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Downscaling of GCM for i’ts use in Agriculture and NRM Research

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Downscaling of GCM for i’ts use in Agriculture and NRM Research

  1. 1. 10/10/2012Carlos Navarro, Julian Ramirez,Andy Jarvis, Peter Laderach
  2. 2. Contents• Brief about climate & agricultre• Climate data, availability, difficulties, options• Our databases
  3. 3. We know…• Any agroecosystem respond to changes of anthropogenic factors, biotics, abiotics.• Weather and climate predictability is fairly limited.• The climate will change.• Each system is an specific case.• Crops are very sensitive to climatic conditions
  4. 4. Climate & Agriculture – Multiple variables – Very high spatial –T° • Max, resolution • Min, Less importance More certainty • Mean – Mid-high temporal (i.e. monthly, daily) resolution –Prec – Accurate weather forecasts and climate – HR projections – Radiation – High certainty – Wind• Both for present and – ……. future
  5. 5. We don’t know… What are the conditions in 30, 50, 100 years? • How our system respond to these conditions? • When, where and what type of change requiere to adapt? • Who should plan? >> UNCERTAINTIES Who should leads the process ? Who should run?
  6. 6. Needs Limitations
  7. 7. Emission Scenarios Pessimistic Intermediate “Bussiness as usual” P Economic P E E Global Regional P P E E Perfect World Environmental Optimistic
  8. 8. In agriculture, the different emission scenarios are not important ... by2030 the difference between the scenarios is minimal
  9. 9. GCM “Global Climate Model” GCMs are the only way Using the past to learnwe can predict the future for the future climate
  10. 10. What are saying the models? Atmospheric concentrations Variations of the Earth’s surface temperature: 1000 to 2100 Anthropogenic changes lead to changes in weather
  11. 11. Resolutions • Horizontal resolution 100 to 300 km • 18 and 56 vertical levels Global scale  Regional or local scale 
  12. 12. Model Country Atmosphere OceanBCCR-BCM2.0 Norway T63, L31 1.5x0.5, L35CCCMA-CGCM3.1 (T47) Canada T47 (3.75x3.75), L31 1.85x1.85, L29CCCMA-CGCM3.1 (T63) Canada T63 (2.8x2.8), L31 1.4x0.94, L29CNRM-CM3 France T63 (2.8x2.8), L45 1.875x(0.5-2), L31CSIRO-Mk3.0 Australia T63, L18 1.875x0.84, L31CSIRO-Mk3.5 Australia T63, L18 1.875x0.84, L31GFDL-CM2.0 USA 2.5x2.0, L24 1.0x(1/3-1), L50GFDL-CM2.1 USA 2.5x2.0, L24 1.0x(1/3-1), L50GISS-AOM USA 4x3, L12 4x3, L16GISS-MODEL-EH USA 5x4, L20 5x4, L13GISS-MODEL-ER USA 5x4, L20 5x4, L13IAP-FGOALS1.0-G China 2.8x2.8, L26 1x1, L16INGV-ECHAM4 Italy T42, L19 2x(0.5-2), L31INM-CM3.0 Russia 5x4, L21 2.5x2, L33IPSL-CM4 France 2.5x3.75, L19 2x(1-2), L30MIROC3.2-HIRES Japan T106, L56 0.28x0.19, L47MIROC3.2-MEDRES Japan T42, L20 1.4x(0.5-1.4), L43MIUB-ECHO-G Germany/Korea T30, L19 T42, L20MPI-ECHAM5 Germany T63, L32 1x1, L41MRI-CGCM2.3.2A Japan T42, L30 2.5x(0.5-2.0)NCAR-CCSM3.0 USA T85L26, 1.4x1.4 1x(0.27-1), L40NCAR-PCM1 USA T42 (2.8x2.8), L18 1x(0.27-1), L40UKMO-HADCM3 UK 3.75x2.5, L19 1.25x1.25, L20UKMO-HADGEM1 UK 1.875x1.25, L38 1.25x1.25, L20 Uncertainties!
  13. 13. Difficulty 1. They differ on resolution
  14. 14. • Difficulty 2. They differ in availability (via IPCC) WCRP CMIP3 A1B-P A1B-T A1B-Tx A1B-Tn A2-P A2-T A2-Tx A2-Tn B1-P B1-T B1-Tx B1-TnBCCR-BCM2.0 OK OK OK OK OK OK OK OK OK OK OK OKCCCMA-CGCM3.1-T63 OK OK NO NO NO NO NO NO OK OK NO NOCCCMA-CGCM3.1-T47 OK OK NO NO OK OK NO NO OK OK NO NOCNRM-CM3 OK OK NO NO OK OK NO NO OK OK NO NOCSIRO-MK3.0 OK OK OK OK OK OK OK OK OK OK OK OKCSIRO-MK3.5 OK OK OK OK OK OK OK OK OK OK OK OKGFDL-CM2.0 OK OK OK OK OK OK OK OK OK OK OK OKGFDL-CM2.1 OK OK OK OK OK OK OK OK OK OK OK OKGISS-AOM OK OK OK OK NO NO NO NO OK OK OK OKGISS-MODEL-EH OK OK NO NO NO NO NO NO NO NO NO NOGISS-MODEL-ER OK OK NO NO OK OK NO NO OK OK NO NOIAP-FGOALS1.0-G OK OK NO NO NO NO NO NO OK OK NO NOINGV-ECHAM4 OK OK NO NO OK OK NO NO NO NO NO NOINM-CM3.0 OK OK OK OK OK OK OK OK OK OK OK OKIPSL-CM4 OK OK NO NO OK OK NO NO OK OK NO NOMIROC3.2.3-HIRES OK OK OK OK NO NO NO NO OK OK OK OKMIROC3.2.3-MEDRES OK OK OK OK OK OK OK OK OK OK OK OKMIUB-ECHO-G OK OK NO NO OK OK NO NO OK OK NO NOMPI-ECHAM5 OK OK NO NO OK OK NO NO OK OK NO NOMRI-CGCM2.3.2A OK OK NO NO OK OK NO NO OK OK NO NONCAR-CCSM3.0 OK OK OK OK OK OK OK OK OK OK OK OKNCAR-PCM1 OK OK OK OK OK OK OK OK OK OK OK OKUKMO-HADCM3 OK OK NO NO OK OK NO NO OK OK NO NOUKMO-HADGEM1 OK OK NO NO OK OK NO NO NO NO NO NO
  15. 15. Difficulty 3. limited ability to represent present climates Depender de un solo GCM es peligroso!
  16. 16. How I can use this information? Options Downscaling by Needs statistical or To increase dynamical resolution, uniformise, methods.. Problem provide high Even the most resolution and precise GCM is contextualised data too coarse (~100km)
  17. 17. The Delta Method• Use anomalies and discard baselines in GCMs – Climate baseline: WorldClim – Used in the majority of studies – Takes original GCM timeseries – Calculates averages over a baseline and future periods (i.e. 2020s, 2050s) – Compute anomalies – Spline interpolation of anomalies – Sum anomalies to WorldClim
  18. 18. Stations by variable: • 47,554 precipitation Mean annualtemperature (ºC) • 24,542 -30.1 tmean 30.5 • 14,835 tmax y tmin Sources: •GHCN •FAOCLIM Annual •WMOprecipitation (mm) 0 •CIAT •R-Hydronet 12084 •Redes nacionales
  19. 19. RCM PRECIS Providing REgional Climates for– They use outputs of Impacts Studies GCMs– Area are limited .. Need boundary conditions.– Performs calculations of atmospheric dynamics and solve equations for each grid.– Daily data– Resolution varies between 25- 50km– More than 170 output variables
  20. 20. Method + - *Easy to implement * Change variable only at big scale Statistical *  resolutions * Variables do not change their relationsdownscaling *Apply to all GCMs with time *Uniforme baseline *  variables *Few platforms (PRECIS, CORDEX) * Robust *Many processes and stockages Dynamic *Apply to GCMs if data *Limited resolution (25-50km)downscaling available *Missing development * variables *Dificulty to quantify uncertainties
  21. 21. We need models to quantify the impacts and adaptation options for effective design Based on process GCMs Statistical Downscaling MarkSim Dynamical downscaling: Regional Climate ModelBased on niches DSSAT Probability EcoCrop Statistical Downscaling Environmental gradient Effective MaxEnt adaptation options
  22. 22. Changes in climate affect the adaptability of crops… There will be winners… Number of crops with more than 5% gain…But muchmore losers indevelopingcountries Number of crops with more than 5% loss
  23. 23. http://ccafs-climate.org
  24. 24. • Downscaling is inevitable.• Continuous improvements are being done• Strong focus on uncertainty analysis and improvement of baseline data• We need multiple approaches to improve the information base on climate change scenarios  Development of RCMs (multiple: PRECIS not enough)  Downscaling empirical, methods Hybrids  We tested different methodologies

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