Recent Developments in Dynamical Climate Seasonal Forecasting

Recent Developments in Dynamical Climate Seasonal Forecasting Francisco J. Doblas-Reyes, Renate Hagedorn, Tim N. Palmer f.doblas-reyes@ecmwf.int European Centre for Medium-Range Weather Forecasts

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.”

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

End-to-end: DEMETER • 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. http://www.ecmwf.int/research/demeter/

Uncertainty initial conditions model formulation Estimation ensemble multi-model multi-model ensemble forecast system N models x M ensemble members Multi-model ensemble approach

DEMETER system: 7 coupled global circulation models Multi-model ensemble system 9 member ensembles ERA-40 initial conditions SST and wind perturbations 4 start dates per year 6 months hindcasts • Hindcast production for: 1980-2001 (1958-2001)

DEMETER system: 7 coupled global circulation models Multi-model ensemble system CNRM (FR) ECMWF (INT) INGV (IT)LODYC (FR)MPI (DE)UKMO (UK) CERFACS (FR) 7 models x 9 ensemble members 63 member multi-model ensemble Feb 87 May 87 Aug 87 Nov 87 Feb 88 ...

DEMETER system: 7 coupled global circulation models Multi-model ensemble system CNRM (FR) ECMWF (INT) INGV (IT)LODYC (FR)MPI (DE)UKMO (UK) CERFACS (FR) Feb 87 May 87 Aug 87 Nov 87 Feb 88 ...

DEMETER system: 7 coupled global circulation models Multi-model ensemble system CNRM (FR) ECMWF (INT) INGV (IT)LODYC (FR)MPI (DE)UKMO (UK) CERFACS (FR) 63 member multi-model ensemble = 1 hindcast Feb 87 May 87 Aug 87 Nov 87 Feb 88 ...

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

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

River basin predictions Multi-model predictions of precipitation over river basins and many other verification diagnostics http://www.ecmwf.int/research/demeter/d/charts/verification/

………… 63 62 4 3 2 1 Downscaling Application model ………… 2 1 63 62 4 3 non-linear transformation 0 0 Probability of Precipitation Probability of Future Crop Yield DEMETER end-to-end methodolgy Seasonal forecast ………… 62 4 3 2 1 63

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., 15-30 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).

Downscaling for s2d predictions http://www.ecmwf.int/research/EU-projects/ENSEMBLES/news/index.html

Wheat yield predictions for Europe 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). Germany France Greece Denmark From P. Cantelaube and J.-M. Terres, JRC

DEMETER Special Issue 2005 Tellus 57A, No. 3, 21 contributions

ECMWF public data server http://data.ecmwf.int/data/ 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

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.

1) Prediction of different time scales Probabilistic seamless forecast system at ECMWF:  1-10 days: medium range EPS (TL399L60)  10 days-1 month: monthly forecast system (TL255L60)  1 month-12 months: seasonal forecast system (TL159L40) 12mth 1mth 10d 01/01 15/01 29/01 01/02 12/02 26/02 01/03

2) 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).

2) Malaria warning: seasonal prediction Relationship between DJF CMAP precipitation and Botswana standardised log malaria incidence for 1982-2002

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

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

Northern box 3) Calibrated downscaled predictions Southern box From Coelho et al. (2005)

4) Anthropogenic effect: T2m predictions 1-month lead, summer (JJA) predictions of global T2m Constant GHG Correlation = 0.52 Variable GHG Correlation = 0.77

5) 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.

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.

Questions?

Uncertainty initial conditions model formulation Estimation ensemble perturbed parameters perturbed parameters ensemble N versions x M ensemble members Generalized ensemble approach

Uncertainty initial conditions model formulation Estimation ensemble with stochastic physics Ensemble with stochastic physics M ensemble members Generalized ensemble approach

Multi-model benefits: Reliability BSS Rel-Sc Res-Sc 0.095 0.926 0.169 0.039 0.899 0.141 0.039 0.899 0.140 -0.001 0.877 0.123 0.047 0.893 0.153 0.065 0.918 0.147 -0.064 0.838 0.099 0.204 0.990 0.213 Reliability for T2m>0, 1-month lead, May start, 1980-2001

River basin predictions Multi-model predictions of precipitation over the Nile basin

ERA / DEMETER data Meteo data JRC’s CGMS in DEMETER Crop Growth Indicator Statistical model Yield Meteo data Jan Feb Aug

Malaria early warning systems gathering cumulative evidence for early and focused response . . . geographic/community focus case surveillance alone = late warning

Areas with epidemic malaria 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)

Forecast assimilation Bayesian procedure: • Climate model ensembles give • But we are interested in , not !!! • Bayes’ theorem updates and obtain

Calibrated South American Precipitation Forecast Assimilation Observations Multi-model • 3 DEMETER coupled models • 1-month lead time DJF precipitation • ENSO composites for 1959-2001 • 16 warm events • 13 cold events r=0.51 r=0.97 r=0.28 r=0.82 (mm/day) From Coelho (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 consultants @ ECMWF • Start date: 1 September 2004, Duration: 5 years • http://ensembles-eu.metoffice.com

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

Recent Developments in Dynamical Climate Seasonal Forecasting