Narrowing CO2 response uncertainty in projections of climate impacts and

adaptationDr. Julian Ramirez-Villegas

Prof. Andy Challinor

• 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

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

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)

Climate change impacts on agriculture: summary of evidence

Challinor et al. (2014) NCC and Chapter 7 IPCC AR5 (2014)

Climate change impacts on agriculture: summary of evidence

Wheeler and von Brauun (2013)

Climate change impacts: with or without CO2 response?

Rosenzweig and Parry (1994) Nature

Rosenzweig et al. (2014) PNAS

Uncertainty decomposition suggests CO2 response is not the largest of uncertainties

(high VPD) (low VPD)

Ramirez-Villegas and Challinor (in prep)Orange = natural variabilityLight green = CO2 responseDark green = Crop modelBlue = GCM

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 groundnutTraits: improved water use efficiency, improved partitioning, heat tolerance, duration

HISTORICAL CHANGE (2030s, RCP4.5)

Ramirez-Villegas and Challinor, Climatic Change (in revision)

Impacts without adaptation

Impacts with adaptation

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

Uncertainty decomposition

Ramirez-Villegas and Challinor, Climatic Change (in revision)

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