EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 1 The ECMWF Seasonal Forecast System-3 Magdalena A. Balmaseda Franco Molteni,Tim Stockdale Laura Ferranti, Paco Doblas-Reyes, Frederic Vitart European Centre for Medium-Range Weather Forecasts, Reading, U.K.

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 2 Overview Introduction to Seasonal Forecasts  End to End Seasonal Forecasting System  Importance of Ocean Initial Conditions ECMWF Seasonal forecasting system 3  Overview  Performance  Web products Calibration of model output Multimodel (EUROSIP) Calibration + Multimodel Summary

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 3 Probabilistic forecast calibration Reliable probability forecasts Tailored products End to End Forecasting System atmos DA atmos obs SST analysis ocean DA ocean obs ocean reanalysis atmos reanalysis land,snow…? sea-ice? initial conditions initial conditions AGCM OGCM ensemble generation

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System El-Niño forecast Initial Conditions Forecast

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System La Niña Initial Conditions

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EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 7 Impact on ECMWF-S3 Forecast Skill In ECMWF S3, ocean Data Assimilation improves forecast skill in the Equatorial Pacific, especially in the Western Part S3 Nodata S3 Assim The impact of ocean initialization in the prediction of SST is comparable to the impact of atmospheric model cycle S2 S2ic_S3model S3

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 8 COUPLED MODEL (IFS + OASIS2 + HOPE) Recent cycle of atmospheric model (Cy31R1) Atmospheric resolution TL159 and 62 levels Time varying greenhouse gasses. Includes ocean currents in wave model INITIALIZATION Includes bias correction in ocean assimilation. Includes assimilation of salinity and altimeter data. ERA-40 data used to initialize ocean and atmosphere in hindcasts Ocean reanalysis back to 1959, using ENACT/ENSEMBLES ocean data ENSEMBLE GENERATION Extended range of back integrations: 11 members, Revised wind and SST perturbations. Use EPS Singular Vector perturbations in atmospheric initial conditions. Forecasts extended to 7 months (to 13 months 4x per year). The seasonal forecast System-3 (implem. March 07)

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 9 Rms error of forecasts has been systematically reduced (solid lines) …. Rms error / spread in different ECMWF systems.. but ensemble spread (dashed lines) is still substantially less than actual forecast error.

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EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 12 ACC for seasonal-mean ( ) 2m-T: DJF from 1 Nov2m-T: JJA from 1 May Precip: DJF from 1 Nov Precip: JJA from 1 May Doblas-Reyes

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 13 New products in the web:ocean reanalysis

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 14 New products from Sys-3: annual-range Nino indices

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 15 New products from Sys-3: ’tercile summary’

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 16 New products from Sys-3: climagrams a) Teleconnection and monsoon indices with verification Predictability barrier

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 17 Climagrams : area-averages of 2mT and rainfall 2m Temperature Amazones Anomaly Correlation Temperature Anomaly Correlation Precipitation

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 18 Climagrams : area-averages of 2mT and rainfall Anomaly Correlation Temperature Anomaly Correlation Precipitation North-East Brasil Target month is more predictable Feb/March as a Window of predictability

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 19 Climagrams : area-averages of 2mT and rainfall Anomaly Correlation Temperature Anomaly Correlation Precipitation South America Atlantic Coast

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 20 Forecast System is not reliable: RMS > Spread To calibrate the model output To sample model error (multi-model): EUROSIP Both A.Can we reduce the error? How much? (Predictability limit) Is the ensemble spread sufficient? Are the forecast reliable? B.Can we increase the spread by improving the ensemble generation?

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 21 Anomaly correlation of seasonal-mean rainfall Franco Molteni

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 22 Can we predict tropical rainfall anomalies?

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 23 Prediction of All India Rainfall: EOF filtered fc. in JAS CC =.50 Franco Molteni

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 24 Prediction of All India Rainfall JJAS CC =.25 JAS CC =.46

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 25 Prediction of East Africa short rains: OND from Aug. Unfiltered fc. : CC = 0.04 EOF-filt. : CC = 0.42 Franco Molteni

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 26 Persistence ECMWF ensemble spread RMS error of Nino3 SST anomalies EUROSIP ECMWF-UKMO-MeteoFrance Sampling model error: The Real Time Multimodel

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 27 TROPICAL CYCLONES Forecasts starting on 1 st June 2005: JASON ECMWF Met Office Meteo-France Obs July-November AtlW-PacE-Pac Multi-model Frederic Vitart

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 28 MULIMODEL: EUROSIP But sometimes the spread with EUROSIP is too large!! ECMWF MULTI-MODEL

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 29 Bayesian Calibration of the Nino Indices: Based on the Forecast Assimilation Framework  It will produce a revised mean and variance Specific Ingredients: 1.Take into account that error in the models can be correlated (remove correlation from errors, not from the signal, by doing SVD of error covariance matrix) 2.Model for the errors: 3.Given the mean and variance, produce the individual plumes

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 30 EUROSIP: Bayesian Combination

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 31 Sampling model error: The Real Time Multimodel Persistence ECMWF ensemble spread RMS error of Nino3 SST anomalies Bayesian Calibration EUROSIP ECMWF-UKMO-MeteoFrance

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 32 Conclusions The new ECMWF seasonal forecast system-3 gives improved predictions of tropical/summer variability respect the previous system. SST predictions are good in the tropical Pacific and eastern Indian Oc., but western Indian Oc. and tropical Atlantic are not better than persistence in NH summer. Difficulty in getting the correct rainfall variability over land. Predictive skill over land can be improved by exploiting teleconnections (calibration) The Multi-Model (EUROSIP) provides skilful predictions of tropical storms. In general it improves reliability, but sometimes the spread is too large Bayesian Calibration can improve the products, but attention should be paid to the estimation of the model error (sensitive to sampling size)

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EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 34 Climagrams :monsoon indices / teleconnections

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 35 Prediction of All India Rainfall JJAS CC =.25 JAS CC =.46

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 36 Can we make use of the larger scale signal?

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 37 Tropical storm annual frequency ( )

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 38 Examples of tropical storm tracks ECMWF System 2 ECMWF System 3

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 39 Interannual variability of tropical storms in EURO-SIP Forecasts issued in June for the period July-November Correlation: 0.72 RMS error: 2.93 Frederic Vitart

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 40 Sampling model error: The Real Time Multimodel Persistence ECMWF ensemble spread RMS error of Nino3 SST anomalies Bayesian Calibration EUROSIP ECMWF-UKMO-MeteoFrance