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Optimal Initial Perturbations for Ensemble Prediction of the Madden–Julian Oscillation during Boreal Winter

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  • 1 Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, and Goddard Earth Sciences Technology and Research Studies and Investigations, Universities Space Research Association, Greenbelt, Maryland
  • | 2 Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 3 Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, and Goddard Earth Sciences Technology and Research, Morgan State University, Baltimore, Maryland
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Abstract

An initialization strategy, tailored to the prediction of the Madden–Julian oscillation (MJO), is evaluated using the Goddard Earth Observing System Model, version 5 (GEOS-5), coupled general circulation model (CGCM). The approach is based on the empirical singular vectors (ESVs) of a reduced-space statistically determined linear approximation of the full nonlinear CGCM. The initial ESV, extracted using 10 years (1990–99) of boreal winter hindcast data, has zonal wind anomalies over the western Indian Ocean, while the final ESV (at a forecast lead time of 10 days) reflects a propagation of the zonal wind anomalies to the east over the Maritime Continent—an evolution that is characteristic of the MJO.

A new set of ensemble hindcasts are produced for the boreal winter season from 1990 to 1999 in which the leading ESV provides the initial perturbations. The results are compared with those from a set of control hindcasts generated using random perturbations. It is shown that the ESV-based predictions have a systematically higher bivariate correlation skill in predicting the MJO compared to those using the random perturbations. Furthermore, the improvement in the skill depends on the phase of the MJO. The ESV is particularly effective in increasing the forecast skill during those phases of the MJO in which the control has low skill (with correlations increasing by as much as 0.2 at 20–25-day lead times), as well as during those times in which the MJO is weak.

Corresponding author address: Dr. Yoo-Geun Ham, NASA Goddard Space Flight Center, Code 610.1, Greenbelt, MD 20770. E-mail: yoo-geun.ham@nasa.gov

Abstract

An initialization strategy, tailored to the prediction of the Madden–Julian oscillation (MJO), is evaluated using the Goddard Earth Observing System Model, version 5 (GEOS-5), coupled general circulation model (CGCM). The approach is based on the empirical singular vectors (ESVs) of a reduced-space statistically determined linear approximation of the full nonlinear CGCM. The initial ESV, extracted using 10 years (1990–99) of boreal winter hindcast data, has zonal wind anomalies over the western Indian Ocean, while the final ESV (at a forecast lead time of 10 days) reflects a propagation of the zonal wind anomalies to the east over the Maritime Continent—an evolution that is characteristic of the MJO.

A new set of ensemble hindcasts are produced for the boreal winter season from 1990 to 1999 in which the leading ESV provides the initial perturbations. The results are compared with those from a set of control hindcasts generated using random perturbations. It is shown that the ESV-based predictions have a systematically higher bivariate correlation skill in predicting the MJO compared to those using the random perturbations. Furthermore, the improvement in the skill depends on the phase of the MJO. The ESV is particularly effective in increasing the forecast skill during those phases of the MJO in which the control has low skill (with correlations increasing by as much as 0.2 at 20–25-day lead times), as well as during those times in which the MJO is weak.

Corresponding author address: Dr. Yoo-Geun Ham, NASA Goddard Space Flight Center, Code 610.1, Greenbelt, MD 20770. E-mail: yoo-geun.ham@nasa.gov
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