JRV presents on CO2 uncertainty at the Adaptation Futures conference in Rotterdam 10–13 May 2016

1. Narrowing CO2 response uncertainty
in projections of climate impacts and
adaptation
Dr. Julian Ramirez-Villegas
Prof. Andy Challinor

2. • Background on direct and indirect
CO2 effects
• Climate change impacts on crop
yields
–Summary of evidence
–With or without CO2 fertilisation?
• A case study with Indian groundnut
using an ensemble of simulations
Outline

3. Direct and indirect CO2 effects
• Direct effects: related to the physiological
changes in the crop as a result of increased
CO2 concentrations (aka CO2 fertilization)
Long et al. (2006) Science

4. Direct and indirect CO2 effects
• Indirect effects: changes associated with the
effect of CO2 concentrations on the climate
system.
Knutti and Sedlacek (2012)Van Vuuren et al. (2011)

5. Climate change impacts on agriculture:
summary of evidence
Challinor et al. (2014) NCC and Chapter 7 IPCC AR5 (2014)

6. Climate change impacts on agriculture:
summary of evidence
Wheeler and von Brauun (2013)

7. Climate change impacts: with or
without CO2 response?
Rosenzweig and Parry (1994) Nature

8. Rosenzweig et al. (2014) PNAS

9. Uncertainty decomposition suggests
CO2 response is not the largest of
uncertainties
(high VPD) (low VPD)
Ramirez-Villegas and Challinor (in prep)Orange = natural variability
Light green = CO2 response
Dark green = Crop model
Blue = GCM

10. An ensemble approach to designing
genotypic adaptation strategies
• General Large Area Model
for annual crops (GLAM)
• Projections as ensemble of:
– Parameters
– Climate models (GCMs)
– GCM bias correction
methods
– CO2 response
• One forcing scenario
(RCP4.5) and time period
(2030s)
Focus on Indian
groundnut
Traits: improved water use
efficiency, improved
partitioning, heat tolerance,
duration

11. HISTORICAL CHANGE (2030s, RCP4.5)
Ramirez-Villegas and Challinor, Climatic Change (in revision)
Impacts without adaptation

12. Impacts with adaptation

13. Robustness and uncertainties in
genotypic adaptation options
• R>0.5: moderately robust projections
• R>0.8: very robust projections
Low GLAM skill –
model
improvement
Very low cropping
intensity

14. Uncertainty decomposition
Ramirez-Villegas and Challinor, Climatic Change (in revision)

15. Key messages
• Better study designs that quantify CO2-response
parameter uncertainty are needed.
• Framework to quantify and partition
uncertainty and assess robustness can help
determining where investment has the lowest
risk, and where and how uncertainties can be
reduced.
• Latest and data knowledge on CO2 fertilization
effect needs to be incorporated into crop
models