Transcript EN_SSII_CC_Data_Analysis_SSalack

Climate Change Data Analysis,
Risks Assessments On agric/Water
Resources and Adaptation Strategies In
Some AAP-Countries
Seyni Salack
(UNOPS-IRTSC, Consultant)
Contributors: Intsiful J., Obuabie E., Moufouma W.
Email: [email protected]
08/04/2015
AAP Countries Meeting, Dakar, Senegal, 12-16 November 2012
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Overall objective of our team
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Help AAP countries build upon their local knowledge
and capabilities.
“Strengthen the strengths and make
weaknesses irrelevant in CC info use
and applications”
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How ?
Focus on few to help many !
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The challenges (1): Understanding the
Complex climate system…
The atmosphere and the chemical components are linked with other components
of the Earth system: oceans; land; terrestrial; plants and animals
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…..GCM outputs…
Hundreds of km
tens of km
Impacts needs…
km
point
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The challenges (2): End users handling the methods in
dynamical and/or statistical downscaling technics
GCM scale
GCM scale
Gamma Distribution,
EOF, Transform. mul.
Statistical
Gauss. etc., Mark. Ch.
Dowscalling
RCM
RCM
???
Zoom 2
Statistical link
Stat
Station data
Station data
a) Classical Methods: Baron et al, (2005), Hansen et al, (2006),
Zoom 1
b)
Dynamical-statistical methods
Schmidli et al. (2006) , Ines & Hansen (2006)
Source: S. Salack (2007)
RMC are used to downscale GCM outputs:
(orgaphic effects, local convection …)
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Capture the sub-grid processes
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The challenges (3): Climate & CC data archiving,
formatting (NetCDF), QC technics
nj
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ni
→ lon(ni), lat(nj) and time(nj,ni) for different levels (nk)
Note: in Netcdf files, lon and lat are often both dimensions (ni and nj)
and name of longitude and latitude vectors → lon(lon), lat(lat), level (nk)
and time (lat, lon)
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Methods and tools provided (1): open source tools
New_locClim (FAO, 2006): to solve problem of data scarcity, data
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interpolation/spatialisation
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Methods and tools provided (2): open source tools
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NCO: NetCDF Command Operators for managing
NetCDF data format
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CDO: Same as NCO + extraction of climate extremes
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R packages and scripts: browse_NCDF.r (for handling
NetCDF files by Salack et al., 2012), Rclimdex.r (for
climate extremes extraction by ETCCM/WMO, 2006)
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Stochastic weather generator for downscaling: LARSWG, EOFs and their limitations in CC info.
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Methods and tools provided (3): open source data
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AMMA-ENSEMBLES & CORDEX data: RCM outputs
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IRI data library: Observations, re-analysis
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NOAA (GHCN), CRU, GPCP, TRMM
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Climate information portal of the CSAG-UCT
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FAO database, including CLIMWAT
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Other data sources such estimated, interpolated,
self-owned data etc.
>>> Because Good and true information is power !
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The achievements (1): Strengthened & sustained capacity
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MZ
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CG
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BF
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GH
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NE
Mauritius
Workshops successfully organized (feedbacks & reports)
Public conference in Congo (special)
National average CC and extremes scenarios reports
National average and local CC risks on agric & water
resources and adaptation measures
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AAP-Mozambique (23 participants)
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AAP-Niger (22 participants)
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AAP-Congo (2x25 participants)
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Results (1): Example of Natl report on CC in Congo
…and output oriented… useful to any other decision making project
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Impacts on agric & water resources
Major challenges:
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Managing uncertainties in CC info.
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Local information to parametrize & validation of crop
models (DSSAT, CROPWAT, SARRAH)
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Information on local water levels and runoff
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Water basin metadata and evaporation data
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Etc…
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Impacts on agric & water resources
Implementations in AAP countries
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Deploy crop models: DSSAT, CROPWAT,
SARRAH
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Deploy hydrological models: SWAT, WEAP
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Deploy GIS tools: ARCGIS, IDV
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The parameterizations and validations are done
using mostly the FAO parameters and data in
most cases but also local data.
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Results 2: Example of Natl report on agric in Congo
…and output oriented… useful to any other decision making project
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Adaptation Measures in agric sector
The “Where” to adapt
 Adaptation
is local. Case to case
approach.
The “how” to adapt
 Technical Adaptation measures have
been suggested.
 Easy to use, to implement and sustained
 Low cost (financially and in manpower)
 Do not oppose indigenous knowledge and
practices
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Results 3: Example of Natl report on agric in Congo
…and output oriented… useful to any other decision making project
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Lessons learnt (Recommendations)
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Open source data sets are very useful (support it)
Open source tools provide precise and good quality
results (Build on the acquired skills).
AAP experiences can increase knowledge of
climate science and can provide breakthrough
ideas for follow up projects (per-review papers)
Strong relationship between AAP and the national
Met. Off. or Agency helps reduce the problem of
local data availability (build on it).
Strong links between AAP and the local
Universities is a long term solution to researchend-users relationship (sustain this process).
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Thank you
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