• Climate and Weather Prediction Needs for Africa and • the African Information System Aida Diongue-Niang, Senegal Met.

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Transcript • Climate and Weather Prediction Needs for Africa and • the African Information System Aida Diongue-Niang, Senegal Met.

• Climate and Weather Prediction
Needs for Africa and
• the African Information System
Aida Diongue-Niang,
Senegal Met. Agency,
co-chair THORPEXAfrica
Pre-technical Conference CASXV
Environmental Prediction into
the Next Decade
16-17 November 2009
COPT81 Côte d’Ivoire
S. Chauzy
Climate and Weather Prediction
Needs for Africa and the African
High-Impact Weather
Information System
Aida Diongue-Niang,
Senegal National Meteorological
Agency
Co-chair African THORPEX
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Variety in ecological zones and mean annual rainfall
West African Monsoon
Indian
Monsoon
Strong
gradients
Climate Prediction Centre (CPC)
Africa lies mostly in tropical regions with reversal of wet and
dry seasons and in subtropical semi-arid
regions
Figure 2.2 - Annual total precipitation over Africa.
In many parts, the annual rain falls in 3-4 months during the rainy
season(s)
Onset of the rainy season highly variable, with active & inactive
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phases, particularly in
the West African Monsoon region
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 Africa highly vulnerable to climate variability and highimpact weather:
 Rainfall patterns
 Fragile ecosystems
 Weak resilience and adaptation capacity
 Weak preparedness
 60-70% of 922 billions African labour force directly
employed by Agriculture, essentially rain-based
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Outline
•
•
•
•
Introduction
Rainfall variability and change in Africa
High-impact weather in Africa
Climate and Weather Prediction Needs in
Africa
• Data assimilation experiments for AMMA
• THORPEX-Africa
• HIWIS
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2009
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Global decrease of rainfall in Africa, particularly
the Sahelian Region
Rainfall (July-August): Difference between [1967à 98] & [1948
à 66]
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2009
source
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0-20N, 20W-20E
West Africa: High degree of persitence of
anamalously wet (e.g. 1930, 1950, 1960) and
dry years (e.g 1970s, 1980s)
25S-10S, 20E-40E
Variability of the West African Monsoon System
with complex feedback mechanisms
(atmospheric,,land surface,ocean-e.g. AMO)
AMMA programme: www.amma-international.org
Climatology 1930-1995 (CRU dataset)
Climatology 1979-2055(GPCP dataset)
10S-10N, 20E-50E
From Giannini et al, 2008
East Equatorial and Southern Africa:
A visible interannual variability
•Eastern Africa positively influenced by
Warm Enso and SST patterns in the Indian
Ocean
•Southern Africa negatively influenced by
Warm Enso
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2009
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Rainfall and cereal yields
Source UNECA
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Rainfall and Runoff Niger Basin
Wet
Runoff index
Rainfall index
Dry
Factor 2
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2009
source
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IPCC AR4 (SRES A1b): Future Rainfall trend in Africa
JAS rainfall (2070-99) minus (1960-99) simulated by different climate models
Consensus Decrease in Northern & Southern Africa and Increase
in Equatorial Eastern
Decrease of rainfall in the Sahel
Increase of rainfall Sahel
Coutesy
Redelsperger
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2009
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AR4 statements in terms of extremes
• In the context of global warming, increasing
intensity and frequency of extremes
• Developing countries highly vulnerable
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2009
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Outline
•
•
•
•
Introduction
Rainfall variability and change in Africa
High-impact weather in Africa
Climate and Weather Prediction Needs in
Africa
• Data assimilation experiments for AMMA
• THORPEX-Africa
• HIWIS
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2009
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Main
High-impact
weather
In Africa
sS
sS
sS
T
Dry spells
Tropical cyclones
Cyclogenis
Flooding
Srong winds
Sand & Dust Storms
Heat waves
Frost
T
T
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Daily rainfall (mm) for 1983 rainy season
Daily rainfall (mm) for 1998 rainy season
Individual MCS developing across West Africa
Too many and/or too intense can lead to flash floods and flooding,
Too few lead to dry spells and drought
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Dust events over northern
Africa
Biomass burning used as
mangement tool across SubSaharan Africa
smoke and haze existing
off the South-Eastern coast
RIVER OF
SMOKE
MODIS Image –
March 2, 2003
Kanyanga, 2003
source
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Outline
•
•
•
•
Introduction
Rainfall variability and change in Africa
High-impact weather in Africa
Climate and Weather Prediction Needs in
Africa
• Data assimilation experiments for AMMA
• THORPEX-Africa
• HIWIS
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2009
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Issues
• How to quantify time-evolving African climate
changes in order to derive reliable scenarios of
human activities?
• In the context of climate change how variability at
different timescales: decadal, interannual,
intraseasonal and weather statistics will be
modified?
• How can we better improve forecasts of high-impact
weather –and its use- that vulnerable societies face
day to day
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Model Evaluation
• Monitoring of global NWP model performance
in tropical regions, particularly in Africa, has
suffered a relative lack of emphasis, hampering predictability
improvements there.
• Climate and NWP model evaluation and
processes studies to determine model errors
that can help to improve models, not enough to
partly to lack of crtical mass
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errors
Africa
(JJA)
Strong Rainfall
link between
climateover
and NWP
model
errors
GPCP: 1992-2007
NWP 1992: Day 1 - GPCP
© Crown copyright Met Office
Climate 20Year:
NWP 1992-2007:
HadGEM2 - GPCP
Day 1 - GPCP
NWP 2005: Day 1 - GPCP
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2009
From Sean Milton, Met Office 19
Improvement of prediction
NWP models
Better representation of
physical process:
Climate models
convection, land-surface
interactions, aerosols
effects, etc
Higher resolution
(Multi-models)
Ensembles
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24N
10km20km
20km
FEWS
1N
20W
17E
20km
BMJ
convective scheme
Eta 24-48h forecasts
accumulated for JAS 2006
Kain-Fritch convective
scheme
Regional NWP model
Strong sensibility to the convective scheme
and its coupling to others processes
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Diongue-Niang , 2007
Soil moisture impact
TRMM
Soil moisture from
analyses
Soil moisture reduced
to fit observations
Accum. Rainfall oover 24 hours
COSMO 2.5 km
COSMO 2.5 km
Cloud
Resolving
Model
, 17-18 November
Standard soil moisturePre-CAS-XV , IncheonModified
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soil moisture
2009
From Ganther et al. 2009
LMDZ
Old Surface
OBS
Rain JAS
From Traore, Fall, et al
Diurnal cycle
LMDZ
New Surface
From Traore, Fall, et al
Sensitivity to
surface scheme
n the LMD climate model
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2009
From Sane et al
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Seasonal Forecasts
Results of ECHAM4.5 driven with observed (JAS) and
persisted June, May and April SSTAs
Skill of JAS Sahel rainfall from GCM (filled) and
EOF1 of Low level zonal wind
used as predictor for Sahel rainfall After correlation using the wind (open bar)
Correlation
0.6
0.5
MOS
0.4
GCM
0.3
0.2
0.1
0
-0.1
-0.2
JAS
June
May
SST boundary conditions
Ndiaye, Goddard and Ward, 2009 International Journal of Climatology
April
Improvement of prediction
NWP models
Monitoring weather
Improving analyses
and forecasts
Evaluate models
Dense network,
ReliableTransmission
Better initial state
Better representation of
physical process:
convection, land-surface
interactions, aerosols
effects, etc
Higher resolution
(Multi-models)
Ensembles
Climate models
Monitoring climate
Evaluate models
Long term reliable,
well distributed data
Archiving
Observations
Better coupling
atmosphere-ocean
Further
understanding,
process studies
Better representation
of teleconnections
Biogeochemical
processes
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GCOS Upper-Air Network (GUAN) and Surface
Network (GSN) performance in Africa
GUAN performance, 2001
GSN performance, 2001
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Observations network: major constraint to weather to
weather improvement
Operational temp received at ECMWF
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Outline
•
•
•
•
Introduction
Rainfall variability and change in Africa
High-impact weather in Africa
Climate and Weather Prediction Needs in
Africa
• Data assimilation experiments for AMMA
• THORPEX-Africa
• HIWIS
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African THORPEX Background
1. 2007-2009: African Science Plan and African
Implementation Plan (English & French) produced &
distributed to All PRs of African countries leading to about
25 nominated national representatives .
2. Ongoing seek for funding and endorsement
3. A workshop to launch THORPEX Africa Implementation
Activities 5-8 October 2009, UNESCO/ICTP, TRIESTE
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PDP1:
Predictive Skill of
high impact
weather
PDP2: Contributing
to the Development
of Seamless forecast
by filling the gap in
intraseasonnal
timescales
DAOS1:
Design of an
optimum
network in Africa
Planned activities
Predictability
and
Dynamical
Processes
TIGGE
Societal and
Economic
Research
Application
Data
Assimilation
observing
systems
DAOS2:
Use of non
conventional
observing
systems
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2009
SERA1:
High-impact
weather database
SERA2:
Forecast
verification and
cost/benefit
assessments
DAOS3:
Improvement of
telecommunications
facilities
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PDP1:
Predictive Skill of
high impact
weather
PDP2: Contributing
to the Development
of Seamless forecast
by filling the gap in
intraseasonnal
timescales
DAOS1:
Design of an
optimum
network in Africa
HIWIS
Predictability
and
Dynamical
Processes
TIGGE
SERA1:
High-impact
weather database
Societal and
Economic
Research
Application
observing
systems
DAOS2:
Use of non
conventional
observing
systems
Pre-CAS-XV , Incheon
, 17-18 November
2009
SERA2:
Forecast
verification and
cost/benefit
assessments
DAOS3:
Improvement of
telecommunications
facilities
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Outline
•
•
•
•
Introduction
Rainfall variability and change in Africa
High-impact weather in Africa
Climate and Weather Prediction Needs in
Africa
• Data assimilation experiments for AMMA
• THORPEX-Africa
• HIWIS
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Goal and scope of the Information System
 The ultimate aim of the HIW Information System is to
collect HIW events in the past two decades and on going
HIW events with a user friendly interface for analysis,
processing and visualisation to facilitate integrated and
multidisciplinary studies.
 While seeking for funding, start the activity with an interim
prototype database with few recent case studies and
simplified interface. This will be used as a pilot
demonstration to gather weather, hydrologic, and impact
data and undertake predictability and (possibly) socio economic studies.
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3 -year demonstration project
HIW
HIW
DATABASE
E
V
E
N
T
S
PREDICTABILITY
STUDIES
Nov.2009-June2010
•Event
documentation
•Data collection
• Metadata
generation
June 2010-June2011
•Data submission to the database
•Forecast Evaluation
•Deterministic Model evaluation
Jan. 2011-Nov. 2012
Ensemble prediction evaluation
High-resolution modelling
Process studies
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Case studies on flooding events Collaboration&partnership
2008
2009?
2009
2007
UNESCO/ICTP to technically
design, host the interim prototype
database
NHMS (~20) : HIW events
analyses and documentation +
data submission
NWP forecasting centres: contact
persons for guidace in model data
& diagnostics, model evaluation
1997
Academics: Modelling and
2007?
processes studies
Other WMO related prog. e.g.
WIS, MEDEX
MunichRe Geo Risk Research
2008
group: experience sharing,
updated review of African HIW
from 1990 and in turn more in
depth impact data
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MunichRe NatCatSERVICE: example Hurricane Ike
Affected
people
Affected
buildings
Affected
infrastructure
Affected lines
of business
Number of deaths
Event description
From Angelika Wirtz, Munich Re
HIWIS
HIW
HIW
DATABASE
E
V
E
N
T
S
PREDICTABILITY
STUDIES
AfRC+NHMS
HIW events
Af RC
ICTP
NHMSs
Data Submission
(weather, hydrology
& impact)
Academic
community
+THORPEX
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NWP Centres
+TIGGE,
JWGFVR
37
01 September 2009 0600
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BURKINA-FASO : OUAGADOUGOU
Courteously by Nakoulma Guillaume
Concluding remarks on prediction Needs in
Africa
Climate regime modes
Interannual variability mode
Seasonal mean anomaly
Weather statistics, Wet/dry
spells, rainy season onset
Weather
Regimes
Mesoscale,
events
daily
Wkly
Mthly
Seasonal
Yearly
Decadal
African THORPEX
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Thanks for your attention.
Questions?
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Burkina Faso High Impact Weather Event, Sep 1, 2009, courtesy
Wassila Thiaw
RFE – 1 Sep 2009
GFS 48h – 1 Sep 2009
GEFS 48h – 1 Sep 2009
X
42
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Initiation
of a MCS
Comparaison avec le canal
15 UTC
15 UTC
TB (K)IR METEOSAT
IR
Aïr
Aïr
Plaine
Plaine
Squall line over Niger: surface rainfall
22 UTC
H
Phase Mature
22 UTC
H
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Diongue et2009
al, 2001
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