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Using Seasonal Forecasts

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  1. Using Seasonal Forecasts Francisco J. Doblas-Reyes f.doblas-reyes@ecmwf.int

  2. Forecasts are relevant for users The user needs climate information to take action and mitigate the adverse effects of climate

  3. Long-range forecast objective “To utilize the ability to predict climate variability on the scale of months to a year and beyond to improve management and decision making in respect to users’ needs at local, regional, and national scales.”

  4. Long-range forecast objective “To utilize the ability to predict climate variability on the scale of months to a year and beyond to improve management and decision making in respect to users’ needs 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

  5. 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.

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

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

  8. ………… 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 Crop Yield/Incidence Extremes for users: end-to-end Climate forecast ………… 62 4 3 2 1 63

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

  10. 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).

  11. Examples of applications • Malaria incidence prediction in an epidemic region (Botswana). • Crop yield prediction for Europe (wheat) and western India (groundnut). • Seasonal streamflow prediction over tropical and subtropical watersheds.

  12. Predictions for large agricultural areas 3-month lead early spring (ASO) precipitation over Eastern Australia 1-month lead spring (MAM) T2m over Ukraine

  13. Seasonal forecast data Meteo data JRC’s CGMS Crop Growth Indicator Statistical model Yield Meteo data Jan Feb Aug

  14. 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

  15. Groundnut yield predictions with a LAM Correlation between de-trended observed and DEMETER ensemble-mean predicted groundnut yields for the period 1987 -1998 From Challinor et al. (2005)

  16. Malaria early warning systems gathering cumulative evidence for early and focused response . . . geographic/community focus case surveillance alone = late warning From M. Thomson (IRI)

  17. Malaria warning: meteorological factors Limiting variables for malaria development as obtained with the MARA rule-based model and ERA40; white areas are influenced by all factors The number of meteorological variables required by the users is large and changes with the region considered From A. Jones (Univ. of Liverpool)

  18. Malaria warning: link to climate Statistical relationship between DJF CMAP precipitation and Botswana standardised log malaria incidence for 1982-2002

  19. Areas with epidemic malaria Climate forecasts for malaria warning Precipitation composites for the five years with the highest (top row) and lowest (bottom row) standardised malaria incidence for DJF DEMETER (left) and CMAP (right) Quartiles define extreme events (epidemics) in malaria prediction

  20. Very low malaria -- high malaria years -- low malaria years Available in March Available in November Very high malaria Malaria warning with statistical model Probabilistic predictions of standardised malaria incidence quartile categories in Botswana with five months lead time

  21. Cumulative frequency Daily rainfall (mm) Rainfall histograms (CERFACS, all Botswana grid points, November start date, 1980-2001) ERA40 raw model correct model Dynamical malaria model: bias correction Daily precipitation as required by the Liverpool Malaria Model Daily rainfall from the CERFACS experiment (25°E, 22.5°S, November start date, 1980-2001), correction applied separately for dry and wet days, with wet days corrected with a ratio End users require probabilistic models that correct biases, downscale to the appropriate grid and are able to produce daily time series with the correct extremal properties From A. Jones (Univ. of Liverpool)

  22. Malaria warning: nonlinearity Malaria index for Botswana from Thomson et al. (2006) and incidence simulated by the Liverpool malaria model (LMM) using ERA40 There is a disagreement between both models for the year 2000: is it due to the impact of extreme temperature or precipitation? Interaction of climate variables may affect the user predictions From A. Jones (Univ. of Liverpool)

  23. Interacting factors in end-user systems • The predictions are designed to be included in an early warning system (decision making). • Tropical disease incidence is an important factor affecting food security in tropical/semi-arid areas (socio-economic interaction). • The previous 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 crop yields (uncertainty). • 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).

  24. Climate change and climate variability • The possibility of adaptation to climate change via a learning process taking place at the interannual time scale is an obvious way to achieve a high degree of integration of climate time scales. • It implies: • Involvement of both climate scientists and end-users • That both scientists and end users/stakeholders consider the whole range of time scales • As an example, crop managers see the adaptation to long-term climate change as a process that takes place on a yearly basis and that benefits from predictions at various time scales.

  25. 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/

  26. Combined/calibrated seasonal predictions 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 et al. (2006)

  27. Calibrated downscaled predictions PAGE agricultural extent PAGE agroclimatic zones

  28. Calibrated downscaled predictions Seasonal predictions of NDJ precipitation (3-month lead time) Northern box Southern box From Coelho et al. (2006)

  29. Calibrated downscaled predictions Seasonal predictions of NDJ precipitation (3-month lead time) From Coelho et al. (2006)

  30. Summary • The multi-model ensemble 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, especially in a probabilistic framework. • There is a clear need to link the research and development carried out about climate variability at different time scales and the users’ needs. • 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 global change.

  31. Questions?