8.0 Applications8.1 Status report on use of and need for research data in

seasonal applications

8.1.1. Experience and progress from recent and ongoing projects (ENSEMBLES, UniCantabria Downscaling Portal, AMMA, QWECI)

Andy MorseSchool of Environmental Sciences,

University of Liverpool, Liverpool, U.K.A.P.Morse@liv.ac.uk

CLIVAR WGSIP13 Buenos Aires, Argentina, 29-31 July 2010

Cyril Caminade, Dave MacLeod 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.

Introduction and Themes

Background, Methods and Results, Discussion

• Update and connects through research projects

• Recent user experiences – NGOs and Government Research

• Plots of distributed seamless activity (works in progress)

• The climate services agenda

Recent User Experiences• NGOs (major UK based international development and aid charities)

Humanitarian Futures Programme, Kings College Londonhttp://www.humanitarianfutures.org/main/

• UK government bodies and commercial bodies in EQUIP and ENHanCE projects

• African government programmes and decision makers through African partners in QWeCI and HealthyFutures

• How do we widen participation?

• How do we leave climate information is a useable way through targeted narratives?

• How does this experience link with current Climate Services Agenda initiatives?

Background, Methods and Results, Discussion

Seasonal Scales

Introduction, Methods and Results, Discussion

Seasonal Ensemble Prediction

Introduction, Methods and Results, Discussion

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

Introduction, Methods and Results, Discussion

Forecast MonthsBelow LT Above Median Above UT

Rainfall JJA 0.073 (0.050) 0.068 (0.041) 0.083 (0.050)

Degree days above 18 ºC ASO 0.342 (0.027) 0.200 (0.021) 0.143(0.031)

Malaria Incidence -

ENSEMBLES multi-

model

ASO 0.148 (0.046) 0.261 (0.057) 0.235(0.056)

Seasonal prediction of malaria epidemic risk in West Africa Potential skill using ENSEMBLES re-forecasts to drive a malaria model

Skill of multi-model forecasts derived from ENSEMBLES May start date, averaged over 15 high-variability incidence grid points in WA. (15N: 17.5W to 7.5W, 12.5N: 2.5W to 12.5E, and 5N: 10E to12.5E). Standard error in brackets. Measured relative to skill of NCEP reanalysis-driven simulations.

-1

0

1

0 1 2 3 4 5 6 7

1. ECMWF2. UK Met Of f ice3. Max Planck

4. INGV5. Met France6. Multi-model

Seasonal prediction of malaria epidemic risk in West Africa

Introduction, Methods and Results, Discussion

Potential skill using ENSEMBLES re-forecasts to drive a malaria model

Model

Skill of above the median forecasts for LMM-simulated incidence over May forecast months 4-6, 13 high-variability grid points in WA. (15N: 17.5W to 7.5W, 12.5N: 2.5W to 12.5E, and 5N: 10E to12.5E). Measured relative to skill of NCEP reanalysis-driven simulations . Scatter points show grid point values, solid black circles show areal mean.

Dabbling with Decadal

Introduction, Methods and Results, Discussion

are working on in

and

Correlation coefficient (ensemble mean vs obs) =

0.233

Distribution of ensemble members from first 5 years of ENSEMBLES decadal forecasts, observations: NCEP reanalysis

Tem

pera

ture

[K]

Predicting the AMO?

Using the first 5 years of decadal hindcast experiments (except for the final 2005-2015 forecast)

after van Oldenborgh et al 2010 GRL

Climate DiagnosticsWorking with users on recent climate variability and trends, towards producing climate products, filling decadal gap with RCM runs

Start with recent past climate using high resolution Eobs for Europe, ENSEMBLES RCM runs for Europe

Need capture real variability – do these runs SRES GCM-RCM runshave even average variability?

Should we even ask that question of GCM-RCM runs?

How do we use RCM to fill decadal gap?

RCMs with s2d initial condition ensembles?

Observed Climatic Trends: 1961-2004

Introduction, Method, Climatic Trends, Health Impact examples

Wetter and warmer winters over Northern Europe, warmer and drier winters over Southern Europe.More drought conditions over the Mediterranean basin in summer

Future Changes: 2030-2050 vs 1960-2000

Introduction, Method, Climatic Trends, Health Impact examples

Warming, faster over northern Europe in winter and southern Europe in summer.

The winters get wetter over northern Europe for both seasons.

Strong drying signal over the Mediterranean basin in summer.

Shading: changesDots: 80% of the climate models agree on the sign of changes

EOBS observation in black

ENSEMBLES RCM CTL ensemble (ERA40 driven) in blue

ENSEMBLES RCM SRESA1B ensemble (GCM driven)

The envelope d(red thin lines) depicts the spread (2stddev) of the CTL (SRESA1B) model ensemble with respect to the mean

Recent climate T2m PDF JJA 1961-2000

Introduction, Method, Climatic Trends, Health Impact examples

T2m PDF JJA 2030-2050 vs 1961-2000

ENSEMBLES RCM SRESA1B ensemble (GCM driven)

1961-2000: Orange

2030-2050: Red

The envelope (thin red lines) The envelope (thin red lines) depicts the spread (2stddev) of the depicts the spread (2stddev) of the model ensemble with respect to model ensemble with respect to the meanthe mean

-> shift to warmer summers-> shift to warmer summers-> spread increases in the future-> spread increases in the future

Introduction, Method, Climatic Trends, Health Impact examples

Health Impact examples for Europe

Mean Bluetongue Risk (OBS): ASO 1961-2008

Health Impact examples: Bluetongue over Europe

From Guis et al, 2010

High BT risk over Spain, Portugal, High BT risk over Spain, Portugal, south western France, Sardegna south western France, Sardegna and Sicilia.and Sicilia.

This misses out observed This misses out observed outbreaks in Corsica outbreaks in Corsica Unrealistic values over mountains Unrealistic values over mountains and Eastern Europeand Eastern Europe

ShadingShading: Ro risk (arbitrary scaled : Ro risk (arbitrary scaled between 0 and 1)between 0 and 1)

Bluetongue Risk changes: 2030-2050 vs 1961-2000

From Guis et al, 2010

MULTI-MODEL CHANGES: MULTI-MODEL CHANGES: MEANMEAN

MULTI-MODEL MULTI-MODEL SPREAD: MAGNITUDESPREAD: MAGNITUDE

MULTI-MODEL MULTI-MODEL SPREAD: SIGN SPREAD: SIGN CONSISTENCYCONSISTENCY

The BT risk increases over UK, Southern France and North-western Spain (Galicia)The BT risk increases over UK, Southern France and North-western Spain (Galicia)

Changes in Northern Europe are related to the pathogen propertiesChanges in Northern Europe are related to the pathogen properties

Changes in Southern Europe are associated with the spread of the Afro-Tropical vector (Imicola spp)Changes in Southern Europe are associated with the spread of the Afro-Tropical vector (Imicola spp)

Health Impact examples: Bluetongue over Europe

Health Impact examples for Africa

Mean seasonal cycle 1990-2007

Health Impact examples: Malaria Climatic Risk over Africa

Hovmoeller like diagram (zonal average between 16W and 16E)

Shading: RainfallContours: Malaria Incidence

Underestimation of the northern extension of the malaria incidence belt by LMM

2-3 months LAG between rainfall and malaria incidence

Mean annual malaria incidence 1990-2007

Health Impact examples: Malaria Climatic Risk over Africa

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 Underestimation of the Northern extension of Northern extension of the malaria incidence the malaria incidence belt by LMMbelt by LMM

ITCZ extends too far ITCZ extends too far north in the RCM worldnorth in the RCM world

Mean Incidence changes SON 2031-50 vs 1990-2010

Health Impact examples: Malaria Climatic Risk over Africa

Simulated changes in Malaria Simulated changes in Malaria Incidence (SON) based on the Incidence (SON) based on the different RCMsdifferent RCMs

-> common feature: decrease -> common feature: decrease of the Malaria Incidence at of the Malaria Incidence at the Northern fringe of the the Northern fringe of the SahelSahel

-> Related to changes in the -> Related to changes in the number of rainy days (and not number of rainy days (and not the seasonal amounts)the seasonal amounts)

Ndione et al, 2008

RVF risk

Dry spell followed by a rainfall peakduring the late rainy season (Sep-Oct)over Northern Senegal Rehydrating ponds mosquitoes hatching + hosts availability high RVF risk

Caminade et al, 2010 (in review)

Rift Valley Fever risk (%) based on rainfall from ERAINTERIM reanalysis (1990-2007). The number of RVF risk events is defined by a dry spell (10 consecutive days with total rainfall below 1mm) followed by a convective event (high precipitation defined by one or two days following the dry spell above the 90th percentile) occurring during the late rainy season (SON). The total number of RVF risk events is then rescaled to range between 0 and 100% to define the risk. The dotted, crossed and filled black areas depict animal host densities (cattle + buffalo + sheep + goats) above 1, 10 and 100 per km2 (FAO, 2005).

RVF risk

RVF climatic risk 1990-2007Health Impact examples: Rift Valley Fever over West Africa

OLR Hovmoeller Diagram (averaged between 12°N and 18°N).

OLR Anomaly for 2002 (NCEP).

Brown: Convective event

Black Box: Senegal location

2 weeks predictability???-> Value of medium range forecasts

RVF outbreak

10-15 days predictability?

Synoptic situation: Senegal RVF outbreak 2002

Health Impact examples: Rift Valley Fever over West Africa

Climate Services Agenda - a seamless one?• Who is doing what, where and with whom e.g. can we share good practise and/or join forces?

• Are Met Services interacting as much as possible with other researchers working on impacts and data use?

• Who is thinking seamlessly across multiple timescales?

• Who is developing this seamlessness with the user community?

• How can WGSIP, CLIVAR, WCRP help connect this Agenda with the impacts community?

Introduction, Methods and Results, Discussion

Summary to seamlessness• Grand ensemble approach – combined ensembles from different systems –

bound uncertainty, maximise skill, model climates

• Impacts model portability – develop models work different climate streams and grand ensemble – impact uncertainty, integrated model value

• Field and Environmental Observations – verification and dynamic insight

• Model data post processing – downscaling, bias correction, dressing

• Continuity to society – decision makers, product tailoring, decision support systems, understanding critical uncertainty and thresholds, agent based models, combination with remote sensing and observations, through to policy and project impact on society

Introduction, Methods and Results, Discussion

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) 1st 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

http://www.equip.leeds.ac.uk/

Introduction

Improving the use of climate prediction by quantifying, understanding and managing uncertainty.

Through working with stakeholders, the EQUIP team will develop new methodologies and analyses for using climate information that will be employed by decision makers in a set of case studies.

We will quantify and understand the uncertainty surrounding future droughts, heatwaves, crop production and marine ecosystems.

EQUIP: End-to-end Quantification of Uncertainty for Impacts Prediction

• Edinburgh, Newcastle, Liverpool• NERC directed research• EQUIP network (external users and

academics) is a core part of our research

Extra Slides

Introduction, Methods and Results, Discussion

Integrated Climate Model Impacts Verification Paradigm

Background, Methods and Results, Discussion

from Morse et al. (2005) Tellus A 57 (3) 464-475

Relative contributions of uncertainties

Hawkins & Sutton, 2009, BAMS, 90(8):1095-1107

Model uncertainty

Scenario uncertianty

Internal variability

Introduction, Methods and Results, Discussion

UK Rainfall and Temperature Trends: EOBS

Rainfall 1961-2004 Temperature 1961-2004

Introduction, Method, Climatic Trends, Health Impact examples

UK extremes in winter: EOBS

Heavy rainy days: 1961-2004 Frost days: 1961-2004

Introduction, Method, Climatic Trends, Health Impact examples

Recent climate T2m PDF DJF 1961-2000

EOBS observation in black

ENSEMBLES RCM CTL ensemble (ERA40 driven) in blue

ENSEMBLES RCM SRESA1B ensemble (GCM driven)

The envelope d(red thin lines) The envelope d(red thin lines) depicts the spread (2stddev) of the depicts the spread (2stddev) of the CTL (SRESA1B) model ensemble CTL (SRESA1B) model ensemble with respect to the meanwith respect to the mean

Problems with 2 models (freezing Problems with 2 models (freezing days too frequent)days too frequent)

Introduction, Method, Climatic Trends, Health Impact examples

T2m PDF DJF 2030-2050 vs 1961-2000

ENSEMBLES RCM SRESA1B ensemble (GCM driven)

1961-2000: Orange

2030-2050: Red

The envelope (thin red lines) depicts the spread (2stddev) of the model ensemble with respect to the mean

-> shift to warmer winters-> spread increases in the future

Introduction, Method, Climatic Trends, Health Impact examples

Observed Climatic Trends: extremes 1961-2004

Introduction, Method, Climatic Trends, Health Impact examples

Largest Increase of winter rainfall extremes over the western coasts of the UK and Norway.

Decrease in the number of frost days in winter over Europe

Significant increase of warm days and warm nights over the Mediterranean basin in summer Health Impacts

Observed Climatic Trends extremes

Introduction, Method, Climatic Trends, Health Impact examples

Wetter and warmer winters over Wetter and warmer winters over Northern Europe, warmer and Northern Europe, warmer and drier winters over Southern Europedrier winters over Southern Europe

More drought conditions over the More drought conditions over the Mediterranean basin in summerMediterranean basin in summer

Bluetongue Risk ASO, Northern Europe

Health Impact examples: Bluetongue over Europe

From Guis et al, 2010

2006: BT outbreak in France Benelux and Germany captured by EOBS and the CTL exp

Increasing trend for the future over Northern Europe

Simulated Relative Ro BT changes (with respect to 1961-2000) over Northern Europe.

Black: BT risk based on EOBSBlue: BT risk based on CTL expRed: BT risk based on SRESA1B exp

The relative envelope depicts the spread within the RCMs ensemble (1 Stddev)

Liver Fluke: Fo=f(T,Rdays)

Work with J. Van Dijk

Fo: The total predicted Fo: The total predicted number of adult progeny number of adult progeny arising from pasture arising from pasture contamination by a single contamination by a single fluke present in a non-fluke present in a non-immune sheep for one year.immune sheep for one year.

X axis: Temperature (°C)X axis: Temperature (°C)Y axis: Fraction of rainy days Y axis: Fraction of rainy days (1 means it rains every day, 0 (1 means it rains every day, 0 no rain, based on the 1mm no rain, based on the 1mm threshold).threshold).

106

103

102

Health Impact examples: Liver Fluke over the UK

Mean Malaria “climatic” Risk: JAS 1990-2008

Based on LMM simulations driven by observed Rainfall and Temperature from different observed datasets.

Northern Italy, some parts of Galicia in Spain and the “Landes” region in France are climatically “at risk”

The incidence values are relatively low in magnitude (20-50%) compared to what can be expected in Africa.

Health Impact examples: Malaria Climatic Risk over Europe

Shift of the epidemic belt 2031-50 vs 1990-2010

Health Impact examples: Malaria Climatic Risk over Africa

Gray: Location of the Gray: Location of the epidemic belt 1990-2010epidemic belt 1990-2010

Black dots: Future location of Black dots: Future location of the epidemic belt 2030-2050the epidemic belt 2030-2050

The epidemic belt location is The epidemic belt location is defined by the coefficient of defined by the coefficient of variation, namely:variation, namely:

Mean Incidence > 1%Mean Incidence > 1%1stddev > 50% of the average1stddev > 50% of the average

Southward shift of the Southward shift of the epidemic belt over WAepidemic belt over WA

-> to more populated areas...-> to more populated areas...

Health Impact examples: Rift Valley Fever over West Africa

RVF Risk based on the control RCMs ensemble (runs driven by ERAINTERIM at the boundaries). The analysis is carried out for the period 1990-2007.Problems as most of the models overestimate the northward extension of the ITCZ.KNMI and DMI pattern relatively realistic with respect to the reanalysis / GPCP driven runs.

RVF risk estimated from ERAINT

RVF climatic risk based on RCMs: 1990-2007

Health Impact examples: Rift Valley Fever over Africa

RVF distribution map according to the Central for Disease Control and prevention. Blue areas show where RVF is endemic

RVF risk as estimated from a) ERAINTERIM and b) GPCP for the whole African continent.

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Introduction, Methods and Results, Discussion