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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...