© Crown copyright Met Office Predictability and systematic error growth in Met Office MJO predictions Ann Shelly, Nick Savage & Sean Milton, UK Met Office

© Crown copyright Met Office Contents This presentation covers the following areas Background Method Tropical systematic errors Tropical performance Teleconnections Conclusions

© Crown copyright Met Office What is the MJO? Mode of sub-seasonal atmospheric variability that influences the location and strength of tropical precipitation and zonal winds Period of days Significant interannual variability Eastward propagation ~5m/s First described by Madden and Julian in 1971 Panels 4-8 days apart U wind anomalies shown as streamlines Cloud symbols show regions of increased convection Multiscale structure Models have problems in simulating MJO

© Crown copyright Met Office Why bother with the MJO? We are now routinely doing 15 day ensemble forecasts using the MetUM The MJO is one of the key phenomena that is potentially predictable on the sub-seasonal timescale Impacts include precipitation, tropical cyclones and extratropical-tropical interactions Links to EL Nino and the NAO which potentially increase its influence to interannual timescales.

© Crown copyright Met Office Method

© Crown copyright Met Office Model description Met Office Global and Regional Ensemble Prediction System (MOGREPS-15) configuration of the MetUM 15 day forecast run twice daily at 00z and12z at ECMWF 24 ensemble members N144L38 resolution (1.25 o x.83 o ), model top ~39km Data archived in TIGGE database at ECMWF

© Crown copyright Met Office Data Based on first two EOFs of the combined fields of 15°S – 15°N averaged anomalies of 850 hPa zonal wind, 200 hPa zonal wind, and satellite- observed OLR data. Projection of the daily observed data onto these EOF’s with the annual cycle and components of interannual variability removed yields principal component time series RMM1 & RMM2 Compositing study on 3 Winters of data (NDJFM) – 06/07, 07/08 & 08/09 Australian Bureau of Meteorology (BOM) index to determine phase and amplitude of MJO Number of W &H phases reduced to 4 Calculation of mean state, anomalies, and error for analysis/observations and forecast

© Crown copyright Met Office Results

© Crown copyright Met Office Tropical systematic errors (a)Composited precipitation forecast error at T+24 in each of the 4 MJO phases (b) with the mean model error removed

© Crown copyright Met Office Tropical performance Composited OLR observed anomalies (solid) and corresponding forecast anomalies (dashed) averaged between 15N & 15S in each of the 4 phases of the MJO

© Crown copyright Met Office Tropical performance Composite analysis OLR (shading) and 200hPa streamfunction (contours) anomalies in each of the 4 MJO phases (b) same as (a) but for forecast at T+240.

© Crown copyright Met Office Teleconnections 200hpa streamfunction analysis anomaly (shading) and OLR observed anomaly (contours: dashed line negative and solid line positive) in phase 0 of the MJO and corresponding forecast (b) Same as for (a) but for phase 2 of the MJO AnalysisForecast (a) (b)

© Crown copyright Met Office Conclusions

© Crown copyright Met Office Conclusions MJO in short range forecasts captured relatively well MJO signature is still visible until day 10, with performance in phase 2 and 3 exceeding that of phase 0 and 1 MOGREPS has problems reproducing the amplitude and position of an MJO when initialised in the Indian Ocean Difficulty in disentangling inherent model bias from that due to MJO signal Evidence for possible impacts on the extratopics through propagation of Rossby waves.

© Crown copyright Met Office Future work Impact of improved horizontal and vertical resolution Seamless investigations of NWP, seasonal and climate model drift. Diagnose why Indian Ocean is a problematic regions for MJO propagation. Calculate diabatic heating rates from analyses and forecast using different methods Examine the impact of coupled ocean on MJO simulations Improvements to convection parameterization

© Crown copyright Met Office References Liebmann B. and C.A. Smith, 1996: Description of a Complete (Interpolated) Outgoing Longwave Radiation Dataset. Bulletin of the American Meteorological Society, 77, Madden, R. A., and P. R. Julian (1971) Detection of a day oscillation in the zonal wind in the tropical Pacific.J. Atmos. Sci., 28, Madden, R. A., and P. R. Julian (1994) Observations of the day tropical oscillation: a review. Mon. Wea. Rev., 122,

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