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CLIMAG Meeting Geneva, 11 May 2005 Recent Developments in Dynamical Climate Seasonal Forecasting Francisco J. Doblas-Reyes, Renate Hagedorn, Tim N. Palmer.

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Presentation on theme: "CLIMAG Meeting Geneva, 11 May 2005 Recent Developments in Dynamical Climate Seasonal Forecasting Francisco J. Doblas-Reyes, Renate Hagedorn, Tim N. Palmer."— Presentation transcript:

1 CLIMAG Meeting Geneva, 11 May 2005 Recent Developments in Dynamical Climate Seasonal Forecasting Francisco J. Doblas-Reyes, Renate Hagedorn, Tim N. Palmer European Centre for Medium-Range Weather Forecasts

2 CLIMAG Meeting Geneva, 11 May 2005 CLIMAG objective “To utilize the ability to predict climate variability on the scale of months to a year to improve management and decision making in respect to crop production at local, regional, and national scales.”

3 CLIMAG Meeting Geneva, 11 May 2005 CLIMAG objective “To utilize the ability to predict climate variability on the scale of months to a year to improve management and decision making in respect to crop production at local, regional, and national scales.” Requirements by the end user: predict climate variability: skilfully deal with uncertainties in climate prediction seasonal-to-interannual time scales: coupled ocean-atmosphere general circulation models variable spatial scale: downscaling

4 CLIMAG Meeting Geneva, 11 May 2005 A user strategy: the end-to-end approach A broad range of forecast products might be offered, but user requirements need to be defined. End-to-end is based on collaboration and continuous feedback. End users develop their models taking into account climate prediction limitations. The level of forecast skill that provides added value is defined by the application: user-oriented verification. End users assess the final value of the predictions. Forecast reliability becomes a major issue.

5 CLIMAG Meeting Geneva, 11 May 2005 Research project funded by the Vth FP of the EC, with 11 partners. Integrated multi-model ensemble prediction system for seasonal time scales. More than a multi-model exercise: seasonal hindcasts used to assess the skill, reliability and value of end-user predictions. Applications in crop yield and tropical infectious disease forecasting. Officially finished in September 2003, but with an operational follow up. End-to-end: DEMETER

6 CLIMAG Meeting Geneva, 11 May 2005 Multi-model ensemble approach Uncertainty initial conditions model formulation Estimation ensemble multi-model multi-model ensemble forecast system N models x M ensemble members

7 CLIMAG Meeting Geneva, 11 May 2005 Multi-model ensemble system DEMETER system: 7 coupled global circulation models Hindcast production for: ( ) 9 member ensembles ERA-40 initial conditions SST and wind perturbations 4 start dates per year 6 months hindcasts

8 CLIMAG Meeting Geneva, 11 May 2005 Multi-model ensemble system Feb 87 May 87 Aug 87 Nov 87 Feb models x 9 ensemble members 63 member multi-model ensemble DEMETER system: 7 coupled global circulation models CNRM (FR) ECMWF (INT) INGV (IT) LODYC (FR) MPI (DE) UKMO (UK) CERFACS (FR)

9 CLIMAG Meeting Geneva, 11 May 2005 Multi-model ensemble system Feb 87 May 87 Aug 87 Nov 87 Feb DEMETER system: 7 coupled global circulation models CNRM (FR) ECMWF (INT) INGV (IT) LODYC (FR) MPI (DE) UKMO (UK) CERFACS (FR)

10 CLIMAG Meeting Geneva, 11 May 2005 Multi-model ensemble system Feb 87 May 87 Aug 87 Nov 87 Feb DEMETER system: 7 coupled global circulation models CNRM (FR) ECMWF (INT) INGV (IT) LODYC (FR) MPI (DE) UKMO (UK) CERFACS (FR)

11 CLIMAG Meeting Geneva, 11 May 2005 Multi-model ensemble system Feb 87 May 87 Aug 87 Nov 87 Feb member multi-model ensemble = 1 hindcast DEMETER system: 7 coupled global circulation models CNRM (FR) ECMWF (INT) INGV (IT) LODYC (FR) MPI (DE) UKMO (UK) CERFACS (FR)

12 CLIMAG Meeting Geneva, 11 May 2005 Forecast quality assessment Forecast quality assessment is a basic component of the prediction process Information about the quality and the uncertainty of the predictions is as important as the prediction itself

13 CLIMAG Meeting Geneva, 11 May 2005 ENSO predictions Multi-model seasonal (MAM) predictions for Niño3.4 SSTs

14 CLIMAG Meeting Geneva, 11 May 2005 Predictions for agricultural areas 1-month lead spring (MAM) T2m over Ukraine 3-month lead early spring (ASO) precipitation over Eastern Australia

15 CLIMAG Meeting Geneva, 11 May 2005 River basin predictions Multi-model predictions of precipitation over river basins and many other verification diagnostics

16 CLIMAG Meeting Geneva, 11 May 2005 DEMETER end-to-end methodolgy 63 ………… Seasonal forecast ………… Downscaling 63 ………… Application model 0 Probability of Precipitation Probability of Future Crop Yield 0 non-linear transformation

17 CLIMAG Meeting Geneva, 11 May 2005 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).

18 CLIMAG Meeting Geneva, 11 May Downscaling for s2d predictions

19 CLIMAG Meeting Geneva, 11 May 2005 France Germany Denmark Greece Wheat yield predictions for Europe From P. Cantelaube and J.-M. Terres, JRC SIMULATIONWEIGHTED YIELD ERROR (%) ± STANDARD ERROR JRC February7.1 ± 0.9 JRC April7.7 ± 0.5 JRC June7.0 ± 0.6 JRC August5.4 ± 0.5 DEMETER (Feb. start) 6.0 ± 0.4 DEMETER multi-model predictions (7 models, 63 members, Feb starts) of average wheat yield for four European countries (box-and-whiskers) compared to Eurostat official yields (black horizontal lines) and crop results from a simulation forced with downscaled ERA40 data (red dots).

20 CLIMAG Meeting Geneva, 11 May 2005 Correlation between de-trended observed and DEMETER ensemble-mean predicted groundnut yields for the period Groundnut yield predictions From Challinor et al. (2005)

21 CLIMAG Meeting Geneva, 11 May 2005 DEMETER Special Issue 2005 Tellus 57A, No. 3, 21 contributions

22 CLIMAG Meeting Geneva, 11 May 2005 ECMWF public data server A service that gives researchers immediate and free access to datasets hosted at ECMWF DEMETER ERA-40 ERA-15 ENACT - Monthly and daily data - Select area - GRIB or NetCDF - Plotting facility

23 CLIMAG Meeting Geneva, 11 May 2005 Future developments Integration of weather and climate predictions at different time scales. Interaction between different climate-related end- user systems. User-oriented verification. Optimisation of the a-posteriori multi-model information through single-model weighting depending on past performance. Anthropogenic impact on seasonal climate predictions. The ENSEMBLES project: probabilistic climate prediction at seasonal, interannual and longer time scales.

24 CLIMAG Meeting Geneva, 11 May ) Prediction of different time scales Probabilistic seamless forecast system at ECMWF:  1-10 days: medium range EPS (T L 399L60)  10 days-1 month: monthly forecast system (T L 255L60)  1 month-12 months: seasonal forecast system (T L 159L40) 10d 1mth 12mth 01/01 01/0201/0315/0129/0112/0226/02

25 CLIMAG Meeting Geneva, 11 May ) Interacting factors: tropical malaria Tropical disease incidence is a major factor affecting food security in tropical/semi-arid areas (socio-economic interaction). The following example deals with uncertainty in malaria prediction using a probabilistic approach to reduce forecast error and can easily be extended to prediction of climate-related yields (uncertainty). The predictions are designed to be included in an early warning system (decision making). Seasonal prediction allows users to become familiar with the use of climate information and understand methods to mitigate the impact of and adapt to future global change (climate change).

26 CLIMAG Meeting Geneva, 11 May ) Malaria warning: seasonal prediction Relationship between DJF CMAP precipitation and Botswana standardised log malaria incidence for

27 CLIMAG Meeting Geneva, 11 May high malaria years -- low malaria years 2) Malaria warning: seasonal prediction Probabilistic predictions of standardised malaria incidence in Botswana five months in advance of the epidemic Very low malaria Very high malaria Available in November Available in March

28 CLIMAG Meeting Geneva, 11 May 2005 From Coelho et al. (2005) 3) Calibrated downscaled predictions PAGE agricultural extent PAGE agroclimatic zones

29 CLIMAG Meeting Geneva, 11 May 2005 Northern box ForecastCorrelationBSS Multi-model Forecast Assimilation ) Calibrated downscaled predictions From Coelho et al. (2005) Southern box ForecastCorrelationBSS Multi-model Forecast Assimilation

30 CLIMAG Meeting Geneva, 11 May 2005 Constant GHG Correlation = ) Anthropogenic effect: T2m predictions Variable GHG Correlation = month lead, summer (JJA) predictions of global T2m

31 CLIMAG Meeting Geneva, 11 May ) The future: 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.

32 CLIMAG Meeting Geneva, 11 May 2005 Summary The multi-model has proven to be an effective approach to reduce forecast error by tackling both initial condition and model uncertainty. The end-to-end approach has shown promising results in seasonal forecasting. There is a clear need to link the research and development carried out about climate variability at different time scales. Seasonal-to-interannual forecasting can evolve into a field where end-users learn to use (and verify) climate information before developing adaptation/mitigation strategies for environmental global change.

33 CLIMAG Meeting Geneva, 11 May 2005 Questions?

34 CLIMAG Meeting Geneva, 11 May 2005 Generalized ensemble approach Uncertainty initial conditions model formulation Estimation ensemble perturbed parameters perturbed parameters ensemble N versions x M ensemble members

35 CLIMAG Meeting Geneva, 11 May 2005 Generalized ensemble approach Uncertainty initial conditions model formulation Estimation ensemble with stochastic physics Ensemble with stochastic physics M ensemble members

36 CLIMAG Meeting Geneva, 11 May 2005 Multi-model benefits: Reliability BSS Rel-Sc Res-Sc Reliability for T2m>0, 1-month lead, May start,

37 CLIMAG Meeting Geneva, 11 May 2005 River basin predictions Multi-model predictions of precipitation over the Nile basin

38 CLIMAG Meeting Geneva, 11 May 2005 JRC’s CGMS in DEMETER Crop Growth Indicator Jan Feb Aug Meteo data Yield Statistical model Meteo data ERA / DEMETER data

39 CLIMAG Meeting Geneva, 11 May 2005 gathering cumulative evidence for early and focused response... case surveillance alone = late warning geographic/community focus Malaria early warning systems

40 CLIMAG Meeting Geneva, 11 May 2005 Malaria warning: seasonal prediction Precipitation composites for the five years with the highest (top row) and lowest (bottom row) standardised malaria incidence for NDJ DEMETER (left) and DJF CMAP (right) Areas with epidemic malaria

41 CLIMAG Meeting Geneva, 11 May 2005 Bayesian procedure: Climate model ensembles give But we are interested in, not !!! Bayes’ theorem updates and obtain Forecast assimilation

42 CLIMAG Meeting Geneva, 11 May 2005 Observations Multi-model Forecast Assimilation (mm/day) r=0.51 r=0.28 r=0.97 r=0.82 Calibrated South American Precipitation From Coelho (2005) 3 DEMETER coupled models 1-month lead time DJF precipitation ENSO composites for warm events 13 cold events

43 CLIMAG Meeting Geneva, 11 May 2005 ENSEMBLES: General information Integrated Project funded by the VI FP of the EC Integrated probabilistic prediction system for time scales from seasons to decades and beyond 69 partners Seasonal-to-decadal hindcasts will be used to assess the reliability of model systems used for climate change experiments Great diversity of climate applications 2 ECMWF Start date: 1 September 2004, Duration: 5 years

44 CLIMAG Meeting Geneva, 11 May 2005 Organization The project is organized in ten Research Themes (RT), ECMWF involvement in red: RT0: Management RT1: Development of the EPS RT2A: Global model engine RT2B: Production of regional climate scenarios RT3: High resolution regional ensembles RT4: Analysis of processes RT5: Evaluation RT6: Assessment of impacts RT7: Scenarios and policy implications RT8: Dissemination and training


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