. Quart. J. Roy. Meteor. Soc.,110, 427–441.

  • Nakazawa, T., 1986: Intraseasonal variations of OLR in the tropics during the FGGE year. J. Meteor. Soc. Japan,64, 17–34.

  • North, G. R., T. L. Bell, R. F. Calahan, and F. J. Moeng, 1982: Sampling errors in the estimation of empirical orthogonal functions. Mon. Wea. Rev.,110, 699–706.

  • Penland, C., and P. D. Sardeshmukh, 1995: The optimal growth of tropical sea surface temperature anomalies. J. Climate,8, 1990–2024.

  • Reynolds, R. W., and T. M. Smith, 1994: Improved global sea surface temperature analyses using optimum interpolation. J. Climate,7, 929–948.

  • Salby, M. L., and H. H. Hendon, 1994: Intraseasonal behavior of clouds, temperature, and winds in the Tropics. J. Atmos. Sci.,51, 2207–2224.

  • Schemm, J. E., H. Van den Dool, and S. Saha, 1996: A multi-year DERF experiment at NCEP. Preprints, 11th Conf. on Numerical Weather Prediction, Norfolk, VA, Amer. Meteor. Soc., 47–49.

  • Slingo, J. M., and Coauthors, 1996: Intraseasonal oscillations in 15 atmospheric general circulation models: Results from an AMIP diagnostic subproject. Climate Dyn.,12, 325–357.

  • von Storch, H., and J. Xu, 1990: Principal oscillation pattern analysis of the tropical 30–60 day oscillation. Part II: Definition of an index and its prediction. Climate Dyn.,4, 175–190.

  • ——, and D. P. Baumhefner, 1991: Principal oscillation pattern analysis of the tropical 30–60 day oscillation. Part II: The prediction of equatorial velocity potential and its skill. Climate Dyn.,6, 1–12.

  • Waliser, D. E., C. Jones, J. E. Schemm, and N. E. Graham, 1999: A statistical extended-range tropical forecast model based on the slow evolution of the Madden–Julian oscillation. J. Climate,12, 1918–1939.

  • Weickmann, K. M., and P. D. Sardeshmukh, 1994: The atmospheric angular momentum cycle associated with a Madden–Julian oscillation. J. Atmos. Sci.,51, 3194–3208.

  • Zhang, C., and H. H. Hendon, 1997: Propagating and standing components of the intraseasonal oscillation in tropical convection. J. Atmos. Sci.,54, 741–752.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 259 111 3
PDF Downloads 216 93 0

Empirical Extended-Range Prediction of the Madden–Julian Oscillation

Fiona LoNOAA/CIRES Climate Diagnostics Center, Boulder, Colorado

Search for other papers by Fiona Lo in
Current site
Google Scholar
PubMed
Close
and
Harry H. HendonNOAA/CIRES Climate Diagnostics Center, Boulder, Colorado

Search for other papers by Harry H. Hendon in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

An empirical model that predicts the evolution of the Madden–Julian oscillation (MJO) in outgoing longwave radiation (OLR) and 200-mb streamfunction is developed. The model is based on the assumption that the MJO can be well represented by a pair of empirical orthogonal functions (EOFs) of OLR and three EOFs of streamfunction. With an eye toward using this model in real time, these EOFs are determined with data only subjected to filtering that can be applied in near–real time. Stepwise lag regression is used to develop the model on 11 winters of dependent data. The predictands are the leading two principal components (PCs) of OLR and the leading three PCs of streamfunction. The model is validated with five winters of independent data and is also compared to dynamic extended range forecasts (DERFs) made with the National Centers for Environmental Prediction’s Medium Range Forecast (MRF) model.

Skillful forecasts of the MJO in OLR and streamfunction with the empirical model are achieved out to about 15 days. Initial skill arises from autocorrelation of the PCs. Subsequent skill beyond about 1 week arises primarily from the cross correlation with the other PCs that define the MJO. Inclusion of PCs not associated with the MJO as predictors appears not to reliably improve skill. Skill is found to be substantially better when the MJO is active at the initial condition than when it is inactive. The empirical forecasts are also found to be more skillful than DERF from the MRF for lead times longer than about 1 week. Furthermore, skill of DERF from the MRF is found to be better when the MJO is quiescent than when it is active at the initial condition. It is suggested that significant improvement of tropical DERF could be achieved by improvement of the representation of the MJO in the dynamic forecast model.

Corresponding author address: Harry Hendon, Climate Diagnostics Center, 325 Broadway, R/E/CD, Boulder, CO 80303.

Email: hhh@cdc.noaa.gov

Abstract

An empirical model that predicts the evolution of the Madden–Julian oscillation (MJO) in outgoing longwave radiation (OLR) and 200-mb streamfunction is developed. The model is based on the assumption that the MJO can be well represented by a pair of empirical orthogonal functions (EOFs) of OLR and three EOFs of streamfunction. With an eye toward using this model in real time, these EOFs are determined with data only subjected to filtering that can be applied in near–real time. Stepwise lag regression is used to develop the model on 11 winters of dependent data. The predictands are the leading two principal components (PCs) of OLR and the leading three PCs of streamfunction. The model is validated with five winters of independent data and is also compared to dynamic extended range forecasts (DERFs) made with the National Centers for Environmental Prediction’s Medium Range Forecast (MRF) model.

Skillful forecasts of the MJO in OLR and streamfunction with the empirical model are achieved out to about 15 days. Initial skill arises from autocorrelation of the PCs. Subsequent skill beyond about 1 week arises primarily from the cross correlation with the other PCs that define the MJO. Inclusion of PCs not associated with the MJO as predictors appears not to reliably improve skill. Skill is found to be substantially better when the MJO is active at the initial condition than when it is inactive. The empirical forecasts are also found to be more skillful than DERF from the MRF for lead times longer than about 1 week. Furthermore, skill of DERF from the MRF is found to be better when the MJO is quiescent than when it is active at the initial condition. It is suggested that significant improvement of tropical DERF could be achieved by improvement of the representation of the MJO in the dynamic forecast model.

Corresponding author address: Harry Hendon, Climate Diagnostics Center, 325 Broadway, R/E/CD, Boulder, CO 80303.

Email: hhh@cdc.noaa.gov

Save