Challinor - Models for adaptation

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Andy Challinor, Using climate models for assessing adaptation options (presentation from Adaptation session at CCAFS Science Workshop, December 2010)

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Challinor - Models for adaptation

  1. 1. School of Earth and EnvironmentAndy ChallinorA.J.Challinor@leeds.ac.uk Using models for assessing adaptation options
  2. 2. The challenge• Increase food production – in the face of climate change – whilst reducing the carbon cost of farming – but not simply by farming at lower intensity and taking more land (because there isn’t enough)• Beddington’s Perfect Storm
  3. 3. G8, IPCC 108 farmers Time ? ? Govts. Farmer ? (Mintzberg) G8, UN, WB, .. SpaceChallinor (2009)
  4. 4. Overview1. Using climate modelling and process studies to understand food production2. Data3. Integration vs ‘parallelisation’
  5. 5. How (un)certain are we?… it depends how far ahead we look Climate predictions focusing on lead times of ~30 to 50 years have the lowest fractional uncertainty. This schematic is based on simple modeling. Cox and Stephenson (2007) Science 317, 207 - 208
  6. 6. … and where we lookSignal to noise ratio for decadal mean surface air temperature predictionsHawkins and Sutton (2009)
  7. 7. Some treatments of uncertainty in crop modelling 2 x CO2 Wheat -100 to Reilly and Schimmelpfennig, N. America +234% 1999 2080s Cereals -10 to +3% Parry et al., 1999 Africa +4oC local ΔT Wheat -60 to +30% IPCC AR4, chap. 5 (Easterling et al., ‘low latitude’ 2007) +4oC local ΔT Wheat -30 to +40% IPCC AR4, chap. 5 (Easterling et al., ‘mid- to high- 2007) latitude’ See Challinor et al. (2007a)
  8. 8. simulation lengthEnsemble size or Uncertainty vs resolution it y Land use: biology, carbon cycle, ex pl water cycle .. m Co Ocean: atmospheric coupling, biology Cryosphere Atmosphere: physics, chemistry Spatial resolution Challinor et al. (2009b)
  9. 9. Modelling methods Challinor et al. (2004)• Climate model ensembles 900 850• Process-based crop model 800 750 Yield (kg ha ) -1designed for use with climate 700 650models 600 550 Model results – Focus on biophysical 500 450 Observed yield (detrended to 1966 levels) processes (abiotic stresses) 400 1965 1970 1975 1980 1985 1990 Year Osborne (2004) Chee-Kiat (2006)
  10. 10. How should investment in adaptation be prioritised? 1 x σ events 2 x σ eventsPercentage of harvests failing Percentage of harvests failing None Temperature Water Temp+Wat None Temperature Water Temp+Wat Adaptation Adaptation Challinor et al. (2010; ERL)
  11. 11. Overview1. Using climate modelling and process studies to understand food production2. Data3. Integration vs ‘parallelisation’
  12. 12. Do we have the real-world varieties to achieve adaptation? Spring wheat in the northern US • Use crop duration data for Climate Number of spring wheat varieties from varieties suitable the CIMMYT database (6,229 trials, 2711 varieties) +0oC 87% of all varieties • Use Thermal Time 5 out of the top 5 Requirement analysis of Challinor et al. (2009a) +2oC 68% of all varieties • Assume T<Topt (i.e. worst- 5 out of the top 5 case scenario) and define suitability as observed current-climate duration of +4oC 54% of all varieties 121 days 2 out of the top 5Thornton et al. (in press)
  13. 13. Adaptation options for one location in India 180,000+ crop simulations, varying both climate (QUMP) and crop response to doubled CO2• Further simulations and 0% Increase in thermalanalysis of crop cardinal time requirementtemperatures suggest a 30% 10%increase may be needed• Field experiments suggest 20%the potential for a 14 to 40%increase within currentgermplasm• Suggests some capacity foradaptation QUMP53 Challinor et al. (2009a)
  14. 14. Overview1. Using climate modelling and process studies to understand food production2. Data3. Integration and ‘parallelisation’
  15. 15. Invest in other agr activities Double cropping Vulnerable Fertiliser, Increasing impact Machinery Agr production Rural capital, population Invest in agr, GDP share of agr Infrastructure RESILIENT Electricity Wheat Increasing exposure Challenge: combining this understanding with thebio-physical crop modelling; see Challinor et al. (2009c)
  16. 16. How should investment in adaptation be prioritised: accounting for vulnerability 1 x σ events 2 x σ eventsPercentage of harvests failing Percentage of harvests failing None Water MinVuln. MeanV. MaxV. None Water MinVuln. MeanV. MaxV. Adaptation Adaptation Challinor et al. (2010; ERL)
  17. 17. Modelling assetts Probability of thriving = resilience?• Stochastic climate variability• Non-climatic drivers, some stochastic• Livelihoods => Assetss asset dynamics• Adaptive management• Tipping points: – Failure thresholds – Poverty threshold trapsJim Hansen failure event T0 Time (out to ~2 decades)
  18. 18. AcknowledgementsElisabeth SimeltonLindsay StringerClaire QuinnTom Osborne www.equip.leeds.ac.uk www.ccafs.cgiar.orgTim BentonJames HansenTim WheelerEd HawkinsDavid Green www.cccep.ac.ukGordon ConwayR. BandyopadhyayMany other co-authors...

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