Climate as Risk Factor. Mean Seasonal Cycle Interannual Variability Seasonal Prediction ...
Transcript of Climate as Risk Factor. Mean Seasonal Cycle Interannual Variability Seasonal Prediction ...
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
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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
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OBS
Linear
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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
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Differences with CMAP
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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
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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
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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
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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
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3000
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forecasted
observed
year
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charg
e (
m3/s
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R2 = 0.6507
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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
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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
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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