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Climate Change Data Analysis and Adaptation Strategies in AAP Countries

Explore how AAP countries can utilize climate data analysis for adaptation strategies. Learn about tools, challenges, methods, and achievements in addressing climate risks on agriculture and water resources.

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Climate Change Data Analysis and Adaptation Strategies in AAP Countries

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  1. 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: seyni.salack@ucad.edu.sn AAP Countries Meeting, Dakar, Senegal, 12-16 November 2012

  2. Overall objective of our team • Help AAP countries build upon their local knowledge and capabilities. “Strengthen the strengths and make weaknesses irrelevant in CC info use and applications”

  3. How ? Focus on few to help many !

  4. 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

  5. …..GCM outputs… Hundreds of km tens of km km Impacts needs… point

  6. GCM scale GCM scale Gamma Distribution, EOF, Transform. mul. Gauss. etc., Mark. Ch. RCM Zoom 1 Statistical Dowscalling Zoom 2 ??? RCM Statistical link Stat Station data Station data Source: S. Salack (2007) a) Classical Methods: Baron et al, (2005), Hansen et al, (2006), Schmidli et al. (2006) , Ines & Hansen (2006) b) Dynamical-statistical methods RMC are used to downscale GCM outputs: Capture the sub-grid processes (orgaphic effects, local convection …) The challenges (2): End users handling the methods in dynamical and/or statistical downscaling technics

  7. The challenges (3): Climate & CC data archiving, formatting (NetCDF), QC technics nj 1 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)

  8. Methods and tools provided (1): open source tools New_locClim (FAO, 2006): to solve problem of data scarcity, data interpolation/spatialisation

  9. Methods and tools provided (2): open source tools • NCO: NetCDF Command Operators for managing NetCDF data format • CDO: Same as NCO + extraction of climate extremes • R packages and scripts: browse_NCDF.r (forhandling NetCDF files by Salack et al., 2012), Rclimdex.r (forclimate extremes extraction by ETCCM/WMO, 2006) • Stochastic weather generator for downscaling: LARS-WG, EOFs and their limitations in CC info.

  10. Methods and tools provided (3): open source data • AMMA-ENSEMBLES & CORDEX data: RCM outputs • IRI data library: Observations, re-analysis • NOAA (GHCN), CRU, GPCP, TRMM • Climate information portal of the CSAG-UCT • FAO database, including CLIMWAT • Other data sources such estimated, interpolated, self-owned data etc. >>>Because Good and true information is power !

  11. The achievements (1): Strengthened & sustained capacity • 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 • MZ • CG • NE • Mauritius • BF • GH

  12. AAP-Mozambique (23 participants)

  13. AAP-Niger (22 participants)

  14. AAP-Congo (2x25 participants)

  15. Results (1): Example of Natl report on CC in Congo …and output oriented… useful to any other decision making project

  16. Impacts on agric & water resources Major challenges: • Managing uncertainties in CC info. • Local information to parametrize & validation of crop models (DSSAT, CROPWAT, SARRAH) • Information on local water levels and runoff • Water basin metadata and evaporation data • Etc…

  17. Impacts on agric & water resources Implementations in AAP countries • Deploy crop models: DSSAT, CROPWAT, SARRAH • Deploy hydrological models: SWAT, WEAP • Deploy GIS tools: ARCGIS, IDV • The parameterizations and validations are done using mostly the FAO parameters and data in most cases but also local data.

  18. Results 2: Example of Natl report on agric in Congo …and output oriented… useful to any other decision making project

  19. 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

  20. Results 3: Example of Natl report on agric in Congo …and output oriented… useful to any other decision making project

  21. Lessons learnt (Recommendations) • 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 research-end-users relationship (sustain this process).

  22. Thank you

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