Towards Probabilistic Projections of Climate Change Impacts on Global Crop Yields
Journal Article

There is a widely recognized need in the scientific and policy communities for probabilistic estimates of climate change impacts, beyond simple scenario analysis. Here we propose a methodology to evaluate one major climate change impact - changes in global average yields of wheat, maize, and barley by 2030 - by a probabilistic approach that integrates uncertainties in climate change and crop yield responses to temperature, precipitation, and carbon dioxide. The resulting probability distributions, which are conditional on assuming the SRES A1B emission scenario and no agricultural adaptation, indicate expected changes of +1.6%, -14.1%, -1.8% for wheat, maize, and barley, with 95% probability intervals of (-4.1, +6.7), (-28.0, -4.3), (-11.0, 6.2) in percent of current yields, respectively. This fully probabilistic analysis aims at quantifying the range of plausible outcomes and allows us to gauge the relative importance of different sources of uncertainty.

A particularly pressing need from a risk analysis standpoint is to provide probabilistic assessments of impacts of climate change. General circulation models (GCMs) are powerful tools for the analysis of future changes in climate variables, and statistical analysis of their output can provide not only point estimates, but also a rigorous evaluation of the uncertainty inherent in future projections [Tebaldi et al., 2004, 2005; Tebaldi and Sanso´ , 2008; R. L. Smith, Bayesian modeling of uncertainty in ensembles of climate models, submitted to Journal of the American Statistical Association, 2007]. Recent work [Lobell and Field, 2007] has quantified through statistical regression analysis the relation between observed changes in temperature and precipitation and recorded changes in agricultural yields of several major crops at the global level. In this work we seek to draw a connection between these two areas of study, by assessing the potential impacts on global yields of three important crops of changes in temperature and precipitation as they are projected in the GCM experiments archived in the Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset. We choose to assess the sensitivity of crop yields to climate change through regression models rather than process-based crop models because of our focus on the quantification of uncertainties, since we are not aware of any systematic means to quantify the dependence of the process-based model results to the choice of a specific model and specific parameter values within each model. Our results are probabilistic projections of percent crop yield changes by 2030, compared to current yields, in the absence of adaptation practices.

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