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Andy Morse

University of Liverpool

A.P.Morse@liv.ac.uk

WP2: WAM microclimate and applications

(Micrometeorology for health applications)

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Thanks

Moshe Hoshen, Liverpool School of Tropical Medicine – Phil McCall,

Anne Jones

ECWMF – Paco Doblas-Reyes and Tim Palmer

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1. Disease background2. Recent malaria model work3. Motivation4. WP2 details5. OWP2 details

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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.

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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

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Malaria Model malaria life cycle

Slide 6 of 14

sporogonic cycle:

temperature dependent

biting/laying:

temperature dependent

larval stage:

rainfall dependent

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Malaria Model comparison new dynamic and existing rules based models

MARA

Slide 7 of 14

Prevalence = proportion of human population infected with malaria

Mapping Malaria Risk in Africa

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Probabilistic Seasonal Forecasting

High

Average

Low

Slide 8 of 14

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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

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Precipitation LT AM UT

Feb 2-4 (MAM) -0.094 -0.009 -0.020

Feb 4-6 (MJJ) -0.012 -0.039 -0.049

Temperature LT AM UT

Feb 2-4 (MAM) 0.080 0.148 0.230

Feb 4-6 (MJJ) 0.104 0.210 0.314

Prevalence LT AM UT

Feb 2-4 (MAM) 0.396 0.461 0.046

Feb 4-6 (MJJ) 0.167 0.289 0.178

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

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Meningitis ModelSpatial 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.

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LowMediumHighVery High

Meningitis Model Model of Predicted Risk

Risk factors:

Land cover typeSeasonal absolute humidityprofile

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.

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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)

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Recent Work

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Malaria Model: Rainfall dependence

Analysis and diagram from Anne Jones

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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

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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’.

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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

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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

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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

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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

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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

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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 CEHEU WP1.4 Scaling Issues IRDEU 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 IRDWP4.3 Remote Sensing CNRS

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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.

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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

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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

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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 site3 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.

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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