integrating seasonal forecasts for health impacts in...
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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
Andy Morse, Department of Geography,
University of Liverpool
Acknowledgements to Anne Jones
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
Introduction -Project Links and Roles
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Introduction
• Important connections between climate and disease
• Climate variability can be important for epidemics
• Climate is not the only factor in causing diseases – even those with strong climate drivers
• Limited understanding of climate by health practitioners (& vice versa) – unlike climate and agriculture.
• Significant challenges to get decision and policy makers to useclimate information – in development and health
• Chance to develop early warning systems to improve preparednessand targeting of meagre resource in area with poor health services
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
IntroductionChallenges to improving climate - health research
• Interdisciplinary – language – working and research practises
• Lack of contact – academic risks – lacking cross disciplinary funding
• Lack of knowledge (and health tailored dissemination) of forecasts/climate products
• Uncertainty in climate forecasts, access and use of climate data
• Health data – rarely integrated, paucity, quality and access
• Critical mass of researchers
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
IntroductionWeather and Climate Models
• Numerical weather forecasting – single high resolution model (25km) few days
• Medium range ensemble prediction systems (EPS) 10 to 15 days (80km) 50 members
• Monthly EPS - ‘just available’ – persisted SST
• Seasonal EPS 180 day integrations 50 members (125km) coupled ocean
• Decadal scale EPS very experimental – currently 13 months out to 10 years
‘decadal gap’ period 2010 to 2050
• Climate models – typically run through late 20th century out to 2100 (100 to 200km)multiple single model runs - range of scenarios
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Background
Source: UNEP GRID Arendalhttp://www.grida.no/
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Background
Source: UNEP GRID Arendal http://www.grida.no/
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Background
WorldMapper – Peters equal area projection
Source: http://www.worldmapper.org© Copyright 2006 SASI Group (University of Sheffield) and Mark Newman (University of Michigan)
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Background
WorldMapper – malaria deaths
Source: http://www.worldmapper.org© Copyright 2006 SASI Group (University of Sheffield) and Mark Newman (University of Michigan)
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Background
WorldMapper – killed by drought
Source: http://www.worldmapper.org© Copyright 2006 SASI Group (University of Sheffield) and Mark Newman (University of Michigan)
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Background - malaria
Mean monthly climatic data at Bukoba(a) and malaria cases reported by Ndolage hospital (b) for the period 1991–1999.
Jones et al. Malaria Journal 2007 6:162 doi:10.1186/1475-2875-6-162
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Background
• Time between trigger threshold to epidemic peak often too short to take effective
intervention – need for skilful and timely seasonal climate forecast
Epidemic Cycle
0
2 04 0
6 0
8 0
100120
140
9745
9748
9751
9802
9806
9809
9812
9815
9818
9821
9824
9827
9830
9833
9841
9844
9847
R ep o r ting w e ek
Nu
mb
er o
f ca
Vaccine
ThresholdEffect
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Background – ensemble prediction systems – seasonal forecasts
Chart from ECMWF
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Background – EPS seasonal
Chart from ECMWF
Sahel
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Background – EPS medium range
Chart from ECMWF
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Banizoumbou AWS Jul-2 to Sept-1 rainfall
0
5
10
15
20
25
30
35
40
4518
3
186
189
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207
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243
Julian Day 2006
Rai
n (m
m)
Background – rainfall – single season
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Background – rainfall interannual
Niamey-Aero, Niger 13.5N 2.1E
0.0
200.0
400.0
600.0
800.0
1000.0
1200.0
1900 1920 1940 1960 1980 2000
Ann
ual R
ainf
all (
mm
)
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
The Forecasting Triangle
DemandForecasts
Training + Product Guidance and Development
DisseminationDissemination
FeedbackFeedback
Providers Users
Developers with users and providers
Morse in prep.
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Background -scalesGlobal model – regional impacts – local and microscale processes
kms to 100s m cm to mm
1000s to 100s km metre
Africa to mosquito 9 orders of magnitudeEarth-Sun distance to
galaxy scale
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Research Examples – verification paradigm
from Morse et al. (2005)Tellus A 57 (3) 464-475
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
Research Examples – verification
Original MARA map Craig et al., 1999
www.mara.org.za
Based on model Craig et al. 1999 www.mara.org.zarun with ERA-40
slide from Anne Jones, University of Liverpool
RGS-IBG CCRG Africa April 2008 – Seasonal Forecasts and Health - Andy Morse
-0.42 -0.35 -0.28 -0.21 -0.14 -0.07 0.07 0.14 0.21 0.28 0.35 0.42
Research Examples – predicting rain anomalies
-0.42 -0.35 -0.28 -0.21 -0.14 -0.07 0.07 0.14 0.21 0.28 0.35 0.42
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
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