Presentation is loading. Please wait.

Presentation is loading. Please wait.

Seasonal forecasting: status and plans David Anderson Tim Stockdale, Magdalena Balmasda, Arthur Vidard, Alberto Troccoli, Paco Doblas-Reyes, Kristian Morgensen,

Similar presentations

Presentation on theme: "Seasonal forecasting: status and plans David Anderson Tim Stockdale, Magdalena Balmasda, Arthur Vidard, Alberto Troccoli, Paco Doblas-Reyes, Kristian Morgensen,"— Presentation transcript:

1 Seasonal forecasting: status and plans David Anderson Tim Stockdale, Magdalena Balmasda, Arthur Vidard, Alberto Troccoli, Paco Doblas-Reyes, Kristian Morgensen, Malcolm MacVean, Laura Ferranti, Frederic Vitart

2 ECMWF System-2 is the same as last year. System-3 is under development. This involves substantial changes to the coupled system, though the changes should be largely transparent to the user. Another major initiative is the development of a real-time, multi-model, multi-analysis system. The UKMO and Meteo France systems are implemented. Other Met Services may join in future: DWD, Spain.

3 System 2 Recap Forecast model TL95L40 atmosphere L29 ocean (Variable resolution 0.3X1in tropics) Ocean Analysis OI analysis of T Corrections to salinity when T is assimilated. Corrections to velocity when density is updated. Ensemble of ocean analyses: 5 analyses perturbed by wind anomalies. Ensemble generation From each ocean analysis, 8 perturbations to SST are made, creating an ensemble of 40 members.

4 Cycle 23r4(24r1) of atmosphere, TL95, L40 Calibration based on 5-member ensemble from 1987 to member ensembles run for November and May for validation. Plumes contain forecasts for Nino3, Nino3.4, Nino4 Various plots on the web e.g. terciles, 15%iles, ensemble means.. Precip, T2m, upper level data

5 The information on which a seasonal forecast is based lies mainly in the ocean. (Soil moisture, snow cover, sea ice, atmospheric state.. may also add some predictability). Considerable effort is put into analysing the ocean state. An ensemble of ocean analyses is created. Five ocean analyses are created by perturbing the wind stress with perceived uncertainty. (These analyses are used to create an ensemble of forecasts.) The purpose of creating an ensemble of ocean analyses is to represent some of the uncertainty in knowing the ocean state. These analyses are used in creating the ensemble of forecasts in System-2 (the current ECMWF seasonal forecast system and in the monthly forecast system).


7 Operating method of ARGO floats. Operating

8 Data coverage for June 1982

9 Data coverage for March 2002

10 Build up of ARGO: Data coverage for February 2005 XBT, MOORINGS, ARGO floats



13 System 3

14 System-3 A new cycle of the atmospheric model - 29r1 with 40 levels or following physics cycle with green house gasses and aerosols and 62 levels. Extended range and size of back integrations. Strawman 11member, back to Include bias correction in ocean assimilation. Include salinity assimilation. Include altimeter assimilation Revised wind and SST perturbations. New sea-ice specification algorithm. Include ocean currents in wave model. Use EPS Singular Vector perturbations in atmospheric initial conditions. Forecasts out to 12 months (4X per year) Will use ENACT/ENSEMBLES ocean data.

15 There will be some extra data archived: Pressure level data for 100HPa, and 50HPa at 12 hourly intervals. A full set for 400 and 300 HPa (currently only T) MSLP every 6 hours (currently 12hours) Potential temperature on PV=2 surface.

16 Prototype of System 3 currents OSCAR currents Velocity fields

17 Zonal velocities: Correlation with OSCAR (NOAA) currents (15m depth) Period , seasonal cycle removed System 2 Prototype of System 3

18 Spring barrier: predictability potential predictability *) seasonal recharge oscillator Estimate of predictability with parameters and noise properties from seasonal fit. From Gerrit Burgers KNMI skill ECMWF operational forecast

19 ERA15-SOC ( ) Wind stress perturbations Standard deviation of zonal wind stress differences (in N/m**2, multiplied by 100) for January ERA40-CORE ( ) ERA40-CORE ( ) ERA40-CORE ( )

20 Constant GHG Correlation = 0.52 Anthropogenic effect: T2m predictions Variable GHG Correlation = month lead, summer (JJA) predictions of global T2m

21 The multi-model

22 Forecast System is not reliable: RMS > Spread A) Can we reduce the error? How much? (Predictability limit) B)Or can we only increase the spread? A)Improve the ensemble generation: Need to sample model error B)Improve calibration: A posteriori use of all available information

23 The Met Office model is very similar to that used in Demeter. The ensemble strategy follows the ECMWF S2 strategy except that it uses ERA- 40/Ops rather than ERA-15/Ops and the wind perturbations used in the ensemble ocean analysis are half amplitude. The ocean analyses use the ENACT data set. The atmospheric resolution is 2.5 X 3.75 degrees, with 19 vertical levels. The ocean has resolution of 1.25X1.25, increasing to in the north south direction near the equator. There are 40 vertical levels. The calibration period is , 15 members ensemble.

24 The Meteo France atmospheric model has 31 vertical levels, TL63 resolution. The ocean model is ORCA: 2X2 at mid latitudes, increasing to near the equator. There are 31 vertical levels. The ocean analyses are produced without in situ data assimilation. Altimeter data are used and a moderate relaxation to observed SST is applied. Forecasts are available from 1993-present. (A 5- member ensemble from 1993 to 2004 inc. The real-time forecast ensemble is 41.)

25 Differences can be considerably larger e.g. in Nino4

26 Results from the real-time multi-model forecast system. Three different models, using three different analysis strategies. Green is ECMWF, blue ECMWF + MO, red ECMWF+MO+MF.



29 Predictions from the 3 multi-model components: Sahel precipitation Met Office Météo France Ecmwf

30 Predictions from the 3 multi-model components: Guinea Coast precipitation Met Office Ecmwf Météo France

31 Results are from DEMETER

32 Precipitation multi-model probability JAS 2005

33 Sea Surface Temp. multi-model probability JAS 2005


35 ENACT Enact was an EU framework V project, seeking to advance ocean data assimilation strategies, to generate an ensemble of ocean analyses for climate assessment and to assess the impact of different assimilation strategies on forecast skill.


37 Uncertainty in Surface fluxes=Uncertainty in ocean state ERA15/OPS versus ERA40 Equatorial Temperature 300m. No assimilation Equatorial Wind Stress Anomaly What happened in 1996?




41 DEMETER and ENSEMBLES Activities

42 The DEMETER heritage DEMETER ended in September However, work has been carried out on forecast quality assessment (additional verification of time series), analysis of the benefits of the multi-model, etc. Research on model calibration and combination has led to strong collaborations with CPTEC (Brazil) and IRI. A special issue of Tellus A has appeared in May Additional work on applications and end-user verification: the case of malaria.

43 Seasonal predictability, Southern Europe 2-4 (JJA) PrecipitationT2m 4-6 (ASO)

44 From Coelho et al. (2005) Calibrated downscaled predictions PAGE agricultural extent PAGE agroclimatic zones

45 Northern box ForecastCorrelationBSS Multi-model Forecast Assimilation Calibrated downscaled predictions From Coelho et al. (2005) Southern box ForecastCorrelationBSS Multi-model Forecast Assimilation

46 ENSEMBLES project Integrated Project funded by the EC within the VIth FP, 69 partners. Start date: 1 September 2004, Duration: 5 years Integrated probabilistic prediction system for time scales from seasons to decades, and beyond. Seasonal-to-decadal hindcasts will be used to assess the reliability of forecast systems used for scenario runs. Comparison of the benefits of the multi-model, perturbed parameters and stochastic physics approaches to assess forecast uncertainty. Great diversity of applications: health, crop yield, energy production, river streamflow, etc.

47 Initial s2d activities (RT1) Main goal: assess best method to estimate model uncertainty among multi-model, perturbed parameter and stochastic physics approaches. Estimates of model uncertainty using a new multi-model ensemble, a recently developed stochastic physics scheme (ECMWF and Met Office) and the perturbed parameters approach (Met Office with 2 different versions of HadCM3). Ocean initial conditions from ENACT and new sets. Common output archived at ECMWF in MARS (atmosphere) and ECFS (ocean). Pre-production for with reduced start dates and expected completion for end Additional experiments to test the consistency of the predictions and the impact of the ensemble size.

48 DEMETER multi-model ENSO predictions Multi-model seasonal (MAM) predictions for Niño3.4 SSTs

49 Three different forecast systems to estimate model uncertainty Multi-model, built from ECMWF, Met Office, Météo- France operational activities and DEMETER experience. Perturbed parameter approach, from the decadal prediction system (DePreSys) at the Met Office. Stochastic physics, from the stochastic physics system developed for medium-range forecasting at ECMWF. Design of a set of common experiments to determine the benefits of each approach.

50 Data storage Archiving at ECMWF (seasonal to decadal) and M&D (decadal to centennial in CERA). Archiving of model levels for Met Office (GloSea), Meteofrance and ECMWF seasonal experiments for use as boundary conditions in limited area models. Use of a common list of variables (minimum requirement) for atmosphere and ocean variables. Atmosphere (GRIB) in MARS and ocean (NetCDF) in ECFS and, eventually, in MARS too. Scripts available for archiving and retrieval. Public data dissemination using a MARS client and an OPenDAP server. Use of the KNMI climate explorer for verification and analysis of extreme events.

51 A service that offers immediate and free access to data from: DEMETER ERA-40 ERA-15 ENACT with monthly and daily data, select area and plotting facilities, GRIB or NetCDF formats Data dissemination Different depending on access granted to ECMWF systems: –access: MARS –no access: public data server and OPenDAP (DODS) server

52 Downscaling for s2d predictions Use dynamical and empirical/statistical methods. Correct systematic errors of global models and obtain reliable (statistical properties similar to the observed data) probabilistic predictions (with only relatively short, i.e., years, training samples). Deal with full ensembles, not a deterministic prediction or the ensemble mean, maximising the benefit of limited simulations with regional models. Consider model and initial condition uncertainty. Generate high-resolution (e.g., daily) time series of surface variables (using, e.g., weather generators with statistical methods).

53 Downscaling for s2d predictions

54 MERSEA Will use the 0.25 degree ocean analyses from MERCATOR. Will couple the 0.25 degree ocean to an atmosphere of comparable resolution to test the impact on an active ocean on medium range forecasts. Will assess the impact of the high resolution ocean analyses on seasonal forecasts. INGV, Meteo France, ECMWF, MERCATOR.

55 EastPac WestPac Eq Atl Eq Ind ERA15/OPS = solid bars ERA40 = dashed bars ERA40/ERA15 Quality of interannual variability: Correlation of SL with Altimeter data Control = solid bars Assimilation= dashed bars EastPac WestPac Eq Atl Eq Ind Impact of Data Assimilation

56 No assim (era40) = solid bars Assim (ERA40) = dashed bars Atl3 NsTrAtl SsTrAtl Natl DIPOLE Impact of Data Assimilation ERA15/OPS = solid bars ERA40 = dashed bars Atl3 NsTrAtl SsTrAtl Natl DIPOLE ERA40/ERA15 Comparison with Altimeter: ATLANTIC REGIONS

57 Impact of number of models Multi-model realizations Single-model realizations

Download ppt "Seasonal forecasting: status and plans David Anderson Tim Stockdale, Magdalena Balmasda, Arthur Vidard, Alberto Troccoli, Paco Doblas-Reyes, Kristian Morgensen,"

Similar presentations

Ads by Google