1. Forecasting the risk of malaria epidemics using climate prediction models Tim Palmer ECMWF

4. Weather/Climate Prediction Weather (1-10 days) Seasonal to Decadal ( 6 months-10 years) Climate change ( 10-100 years)

6. El-Niño

7. Global impact of El-Niño

8. The thermohaline circulation

10. Numerical Models of Weather and Climate Weather – atmosphere Seasonal – atmosphere- ocean Climate – Earth System

13. … one flap of a sea-gulls wing may forever change the future course of the weather (Lorenz, 1965)

15. In a nonlinear dynamical system, the finite-time growth of initial uncertainties is flow dependent. Scientific basis for ensemble forecasting Lorenz (1963): prototype model of chaos October 1987!

16. Ensemble Forecasting in Weather Prediction 50 ………… 49 4 3 2 1 Perturb initial conditions consistent with uncertainty in observations Forecast Probability of Temp or Precip… 0

17. MLSP 66-hour forecasts, VT: 16-Oct-1987, 6 UTC TL399 EPS with TL95, moist SVs

18. Risk of Beaufort force 12 winds 6- 12am October 16 th 1987

20. The influence of f on the state vector probability function is itself predictable. f=0 f=2 f=3 f=4 Add slowly-varying term f to the Lorenz (1963) equations to represent effect of ocean / CO 2 etc

21. Probability Maps Risk of cold / warm winter 2002/03 Risk of wet / dry winter 2002/03

22. Representing model uncertainty Multi-model ensembles Perturbed parameter ensembles Stochastic physics ensembles

23. Development of a European Multi-Model Ensemble System for Seasonal to Interannual Climate Prediction

24. DEMETER Multi-model ensemble system 7 global coupled ocean-atmosphere climate models Hindcast production for: 1980-2001 9 member ensembles ERA-40 initial conditions SST and wind perturbations 4 start dates per year 6 months hindcasts

25. Multi-Model Ensemble Climate Forecast System 2 Forecast Probability of Temp or Precip… 134 95678 CLIMATE MODEL A 2 1 34 95678 2 1 3 495678 … CLIMATE MODEL B CLIMATE MODEL G

26. ECMWF model only DEMETER multi-model ensemble Palmer et al, 2004; Hagedorn et al 2005 Predicting El Niño

27. IPCC (AR4) multi-model multi-scenario ensemble - seasonal mean near-surface temperature - Models

28. Probability of above average seasonal precipitation associated with global warming

29. Probability of seasonal temperature above 95% ile. associated with global warming

30. Probability of seasonal precipitation below 5% ile. associated with global warming

31. DEMETER End-to-end Forecast System 63 ………… 62 4 3 2 1 Seasonal forecast ………… 63 62 4 3 2 1 Downscaling 63 ………… 62 4 3 2 1 Application model 0 Probability of Precip & Temp… Probability of Crop Yield/ Malaria Incidence 0 non-linear transformation

32. DEMETER and Malaria A forecast quality assessment of an end- to-end probabilistic multi-model seasonal forecast system using a malaria model.. A. P. Morse, F.-J. Doblas-Reyes, Moshe B. Hoshen, R. Hagedorn, T.N.Palmer. Tellus, 57a, 464-498 Malaria early warnings based on seasonal climate forecasts from multi- model ensembles. M.C. Thomson, F.J.Doblas-Reyes, S.J.Mason, R.Hagedorn, S.J.Connor, T.Phindela, A.P.Morse and T.N.Palmer. Nature to appear Special issue of Tellus (vol 57a number 3) devoted to DEMETER

33. Thomson, M.C., S.J.Connor, T.Phindela, and S.Mason: Rainfall and sea surface temperature monitoring for malaria early warning in Botswana. Am.J.Trop.Med.Hyg., 73, 214- 221 (2005)

34. Precipitation composites for the five years with the highest (top row) and lowest (bottom row) standardised malaria incidence for DEMETER (left) and CMAP (right) Areas with epidemic malaria

35. DEMETER-based PDFs of malaria incidence for Botswana (forecasts made 5 months in advance of epidemic) 5 years with lowest observed malaria incidence 5 years with highest observed malaria incidence

36. Cumulative PDFs of standardised malaria incidence in Botswana five months in advance of the epidemic -- high malaria years -- low malaria years ROC ScorePrecipitationIncidence EventCMAPDEMETERCMAPDEMETER Low1.00 (1.00-1.00) 0.95 (0.82- 1.00) 1.00 (1.00- 1.00) High1.00 (1.00-1.00) 0.52 (0.25- 0.78) 0.94 (0.80- 1.00) 0.84 (0.65- 0.98) Low malaria incidence High malaria incidence

37. Overview of Liverpool Malaria Model Hoshen and Morse, 2004 Malaria Journal 3(32) 10 day rainfall Daily temperature Mosquito population Malaria transmission - mosquito Malaria transmission - human Daily temperature Humidity (10 day rainfall) Daily Malaria incidence (number of new cases) and prevalence (proportion of population infected) Daily temperature

38. Malaria Model malaria life cycle sporogonic cycle: temperature dependent biting/laying: temperature dependent larval stage: rainfall dependent

39. Mosquito Population Model

40. Malaria Transmission Model simplified schematic of Liverpool model Underlying model is similar to that described by Aron and May (1982) Model assumes no immunity, no superinfection death Maturing larvae Mosquito Human UninfectedInfectedInfectious UninfectedInfectedInfectious Infection (Sporogonic cycle) Recovery

41. where x 1 = proportion infected humans x 2 = proportion infectious humans y 1 = proportion infected mosquitoes y 2 = proportion infectious mosquitoes a = human biting rate of mosquito b = human susceptibility to infection c = mosquito susceptibility to infection m = mosquito to human population ratio r = human recovery rate = mosquito mortality rate x = latent period in human y = latent period in mosquito (sporogonic cycle) and, indicate those variables at time t - Malaria Transmission Model

42. DEMETER: malaria prediction Verification DEMETER-MM: Ensemble-mean Terciles Time series for grid point in South Africa (17.5 S, 25.0 E) Morse et al, 2005

43. http://www.ecmwf.int/research/demeter DEMETER data can be freely downloaded

44. Meningococcal (epidemic) meningitis – land erosion Neisseria meningitidis Transmission of N.meningitidis is by direct droplet contact 20-40% of the population in West Africa are symptomless carriers Meningococcal meningitis occurs when the bacteria penetrate the mucous membrame Changes in the proportion of clinical to subclinical infections rather than the risk of infection are thought to explain changes in the incidence of disease

45. Affected districts (n = 1232 / 3281) Reported to district Reported to province The spatial distribution of epidemics Molesworth, A.M. Thomson, M.C. Connor, S.J. Cresswell, M.C. Morse, AP. Shears, P. Hart, C.A. Cuevas, L.E. (2002). Where is the Meningitis Belt? Transactions of the Royal Society of Tropical Medicine and Hygiene 96, 242-249

47. Potential for predicting dust using Sea Surface Temperatures SST anomaly pattern associated with dustiest years in Niger Ben Mohamed and Neil Ward Dustiest years inferred from visibility data are 1974, 1983, 1985, 1988, 1991, 1994

48. Lagged correlation between SSTs and cholera in Dhaka, Bangla Desh (data from International Centre for Diarrhoeal Disease Control) over 1980-2002 Cholera and climate From X. Rodo (Univ. of Barcelona)

49. Conclusions Climate models are sufficiently realistic that reliable predictions of temperature and rainfall are possible on weather and climate timescales Uncertainties in prediction are associated with sensitivity to initial conditions and model formulation. The effect of these uncertainties can be represented using ensemble prediction techniques Application models can be coupled to climate models allowing probabilistic predictions of user-relevant variables; weather/climate variables are intermediate Health-based applications include studies of epidemic malaria in Africa – there is the potential for other quantitative health-based applications.