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

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Forecasting the risk of malaria epidemics using climate

prediction models

Tim Palmer

ECMWF

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Weather/Climate Prediction

• Weather (1-10 days)

• Seasonal to Decadal ( 6 months-10 years)

• Climate change (10-100 years)

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El-Niño

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Global impact of El-Niño

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The thermohaline circulation

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Numerical Models of Weather and Climate

Weather – atmosphere

Seasonal – atmosphere-ocean

Climate – Earth System

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

X X Y

Y XZ rX Y

Z XY bZ

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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!

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Ensemble Forecasting in Weather Prediction

50………… 494321

Perturb initial conditions consistent with uncertainty in observations

Forecast Probability of Temp or Precip…

0

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956

AN 19871016, 06GMT

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EPS Cont FC +66 h

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MLSP 66-hour forecasts, VT: 16-Oct-1987, 6 UTC

TL399 EPS with TL95, moist SVs

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

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20The 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 / CO2 etc

( )

( )

X X Y

Y XZ rX Y

Z XY

f

Z

f

b

t

t

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Probability MapsRisk of cold / warm winter 2002/03Risk of wet / dry winter 2002/03

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Representing model uncertainty

• Multi-model ensembles

• Perturbed parameter ensembles

• Stochastic physics ensembles

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Development of a

European Multi-Model Ensemble System

forSeasonal to Interannual Climate

Prediction

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DEMETER Multi-model ensemble system

Partner Atmosphere Ocean

ECMWF IFS HOPE

LODYC IFS OPA 8.3

CNRM ARPEGE OPA 8.1

CERFACS ARPEGE OPA 8.3

INGV ECHAM-4 OPA 8.2

MPI ECHAM-5 MPI-OM1

UKMO HadCM3 HadCM3

• 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

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Multi-Model Ensemble Climate Forecast System

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Forecast Probability of Temp or Precip…

1 3 4 95 6 7 8

CLIMATE MODEL A

21 3 4 95 6 7 8 21 3 4 95 6 7 8…

CLIMATE MODEL B

CLIMATE MODEL G

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ECMWF model only

DEMETER multi-model ensemble

Palmer et al, 2004; Hagedorn et al 2005

Predicting El Niño

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Model 1971-1990 Resolution Reference20c3m A1B A2 B1

1 CCSM3 X X X X T85 L26 Collins et al. (2005)2 CGCM3.1(T47) X X X X T47 L313 CGCM3.1(T63) X T63 L314 CNRM-CM3 X X X X T63 L45 Salas-Melia et al. (2005)5 CSIRO-Mk3.0 X X X X T63 L18 Gordon et al. (2002)6 ECHAM5/MPI-OM X X X X T63 L31 Jungclaus et al. (2005)

7 FGOALS-g1.0 X X X 2.8ox2.8o L26 Yongqiang et al. (2004)

8 GFDL-CM2.0 X X X X 2.5ox2o L24

9 GFDL-CM2.1 X X X X 2.5ox2o L24

10 GISS-AOM X X X 4ox3o L12 Russel et al. (1995)

11 GISS-EH X X 5ox4o L20

12 GISS-ER X X X X 5ox4o L20

13 INM-CM3.0 X X X X 5ox4o L21 Diansky & Volodin (2002)

14 IPSL-CM4 X X X X 2.5ox3.75o L19 IPSL (2005)15 MIROC3.2 (hires) X X X T106 L5616 MIROC3.2 (medres) X X X X T42 L2017 MRI-CGCM2.3.2 X X X X T42 L30 Yukimoto & Noda (2002)18 PCM X X X X T42 L18 Dai et al. (2004)

19 UKMO-HadCM3 X X X X 3.75ox2.5o L19 Pope et al. (2000)

Hasumi & Emori (2004)

SRES 2081-2100

Flato et al. (2005)

Delworth et al. (2005)

Schmidt et al. (2005)

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

Models

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

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

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DEMETER End-to-end Forecast System

63………… 624321Seasonal forecast

………… 63624321 Downscaling

63………… 624321Application

model

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Probability of Precip & Temp… Probability of Crop Yield/ Malaria Incidence

0

non-linear transformation

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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. 2006, 439, 576-579.

Special issue of Tellus (vol 57a number 3) devoted to DEMETER

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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)

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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

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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

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

-- high malaria years

-- low malaria years

Low malaria

incidence

High malaria

incidence

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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

Andy Morse

[email protected]

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Mosquito Population Model

15 Days

Immature mosquito Adult mosquito

Gonotrophic cycle

Daily Survival

Probability

2

1

D

D

R

RP

DRrfacNeggs .laying

Adult mortality

9

371

Tg

15 Days

Immature mosquito Adult mosquito

Gonotrophic cycle

Gonotrophic cycle

Daily Survival

Probability

2

1

D

D

R

RP

DRrfacNeggs .laying

Adult mortality

9

371

Tg

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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 death death

Maturing larvae

Mosquito

Human

Uninfected Infected Infectious

Uninfected Infected Infectious

InfectionInfection

(Sporogonic cycle)

Recovery

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12122121 )exp()ˆˆ1(ˆ)1( rxrxxyabmxxabmyx x

22122 )exp()ˆˆ1(ˆ rxrxxyabmx x

12122121 )exp()ˆˆ1(ˆ)1( yyyxacyyacxy y

22122 )exp()ˆˆ1(ˆ yyyxacy y where

x1 = proportion infected humansx2 = proportion infectious humansy1 = proportion infected mosquitoesy2 = proportion infectious mosquitoesa = human biting rate of mosquitob = human susceptibility to infectionc = mosquito susceptibility to infectionm = mosquito to human population ratior = human recovery rate = mosquito mortality ratex = latent period in humany = latent period in mosquito (sporogonic cycle)and , indicate those variables at time t -

Malaria Transmission Model

x y

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DEMETER: malaria predictionVerification DEMETER-MM: Ensemble-mean Terciles

Time series for grid point in South Africa (17.5 S, 25.0 E)

Morse et al, 2005

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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

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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

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Potential for predicting dust using Sea Surface TemperaturesSST anomaly pattern

associated with dustiest years in NigerBen Mohamed and Neil Ward

Dustiest years inferred from visibility data are 1974, 1983, 1985, 1988, 1991, 1994

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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)

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

DEMETER data can be freely downloaded

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Conclusions• Climate models are beginning to become sufficiently realistic

that reliable predictions of temperature and rainfall are possible on weather and seasonal 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.