Joint IPSL/NCAS-Climate meeting Paris, 14-16 May 2007

1. The impact of basic state errors on monsoon intraseasonal variability and the intraseasonal-interannual relationship Joint IPSL/NCAS-Climate meetingParis, 14-16 May 2007 A. G. Turnera,b, P. K. Mohantyc, J. M. Slingoa,b, P. M. Innessa,b a National Centre for Atmospheric Science b Walker Institute, University of Reading, UK c Department of Marine Sciences, Meteorology and Physical Oceanography Laboratory, Berhampur University, India

2. Outline • Model framework • Spatio-temporal behaviour of intraseasonal variations • Active-break composition using an OLR index • Evolution of selected fields • Combined EOF analysis in model integrations • Intraseasonal-interannual relationships and their link to the basic state • Summary

3. Model framework • Control integration (HadCM3) run for 100 years with daily diagnostics. • A further integration uses limited-area heat flux adjustments to counteract the effect of systematic model error (HadCM3FA). Also run for 100 years (only 85 years daily data available). • Comparisons made with ERA-40 reanalysis (Uppala et al. 2005)

4. Heat flux adjustments • Traditionally used in older models (e.g. HadCM2) to prevent climate drift; HadCM3 does not have this problem. • Used here to counteract biases in the mean state. Devised by Inness et al. (2003) to investigate the role of systematic low-level zonal wind and SST errors on the MJO. Coupled model run for 20 years, Indian and Pacific SSTs within 10S-10N relaxed back towards climatology. Anomalous heat fluxes generate a mean annual cycle which is applied to a new 100 year integration (HadCM3FA, described in Turner et al. 2005).

5. Heat flux adjustments Large fluxes (up to 186Wm-2 at 120W) into the cold tongue. Much smaller (~30W.m-2) over Maritime Continent and Indian Ocean. Annual Mean Amplitude of annual cycle Small annual cycle apart from upwelling region off African coast, and central Pacific.

6. Improvements to the mean state HadCM3FA mean summer (JJAS) surface temperature differences with HadCM3 HadCM3 differences with ERA-40

7. Spatio-temporal behaviour in intraseasonal bands • Daily anomalies to seasonal cycle split into 10-20 day and 30-60 day bands using Lanczos filter. • Bands chosen representative of observed modes: models show reasonable power spectra at these frequencies. • 10-20 day band commonly associated with westward propagation (recently suggested this is movement of depressions along the monsoon trough; Krishnamurthy & Shukla 2007). • 30-60 day band displays northward and eastward propagation (the NPISO, boreal summer ISO, related to MJO?).

8. Spatial variation in intraseasonal bands • Percentage of total intraseasonal variance explained in each band in reasonable spatial agreement with reanalysis (850hPa zonal winds). • Similar pattern for OLR. 10-20day ERA-40 HadCM3 HadCM3FA 10-20day 30-60day

9. Temporal variation in intraseasonal bands • Lag regressions of u850 averaged 5-15°N (top) and 70-90°E (bottom) against reference timeseries (85-90°E, 5-10°N) after Goswami & Xavier 2005. • Some evidence of westward and northward propagation. ERA-40 HadCM3 HadCM3FA 10-20day 30-60day

10. Active-break composites using an OLR index • OLR index of Vecchi & Harrison (2002)1 used to define active and break events. • Those events which persist for five or more days σ above or below the mean are selected as active and break events respectively. • Composites at different lags generated with respect to event onset. 1difference between normalized 7-day boxcar smoothed OLR anomalies (10-30°N, 65-85°E) and (10°S-5°N, 75-95°E) minus 50 day centred mean.

11. HadCM3 HadCM3FA -10 -5 -3 -1 0 Active Break Active Break Evolution of active and break events: 10-20 day precip

12. HadCM3 HadCM3FA -20 -15 -10 -5 0 Active Break Active Break Evolution of active-break events:30-60 day precip

13. HadCM3 HadCM3FA Active Break Active-break composites: mixed layer depth anomalies

14. HadCM3 HadCM3FA Active Break Active-break composites: wind stress curl anomalies

15. HadCM3 HadCM3FA -20 -15 -10 -5 0 Active Break Active Break Evolution of active-break events:30-60 day wind stress curl anomalies

16. HadCM3 HadCM3FA -20 -15 -10 -5 0 Active Break Active Break Evolution of active-break events:30-60 day mixed layer depth anomalies

17. Combined EOF analysis • EOF analysis performed on combined wind and precipitation fields. • Data from HadCM3 and HadCM3FA combined into one timeseries to find common modes of variation and assess differences in interannual-intraseasonal relationships. • Anomaly timeseries calculated using two methods (after Krishnamurthy & Shukla 2000): 1: daily rainfall anomaly including seasonal anomaly R’(m,n) for day n of year m, calculated by removing daily rainfall climatology.

18. Combined EOF analysis #1 • First two combined EOFs show evidence of seasonal mean monsoon behaviour

19. HadCM3 HadCM3FA Combined EOF analysis #1: stratify PCS by JJAS mean AIR red=+1σ, blue=-1σ. • PC1 much more strongly perturbed by JJAS mean AIR in HadCM3FA. • Suggests basic state errors influence projection of IS onto IA variability.

20. HadCM3 HadCM3FA Combined EOF analysis #1: stratify PCS by JJAS mean DMI red=+1σ, blue=-1σ. • Both PC1 and PC2 more strongly perturbed by JJAS mean DMI in HadCM3FA. • Suggests basic state errors influence projection of IS onto IA variability.

21. HadCM3 HadCM3FA Combined EOF analysis #1: stratify PCS by JJAS mean Niño-3 red=+1σ, blue=-1σ. • PC1 more strongly perturbed by JJAS mean ENSO forcing. • Suggests basic state errors influence large scale forcing on monsoon ISV.

22. Combined EOF analysis • Anomaly timeseries calculated using two methods (after Krishnamurthy & Shukla 2000): 2: daily rainfall anomaly with removed seasonal residual R’’(m,n) for day n of year m, calculated by removing daily rainfall climatology and seasonal mean anomaly from rainfall timeseries.

23. Combined EOF analysis #2 • First two combined EOFs show evidence of seasonal mean monsoon behaviour Pattern correlations with other method: 0.97, 0.96, 0.95, 0.83

24. HadCM3 HadCM3FA Combined EOF analysis #2: stratify PCS by JJAS mean AIR red=+1σ, blue=-1σ. • Less obvious difference in PC1 perturbation by JJAS mean AIR. • Suggests most of IS-IA link is due to seasonal residual in the anomaly.

25. HadCM3 HadCM3FA Combined EOF analysis #2: stratify PCS by JJAS mean DMI red=+1σ, blue=-1σ. • PC2 more strongly perturbed by JJAS mean DMI in HadCM3FA, similar to method #1. • PC1 unperturbed by stratification, like method #1.

26. HadCM3 HadCM3FA Combined EOF analysis #2: stratify PCS by JJAS mean Niño-3 red=+1σ, blue=-1σ. • PC2 more strongly perturbed. • Is seasonal residual completely removed?

27. Combined EOF analysis • Initial stratification of PCs suggests stronger links between interannual and intraseasonal variations in HadCM3FA • Removal of residual of seasonal mean anomaly by simple method tends to suggest little projection of intraseasonal behaviour onto interannual variation. • Projection of IS onto IAV occurs most in HadCM3FA, where interannual variability of the monsoon-ENSO system is much stronger (Turner et al. 2005). • More analysis required (careful choice of filtering) to remove any remaining residual from the daily precipitation anomalies.

28. Summary • Basic state has limited impact on spatio-temporal evolution of monsoon intraseasonal variability using flux adjustment method. • Coherent active/break structures in wind stress curl, MLD, and other ocean fields. • Suggestion of greater projection of intraseasonal monsoon variability onto interannual variations in HadCM3FA, however this may be due to residual from seasonal mean.