RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse Integrating seasonal forecasts for health impacts in Africa – the story so far Andy Morse, Department of Geography, University of Liverpool [email protected]Acknowledgements to Anne Jones
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RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Integrating seasonal forecasts for health impacts in Africa – the story so far
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
“Our planet is filled with marvelous science-based opportunities for improving human welfare at a tiny cost,
but these opportunities are often unrecognized by policymakers and the public.”
Jeffery Sachs, Director, Earth Institute at Columbia University
writing about Neglected Tropical Diseases in Scientific American
A thought
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Talk Themes
• Introduction• Background• Research Examples• Discussion & Not Conclusions – Ways Ahead
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Ensemble prediction systems
EU FP5 DEMETER – seasonal ‘end-to-end’ in practice EU FP6 ENSEMBLES – s2d, ACC (AOGCM, ESSM, RCM) – towards seamless ideas and user challengesEU FP6 and NERC-UK AMMA –observation, user validation, model development, model applications EPS, trainingTHORPEX & THORPEX-Africa out to 15 days
NDJF DEMETER ensemble mean precipitation anomaly (mm/day) for i) five highest malaria years, ii) five lowest malaria years in Botswana
from 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). Malaria early warnings based on seasonal climate forecasts from multi-model ensembles, Nature, 439, 576-579.
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Research Examples – statistical models
Quadratic malaria relationship from Thomson et al. (2005) Malaria Index for Botswana (1982 to 2002)
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Research Examples – dynamical modelsbiting/laying:
temperature dependent
sporogoniccycle:
temperature dependent
larval stage:
rainfall dependent
After CDC etc.
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Research Examples – malaria modelling
TemperatureTemperature
Mosquito survivalafter Martens (1995) slide from Anne Jones unpublished Ph.D. thesis
At T = 25°C sporogonic cycle length = 15.9 days
2.9% survive to infectious stage
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
00.05
0.10.15
0.20.25
0.30.35
0.40.45
1 31 61 91 121 151
Forecast Day
Mal
aria
Pre
vale
nce
Research Examples – malaria prediction plume
95
85
65
35
15
5ERA
Botswana malaria forecast for February 1989, LMM driven by DEMETER multi-model
(ERA-driven model shown in red)
Plot from Anne Jones unpublished Ph.D. thesis University of Liverpool
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Research Examples – malaria modellingTier-2 malaria runs - ROC Skill Scores Above Median Event
Nov 2-4 Nov 4-6
DEMETER data set. Areas of high interannual variability were selected and persisted forecast skill was removed from the scores.
Jones, A. and Morse, A. (2007) CLIVAR Exchanges, 43
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Fig. 2: (A) Differences in the annual average model prevalence (in %) and (B) in the standard deviation regarding the annual maximum of the model prevalence (in %) between the last decade of the A1B scenario (2041-2050) and the past period (1960-2000).
Changes in the malaria distribution University of Liverpool, A. Morse & A. JonesUniversity of Cologne, V. Ermert & A. FinkUniversity of Würzburg, H.Paeth
LMM malaria scenarios (2041-2050):• decreased malaria transmission due to precipitation reduction• reduced model prevalence variability in N-Sahel ⇒ fewer epidemics/malaria retreat• 13-16°N: increased variability in the S-Sahelian zone ⇒ more frequent epidemics
in denser populated areas• farther south: malaria transmission remains stable
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
• Allow non-linear mapping of combined ensemble PDFs through time • Allow assessment of downscaling, dressing of ensembles etc.• Define forecast skill and potential user/societal value• Make link to decision makers/stakeholders• Allow linkage across modelling streams – semi seamless approach• Allow assessment of skill improvement across model cycles.
• African users – clear forecasting needs for rains – onset, break cycles, cessation – intraseasonal and interseaonal – early warning of high impacts events
Discussion - Climate Impacts – Integration of users
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Not Conclusions – Ways Ahead
Increasing interest in climate-health links particularly with operational predictions-EPS at medium range, seasonal and climate scales
Need to undertake underpinning health science and integrated surveillance
Need to raise awareness at all levels – students and practitioners to researchers to decision and policy makers
Need to build wider community – few clinicians & further links to zoonoses etc.
Education and training - public and health community and climate community
Funding for short term embedding in climate groups, short courses and pilot projects
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Questions
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
DEMETER, ENSEMBLES, AMMA, THORPEX, CLIVAR
Integration impacts models – Ensemble Prediction SystemsProbabilistic – all lead timesPost processing – downscaling
Continuum: forecast model to customerInterdisciplinary – networking – cross cuttingTimely use of existing climate information
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
User driven – tailoring product, skill requirements, ‘acceptable’ uncertaintyScience – seamless approach, impact models, downscaling, risks, feedback model development, adaptationPolicy – decisions to impact reductionTechnical – ensembles, data, cross cutting, model climates, mitigationTraining – probabilistic – use, validation & uncertainty