Challinor - Models for adaptation
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The document discusses the challenges of increasing food production amid climate change while minimizing the carbon footprint of farming. It highlights the use of climate modeling and process studies to assess adaptation strategies, emphasizing the importance of prioritizing investments based on vulnerability and resilience. Various data analyses suggest potential adaptation options for different crops, particularly in the context of changing temperatures and their impact on yields.
Challinor - Models for adaptation
- 1. School of Earth and Environment Andy Challinor A.J.Challinor@leeds.ac.uk Using models for assessing adaptation options
- 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. G8, IPCC 108 farmers Time ? ? Govts. Farmer ? (Mintzberg) G8, UN, WB, .. Space Challinor (2009)
- 4. Overview 1. Using climate modelling and process studies to understand food production 2. Data 3. Integration vs ‘parallelisation’
- 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. … and where we look Signal to noise ratio for decadal mean surface air temperature predictions Hawkins and Sutton (2009)
- 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. simulation length Ensemble 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. Modelling methods Challinor et al. (2004) • Climate model ensembles 900 850 • Process-based crop model 800 750 Yield (kg ha ) -1 designed for use with climate 700 650 models 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. How should investment in adaptation be prioritised? 1 x σ events 2 x σ events Percentage 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. Overview 1. Using climate modelling and process studies to understand food production 2. Data 3. Integration vs ‘parallelisation’
- 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 5 Thornton et al. (in press)
- 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 thermal analysis of crop cardinal time requirement temperatures suggest a 30% 10% increase may be needed • Field experiments suggest 20% the potential for a 14 to 40% increase within current germplasm • Suggests some capacity for adaptation QUMP53 Challinor et al. (2009a)
- 14. Overview 1. Using climate modelling and process studies to understand food production 2. Data 3. Integration and ‘parallelisation’
- 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 the bio-physical crop modelling; see Challinor et al. (2009c)
- 16. How should investment in adaptation be prioritised: accounting for vulnerability 1 x σ events 2 x σ events Percentage 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. 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 traps Jim Hansen failure event T0 Time (out to ~2 decades)
- 18. Acknowledgements Elisabeth Simelton Lindsay Stringer Claire Quinn Tom Osborne www.equip.leeds.ac.uk www.ccafs.cgiar.org Tim Benton James Hansen Tim Wheeler Ed Hawkins David Green www.cccep.ac.uk Gordon Conway R. Bandyopadhyay Many other co-authors...