Andy Morse University of Liverpool A.P.Morse@liv.ac.uk WP2: WAM microclimate and applications

1. Andy Morse University of Liverpool A.P.Morse@liv.ac.uk WP2: WAM microclimate and applications (Micrometeorology for health applications)

2. Thanks Moshe Hoshen, Liverpool School of Tropical Medicine – Phil McCall, Anne Jones ECWMF – Paco Doblas-Reyes and Tim Palmer

3. Disease background • Recent malaria model work • Motivation • WP2 details • OWP2 details

4. WP2: WAM microclimate and applications. We aim to quantify the microclimate of the region in the sub-canopy layer in order to downscale global model predictions and earth observation products to the scales and parameters required for disease prediction.

5. Malaria Background • Malaria kills more than 2,000,000 people per year • 90% deaths sub-Saharan Africa -mostly children • Mechanisms of the disease known for over 100 years • Anopheline mosquitoes and parasite Plasmodium spp. with P. falciparum most dangerous and cause of African epidemics Slide 5 of 14

6. Malaria Model malaria life cycle biting/laying: temperature dependent sporogonic cycle: temperature dependent larval stage: rainfall dependent Slide 6 of 14

7. Malaria Model comparison new dynamic and existing rules based models MARA Mapping Malaria Risk in Africa Prevalence = proportion of human population infected with malaria Slide 7 of 14

8. High Average Low Probabilistic Seasonal Forecasting Slide 8 of 14

9. Probabilistic Seasonal Forecasting • EU FP5 DEMETER – multi-model ensemble system www.ecmwf.int/research/demeter • Seven modelling groups running AOGCMs in full forecast mode, 4 start dates per year running out to 6 months, hindcasts 1959 to 2000 • Data available from data.ecmwf.int/data/ • EU FP6 ENSEMBLES www.ensembles-eu.org DEMETER - hindcast biases

10. Brier Skill Scores Feb 2-4 and 4-6 LT is the lower tercile event, AM the above the median event and UT the upper tercile event After Morse et al. 2005

11. Meningitis Model Spatial Distribution Meningitis Epidemics 1841-1999(n = c.425) 1 • Statistical Model to produce a map of risk • Epidemiological data and climatic and environmental variables • Second model under development to predict location, onset and size of epidemics • Initial results promising – needs to be revisited 1 Molesworth A.M., Thomson M.C., Connor S.J., Cresswell M.P., Morse A.P., Shears P., Hart C.A., Cuevas L.E. (2002) Where is the Meningitis Belt?, Transactions of the Royal Society of Hygiene and Tropical Medicine, 96, 242-249.

12. Meningitis Model Model of Predicted Risk • Risk factors: • Land cover type • Seasonal absolute humidity • profile • Seasonal dust profile* • Population density* • Soil type* • Significant but not included in final model • Human factors not included • Molesworth, A.M., Cuevas,L.E., Connor, S.J., • Morse A.P., Thomson, M.C. (2003). • Environmental risk and meningitis epidemics in Africa, Emerging Infectious Diseases, 9 (10), 1287-1293.

13. Selected Recent Papers Molesworth, A.M., Cuevas, L.E., Connor, S.J., Morse A.P., Thomson, M.C. (2003). Environmental risk and meningitis epidemics in Africa, Emerging Infectious Diseases, 9 (10), 1287-1293. Hoshen, M.B., Morse, A.P. (2004) A weather-driven model of malaria transmission, Malaria Journal, 3:32 (6th September 2004)  doi:10.1186/1475-2875-3-32 (14 pages) Morse, A.P., Doblas-Reyes, F., Hoshen, M.B., Hagedorn, R.and Palmer, T.N. (2005) A forecast quality assessment of an end-to-end probabilistic multi-model seasonal forecast system using a malaria model, Tellus A, ( in press)

14. Recent Work

15. Malaria Model: Rainfall dependence Analysis and diagram from Anne Jones

16. Temperature Malaria Model: Temperature dependence Mosquito survival after Martens (1995) At T = 25°C sporogonic cycle length = 15.9 days 2.9% survive to infectious stage Analysis and diagram from Anne Jones

17. Applying ‘malaria models’ key questions and motivation (non exhaustive list) Human Questions – immunity, dry season transmission, clinical records, intervention, early warning systems etc. Mosquito and Parasite Questions – development rates, survivability, pesticide and drug resistance, dry season transmission Physical Environment Questions – local temperature and humidity regimes (in and out), breeding sites and water temperature, - downscaling, rainy season – onset, cessation and break cycle timing, prediction of WAM, heterogeneity of rainfall and vegetation as ‘refuges’.

18. WP2 WAM microclimate and applications: Liverpool PDRA (Morse, Taylor, Parker) WP2.1 To make sub-canopy observations alongside the flux station array, and thereby to quantify the microclimates of the region, in relation to spatial patterns inferred by satellite and aircraft data. Microclimate measurements temperature & RH plus soil and water temperature, radiation, wind speed. Reference to local surface heat towers and aircraft soundings. Link to satellite and aircraft radiometry (Links to WP1 plus WP3 and OWPs 1 and 4) Field experiment EOP

19. WP2.2 To use mesoscale model simulations (at smallest spatial resolutions) to simulate the control of the microclimate by spatial inhomogeneities of surface properties (as provided by WP1 remote sensing). This modelling will be in the form of case studies and idealised simulations. Comparisons made where observations exist. Define spatial variability of variables T & RH etc. across large areas. Link to surface schemes or UCD Advanced Canopy-Atmosphere-Soil-Algorithm ACASA (Pyles et al. 2000) Links to WP1, WP3 and OWP 4 (radiometry) Model lead study linked to remote sensing and measurements

20. WP2.3 To use the observations, along with global, mesoscale / microscale model (UMs) results to explore the sensitivity of environmental malaria development parameters to the model resolution. (i) examine local scale temperature distribution and rainfall variability, particularly with regard to landscape and land use factors, to determine suitability for sustained breeding sites, (ii) examine the daily and seasonal humidity cycles. Links very closely to WP1, WP3 and UEA studentship Observation and model produced drivers to drive an application model Nested models downscaling vs. observations

21. WP2.4 To develop sub-canopy and dwelling microclimate models to use in association with satellite data. To allow extension after detailed observations i. statistical model between microclimate observations and flux stations/regional models ii. statistical models between observations and radiance derived surface temperatures iii Surface schemes – JULES 2.5km WP1 iv Canopy models (Challinor 1D drag and ACASA drive from mesoscale models link WP2.2) can this relink R/S? v. Hut model – simple energy balance model All link to WP2.3, WP1, OWP1. Local models driven by Remote Sensing/ regional models. Modelling studies linked to observations

22. Models Nested Met. Office UM Mesoscale UM Maximising interactions with WP1 and WP3 ? Use only WP1 products or add additional model products? Depends on PDRA. Liverpool job spec. needs to be clear Support of AMMA modelling group (Leeds PDRA, Matthews, Morse, Parker, Pyle, Taylor). Training and support CGAM Support of Leeds and CEH Dynamic R&D malaria model for sensitivity studies Development of different local scale canopy and dwelling modelling techniques – statistical and dynamic energy budget

23. Comment WPs 2.2 and 2.3 link very closely to WPs 1.3 and 1.4 Links to AMMA-EU and AMMA-Africa EU links include EU WP1.2 Surface-atmosphere feedbacks CEH EU WP1.4 Scaling Issues IRD EU WP2.3 Physical and Biological Processes over Land Surfaces (FZK) Impacts Studies EU WPs 3.1, 3.2, 3.3, 3.4 - Land Productivity, Human processes, Water Resources, Health; CIRAD, IGUC, AGHYMET, Liverpool EU WP 4.2 Field Campaigns EOP/LOP IRD WP4.3 Remote Sensing CNRS

24. Liverpool Project Partner Dr Phil McCall, Liverpool School of Tropical Medicine (LSTM). Involvement in planning observations and the conduct of data analysis, ensure our activities meet requirements of the research community working on development and distribution of disease vectors. Liverpool PDRA Oct 05 36 months Work on the data collection and modelling studies, working closely with Leeds, CEH and the LSTM.

25. Observational Work OWP2 Micrometeorology (Liverpool: Morse, Lloyd) EOP activity Micrometeorological measurements included at/near selected flux stations. Temperature, humidity vertical and horizontal profiles within canopy Soil and puddle temperatures with soil moisture Limited windspeed and radiation At least one dwelling will be instrumented for temperature and humidity. Site characterisation for use in surface schemes and canopy models Links to OWP 1

26. Microclimate Sites 2 sites (major and minor) plus and a simple satellite or roving set Primary site Banizoumbou (13˚26΄ N 2˚41΄ E), to the east of Niamey, Niger to include an instrumented straw roof dwelling. Second site Djougou, Benin (9˚40΄ N 1˚34΄ E) Rover either nearby one of the sites or embedded with AMMA-EU health group

27. Equipment Configuration – wish list 1. Basic T & RH (Rover) 10 paired T&RH solid sate sensors, 1 reference T&RH e.g. Vaisala all with mini beehive radiation screens + logger and power. Mini tower and / or means of attachment to vegetation etc. 2. T & RH + secondary site. 1 x Rover + extra T&RH ref (1 vertical profile through plant canopy with a few ‘spatial’ measurements) plus 1 wind speed (basic instrument), 1 net radiation, 2 off soil heat flux, 2 off soil temperature, tipping bucket rain gauge. Infrastructure for deployment within and through plant canopy. 3. T & RH +++ main site 3 x Rover + extra T&RH ref - Justification paired inside and outside measurements on dwelling plus spatial and vertical variability vegetation microclimate. Plus 2 wind speed, two net radiometers, two PAR, 4 soil heat flux, 5 soil temperature – one for use in shallow water pool, tipping bucket rain gauge. Assume solar will be OK from flux station.

28. Links to projects and related activities • FP6 EU ENSEMBLES 15MEu • joint leader RT6 applications and impacts 1.96MEu 28 partners • – leader WP 6.3 and WP 5.5 Probabilistic prediction at seasonal to interannual timescales– 11 partners working on variety applications • FP6 EU AMMA 12.5MEu • - Leader WP 3.4 Health Impacts Climate/health - Benin, Niger, Senegal - malaria, RVF, meningitis • NERC e-Science Ph.D. Anne Jones DEMETER hindcasts - malaria model – GRID Liverpool Cluster jointly with Physics • WCRP CLIVAR WG Seasonal to Interannual Predictions - applications • AMMA ISSC WG 4 Impact and Applications – joint leader • Washington, R., Harrison, M, Conway, D., Black, E., Challinor, A., Grimes, D., Jones, R., Morse, A. and Todd, M (2004). African Climate Report - A report commissioned by the UK Government to review African climate science, policy and options for action, DFID/DEFRA, London, December 2004, pp45 http://www.defra.gov.uk/environment/climatechange/ccafrica-study/index.htm