Providing seamless seasonal to centennial projections for health

impacts of climate changeAndy Morse

School of Environmental Sciences,University of Liverpool, Liverpool, U.K.

A.P.Morse@liv.ac.ukEarth System Science: Global Change, Climate and People,

AIMES Open Science Conference, Edinburgh May, 2010Thanks to: Cyril Caminade and Anne Jones, School of Environmental Sciences, University of Liverpool, Liverpool, U.K.; Matthew Baylis, School of Veterinary Science, University of Liverpool; Helene Guis, CIRAD, Montpellier, France.

Themes

Background, Methods and Results, Discussion

• Reflections on a decade+ of end-to-end (-to end) modelling • Simple thoughts on climate, disease and model integration

• Ensemble prediction, malaria models and towards being seamless from seasons to decades

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• Future integration of ESM dynamic surface fields

Over A Decade of End-to-End Research • Integrated climate impacts with model outputs major European climate

modelling centres, ECMWF, The Met. Office and Metéo France

range of applications at number research institutes, many in Africa

• FP4 PROVOST – data used

• FP5 DEMETER – led impacts groups – seasonal EPS

• FP6 ENSEMBLES – co-led impacts section - EPS and RCM

• FP6 AMMA; NERC AMMA linked to FP6 ENSEMBLES

• NERC EQUIP decadal prediction

• FP7 NERC ERA-NET ENHanCE co-lead and FP7 QWeCI coordinator

• Initial condition multi-model ensemble predictions (probabilistic):

days to decades Climate Variability. Seasonal scales.

• Climate projections - GHG driven global climate models & RCMs

multi-model Climate Change.

• Developed skill base/team/network to extract useful information and integrate with impacts models (and society) for health (food security and water).

• Climate Services agenda.

Background, Methods and Results, Discussion

Introduction• Climate variability important component determining incidence number of diseases (vector-born especially) with significant human and animal health impacts.

• Observed and simulated climate datasets drive models and map the risk of key relevant (animal) and human diseases for the recent past, to verify seasonal scale hindcasts and to project them into the future based on climate simulations.

• Other important non-climatic factors also need to be considered in the disease modelling approach.

• Current few if any disease model have realistic land surfaces and ESM gives an opportunity to develop this aspect of the modelling for future projections

Background, Methods and Results, Discussion

Background, Methods and Results, Discussion

Background, Methods and Results, Discussion

Integrated Climate Model Impacts Verification Paradigm

Background, Methods and Results, Discussion

from Morse et al. (2005) Tellus A 57 (3) 464-475

Seasonal Ensemble Prediction

Introduction, Methods and Results, Discussion

Malaria Prediction Plume

Introduction, Methods and Results, Discussion

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Botswana malaria forecast for February 1989

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LMM (Hoshen and Morse, 2004) driven by DEMETER multi-model 63 members

(ERA-driven model shown in red)

Seasonal Forecasts –decision making contexts

Introduction, Methods and Results, Discussion

Event forecast

Event observed

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‘User defined’ Decision threshold, P

DEMETER multi-model-driven malaria forecasts for above upper tercile malaria, Botswana, November forecast months 4-6 (FMA), compared to observed anomalies from 1982-2001 published index.

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Potential Seasonal Skill in Epidemic Zones for Malaria

Based on the Liverpool Malaria Model simulations driven by seasonal ensemble multi-model outputs (Rainfall and Temperature)

ENSEMBLES Seasonal EPS May 4-6 (ASO) upper tercile epidemic transmission zone ROCSS

Introduction, Methods and Results, Discussion

Mean Annual Malaria Modelled Incidence 1990-2007

Introduction, Methods and Results, Discussion

Mean annual simulated malaria incidence (1990-2007) driven by“Observed datasets” and the ENSEMBLES RCM ensemble

Endemic areas >80%

“Endemic and seasonal” areas between 20-80%

Epidemic Areas (<20%)-> Northen fringe of the Sahel-> Strongly connected to climate variability

Underestimation of the Northern extension of the malaria incidence belt by LMM

ITCZ extends too far north in the RCM world

Shift of the epidemic belt 2031-50 vs 1990-2010

Introduction, Methods and Results, Discussion

Grey: Location of the epidemic belt 1990-2010

Black dots: Future location of the epidemic belt 2030-2050

The epidemic belt location is defined by the coefficient of variation, namely:

Mean Incidence > 1%1stddev > 50% of the average

Southward shift of the epidemic belt over WA

-> to more populated areas...

Earth System Model integration with disease modelling

• Disease models shown here have no realistic land surface• Dynamic vegetation for vector habitats?• Realistic vegetation to constrain temperature cycle/ ranges?• Realistic surface hydrology forvector and parasite life cycle. • Away from ESM – models of society and social systems –

ESM integrated with large agent based modelling???• How do we build seamless systems FP7 QWeCI (months to

decades)

Introduction, Methods and Results, Discussion

Summary

• Demonstrated disease model – seasonal ensemble prediction system integration and impact verification

• Will most diseases respond to climate change or just a few?

• Is it possible that the diseases ‘that matter most’ are the least likely to respond to climate change?

• Society will change +/- disease threat• The use of ESM inputs to improve future disease

projections?

Introduction, Methods and Results, Discussion

QWeCI

FP7 SEVENTH FRAMEWORK PROGRAMME THEME ENV.2009.1.2.1.2 Methods to quantify the impacts of climate and weather on health in developing low income countries

Collaborative Project (small- or medium scale focused research project) for specific cooperation actions (SICA) dedicated to international cooperation partner countries Quantifying Weather and Climate Conditions on health in developing countries (QWeCI)

3.5 MEu EC contribution (~4.7MEu total) 1st Feb 2010 start

13 partners = 7 Africa, 6 EU, Liverpool coordinator, 42 months

UNILIV, CSE, CSIC, ECMWF, IC3, ICTP, ILRI, IPD, KNUST, UCAD,UNIMA, UOC, UP