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Forecasting the risk of malaria epidemics using climate prediction models. Tim Palmer ECMWF. Weather/Climate Prediction. Weather (1-10 days) Seasonal to Decadal (  6 months-10 years) Climate change (10-100 years). El-Niño. Global impact of El-Niño. The thermohaline circulation.

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weather climate prediction
Weather/Climate Prediction
  • Weather (1-10 days)
  • Seasonal to Decadal ( 6 months-10 years)
  • Climate change (10-100 years)
slide10

Numerical Models of Weather and Climate

  • Weather – atmosphere
  • Seasonal – atmosphere-ocean
  • Climate – Earth System
slide13

“… one flap of a sea-gull’s wing may forever change the future course of the weather” (Lorenz, 1965)

slide15

Lorenz (1963): prototype model of chaos

In a nonlinear dynamical system, the finite-time growth of initial uncertainties is flow dependent. Scientific basis for ensemble forecasting

October 1987!

ensemble forecasting in weather prediction
Ensemble Forecasting in Weather Prediction

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Perturb initial conditions consistent with uncertainty in observations

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Forecast Probability of Temp or Precip…

representing model uncertainty
Representing model uncertainty
  • Multi-model ensembles
  • Perturbed parameter ensembles
  • Stochastic physics ensembles
slide21



Development of a

European Multi-Model Ensemble System

for

Seasonal to Interannual Climate

Prediction

demeter multi model ensemble system
• 7 global coupled ocean-atmosphere climate modelsDEMETER 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

multi model ensemble climate forecast system
Multi-Model Ensemble Climate Forecast System

CLIMATE MODEL B

CLIMATE MODEL A

CLIMATE MODEL G

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Forecast Probability of Temp or Precip…

slide24

Predicting El Niño

ECMWF model only

DEMETER multi-model ensemble

Palmer et al, 2004; Hagedorn et al 2005

slide25

Models

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

demeter end to end forecast system
DEMETER End-to-end Forecast System

Seasonal

forecast

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Downscaling

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Application

model

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non-linear transformation

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Probability of Crop Yield/ Malaria Incidence

Probability of Precip & Temp…

slide29

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

slide30

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)

slide31

Areas with epidemic malaria

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

demeter based pdfs of malaria incidence for botswana forecasts made 5 months in advance of epidemic
DEMETER-based PDFs of malaria incidence for Botswana (forecasts made 5 months in advance of epidemic)

5 years with highest observed malaria incidence

5 years with lowest observed malaria incidence

slide33

-- high malaria years

-- low malaria years

Low malaria incidence

High malaria incidence

Cumulative PDFs of standardised malaria incidence in Botswana five months in advance of the epidemic

slide34

Overview of Liverpool Malaria Model

10 day rainfall

Mosquito population

Daily temperature

Malaria transmission -mosquito

Daily temperature

Daily temperature

Humidity (10 day rainfall)

Daily Malaria incidence(number of new cases) and prevalence (proportion of population infected)

Malaria transmission - human

Hoshen and Morse, 2004 Malaria Journal 3(32)

slide36

Malaria Transmission Model simplified schematic of Liverpool model

Uninfected

Infected

Infectious

death

death

death

Maturing larvae

Uninfected

Infected

Infectious

(Sporogonic cycle)

Mosquito

Infection

Infection

Human

Recovery

  • Underlying model is similar to that described by Aron and May (1982)
  • Model assumes no immunity, no superinfection
slide37

Malaria Transmission Model

  • where
    • x1 = proportion infected humans
    • x2 = proportion infectious humans
    • y1 = proportion infected mosquitoes
    • y2 = 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 - 

demeter malaria prediction
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

http www ecmwf int research demeter
http://www.ecmwf.int/research/demeter

DEMETER data can be freely downloaded

meningococcal epidemic meningitis land erosion neisseria meningitidis
Meningococcal (epidemic) meningitis – land erosionNeisseria 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
slide41

The spatial distribution of epidemics

Affected districts

(n = 1232 / 3281)

Reported to district

Reported to province

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

slide43

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

slide44

Cholera and climate

Lagged correlation between SSTs and cholera in Dhaka, Bangla Desh (data from International Centre for Diarrhoeal Disease Control) over 1980-2002

From X. Rodo (Univ. of Barcelona)

conclusions
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.