Charles Jones and Leila M. V. Carvalho University of California Santa Barbara

1. Changes in the Activity of the Madden-Julian Oscillation during 1958-2004 Charles Jones and Leila M. V. Carvalho University of California Santa Barbara • Extensively studied over the years but no comprehensive theory • Behavior on time scales longer than interannual is unknown Case to Case Seasonal Variations Interannual Variations Long-term Behavior Time scales ??? ???? ? ??

2. This Study • Are there linear trends in the activity of the MJO? (intensity & number of events) • Does the MJO exhibit regimes of high and low activity? • Are there significant seasonal differences in the activity of the MJO on time scales longer than interannual? Note: MJO refers to summer and winter events

3. Identification of MJO events • NCEP/NCAR U200, U850 (1958-2004) 20-100 day anomalies • OLR (1979-2004) 20-100 day anomalies • EOF analysis • Summer domain: south Asian monsoon (Lawrence and Webster 2001) • Winter domain: Equatorial region (Kessler 2001) • Event: amplitude PC1 exceeds 1 sigma • within 20 days PC2 exceeds 1 sigma • Events registered at pentads in which PC1 exceed 1 sigma • 1958-2004: 158 events (75 in summer; 83 in winter) (U200,U8500) • 1979-2004: 90 events (49 summer; 41 winter) (OLR)

7. MJO Events more frequent in some years than in other periods • 158 events (1958-2004) identified with CEOF (U200, U850) (correlation matrix) • Each bar represents an event registered at peak in PC1 • Amplitude is the variance (15S-15N; all longitudes) of eastward wavenumbers 1-6 20-100 day anomalies (Hendon et al. 1999)

8. Does the MJO exhibit regimes of high and low activity? MJO Occurrences (1958-2004) SK • Low-Frequency diagram • Consider XT, T=1, 3431 pentads, XT=1 event, XT= 0 no event • Define moving window SK and compute percentage of MJO events in SK: PK,T = MK,T / N where MK,T is number of events and N total number of events • Similar for summer: PSK,T = MSK,T / NS where MSK,T is number of summer events and NS total number of summer events • Similar for winter: PWK,T = MWK,T / NW where MWK,T is number of winter events and NW total number of winter events • SK odd number and varied from smallest (1 pentad) to largest possible (3431 pentads)

9. Hypothetical Case: events evenly spaced in time SK Low-frequency diagram Cone of Influence Cone of Influence

10. Winter & Summer

13. Overall Mean MJO Low-frequency Variability Mean PK,T Mean PSK,T Mean PWK,T SK = 145 to 657 (1.98 to 9 years) Summer Winter

14. Are regime changes different than random occurrences? 1% of time series with random events correlated with observed at 0.7 or higher (~50% or more of the observed variance in the mean LF MJO variability) • Monte Carlo simulations • Summer • XT, XT=1 summer event, XT= 0 no event • Randomize seasons 999 times • Each batch: • compute LF diagram • mean RSK,T (SK =145 to 657 (1.98 to 9 years) • correlation PSK,T and RSK,T • Frequency distribution of correlation > Cr • Same for winter

15. Summary and Conclusions • MJO exhibits substantial changes in regimes longer than interannual • Two regimes of high activity (1974-1978, 1988-1992), and low activity (1981-1986). • Markedly different changes in summer and winter activity • Summer: increased from early 1960’s to mid 1970’s and decreased steadily until the 1990’s • Winter: more regular changes with peaks in 1967, 1976 and 1989 and lows in 1971, 1983 and 1997. • Changes in summer and winter MJO activity are statistically different from random occurrences • There are positive linear trends in (1958-2004): • U200 amplitudes of summer and winter MJO events • Number of summer MJO events • Linear trends are not higher than random occurrences (5% level) • Trends in U200 amplitudes of the MJO are consistent between EOF metric (this study) and equatorial zonal mean intraseasonal index (Slingo et. 1999)

18. No-sat Sat Slingo et al. (1999) Intraseasonal Index (QJRMS) • Interannual variations in the MJO; NCEP/NCAR 1958-1997 • Zonal mean of U 200hPa (10°S - 10°N) • 20-100 day anomalies; square and apply 100 day smoothing • 40-yr integration of HadAM2a model driven with observed SST reproduced general trends in “MJO activity” • Index contains up to 40%unrelated MJO variability (Hendon et al. 1999)

19. Are MJO events becoming more intense?

20. Is the MJO becoming more frequent?