Climate as Risk Factor

Mean Seasonal Cycle

Interannual Variability

Seasonal Prediction

Operational Seasonal Forecast

IRI

Ouest African Monsoon Variability and Predictability

Sylwia TrzaskaInternational Institute for Climate and Society

Earth Institute at Columbia UniversityNew York, USA

With contribution of many others

West African Monsoon

Region in west Africa ca.10-20N One rainy season July-Sept Seasonal transequatorial south-westerly low level wind

Mean Seasonal Cycle

Seasonal circulations over Africa

• Seasonality of the rainfall linked to seasonal large scale circulations •SWly circulations bring moisture inland• flux convergence forces ascent and precipitation• moisture fluxes influenced by thestate of the SST and of the land surface

Jul-Sept

WAfr Monsoon – Seasonal cycle

AMJ – first rainy season on the coast JAS – rainy season in the Sahel, little dry season on the coast OND – slow southward retreat of the rainbelt, second rainy season on the coast the installation of the rainbelt in the Sahel is NOt progressive – ‘monsoon jump’

J F M A M J J A S O N D

strong meridional gradient of annual totals; 100mm/100km Sahel region on the edge of sustainable agriculture (ca 600mm/yr), very sensitive to small rainfall deficits

Hoevmoller RR diagramme: 10E-10W Annual Rainfall totals (in mm)

WAf Monsoon – Meridional View

TEJ

Source: C. Thorncroft

Intertropical discontinuity (ITD) – boundary btwn monsoon and saharan air masses Shallow heat low to the north of ITD Northern part of the rainbelt – intermittent convection More sustained convection ca 10N

Rainy events

Rainfall occurs within Mesoscale Convective Systems (MCS)propagating westward

Seasonal Cycle - upwelling

JAS – equatorial and coastal upwelling in the Gulf of Guinea Enhances meridional sea-land thermal gradient

SST anomalies / mean annual SST

pro

bab

ility

of o

ccu

rren

ce

Climatological HL detectionERA 40: 1967-2001 ERA 401 value / day (0600 UTC)

Using the Low Levels Atmospheric Thickness (LLAT). upper boundary : 700 hPa,top of upward wind

lower boundary : 925 hPato avoid topography below 800m

- HL occ prob- shaded- 925hPa wind- Horizontal wind CV - blue lines

Seasonal Cycle – Heat LowMean seasonal evolution of the Heat Low location

Courtesy C. Laveysse

WAfr Monsoon – mean features

AEJ

Cold Tongue

SAL

ITCZ

Heat Low

Source: C. Thorncroft

Mean Features and Seasonal Cycle Summary

strong meridional gradient of annual RR

meridional shift of the rainbelt

• 2 rainy seasons over the coast• JAS rainy season over the Sahel

seasonal features

• upwelling in Gulf of Guinea and equatorial Atlantic• heat-low over Sahara• AEJ

Rainfall within eastward progating MCS

Interannual Variability

WAfr Monsoon Variability

Rainfall RegionalizationBased on automatic classification of station data

Janicot, 1991, J Clim

Sahel Rainfall – Variability

200

250

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350

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650

700

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

OBS

Linear

Decadal

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-150

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1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

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1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

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-150

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200

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

27% Variance

18% Variance

55% Variance

Observed rainfall in theSahel

! Strong decadal component

WAfr Monsoon Variability2 initial hypotheses Land surface feedback

‘Charney mecanism’ Early 70’s Positive feedback between reduced rainfall and albedo

Reduced rainfall => reduced vegetation/desertification=> change in albedo => change in energy balance at the surface => compensation by subsidence => further reduction in rainfall

does not explain rainfall recovery in mid 70’s or late 90’s amplitude of albedo changes needed >>observed

Sea Surface Temperatures Atlantic Ocean - direct impact on moisture flux unstable ENSO influence trend linked to large scale SST gradient (interhemispheric gradient or ‘Folland mode’) and/or Indian Ocean warming-

GCM forced with observed SST can successfully reproduce some of the features of Sahelian Rainfall Variability

AGCM simulations

Models can usually capture decadal and interannual rainfall variability in the Sahel based on SST

Giannini et al. 2003, Science

AGCM Simulations

Early simulations by Folland et al.1986

• SST composite between Dry Sahel (1972-73,1982-84) and Wet Sahel (1950,1952-54,1958)

• Rainfall response in 180d simulations forced by composite SST anomalies (exp-ctrl, in mm/day)

Dipolar RR response to global SST forcing Role of each basin unclear

Atlantic Ocean

Direct influence on thermal gradient and moisture fluxes Lamb (1978) found a well defined SST anomalies in the tropical Atlantic associated with 1968 drought

Atlantic ‘Dipole’ debate in the 1990’s• 2nd EOF of tropical Atlantic Variability• ‘Dipole Index’ associated with Sahel rainfall Variability (r=0.42), Servain (1991)• Challenged by Houghton and Tourre(1992) as artifact of EOF method• Mechanism of Wind-SST positive feedback proposed by Chang et al. (1997) but did not explain phase changes • although systematic out-of-phase variability has been ruled out Tropical Atlantic Variability is still not well understood• part of Atlantic Meridional Overturning Circulation (MOC)

Numerical studies of the influence of Atlantic SST alone did not generate significant anomalies in the Sahel, only in the coastal area (Vizy and Cook, 2001, personal experience)

Relationship with SST

Sahel RR Index and Global SSTEpoch Correlation

1950-69

1970-90

Janicot et al. 2001 Clim. Dyn

Strong influence of the 1982-83 ENSO

Sahel RR Index and SST IndicesRunning Window Correlations

Sahel-ENSO Sahel-ENSO partial (otherl removed)

Sahel-Coast Sahel- SAtl EOF Sahel – NAtl EOF Sahel – ‘global mode Sahel-ENSO Atl removed

Long term SST changes

Slow change in the background SST state

Can this change explain change in the teleconnexions?

Idealized SST forcing experiments

CLIVARCLIVAR

NA

ESA South Atlantic

ENSO Interannual

Global decadal&

trend

North Atlantic

AGCM (ARPEGE-Climat)

•Forced by idealized SST anomalies

•Linear combinations of global SST REOF & basin forcings

Main Results:

•Increase of impact of ENSO in recent context

• no clear signal in Atlantic only forcing => need for larger scale anomalies

• decreased Rainfall in recent global context

• decreased rainfall in Indian Ocean forcing

Warmer Indian Ocean influences the location of ENSO related subsidence and generates subsidence over Sahara

! Result model-dependant

Impact of the Indian Ocean

Bader and Latif, 2004, GRL

AGCM (ECHAM) forced by

•observed global SST anomalies (Wet-Dry)

•obs SST anomalies in the Ind Oc only

•Uniform -1K anomaly in the Ind Oc

Main Results:

• Increased rainfall in all cases, stronger in global and uniform Ind case

Rowell (2003) Impact of E Mediterranean via moisture advection

Land surface &

Vegetation

WAfr Monsoon – Land&veg feedback

Zeng et al. 1999, Science

SST – primary driver of the variabilityLand and Vegetation feedbacks add multiyear persistenc of anomalies

WAfr Monsoon Variability - Summary

Strong decadal component SST primary driver of the variability

Land surface & vegetation may act as amplifiers

Multiple influences- Atlantic- ENSO- Indian Ocean/interhemispheric dipole

Intstability in the teleconnections: periods of stronger Atlantic/ENSO influence Captured by GCM forced by observed SST

Climate Change

WAfr Monsoon – Climate ChangeIPCC rainfall projection in JJA Inconsistency between models

Models that reproduced late XX century drying disagree in the future

most likelychange

model agreement

Biasutti and Giannini, 2006

XX century anomalies Future anomalies

IPCC 2008

Seasonal Prediction

WAfr Monsoon – Seasonal Prediction

Basis – slow evolution of SST, predictability of ENSO

2 methods

Statistical: based on statistical relationships btwn variables established on past data

Advantages : easy to establish, understand, implement Drawbacks: fixed relationships based on past observations – unstabilities exist

Dynamical: uses GCMs, 1tier – fully coupled models2 tier – atm GCM forced by predicted SST

Advantages: no prior assumptions on linkages Drawbacks: heavily relies on abilities of GCM to correctly represent the monsoon and the teleconnections

Pbs : equatorial Atlantic

Ex. Multiple regression onto 4 SST predictors (indices)

WAMME AGCM seasonal cycle

Courtesy Y. Xue

GCMs and the equatorial Atlantic SST

Error in RR prediction linked to error in SST

• Difficulties to predictin May the JAS equatorial Atlantic SST

• Interaction btwn monsoon flow and SST in the Gulf of Guinea

• Coupled models have strong systematic biases : wrong equatorial SST gradient, location of the Subtropical High Pressure Center...

In Forced models problems in propagation of eq Atlantic related anomalies into Sahel (Vizy and Cook, 2001, Giannini et al, 2003, personal experience)

Goddard and Mason, 2002 Clim Dyn

CCA btwn errors in SST and errors in RR forecats

GCMs and Teleconections

Coupled Models do not reproduce main teleconnection patterns

Most of the models DO NOT simulate Teleconnection to Equatorial and Tropical S Atlantic.

Models DO NOT simulate consistent decadal variability.

Most of the models DO simulate RR Sahel - Tropical Pacific teleconnection,some with reversed sign.

Observed RR-SST teleconnection patterns,

Joly et al. 2007, Clim Dyn

Simulation of the seasonal cycle with different SST in the Atlantic

JAS mean

Re

yn

old

s S

ST

EC

HA

M-S

OM

EC

HA

M-M

OM

Differences with CMAP

mo

nth

mo

nth

mo

nth

Mean Seasonal cycleSST Average eq-5S

ECHAM4.5 forced, with SOM and fully coupled

Operetional Seasonal Forecast

Seasonal Prediction in Africa

PRES-AO (11)

GHACOF (22)

PRES-AC (3)

SARCOF (11)

PRESANOR

11 years of COFs 1997-2008

(Climate Outlook Forums)

Seasonal Forecast in West AfricaPRESAO

Consensus Rainfall Forecast for JAS2008 issued early June 2008

Zone 1 : Humid ; Zone 2: Very Humid; Zone 3: NormalNB: Chances of rainfall deficit are negligible

Source: ACMAD

Seasonal Forecast in west AfricaPRESAO

Statistical Hydrological Forecast for JAS2008 issued early June 2008

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#Bui

Lai

Ati

BaroSeme

BaKel

BonouBambou

Niamey

Lokoyo

Simenti

Moundou

FaranahBeterou

Kedougou

Makourdi

N'Djamena

Kouroussa

Dialakoro

GouloumbouGarbe-Kouro

Douna (Bani

Dioila (Bao

Pankourou (Mallanville

Sarh

STATIONS HYDROMETRIQUESPRESAO II

BassinsChariComoéGambiaGorubalKomaduguNigerSénégalVolta

Limite des PaysLimite Cours d'eauCours d'eau

404020

453520

254530

503020

503020

354025

453520

403525

354520

Source: AGRHYMET

PRESAO – a consensus process

Regional Forecast

NationalForecasts

(« local» scales)

18 CountriesGCMs

Forecasts(« large» scales

and SSTs)

4 « Global » Producing Centres

OtherForecasts

African Desk

Consesnsus discussion

Verification

PREASO –National Forecasts

Statistical Method NAEANINO

EOF3

2 4 6 8 10 12 14 16

12

14

16

18

20

22

BKonni

Dosso M agariaM aine

M aradiNiam ey

Tahoua

TillabéryZinder

Diffa

GouréNguigm i

Gaya

Zone1 Zone2

Zone3

- 5 - 4 - 3 - 2 - 1 0 1 2

- 5 - 4 - 3 - 2 - 1 0 1 2

1 0

1 1

1 2

1 3

1 4

1 5

1 0

1 1

1 2

1 3

1 4

1 5

Zone_Nord = 0.34 - 0.2 * EOF45Skill = 0.45

Zone_Centre = 0.38 - 0.03 * NIN67 - 0.17 * EOF34Skill = 0.52

Zone_Sud = 0.12 - 0.14 * EOF45 + 0.04 * EA56SKill = 0.49

1 2 3

7

8

9

10

11

12

CENTRE

NORD

Sud Z NORD

SUD

• Multiple regression on SST indices; model trained on 1960-91 period

• Each country divided in several ‘zones’

• Probabilities estimated from past model performance

PRESAO – use of global products (1)

Currently• Analysis of global forecasts

issued by International Centers (ECMWF, UKMet, Meteo-France, NCEP, IRI etc)

• Analysis of current SST statet and its forecasted evolution

• Evaluation of the consistency between global and national forecasts

PRESAO – use of global products (2)

In progress• Using GCM outputs via CPT –

MOS correction, downscalling• Introduced since a couple of years

… work in progress

Ex. Evaluation of predictability in Gambia using ECHAM4.5 2-tier forecasts started in May, June and July

Courtesy Lamin Mai Touray, Dept. Water Resources, Gambia

May

ROC

Jul

ROC

Jun

ROC Importance of

updates during the rainy season

Examples of Use of forecast

Evénement et échelle

Population potentiel-

lement affectée

Impact

(Fréquence) Mai Juin Juillet Août Sept

Famine à échelle régionale

Millions d'habitants

Survie dépendante de l'aide alimentaire et de l'action des Organisations internationales

Alerte par PRESAO

Confirmation alerte par le

FIT. Mobilisation internationale

Prévision ZAR et mission terrain pour dimension catastrophe Planification logistique et mobilisation aide alimentaire

Identification zones les plus vulnérables - Distribution des stocks de sécurité dans ces zones prioritaires

Distribution des stocks nationaux

de sécurité Envoi de l'aide

alimentaire internationale

(1 an sur 10)

Crise alimentaire diffusée dans plusieurs pays

Centaines de milliers d'habitants

Survie dépendante de l'aide alimentaire internationale

Pre-alerte par lPRESAO

Alerte par le FIT.

Confirmation des alertes par ZAR - Front de

Végétation

Suivi des zones à risque par biomasse et FV - Prévision des rendements (DHC-SISP) Mission terrain Mobilisation aide alimentaire internationale

Identification des zones et groupes vulnérables Mobilisation stocks nationaux leur distribution dans les zones les plus vulnérables

(1 an sur 5)

Crise alimentaire dans zones de différents pays

Dizaines de milliers d'habitants

Provision alimentaire par système national à travers l'aide alimentaire et le commerce régional

Pré-alerte par le FIT.

Alerte par ZAR - et Front de végétation

Suivi des zones à risque et confirmation des alertes par DHC - SISP et d'autres modèles (Biomasse Front de végétation)

Identification des zones vulnérables Mobilisation des stock nationaux de sécurité

(1 an sur 2)Suivi des marchés

Suivi des marchés à la suite de la campagne précédente

Suivi des marchés à la suite de la campagne précédente

Informations et actions

CHRONOGRAMME DES ACTI ONS DE PREVENTI ON DES CRI SES ALI MENTAI RES

Using PRESAO forecast in the food security Early Warning System in Niger

Courtesy Adamou Aïssatou SITTA, DMN Niger

Examples of Use of forecast

Courtesy J.-P Ceron, Meteo-France

Using Seasonal Forecast for Senegal River Basin Management

MANANTALI

KAEDI, october 1999

BAKEL

0 50 100 150 200 250 km

Multi-user dam• Hydropower, • flow regulation: flood control, irrigation,water for flood recession agriculture,minimum ecological impact

Manantali Dam, Senegal River

Examples of Use of forecast

0

500

1000

1500

2000

2500

3000

1975 1980 1985 1990 1995 2000 2005

forecasted

observed

year

natu

ral dis

charg

e (

m3/s

)

R2 = 0.6507

0

500

1000

1500

2000

2500

3000

0 500 1000 1500 2000 2500

1979-2000 (calibration)2000-2005 (validation)80% interval90% interval

Qd = f (V1,…V5) : forecasted natural discharge (m3/s)

observ

ed n

atu

ral dis

charg

e (

m3/s

)August 20 – reservoir management decision for water release for traditional agriculture Sept-Oct, given electricity and irrigation demands Sept-July

Management strategy using Aug-Oct seasonal forecast made at Meteo-France end of July

=> Forecast water stock in the reservoir at the end of the monsoon season

Courtesy J.-P Ceron, Meteo-France

Using Seasonal Forecast for Senegal River Basin Management

Forecast Verification

• Lack of knowledge that the forecast exists

• Lack of understanding of the forecast and its validity domain

• Inadequate format of the forecast

• Lack of confidence in the forecast

•Forecast communication (media)

•Users capacity building

•Forecasts tailoring

•Forecasts verification

What hinders forecast uptake

Verification of forecast quality ≠ Verification of forecast Value

Standard WMO recommended Methods for Probabilistic Forecast Verification: Reliability Diagram, RPSS…

Forecast Verification 1st Attempt to Verify PRESAO product Only 10 year sample – big sampling problem Verification using Gridded rainfall dataset

• Consistently under-forecasting above normal and overforecasting normal category

• Forecasting system has capacity to discriminate btwn above- and below-normal seasons.

• Better skill to predict dry than non-dry years

• Skill Score indicates that PRESAO is better than climatology for JAS

Managing Climate related risk

Managing the Full Range of Variability: Prevent negative outcomes in unfavorable climatic conditionsMaximize advantages in favorable climate conditions

Climatic outcome (e.g. production, income)

Pro

ba

bili

ty d

en

sit

y

CR

ISIS

HA

RD

SH

IP

FOR

EFI

TED

OPPO

RTU

NIT

Y

Into the future

WHY?• Lack of real use of SIP, notably because of the lack of relevant and tailored

products (nature of the product, forecast period, etc.).

• Needs for recalibration of statistical models used in the RCOF process so far (mainly calibrated over the 1961-1990 period), keeping in view the multi-decadal variability.

• The potential of products based on Coupled General Circulation Models (CGCMs), mainly in terms of the possible forecast periods, possible range of products and future improvements.

• A new WMO framework for LRF notably GPCs issuing seasonal forecasts in operational mode.

• Substantial improvement in the skill can be achieved in correcting bias GCMs (notably spatial) and adapting GCM outputs in terms of relevant spatial scales for the benefit of SIP product users.

• While the science of SIP indicates that one should start from the large-scale signal and large-scale forecast and then proceed to perform downscaling and tailoring the current system tends to work the opposite way (from country up-scale)

Targeted framework

NMHSsDownscaling

Tailoring

Large Scale ClimateInformation

Monitoring - Forecasts

ACMAD

Users

NationalPartners

RegionalPartners

InternationalPartners

National Organisation& NGO

MWG

MEDIA

RegionalUsers

MEDIA

Summary

Strong decadal component and multiple SST influences make the forecasting of West African Monsoon challenging

Nevertheless attempts have been made forecast 11 years and the results are encouraging

Numerous improvements in understanding and simulation WAfr Monsoon are expected from recent AMMA (African Monsoon Multidscioplinary Analysis) programme (2004-2009)

About the IRI

IRI dynamical Forecast System

1230

24

12

24

24

24

10

FORECAST SST

TROP. PACIFIC (multi-models, dynamical and statistical)

TROP. ATL, INDIAN (statistical)

EXTRATROPICAL (damped persistence)

GLOBAL ATMOSPHERIC

MODELS

ECPC(Scripps)

ECHAM4.5(MPI)

CCM3.x(NCAR)

NCEP(MRF9)

NSIPP(NASA)

COLA2

GFDL

POSTPROCESSING

MULTIMODELENSEMBLING

PERSISTED

GLOBAL

SST

ANOMALY

OCEAN ATMOSPHERE

10

PersistedSST

Ensembles3 Mo. lead

3030

ForecastSST

Ensembles3/6 Mo. lead

2-tier system

SST Scenarios

JAS 2008SST forecast

MEAN

Model Outputs postprocessing

Combining based on model

Climo Fcst“Prior”

GCM Fcst“Evidence”

t=1 2 3 4

…

Combine “prior” and “evidence” to produce weighted “posterior” forecast probabilities, by maximizing the likelihood.

…

…

(Rajagopalan et al. 2001, MWR; Robertson et al., 2004, MWR)

Bayesian Model Combination

Multimodel Ensemble Verification

Major Goal of Probabilistic Forecasts Reliability!

Forecasts should “mean what they say”.

Courtesy Tony Barnston

Participants PRESAO11, May 23-25 2008, Niamey Niger

Thank you

WAfr Monsoon – Land surface

Koster et al. 2004, Science