Presentation on theme: "The ECMWF Monthly and Seasonal Forecast Systems"— Presentation transcript:
1 The ECMWF Monthly and Seasonal Forecast Systems D. Anderson, M. Balmaseda, L. Ferranti,F. Molteni, T. Stockdale, F. VitartECMWF, Reading, UK
2 Main topics Monthly forecast system: merging with VAREPS new atmospheric model cycle : improved simulation of tropical intra-seasonal variabilitysome recent results from the operational versionSeasonal forecast system: from System 2 to System 3improved ocean data assimilationbetter predictions of tropical SSTreduced systematic errors in atmospheric fields
3 Monthly Forecasting System Real-time forecast:Coupled ocean-atmosphere integrations: a 51-member ensemble is integrated for 32 days every Thursday.Atmospheric component: IFS with the latest operational cycle and with a TL159L62 resolutionOceanic component: HOPE (from Max-Planck Institute) with a zonal resolution of 1.4 degrees and 29 vertical levelsCoupling: OASIS (CERFACS). Coupling every ocean time step (1 hour)Background statistics:5-member ensemble integrated at the same day and same month as the real-time time forecast over the past 12 yearsYes/no, error bars, probabilities?Users should be told about past records of forecast quality
4 Model changes during the past year New versions of IFS :Cycle 29R2 in June new sea ice treatment*Cycle 30R1 in Feb change of vertical resolution (62 vertical levels)The sea-ice cover is persisted from the atmospheric initial conditions till day 10, then relaxed towards climatology. After day 30, the sea-ice cover is the climatologocal sea-ice cover (from ERA40).Other changes:Archiving of probabilitiesVerification web siteNew products (Madden Julian Oscillation, products for the Southern Hemisphere)
5 TL399L62 twice a day uncoupled Future developments: Merging the monthly forecasting system with VAREPSPresent situation: 2 separate systemsEPS:TL399L62 twice a day uncoupledDay 0Day 10TL159L62 once a weekMOFC:Day 0Day 32Ocean model
6 Future developments: Merging the monthly forecasting system with VAREPS Q1 2007: single systemVAREPS:Once a weekTL399L62TL255L62Day 0Day 32Day 10MOFC:Day 10Day 0Day 32Ocean model
7 48 5-ensemble member cases, CY30R1 Prob. 2m temp. in upper tercile. NH. Day 12-18VAREPSOp. TL15932/ % significanceROC Area:Tl159VAREPS
13 Precipitation over India 08/05-15/0515/05-22/0522/05-29/0529/05-05/06ANADay5-11Day12-18
14 Precipitation over India 08/05-15/0515/05-22/0522/05-29/0529/05-05/06ANADay19-25Day26-32
15 Indian monsoon Probability of precipitation in the upper tercile 27 real-time cases covering the period May-June-July-August 2002, 2003, 2004 , 2005Monthly ForecastPersistence of the probabilitiesof the previous weekDAY 19-25DAY 26-32DAY 12-18ROC score:ROC score:ROC score:
16 System-2 ECMWF seasonal forecast COUPLED MODEL Atmospheric model cycle 23R4Atmospheric resolution TL95 and 40 levelsHope ocean model (1x1)Oasis couplerINITIALIZATIONERA-15 data to initialize ocean and atmosphereAssimilation of subsurface temperature only“Multivariate” corrections to the salinity and velocity fieldsEnsemble of 5 ocean analyses back to 1987.ENSEMBLE GENERATIONReal time FC: 40 ensemble members (SST and wind perturbations)Back integrations:5 members, for calibration.40 members (Nov and May starts) for skill assessment.
17 System-3 (expected late 2006) COUPLED MODELNew cycle of atmospheric model (Cy31R1)Higher atmospheric resolution TL159 and 62 levelsGreen house gasses and new aerosols.New sea-ice specification algorithmInclude ocean currents in wave modelINITIALIZATIONERA-40 data to initialize ocean and atmosphereInclude bias correction in ocean assimilation.Include assimilation of salinity and altimeter data.Ocean reanalysis back to 1959, using ENACT/ENSEMBLES ocean dataENSEMBLE GENERATIONExtended range of back integrations: 11 members,Revised wind and SST perturbations.Use EPS Singular Vector perturbations in atmospheric initial conditions.Forecasts out to 12 months (4x per year)
18 EUROSIP: EUROpean multi-model Seasonal-to-Interannual Prediction system Currently composed of ECMWF (System 2), Meteo-France (Arpege/ORCA) and UK MetOffice (GloSea) coupled systemsEnsemble integrations performed at ECMWFMulti-model products to be computed and made available to member statesGraphical products available on the ECMWF web siteData access/distribution policy to be agreed
19 New ECMWF operational ocean (re)analysis Basic (existing) Setup:Ocean model: HOPE (~1x1 going to 1x.3 at the equator)Assimilation Method OIAssimilation of T + Balanced relationships (T-S, ρ-U)10 days assimilation windows, increment spread in timeEnsemble of 5 ocean analyses to represent uncertaintySystem-3+New FeaturesERA-40 fluxes to initialize oceanRetrospective Ocean Reanalysis back to 1959.Multivariate on-line Bias Correction .Assimilation of salinity data.Assimilation of altimeter-derived sea level anomalies.3D OIECMWF has a new operational ocean (re)analysis to initialize seasonal and monthly forecastsTo the basic setup (existing SYSTEM 2), we have added new features. The new system assimilates salinity and altimeter.
20 Reanalysis time series : trends and variability (with uncertainty from 5 ocean analysis)North Atlantic:T300 anomalyClimate signals….North Atlantic:S300 anomalyThe shading in the figures is a representation of the uncertainty in our analysis. It comes from the ensemble of 5 analysis in which the wind stress has been perturbed.
21 THC: Atlantic Meridional Transport (30N) Values from Bryden et al 2005
22 Tropical Pacific SST indices: bias ---- System 2---- System 3 (exp.)
23 Tropical Pacific SST indices: skill scores ---- System 2---- System 3 (exp.)
24 Tropical Pacific SST indices: error time series ---- System 2---- System 3 (exp.)
25 JFM rainfall standard deviation and regression against the Nino3 JFM rainfall standard deviation and regression against the Nino3.4 index: GPCP
26 JFM rainfall standard deviation and regression against the Nino3 JFM rainfall standard deviation and regression against the Nino3.4 index: System 2
27 JFM rainfall standard deviation and regression against the Nino3 JFM rainfall standard deviation and regression against the Nino3.4 index: System 3
28 Systematic errors in atmospheric fields: 500-hPa geopotential height in JFM (m.4-6)
29 Systematic errors in atmospheric fields: sea level pressure in JAS (m
30 Systematic errors in atmospheric fields: 2-m. temperature in JAS (m
31 Latest Niño 3.4 Forecast EuroSIP ECMWF The EUROSIP NINO plumes for the forecast started in March06 indicate a fast return to near-normal conditions by May and the tendency of warm after that.Here is the latest collection of forecasts of 20 ENSO models. Time Series of predicted sea surface temperature anomalies (SSTA) for the Niño 3.4 index (°C) from various dynamical and statistical models for nine overlapping 3-month periods. Figure provided by the Forecasting and Prediction Research Group of the IRI.Plume of 20 ENSO forecast models from March 2006.There is quite a range of forecasts from later northern spring onward. Some clustering of model forecasts is apparent. Four models (NCEP/CFS, ECHAM/MOM, CPC Markov and Zhang intermediate coupled model) see the mild cold even continuing into andbeyond the middle of 2006, while the others generally call for a return to neutral by around June, with a few warming to weak El Nino anomaly levels by July or August.IRI defines La Nina as SSTs in the Nino3.4 region being more than the following anomaly amounts, varying as a function of the season:DJF AMJ ASOJFM MJJ SONFMA JJA ONDMAM JAS NDJThe above anomaly thresholds approximately mark the 25%iles for the distributions of the respective seasons.
34 Seasonal forecasting of tropical storms ECMWFForecasts starting on 1st June 2005: JASONOBS: JASON 2005EuroSIPWNPENPAtl
35 Tropical storm seasonal forecast for JASON 2006 ECMWF forecast issued in May
36 Tropical storm seasonal forecast for JASON 2006 ECMWF forecast issued in June
37 SummaryIntegration of VarEPS and monthly forecast: a step towards a “seamless prediction” system.Improvements in physical parametrizations (to be included in cy 31R1) reduce systematic errors and improve tropical intra-seasonal variability in monthly and seasonal forecasts.New ocean (re-)analysis beneficial to both seasonal forecasting and climate researchMulti-model predictions moving from experimental (DEMETER, ENSEMBLES) to operational phase (EUROSIP).