1. Providing seamless seasonal to centennial projections for health impacts of climate change Andy Morse School of Environmental Sciences, University of Liverpool, Liverpool, U.K. A.P.Morse@liv.ac.uk Earth System Science: Global Change, Climate and People, AIMES Open Science Conference, Edinburgh May, 2010 Thanks 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.

2. 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 Future integration of ESM dynamic surface fields

3. 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

4. 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

7. Integrated Climate Model Impacts Verification Paradigm Background, Methods and Results, Discussion from Morse et al. (2005) Tellus A 57 (3) 464-475

8. Seasonal Ensemble Prediction Introduction, Methods and Results, Discussion

9. Malaria Prediction Plume Introduction, Methods and Results, Discussion Botswana malaria forecast for February 1989 95 85 65 35 15 5 ERA LMM (Hoshen and Morse, 2004) driven by DEMETER multi- model 63 members (ERA-driven model shown in red)

10. Seasonal Forecasts –decision making contexts Introduction, Methods and Results, Discussion Event forecast Event observed YesNo YesHit (a)False alarm (b) NoMiss (c)Correct rejection (d) ‘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.

11. 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

12. 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

13. 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...

14. 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

15. 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

16. 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) 1 st 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